diff --git a/.DS_Store b/.DS_Store
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diff --git a/Iris/iris_identification_DT.ipynb b/Iris/iris_identification_DT.ipynb
new file mode 100644
index 0000000..def4082
--- /dev/null
+++ b/Iris/iris_identification_DT.ipynb
@@ -0,0 +1,1140 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e96a1dd-303c-4920-8d24-d5d7e58dc02d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>Species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                5.1               3.5                1.4               0.2   \n",
+       "1                4.9               3.0                1.4               0.2   \n",
+       "2                4.7               3.2                1.3               0.2   \n",
+       "3                4.6               3.1                1.5               0.2   \n",
+       "4                5.0               3.6                1.4               0.2   \n",
+       "\n",
+       "       Species  \n",
+       "0  Iris-setosa  \n",
+       "1  Iris-setosa  \n",
+       "2  Iris-setosa  \n",
+       "3  Iris-setosa  \n",
+       "4  Iris-setosa  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Loading the iris dataset\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
+    "df = pd.read_csv(url, header=None, names=['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
+    "                                          'petal width (cm)', 'Species'])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1a8f6c2f-d63a-4fc0-9e06-3c360e451852",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(150, 5)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To know number of rows and collumns\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "dc5283cb-d268-4373-99d1-94cf90c33edd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 150 entries, 0 to 149\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   sepal length (cm)  150 non-null    float64\n",
+      " 1   sepal width (cm)   150 non-null    float64\n",
+      " 2   petal length (cm)  150 non-null    float64\n",
+      " 3   petal width (cm)   150 non-null    float64\n",
+      " 4   Species            150 non-null    object \n",
+      "dtypes: float64(4), object(1)\n",
+      "memory usage: 6.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the dataframe information\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "68c02ddc-9a88-45b1-8f86-9b2c9ebfc713",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal length (cm)    0\n",
+       "sepal width (cm)     0\n",
+       "petal length (cm)    0\n",
+       "petal width (cm)     0\n",
+       "Species              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To find if any null value is present\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "15fa05e3-9e35-44dc-a053-4021f64ec00b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sepal length (cm)</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>5.843333</td>\n",
+       "      <td>0.828066</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>5.80</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>7.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3.054000</td>\n",
+       "      <td>0.433594</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3.758667</td>\n",
+       "      <td>1.764420</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>4.35</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>6.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>1.198667</td>\n",
+       "      <td>0.763161</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>2.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count      mean       std  min  25%   50%  75%  max\n",
+       "sepal length (cm)  150.0  5.843333  0.828066  4.3  5.1  5.80  6.4  7.9\n",
+       "sepal width (cm)   150.0  3.054000  0.433594  2.0  2.8  3.00  3.3  4.4\n",
+       "petal length (cm)  150.0  3.758667  1.764420  1.0  1.6  4.35  5.1  6.9\n",
+       "petal width (cm)   150.0  1.198667  0.763161  0.1  0.3  1.30  1.8  2.5"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To see summary statistics\n",
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25225fd1-313b-4dd7-81e4-63e8a09490db",
+   "metadata": {},
+   "source": [
+    "### other options for palettes \n",
+    "\n",
+    "- \"PRGn\" \n",
+    "- \"flare\"\n",
+    "- \"colorblind\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6baff895-78f5-4c65-a804-32b372f95814",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1900x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "#%matplotlib inline # inline render graphs below cell (but maybe not necessary anymore) \n",
+    "\n",
+    "columns = df.columns # my data has 4 columns.\n",
+    "\n",
+    "fig, ax = plt.subplots(ncols = 4, figsize=(19,10))\n",
+    "plt.subplots_adjust(wspace = 0.5) # wspace = width space\n",
+    "\n",
+    "for i in range(0,4):\n",
+    "    s = sns.boxplot(ax = ax[i], data = df[columns[i]], showfliers = True)\n",
+    "    ax[i].set_xlabel(columns[i])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "32b155aa-a95b-438d-a0ee-0d1336bc462d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(146, 5)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "    # To remove outliers from 'sepal width (cm)'\n",
+    "q1 = df['sepal width (cm)'].quantile(0.25)\n",
+    "q3 = df['sepal width (cm)'].quantile(0.75)\n",
+    "iqr = q3 - q1\n",
+    "df = df[(df['sepal width (cm)'] >= q1-1.5*iqr) & (df['sepal width (cm)'] <= q3+1.5*iqr)]\n",
+    "df.shape # To find out the number of rows and column after outlier treatment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "665e444d-3d65-4dc6-94c1-60fb0677dc77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Boxplot for sepal width (cm) after outlier treatment\n",
+    "sns.boxplot(y=df['sepal width (cm)'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4e7a2ded-1e62-479f-82c9-2fe73e2af3b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Splitting the data into train and test sets\n",
+    "X = df.drop(\"Species\",axis=1)\n",
+    "y = df[\"Species\"]\n",
+    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3, random_state= 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "139a1941-1c75-4ead-81ed-83031c9f3ad6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, min_samples_leaf=10, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=3, min_samples_leaf=10, random_state=1)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(max_depth=3, min_samples_leaf=10, random_state=1)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "# Defining an object for DTC and fitting for whole dataset\n",
+    "dt = DecisionTreeClassifier(max_depth=3, min_samples_leaf=10, random_state=1 )\n",
+    "dt.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9c6be7f6-ae65-4685-be32-5f945f53e869",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cpAOhMEojRoxg0KBB+terV6+mXr16+tdNmjTRT3X83XffkZCQ8NJ9Lly4EGdnZ/3riRMn8u2339KsWTO6du1KdHQ0jRo1emFMn332WZb3xo4dS3BwMBUqVMDT05MOHTrg5OTEN998UySJkxDCOEjLgChUY8eO5cCBAwD88ccftGvXDoDBgwfrf3WfPHkyyzY3b96kU6dO+tcODg4AxMbG4uvri7e3N7169SIlJaVAYkxJSWHnzp307Nkzx+WbN2/O8mhiTnbt2oW7u3uWJx5iY2Np1qwZAC1btuTo0aMv3MeqVavw8PDg22+/1ceVlpZG9erVKVeuHLa2tly7du1VPpoQQuSJJAOiUA0cOFA/Tv+mTZvo27cvAEuXLkWr1bJx48YcR/vLycSJE5kzZ45+KOHMx/MyHT9+PNsIgRqNhpiYmBfu99tvv+Wjjz7KcQjh2NhYdDoddevWzXX79PR0li1blm28gYYNGxISEkJaWhqHDh3SD4iUk/fee4+oqCgOHjzI7t27+eWXX7h//z6VKlXSr2Ntbf3CfQghRH7JbQJRqJycnIiOjubx48ds376dPXv2oNPpmDp1KuHh4ZiamhIfH59lm+f/KGc+/Xrp0iVGjRoFZPxqzhxBMJO7u7u+aT+vEhIS0Gq1TJgwgcDAwGzLn01gchMUFESXLl0oU6ZMlvfnz5/PyJEjmTNnDrVq1aJatWq57iPzj76ZmRldu3bl7NmzNG/ePMvtiYcPH2JtbZ33DyeEEHkkyYAodJ07d2bu3LnUrFmTChUqEBERQWxsLEePHiU6Ojpb83ylSpX0ww5HR0eTnJwMQKNGjZgxYwYNGzYEMmYBfNbx48dznGBozZo11KlTJ8fYoqOjiYuLo3379ty5c4fU1FSaNWuGj48PAFu2bGHv3r369R89eoROp8vyi/3ChQuEh4fz448/cv78efr06cOhQ4eoVasWu3btQqfT0bt3bzp06ABkzEdQrVq1LBMWJSQkULFiRRRF4ciRIwwfPpwyZcpgZmbG3bt3qVChAlevXqV+/fp5OudCCPEqJBkQhW7gwIHY2tqyfft2IKP5PD4+Hm9vb9zc3LKtX7FiRdzd3XFzc8PDwwNLS0vgf7+0k5KSAJg6dSre3t767fLTMuDi4sLp06eBjB79cXFx+kQgKiqKKlWqULVqVf36W7Zs4enTp/z73//Wv/f111/r/63RaNiyZQsA69evZ/Xq1ZiYmDB69GjeeOMNAPr378/u3bspX768fjt/f3/279+v/xyZMfj7+9OjRw/S09Px8/PD3FyqrBCiEChC5FF4eLgCKOHh4WqH8lq2bdumODo6KuvWrXvlbceOHav89ddf+T52amqqMnTo0HxvryiKEhYWpri4uCj/93//90rblZTyE0IUPBmOWOSZDGdbvEn5CSFyI08TCCGEEEZOkgEhhBDCyElvJPHKLl++rHYIIh+k3IQQuZFkQOSZjY0NlpaWDBw4UO1QRD5ZWlpiY2OjdhhCCAMjHQjFK7l165Z+Ih5DsnDhQrZs2UJQUNALRwssTDdu3KB///707duXMWPGqBLDy9jY2PDWW2+pHYYQwsBIMiCKvRMnTuDh4cHXX3/NhAkTVI1l7ty5TJ48mePHj+Pq6qpqLEIIkVeSDIhiLTk5mWbNmmFjY8Px48cxMzNTNR6dToe7uzvx8fFERkbqB0wSQghDJk8TiGJtypQp/PbbbwQGBqqeCEDG3AKBgYHcvn07x6GRhRDCEEkyIIqto0ePsnjxYmbOnKmfr8AQNGzYkJkzZ/LNN99w7NgxtcMRQoiXktsEolhKSkqiadOmVKtWjSNHjhhEq8CzdDodXl5e3Lt3j8jISKysrNQOSQghciUtA6JYmjx5Mnfv3mXNmjUGlwhAxu2C1atXc+fOHaZMmaJ2OEII8UKSDIhi5/Dhw3z33XfMmTMHW1tbtcPJVYMGDZg9ezaLFy/myJEjaocjhBC5ktsEolhJTEykSZMmvPXWWxw+fBhTU8POZ9PT09FoNPz222+cP3+ecuXKqR2SEEJkY9hXUiGeM3HiRP78809Wr15t8IkAgKmpKWvWrOHevXtMmjRJ7XCEECJHhn81FeL/O3jwIMuWLWPu3LnUq1dP7XDyrF69enz99dcsXbqUkJAQtcMRQohs5DaBKBb+/vtvmjRpQr169Th48GCxaBV4Vnp6Ou+88w4xMTFcuHCB8uXLqx2SEELoFa8rqjBan332GfHx8QQEBBS7RAAybhesXr2auLg41YdMFkKI5xW/q6owOvv372flypXMnz+fOnXqqB1OvtWpU4d58+axYsUKDhw4oHY4QgihJ7cJhEFLSEjAwcGBRo0asX//fkxMTNQO6bWkp6fz7rvvcvXqVS5evEiFChXUDkkIIaRlQBi2cePGkZCQwKpVq4p9IgAZtwsCAgJ48OAB48ePVzscIYQAJBkQBmzPnj2sXr2aBQsWUKtWLbXDKTC1atViwYIFrFq1ip9//lntcIQQQm4TCMP04MEDHBwcaNKkCXv37i0RrQLPUhSF9u3bExUVxcWLF6lUqZLaIQkhjJi0DAiDNHbsWBITE1m5cmWJSwQATExMWLVqFY8ePWLcuHFqhyOEMHKSDAiDs2vXLtauXcs333zDm2++qXY4hebNN99k4cKFrFmzhp9++kntcIQQRkxuEwiDcv/+fezt7WnRogW7d+8uka0Cz1IUhY4dOxIZGUlUVBTW1tZqhySEMELSMiAMyqhRo0hJSeH7778v8YkAZNwuWLlyJcnJyYwePVrtcIQQRkqSAWEwfvzxRzZu3MiiRYuoUaOG2uEUmRo1arB48WLWr1/Pzp071Q5HCGGE5DaBMAhxcXHY29vj4uLCjh07jKJV4FmKotClSxfCwsK4ePEiVapUUTskIYQRkZYBYRA+/fRTUlNTWbFihdElApBxu2DFihU8efKEUaNGqR2OEMLISDIgVLd9+3Y2b97Md999R7Vq1dQORzXVq1fn22+/JSgoiB9++EHtcIQQRkRuEwhV/fXXX9jb2+Pm5sYPP/xglK0Cz1IUhe7du3Py5EmioqKwsbFROyQhhBGQlgGhqk8++YT09HSWL19u9IkAZNwuWLZsGWlpaYwcOVLtcIQQRkKSAaGarVu3sm3bNpYsWcI///lPtcMxGFWrVmXJkiVs2bKFbdu2qR2OEMIIyG0CoYp79+5hb29PmzZt2Lp1q7QKPEdRFHr16sWRI0eIiorijTfeUDskIUQJJsmAKHKKotCjRw+OHz9OVFQU//jHP9QOySD9+eef2Nvb4+XlxbZt2yRhEkIUGrlNIIrc5s2b+e9//8vSpUslEXiBN954g6VLl7J9+3a2bNmidjhCiBJMWgZEkbp79y729va8++67bN68We1wioU+ffpw8OBBoqKiqFq1qtrhCCFKIEkGRJFRFIWuXbty+vRpeWzuFWSOzti6dWv++9//yu0CIUSBk9sEoshs2LCBXbt2sXz5ckkEXoGNjQ3Lly9nx44dBAUFqR2OEKIEkpYBUSR+//137O3t8fX1ZePGjWqHUywNGDCAvXv3EhUVhaIolC1bVqY8FkIUCEkGRKFTFIXOnTsTHh4uk/C8hvj4eOzt7WnVqhVPnz6ldu3aLF++XO2whBAlgNwmEIVu7dq1/PTTT6xYsUISgdfw+PFj/Pz82LVrFw8fPiQ6OlrtkIQQJYS0DIhC9dtvv2Fvb897773H2rVr1Q6nWJswYQILFiygXr163Lx5kzfeeIPffvtN7bCEECWAJAOi0CiKgq+vL+fPn+fixYtyf/s1PX36lKVLl+Ln50dCQoL+vVKlSqkcmRCiuJPbBKLA6XQ6AFavXs3PP//MypUrJREoABYWFowZM4br16/TpUsXLCwsSE1NVTssIUQJIC0DokA9ePCAOnXqsGnTJvr06UPPnj1ZvXq12mEJIYR4AUkGRIH65ZdfaN26NS4uLty5c4fTp09TvXp1tcMSQgjxAuZqByBKlps3bwJw+vRp3N3defPNN7l3716xHGTo1q1bxMXFqR2GKGA2Nja89dZbaochhEGRZEAUqIiICABMTU2JiYlh/fr1xTYRsLOzIzk5We1QRAGztLTk8uXLkhAI8QxJBkSBOnv2LCYmJkyePJnPP/8cS0tLtUPKl7i4OJKTk9mwYQN2dnZqhyMKyOXLlxk4cCBxcXGSDAjxDEkGRIH68ccfefToUYmZXc/Ozo4WLVqoHYYQQhQqSQZEgbKyssLKykrtMIQQQrwCGWdACCGEMHLSMlDEpId6wTLEnuGRkZEkJyfj6uqa6zp+fn44ODjQs2fPPO3zjz/+YNGiRcyePTvH/bRt25Y9e/bQv39/ADQaDcHBwS/svDl9+nTat2+Pi4tLnmJ43scff8zUqVOpWbNmvrZ/kdTUVDQaDVFRUaxatSrH8/TOO+/oB7g6deoUf/zxBwkJCTg5OdGkSRMAvvnmG5o3b17g8QlR0kgyUISkh3rBM8Se4ZGRkcTFxb0wGXhVVatWzZYIPOvhw4cEBQXpk4GXefz4MSdPnmTatGn5jmnYsGEsXryYuXPnvnTdpKSkV7p9ZG5uTnBwMCtWrMh1nUOHDgEQFRXF2LFjsba2JiEhgdatW7N79+48H0sIIclAkZIe6gWrKHuGa7Va5s2bh4WFBTExMXz55Zd06dKFc+fOMW7cOHQ6HQ0bNmT58uUsWrSIv//+m927d7Njxw5GjRpFbGwsiYmJLF68ONckYfDgwUyZMoXSpUtTv3594uPjOX/+PDt37uSTTz5h5MiR7N69G61Wy9ixY6lZsyYWFhY4ODiwaNEiTp06hUajYfr06QD4+/tz+vRprK2t2b59e5ZjHTp0iNatW+tf+/v7s23bNsqUKcO4ceOoUKECM2fOxMrKiitXrjBnzhxWrlzJzZs3CQwMxNnZmZYtWzJy5Mhcz1l6ejqHDx9m3bp13Lp1i8OHD+f5fJuYmFCtWrU8rRsUFES/fv30r0NDQ/Hw8KBZs2bMnz+fMmXK5Pm4QhgrSQZUID3Ui6fff/+d0NBQEhMTcXd3p0uXLowZM4ZNmzZRtWpVxo8fz/79+xk9ejRxcXF89tlnACxdulT/R3X06NH8/PPPOe5fo9Fw+PBhSpcuja+vL8eOHePs2bNoNJos602cOJHdu3dTvXp12rZtC8Do0aP59ddfs/wi9vLyYvbs2XTu3JkLFy7om84hI5GqX78+ABcuXGDfvn2cOHECMzMzdDodx44dIzk5mf3797Nz5078/PwICwtDq9WycuVKnJ2d9ftKSUnJ8gc3NjaWFStWcPLkSTw8PPj8889p0KABANeuXWP48OHZPvvs2bOzJCev4r///S+nTp0CoFq1aly/fp1y5coxbdo0Fi1axKRJk/K1XyGMiSQDQuSRs7Mz5ubmVKpUiXLlypGUlMTFixfp27cvAImJiTg4OGBiYqLfRqfTMXXqVMLDwzE1NSU+Pj7X/bdp04ZJkyZhaWnJtGnTCAoK4sKFC4wePZr79+/r13v8+DE1atQAoGXLlrnuz8nJCYBatWpl2R4yZpTMdPnyZTw8PDAzMwPQ/7958+aYmJhQo0YNmjZtipmZGTVq1MiyLxMTE54f0Tw0NJTt27fzwQcfMGDAgCx9CurXr49Wq8015lf1yy+/0LBhQypWrAhA6dKlKV26NAB9+vTRt5IIIV5MniYowQIDAzly5Ei+l7/MypUrcXV1RaPRcOPGjWzLQ0NDcXV1xdXVtUTcww0PD0en05GQkEBiYiJWVlY0adKE4OBgtFotYWFhDBw4EAsLC9LS0gA4d+4csbGxHD16lOXLl2f7w/ms2rVrc/PmTR4+fIiTkxNXrlzhyZMnVKhQIct6ZcuW5e7duyiKQlhYGECWY2Z6Nil5/rh2dnZcu3YNgMaNG3P8+HHS09MB9P9/dvvc9pWenk7ZsmWz7Ltnz55ERUXRuHFjxo8fj6+vL2vWrAEyWgY0Gk22/zJ/2b+q5/tJ/P333/p/a7VafeuHEOLFpGWgBBsyZMhrLX+R+/fvExAQwIkTJzh79iyTJ09m69atWdYZO3YswcHBVKhQAU9PTzp06KD/1VkcVa1alW7dunH79m1mzZoFwMKFC+nXrx9paWmYmprq+wQsWbKEsLAwAgICiI+Px9vbGzc3t5cew87OTv8EgI2NTY59S+bOnYuvry/Vq1fXJwqZgzz17NlTf3viRd555x2WLFkCgIODAz4+Pri6ulK2bFnGjh2bLQHJSVhYGJ6enjkuMzc3p3PnznTu3JkHDx6wefNm4NVaBnr37k1YWBjlypXj1KlT+Pv7ExgYSJ06dfDy8kKn07F3716+/vpr/TZHjx5l2rRpWFlZYW1tTWBgYJ6OJYTRU0SRCQ8PVwAlPDy8QPebmpqq9OnTR9FoNMqUKVMUe3t7RVEUZdq0acq2bduUmJgYxdXVVRk0aJDSpEkTZdOmTVmW58eePXuUSZMm6V83a9Ysy/LHjx8rLi4u+te9e/dWoqOj83Ws3BTW+cxp34cPH1Y++eSTAj+Omvz8/JRffvkl39t/9NFHyq1btwowosJXmN8ZIYozaRkoAX788UeqVavG5s2bOXbsGDt27Mi2zp07dwgJCeHRo0e0b99ef5/7eXnt4PXgwQMqVaqkf53ZtJzp/v37WZZbW1tnu28t1PU6jxUCLF++vIAiEUKoTZKBEuDatWv63t25dSizt7fXd65KSUnJdV95bca1trbmwoUL+templm7n1SuXJmEhAT964cPH2Jtbf3S/RqqzHvbQghREkkHwhLA1tZW35EsNDQ0x3We7QD2Innt4OXi4oJWq0Wn0xEREYGtrW2W5WXKlMHMzIy7d++SlJTE1atXpTNXIVO7w6hGo8HNzQ2NRsOcOXPyfRwhRNGTloESoGvXrmzbtg2NRoOzszMWFhb53ldeWwYqV67M4MGD8fDwoFSpUgQEBABk6eDl7+9Pjx49SE9Px8/PD3Nz+boVJrU7jALs2LHjhUMgCyEMk1ydSwBzc3PWr19PqVKlOHLkCLdv3wYyxq3P9OyjfRcvXsy2PD8+/vhjPv744yzvPfsHx8XFhZMnT77WMUR2aWlpDBw4kHv37uHq6sqOHTu4ePGifp4CZ2dnBgwYQL169YiMjGTKlCn07dv3ledDeNbp06fRaDSYmZnh7OzM1atXs61jYmJCt27dsLS0ZO7cuTRr1qwgPq4QoghIMlBC9OzZkwcPHpCamqr/lS5KJkPsMAoQHBxMlSpViIqKYtCgQUREROTj0wkh1CDJQAmR0x8EUTIZYodRgCpVquiPbW5uzuPHj7MNSCSEMEzSgdDIabXaF042U5ASEhKwsbEhODgYgAMHDug7KDo4ONC9e/ciiaO4M8QOo/C/0f/++OMPEhMTJREQohiRlgFRZObNm4eLi4v+tY+PDz4+PgB89tlnODo6qhVasWKIHUY9PDxo06YNlpaWpKamsnjx4nzHJIQoepIMGLiYmBgGDRpEqVKlsLS05KeffiIoKIhVq1bx6NEj2rVrx4wZM9BqtS+dctbe3p727dsTGhqKra0tq1atynKskJAQ/Pz8UBSFtm3bMm3aNHbv3s1XX31F2bJlcXd3Z8aMGfn6HPfu3SMmJibHZm1FUdi1a9drd2g0FobaYTQ8PPy19i+EUI8kAwbu8OHDdO/enXHjxuk7bXXt2pX+/fujKAru7u7cvXsX4KVTziYlJdG/f3/8/f0ZOnQoBw8epFSpUkDGH+RJkyah1WqxsrKiZ8+eXLlyha1bt7Js2TJatGiRY6exTp06kZiYmOW9Pn36MGLEiCzvzZgxg0mTJvHDDz9k28fRo0dxdHSkXLlyBXLOjIF0GBVCFCRJBgxcnz59mDFjBoMGDcLBwYFJkyYREhLCwoUL0el0XL9+nd9//x14+ZSzpUqV0k9r6+LiwtWrV7G3twcgLi6OGzdu0LFjRyBjxMBbt24xbdo05s2bR2JiIr169aJr165Z4svLbIQxMTEkJCTQtGnTHJOBTZs20a9fv/yfJCMkHUaFEAVJkgEDZ2JiwuzZs4GMe+zdunXjiy++QKvVUrFiRVxdXfVTyr5sytnU1FTOnj2Lo6MjZ86cyfIH2MbGhgYNGrB3717Kli1Leno6iqLw5MkTli9fztOnT7G3t8+WDOSlZeDs2bNcv36d9u3bc+3aNcqXL0+DBg1o2rQpqampHDp0SO4xFxGtVktwcDDfffddoR5nyZIlrF+/HgsLCxYtWoSjoyOJiYm8//773L9/n5o1a7Jq1SrKlClTqHEIIfJGkgEDt3v3bhYvXoyZmRnVqlWjbt269O3bFy8vL+zs7LCyssrzvqysrFi3bh2jR4+mfv36tG3bVj88rYmJCTNnzqRDhw6YmJhQqlQpNmzYwIIFCzh58iSpqak5Po+el5aB7t27658UyBz4pmnTpgDs378fLy+v1+oEJwzLvXv32LBhAydOnODevXsMGDCAw4cPs2LFCry8vBg9ejQLFiwgMDAwWx8EIYRKVJwx0eioPX1q5tTGJUVRTmFcVG7cuKG4ubkpGo1G8fX1VRRFUTZu3Ki0adNGcXZ2Vj7//HNFUTKmVG7btq3StWtXpVGjRsqPP/6odOzYUbG3t1dCQ0MVRVGUxo0bK+PGjVM8PDyUDz74QElPT88yFfOhQ4cUDw8Pxd3dXfHz81MURVF27dqltGrVSvHy8tIf61X98ssvyvDhw/Wv69Wrp6SkpCgff/yxcvz4cUVRFOXYsWNKv3798neSXoPadVAIQyUtA0IYkJLQYbR+/fqEhYWRnJxMbGwssbGxPHjwAAcHB/bt24ebmxs///yzTGkthAGRZMCIZD5iJgxXSegwWqVKFSZOnEiHDh2oVasWzZo1w8bGhmHDhjF69Gi8vb1p0aIF1apVK7DzJoR4PTICYTHh5+enH7mvMAUGBlKvXj3Wr1+vf0+n09GoUSPmz58PwIULF/Qj1Tk7O9OiRYsX7vNlU9++bN2AgABq165dJJ9fbZkdRtevX8/Bgwe5evUqX3zxBT/88ANarZY6deq8codRgDNnztCgQQP9Os92GNVqtURERODt7U21atVYvnw5q1ev5rPPPssWX6dOnbKNVrhs2bJs6/Xr148jR47wf//3fzRq1Ahzc3PKlCnDihUrCAkJwdraWkacFMKASMuAyGbEiBEMGjRI/3r16tXUq1dP/7pJkyb6Ueu+++47EhISct1XXqe+fdG6w4YN0w+sU9KVhA6jAAMGDODu3btUqlSJpUuXAhlJ5Keffoq5uTkeHh507tw5z59FCFG4JBlQ0dixY/H19cXHx4c//viDwYMHs2/fPgYPHkxsbCyJiYksXrwYV1dX/TY3b95k5MiR+ouyg4MDFy9eJDY2lhEjRpCSkkKVKlVYv359gTy2lZKSws6dO+nZsyfx8fHZlm/evJl169blun1epr7Nz7olVe/evendu3eW9yZPnszkyZOzravRaABwdnYmMDAQgEaNGulbUExMTFi4cGG2bTK38/b2xtvbO8vyOXPmFMCngI0bN2Z779kkUghhWOQ2gYoGDhyov2hu2rRJP83s0qVL0Wq1bNy4kS+//DJP+5o4cSJz5swhJCQET09P1q5dm2X58ePHc5yQJiYm5oX7/fbbb/noo49ynPgmNjYWnU5H3bp1c90+L1Pf5mddIYQQBUdaBlTk5OREdHQ0jx8/Zvv27ezZswedTsfUqVMJDw/H1NQ026/x5/8oZ94fvnTpEqNGjQIyfs1ndgzL5O7u/sq/yhISEtBqtUyYMEH/y/NZzyYwucnL1Lf5WVe8nHQYFULklSQDKuvcuTNz586lZs2aVKhQgYiICGJjYzl69CjR0dH07Nkzy/qVKlXS9yaPjo4mOTkZyGgenjFjBg0bNgTg6dOnWbY7fvw4U6dOzXb8NWvWUKdOnRxji46OJi4ujvbt23Pnzh1SU1Np1qyZfqbBLVu2sHfvXv36jx49QqfTZfl17+LiwpdffolOp+PcuXP6qW9fZV0hhBCFS5IBlQ0cOBBbW1u2b98OQMOGDYmPj8fb2xs3N7ds61esWBF3d3fc3Nzw8PDA0tISgPnz5zNy5EiSkpIAmDp1apb7wflpGXBxceH06dNAxlMGcXFx+kQgKiqKKlWqULVqVf36W7Zs4enTp/z73//Wv5fb1Levsq4xyRyh8fkksKAFBgby1Vdf4efnx6BBg7h+/TrDhg0jNTWVIUOG8OGHH75we51Oh729PcOHD+ezzz4jPT2dYcOGcePGDSpWrEhgYCCVK1fOdXsrKyv9DJaTJ0+mffv2PH78mCFDhvD777/j4ODAkiVLMDU1pVevXhw+fJi4uLgCPQdCiGeoOuSRkSkOo59t27ZNcXR0VNatW/fK244dO1b566+/CnzdVatWKc2aNVP27NmT5f2SOALhtGnTlG3bthX6cdasWaPMmzdP/7pnz55KWFiYkpqaqri4uCjx8fEv3P77779XfH199fsIDg5WxowZoyiKomzfvl2ZNGnSC7fPaTTM7777Tpk/f76iKIryySefKD/99NML18+P4lAHhVCD3JQVWfTs2ZOIiIgsjxbm1YIFC7CxsSnwdYcNG0ZkZCQdOnR45ZgMwdixYzlw4AAAf/zxB+3atQNg8ODB+rEaTp48mWWbmzdv0qlTJ/1rBwcHIKPTpq+vL97e3vTq1YuUlJQCifHq1as4OTlhbm6Ol5cXoaGhua777BMmma5du4azszMArVq14ujRoy883u3bt/H09GTQoEH6QZKOHj2q7+vSsWNHjh079rofSwiRR5IMCFHIisNTI8r/74gKGR05XzRUcE5PmDg4OLB//36APA01fOPGDY4ePUqbNm34/PPPgaxPk7wsBiFEwZI+A0IUMkN/agSyPrnx8OFDmjdvnuN6uT1h4uvrq09E3NzcqFGjxguPV6VKFQD69u3LihUrgIwEICEhgapVq/Lw4UOsra1f+XMIIfJHkgEVXL58We0QSoTidB4N+akRAFtbWyIiImjWrBlHjhxh4sSJQEZz/ptvvqlf70VPmMyePRvIGHAos2NpTk+NJCUlUaZMGczMzNBqtdSvXx8AT09PfvrpJxo2bMiePXv0t1OEEIVPkoEiZGNjg6WlJQMHDlQ7lBLD0tIyz30P1GTIT40AzJ49O8vTBJUrVyYtLY0ePXpw5swZ/Xq5PWHy119/0atXL8zNzWncuDH+/v5Azk+NREdHM3z4cMqXL4+FhYX+qZGhQ4cyZMgQPDw8aNy4cbHtIyJEcWSiPHuzUBS6W7duySNSBcjGxoa33nqrwPcbERGBk5MT4eHhL52IqTgKDg5m1qxZjB07NtfOor/88gsRERFZ/pC/qnHjxjFlypTXSth69erF1atXOXfuXL73kamkl6sQ+SXJgBA5kD8aJZOUqxA5k6cJhBBCCCMnyYAQQghh5KQDoRAvUJyeWBAvJ+UpRM4kGRAiB/LkR8lVXJ5AEaIoSQdCIXJRWE9+KIrCtm3b8Pf3p379+syZMyfLs/zG6NatW0yePJnr168zfvx4evXqlW3gpYJSWE+gCFGcSTIgRBF6+PAhH374IcHBwYwcOZL58+dTunRptcMyCCkpKXz22WcsWbKEnj17smrVKipWrKh2WEIYBUkGhCgioaGh9OnTh/v37xMQEECPHj3UDskgbd++nWHDhlG5cmW2bNmin+pYCFF45GkCIQqZoih88803uLm5YWNjw9mzZyUReIEePXoQERGBjY0Nbm5uLFq0CPnNIkThkmRAiEJ0//593nvvPcaOHcvIkSM5fvz4C+cIEBnq1q3L8ePH+eSTTxgzZgzdunWTWQyFKERym0CIQnLq1Cn69u3Lo0ePCAwMpEuXLmqHVCzt3LmTIUOGUL58ebZs2cLbb7+tdkhClDjSMiBEAUtPT2fevHl4enpSo0YNIiMjJRF4DV26dOHs2bNUr14dDw8P5s+fT3p6utphCVGiSDIgRAGKi4ujc+fOTJw4kXHjxnHkyBF5jK0A1KpVi6NHjzJu3DgmTJhAly5dZMIvIQqQ3CYQooAcP36cvn37kpKSwrp16/D19VU7pBJpz549vP/++5QtW5ZNmzbh7u6udkhCFHvSMiDEa0pPT2fWrFloNBrq1q1LZGSkJAKFyNfXl8jISGrXro1Go2H27Nly20CI1yTJgBCv4c8//6RDhw5MnTqV//znP4SEhFCzZk21wyrxatasyeHDh5k8eTKff/45HTp04M8//1Q7LCGKLblNIEQ+HT58mP79+5Oens6GDRvw8fFROySjdODAAQYMGIC5uTlBQUFoNBq1QxKi2JGWASFekU6nY/r06bRt2xY7OzsiIyMlEVCRj48P586do1GjRrzzzjt8+eWX6HQ6tcMSoliRlgEhXsHdu3cZOHAghw8fZtq0aUydOhUzMzO1wxJkJGlfffUVX375JW3atGHjxo1UrVpV7bCEKBYkGRAijw4cOMDAgQMxNTUlKCiINm3aqB2SyEFISAgDBgwgPT2djRs30rZtW7VDEsLgyW0CIV4iLS2NqVOn0q5dO5o3b865c+ckETBg3t7eREZG0qxZM959912++OIL0tLS1A5LCIMmLQNCvMCdO3fo168fJ06c4KuvvmLy5MmYmkoOXRykp6czZ84cvvjiC9zd3QkKCqJGjRpqhyWEQZJkQIhc7N27l/fff5/SpUuzadMmPDw81A5J5MOxY8fo168fT548Yf369bRv317tkIQwOPITR4jnpKamMmnSJHx9fWnVqhWRkZGSCBRjHh4eREZG0rJlSzp06MDkyZNJTU1VOywhDIq0DAjxjFu3btGvXz/OnDnDrFmzGD9+vNwWKCHS09Px9/fnP//5Dy4uLmzevJk333xT7bCEMAiSDAjx/z07Ve7mzZtp3bq12iGJQnDq1Cn69OlDUlISgYGBdO7cWe2QhFCd/OQRRu/p06eMGzeOrl274uHhwdmzZyURKMFat25NZGQk7u7udOnShfHjx/P06VO1wxJCVdIyIIxaTEwMffv25ezZs8ybN49Ro0ZhYmKidliiCCiKwqJFi5g4cSKOjo5s3ryZOnXqqB2WEKqQlgFhtH744QccHR3566+/OHHiBKNHj5ZEwIiYmJgwZswYTpw4wZ9//omjoyP//e9/1Q5LCFVIMiCMzpMnT/j000/p0aMHbdu2JSIigpYtW6odllBJy5YtOXv2LO+88w7du3dn1KhRPHnyRO2whChScptAGJVr167Rp08fLl68yIIFC/j3v/8trQECyLhtsHTpUsaNG0eTJk3YsmUL9erVUzssIYqEtAwIo7F161ZatGjB33//zS+//MInn3wiiYDQMzEx4ZNPPuHUqVM8fPgQR0dHtm7dqnZYQhQJSQZEiff48WM+/vhj+vTpQ8eOHQkPD8fR0VHtsISBatGiBREREfj6+tKnTx9GjBjB48eP1Q5LiEIltwlEiXblyhV69+7N1atXWbx4McOHD5fWAJEniqKwcuVKRo0aRcOGDdm6dSsNGzZUOywhCoW0DIgSa8OGDTg5OfHkyRNOnz7Nhx9+KImAyDMTExP+9a9/cebMGZ48eYKTkxMbN25UOywhCoUkA6LESU5OZtiwYQwaNIju3bsTFhZG06ZN1Q5LFFNNmzYlLCyMbt26MXDgQIYPH05ycrLaYQlRoOQ2gShRLl26RO/evblx4wZLlixhyJAh0hogCoSiKAQGBvLJJ59Qt25dtm7dSuPGjdUOS4gCIS0DosQIDAzE2dkZgLCwMIYOHSqJgCgwJiYmDB06lNDQUBRFoWXLlgQGBqodlhAFQpIBUewlJiYyePBghg4dqp9xUH6xicJib29PaGgoffv2ZejQoQwePJjExES1wxLitchtAlGsXbhwgd69e3P79m2WL1/OwIED1Q5JGJH169czYsQI3nzzTbZu3UqTJk3UDkmIfJGWAVEsZT721apVKywsLAgPD5dEQBS5QYMGERYWRqlSpWjVqhWrVq1Cfl+J4kiSAVEspKenExQURFJSEn///Tf9+/fnX//6F4MHD+aXX36R57+Faho1asTp06cZPHgwH374IQMGDODRo0ckJiYSFBREenq62iEK8VKSDIhiITg4mAEDBrB//36cnJz46aef2Lx5M8uXL6ds2bJqhyeMXNmyZVm+fDmbNm1i9+7dtGjRggMHDjBgwAC2b9+udnhCvJT0GRAGT6fT0bRpUxRF4fr16zg4OLBlyxbq16+vdmhCZPPrr7/Sp08fLl26RN26dTE1NeX8+fOYmspvL2G4zNUOQIiXWbt2LZcuXQLAxcWFhg0bYmZmpnJUQuTM3NycJk2aYGFhwenTp4GM7/DQoUNVjkyI3EmqKgzexIkT9f/+9ddf+e2336STljBYiqLw22+/cfXqVf17z36HhTBEcptAGLytW7fy5MkTvL29qV69ugwkJIoFRVH4/fffOXToEGXKlKF3795qhyREriQZEEIIIYyc9BnIg1u3bhEXF6d2GKKQ2djY8NZbb6kdhihCUreNi9Tx3Eky8BK3bt3Czs5OZikzApaWlly+fFkuFkZC6rbxkTqeO0kGXiIuLo7k5GQ2bNiAnZ2d2uGIQnL58mUGDhxIXFycXCiMhNRt4yJ1/MUkGcgjOzs7WrRooXYYQogCJnVbCHm0UAghhDB6kgwUosjISE6ePPnCdfz8/AgODs7ynlarZeTIkYUSg4ODw0u36d27d76nZE1LS6N79+6FPh77jBkzcv0sZ86cwcfHBy8vL77++msAAgMDqVevHhqNBo1GU6ixCfGs3OpzXupiXjx8+JCgoCD9a41G89JOkdOnT9cPiJQfH3/8Mb/99lu+t3+ZnOpwpqSkJHx8fPDw8MDV1ZUzZ84AcPPmTapUqaKv45GRkYUWX0kktwkKUWRkJHFxcbi6uhabGE6ePEmdOnUoV65cvo5nbm5O69at+fnnn/H19X3p+omJia98rPj4eP2IhM978uQJ06dPZ8eOHVhaWmZZNmLECD777LNXOpYQhi4zGejfv3+e1n/8+DEnT55k2rRp+T7msGHDWLx4MXPnzn3puklJSVhZWeV53y+qw5BxjQkICOCtt94iOjqakSNHcvDgQQBat27N7t278/5BhJ60DLwirVZLx44d6datG82bN2fnzp0AnDt3jnfeeQeNRsNHH32EoigsWrSIZcuWodFoSEhIYPDgwWg0GpydnV/aYpApJCQET09PPDw8mD59OpDRmjBs2DA6depEq1atuHfvHgDz5s2jVatWDBo0CCcnJ+Li4rLF8OTJE0aMGIGzszPz58/PdrwffviBDh06ABmV8v3338fT05M2bdpw69Yt/Pz86Nu3L76+vri7uxMUFISPjw9vv/02CQkJALRv3/6Fk7MkJycTFBREp06d8PPzy/O5zzRnzpxc/6ifOnUKS0tLevbsSbt27bh48aJ+2apVq/Dw8ODbb7995WMK4xATE4O7uztt2rShY8eOAMTGxuLr64u3tze9evUiJSUl1+vAggUL8Pb2pkWLFixbtixPx8xt/506daJ37944ODhw5MgRION64OjoSJcuXejRowfBwcEsWrSIU6dOodFo9Ov5+/vj7e1Njx49sh3v0KFDtG7dWv/a39+ft99+G41Gw86dO9Fqtfj4+PDee+9hZ2fHjh076NSpEw4ODoSFhQHQsmVL/bFykp6ezqFDhxg8eDCdOnXK03nI9KI6DFC6dGl9B0Bzc/MsQ5OHhobi4eHByJEjSUlJeaXjGj1FvFB4eLgCKOHh4YqiKMrhw4eV5s2bK6mpqcqDBw8Ue3t7RVEURaPRKHfv3lUURVHGjRun/Pzzz8qaNWuUefPm6feVmJioKIqiREdHK+3atVMURVGmTZumbNu2LcsxDx8+rHzyySdKenq64uzsrN+uR48eSnR0tDJt2jRl0qRJiqIoyvz585XFixcrf/zxh+Li4qKkpaUpDx8+VKytrZW//vorWwxWVlZKXFyc8uTJE8XW1jbb5/X19VVu376tKIqifPvtt8r06dP1y3Q6nTJt2jRl8uTJiqIoypgxY5SRI0cqiqIoU6dOVYKCghRFUZTk5GTFxcUl275PnTqlfPDBB4qPj4/y7bffKn/99Zd+2bZt2xQvL69s/yUnJ2fZx+3bt5UBAwYoiqLoz/2zgoKCFDs7OyUxMVG5dOmS4u7uriiKojx48EBJS0tTUlJSlHfffVc5depUlu2eL2dR8uVU5gEBAYq/v7+iKBnfd0VRlN69eyvnzp1TFEVRFi9erCxfvjzX60BmXX3y5InSqFEjRafT6evz8zK3yW3/rq6uik6nU8LCwpTu3bsriqIozs7Oyh9//KGkp6cr77zzjrJt2zYlJiZG6dixo36/Xl5eyt69exVFUZROnTop58+fz3LcuXPnKuvXr1cURVHOnz+v+Pj4KGlpaYqiKEpaWpr+2Onp6cqPP/6oNG/eXElLS1MOHjyo/Otf/9Lvp1WrVsrjx4+z7PvmzZvKf/7zH8XLy0uZOnWqcuXKFf2yX3/9Ncc6fvLkySz7yK0OP0+n0ym+vr7KoUOHFEVRlJSUFOXRo0eKoijK//3f/ylz5szJsr7U8ReT2wT54OzsjLm5OZUqVaJcuXIkJSVx8eJF+vbtC2Q0fTs4OGQZNlen0zF16lTCw8MxNTUlPj7+pceJi4vjxo0b+l8oDx8+5NatWwA4OTkBUKtWLaKiooiJiaFJkyaYmZlRsWJFGjRokOM+a9WqRZUqVQCwsLDItlx5ZkDKS5cu0a9fP/3rzFnXMnte16hRQ7+vGjVqcP/+fQBMTExynDtg586dREZGMmrUKLp370758uX1y3r27EnPnj1fek6+/PJLpkyZkutya2tr3NzcsLKyws7OTt9aUalSJQDMzMzo2rUrZ8+e5e23337p8YRx6dOnDzNmzGDQoEE4ODgwadIkLl26xKhRowBISUmhY8eONGzYMMfrwJYtWwgMDMTExIS7d+/y4MGDlx4zt/03b94cU1NTatWqpa9bKSkp/POf/wQyrkO5efb6kLltpmfr5uXLl/Hw8ND/us78f/PmzTExMaFGjRo0bdoUMzOzLHUccq7noaGhbN++nQ8++IABAwZQs2ZN/bL69euj1Wpfej5yq8PPGzNmDG3atMHb2xvIaDEoXbo0kFGOmS2pIm8kGciH8PBwdDodiYmJJCYmYmVlRZMmTdi6dSs2NjYApKamsm3bNtLS0oCM2wixsbEcPXqU6OjoPP3hs7GxoUGDBuzdu5eyZcuSnp6OoiicOHEiS6KhKAp16tThwoUL+rgyJ0mxsLDQxwC8dFx/Ozs7rl27Rs2aNbG3t+fIkSN4eHgA6DsFPruP5+MAuHbtGvb29tn2PWvWLCZNmsTWrVvp0aMHNjY2/Otf/0Kj0RAcHMx3332XbZvMz54pJiaGcePGARnNqxMnTsxy39LFxYVZs2aRnp7OvXv39NsmJCRQsWJFFEXhyJEjDB8+/IXnQRgnExMTZs+eDYCPjw/dunWjUaNGzJgxg4YNGwLw9OlTTp48meN1YPbs2Vy6dIn09HQaNGiQpwm1ctt/TnWrTJky3Lt3jzfeeIOwsDCcnZ2z1fHMz/H8tpns7OwIDw8HoHHjxgQEBJCeno6pqWme6zhkXA+erZuQkdS/99577N27l/Hjx/Po0SN69erF0KFDuXbtWo71bvbs2VluW+RWh5+V2anw2duFf//9NxUqVAAybufKFOevRpKBfKhatSrdunXj9u3bzJo1C4CFCxfSr18/0tLSMDU1ZfHixbi6urJkyRLCwsIICAggPj4eb29v3Nzc8nQcExMTZs6cSYcOHTAxMaFUqVJs2LAhx3X/+c9/0qNHD95++21sbW2pWbMmFhYWWWJYs2bNS4/Zo0cPdu7ciUajYfjw4QwfPhxPT0/MzMxYu3ZtnuL++eef6d69e47LKlasyIcffsiHH37I9evX9fcg89oycODAAf2/HRwc9InAmDFjmD59OtbW1gwdOhQvLy/S0tLw9/cHMu6L7t+/HwB3d3d8fHzy9FmEcdm9ezeLFy/GzMyMatWqUbduXebPn8/IkSNJSkoCYOrUqZiamuZ4HXj33XdxdXXFwcGBypUr5+mYue0/J19//TXt27enevXqWFpaYmFhQdWqVYGMOpSXDrLvvPMOS5YsATLqkI+PD66urpQtW5axY8fq/6C+SFhYGJ6enjkuMzc3p3PnznTu3JkHDx6wefNm4NVaBnKqw4GBgdSpUwdbW1umTJmCm5sbGo2GGjVqsHHjRo4ePcq0adOwsrLC2tqawMDAlx5LPEOduxPFR059BnK6/2cInj59qihKxv3xhg0b5ns/vXr10t97e1WpqanKe++9p7/fWlzI/UTj8zplrtZ1ILOOp6enK97e3kpUVFS+9uPn56f88ssv+Y7jo48+Um7dupXv7dUgdfzFpGWgBJk5cyZarZaEhARmzJiR7/1s3bo139uam5vz3//+N9/bCyFyt2fPHhYsWEBSUhJt27alcePG+drP6zxWCLB8+fLX2l4YHkkGXpEhD1qTn8f0hBCvTq3rQNeuXenatWuRH1eUfDLOQAkQGBj4wmd+X7b8ZVauXImrqysajYYbN25kWz5y5EiqV6+eZZS1AwcO6C+YDg4O+j4Ea9asoWHDhgU2+poQJZ3a9Ts0NBRXV1dcXV2zDeizfv36LE8FzZw5E09PT1q1asWECRPyHZNQgdr3KQydsd9nio+P149fEBoaqvTq1SvbOnfu3FFCQkJyvYc6fvx4ZcOGDYqiKMqff/6pPH36NMcxAtRk7OVsjKTM81a/3dzclDt37iiPHj1SHB0d9WMSPHnyROnSpYtiZ2enX/fJkyf6f3t6eiq//vpr4X+IPJLyfjFpGShG0tLS6Nu3L23atOHzzz/X/7rOnN/g5s2buLm58f7779O0aVN9L96c5j/Iq9OnT6PRaDAzM8PZ2Vn/yOKzqlevnusji4qisGvXLn3T5j/+8Q9KlSqVr1iEKMkMsX6npKSQlpZG9erVKVeuHLa2tly7dg2AFStWMGTIkCxPPmSOXZKamoq1tbV+TARh+CQZKEZ+/PFHqlWrxuHDh2nfvn2O69y5c4eVK1cSEhKS43DDma5du6Zvxn/2v1OnTmVZ78GDB/oBe4BXnoDo6NGjODo65nuuAyGMhSHW7/v372dZbm1tzf3790lKSmLfvn1069Yt27EnTpxI/fr1sbGxeaU5CYS6pANhMXLt2jX9qGMtW7bMcR17e3v9SFwvGpv7VZ75vXDhgv51bs8/52bTpk1ZRjEUQuTMEOt35cqVs4wA+PDhQ6ytrVm4cGGuM6vOnTuX2bNn07dv3zxPWCbUJy0DxYitra1+kJ7Q0NAc13nZCIOZ8vrLwcXFBa1Wi06nIyIiAltb2zzHm5qayqFDh/QTHwkhcmeI9btMmTKYmZlx9+5dkpKSuHr1KvXr1yc6OppvvvmG9u3bExsby+DBg4GMyc0gY1jjChUq5Dh6oDBM0jJQjHTt2pVt27bpZz7MaW6BvMrrL4fKlSszePBgPDw8KFWqFAEBAcD/RgPz8vJi5syZ/PDDD/z5559ER0frpxPdv38/Xl5eWeLcvXs333zzDTdv3qRt27b4+/vTrFmzfH8OIUoKQ63f/v7+9OjRg/T0dPz8/DA3N88yEqqDg4N+dNIRI0Zw48YNUlNTad26NW3atMn3ZxBFy0RR8jB4thGLiIjAycmJ8PBw/QQ9akpNTaVUqVIcOXKEpUuXsmXLFrVDKhEMrZxF4TPEMpf6XXgMsbwNibQMFDM9e/bkwYMHpKam6rN4IUTJIPVbqEWSgWJmx44daocghCgkUr+FWqQDoRBCCGHkJBkwclqtNtdHhAqSRqPRTzk6Z84cAM6dO4erqyteXl5oNBp+++23Qo9DCGNSVPUbICEhARsbmywDIP300094e3vj6enJunXriiQOkT9ym0AUmR07dmBjY6N/3bhxY06ePAnA6tWrWb58+WvNtiiEUM+8efNwcXHRv/7zzz9ZvXo1Bw4cwMzMTMXIRF5Iy4CBi4mJwd3dnTZt2tCxY0cAgoKC8Pb2pmXLlkydOhXI+AXg4+PDe++9h52dHTt27KBTp044ODjon122t7dn/PjxeHp6MmzYMJ5/kCQkJARPT088PDyYPn06kPEooIuLCxqNRn+s/DAxMaFbt260a9eOc+fOAWQZljgxMZHmzZvne/9CFEclpX7fu3ePmJiYLIMl7dmzh/Lly9OhQwe6du0qLX8GTloGDNzhw4fp3r0748aN0w8V2rVrV/r374+iKLi7u3P37l0AkpOT2b9/Pzt37sTPz4+wsDC0Wi0rV67E2dmZpKQk+vfvj7+/P0OHDuXgwYP6P8iKojBp0iS0Wi1WVlb07NmTK1eusHXrVpYtW0aLFi1yHIq4U6dOJCYmZnmvT58+jBgxIst7wcHBVKlShaioKAYNGkRERASQcYGaPHkyCQkJ/PTTTwV+/oQwZCWlfs+YMYNJkybxww8/6N+7e/cut2/fZt++fWi1Wj777DP9fArC8EgyYOD69OnDjBkzGDRoEA4ODkyaNImQkBAWLlyITqfj+vXr/P777wA0b94cExMTatSoQdOmTTEzM6NGjRrcv38fyPgl7uTkBGSMPHb16lXs7e0BiIuL48aNG/pfJw8fPuTWrVtMmzaNefPmkZiYSK9evbLNpf78lKa5qVKlCpDx68Xc3JzHjx9TtmxZvL29OXPmDDt27GDKlCls3br19U+aEMVESajfMTExJCQk0LRp0yzJgLW1Nd7e3pibm9O2bVvGjx//+idMFBpJBgyciYkJs2fPBsDHx4du3brxxRdfoNVqqVixIq6urvrmwGeHKn3235nLU1NTOXv2LI6Ojpw5cybLnAE2NjY0aNCAvXv3UrZsWdLT01EUhSdPnrB8+XKePn2Kvb19totFXn85/P3331SoUIE//viDxMREypYty5MnTyhdujSQMRKaDF0qjE1JqN9nz57l+vXrtG/fnmvXrlG+fHkaNGiAl5eX/tZDZGQktWvXLoAzJgqLJAMGbvfu3SxevBgzMzOqVatG3bp16du3L15eXtjZ2b3SrGBWVlasW7eO0aNHU79+fdq2bcuRI0eAjIvLzJkz6dChAyYmJpQqVYoNGzawYMECTp48SWpqKsOHD88xvpdJT0+nTZs2WFpakpqayuLFiwHYuXMn3333HaamppQqVYrvv/8+z59FiJKgJNTv7t270717dyBjOmUHBweaNm0KQIsWLfDy8kJRFFasWJHnzyKKngxH/BIlaQhLBwcHLl68qHYYBqkklbPIm5JW5lK/X6yklXdBk6cJhBBCCCMnyYARkV8NQpRcUr/F65BkoJjw8/PLMrJXYQkMDKRevXqsX78egOvXr+tHD1y5cuVLt9fpdDRq1Ij58+fr3xs5ciStW7emVatWhISEvHB7Kysr/dzrP//8MwABAQHUrl27SD6/EGooDvV71qxZ1K5dm06dOuXpWDldC6R+Gy7pQCiyGTFiBIMGDQJg8uTJ+Pv706xZM9zd3enRoweVK1fOddvVq1dTr149/etff/2VS5cucerUKWJjYxk0aBDe3t65bl+nTp1s87APGzaM27dvv96HEkIA+a/fH3zwAX369GH06NF5Os7z1wKQ+m3IpGVARWPHjuXAgQMA/PHHH7Rr1w6AwYMHo9FocHZ21g/Xm+nmzZtZMnMHBwcAYmNj8fX1xdvbm169epGSklIgMV69ehUnJyfMzc3x8vIiNDQ013VTUlLYuXMnPXv21L/3z3/+EysrK9LS0khISOCNN9544fFu376Np6cngwYN0j8/LURxVNLqd9WqVfM8rHBO1wKQ+m3IJBlQ0cCBA9m4cSMAmzZtom/fvgAsXboUrVbLxo0b+fLLL/O0r4kTJzJnzhz9kKNr167Nsvz48eP65rln/4uJiXnhfp992MTa2vqFFfjbb7/lo48+yvIMdPny5alVqxYNGzakXbt2TJgw4YXHu3HjBkePHqVNmzZ8/vnnL1xXCENW0ur3q8jpWgBSvw2Z3CZQkZOTE9HR0Tx+/Jjt27ezZ88edDodU6dOJTw8HFNTU+Lj47Ns83zlyqzMly5dYtSoUUBGVp450lgmd3f3bM1zeWFq+r988eHDh7nOH5CQkIBWq2XChAkEBgbq3z9w4ABxcXH8+uuv3Lt3j06dOhEeHp7r8TJHKuzbt688lyyKtZJUv19FbtcCkPptyCQZUFnnzp2ZO3cuNWvWpEKFCkRERBAbG8vRo0eJjo7O1sxWqVIl/fCk0dHRJCcnA9CoUSNmzJhBw4YNAXj69GmW7Y4fP57jRCRr1qyhTp06ucZna2tLREQEzZo148iRI0ycOBHIaO5788039etFR0cTFxdH+/btuXPnDqmpqTRr1oz09HQqV66MqakpFSpUICkpCYBHjx6h0+moVKmSfh9JSUmUKVMGMzMztFot9evXz+tpFMIglZT6/SJ5vRa4urpK/TZgkgyobODAgdja2rJ9+3YAGjZsSHx8PN7e3ri5uWVbv2LFiri7u+Pm5oaHhweWlpYAzJ8/n5EjR+r/2E6dOjVLR738/nKYPXs2w4YNIzU1lSFDhlC5cmXS0tLo0aMHZ86c0a/n4uLC6dOngYwey3Fxcfj4+KDT6di4cSMeHh6kpKTwxRdfALBlyxaePn3Kv//9b/0+oqOjGT58OOXLl8fCwoKAgIBXjlcIQ1JS6jdk9Ppfs2YNV65coW3btmzcuJEqVark+VoQHh4u9duQKeKFwsPDFUAJDw9XO5QisW3bNsXR0VFZt25druucOnVKWbJkyWsdZ+zYscpff/2Vp3VXrVqlNGvWTNmzZ89rHfNFjK2chXGWeUHX74K4FhRF/VYU4yzvVyHDEb+EDGFpHKScjY+UuXGR8n4xeZpACCGEMHKSDAghhBBGTjoQ5tHly5fVDkEUIilf4yVlbxyknF9MkoGXsLGxwdLSkoEDB6odiihklpaW2NjYqB2GKCJSt42P1PHcSQfCPLh16xZxcXFqh/FSiqLw2WefERkZSXBwMNbW1qrE8eDBA3r27ImjoyPz5s3LNpCKobKxseGtt95SOwxRhIpL3Qap3wVB6njupGUgD956661i8QUKCgpCq9USHBzMO++8o2osK1eupFevXly9epV+/fqpGosQuSkudRukfovCJS0DJcTdu3ext7enXbt2bNq0Se1wgIwhR/fv309UVBTVqlVTOxwhii2p36KwSTJQAiiKQteuXTlz5gxRUVH68b/VFhcXh729PS4uLuzYsaNYNScKYSikfouiII8WlgDr169n165drFixwmAuFJBxf2758uXs2rWLDRs2qB2OEMWS1G9RFKRloJi7c+cODg4OdOzY0WAr5IABA9izZw9RUVFUr15d7XCEKDakfouiIslAMaYoCp06dSIiIoKoqCgqV66sdkg5io+Px8HBAScnJ3bt2iXNiULkgdRvUZTkNkExFhgYyJ49e/j+++8N9kIBGXOYr1ixgp9++om1a9eqHY4QxYLUb1GUpGWgmLp9+zYODg5069aNwMBAtcPJk8GDB/Pjjz8SFRVFzZo11Q5HCIMl9VsUNUkGiiFFUejQoQMXLlwgKiqKSpUqqR1Snjx48AAHBweaNGnC3r17pTlRiBxI/RZqkNsExVBAQAD79u1j5cqVxeZCAWBtbc3333/Pvn37WL16tdrhCGGQpH4LNUjLQDETGxtLkyZN6NWrFwEBAWqHky8ffPABwcHBXLx4sdiM/iZEUZD6LdQiyUAxoigKPj4+XLlyhYsXL1KxYkW1Q8qXhIQEHBwcaNSoEfv375fmRCGQ+i3UJbcJipEVK1Zw6NAhAgICiu2FAqBixYqsWrWKgwcP8v3336sdjhAGQeq3UJO0DBQTMTExNGnShP79+5eYCvbhhx+yefNmLly4QO3atdUORwjVSP0WapNkoBhIT0+nbdu2XL9+nQsXLlChQgW1QyoQf//9Nw4ODtja2nLgwAFMTaWhShgfqd/CEEjpFAPLli3j8OHDBAQElJgLBUCFChVYvXo1ISEhLF++XO1whFCF1G9hCKRlwMBdv36dpk2bMnjwYJYuXap2OIVixIgRrFu3jgsXLlC3bl21wxGiyEj9FoZCkgEDlp6eTps2bbh16xYXLlygXLlyaodUKB49ekTTpk2pVasWISEh0pwojILUb2FIpFQM2HfffcfRo0dZs2ZNib1QAJQvX57Vq1dz5MgRlixZonY4QhQJqd/CkEjLgIH69ddfadasGcOGDePbb79VO5wiMXLkSFavXs358+epX7++2uEIUWikfkv9NjSSDBggnU6Hl5cXd+/e5fz581hZWakdUpFITEykWbNmVKtWjSNHjmBmZqZ2SEIUOKnfUr8NkdwmMECLFi3i5MmTBAYGGs2FAqBcuXKsWbOGEydOsHjxYrXDEaJQSP2W+m2IpGXAwFy5coXmzZvz8ccfs3DhQrXDUcWYMWNYsWIF586do0GDBmqHI0SBkfot9dtQSTJgQHQ6He7u7sTHxxMZGYmlpaXaIakiOTmZZs2a8Y9//INjx45Jc6IoEaR+Z5D6bZjkNoEBWbBgAadPn2bNmjVGe6EAsLS0ZM2aNfzyyy9G++tJlDxSvzNI/TZM0jJgIC5fvoyjoyMjR45k/vz5aodjEMaPH8+SJUs4e/YsdnZ2aocjRL5J/c5O6rdhkWTAAKSlpeHm5kZCQgJnz56lbNmyaodkEB4/fkzz5s2pVKkSJ06cwNzcXO2QhHhlUr9zJvXbsMhtAgMwf/58wsLCCAwMlAvFM8qWLcvatWsJCwvD399f7XCEyBep3zmT+m1YpGVAZRcvXsTJyYkxY8bw9ddfqx2OQZo4cSKLFi0iIiICe3t7tcMRIs+kfr+c1G/DIMmAilJTU2ndujXJyclERERQpkwZtUMySCkpKTg6OlKuXDlOnTolzYmiWJD6nTdSvw2D3CZQ0dy5c4mMjGTt2rVyoXiBMmXKsHbtWiIiIpg7d67a4QiRJ1K/80bqt2GQlgGVnD9/HmdnZyZMmMDMmTPVDqdYmDJlCvPnzyc8PJwmTZqoHY4QuZL6/eqkfqtLkgEVpKam0qpVK9LS0ggLC6N06dJqh1QsPHnyBCcnJywsLDh9+jSlSpVSOyQhspH6nT9Sv9UltwlUMGvWLC5cuEBgYKBcKF5B6dKlWbt2LefPn2f27NlqhyNEjqR+54/Ub3VJMlDEIiMjmTFjBlOmTMHJyUntcIodJycn/vOf//DVV18RGRmpdjhCZCH1+/VI/VaP3CYoQk+fPqVly5YAhIaGYmFhoXJExVPmeTQxMeHMmTNyHoVBkPpdMKR+q0NaBorQjBkzuHTpEmvXrpUv+GuwsLAgMDCQqKgo6ZwlDIbU74Ih9VsdkgwUkfDwcGbNmsUXX3xB8+bN1Q6n2HN0dGTq1KnMnDmTiIgItcMRRk7qd8GS+l305DZBEZBesoVDem0LQyD1u3BI/S5a0jJQiC5fvoxOp+PLL7/k6tWrBAYGyoWiAJUqVYrAwECuXLnCV199hU6nIzo6Wu2whJGQ+l24pH4XMUUUit9//10xNTVVVq1apZiamiozZsxQO6QS66uvvlLMzMyUlStXKqampsrvv/+udkiihJP6XXSkfhcNaRkoJL/++ivp6enMmjWLZs2a4eHhgSJ3ZAqcoih4enrStGlT5syZQ3p6OteuXVM7LFHCSf0uGlK/i44kA4UkJiYGgNjYWO7fv0/btm2Jj49XOaqSJz4+nrZt23L//n1u3rwJ/O/cC1FYpH4XDanfRUeSgUJy7NgxAHQ6HQ0aNCA8PBwbGxuVoyp5bGxsCA8Px9bWFp1OB/zv3AtRWKR+Fw2p30VHkoFCkpKSQoUKFdixYwf79u2TiTcKUZMmTdi/fz8//vgjFSpU4PHjx2qHJEo4qd9FR+p30ZBHC4UQQggjJy0DQgghhJEzV+vAt27dIi4uTq3Dlzg2Nja89dZbaoeRKynvgiXlbVykvI2LGuWtSjJw69Yt7OzsSE5OVuPwJZKlpSWXL182yAuGlHfBk/I2LlLexkWN8lYlGYiLiyM5OZkNGzZgZ2enRgglyuXLlxk4cCBxcXEGebGQ8i5YUt7GRcrbuKhV3qrdJgCws7OjRYsWaoYgipCUt3GR8jYuUt7Fm3QgFEIIIYycJANCCCGEkSuxyUBgYCBHjhzJ9/KXWblyJa6urmg0Gm7cuJFteWhoKK6urri6urJ7924Abt68SZUqVdBoNGg0GiIjI/N9fKF+GQMkJCRgY2NDcHCw/j0/Pz/c3d3p0KGDvod1eHg4b7/9Nh4eHvTv318/mprIO7XLe+TIkVSvXp2RI0dmef+nn37C29sbT09P1q1bB2R8B+zt7dFoNPTt2zffMYns1P4e5HRtLxHUmB0pPDxcAZTw8HA1Dv/a4uPjFRcXFyUtLU0JDQ1VevXqlW0dNzc35c6dO8qjR48UR0dHJS0tTYmJiVE6duxY4PEY+vk09PhykpcyVhRF+fzzzxVfX19l27ZtiqIoSlRUlNKpUydFURRl27ZtyoQJExRFUZT+/fsrR44cURRFUYYOHaocOnQo37EZ+vk09PhykpfyvnPnjhISEqJ88skn+vfu3bundO/eXUlLS8uy7rRp0/Tfiddl6OfT0ON7Ffm9thcktc5nsW8ZSEtLo2/fvrRp04bPP/8cBwcHICMzDw4O5ubNm7i5ufH+++/TtGlTNm/enGV5fpw+fRqNRoOZmRnOzs5cvXo1y/KUlBTS0tKoXr065cqVw9bWVj/TVmhoKB4eHowcOZKUlJTX+OTGwxDLGODevXvExMTQsmVL/XtHjhyhY8eOAHTs2JHjx48DGUOqJiQkAPD333/zj3/8I19xGQNDLe/q1atjYmKS5b09e/ZQvnx5OnToQNeuXfntt9/0y2bMmIGnpydbt27NV0zGzhC/By+6thd3xT4Z+PHHH6lWrRqHDx+mffv2Oa5z584dVq5cSUhICPPnz891X9euXdM34T/736lTp7Ks9+DBAypVqqR/nZ6enmX5/fv3syy3trbm/v37VKtWjevXr3Ps2DGqVKnCokWLXv0DGyFDLGPIuNhPmjQp1+3Kli1LYmIiAL6+vowaNQo7OztMTEz0FzaRnaGWd07u3r3L7du32bNnD59++imfffYZAJ9++imRkZHs2rWLuXPncvv27TztT/yPIX4Pcru2lwSqPlpYEK5du4azszNAll9oz7K3t6d06dKULl36hb/G69evj1arfekxra2tuXDhgv61qWnWnKpy5cr6X4EADx8+xNraWh8DQJ8+fZg+ffpLjyUMs4xjYmJISEigadOm/PDDD1m2yyz7lJQUrKysABgxYgS7d+/G3t6eiRMnEhQUxIABA14ahzEyxPJ+0Xbe3t6Ym5vTtm1bxo8fD0CVKlUAqFixIt7e3ly8eJE333wzT/sUGQzxe5Dbtb0kKPYtA7a2toSFhQEZTfA5eb5pLzd5zR5dXFzQarXodDoiIiKwtbXNsrxMmTKYmZlx9+5dkpKSuHr1KvXr1+fvv//Wr6PVaqlfv/6rfFSjZYhlfPbsWa5fv0779u3ZsGEDM2fO5Pz583h6erJnzx4gownZ3d0dAEVR9H8gqlSpwoMHD/J+AoyMIZZ3bry8vIiIiAAgMjKS2rVrA+j/YKSlpXHy5Enq1auXp/2J/zHE70Fu1/aSoNi3DHTt2pVt27ah0WhwdnbGwsIi3/vKa/ZYuXJlBg8ejIeHB6VKlSIgIADI6MVap04dvLy88Pf3p0ePHqSnp+Pn54e5uTlHjx5l2rRpWFlZYW1tTWBgYL5jNSaGWMbdu3ene/fuQMY9SgcHB5o2bQpAs2bNcHd3p1y5cqxfvx6AmTNn0q1bNywsLLC0tNTf3xTZGWJ5e3l5MXPmTH744Qf+/PNPoqOjOXjwoH6gHS8vLxRFYcWKFQCMHz+eS5cu6e97N2jQIN+fwVgZ6vcgp2t7iVCk3RX/v4LuLfn06VNFURRFq9UqvXv3LpB9FieG3pu3IOIz9jJ+lpS3cTGG8s6NMX4P1CrvEpHS9OzZkwcPHpCamqrP5ETJImVsXKS8Bcj3oCiViGRgx44daocgCpmUsXGR8hYg34OiVOw7EBYUrVabbWSxwtCjRw+8vLxwdnbW3zdOTEyke/fuaDQaBg4cKOMPFIGiKu/cRq0DWL9+PeXLly/0GIS65f3777/j7OxMuXLl9B3iROFS83peXEealWSgiG3atIkjR44QEhKCn58fACtWrMDLywutVkuLFi2kY2EJMmXKFDZu3Jjt/adPnxIcHCyPm5UwOZV35cqV2bdvHz179lQpKlFYcrqeA7Ru3RqtVotWq6V58+aqxfcqik0yEBMTg7u7O23atNGP8BYUFIS3tzctW7Zk6tSpQEZG6OPjw3vvvYednR07duygU6dOODg46LNye3t7xo8fj6enJ8OGDUNRlCzHCgkJwdPTEw8PD/1YALt378bFxQWNRqM/Vn5k9ohNSkrS9z5/9nnaVq1acfTo0Xzvv6QoKeWd06h1kJEADhkyJM/Ps5d0Jbm8y5Qpo3+sVGQoKeWd0/UciudIs8Wmz8Dhw4fp3r0748aN048K1bVrV/r374+iKLi7u3P37l0AkpOT2b9/Pzt37sTPz4+wsDC0Wi0rV67E2dmZpKQk+vfvj7+/P0OHDuXgwYOUKlUKyHgefNKkSWi1WqysrOjZsydXrlxh69atLFu2jBYtWuQ4OlmnTp30o81l6tOnDyNGjMi2ro+PD+fOnWPWrFkAODg4sG/fPtzc3Pj5559LzIhWr6MklffzkpKS2LdvH7t37+aLL7543VNVIpTk8hbZlaTyfv56njnSbLly5Zg2bRqLFi3KNlKpISo2yUCfPn2YMWMGgwYNwsHBgUmTJhESEsLChQvR6XRcv36d33//HYDmzZtjYmJCjRo1aNq0KWZmZtSoUUP/R7ZUqVI4OTkBGYNMXL16FXt7ewDi4uK4ceOGPlt9+PAht27dYtq0acybN4/ExER69epF165ds8T3KrNXHThwgIcPH9KqVSt69erFsGHDGD16NN7e3rRo0YJq1aq99vkq7kpSeT9v4cKFRXI/szgpyeUtsitJ5f389bxixYrFcqTZYpMMmJiYMHv2bCAjE+vWrRtffPEFWq2WihUr4urqqm8eeraZ7tl/Zy5PTU3l7NmzODo6cubMGfr166dfx8bGhgYNGrB3717Kli1Leno6iqLw5MkTli9fztOnT7G3t8/25clLJpmeno5Op6NUqVJYWlpiaWlJ6dKlKVOmjH6wkpkzZ+Ll5VUQp6xYKwnlnZvo6GiOHz/ON998Q2xsLIMHD2bt2rWveIZKlpJc3iK7klDeuV3P//77bypUqAAUr5Fmi00ysHv3bhYvXoyZmRnVqlWjbt269O3bFy8vL+zs7PRjwOeFlZUV69atY/To0dSvX5+2bdvq5782MTFh5syZdOjQARMTE0qVKsWGDRtYsGABJ0+eJDU1leHDh+cY38skJSXRuXNnIKMD2YQJEyhTpgwXLlzg008/xdzcHA8PD/06xqwklDeQ46h1GzZs0C93cHAw+kQASnZ5p6am0qFDBy5dusTly5fp2bMnEyZMyPPnKYlKQnnndj0/ePBg8RxptujGN/oftUfUsre3V+W4hUXt8/kyascn5V201I5PyrtoqR2flHfBkK7MQgghhJEzymTg4sWLaocgipCUt3GR8jYuUt4Fo9glA35+fgQHBxf6cQIDA6lXr55+1rnr16+j0Whwc3Nj5cqVL9z2k08+0Y9K9c033+jfHzlyJK1bt6ZVq1aEhITkun1qaipubm5UqlQpy2cNCAigdu3aRfL5DUVxKO9Zs2ZRu3ZtOnXqlOX9lStX4urqikaj4caNGy+N4fjx45iYmBAXFwdIeRemwijvV1k3p++GlHfheZ3yLohrv5WVlX5Ewp9//hkwvPIuNh0I1TBixAgGDRoEwOTJk/H399dPT9ujRw8qV66c43YLFy7EwsKCtLQ0GjduzIgRI7h16xaXLl3i1KlTxMbGMmjQILy9vXPc3tzcnODgYP0TBpmGDRvG7du3C/ZDCr38lvcHH3xAnz59GD16tP69+/fvExAQwIkTJzh79iyTJ09m69atLzz+woUL9YNPgZR3YSvI8s7Nq3w3pLwLV37L+3Wv/aVLl6ZOnTrZplA2tPI2iJaBsWPHcuDAAQD++OMP2rVrB8DgwYP1c1mfPHkyyzY3b97Mkm07ODgAEBsbi6+vL97e3vTq1avARn+6evUqTk5OmJub4+XlRWhoaK7rZo5KlZKSgq2tLRYWFvzzn//EysqKtLQ0EhISeOONN3Ld3sTEpESPNVDSyrtq1aqYmZllee/06dNoNBrMzMxwdnbm6tWrLzzerl27cHd3f6Ve1MWFMZT3q6z7qt+N4qaklffrXvsBbt++jaenJ4MGDTLYQeUMIhkYOHCgfjzvTZs20bdvXwCWLl2KVqtl48aNfPnll3na18SJE5kzZ45+CMrnH9s6fvy4vrnm2f9iYmJeuF/lmSEura2tX1qg77//PvXr16dly5aYmJhQvnx5atWqRcOGDWnXrp1RP1pUEsv7eQ8ePKBSpUr61zmNcvbssmXLlpXYZ9aNobxfxat8N4qjklber3vtB7hx4wZHjx6lTZs2fP755y/cXi0GcZvAycmJ6OhoHj9+zPbt29mzZw86nY6pU6cSHh6Oqakp8fHxWbZ5fvzvzAK7dOkSo0aNAjKys8yRpzK5u7tna67Ji2fHkH/48OFLJ59Yt24dKSkpeHt707t3b3777Tfi4uL49ddfuXfvHp06dSI8PPyV4ygJSmJ5P8/a2poLFy7kuL/nBQUF0aVLF8qUKfPKcRYHxlDer+JVvhvFUUkr79e99jdu3Fg/N0Xfvn2z3f41FAaRDAB07tyZuXPnUrNmTSpUqEBERASxsbEcPXqU6OjobDN+VapUST9cZXR0NMnJyQA0atSIGTNm0LBhQyBjMIhnHT9+PMeJKdasWUOdOnVyjc/W1paIiAiaNWvGkSNHmDhxIpDR/PP8zHNPnjyhdOnSlC5dGisrK/3IV5UrV8bU1JQKFSqQlJQEwKNHj9DpdFl+KRiDklTeOXFxceHLL79Ep9Nx7tw5bG1tgZzL+8KFC4SHh/Pjjz9y/vx5+vTpw6FDh156jOKkpJf3q6yb23ejJClJ5f261/6kpCTKlCmDmZmZQY9IaDDJwMCBA7G1tWX79u0ANGzYkPj4eLy9vXFzc8u2fsWKFXF3d8fNzQ0PDw8sLS0BmD9/PiNHjtT/sZ06dWqWjnr5zSRnz57NsGHDSE1NZciQIVSuXJm0tDR69OjBmTNnsqzbrVs3kpOTefr0KT169KBOnTq89dZbbNy4EQ8PD1JSUvQT1GzZsoWnT5/y73//O8s+evfuTVhYGOXKlePUqVP4+/u/csyGrCSVd0BAAGvWrOHKlSu0bduWjRs38s9//pPBgwfj4eFBqVKlCAgIAHIu76+//lr/b41Gw5YtW145XkNX0su7SpUqr/3dKElKUnm/7rU/PDyc4cOHU758eSwsLAy3vIt0iKP/T+0Rq/Ji27ZtiqOjo7Ju3bpc1zl16pSyZMmS1zrO2LFjlb/++itP665atUpp1qyZsmfPnizvG/r5NPT4FEXKuyAZenyKUvDlXRDfDSnvwiPl/XKSDJQAhn4+DT2+4sbQz6ehx1fcGPr5NPT4ihsZjlgIIYQQqpBkQAghhDByqnYgvHz5spqHLzGKy3ksLnEauuJyHotLnIauuJzH4hKnoVPrPKqSDNjY2GBpacnAgQPVOHyJZGlpiY2Njdph5EjKu+BJeRsXKW/jokZ5myjKM8MrFaFbt27pJ2QRr8/Gxoa33npL7TByJeVdsKS8jYuUt3FRo7xVSwaEEEIIYRikA6EQQghh5CQZEEIIIYycJANCCCGEkZNkQAghhDBykgwIIYQQRk6SASGEEMLISTIghBBCGDlJBoQQQggjJ8mAEEIIYeQkGRBCCCGMnCQDQgghhJGTZEAIIYQwcpIMCCGEEEZOkgEhhBDCyEkyIIQQQhg5SQaEEEIIIyfJgBBCCGHkJBkQQgghjJwkA0IIIYSRk2RACCGEMHKSDAghhBBGTpIBIYQQwshJMiCEEEIYOUkGhBBCCCMnyYAQQghh5CQZEEIIIYycJANCCCGEkZNkQAghhDBykgwIIYQQRk6SASGEEMLISTIghBBCGDlJBoQQQggjJ8mAEEIIYeQkGRBCCCGMnCQDQgghhJH7f0sSHpBPeH+DAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn import tree\n",
+    "\n",
+    "#print(dt.feature_names_in_)\n",
+    "\n",
+    "t = tree.plot_tree(decision_tree = dt, feature_names=dt.feature_names_in_) # returns a array with each leaf and their values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "80282a49-1d47-4146-afd8-a9aa359e9791",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Defining an object for DTC and fitting for train dataset\n",
+    "dt = DecisionTreeClassifier(random_state=1)\n",
+    "dt.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_train = dt.predict(X_train)\n",
+    "y_pred = dt.predict(X_test)\n",
+    "y_prob = dt.predict_proba(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "af602439-ec2e-4ef7-88eb-6d3684fd70ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of Decision Tree-Train:  1.0\n",
+      "Accuracy of Decision Tree-Test:  0.9545454545454546\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "print('Accuracy of Decision Tree-Train: ', accuracy_score(y_pred_train, y_train))\n",
+    "print('Accuracy of Decision Tree-Test: ', accuracy_score(y_pred, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0ae9915b-5145-4ffe-a49f-18d44064dfa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00        15\n",
+      "Iris-versicolor       1.00      0.87      0.93        15\n",
+      " Iris-virginica       0.88      1.00      0.93        14\n",
+      "\n",
+      "       accuracy                           0.95        44\n",
+      "      macro avg       0.96      0.96      0.95        44\n",
+      "   weighted avg       0.96      0.95      0.95        44\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report \n",
+    "#Classification for test before hyperparameter tuning\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6906db27-13f0-4e27-b08c-924a303e61ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'max_depth': 3, 'min_samples_leaf': 3, 'min_samples_split': 2}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "\n",
+    "# Hyperparameter Tuning of DTC\n",
+    "\n",
+    "dt = DecisionTreeClassifier(random_state=1)\n",
+    "\n",
+    "params = {'max_depth' : [2,3,4,5],\n",
+    "        'min_samples_split': [2,3,4,5],\n",
+    "        'min_samples_leaf': [1,2,3,4,5]}\n",
+    "\n",
+    "gsearch = GridSearchCV(dt, param_grid=params, cv=3)\n",
+    "\n",
+    "gsearch.fit(X,y)\n",
+    "\n",
+    "gsearch.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "56a75b7b-2915-47e7-9f8b-1f12c72c2196",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Passing best parameter for the Hyperparameter Tuning\n",
+    "dt = DecisionTreeClassifier(**gsearch.best_params_, random_state=1)\n",
+    "\n",
+    "dt.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_train = dt.predict(X_train)\n",
+    "y_prob_train = dt.predict_proba(X_train)[:,1]\n",
+    "\n",
+    "y_pred = dt.predict(X_test)\n",
+    "y_prob = dt.predict_proba(X_test)[:,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "59d0f61b-c0c7-4746-9706-2b0816dca073",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.4, 0.875, 'petal width (cm) <= 0.75\\ngini = 0.666\\nsamples = 102\\nvalue = [32, 34, 36]'),\n",
+       " Text(0.2, 0.625, 'gini = 0.0\\nsamples = 32\\nvalue = [32, 0, 0]'),\n",
+       " Text(0.6, 0.625, 'petal width (cm) <= 1.65\\ngini = 0.5\\nsamples = 70\\nvalue = [0, 34, 36]'),\n",
+       " Text(0.4, 0.375, 'petal length (cm) <= 4.95\\ngini = 0.149\\nsamples = 37\\nvalue = [0, 34, 3]'),\n",
+       " Text(0.2, 0.125, 'gini = 0.0\\nsamples = 33\\nvalue = [0, 33, 0]'),\n",
+       " Text(0.6, 0.125, 'gini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
+       " Text(0.8, 0.375, 'gini = 0.0\\nsamples = 33\\nvalue = [0, 0, 33]')]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree.plot_tree(decision_tree = dt, feature_names=dt.feature_names_in_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "eb0b0037-7305-4085-a668-92882fec7302",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Passing best parameter for the Hyperparameter Tuning\n",
+    "dt = DecisionTreeClassifier(**gsearch.best_params_, random_state=1)\n",
+    "\n",
+    "dt.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_train = dt.predict(X_train)\n",
+    "y_prob_train = dt.predict_proba(X_train)[:,1]\n",
+    "\n",
+    "y_pred = dt.predict(X_test)\n",
+    "y_prob = dt.predict_proba(X_test)[:,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d191383c-ba55-43c8-97bb-88816b9763d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrix - Train: \n",
+      " [[32  0  0]\n",
+      " [ 0 33  1]\n",
+      " [ 0  0 36]]\n",
+      "\n",
+      " Confusion Matrix - Test: \n",
+      " [[15  0  0]\n",
+      " [ 0 13  2]\n",
+      " [ 0  0 14]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix \n",
+    "\n",
+    "print('Confusion Matrix - Train:','\\n',confusion_matrix(y_train,y_pred_train))\n",
+    "print('\\n','Confusion Matrix - Test:','\\n',confusion_matrix(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2e5cdce5-ada0-493e-aefa-e3415abb1d54",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00        15\n",
+      "Iris-versicolor       1.00      0.87      0.93        15\n",
+      " Iris-virginica       0.88      1.00      0.93        14\n",
+      "\n",
+      "       accuracy                           0.95        44\n",
+      "      macro avg       0.96      0.96      0.95        44\n",
+      "   weighted avg       0.96      0.95      0.95        44\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Classification for test after hyperparameter tuning\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "55c4bb77-7646-4c0e-b2e2-24ae9e23e1db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of Decision Tree-Train:  0.9901960784313726\n",
+      "Accuracy of Decision Tree-Test:  0.9545454545454546\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Accuracy of Decision Tree-Train: ', accuracy_score(y_pred_train, y_train))\n",
+    "print('Accuracy of Decision Tree-Test: ', accuracy_score(y_pred, y_test))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/LinearRegression/california_housing_LineaRegression.ipynb b/LinearRegression/california_housing_LineaRegression.ipynb
new file mode 100644
index 0000000..ca0230f
--- /dev/null
+++ b/LinearRegression/california_housing_LineaRegression.ipynb
@@ -0,0 +1,1036 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "407d4131-b3c5-4b66-a214-baf5ee98aae3",
+   "metadata": {},
+   "source": [
+    "### Install"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "cd0826ea-da42-4fd3-90a9-abc13ed05203",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.11/site-packages (1.4.0)\n",
+      "Requirement already satisfied: numpy<2.0,>=1.19.5 in /opt/conda/lib/python3.11/site-packages (from scikit-learn) (1.26.3)\n",
+      "Requirement already satisfied: scipy>=1.6.0 in /opt/conda/lib/python3.11/site-packages (from scikit-learn) (1.12.0)\n",
+      "Requirement already satisfied: joblib>=1.2.0 in /opt/conda/lib/python3.11/site-packages (from scikit-learn) (1.3.2)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.11/site-packages (from scikit-learn) (3.2.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad1b4816-64f8-427f-8af4-b862d7561bfb",
+   "metadata": {},
+   "source": [
+    "### Import california housing dataset\n",
+    "\n",
+    "print out dataset --> in JSON format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9a456fe9-2741-43e3-b3d1-f8f98595a466",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'data': array([[   8.3252    ,   41.        ,    6.98412698, ...,    2.55555556,\n",
+       "           37.88      , -122.23      ],\n",
+       "        [   8.3014    ,   21.        ,    6.23813708, ...,    2.10984183,\n",
+       "           37.86      , -122.22      ],\n",
+       "        [   7.2574    ,   52.        ,    8.28813559, ...,    2.80225989,\n",
+       "           37.85      , -122.24      ],\n",
+       "        ...,\n",
+       "        [   1.7       ,   17.        ,    5.20554273, ...,    2.3256351 ,\n",
+       "           39.43      , -121.22      ],\n",
+       "        [   1.8672    ,   18.        ,    5.32951289, ...,    2.12320917,\n",
+       "           39.43      , -121.32      ],\n",
+       "        [   2.3886    ,   16.        ,    5.25471698, ...,    2.61698113,\n",
+       "           39.37      , -121.24      ]]),\n",
+       " 'target': array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894]),\n",
+       " 'frame': None,\n",
+       " 'target_names': ['MedHouseVal'],\n",
+       " 'feature_names': ['MedInc',\n",
+       "  'HouseAge',\n",
+       "  'AveRooms',\n",
+       "  'AveBedrms',\n",
+       "  'Population',\n",
+       "  'AveOccup',\n",
+       "  'Latitude',\n",
+       "  'Longitude'],\n",
+       " 'DESCR': '.. _california_housing_dataset:\\n\\nCalifornia Housing dataset\\n--------------------------\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 20640\\n\\n:Number of Attributes: 8 numeric, predictive attributes and the target\\n\\n:Attribute Information:\\n    - MedInc        median income in block group\\n    - HouseAge      median house age in block group\\n    - AveRooms      average number of rooms per household\\n    - AveBedrms     average number of bedrooms per household\\n    - Population    block group population\\n    - AveOccup      average number of household members\\n    - Latitude      block group latitude\\n    - Longitude     block group longitude\\n\\n:Missing Attribute Values: None\\n\\nThis dataset was obtained from the StatLib repository.\\nhttps://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\\n\\nThe target variable is the median house value for California districts,\\nexpressed in hundreds of thousands of dollars ($100,000).\\n\\nThis dataset was derived from the 1990 U.S. census, using one row per census\\nblock group. A block group is the smallest geographical unit for which the U.S.\\nCensus Bureau publishes sample data (a block group typically has a population\\nof 600 to 3,000 people).\\n\\nA household is a group of people residing within a home. Since the average\\nnumber of rooms and bedrooms in this dataset are provided per household, these\\ncolumns may take surprisingly large values for block groups with few households\\nand many empty houses, such as vacation resorts.\\n\\nIt can be downloaded/loaded using the\\n:func:`sklearn.datasets.fetch_california_housing` function.\\n\\n.. topic:: References\\n\\n    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\\n      Statistics and Probability Letters, 33 (1997) 291-297\\n'}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "housing = fetch_california_housing()\n",
+    "housing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e52ae3d2-8d34-4569-aa24-00269f9f51a0",
+   "metadata": {},
+   "source": [
+    "### show keys of dataset\n",
+    "show all the keys of the dataset, to get an overview, what's inside the dataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "8911162c-2a9a-43aa-9cad-362dedb43b13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "#show the keys of the JSON california housing\n",
+    "print(housing.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac4cc0f9-cc2e-4c90-865a-6731be8964a5",
+   "metadata": {},
+   "source": [
+    "### import pandas\n",
+    "convert the dataset from a json format to a pandas dataframe \n",
+    "!import housing.data into the rows and feature_names as column names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "19373cf8-4274-4528-a82b-809871ef7008",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MedInc</th>\n",
+       "      <th>HouseAge</th>\n",
+       "      <th>AveRooms</th>\n",
+       "      <th>AveBedrms</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>AveOccup</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>6.984127</td>\n",
+       "      <td>1.023810</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>2.555556</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>-122.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>6.238137</td>\n",
+       "      <td>0.971880</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>2.109842</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>-122.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>8.288136</td>\n",
+       "      <td>1.073446</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>2.802260</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>-122.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>5.817352</td>\n",
+       "      <td>1.073059</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>2.547945</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>-122.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>6.281853</td>\n",
+       "      <td>1.081081</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>2.181467</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>-122.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20635</th>\n",
+       "      <td>1.5603</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>5.045455</td>\n",
+       "      <td>1.133333</td>\n",
+       "      <td>845.0</td>\n",
+       "      <td>2.560606</td>\n",
+       "      <td>39.48</td>\n",
+       "      <td>-121.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20636</th>\n",
+       "      <td>2.5568</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>6.114035</td>\n",
+       "      <td>1.315789</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>3.122807</td>\n",
+       "      <td>39.49</td>\n",
+       "      <td>-121.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20637</th>\n",
+       "      <td>1.7000</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>5.205543</td>\n",
+       "      <td>1.120092</td>\n",
+       "      <td>1007.0</td>\n",
+       "      <td>2.325635</td>\n",
+       "      <td>39.43</td>\n",
+       "      <td>-121.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20638</th>\n",
+       "      <td>1.8672</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>5.329513</td>\n",
+       "      <td>1.171920</td>\n",
+       "      <td>741.0</td>\n",
+       "      <td>2.123209</td>\n",
+       "      <td>39.43</td>\n",
+       "      <td>-121.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20639</th>\n",
+       "      <td>2.3886</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>5.254717</td>\n",
+       "      <td>1.162264</td>\n",
+       "      <td>1387.0</td>\n",
+       "      <td>2.616981</td>\n",
+       "      <td>39.37</td>\n",
+       "      <td>-121.24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20640 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
+       "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
+       "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
+       "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
+       "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
+       "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
+       "...       ...       ...       ...        ...         ...       ...       ...   \n",
+       "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
+       "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
+       "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
+       "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
+       "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
+       "\n",
+       "       Longitude  \n",
+       "0        -122.23  \n",
+       "1        -122.22  \n",
+       "2        -122.24  \n",
+       "3        -122.25  \n",
+       "4        -122.25  \n",
+       "...          ...  \n",
+       "20635    -121.09  \n",
+       "20636    -121.21  \n",
+       "20637    -121.22  \n",
+       "20638    -121.32  \n",
+       "20639    -121.24  \n",
+       "\n",
+       "[20640 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd  \n",
+    "cal_housing = pd.DataFrame(housing.data, columns=housing.feature_names) ## definition of rows and columns\n",
+    "cal_housing ## show dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b9c0f611-2e5a-4006-881b-dc2a0a397df2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20640, 8)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cal_housing.shape ## show shape of dataframe, means 20640 rows by 8 columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "48477f2c-27ae-458c-a38a-a2d28b3c05bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MedInc</th>\n",
+       "      <th>HouseAge</th>\n",
+       "      <th>AveRooms</th>\n",
+       "      <th>AveBedrms</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>AveOccup</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>MedHouseVal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>6.984127</td>\n",
+       "      <td>1.023810</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>2.555556</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>4.526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>6.238137</td>\n",
+       "      <td>0.971880</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>2.109842</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>3.585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>8.288136</td>\n",
+       "      <td>1.073446</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>2.802260</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>3.521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>5.817352</td>\n",
+       "      <td>1.073059</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>2.547945</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>3.413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>6.281853</td>\n",
+       "      <td>1.081081</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>2.181467</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>3.422</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20635</th>\n",
+       "      <td>1.5603</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>5.045455</td>\n",
+       "      <td>1.133333</td>\n",
+       "      <td>845.0</td>\n",
+       "      <td>2.560606</td>\n",
+       "      <td>39.48</td>\n",
+       "      <td>-121.09</td>\n",
+       "      <td>0.781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20636</th>\n",
+       "      <td>2.5568</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>6.114035</td>\n",
+       "      <td>1.315789</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>3.122807</td>\n",
+       "      <td>39.49</td>\n",
+       "      <td>-121.21</td>\n",
+       "      <td>0.771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20637</th>\n",
+       "      <td>1.7000</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>5.205543</td>\n",
+       "      <td>1.120092</td>\n",
+       "      <td>1007.0</td>\n",
+       "      <td>2.325635</td>\n",
+       "      <td>39.43</td>\n",
+       "      <td>-121.22</td>\n",
+       "      <td>0.923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20638</th>\n",
+       "      <td>1.8672</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>5.329513</td>\n",
+       "      <td>1.171920</td>\n",
+       "      <td>741.0</td>\n",
+       "      <td>2.123209</td>\n",
+       "      <td>39.43</td>\n",
+       "      <td>-121.32</td>\n",
+       "      <td>0.847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20639</th>\n",
+       "      <td>2.3886</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>5.254717</td>\n",
+       "      <td>1.162264</td>\n",
+       "      <td>1387.0</td>\n",
+       "      <td>2.616981</td>\n",
+       "      <td>39.37</td>\n",
+       "      <td>-121.24</td>\n",
+       "      <td>0.894</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20640 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
+       "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
+       "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
+       "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
+       "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
+       "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
+       "...       ...       ...       ...        ...         ...       ...       ...   \n",
+       "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
+       "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
+       "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
+       "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
+       "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
+       "\n",
+       "       Longitude  MedHouseVal  \n",
+       "0        -122.23        4.526  \n",
+       "1        -122.22        3.585  \n",
+       "2        -122.24        3.521  \n",
+       "3        -122.25        3.413  \n",
+       "4        -122.25        3.422  \n",
+       "...          ...          ...  \n",
+       "20635    -121.09        0.781  \n",
+       "20636    -121.21        0.771  \n",
+       "20637    -121.22        0.923  \n",
+       "20638    -121.32        0.847  \n",
+       "20639    -121.24        0.894  \n",
+       "\n",
+       "[20640 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cal_housing['MedHouseVal'] = housing.target # add column: MedHouseVal which can be found in the JSON Object by target key\n",
+    "cal_housing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "62a4c8c5-f0df-4f48-b473-18dfe7492c0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MedInc         0\n",
+       "HouseAge       0\n",
+       "AveRooms       0\n",
+       "AveBedrms      0\n",
+       "Population     0\n",
+       "AveOccup       0\n",
+       "Latitude       0\n",
+       "Longitude      0\n",
+       "MedHouseVal    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cal_housing.isnull().sum() ## look for null values inside dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "e812be9e-af15-4f7c-b309-9c0cce3e98c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x2000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt ## visualization library\n",
+    "\n",
+    "cal_housing.hist(bins=120, figsize=(16,20)) \n",
+    "plt.subplots_adjust(hspace=0.7, wspace=0.4)\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "abb96d05-6c16-4f31-a90d-7032e346457e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        8.3252\n",
+       "1        8.3014\n",
+       "2        7.2574\n",
+       "3        5.6431\n",
+       "4        3.8462\n",
+       "          ...  \n",
+       "20635    1.5603\n",
+       "20636    2.5568\n",
+       "20637    1.7000\n",
+       "20638    1.8672\n",
+       "20639    2.3886\n",
+       "Name: MedInc, Length: 20640, dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cal_housing[cal_housing.columns[0]] ## show data of first column in that case: Median Income"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "5bbceaef-a9af-4573-bcd3-a0cc8d3e472b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "range(0, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(range(9)) ## means inclusive beginning, exclusive at the end! --> 0-8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6464dc1a-b433-4606-801f-e838cfb5488c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(('John', 1), ('Charles', 2), ('Mike', 3))\n"
+     ]
+    }
+   ],
+   "source": [
+    "## How ZIP work's?\n",
+    "a = (\"John\", \"Charles\", \"Mike\")\n",
+    "b = (1, 2, 3, 4)\n",
+    "x = zip(a, b)\n",
+    "print(tuple(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "49540a5c-f083-4c03-b6bb-9af70d02800f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qx/ML9Nb/3CGPkTr3Plu9Jk6Qt1tXSVJM585KH3+lfOf3VZSkt2fMUcXhYkf7C6D9OR6wPHr0qKZMmaKlS5eqW7du9nFjjBYuXKg5c+Zo4sSJysjI0FNPPaXjx49r5cqVkqTi4mItW7ZMDz/8sEaPHq3BgwdrxYoV2rFjh9atWydJ2r17t3JycvSHP/xBmZmZyszM1NKlS/Xyyy9rz549jowZAAAAAIDTkTFGW2f9ryqKS3QgJkrpV49TVHR0SBsrKkppo0fqcHSUSgu+Uu79cx3qLQCnOB6wvPPOO3X11Vdr9OjRIcf37t2rgoICjR071j7m9Xo1YsQIbdq0SZKUm5urysrKkDZpaWnKyMiw22zevFk+n0/Dhg2z21xyySXy+Xx2m4aUl5erpKQk5AEAAAAAAFrv38/+RQUb/k/RXq/e6BqvqJiYBttFxcTozW5xsqKi9Nnql3Xw3ffbuacAnORowHLVqlXavn275s2bV+9cQUGBJCklJSXkeEpKin2uoKBAsbGxIZmZDbVJTk6ud/3k5GS7TUPmzZtn73np8/mUnp7essEBAAAAAABb+eHDev/XCyRJA396j4pjok/a/kCsR73/e4IkacfDi9q8fwDcw7GA5b59+3TPPfdoxYoViouLa7SdZVkh3xtj6h2rq26bhto3dZ3Zs2eruLjYfuzbt++k9wQAAAAAAI3btXCJKopL5Lugn/r+zw3Nes5F99wuy+NRwVubVPh2bhv3EIBbOBawzM3NVWFhoYYMGSKPxyOPx6MNGzbo0UcflcfjsTMr62ZBFhYW2udSU1NVUVGhoqKik7b56quv6t3/wIED9bI3a/N6verSpUvIAwAAAAAAtNyRvZ/po6dXSZIG/2JWvX0rG9P57LN0zvcmSpJ2P7a0zfoHwF0cC1iOGjVKO3bsUF5env0YOnSopkyZory8PJ1zzjlKTU3V2rVr7edUVFRow4YNGj58uCRpyJAhiomJCWmTn5+vnTt32m0yMzNVXFysrVu32m3efvttFRcX220AAAAAAEDb2bngdzJVVep5xbeU+p+ZLXruBbf+jyQpf/1GHf2M1Y/A6cDj1I0TExOVkZERciwhIUFJSUn28aysLM2dO1d9+/ZV3759NXfuXHXq1EmTJ0+WJPl8Pt18882aMWOGkpKS1L17d82cOVMDBgywi/j0799fV155pW655RY9/vjjkqQf/ehHGj9+vM4///x2HDEAAAAAAKefko8/0Wd/f1WSNHDm3S1+fmLvs5U64lIVbPg/ffzsn/WN+2aEu4sAXMbxKuEnM2vWLGVlZemOO+7Q0KFD9cUXX2jNmjVKTEy02yxYsEDXXXedJk2apEsvvVSdOnXSSy+9pOha6eXPPvusBgwYoLFjx2rs2LEaOHCgnnnmGSeGBAAAAADAaWXXo7+XjNGZY69Qt4z+rbpG36nXS5I+ee4F+cvKw9k9AC7kWIZlQ9avXx/yvWVZys7OVnZ2dqPPiYuL06JFi7RoUeMVw7p3764VK1aEqZcAAAAAACDoOxOu1aHCwgbPne3rqiv/Vb2MOyPrjlbfo+eoEeqUlqrjXxZo/z/Wqde1V7f6WgDcz1UBSwAAAAAAEFkOFRbqt5Marvr9lyeWSsYo7YoRrc6ulKSo6Gj1/s61+uDRx/XZ318lYAl0cK5eEg4AAAAAACJT5bHj6ne8UpJ0we03n/L1gkHK/PUbVV50+JSvB8C9CFgCAAAAAICwO5T3vjySkv5jkM745n+c8vV8fc9V14sukKmq0r5X15x6BwG4FkvCAQAAAABAWPkrKnTo/V2SpP63/UCWZYXlur0mfFuHd/1Ln7/4qs6bMqnBNifbU7N7crL++uLfw9IXAG2HgCUAAAAAAAirop0fKFBersPRUTpz7BVhu+7ZE67Se/MeUeGWd1R24GvFndGjXpuT7al5z58pyAtEApaEAwAAAACAsAn4/Tq4/T1J0nudY2VFhS/0kHBmmroPvEgyRl+sWx+26wJwFwKWAAAAAAAgbEr2fKSqo8fk6dRJH3eKCfv1gxmbX6x5I+zXBuAOBCwBAAAAAEBYGGN0YNt2SVL3wQPlD9PelbWdOW6UJKlg42ZVHjsW9usDcB4BSwAAAAAAEBZH/v2JKooOK8rrVfeBGW1yD1+/85RwdroC5RUqeGtTm9wDgLMougMAAAAAAE65urYxRge21mRXDspQtDf2lPqz58MPdcUlmQ2eG1lpqZ+kL9a+qfSrxpzSfQC4DwFLAAAAAABwytW1j32+T2WFB2R5PEoaPFDSyYOOH3/40UmvZ6qqGu3Pr59Zrn6SCjZslAkEwlrYB4DzCFgCAAAAAIBTFty7slvGhfLEx0s6edBxXPZ9rb5XQWy0PJ3iVXbgoA7v3qNuF/Vv9bUAuA8fQQAAAAAAgFNy/Mt8Hd//payoKPUYMqjN7xewLCUPHyZJyl+/sc3vB6B9EbAEAAAAAACn5MDWXEmSr//5iklMbJd79hx5mSQClkBHRMASAAAAAAC0Wmp5lY5++rlkWeoxdHC73bfniOqA5de5eaooOdJu9wXQ9ghYAgAAAACAVjHG6Jsl5ZKkbhn95e3Wtd3u3blXuhL79JKpqlLh5q3tdl8AbY+AJQAAAAAAaJUv1ryh1Eq/LI9HZwy7uN3vn3LZJZKkwk0ELIGOhIAlAAAAAABosarSUm1/4FeSpKTBAxXTOaHd+5CcWV1456tNb7f7vQG0HQKWAAAAAACgxT5Y/ISO7/9SR6ItnfHNIY70ITmzOquzeM9HKjt4yJE+AAg/ApYAAAAAAKBFinbt1r9+/0dJ0uYucYqKiXGkH3FJ3eU7v68kqXDzNkf6ACD8CFgCAAAAAIBmqzp+XJunz1KgskpnjhulT+M8jvYnZXj1svDCzSwLBzoKApYAAAAAAKBZjDF6Z87/p5KPP1F8SrK++esHJMtytE/Jw78pSfqKwjtAh0HAEgAAAAAANMv783+rT59/UVZUlC5ZOE/e7t2c7pKShw2VLEtH/r1XpQWFTncHQBg4m7cNAAAAAABczzJGeXMftvetHPqrbKVceolj/dnz4Ye64pJM+/uJHks9Ko3uHvttfbz/U8f6BSA8CFgCAAAAAIBGlRcd1pWHjtvBykE/+7HOvf6/He2TqarSbyfdYH9f8Nb/6eD29/TfZ/XWK59+5GDPAIQDAUsAAAAAAE5TgcpKVR47JgWM4v0BVR49JhMIyF9aqvKDh3Tkk09V8u+9SjdG0V6vvvmb/0+9rr3a6W7Xk5B+pg5uf0/H933hdFcAhAEBSwAAAAAATiOlBYX6+Nk/64t1b6rkw48VqKySJE2V9OEfnmrwOZ95Pbrt1b/I1/fcduxp83VKS5MsSxXFJepqRTvdHQCniIAlAAAAAACnA2O0e8ky7fjNo3aQsh7LkhUVpeg4r2J9XdTprDPV5bxz9MT6f+inLg1WSlK0N1bxyWeo9KtCnefxOt0dAKeIgCUAAAAAAB1coMqvsUWlem/eI5KkHhf/h8674Xs64+L/UHxqsqzoaI3KHB6yL2SkSUg/szpgGUPAEoh0BCwBAAAAAOjAjDH6ct0b6l1WpShvrIY8cJ/O+f/Zu/cwp+sz///PnCZzzpyYE2cUEBxQRMtBq1QRtEW0rktbKtXvutbvqrRUXb+17nan2xZ+y66nhV23ta66IqXttrRaKwpaUQoooiggcpAzzIk5ZM7JJPn8/sgkMByHmSSfHF6P68pVmbwnuVNxPpk79+Ebt2GxWMwOLaIyBw2EDz5ihE0JS5FEp4SliIiIiIiISBI79v5m3J/tJgBc8+xSyq6+0uyQoiKzrBQsFopsdrpaWnHkZJsdkoj0kdXsAEREREREREQkOjrrG6h97wMA3nWlJ22yEoJzLNMHFAHQfqTK5GhEpD+UsBQRERERERFJQsFW8LchECDnguHszEozO6SoyxxYBkDb0aMmRyIi/aGEpYiIiIiIiEgScn+2i46qaqwOB2XTrjI7nJjIGlgOqMJSJNEpYSkiIiIiIiKSZIxAgLr3NwNQ9IWJOHJyTI4oNjLLgxWWnvoGfB2dJkcjIn2lhKWIiEgELFq0iCuuuIKcnByKi4u55ZZb2LlzZ48zd955JxaLpcdt8uTJPc54PB7mz59PUVERWVlZzJ49m8OHD/c409jYyLx583C5XLhcLubNm0dTU1O0X6KIiIgkkObP9+FtbMLqdFJwSYXZ4cSMPTODan8XAO1HVWUpkqiUsBQREYmAtWvXct9997Fx40ZWr16Nz+djxowZtLW19Th3ww03UFVVFb796U9/6nH/ggULWLlyJStWrGDdunW0trYya9Ys/H5/+MzcuXPZsmULq1atYtWqVWzZsoV58+bF5HWKiIhI/DMMg2Pd1ZWFl47Dlpb8sytPtNfnAaD9iOZYiiQqu9kBiIiIJINVq1b1+PNzzz1HcXExmzdv5uqrrw5/3el0UlpaetrHcLvdPPvss7z44otMnz4dgGXLljF48GDWrFnDzJkz2bFjB6tWrWLjxo1MmjQJgGeeeYYpU6awc+dORo8eHaVXKCIiIomio6qazrpjWOx2Ci4db3Y4MbfX52GqM5s2JSxFEpYqLEVERKLA7XYDUFBQ0OPrb7/9NsXFxYwaNYq7776b2tra8H2bN2+mq6uLGTNmhL9WXl5ORUUF69evB2DDhg24XK5wshJg8uTJuFyu8JmTeTwempube9xEREQkeTVu3wGAa9SF2DPSTY4m9vb6vAB01h7D7/WaHI2I9IUSliIiIhFmGAYPPPAAV111FRUVx2dG3Xjjjbz00ku89dZbPPbYY2zatIlrr70WjyfYtlRdXU1aWhr5+fk9Hq+kpITq6urwmeLi4lOes7i4OHzmZIsWLQrPu3S5XAwePDhSL1VERFKYz+fjH/7hHxg+fDgZGRmMGDGCf/7nfyYQCITPGIZBZWUl5eXlZGRkMG3aNLZv397jcXozv1l6zxEwaN61B4C8i8eYHI05mgw/jtwcMAw6qk7//khE4psSliIiIhF2//3388knn/DLX/6yx9e/9rWv8ZWvfIWKigpuuukmXnvtNXbt2sWrr7561sczDAOLxRL+84n/fKYzJ3rkkUdwu93h26FDh/rwqkRERHr6l3/5F/7rv/6LpUuXsmPHDhYvXsy//uu/smTJkvCZxYsX8/jjj7N06VI2bdpEaWkp119/PS0tLeEzvZnfLL03orOLQJePtPw8MstPP4YmFWQOLAeg7YgW74gkIiUsRUREImj+/Pm8/PLL/PnPf2bQoEFnPVtWVsbQoUPZvXs3AKWlpXi9XhobG3ucq62tpaSkJHympqbmlMeqq6sLnzmZ0+kkNze3x01ERKS/NmzYwM0338xXvvIVhg0bxm233caMGTP44IMPgOCHaU8++SSPPvoot956KxUVFbzwwgu0t7ezfPly4Pj85scee4zp06czYcIEli1bxtatW1mzZo2ZLy9hjWwPbsjOG3vRGT/MTAVZA8sAaFfCUiQhKWEpIiISAYZhcP/99/O73/2Ot956i+HDh5/ze+rr6zl06BBlZcE31BMnTsThcLB69erwmaqqKrZt28bUqVMBmDJlCm63m/fffz985r333sPtdofPiIiIxMJVV13Fm2++ya5duwD4+OOPWbduHV/+8pcB2LdvH9XV1T1mMzudTq655prw3OXezG8+mWYzn1ln3THKvMHKVNfokSZHY65QhWVHdQ0Bn8/kaETkfGlLuIiISATcd999LF++nD/84Q/k5OSE50m6XC4yMjJobW2lsrKSv/qrv6KsrIz9+/fzgx/8gKKiIr761a+Gz9511108+OCDFBYWUlBQwEMPPcS4cePCW8PHjBnDDTfcwN13383PfvYzAL797W8za9YsbQgXEZGY+n//7//hdru56KKLsNls+P1+fvrTn/KNb3wDIHwtPLkDoKSkhAMHDoTPnGt+88kWLVrEj370o0i/nKRw+PW3sAAZJcWk5eaYHY6p0vJc2DMz8LV30FFTS1Z3AlNEEoMqLEVERCLg6aefxu12M23aNMrKysK3X/3qVwDYbDa2bt3KzTffzKhRo7jjjjsYNWoUGzZsICfn+C8UTzzxBLfccgtz5szhyiuvJDMzk1deeQWbzRY+89JLLzFu3DhmzJjBjBkzGD9+PC+++GLMX7OIiKS2X/3qVyxbtozly5fz4Ycf8sILL/Bv//ZvvPDCCz3OndyWfLa5y705o9nMZ3botWCXRs6FI0yOxHwWiyVcZam2cJHEowpLERGRCDAM46z3Z2Rk8Prrr5/zcdLT01myZEmPhQUnKygoYNmyZecdo4iISCT9/d//Pd///vf5+te/DsC4ceM4cOAAixYt4o477qC0NLjwpbq6Ojz+BE6dzRya33xilWVtbe0ZR504nU6cTme0XlbC8jQ1UbshODImVwlLADIHltG8+3PajxwFJpodjoicB1VYioiIiIiIyHlrb2/Hau35K6XNZiMQCAAwfPhwSktLe8xm9nq9rF27NpyM7M38Zumdo2++g+HzUW+34szPMzucuBBqA28/Wo3R/fdSRBKDKixFRERERETkvN1000389Kc/ZciQIVx88cV89NFHPP744/zN3/wNEGzJXbBgAQsXLmTkyJGMHDmShQsXkpmZydy5c4HezW+W3ql6+10ADqTr1/wQZ2EB1rQ0Al4vnXXHyCgpNjskEekl/SQTERERERGR87ZkyRL+8R//kXvvvZfa2lrKy8u55557+OEPfxg+8/DDD9PR0cG9995LY2MjkyZN4o033jhlfrPdbmfOnDl0dHRw3XXX8fzzz/eY3yxnF/D7qX4nuFX9sFO/5odYrFYyB5bRuu8AbUeqlLAUSSD6SSYiIiIiIiLnLScnhyeffJInn3zyjGcsFguVlZVUVlae8Uxv5jfL2TVu+xRvYxOOnGxq0s6+0CjVZA0sp3XfgeAcy8suMTscEeklzbAUERERERERSWDVa/8CQMmVkzHOsYE91WSWBxc+tR+pOueSRBGJH0pYioiIiIiIiCSwqrfXAVB69ZUmRxJ/0ksGYLHb8Xd24mloNDscEekltYSLiIiIiIiIJJDbZt9MQ20tAI6AwR3VLViBB558jD17Pzc3uDhjtdnILCuh7dCRYFu4iCQEVViKiIiIiIiIJJCG2lqemnM7T825nX+adDVWIM2Vy0/nfgufr8vs8OLOiW3hIpIYlLAUERERERERSVBth4NVg5mDyk2OJH6F/r9pO3IUNMdSJCEoYSkiIiIiIiKSoEJtzlkDlbA8k8zSErBa8bW2keNXwlIkEShhKSIiIiIiIpKA/F4vHTXBWZaZgwaaHE38sjocZBQPAKDU6zM5GhHpDSUsRURERERERBJQx9FqMAwcuTmk5eaYHU5cy+yuQC3z+k2ORER6Q1vCRURERERERBJQ23m2g+/ctYtrJ0854/17du3u0/ee7fviRdagMuo3f0SpRwlLkUSghKWIiIiIiIhIAgptve7twh3D5+OpObef8f6ZlT/o0/ee7fviRWZZcFN4nj9AR21duEVcROKTWsJFREREREREEkzA7z8+v7Ks1ORo4p8t3Ul6USEAde9/aHI0InIuSliKiIiIiIiIJBhP3TEMvx9bupO0/Dyzw0kIoUrUuvc/MDkSETkXJSxFREREREREEkx7VQ0AGaUlWCwWk6NJDJnlwbbwuvc3mxyJiJyLEpYiIiIiIiIiCaa9qhpQO/j5CC0natqxC09Do8nRiMjZKGEpIiIiIhIlvo5O2g4dwTAMs0MRkSTTEaqwVMKy1+xZmTTYrWAY1KzbaHY4InIWSliKiIiIiESBYRgc/P0f2f/bP3Bg5St0tbWZHZKIJIkMf4CulpbgP5cWmxxNYjnstANQvW6DyZGIyNkoYSkiIiIiEgVth46EN/i2HTzM/v/9A5oyJyKRUOL1A+AsKsCWlmZyNInliNMGBBOWqn4XiV9KWIqIiIiIREH9h1sAyLlwBFaHHW9jEwMtdnODEpGkMKArmLDMLFU7+PmqSrNjddhpP3yU1v0HzQ5HRM5A75hERERERCKss74h/Itw6VVTqA4YtOzdxxibKqFEpP8GdFdYppeoHfx8+awWCidOoG7jJqrfXU/O8KG9/t7bZt9MQ23tae8rKC7mf1/+Q6TCFEl5SliKiIiIiERY8+7PAcgZMYy0PBfZw4YEE5ZWJSxFpH8MwwhXWGaUDDA5msRU+sUpwYTl2r8w8lvf6PX3NdTW8tSc209733d/vSxS4YkIagkXEREREYm4ztpjAGQNGghA9rAhAAyzOvA0NZkVlogkgdYDh3AaYLHZcBYWmB1OQir/0tUAVK/biL/TY3I0InI6SliKiIiIiERYZ10dAOnFweqntNwcnIUFWC0Wqt9Zb2ZoIpLgGj/ZDkB6USFWm83kaBJT3sUXkVFSjL+jg9r3NpkdjoichhKWIiIiIiIR5OvooKulFYD0AUXhr4eqLKvf3WBKXCKSHBq2dicsNb+yzywWC2XXBqssj775jsnRiMjpKGEpIiIiIhJBoXbwtDwXNufxmZWZpSUAuHfsNCUuEUkODZ9sAzS/sr/Kr7sGgKNvrsUwDJOjEZGTKWEpIiIiIhJBnXXBhOWJ1ZVAeNace/dejEAg5nGJSOIzAgEatn4KQIYqLPul5MpJWNMctB06TPOevWaHIyInUcJSRERERCSCOmp7zq8MSctz4TMM/B0dtB06YkZoIpLgWg8cwtfahg9wFuSbHU5Cc2RlUTJ1EgCHX3/T5GhE5GRKWIqIiIiIRFCowjLjpApLi9VKjeEDwL1rT8zjEpHE17j9MwAaHFYsVv0631+DbrwegEOvvm5yJCJyMv2EExERERGJEL+3C29jEwDpxUWn3F8d8ANKWIpI3zR9GkxY1ju0HTwSBs28DovNRtP2z2jZf8DscETkBKYmLJ9++mnGjx9Pbm4uubm5TJkyhddeey18v2EYVFZWUl5eTkZGBtOmTWP79u09HsPj8TB//nyKiorIyspi9uzZHD58uMeZxsZG5s2bh8vlwuVyMW/ePJqammLxEkVEREQkhXib3ADYMtKxZ2aecn91uMLy85jGJSLJoVEJy4hyFuRT3N0WfujVN0yORkROZGrCctCgQfx//9//xwcffMAHH3zAtddey8033xxOSi5evJjHH3+cpUuXsmnTJkpLS7n++utpaWkJP8aCBQtYuXIlK1asYN26dbS2tjJr1iz8fn/4zNy5c9myZQurVq1i1apVbNmyhXnz5sX89YqIiIhIcutqbgYgLTf3tPdXBUIJy90xi0lEkkfT9lDCUs2SkTL4y6G2cCUsReKJqT/lbrrpJr785S8zatQoRo0axU9/+lOys7PZuHEjhmHw5JNP8uijj3LrrbdSUVHBCy+8QHt7O8uXLwfA7Xbz7LPP8thjjzF9+nQmTJjAsmXL2Lp1K2vWrAFgx44drFq1il/84hdMmTKFKVOm8Mwzz/DHP/6RnTt3mvnyRURERCTJeJuDH6w7cnNOe3+1EfxQvWXPPgInfMAuInIuncfq6aipBYuFBrsqLCNl0A3TsdhsNG77FPduVb+LxIu4+VjG7/ezYsUK2tramDJlCvv27aO6upoZM2aEzzidTq655hrWr18PwObNm+nq6upxpry8nIqKivCZDRs24HK5mDRpUvjM5MmTcblc4TMiIiIiIpEQqrB0nKHCst7wY3M68Xs8tB08fNozIiKn0/RpsOAmZ9gQuqwWk6NJHumFBZRfezUA+3690uRoRCTE9ITl1q1byc7Oxul08n//7/9l5cqVjB07lurqagBKSkp6nC8pKQnfV11dTVpaGvn5+Wc9U1xcfMrzFhcXh8+cjsfjobm5ucdNRERERORsurorLNPOUGHZ1NTE/o5WAL46eSqlAwaccpswbnzM4hWRxBGaX5k39iKTI0k+w//6FgD2/+4VAj6fucGICAB2swMYPXo0W7Zsoampid/+9rfccccdrF27Nny/xdLzkyPDME752slOPnO68+d6nEWLFvGjH/2oty9DREREROR4S7jr9BWW/kCAi0dfRPPuz/mHGV+hcMIlp5y5flFlNEMUkQQV2hCed/FF8NEGk6NJLmXXXo2zIJ/OumNUr/0L5dddY3ZIIinP9ArLtLQ0LrzwQi6//HIWLVrEJZdcwlNPPUVpaSnAKVWQtbW14arL0tJSvF4vjY2NZz1TU1NzyvPW1dWdUr15okceeQS32x2+HTp0qF+vU0RERESS3/GlO6evsITj8y297pYznhEROVnTjl0A5F00yuRIko8tLY2hX50FwOfLf2NyNCICcZCwPJlhGHg8HoYPH05paSmrV68O3+f1elm7di1Tp04FYOLEiTgcjh5nqqqq2LZtW/jMlClTcLvdvP/+++Ez7733Hm63O3zmdJxOJ7m5uT1uIiIiIiJnkomFgLcLOPPSHTi+QbxLI4dEpJf8Xi/Nn+8DlLCMlgtv/xoAR9a8Tcu+AyZHIyKmtoT/4Ac/4MYbb2Tw4MG0tLSwYsUK3n77bVatWoXFYmHBggUsXLiQkSNHMnLkSBYuXEhmZiZz584FwOVycdddd/Hggw9SWFhIQUEBDz30EOPGjWP69OkAjBkzhhtuuIG7776bn/3sZwB8+9vfZtasWYwePdq01y4iIiIiyaXAEtzaa8/MxGo/89vscIVlsyosRaR3WvYdwPD5sGdnkTmwzOxwklLuBcMpv+4ajr65ll3//SITf/wPZockktJMTVjW1NQwb948qqqqcLlcjB8/nlWrVnH99dcD8PDDD9PR0cG9995LY2MjkyZN4o033iAn5/gn1k888QR2u505c+bQ0dHBddddx/PPP4/NZgufeemll/jOd74T3iY+e/Zsli5dGtsXKyIiIiJJLZSwPFt1JUCaK3h/V3Nzr+azi4i4d+4GwDV6pH5mRNHov/0WR99cy95f/56KB+/HmZdndkgiKcvUhOWzzz571vstFguVlZVUVlae8Ux6ejpLlixhyZIlZzxTUFDAsmXL+hqmiIiIiMg5FViD05bOlbB0dLeEB7xd+D0e7OnpUY9NRBKb+7NgwjJv9EiTI0luxVMnkTd2NE2f7mTnz19g/MPfNTskkZQVdzMsRUREREQSUajCMu0MG8JDrHY79swMALrcmmMpIud2YoWlRI/FYqHie/cBsPPZF+morTM5IpHUpYSliIiIiEgEFPayJTx4JrR4R3MsReTcmrorLF0XKWEZbQNnXEvhhPH4Ozr4dMnPzA5HJGUpYSkiIiIiEgF5lu6W8JzeJCxDi3dUYSkiZ9fV1kbbocOAWsJjwWKxhFvB97z0G5o+22VyRCKpSQlLEREREZEIyA1VWGZlnvNsqG1cFZYici7Nuz4HIH1AEc6CfJOjSQ0lV05m4MzrMHw+Nj3yI4xAwOyQRFKOEpYiIiIiIv0U6Ooip7vC0t6LhGW4wlIzLEXkHJo0v9IUE//5B9izs6jfvIU9y35ldjgiKUcJSxERERGRfuo8Vh/8B6sVW0bGOc+naYaliPRS8+5ghaVr1AUmR5JaMstKw63hW37yb7h37TE5IpHUooSliIiIiEg/ddYeA8CemYHFYjnn+eMzLFswDCOqsYlIYgslLHNHKmEZayO/9Q1Kr56Kv7OT9ff/PTb9vBaJGSUspU9a9h+ktXvws4iIiEiq66itA3rXDg7gyMkGwPD58Hs8UYtLRBKfe89eAHIvVMIy1ixWK5MeX4izqBD3Z7u4qqlTHzKJxIjd7AAksQR8fqrffpfGbZ8CcMHtXyO9qNDkqERERETMFUpYOrKyenXeardjS0/H39mJr7UNe3p6NMMTkQR02+ybaa6u4W+qg6Mj7ph/Lx5rsOZoz67dZoaWUjKKBzDl3xez9va7Gd3RRePW7RSMrzA7LJGkpwpLOS81764PJysBate/Z2I0IiLxY9GiRVxxxRXk5ORQXFzMLbfcws6dO3ucMQyDyspKysvLycjIYNq0aWzfvr3HGY/Hw/z58ykqKiIrK4vZs2dz+HDPivbGxkbmzZuHy+XC5XIxb948mpqaov0SReQsjreE967CEsCeHUxudrW0RiUmEUlsDbW1/ORLMwGwZWSw+Ovf4qk5t/PUnNvx+bpMji61lF41mfHf/x4A1W+vo/1otckRiSQ/JSyl1yyAu3tDXfHUL4DFQsve/bQfrTI3MBGROLB27Vruu+8+Nm7cyOrVq/H5fMyYMYO2trbwmcWLF/P444+zdOlSNm3aRGlpKddffz0tLceXbixYsICVK1eyYsUK1q1bR2trK7NmzcLv94fPzJ07ly1btrBq1SpWrVrFli1bmDdvXkxfr4j0dL4t4QCO7oSl74SfEyIiJ/I0NALgLMg3ORK56J7/w+fpdoxAgEOvrqJLP7tFokot4dJrI+xO/J2dWJ1Oii6/DG9zC03bdnBs8xaGlJeZHZ6IiKlWrVrV48/PPfccxcXFbN68mauvvhrDMHjyySd59NFHufXWWwF44YUXKCkpYfny5dxzzz243W6effZZXnzxRaZPnw7AsmXLGDx4MGvWrGHmzJns2LGDVatWsXHjRiZNmgTAM888w5QpU9i5cyejR4+O7QsXEQA6+5SwDM6xVIWliJxJOGFZqISl2SwWC2vzMhjrteNpaOTwq68z9K9uxmqzmR2aSFJShaX02jhHBgDZQwdjsVrJv3gMAO1HqzR4WETkJG63G4CCggIA9u3bR3V1NTNmzAifcTqdXHPNNaxfvx6AzZs309XV1eNMeXk5FRUV4TMbNmzA5XKFk5UAkydPxuVyhc+czOPx0Nzc3OMmIpF1vMKydzMs4YSWcFXpiMgZeOpVYRlPfFYLg2+6EWtaGu1Hq6l55/TvvUSk/5SwlF4blxZMWOYMHwpA+oABWGw2/B2deJvcZoYmIhJXDMPggQce4KqrrqKiIjiUvbo6OOuopKSkx9mSkpLwfdXV1aSlpZGfn3/WM8XFxac8Z3FxcfjMyRYtWhSed+lyuRg8eHD/XqCInKIjNMOyLy3hLUpYisjpeRpDCcsCkyOREGd+HoNuCHbCNHy8FfeuPSZHJJKclLCUXmmvrmGI3QlA9tAhAFjtNtKLBwDQUaWhwyIiIffffz+ffPIJv/zlL0+5z2Kx9PizYRinfO1kJ5853fmzPc4jjzyC2+0O3w4dOtSblyEivWQEAniOBROWjr60hLepJVxETmU1jHBhiCos40vOiGEUXXEZAFVvvaNKeZEoUMJSeqVm3UYAMkpLsGdmhL+eWRasFGqvqjElLhGReDN//nxefvll/vznPzNo0KDw10tLSwFOqYKsra0NV12Wlpbi9Xpp7K6mONOZmppTf+bW1dWdUr0Z4nQ6yc3N7XETkcjxNrkJdPkAsPVhS7gqLEXkdHJ9ATAMrGmO86reltgYMPkK0gcU4e/s5Oiat0Fj0kQiSglL6ZWmz3YBkFHasw0xsyz4C7gqLEUk1RmGwf3338/vfvc73nrrLYYPH97j/uHDh1NaWsrq1avDX/N6vaxdu5apU6cCMHHiRBwOR48zVVVVbNu2LXxmypQpuN1u3n///fCZ9957D7fbHT4jIrEVml/ZagTOa/lCqMLS7/EQ6OqKSmwikrhcvgAAaXl55+zGkNiz2mwMnHkdFpuV1n0HGNbpMzskkaSiLeHSK83dczmchT1np2R0Jyw76xvwe70xj0tEJF7cd999LF++nD/84Q/k5OSEKyldLhcZGRlYLBYWLFjAwoULGTlyJCNHjmThwoVkZmYyd+7c8Nm77rqLBx98kMLCQgoKCnjooYcYN25ceGv4mDFjuOGGG7j77rv52c9+BsC3v/1tZs2apQ3hIiYJJSybjcB5fZ/VmYbFbsfw+ehqbcOZnxeF6EQkUbn8oYSly+RI5EzSiwopnDiBY+9vZkpzJ772duznUWkvImfWp4TliBEj2LRpE4WFhT2+3tTUxGWXXcbevXsjEpzEj6adwYRl+kkJS0d2Fo6cHLpaWuioqTUjNBGRfonUNe3pp58GYNq0aT2+/txzz3HnnXcC8PDDD9PR0cG9995LY2MjkyZN4o033iAnJyd8/oknnsButzNnzhw6Ojq47rrreP7557GdULX10ksv8Z3vfCe8TXz27NksXbr0fF+6iERIZ03fEpYWiwVHTjbexiZ8SlhKhOh3tcRx2+ybaag98+9Ql9Q3giNTPxvi3IArLsO9Yyc5La18+p+/YPxD3zE7JJGk0KeE5f79+/H7/ad83ePxcOTIkX4HJfHF624Ot3yfXGEJwTbxrpYWPHX1sQ5NRKTfInVNM3oxt8hisVBZWUllZeUZz6Snp7NkyRKWLFlyxjMFBQUsW7as17GJSHR11gUX7rQYp/4sORdHVhbexia6WrV4RyJDv6sljobaWp6ac/sZ7//Dvz0BqMIy3lkdDkqvuZJDf3ydj5f8jEd+9RIdtp7T9wqKi/nfl/9gUoQiiem8EpYvv/xy+J9ff/11XK7jPzj9fj9vvvkmw4YNi1hwEh/c3e3g9X4fNqfzlPtDF1BPU1MswxIR6Rdd00QkUjobgouyWvqwcMGeE1y809WqxTvSP7quJZ8B1uCv62mqsIx7OReM4IDPy1B7Gt8tG0HZtKt63P/dX+uDZpHzdV4Jy1tuuQUIVojccccdPe5zOBwMGzaMxx57LGLBSXwIJSyP+k8/ozItLw8IbsgUEUkUuqaJSKR46hsAaOP8WsIhWGEJ4FPCUvpJ17Xk4vd24bIGx8E4VWEZ9ywWC3/qdPN32QNo3LqNwssuIS0359zfKCJndF4Jy0Ag+CZs+PDhbNq0iaKioqgEJfHFvTOUsDz99kpnfvAC6m1silVIIiL9pmuaiESKp7vCsvU8Z1gC2Ls3haslXPpL17XkEioGsWWkY0tPNzka6Y3dPg9ZgwbSdvgI9R9+fEqVpYicH+u5j5xq3759ugCmEPeu3QAcOUeFZVdLKw4ssQpLRCQidE0Tkf4KVVj2JWHpyA5uk/W1tUc0Jklduq4lB2/3uK3Q71qSGIquuAyAxm2f4uvoNDkakcTWp6U7AG+++SZvvvkmtbW14U/zQv77v/+734FJ/AhVWB7xnb7C0paRjtXpJODxMMDW579SIiKm0TVNRPqjsz8VlpndLeFKWEoE6bqW+ELda2oHTyxZQwaRPqCIzrpjNHyyjeJJl5sdkkjC6lOF5Y9+9CNmzJjBm2++ybFjx2hsbOxxk+ThaWoKVw1Un6El3GKxhC+kJTZHzGITEYkEXdNEpL88x7orLPsww9Ke1V1h2d6G0YelPSIn03UtOXi6W8K1cCexWCwWiiZOAKBhyycEfD6TIxJJXH0qh/uv//ovnn/+eebNmxfpeCTOtB06CkD6gCK89fvPeC4tz0VHTS0lVlVYikhi0TVNRPrD196OvzPY9tfaly3h3Ut3DH8Af6cHe4Zm1Un/6LqWHEIVlmmqsEw4uaMuwL5uPb7WNpp3f07emNFmhySSkPpUYen1epk6dWqkY5E41Hb4CABZgwee9Vzok79iVViKSILRNU1E+qOzPlixZnWm4eH8E5ZWuw1buhMAX5s2hUv/xfq6duTIEW6//XYKCwvJzMzk0ksvZfPmzeH7DcOgsrKS8vJyMjIymDZtGtu3b+/xGB6Ph/nz51NUVERWVhazZ8/m8OHDMXsN8Si0dMepCsuEY7FaKRh3MQANH28zORqRxNWnhOXf/u3fsnz58kjHInGo7VB3wnJQ+VnPpaklXEQSlK5pItIfnoZgO7izoKDPjxGqstQcS4mEWF7XGhsbufLKK3E4HLz22mt8+umnPPbYY+SdsChm8eLFPP744yxdupRNmzZRWlrK9ddfT0tLS/jMggULWLlyJStWrGDdunW0trYya9Ys/H5/TF5HvPF1doYrt1VhGXk7d+3i2slTTnu7bfbNEXmO/IqxWKxWOqpr6Kiti8hjiqSaPvXvdnZ28vOf/5w1a9Ywfvx4HI6eSarHH388IsGJ+Y5XWA466zlnuMJSLeEiklh0TROR/vB0V1imF+bD3r49hj0rE099gyosJSJieV37l3/5FwYPHsxzzz0X/tqwYcPC/2wYBk8++SSPPvoot956KwAvvPACJSUlLF++nHvuuQe3282zzz7Liy++yPTp0wFYtmwZgwcPZs2aNcycOTNi8SYKb2OwurIp4MPqUEFIpBk+H0/Nuf20933318si8hz2rExyR16Ae+duGj/Zfu5vEJFT9Cm79Mknn3DppZcCsG1bzxJni8XS76AkfpxvhWWe1U5XWxuO7koBEZF4p2uaiPRHaDmhsyC/z48RqrDsUoWlREAsr2svv/wyM2fO5K//+q9Zu3YtAwcO5N577+Xuu+8GYN++fVRXVzNjxozw9zidTq655hrWr1/PPffcw+bNm+nq6upxpry8nIqKCtavX3/ahKXH48Hj8YT/3NzcHNHXZTZvUxMAdX4tbIm1UPXlmezZtbvXj5U/bizunbtx79qDrdAZifBEUkqfEpZ//vOfIx2HxKlwwvIcMyxtTifWtDQCXi/tR6txjbwgFuGJiPSbrmki0h+ehmCFpbOw7y3hjtCmcFVYSgTE8rq2d+9enn76aR544AF+8IMf8P777/Od73wHp9PJt771LaqrqwEoKSnp8X0lJSUcOHAAgOrqatLS0sjPzz/lTOj7T7Zo0SJ+9KMfReEVxYfQ/MpjASUsY+1s1ZcAMyt/0OvHyhxYjiM3h67mFoZ12CIRnkhK6dMMS0kNhmEcbwkfdPaEJYAjJxuA9qrTv7EQERERSTadx+qByFRYaoalJJpAIMBll13GwoULmTBhAvfccw933303Tz/9dI9zJ1d2GoZxzmrPs5155JFHcLvd4duhQ4f690LijKd7Q3itEpYJzWKxhDeEj+rwmhyNSOLpU4Xll770pbNeYN56660+ByTxw9vYhK+9A4CsgWdvCQdwZGfjqW+g/agSliKSOHRNE5H+CFVYphcW9vkx7KqwlAiK5XWtrKyMsWPH9vjamDFj+O1vfwtAaWkpEKyiLCsrC5+pra0NV12Wlpbi9XppbGzsUWVZW1t7xm3nTqcTpzN5W2y93QnLY2oJT3h5Y0ZT994HDPT4aa+uIbO05NzfJCJAHyssL730Ui655JLwbezYsXi9Xj788EPGjRsX6RjFJK2HDgOQUVKMzZl2zvPhCkslLEUkgeiaJiL9EVq64yzUDEuJD7G8rl155ZXs3Lmzx9d27drF0KFDARg+fDilpaWsXr06fL/X62Xt2rXhZOTEiRNxOBw9zlRVVbFt27YzJiyTmWEY4ZbwOlVYJry0PBeZA8uwAvt/+7LZ4YgklD5VWD7xxBOn/XplZSWtra39CkjiR9uho8C5F+6E2NUSLiIJSNc0EemPzobupTv9mWGZ3V1h2dqGYRgRiUtSVyyva9/73veYOnUqCxcuZM6cObz//vv8/Oc/5+c//zkQbIldsGABCxcuZOTIkYwcOZKFCxeSmZnJ3LlzAXC5XNx11108+OCDFBYWUlBQwEMPPcS4cePCW8NTia+9nUBXF1gs1CthmRTyxoym/UgV+3/7MmPu/VstdRTppYjOsLz99tv57//+70g+pJgoPL9yyKBenXdkq8JSRJKHrmki0huhCsv0CMywNPx+Ah7NOZPoiMZ17YorrmDlypX88pe/pKKigh//+Mc8+eSTfPOb3wyfefjhh1mwYAH33nsvl19+OUeOHOGNN94gJycnfOaJJ57glltuYc6cOVx55ZVkZmbyyiuvYLOl3qISb2OwutKRm4Pf5FgkMnJHXojPAs179tLw8VazwxFJGH2qsDyTDRs2kJ6eHsmHFBO1dbeE97bCMtQS3qEKSxFJArqmiUhveCJQYWm127GmpRHwevG1a46lREe0rmuzZs1i1qxZZ7zfYrFQWVlJZWXlGc+kp6ezZMkSlixZEvH4Eo23qQkAZ54LDpgbi0SGzZnGvnQHIzu62Peb31N46XizQxJJCH1KWN566609/mwYBlVVVXzwwQf84z/+Y0QCE/O1HakCerdwB3puCe/N5j8RkXiga5qI9JW/0xPe7N2fLeEAjuwsPA1eulo1x1L6R9e1xOZ1NwPgcLlMjkQiaVdGMGF58OXXmPDD7/dqR4RIqutTwtJ10g9Pq9XK6NGj+ed//mdmzJgRkcDEfB3VNQBklJX26rwjO9jO5Gtrp6u5hTRXbtRiExGJFF3TRKSvQvMrLXY7jtycc5w+O3tWJp6GRm0Kl37TdS2xhRKWaa7+/UyR+HLUaSOjpJiOmlqq3n6XQTOvMzskkbjXp4Tlc889F+k4JA51VNcCkFla3KvzVoeDloCfHKuN9qpqJSxFJCHomiYifeVtbALAme/qd2eJPat78Y42hUs/6bqW2LqaWwD0u1SSMSwWhtz8ZXb+/HkOrPyjEpYivdCvGZabN29mx44dWCwWxo4dy4QJEyIVl5jM7/HiaQgOkc8oLen19zUGfMGE5dFq8i4aFa3wREQiTtc0OR8Txo2nqrrqrGfKSsv4aOsnMYpIzODpXo6RlpfX78cKLd7pUoWlRIiua4nJ29zdEp6rhGWyGXbLLHb+/HmOvPk23uYW0vpZmS+S7PqUsKytreXrX/86b7/9Nnl5eRiGgdvt5ktf+hIrVqxgwIABkY5TYqyjJlhdaXWmkZbX+/kpDQE/Q9CmcBFJHLqmSV9UVVex+pHKs565ftHZ75fEd7zCMq/fjxVKWKrCUvpL17XE5fd24W/vACBNCcukk3fxReReOILmPXs5vGoNI+Z81eyQROKatS/fNH/+fJqbm9m+fTsNDQ00Njaybds2mpub+c53vhPpGMUEoYRlRnHxebU4NQZ8ALRXnb3qREQkXuiaJiJ95ene5psWgYSlQy3hEiG6riWuru7qSqvTiS3daXI0EmkWi4WhX50FwIGVfzQ5GpH416cKy1WrVrFmzRrGjBkT/trYsWP5j//4Dw1yThKh+ZUZvZxfGdLg9wOqsBSRxKFrmoj0VXQqLNUSLv2j61ri0vzK5LVz1y6unTyFHF+AbwDVf9nIrCsm0W6zUlBczP++/AezQxSJO31KWAYCARwOxylfdzgcBAKBfgcl5uuoCW4IzzyP+ZVwYoVlTcRjEhGJBl3TRKSvPN0Jy/MZn3Mmx5fuKGEp/aPrWuIKbwjXbMOkY/h8PDXndgD2/up3dFRV8+Do8RRddinf/fUyk6MTiU99agm/9tpr+e53v8vRo0fDXzty5Ajf+973uO46bbtKBh01dQBklJzfjBt3IFhh2Vl3LOIxiYhEg65pItJX3u6lO5GssAx0+XDSv43jktp0XUtcoYSlQxWWSS20nNb92W6TIxGJb31KWC5dupSWlhaGDRvGBRdcwIUXXsjw4cNpaWlhyZIlkY5RouyikaPIycrqcXvh34P/Hn/81JPhr3V0dJ7zsdxGd8Kyti6qMYuIRIquaSLSV5GcYWlLc2BNC1bFuSx9eosuAui6lshCMyy1cCe55Y66AKxWOmvr8DQ0mB2OSNzqU0v44MGD+fDDD1m9ejWfffYZhmEwduxYpk+fHun4JAaOHD3C+h8v7vG1fb/5Pe1HjvLAbV/jR6NHAnDpg/ef87FCFZZedzN+jxebMy3yAYuIRJCuaSLSV5GcYQnBKkuvt4lcJSylH3RdS1zeZlVYpgJ7RgbZQwfTuu8ATaqyFDmj83o39NZbbzF27Fiau3+QXn/99cyfP5/vfOc7XHHFFVx88cW8++67UQlUYsvX2gqAPTvrvL6v3QiEqwPUFi4i8UzXNIk2d1MTpQMGnPU2Ydx4s8OUfjg+wzIvIo9nzwzOsVTCUvpC17XEZhgGXZphmTJ6tIUbhsnRiMSn86qwfPLJJ7n77rvJPU2Jusvl4p577uHxxx/ni1/8YsQClNgzDIOutnYAHFnnl7AESB9QRPuRKjpq68gaVB7p8EREIkLXNIk2fyDA6kcqz3rm+kVnv1/i2/EKy/4v3QFwdH9QrISl9IWua4nN39FJoCu4wNShhGXSyxkxDKvDQVdzMyVpmWaHIxKXzuvd0Mcff8wNN9xwxvtnzJjB5s2b+x2UmCvg8WD4ghfL862wBMgoDi7qUYWliMQzXdNEpD8Cfj/e5hYgki3h3RWW2CLyeJJadF1LbKF2cHtWFlZ7nya3SQKxOhzkXDgcgAvbu0yORiQ+nVfCsqamBofDccb77XY7dXVatpLoulrbALClO/t0sUzvTlh2aPGOiMQxXdNEpD+6mlvCbXxpeZGpsAwnLFVhKX2g61piC7eDa35lysgbHWwLH9HpI9ClpKXIyc7r3dDAgQPZunXrGe//5JNPKCsr63dQYi5fd8LS3od2cICMAUUAdNaqwlJE4peuaSLSH6H5lfbsLKxnSRKdj9B7L20Jl77QdS2xed2hhTtqB08VWUMGYcvMICNgUP3OerPDEYk75/Vu6Mtf/jI//OEP6ezsPOW+jo4O/umf/olZs2ZFLDgxR1dbMGHp6EM7OEB6cTBhqQpLEYlnuqaJSH9EekM4HJ8drgpL6Qtd1xJbV/eIibTTzCCV5GSxWnGNuhCA/b//o8nRiMSf8+r3/Yd/+Ad+97vfMWrUKO6//35Gjx6NxWJhx44d/Md//Ad+v59HH300WrFKjPi6F+70ucIyNMNSCUsRiWO6polIf0R6QzioJVz6R9e1xBaaYelQwjKluC4aRcOWrRx54890tbX1aemtSLI6r4RlSUkJ69ev5+/+7u945JFHMLrn9lgsFmbOnMl//ud/UlJSEpVAJXZ87R0A2DMz+vT96aGWcC3dEZE4pmuaiPRHpDeEw/EPizMsVnzt7dgztTlWek/XtcTm1QzLlJRRUozbZsXV0cGR199i2K03mR2SSNw4740qQ4cO5U9/+hONjY3s2bMHwzAYOXIk+fn50YhPTOBr766w7OOb5Izw0h0lLEUkvumaJiJ95WlqAiAtgi3h1jQHFrsdw+ejo7aOnGFDI/bYkhp0XUtMRiBAV0srAGmaYZlSLBYLezLsTGz1cuD3f1TCUuQE578Cult+fj5XXHFFJGOROHG8JbxvCcvQDMvOY/UYgQAWq9qaRCS+6ZomIufL2+gGIjvD0mKx4MjKxOtupqPmmBKW0me6riWWrtY26P69qa9juSRx7c50MLHVS/U76+msOxbuWBRJdcokySn6W2GZXlQIFguGzxee7yQiIiKSTKIxwxKCW8cBOmtqI/q4IhK/ukIbwnNzVOyRgprtNgouqcAIBDj4yiqzwxGJG/ppKKfob4Wl1eHAWRBsO9HiHREREUlG0dgSDsc/MO7QeyiRlKGFOzLsq8FWcG0LFzlOCUvpIeDzEfB6gb5XWMLxxTt6sy0iIiLJ6PgMy8gt3YHji3ci8R5qwrjxlA4YcNbbhHHj+/08ItI/xxfuaH5lqhpy0w1YbDYatmylZd8Bs8MRiQt9nmEpySm0Idxis2F1pvX5cTKKi3B/totOLd4RERGRJBStCktHd0t4R03/E5ZV1VWsfqTyrGeuX3T2+0Uk+rpUYZny0gcUUfrFKVS9vY69v/odl3z/e2aHJGI6VVhKD+F28MxMLBZLnx8nvah78U59Q0TiEhEREYknntDSnUjPsOweyaOxOiKpo6u5BYC0XFVYprIR37gNgL2/Xom/u+tRJJUpYSk9HF+4k9Gvx3EWBmdYepSwFBERkSQUqrBMi/QMyyzNsBRJNV0trQA4cpSwTGUDp08jfUARnmP1HF39Z7PDETGdEpbSQ38X7oSkFxYA0Hmsvt8xiYiIiMQTX0cHfo8HiMLSnazItYSLSPwzAgG6WtsAcORmmxyNmMnqcDDia7cCsGf5/5ocjYj5lLCUHo5XWPYvYeksKgTA09DY75hERERE4om3KdgObrHbsXfPnIwUR/eHxl3Nzfg6OyP62CISf3xt7WAYYLX2+3cwSXwjvv5XANS8u56W/QdNjkbEXEpYSg+Rr7BUS7iIiIgkF0+oHTzP1a+Z36djdTrpMgwALS8USQHhdvCsTCxW/Xqe6rKHDKL06qkA7F3xW5OjETGXfiJKD5GrsAwmLD31agkXERGR5BLeEJ7nivhjWywW3EYAgI6a2og/vojEl66W4MIdza+UkAu+OQfQ8h0RJSylh3DCst8VlsGW8M76BozuKgERERGRZBDaEB7phTshzYYf0KZwkVTQ1RpauKP5lRIUXL5TqOU7kvKUsJQefG0dQAQqLLu3hAc8XnzdQ6RFREREkkG4wjJaCUu6KyzVEi6S9EIt4fZsJSwlyOpwMHxOcPnO7hdXmByNiHmUsJQwwzBOqLDM6Ndj2TMywlWanWoLF5EU8c4773DTTTdRXl6OxWLh97//fY/777zzTiwWS4/b5MmTe5zxeDzMnz+foqIisrKymD17NocPH+5xprGxkXnz5uFyuXC5XMybN4+mpqYovzoRCfF0//cWvQpLtYSLpIrwDEtVWMoJLrx9Dhabjdr179O0Y6fZ4YiYQglLCQt4uzB8PqD/FZYAzu62cI8W74hIimhra+OSSy5h6dKlZzxzww03UFVVFb796U9/6nH/ggULWLlyJStWrGDdunW0trYya9Ys/H5/+MzcuXPZsmULq1atYtWqVWzZsoV58+ZF7XWJSE/RnGEJJyQs1RIukvSUsJTTyRpYzqCZ1wGw67mXTI5GxBx2swOQ+OHrCLaDWx0OrA5Hvx8vvTCftoOH6KxXwlJEUsONN97IjTfeeNYzTqeT0tLS097ndrt59tlnefHFF5k+fToAy5YtY/DgwaxZs4aZM2eyY8cOVq1axcaNG5k0aRIAzzzzDFOmTGHnzp2MHj06si9KRE7hiXZLeHfCUjMsRZKfZljKmYz6m9s59Kc3OLDyj1zy/e/hLMg3OySRmFKFpYT5u9vBbZn9awcPcRaGNoUrYSkiEvL2229TXFzMqFGjuPvuu6mtPd7yuXnzZrq6upgxY0b4a+Xl5VRUVLB+/XoANmzYgMvlCicrASZPnozL5QqfOZnH46G5ubnHTUT6zhvlpTvHt4QrYSmSzGyGgb89WDSihKWcrOiKy8ivGIvf42HP8t+YHY5IzClhKWG+7oulPSMyCcv0ouObwkVEJFiB+dJLL/HWW2/x2GOPsWnTJq699lo8Hg8A1dXVpKWlkZ/f8xP0kpISqqurw2eKi4tPeezi4uLwmZMtWrQoPO/S5XIxePDgCL8ykdQSmmEZvQrL4AgItYSLJLcsvwGAxW7Hlp5ucjQSbywWC6P+5nYA9vzPCgJdXSZHJBJbSlhKWDhhGYH5lXBChaVmWIqIAPC1r32Nr3zlK1RUVHDTTTfx2muvsWvXLl599dWzfp9hGFgslvCfT/znM5050SOPPILb7Q7fDh061L8XIpLiQjMso710x9vYhN/jjcpziIj5svzB/9YdOdlnvIZLahty042kDyiko7qGQ6+tNjsckZhSwlLCQjMs7RFqCU/vTlhqS7iIyOmVlZUxdOhQdu/eDUBpaSler5fGxsYe52praykpKQmfqampOeWx6urqwmdO5nQ6yc3N7XETkb4Lz7DMy4vK47dhYHUER8131h2LynOIiPmyQwnLbLWDy+nZnGlcePvXAdj17IsmRyMSW1q6I2ERn2FZ1J2wVIWliMhp1dfXc+jQIcrKygCYOHEiDoeD1atXM2fOHACqqqrYtm0bixcvBmDKlCm43W7ef/99vvCFLwDw3nvv4Xa7mTp1qjkvRCSFBPx+vO7gHNi0/OhsCQdILx5A+5EqOmrryBpUHrXnERHzZHe3hGt+pZzJbbNvpqOqmrlA/Uef8PXLrqA27XgaZ++BA4wYOvS031tQXMz/vvyHGEUqEnmmVlguWrSIK664gpycHIqLi7nlllvYuXNnjzOGYVBZWUl5eTkZGRlMmzaN7du39zjj8XiYP38+RUVFZGVlMXv2bA4fPtzjTGNjI/PmzQvP75o3bx5N3fOHJOh4S3ikKiyDMyy1dEdEUkVraytbtmxhy5YtAOzbt48tW7Zw8OBBWltbeeihh9iwYQP79+/n7bff5qabbqKoqIivfvWrALhcLu666y4efPBB3nzzTT766CNuv/12xo0bF94aPmbMGG644QbuvvtuNm7cyMaNG7n77ruZNWuWNoSLxEBXcwsYwSRDWl70EpYZxQMA6KipPcdJEUlU2Se0hIucTkNtLf/fN75F4diLAPhWfhlPzbk9fPO2tfX484m3hlpdPySxmZqwXLt2Lffddx8bN25k9erV+Hw+ZsyYQVtbW/jM4sWLefzxx1m6dCmbNm2itLSU66+/npaWlvCZBQsWsHLlSlasWMG6detobW1l1qxZ+P3+8Jm5c+eyZcsWVq1axapVq9iyZQvz5s2L6euNd5FfuhNqCVfCUkRSwwcffMCECROYMGECAA888AATJkzghz/8ITabja1bt3LzzTczatQo7rjjDkaNGsWGDRvIyckJP8YTTzzBLbfcwpw5c7jyyivJzMzklVdewWazhc+89NJLjBs3jhkzZjBjxgzGjx/Piy+qTSie+Ds93JRdyKFXX8frdpsdjkSQtyn479OenYUtLS1qz5NRElyu1anFOyJJK0sVltJLhRPGA9C8Zy/e5pZznBZJDqa2hK9atarHn5977jmKi4vZvHkzV199NYZh8OSTT/Loo49y6623AvDCCy9QUlLC8uXLueeee3C73Tz77LO8+OKL4eqTZcuWMXjwYNasWcPMmTPZsWMHq1atYuPGjUyaNAmAZ555hilTprBz505VpHTzdbeER2zpTkEwYeltaCTg92M94ZdtEZFkNG3aNIzuyqvTef3118/5GOnp6SxZsoQlS5ac8UxBQQHLli3rU4wSfR21dez/7cv8tWsAzbs/p/1IFUO/Oov0AUVmhyYREJ5fGaWFOyHpxcG/L9oULpK8NMNSAHbu2sW1k6ec9r49u4JzztMHFJE1eCBth47Q8PFWSr+oMUCS/OJqhqW7uwKhoDvRtW/fPqqrq5kxY0b4jNPp5JprrmH9+vXcc889bN68ma6urh5nysvLqaioYP369cycOZMNGzbgcrnCyUqAyZMn43K5WL9+/WkTlh6PB4/HE/5zc3NzxF9vvPF3L92J2AzLgjwAjEAAb5M7vIRHREQkWRmGQfXavxDweDja5WF4aRme+gb2r3yFkXfeji3NYXaI0k/hDeGu6LWDw4kt4UpYiiSrcMIyVwnLVGb4fDw15/bT3jez8gfhfy6ccAlth47QuPVTBky6Qu8pJOnFzZZwwzB44IEHuOqqq6ioqACguroa4JStpyUlJeH7qqurSUtLIz8//6xniouLT3nO4uLi8JmTLVq0KDzv0uVyMXjw4P69wDhn+P34O4MJ2kjNsLQ6HOHZTppjKSIiqaDt4GHajxzFYrOxuP4Qw//6qzhcufjbO2javsPs8CQCQhWWaVGvsAwmLNUSLpKculpaSetuyrBn55z9sAiQPXwoafl5BLxemj7VewpJfnGTsLz//vv55JNP+OUvf3nKfRaLpcefDcM45WsnO/nM6c6f7XEeeeQR3G53+Hbo0KHevIyE5evoDP6DxYItPT1ij5teFFy8ozmWIiKS7AzDoHb9ewAUjK+gwe/Dlu6k6LJLgeB2TyMQMDFCiQRv99JGZxQ3hMPxGZZqCRdJTu1Hg4UzVqdTlXLSKxaLhcJLg7MsG/SeQlJAXCQs58+fz8svv8yf//xnBg0aFP56aWkpwClVkLW1teGqy9LSUrxeL42NjWc9U1NTc8rz1tXVnVK9GeJ0OsnNze1xS2bHF+6knzMZfD6c3W3gnmNKWCabi0aOIicr65y3i0aOMjtUEZGY8Byrp6OmFovNRtEVE8Jfzxs7Glt6Ol3NzTTv2WtihBIJ4RmWBflnP9hPGaEZlmoJF0lKbUerAC3ckfOTN3Y0NqcTr7uZln0HzA5HJKpMnWFpGAbz589n5cqVvP322wwfPrzH/cOHD6e0tJTVq1eHN656vV7Wrl3Lv/zLvwAwceJEHA4Hq1evZs6cOQBUVVWxbds2Fi9eDMCUKVNwu928//77fOELXwDgvffew+12M3WqhtUC+DuCC3dsEVq4ExKaW9lZXx/RxxXzHTl6hPU/XnzOc1P/8eEYRCMiYr6WvfsByB46uMcCO6vDQcElFdS99wGN2z7FNepCkyKUSPA0NAGQlpcX1ecJzbD01DcQ6OrC6lAFlkgyaVfCUvrA6nCQP24sxz74iPoPPzY7HJGoMjVhed9997F8+XL+8Ic/kJOTE66kdLlcZGRkYLFYWLBgAQsXLmTkyJGMHDmShQsXkpmZydy5c8Nn77rrLh588EEKCwspKCjgoYceYty4ceGt4WPGjOGGG27g7rvv5mc/+xkA3/72t5k1a5Y2hHcLV1hGaH5liLOou8JSLeEiIpLkwgnL4cNOuc910Sjq3vuAtsNH8Xs82JzO2AYnEeMNV1jmRfV5nIUFWGw2DL+fzmP1ZJaVRvX5RCS22quCv/tqQ7icr4JLxnHsw49pP3KUgTZ9mCXJy9SW8Keffhq32820adMoKysL3371q1+Fzzz88MMsWLCAe++9l8svv5wjR47wxhtvkJNzfDDxE088wS233MKcOXO48soryczM5JVXXsFms4XPvPTSS4wbN44ZM2YwY8YMxo8fz4svvhjT1xvPjreERzZhmV7YPcNSLeEiIpLEutra6aipBSBnxNBT7nfm5wWXtAQCtB5I7rnYyc7TPYbIGeWlOxarlfQBobbw2qg+l4jEXvsRVVhK3zhysnGNvACAq536+yPJy/SW8HOxWCxUVlZSWVl5xjPp6eksWbKEJUuWnPFMQUEBy5Yt60uYKeF4hWVkW8JVYSkiIqmgdd9+ILgoxZGVddozOcOHUt/YRMve/WoLT2CeRjcAzvzozrAEyCgZQEd1jeZYiiShcIWlEpbSB4UTxuPeuZsJjkx87e0R/z1eJB7ExdIdMZ+/PTTDMsIVlt0D6TuPaYaliIgkr9Dg++wRw854Jqf7vtb9B7TZM4F5GoIVlmlRrrAEyCwPtoGHKrFEJHmEtoQrYSl9kVFaQkZJMXaLhcbtn5kdjkhUKGEpAPg6ojXDMtgSHnpzLyIikmwMwwhXymQPHnjGc5nlZdicTvydnvB5SSyGYeBtagKiP8MSIGtgOXB8m7CIJIcTrxtKWEpf5Y+/GIDGrZ/2qntVJNEoYSlA9JbuhLeEa4aliIgkqa7mFvztHcGZg92bnU/HYrWSNXQwAG2HjsQqPIkgX1s7AW8XEP0ZlhBMcoMqLEWSjae+gYDHiwHYs5SwlL5xjbqQDiNAV3MzbZqPLUlICUsBwNfdEh7ppTuhGZZdzc34vd6IPraIiEg86KiuAcA5oBCr/ezjwTMHdiegVDGXkDzdG8JtTie2CL9nOh39fRFJTqF28A6rBavddo7TIqdndTjY5G0DoGHrdpOjEYk8JSwFAH93S7gtwsN603JzsXT/8qa2cBERSUbt3QnLzNLSc57N6q6Y66iq1hzLBOQ9YX6lxWKJ+vOFW8IPH436c4lI7ITawVtt0f85IsltgyeYsGzZu5+ullaToxGJLCUshQyLBcMf/KUp0i3hFqsVZ/fiHY/awkVEJAmFKiwzSovPedZZVIjV6STQ5aOz7li0Q5MI84TmV+a7YvJ8oQrLzrpj+D3qVBFJFqExD202/Tou/VMT8JE5sBwMg8btO8wORySi9BNSyLUE2xCsaY5ztrL1RXp3W3hnvTaFi4hIcgn4/XTWBhOPGWXnrrC0WCzhzc9tmkuYcDwNTQCk5efH5PmcBfnY0tMBtKhJJIkcr7DUr+PSfwXjupfvbPtU3RuSVPQTUsixBhOWkZ5fGeIsDG4K1+IdERFJNp66Yxh+P7b0dNJcub36nuOLVNTmm2i83TMsY7FwB7oT3JpjKZJ0QjMs1RIukZBz4QhsGen4Wtto3X/Q7HBEIkYJSwknLCM9vzIkVGHpqVfCUkREkktHTR0AGSXFvZ5pmKUEVMLyNAZnWDoL8mL2nKEEd5sS3CJJI/TzXxWWEglWu428i0YD0LRjp8nRiESOfkJKuCU80vMrQ8IzLJWwFBGRJBMad5I+oLDX35NeXIzFZsXf0UmBRW/FEkloS3hajCos4YQEt0YIiCSNNlVYSoTljQ0mLFv27sPX2WlyNCKRoXfJQq41+NcgWi3h6aGWcCUsRUQkyYQWyjmLep+wtNpt4XEpgy2OqMQl0eFtdAPgjNEMSyA88zTUQioiiS3g89FZUwto6Y5ETvqAIpxFhRj+AM279pgdjkhE6CekHJ9hmRWdlnBnaOnOMS3dERGR5GEYRvjDuPTCgvP63oziAQAMtkZ+2Z1Ej6ehuyU8phWW5YCWNIkki47aOoxAAIvdTodVFZYSOXlj1BYuyUUJSyGnuyXcFrUKS82wFBGR5ONrayPg8YDFct5bo9NLigEYbFWFZSLxNDUBMZ5hOVBLmkSSSWi8Q0ZpMUYvZx+L9IbrolFgsdBRVRMeYSKSyJSwFHKt0Z1hmV6klnAREUk+naF28Pw8rHbbeX1vRkmwwnKQ1Y5hGBGPTaLD09AExHaG5fGt8lUYgUDMnldEoqO9KjjeIav7v22RSHFkZZI9dDCgKktJDkpYyvGW8Kgv3WmMyuOLiIiYwdO9cMd5nu3goe+x2KxkWay0HTwc6dAkSrzdFSuxnGGZNbAMi82G3+Oho7YuZs8rcr4WLVqExWJhwYIF4a8ZhkFlZSXl5eVkZGQwbdo0tm/f3uP7PB4P8+fPp6ioiKysLGbPns3hw8n7czE0jzY0n1YkkkJt4e4du0AfiEqCU8JSyO3eUBq1hGX3DEt/Rwe+9vaoPIeIiEis9WXhTojVZgt/X8Mn289xWuKBr6MDf/fm1VjOsLQ6HGQNHghAy74DMXtekfOxadMmfv7znzN+/PgeX1+8eDGPP/44S5cuZdOmTZSWlnL99dfT0tISPrNgwQJWrlzJihUrWLduHa2trcyaNQu/3x/rlxET4YRlmRKWEnk5FwzHmpZGV0sLZd7k/G9IUocSlinO7/WSZQ3NsIzO0h17Zia29HTgePuciIhIouvrwp2QjOLgHMuGrUpYJoJrL/8CAD7DYPCI4ZQOGHDKral7xmWk5QwbAkDr/oNReXyR/mhtbeWb3/wmzzzzDPknVB8bhsGTTz7Jo48+yq233kpFRQUvvPAC7e3tLF++HAC3282zzz7LY489xvTp05kwYQLLli1j69atrFmzxqyXFFG3zb6ZaydPCd9Wr/gVAM8sf4k9u3abHJ0kG6vdTu6oCwAY2dFlcjQi/aPVlCkutO0SqxVbujMqz2GxWHAW5tN+pApPQwPZQwZF5XlERERixcLxZXJ9qbCE4BzLxq2qsEwUbXXHIL2A9KwsVn/vvtOemfj334nKc2crYSlx7L777uMrX/kK06dP5yc/+Un46/v27aO6upoZM2aEv+Z0OrnmmmtYv34999xzD5s3b6arq6vHmfLycioqKli/fj0zZ86M6WuJhobaWp6ac3v4z58v/w2dnXXMveZafvc/PzcxMklWeaNH0bRtB8M7uvB7vdjS0swOSaRPlLBMcaF2NntGOpYobqlLLyyg/UiVKixFRCQpFFisGH4/FpuNtNycPj1GeneFZeO2TzEMI6rXYem/rO5/P7aM9Jg/d/bQYMKyRQlLiTMrVqzgww8/ZNOmTafcV10dbH0uKSnp8fWSkhIOHDgQPpOWltajMjN0JvT9p+PxePB4POE/Nzc39/k1xFpXSysA9uxskyORZJU5sAx7Zia0t1Pz7gbKr7vG7JBE+kQt4Smu89gxAOwZ0ZlfGRKqPvFoU7iIiCSBAZbgZ75peS4s1r69nUovzKfLMOhqbqH1gBJR8S6re+Z3aMxNLIVbwvX3ROLIoUOH+O53v8uyZctIP8t/Fyd/GNObD2jOdWbRokW4XK7wbfDgwecXvEkCPh/+jg4AHLlKWEp0WKzWcFv4wVdWmRyNSN8pYZniQhWPtigt3AkJbQrvPFYf1ecRERGJhWJLcP5zf5avWGw2jho+ABo/+TQSYUkUZXW/bTalwvKElnBDW18lTmzevJna2lomTpyI3W7Hbrezdu1a/v3f/x273R6urDy5UrK2tjZ8X2lpKV6vl8bGxjOeOZ1HHnkEt9sdvh06dCjCry46QtWVFrsdmzM647hEAFyjLgTg8Btv4u/0nOO0SHxSwjLFddZ1V1hmRWfhTkhoIUF4ZqaIiEgCG9C9sC6tn9uiDwWCA/G1eCf+hVrC7SZUWGYNHojFasXX3kFn7bGYP7/I6Vx33XVs3bqVLVu2hG+XX3453/zmN9myZQsjRoygtLSU1atXh7/H6/Wydu1apk6dCsDEiRNxOBw9zlRVVbFt27bwmdNxOp3k5ub2uCWCrtZgwtKRk60xIBJVGWWltFot+FrbqHr7XbPDEekTzbBMcaGKR3tmlBOW3S3hmmEpIiLJoDjcEp7Xr8c5FAhWWCphGf/CLeEmVFja0tLIHFhO26HDtOw/SEbJgJjHIHKynJwcKioqenwtKyuLwsLC8NcXLFjAwoULGTlyJCNHjmThwoVkZmYyd+5cAFwuF3fddRcPPvgghYWFFBQU8NBDDzFu3DimT58e89cUbb6W4wlLkWiyWCzszXAwvs3LwVdWMeiG5PvvSZKfEpYpLlxhGeWEZagl3FOvlnAREUl8A8It4a5+PU4oYdm49VOMQKDP8zAl+sxsCYdgW3jbocO0HjhI8aSJpsQgcr4efvhhOjo6uPfee2lsbGTSpEm88cYb5OQcX1b2xBNPYLfbmTNnDh0dHVx33XU8//zz2Gw2EyOPDq8SlhJDn3cnLI+seRtfe3vUf+cXiTQlLFNcZ12owjJGS3fUEi4iIgnO19FBQYRawqsNHzank66WVloPHCJn+NAIRCjRYGZLOEDOsMHUvBucYykSr95+++0ef7ZYLFRWVlJZWXnG70lPT2fJkiUsWbIkusHFgXCFpTaESwzUOaxkDR5E26HDHH3zHYbcdIPZIYmcF32Mn+JiPcNSLeEiIpLoQgkjW7oTe0b/PvALAHljRwPQ8Mm2/oYmUZRpYks4EE5mN3++15TnF5H+61KFpcSSxcKQWTMBOPjH10wORuT8KWGZ4mI1w9JZeLwlXNstRUQkkbXs3Q/0f35lSP64sQA0btsRkceT6MgOtYSbVGHpGj0KAPdnu015fhHpv66WFgAcJ7TEi0TTkJtuBKDqrXfpam0zORqR86OEZQoLdHWFW7Sjn7As6H5OX/iTRRERkUTUvO8A0P928JCCcRcDWrwT7zK7W8Jt/ayq7au8McGEZcv+g/ja202JQUT658Qt4SKxkHfxRWQPG4Lf4+HoW2vNDkfkvChhmcI89cFkpd8wot7eZE9Px56d1f28agsXEZHE1fL5PqD/C3dCTqywVBdCfPJ7vKR3t4Tb052mxJBeVEj6gEIwDNy79pgSg4j0nd/jIeDtApSwlNixWCwM/kqwLfzQq6+bHI3I+VHCMoV1dM+vbDH8WLqrBqLp+BxLbQoXEZHE1dI9wzJSLeGukRdgdabR1dxC6wEtVIlH3sam4D9YLFid5iQsAVwXBeedNu3YZVoMItI3oS4zm9OJ1eEwORpJJUO6E5ZVb71LV5vawiVxKGGZwkKJw+aAPybPF2oL16ZwERFJZG0HDwGQ5sqNyONZHQ7yLgq2+zZu1RzLeORpDL53saWnx+RD3jMJtYUrYSmSeLRwR8ySd/FFZA8dHGwLf1Nt4ZI4lLBMYaEN4e4YJSxVYSkiIomuq62NzrrgdSzNFZmWcNAcy3jn6a6wtJnUDh4SSmw3faaEpUiiCSUs7UpYSozs3LWLaydP4bopU3m3vgaA5Q89wrWTp3Db7JtNjk7k3OxmByDmCSUsWwKBmDxfuMJSMyxFRCRBtR06EvxfIxDR5FV4juXWTyP2mBI5noYmAOwmLdwJCVVYunfsxDAMU6s9ReT8aOGOxJrh8/HUnNsB6KitY+/y3zCiy+DxW77GA7//lcnRiZybKixTWLjC0ohchaWno5OcrKzT3pb+97MAPPnjn3LRyFERe04REZFYaT0QbAevj+C1EyC/IrR451Mt3olD3nCFZXSXFJ5L7oUXYLHZ8Lqb6aiuMTUWETk/XS0tADhyckyORFJR+oAi0ly5GH4/rfv2mx2OSK+owjKFRWOGpd8IsP7Hi097X/2HH1P9zl/46iWXsWLDmog9p4iISKyEEpbHIjxOxTX6QqxpDrzuZtoOHSF7yKCIPr70T3iGZYa5CUubM43cC4bj3rWHxq2fkllWamo8ItJ7mmEpZrJYLOSOvIBjH3xE8+7PzQ5HpFdUYZnCQhWWsVq6Y8sMtlH5Ozpi8nwiIiKR1nowOhWWtrQ0XKNDi3c0xzLeeOKkwhKg8LJLAKjb9KHJkYjI+fCFEpbZSliKOXJHXQhAy/6D2APq5pD4p4RlCgstDYhVwjI098nXroSliIgkpnCFZYQTlgAF3XMsGzTHMu4cn2FpfsKyeMoXAKjd8L7JkYhIbxmGQVdrG6AKSzFP+oAiHK5cDJ+PIR6f2eGInJMSliksGjMsz8auCksREUlwbQcPA5GvsITjcyyVsIw/3qYmwPyWcIDiyZcD0LhtR7jFVETim7+9A8MfvG7Ys7NMjkZSlcViwTXyAgBGdHSZHI3IuSlhmaL8Hi9edzMQ+5ZwX0cn2mkpIiKJJuD303Y4uCU8mhWWjVu3a/FOnPE0dM+wjIOW8MyyUrKHDsYIBNQWLpIgQhvC7VmZWG02k6ORVBZqCx/i8eFrbzc5GpGzU8IyRXXWB9vBrQ477UYgJs9pD73JNwyyLPqrJyIiiaX9aDWBLh/WNAfuKFw7XReNwuqw421y036kKuKPL33XWd8AHO8WMVu4LXzjJpMjEZHe6NL8SokTobZwuwFH33zH7HBEzkpZoxTl6Z5f6SwsJFY1HBabDZvTCUC2VZ8siohIYmnrXriTNWhgVK6dNmcartEjAWjQ4p24YRgGnmPxlbAcMCnYFq6EpUhi6GppAcCRm2NyJJLqTmwLP/Tq6yZHI3J2SlimqI7u+ZXpA4pi+ryhtvBcVViKiEiCCS3cyR4yKGrPEZpj2ag5lnHD19aOv7MTAHtmpsnRBJVMDVZYNn6ynfaqapOjEZFzUYWlxJPc7oTl0bfeCS+DEolHyhqlqM5jwQrL9AGFMX3e0KbwHFVYiohIgmk7fBSArCgmLAvGXQxA/cfbovYccn5C75k8hoHV4TA5mqDMslIGTL4CIxBg3//+wexwROQcQglLuzaESxxILx5Ak82Kv7OTw6vWmB2OyBkpYZmiQhvC04tim7AMVVjmWJSwFBGRxBJauJM1aGDUnqNwwngAGj7eihGIzYxpOTtP9/zK1hjN/O6tEXO+CsDeX63U3xWROBdauuNQwlLigMViYXdm8AO4/Sv/aHI0ImemhGWK6qwLVVjGtiVcFZYikszeeecdbrrpJsrLy7FYLPz+97/vcb9hGFRWVlJeXk5GRgbTpk1j+/aeswo9Hg/z58+nqKiIrKwsZs+ezeHDh3ucaWxsZN68ebhcLlwuF/PmzaOpqSnKr05aDwUTltmDo5ewdF00EntmBl3NLTTv3hu155HeCy3caSG+koKDv3w99uws2g4eova9D8wOR0TOItwSroSlxIk9GcGEZe1fNtJRXWtyNCKnp4RlijKrwjI0rD7Hqr96IpJ82trauOSSS1i6dOlp71+8eDGPP/44S5cuZdOmTZSWlnL99dfT0j2MH2DBggWsXLmSFStWsG7dOlpbW5k1axZ+vz98Zu7cuWzZsoVVq1axatUqtmzZwrx586L++lJd26HoV1ha7XYKLqkA4NiHW6L2PNJ7ofdM8VZhac/MZMhNNwLw6b//TFWWInHKYhj42toBJSwlfrTYrRRNvBQjEODAK38yOxyR01LWKEWF5jFlxHrpTkZo6Y4qLEUk+dx444385Cc/4dZbbz3lPsMwePLJJ3n00Ue59dZbqaio4IUXXqC9vZ3ly5cD4Ha7efbZZ3nssceYPn06EyZMYNmyZWzdupU1a4Izhnbs2MGqVav4xS9+wZQpU5gyZQrPPPMMf/zjH9m5c2dMX28q8Xd66KytAyArihWWAIUTLgGg/qNPovo80juhlvCWOEtYAlx0z//Blp5OzV82suu/l5kdjoicRqbfAMPAYrXGzeIuEYBht84G4IDawiVOKWGZokLVAs5YL93prrDMVku4iKSYffv2UV1dzYwZM8JfczqdXHPNNaxfvx6AzZs309XV1eNMeXk5FRUV4TMbNmzA5XIxadKk8JnJkyfjcrnCZ07m8Xhobm7ucZPz03YkuHDHnpVJWn5eVJ+r6LJgwvLY5i1RfR7pnc5j8TnDEiB3xDAm/OPDAHz8/z3Ozl+8gN/rNTkqETlRtj/4s8OenY3FYjE5GpHjBs+aidVhp3HbDtw795gdjsgplLBMUWZVWNrDFZb6qyciqaW6uhqAkpKSHl8vKSkJ31ddXU1aWhr5+flnPVNcXHzK4xcXF4fPnGzRokXheZcul4vBgwf3+/WkmhMX7kT7F87C7oRl8+7P8bqVXDZb6D1TPFZYAlxw+xwG3Xg9AW8XH/3zYn578ST+dO1NvHPnvcyyZ4WXfYiIObL9BqB2cIk/zvw8yr50NQD7f68qS4k/yhqlIH+nh67m4Lw007aEq8JSRFLUyckuwzDOmQA7+czpzp/tcR555BHcbnf4dujQoT5EntraDnYnLKPcDg7Ba3P20GBSuX6L2sLN5qkPJixb42zpTojFYmHqfz7GFYv/mfQBRQQ8Xpr37OXoW2uZ7shi93MvUbfpQ7PDFElZoQpLR06WyZGInGrYV2cBcOD3f9QsZIk7SlimoFClgDXNgcOVG9PnDlVYZlttBLq6YvrcEnmG309HbR3uXXvoamszOxyRuFZaWgpwShVkbW1tuOqytLQUr9dLY2PjWc/U1NSc8vh1dXWnVG+GOJ1OcnNze9zk/JxYYRkLRZdPAKDuvc0xeT45s1BLeLxWWAJYbTYu+PpfMfu9N5m17nWmvfQLLl/4T3zu92L4/dT+ZSON2z8zO0yRlJQVCCUsc0yORORU5ddNw5GTTfuRKmo3bjI7HJEelLBMQSduCI/1HBVbuhO6n9PT0BTT55bIKrU62P3CcvYu/w2H//QGe54PVnDokzmR0xs+fDilpaWsXr06/DWv18vatWuZOnUqABMnTsThcPQ4U1VVxbZt28JnpkyZgtvt5v333w+fee+993C73eEzEnlth4MzLLMGl8fk+YqnfAGAmg3vn+OkRFs8L905mdVuJ3vIIEq/OIULb5/DEm8TAyZdDkDVW2vp6F4cJSKxE24Jz1ZLuMQfW7qTIbO/DMDnv/xfk6MR6UkJyxQUTljGeH4lENyO111lGYpDEk/Tjp38vauUruYWrGlppOW5CHT5qP3LRure+8Ds8ERM09raypYtW9iyZQsQXLSzZcsWDh48iMViYcGCBSxcuJCVK1eybds27rzzTjIzM5k7dy4ALpeLu+66iwcffJA333yTjz76iNtvv51x48Yxffp0AMaMGcMNN9zA3XffzcaNG9m4cSN33303s2bNYvTo0Wa99KTXeugwANmDB8Xk+UIJy4aPt6mC3UQBnw9PYxMQn0t3emPA5CvIHj4Uw++neu1fzA5HJOUcbwlXwlLi0wVz/xqAw6+txtPQeI7TIrGjhGUK6jihwtIM9qzMHnFIYvF3elh3zwJyrTbSiwcw8v98kwvvmEvpNVcBUPfeB7QdOmJylCLm+OCDD5gwYQITJgTbeR944AEmTJjAD3/4QwAefvhhFixYwL333svll1/OkSNHeOONN8g5oU3siSee4JZbbmHOnDlceeWVZGZm8sorr2CzHZ/9+9JLLzFu3DhmzJjBjBkzGD9+PC+++GJsX2yKCVdYxqglPHvwQDIHlWP4fBz74KOYPKecytPQCIYBFgttGGaH0ycWi4Xya68Bq5X2I0dVZSkSY1lauiNxrmDcWArGX0zA28W+3/ze7HBEwpSwTEGhGZZmVFgC2LsX73QeU8IyEX328+do3X+QpoCPYbfehD0jA4vFQuGE8eRdfBEAh19fQ8DnMzlSkdibNm0ahmGccnv++eeBYOKgsrKSqqoqOjs7Wbt2LRUVFT0eIz09nSVLllBfX097ezuvvPLKKVu9CwoKWLZsGc3NzTQ3N7Ns2TLy8vJi9CpTj6+jA0/3tTNrUGxawgGKJ18BQO0GzZQyS6gd3FmQn6DpyiBHTjaukRcAUP/hFnODEUkhvo4OMgJKWEr8u+CbcwDY89KvNeJL4oYSlinoeEu4SRWWmcEKy85aJSwTTeuhI3y65OcA/KatEVt6eo/7y6Z9EUdODr7WNhq3fWpGiCIiEReqGnfk5pCW54rZ85Z0t4VrCL55Qgt3zOpKiaTCyy4BwL3rc7paWk2ORiQ1tFcFl+RZHXasTqfJ0Yic2ZDZN+LIzaF1/0GOvvVOzJ//ttk3c+3kKae93Tb75pjHI/HBbnYAEnuddd0VlkXmVFjaQgnL7moVSRyf/dez+D0eBky+gvdf/c0p91sdDoqumEDVW+9w7IOPyK+42IQoRUQi63g7eOyqK6HnHEuvu5k0l7a7x1rovYqzsMDkSPovo6SYzPJS2o9W07z783ACU0Sip/1oNQD27OyYLzsVOR+OrCwu+MZtfPaz59j5ixcYOH1aTJ+/obaWp+bcftr7vvvrZTGNReKHKixTUGh2UUbJAFOePzTDUkt3EounqYl9//syABUL/u6M5/LGXoQ9KwtfaxtNn34Wq/BERKKm7XCwwjIrRgt3QrIGlZN74YjgspR31sf0uSUoNL4mvSjxE5YAORcG28Jb9u03NxCRFNFeVQWA44RZ1SLxauT/+SYWm43a9e/TuG2H2eGIKGGZijqqg60JGaUlpjy/I1MJy0T0+Uu/wd/RQd7Y0eGqn9Ox2u0UTbwUgIZPtsYoOhGR6Am1hMe6whKg7NqrATj61tqYP7ccH1+TUVJsciSRkTNiGABtR6rwd3rMDUYkBbQfCSUsNb9S4l9WeRmDZ80EYMd/PWtyNCJKWKYcIxCg0+QKS1tW99KdOrWEJ4qAz8fuF34JwKi75p2zpSVv7GgsNhueYw0MsaXFIkQRkag5nrCMzYbwE5V3Jyyr3l5HwO+P+fOnuo6aWgAyis15zxRpzjwXzoJ8CARoPXDQ7HBEkl6oJVwJS0kUY/7v3wBw8JVVuHfuMTkaSXVKWKYYT0MjgS4fWCymvfm2q8Iy4VS/u4GO6hqcBfkMnf2Vc563paeTc8FwAK506g2aiCS21u6EZfbg2CcsB1xxGY6cbDz1DTR8vC3mz5/qQmN00k36kDcasocPA6Bl735T4xBJBe1VSlhKYsm/eAyDbrweDINtT/6n2eFInPG6m/E2t+AMBAj4fFF/Pi3dSTEd1cFKgfSiAqwOhykxhBKWXnczfo8Xm1MVePHuwMpXABgy+8u9/veVN/Yimnft4QvOLP17FpGEdnyGZewTllaHg9Krr+TQq69z9K21FGlRSkx11IS6UpKjJRwg94Jh1G/+iNb9BzEMw+xwRJLa8ZZwzbCU+LJz1y6unTzltPcNy3Exw2Lh0Kuv07D1UwrGjY1xdBKP6t7fTO369wC4A6jd+AGlV02O6nMqYZli2kPzK0vMmV8JYEt34jMM7BYLnfX1ZJWXmRaLnFtXaxuHV70JwLBbZ/X6+7KHDMKenUV2axtH31rL4Buvj1aIIiJR09XahrexCTCnJRygfPo0Dr36Oof/tJpxD87XptkYCreExzhh6W5qonTA2as6y0rL+GjrJ+f92BmlJVgddvweD576hr6GKCLnYBgGbYePAuDIVcJS4ovh8511K/fQm7/Mgd+/yoc//CnX/fZFLFY156aytkNHqN3wPgAWmw3D78fRvUw5mpSwTDHH33ib19pksVhoDvgpsNnprDumhGWcO7xqDf7OTnJGDKPgknG9/j6L1Ypr1IXUf/gxh1etUcJSRBJSqLoyLc9lWkvfoBnXssmZRvOevTR9+hn5F48xJY5U09Xahq+1DYj9DEt/IMDqRyrPeub6RWe//0wsVisZpSW0HToSnq8nIpHnaWjE39kJgCNbLeGSWC555AGOvPEWxzZvYf9vX2b4X99idkhiEr/Hw+HX3gDDIG/sRQyccS0LfvUic8ZfHPXnVsIyxYRaws3aEB7SbPgpwK7FOwngwB9eBWDoV2edd1VP7oUjqP/wY46ueRu/14stTW3hIpJYor1wp7eVdEuuvZbDr63mwB/+pIRljITmV9qzMnFkZ5kcTWRllpcFE5ZVVWaHIpK02rurK9usFqx2m8nRiJyfzLJSLv7u3/HxosfZ8tN/o/TqK/td9HTb7JtpqK097X17du3u12NL9Lg/242vvYO0PBdlX/oiAIbFgtUW/Z9rSlimmHCFZam5s5iaA8FNp1q8E9+87mZq/hKcUzHkphvP+/szykppCvjIa2ml5i/vUd79A05EJFGE2vmyBpdH5fF7W0k39OYvc/i11Rx85TUu+f731JoVA521yTe/MiSzrBRAFZYiUdR2JHj9aLXp57UkplF3zePA7/9I045dbPzeI0xb9vN+vf9oqK09Yxv6zMof9PlxJboat+8AoOCSipjvQdFPzxTT0T3DMtPsCstQwvKYKizj2dG33sHw+cgdeQG5I4ad9/dbLBY+8rYDcPi11RGOTkQk+toOHQYga/AgU+Mou/Zq7NlZtB+pom7TR6bGkiriYYxOtGSUBd8HdrmbydGvAyJRcTxhqbnDkphsaWlMWfpv2NLTqVm3gW1PPm12SBJjHbV1dNbWYbFZcV00OubPr3coKSZe3ny7VWGZEA6vWgPAoBuu6/NjfOgJJiyPvPEWAb8/InGJiMRKuMJyUHQqLHvLnp7O4K/MBGDvL//X1FhSRTJuCA+xOZ04iwoBGG6NbbWESKpoPxwcudBi16/ckrhcIy9g4o8fBWD7k//J7hdXmByRxFLjtmB1Zc4FI7BnpMf8+fXTM8W0x9EMS4DOWiUs45Wvs5Oqt9cBMGjm9D4/zi5fJ47cXDwNjTRs2Rqp8EREYqK1u8Iy2+QKS4ALv/nXABx8dRWe7s3lEj2hD3nTY7xwJ1Yyy4Nt4SNsSliKRINawiVZjPjarVz83f8LwOZ/+Anbl/wMwzBMjkqizjBo3vM5APkXX2RKCPrpmUL8nR683b/gmD3DMlRh2aEKy7hV8856/B0dZA4sI3/c2D4/TgAom3YlAEffXBuh6EREYiO8dGdwdJbunI+CS8aRXzGWgMfLvt/83uxwkl5HeIZlkiYsuz+8HmTRSHuRaAglLFvUEi5JoOKB+xl99x1gGGz913/nnTv+L+5de875fYZh0FFTR9U7f2Fsm5fajZuo3fA+xz74iOY9e+lqaY1B9NIXhV0B/O0dWB0OMqO0fPJc9A4lhYQqBWxOJ2kul6mxNIUSlt3tVhJ/jv75XQAGXv+l894OfrLy66Zx8OXXOPrm24x/+LuRCE9EJOq8TW66mluA+EhYWiwWLrx9Dpu+X8meZb9i1F3zYrKhMVUlc0s4QPqAIgAGWe0YgYAWOYlEWGhLuCosJRlYLBYm/OPD5F44gs3/+BOq3l5H9TvrKZ46ibIvXUXOsKHYMzPwdXTSuv8gzZ/vo/nzfbh37g4XTV0F1G3cdMpjZ5SVMtGRqWtRnBns8QHB98Bmvd9UwjKFhFubSor7nYDqr/AMy5paDMMwPR7pyTAMqtYG28HLpvV/s3fZtKuwWK007dhF25GjZA00dxaciEhvtB4MtoOnDyjEnpFhcjRBQ2/5ClsWPk7r/oMcef1NBn95htkhJa3O8Nzv5ExYOgvysdhspPuh9eAhcoYNNTskkaTR1dqG190MKGEpyeWCb9zGgCsu45PFT3F41Rpq1m2gZt2Gs35PAGi2Wznc2c4XL70MrBYCXi+e+kY6j9XTUVXNN7MK2Lv8N5RNn2b6gmAJGtSdsMweOsS0GJSwTCEd3fMrM01uB4fjFZZ+j4cudzNpeeZWfEpPLXv30374KFZnGsWTL+/34znz8yiceCnHNn3I0bfeYeS8r0cgShGR6IqXDeEnsmdmMvLOb/Dpv/+MT//jFwy68Xp96BcFwRa2+FhUGC0Wmw1nUQGdNXU0bvtMCUuRCAq1g6e5culSvlKSTO6FI7jq50/Rsu8Ah1etof6jT2g7fJSA18uefXu5YNhw0vLzcObn4SwswFmQj9VuZ2blD/ja9Gk9HqurtY2mTz/jwF82wLF69v96JSVXX0nBJRV6f2OirpZWSr3BnE320MGmxaEfnymk7WhwU11oyLqZfBjhJKXawuNPaNnOgC9cjj0zMyKPOfC6awDNsRSRxNEaR/MrTzTq/9yOLT2dxq3bqXn37FUN0je+1jZ87R0AZCTp0h2AjAHB19a4fYfJkYgkl1A7eOYgdRVJ8soZPpQxf3cXV/38KWb+6TfcuOYPrByQzeCvzKRk6iTyxowmo3gAVvuZ6+Qc2VkM+MJEFjZXk3vhCIxAgOq336Vm3UYt9jFRzYb3sRL80MXM4jIlLFNIeyhhGSftuKEWq1AFg8SP6lA7+DVXRuwxy6+bBkDNXzbia2+P2OOKiERLWxxtCD9RemEBF3zjNgA+/c9nTI4mObVXVQPgyM2J2Ad38Sg0x7JJCUuRiApVWGoMkkjvtBkBBn1lJiVXTQGgfvNH1Ly73uSoUlftX94DIMvE6kpQwjKltHV/0pdVXmZyJEGhFislLOOLr7OT2g3BYchl11wVscfNHXUBWYMHEvB4qen+ASgiEs/iaUP4yUbfcycWu53a9e9T/9EnTBg3ntIBA856mzBuvNlhJ4zwe6aB8fGeKVrSi4MJS1VYikTW8Z8hSliK9JbFYqHo8gmUXXs1APUffkz9lk9Mjio11X3wEWD+zzDNsEwh7UfiqzXheIWlWsLjSd17H+D3eMgoKyV31AURe1yLxUL5ddew+/nlHH1zLQOv/1LEHltEJBpCLeHZQ+KrwhKCHz4Ou/Um9v16JZ/+xzNUVVex+pHKs37P9YvOfr8cF66OGhR/yepISi8qJGAYdNbV01FTl7TzOkVirf1Id2fboOT+0EMkGgrGVxDwdlGzbgPVa//C4Px0s0NKKV1twbmiYP44QVVYppC2I8H2pvipsOxOWNaqwjKehOZXll1zZcQHHZefMMdSM0lEJJ4ZgQBth0MVlvGXsAQY83d3gcXCkTfeotRiMzucpBKeP5fk1VFWh4NaIzhUv7H7lxMR6T+1hIv0T+HES8m7eAwYBl9q6qS9usbskFJGw5atGH4/rTYLjpwcU2NRhWWK6Gpppau5GYifT/rUEh6fqtf+BYCyaZFrBw8pnnQF9swMOmpqadr+GfkVYyL+HCIikdBRW0fA48Vis5n+6fKZ5F4wnEE3TOfwa6u51p68cxbNcLzCMvmTDVUBH6VWO827P6f8S180OxyRpKCWcElWt82+mYazFBzt2bU7Is9jsVgo+9LVdNbWQd0xNnzn//GlXz6L1aYPaKPt2OYtANQ4zP//2tQKy3feeYebbrqJ8vJyLBYLv//973vcbxgGlZWVlJeXk5GRwbRp09i+fXuPMx6Ph/nz51NUVERWVhazZ8/m8OHDPc40NjYyb948XC4XLpeLefPm0dTUFOVXF19CG8LT8lw4srJMjiZILeHxp/XQEZr37MVis1Fy5eSIP74t3UnJF6cCcOTNtyP++CIikdJ2MPheIrO89KzbLc025t67AJhoS8fb3GJyNMmjrbudM9lnWAJUGz4A3Lv2mByJSHLwd3qCSRbiZxSXyPnYuWsX106ectrb++v+wlNzbj/jzefrilgcVruNQV+eQZcF6jZuYtezL0bsseXMjnXPr6xOM//9r6kJy7a2Ni655BKWLl162vsXL17M448/ztKlS9m0aROlpaVcf/31tLQcf0O+YMECVq5cyYoVK1i3bh2tra3MmjULv98fPjN37ly2bNnCqlWrWLVqFVu2bGHevHlRf33x5HhrU/y88daW8PgTqq4svOwS0ly5UXmOE9vCRUTiVWscL9w5UeEl4yi5cjI2i4X6D7eYHU7SCL9vSoFkQ3Ug+J65effnJkcikhzaq4JjuGzp6TgL8k2ORuT8GT5fTBKSveHMz2N9bnCG5dZ//Xda9h+I6fOnGiMQ4NiHHwNQnWZ+haWpKdMbb7yRG2+88bT3GYbBk08+yaOPPsqtt94KwAsvvEBJSQnLly/nnnvuwe128+yzz/Liiy8yffp0AJYtW8bgwYNZs2YNM2fOZMeOHaxatYqNGzcyadIkAJ555hmmTJnCzp07GT16dGxerMlCFZbx1JYQ2kzZWVuHYRgRn5co56/6nei1g4eUfym49a1hy1Y6646RPqAoas8lItJXbYeCFZbxML/S3dRE6YAzL0MZZXVwrzOfxm07GDDpcuwZGTGMLvn4vV46uquj4ul9U7SEKyx3f673YyIREG4HH1Su/55EImBnpoM5FZdS85eNbPp//8SXVjyn/7aixL37c7qaW7BnZtDgMH/ljfk1nmewb98+qqurmTFjRvhrTqeTa665hvXr13PPPfewefNmurq6epwpLy+noqKC9evXM3PmTDZs2IDL5QonKwEmT56My+Vi/fr1Z0xYejwePB5P+M/N3fMfE1W4UiBOFu4A4URVoMuHt7FJn0CaLNDVRfW6DQCUXRO9hGVGyQAKxl9MwyfbOfrndxkx56tRey4Rkb5qC20Ij4MKS38gcNYN4IZh8Oq/Ps5wMmjYspXiKV+IXXBJqP1oNRhGsDqqsMDscKLumOHHYrfja22jvao6bpYziiSq0AzceOpsE0loFgtX/MuPeG36zdRu2MTBV15j6Owvmx1VUmr4ZBsA+eMrMA7uNDmaON4SXl0dLKUvKSnp8fWSkpLwfdXV1aSlpZGfn3/WM8XFxac8fnFxcfjM6SxatCg889LlcjF48OB+vR6zxePweFtaWvgXAbWFm+/Yhx/ja23DWVgQ0WU4no5OcrKyetye3xjcRP70/O+Rk5XFRSNHRez5REQi4XhLuPkVludisVj4Y0sDEKxe93tj266VbNpPSDakQgWHH8gZPhSAZs2xFOm3dm0IF4m47CGDGHPf3wKw5aeP4WtvNzmi5NTwcTBhWTD+YpMjCYrbhGXIyW8Ue9OqcvKZ050/1+M88sgjuN3u8O3QoUPnGXl8ae8eHh9PFZagTeHxpOrtYBKx9OqpWKyR+9HgNwKs//HiHrcFd90NwBXZeaz7p0UcOXokYs8nIhIJoZbweKiw7I0POltIy3Ph93ho3Lb93N8gZ3R8u298vWeKJtfIC4BgK5iI9E9bCs3AFYmli+75P2QNHkhHVTWf/ucvzA4nKTV8EnwPWTi+wuRIguI2YVlaWgpwShVkbW1tuOqytLQUr9dLY2PjWc/U1NSc8vh1dXWnVG+eyOl0kpub2+OWyMLbLgfF15vv0OKd9qpT/x1JbFWvDSYso9kOHpJePAB7ViaBrq7wp9AiIvHC7/UG24KBrCHxX2EJYACFEy8FglWWRiBgajyJLJxsSKHqqNxRFwLg3qWEpUh/hX/vSqGfISKxYE9P59J/+HsAPvvZc7QeSOyisnjj93pp+vQzAPJVYXl2w4cPp7S0lNWrV4e/5vV6Wbt2LVOnTgVg4sSJOByOHmeqqqrYtm1b+MyUKVNwu928//774TPvvfcebrc7fCbZ+b3ecAVjvFVYZpYFE9NKWJqro7aOxm07gGCF5UUjR53Sxn3yraOjs8/PZ7FYyO5uP2vZp01vIhJf2o9WhWcYJtJisLyLRmFzOulqbqF1/0Gzw0lY8ThGJ9pCFZbaFC7Sf8dbwuPr9y6RRLVz1y6unTyFaydP4Y7Kf+Rwmo2Ax8uS62dx7eQp3Db7ZrNDTArunXsIeLtIc+WSPTQ+RiKaunSntbWVPXuOz8rZt28fW7ZsoaCggCFDhrBgwQIWLlzIyJEjGTlyJAsXLiQzM5O5c+cC4HK5uOuuu3jwwQcpLCykoKCAhx56iHHjxoW3ho8ZM4YbbriBu+++m5/97GcAfPvb32bWrFmpsyH88NHgL14ZGXH3i1doGHV79xZzMUf1O+sByB93MelFhRw5eoT1P1581u+59MH7+/WcOcOH0bRtBy379vfrcUREIq3tYPf8ygTb8Gp1OMirGEP95i00fLyVnBHDzA4pIaXi/DlXd4VlszaFi/RLwO8PF2KoJVwkMgyfj6fm3B7+c2d9A58v+xXDO338dPI0Ht34tnnBJZETF+7Ey/sAUxOWH3zwAV/60pfCf37ggQcAuOOOO3j++ed5+OGH6ejo4N5776WxsZFJkybxxhtvkJOTE/6eJ554Arvdzpw5c+jo6OC6667j+eefx2azhc+89NJLfOc73wlvE589ezZLly6N0as87qKRo3o1q29g+UA+270rYs8bqrLIGTY4bv7ihYQrLI+eeQGSRF9VqB182pUxe86swYOw2Kx0uZsptTpi9rwiIufy/bu/zQ3Ahp07+JsBA057pqmpKaYx9VbB+Iup37yF1gOH8DQ24czPMzukhNOyN1j5nz1siMmRxE728CFY7Ha6WlrpqK4Jvz8TkfPTUVOL4fNhsdvDo69EJLLSCwsoGF9Bw8dbqXl3PdgNs0NKCvG2cAdMTlhOmzYNwzjzXy6LxUJlZSWVlZVnPJOens6SJUtYsmTJGc8UFBSwbNmy/oQaEb2pWgOY+o8PR/R5QwnLeHzjnVkeSliqwtIsAb8/XGEZi/mVIbY0B1mDBtJ64BDj0zJi9rwiIudib24FRxZfvGwic7509WnPTPz778Q4qt5Jc7nIHj6U1n0HaNy2g9IvTjE7pITS1doWHqOTm0IVqra0NHKGDaF5z17cuz9XwlKkj0Iz9bIGlWM9oYBGRCJrwKTLadqxk866Y4zMSzc7nKTQsDW4cCeeEpZxO8NSIqcllLAcGo8Jy1BLePVZk9cSPY3bPsXb2IQjN4fCyy6J6XNnDx8GwPi0zJg+r4jI2RRYgjQLC3UAAGbBSURBVL9kOhJ04V7+xWMAcO/YqeU756ll734AnIUFpOW5zA0mxnJDcyx37TnHSRE5k7aDhwHiZv6bSLKyZ2ZQdMVlAFzR7MHX2ff9CgK+zk7cO4PX/4JL4mNDOChhmRJa9wdbm3K6l5zEk9An+P7OTrxNbpOjSU1VbwfbwUuunITVHtui69DfyQvtTv37F5G4UWgNJizTXDnnOBmfsocPxZaRga+9Xct3zlPz3n0AKTn/0zUqmLDUpnA5H4sWLeKKK64gJyeH4uJibrnlFnbu3NnjjGEYVFZWUl5eTkZGBtOmTWP79u09zng8HubPn09RURFZWVnMnj2bw4cPx/KlRETrwWCFZfaQQSZHIpL8CieMx5GTTXbAYNcvXjQ7nITW9OlODJ8PZ1FhXHVZKGGZAlriuCXc5kzDWVQIQPsRtYWbobo7YRnLdvCQNFcuzsICbBYLVe/8JebPLyJyOoleYWm12cgbMwqAxu07TI4msbR8vh+A3AuGmxuICVwjg4t33NoULudh7dq13HfffWzcuJHVq1fj8/mYMWMGbW1t4TOLFy/m8ccfZ+nSpWzatInS0lKuv/56WlpawmcWLFjAypUrWbFiBevWraO1tZVZs2bh9/vNeFl91hqqsByiCkuRaLPa7RRfORmAT//zGTqP1ZscUeIKLdwpjKOFO6CEZdILdHUFt4QDOXGYsATI0hzLmLlo5ChysrLCt5LsHGo/+AiAa++5K/z1jo7YldSHqiyPvrn2nGdPjv90t4tGjop2yCKSxLra2sixBN8epSVowhIgb+xFALTsO4Cvvd3kaBJHqCU8FSssc7srLEObwkV6Y9WqVdx5551cfPHFXHLJJTz33HMcPHiQzZs3A8HqyieffJJHH32UW2+9lYqKCl544QXa29tZvnw5AG63m2effZbHHnuM6dOnM2HCBJYtW8bWrVtZs2aNmS/vvIVawrNUYSkSE67RI6lzWPG1trH9qafNDidhhRbu5MfR/EoweemORF/bkSoMnw+b0xm3m+oyy8to+GQ77VXaFB5tJy9+avpsF0dWrcFZWMAfF9wb/vqlD94fs5iyhw/j2AcfUfXndwn4fGdtS+/N4qpIL60SkdQSWphgS0/Hlu40OZq+Sy8qJKOkmI6aWpo+201RjGcUJ6pQS3gqLdwJyRk+DIvNRldzCx01tWSWlpgdkiQgtzs44qegoACAffv2UV1dzYwZM8JnnE4n11xzDevXr+eee+5h8+bNdHV19ThTXl5ORUUF69evZ+bMmbF9Ef1wvMJSCUuRWLBYLGzMTeem+nb2LPs1I+/8Zkp2SfRXwyfxt3AHVGGZ9ELzK7OHDsZijc9/3aEZCW1qCY+5eKgkySwroTXgx9vk5tjmLabFISIC0LoveN1MhoUreRcHqyybtu9QxVwvGIEALXu7536n4C87NmdaeHxQs+ZYSh8YhsEDDzzAVVddRUVFcGlDdXWwIKGkpGcCvKSkJHxfdXU1aWlp5Ofnn/HMyTweD83NzT1uZutqbcNT3wBA1mAlLEVipcppp3z6NAy/n48XPW52OAmnq62N5j17ASUsJcbC8yvjcOFOSObA7k3hqrCMKcPvD1cSmbmQyWK18om3A4ADv3/VtDhEROD4dTMZEpauUSOx2Gx46hvorKk1O5y4115dg7+jA4vdnrLVUeHFO5pjKX1w//3388knn/DLX/7ylPtOnolmGMY556Sd7cyiRYtwuVzh2+DB5s+MDC3cScvPIy03MZe2iSSqSx55AIvNxpE33qL2vQ/MDiehNG7bAYZBRlkpGcUDzA6nByUsk1xoO2i8zq+E4xWW7UeVsIyl9qPVBDwebBnpZJjc9rXB0wrAwVdW4e/0mBqLiKS20Ac5yZCwtKU7yb1wBACNn35mcjTxL7RwJ3vIIKwOh7nBmCR3ZGhT+B6TI5FEM3/+fF5++WX+/Oc/M2jQ8YR/aWnwff7JlZK1tbXhqsvS0lK8Xi+NjY1nPHOyRx55BLfbHb4dOnQoki+nT9rUDi5iGtfIC7jgG7cBsOUn/4oRCJgcUeIILdyJt+pKUMIy6bXsC7WEx3HCMlRhqaU7MdWybz8A2cOGmj4uYKevk8zyUrqamzn65tumxiIiqS2ZKizheFt488492EyOJd41fx5sh0rFhTshrlHBTeHNqrCUXjIMg/vvv5/f/e53vPXWWwwf3nOcwvDhwyktLWX16tXhr3m9XtauXcvUqVMBmDhxIg6Ho8eZqqoqtm3bFj5zMqfTSW5ubo+b2bQhXMRcFd+7F3tWJg0fb+PgK6+ZHU7CCC3cKbikwuRITqWEZZJr+mw3cLzFJx5ldm8J76iuJeD3mxxN6ggls+PhFzMDGHrLLAD2/fZlc4MRkZQWnmHpSo6EZdaggdizsvB7PFxsTdwlQrHQuD1YhZo3ZpTJkZjHNVKbwuX83HfffSxbtozly5eTk5NDdXU11dXVdHQEx/1YLBYWLFjAwoULWblyJdu2bePOO+8kMzOTuXPnAuByubjrrrt48MEHefPNN/noo4+4/fbbGTduHNOnTzfz5Z2XUEu4NoSLmCN9QBFj7v1bAD7+lyfVuddL8bpwB5SwTGreJjcd3XMhXaNHmhzNmaUPKMKa5sDw++morjE7nJTgaWzC29iExWqNm0+Bh/3VbACq/vwu7fp7ICIm8LW309E96zEtPzkSlharFddFwQTc5fZ0k6OJb41bu9+wj4u/N+yxkjN8GBarFa+7mc66Y2aHIwng6aefxu12M23aNMrKysK3X/3qV+EzDz/8MAsWLODee+/l8ssv58iRI7zxxhvk5Byf8/jEE09wyy23MGfOHK688koyMzN55ZVXsNkSpzY8NFJELeEi5hn9t98io7SE9sNH2f3CcrPDiXveJnd4jKASlhJTTTuD84cyy0tJc5nfJnEmVpuNrEEDgeMXeomuUDt45qBybM40c4Pp5hp5AQO+MBHD7+fzl35jdjgikoJC7XxtRgB7evIk90IVg2OtaXgam8wNJk75Oz24uzdj548ba3I0veNuaqJ0wICz3pqams7rMW3pTrKHBj/IVFu49IZhGKe93XnnneEzFouFyspKqqqq6OzsZO3ateEt4iHp6eksWbKE+vp62tvbeeWVV+Jikc75aNnXvew0jncHiCQ7e0YG4//+OwBsX/Izve85h4atnwKQNWQwzrw8c4M5DbvZAUj0uHd1t4NfFP+tTdlDBtGydz+tBw9TMnWS2eEkvZa9+wFzt4Ofzsg75lL3/mY+X/5rxs7/Nra0+EimikhqCI3KOGYk13iS9KJC0gcU0Vl3jIOvvMbIb33D7JDiTtNnOzF8PpwF+WSWl5kdTq/4AwFWP1J51jMTu39pOx+5Iy+gZd8B3Ls/p+TKyX2MTiS1+L1e2g8fASB3+DBzgxFJcUNvvYmdz/4PTZ/uZOtjS7j8J/9odkhxK54X7oAqLJOa+7NdAOTFcTt4SGjWS2i7nkSPv7OT9iPBBUc5cfaGatAN15FePIDOunoO/ekNs8MRkRQTaok5FkiuhCWAa8xoAPZrTvBphSoM8seNxWKxmByNuXJPmGMpIr3TdvAwRiCAPTOD9JIBZocjktKsNhsTfvj/ANjz4q849uHHJkcUv0IJy8I4XLgDSlgmtfDCnYSosAy2fISGVUv0tO4/BIaBsyA/7rbgWh0OLrx9DgA7f/68Bv6LSEyFNoTXJVmFJQRnWfsNg/qPPqG5u8pejmvsTlim8vzKkNDiHffuvSZHIpI4QhX62cOHpvyHHiLxoGTqJIbddjMYBpu+X0mgq8vskOJSaOFOviosJZYMw8C9M5iwzLso/issQ8OpW1VhGXXuPcGKiXjYDn46I7/1DeyZGTRu28HRN9eaHY6IpJDW/cnZEg7gyMpkZ8ALwP7fqcryZI3bjldYpjpVWIqcv5bu60fOsPgatySSyib8w9+Tlp+H+7NdfPbz580OJ+50HqsPdl5aLBRUxOf7HyUsk1R7VTVdzS1Y7HZyLhhudjjnpJbw2HBiobX7E+DcUReaHM3pOQvyubB7vtr2p55WlaUklcrKSiwWS49baWlp+H7DMKisrKS8vJyMjAymTZvG9u3bezyGx+Nh/vz5FBUVkZWVxezZszl8WD87I6F5zz4AagM+kyOJjk3+TgAOrPwjRiBgcjTxw+/xhj/kVYUl5F44HCwWPPUNdNY3mB2OSEJo2dudsByhhKVIrO3ctYtrJ0855Xbjl7/MupzgToTtTz4d7qSRoPotWwHIvWA4jpxsk6M5PSUsk5S7ux08d8SwhFhckj04mLD0NDTS1dJqcjTJa3xaJobfT1qei/QBRWaHc0YXfftObBkZNHy8jaNr3jY7HJGIuvjii6mqqgrftm7dGr5v8eLFPP744yxdupRNmzZRWlrK9ddfT0tLS/jMggULWLlyJStWrGDdunW0trYya9Ys/P7kqwqMJW9zC511xwCoTcIKS4Btfg/27CzaDh2hbtOHZocTN459uIVAl4/0AYVkDkyMhTvRZM/IIGvQQACa96gtXKQ3Qi3hqrAUiT3D5+OpObef9ralq4OSKyfj93jY9P1KfWB7gvru2Z6Fl11iciRnpoRlkgply/MuvsjkSHrHkZONsyAf0BzLaLo8LRMItnvF83yd9KJCRt05F4AtP/lX/F6vyRGJRI7dbqe0tDR8GzAgOJzfMAyefPJJHn30UW699VYqKip44YUXaG9vZ/ny5QC43W6effZZHnvsMaZPn86ECRNYtmwZW7duZc2aNWa+rITXsjdYXZlePIBOkrOyuwsY/JWZgJbvnKjm3Q0AlFw1Ja6vjbEUmmPZvEtt4SK9ERopkjNcCUuRuGKxcPmiH2JLT6d2/XtqDT9BaBlRkRKWEmvHNm8BoOjyCeYGch6yNMcyqrpaWqlIywAgd2R8toOfaOz93yZ9QCEt+w6w539+aXY4IhGze/duysvLGT58OF//+tfZuzdYwbRv3z6qq6uZMWNG+KzT6eSaa65h/fr1AGzevJmurq4eZ8rLy6moqAifOR2Px0Nzc3OPm/QUagfPvTD+x6j0x7BbbwLg0Kuv4+vsNDma+FD9bvC/ndKrppgcSfzIHTkCALfmWIqck6+jg/aj1UD8zogXSWU5w4ZyWeX3Afhk8VPUf/SJyRGZL+D30/BxsMhNFZYSUwG/n/qPurPlEy81N5jzkK05llF16E9vkGaxkpafR/qAQrPDOSdHTjbj/v67AGx74j9pr64xOSKR/ps0aRL/8z//w+uvv84zzzxDdXU1U6dOpb6+nurq4C87JSUlPb6npKQkfF91dTVpaWnk5+ef8czpLFq0CJfLFb4NHjw4wq8s8TV/3p2wTIC5z/1RPOlyMgeW0dXSytHVb5sdjuk8TU3hDZklX1TCMiR3lBbviPRW64Fgd5gjN5e0/DxzgxGR0xrxjdsY/JWZGD4f6+//e7zNLef+piTWvGsPvrZ27NlZ4WV78UgJyyTUvGsPvtY27NlZuEbH/4bwkOwhwV+g1RIeHfv+9w8A5I29KGFa3ob/9S0UXDqOrpZWPvjBP5sdjki/3XjjjfzVX/0V48aNY/r06bz66qsAvPDCC+EzJ//3aRjGOf+bPdeZRx55BLfbHb4dOqSfsydrCScsR5gcSXRZrFaGfTVYZbnvNytNjsZ8tX95DwyD3JEXkFlacu5vSBGhlnBVWIqcW3h+5YihCfMeWyRVhBbyXDdlKj/evJ5mm4W2Q4f518uv4rabZpsdnmlC7eCF/3979x3fRP3HcfyVpHvTRQelZZU9y94bBQVEFBcbFSdDRUQUXPADBfcAB6CoOAFRBCqjLIFSNpTRUuimLaV7N/f7ozZS6QLaXNp+no9HHpq7y907X9Jc7nvf0b4tWp1O5TRlkwrLWijx8FEAXDq2N+kP33/Z+RZVWKZfkgvpqpYRGU3iwcPoFQWnFv5qx6k0rU5Ht7ffRGtuRuxfu+hqYat2JCGqlK2tLW3btuXChQuG2cL/21IyISHB0OrSw8ODvLw8rl27VuY2pbG0tMTBwaHEQ5RUPLmIfS3vEg5FN4MA4oL2kRkTq24YlcX/M36lh7SuLMGhaVGFZU5CInkpqSqnEcK0FbfQt2/kp24QIcQNrp+Q5+0HxtN+7D2g0dAkpwCviLrbs7O4W7xLp3YqJymfVFjWQkmHjwE1qzs4/DvmS3q4zEhZ1YonVwjNz8Hc3k7lNDfHsXlTWj3zOAAP27rU+eb7onbJzc0lNDQUT09PGjVqhIeHB4GBgYb1eXl5BAUF0bNnTwACAgIwNzcvsU1cXBynTp0ybCNunr6ggIzLkUDt7hKempKCh5sbzbp25nxhHigKT3XuiYebGx5ubnRsa9o/WquaPj+fmMAdAHj0661yGtNibmeLjVfRTRSZKVyI8qVdKPobcTThbpVCiCI2nh6GIWC6p+USu3OPyonUUTzniUtH0x2/EqTCslb6d8KdDqrmuFnFA7xnxcaTn5mpcpraQ19QwMUffgHg79wMldPcmlZPPYpzh7bYaLXEbP0LRa9XO5IQt+T5558nKCiIiIgIDh48yNixY0lLS2PixIloNBpmzpzJokWLWL9+PadOnWLSpEnY2Njw0EMPAeDo6MjUqVN57rnn2L59O0ePHuWRRx4xdDEXtyYzKgZ9fgE6a2tsPD3UjlNtCvV6Al9aSOBLCxk4YgQAd9VzZ9uLrxL40kLi4uNUTlg9OrZtZ6iUvf4xpIEfOYlXSVf03P3Mk2rHNDkO0i1ciEopHuu1+FpGCGHaXDq2x6l1C7TA/idmGbpH1xXZCYlFQyFpNLiZ+CTNZmoHEFUr43IUmZFRaHQ6XDrUrJYSlk5OWLq6kJt0lfSwCJzbt1E7Uq0Quz2IrNh4LJ3rEXL1stpxbonW3JweHyzllz7DICaOpMNHcOvaWe1YQty06OhoHnzwQZKSknBzc6N79+4cOHAAX19fAObMmUN2djZPPvkk165do1u3bmzbtg17e3vDPt59913MzMy4//77yc7OZtCgQaxevRpdDRoCxNQYuoM39kWjrRv3cu2bNkJnZUVBRiYZlyJr9cy2cfFxBL608IblkZv+JD08Ar+AjsTsC7zxhXWcY7MmxAftk4l3hCiHotf/O2mbtLAUokbQaDR4DuzHqQsXaJCVTdCExxm4bhX12rRUO5pRJB4MAcCpVXMsnBxVTlO+uvGrvA6J3rodALdunbFwsK9ga9NT3BWv+MQvbt+FNd8D0HjcGApQVE5z6+z9GvJd5lUAEv4OJitOZg0XNc+6deuIjY0lLy+PmJgYfvnlF1q1amVYr9FoWLhwIXFxceTk5BAUFESbNiVv3lhZWfHhhx9y9epVsrKy2LRpk8z6fZtSz4cB4NC07rSO0ZqZ4dS6BQBXj51QOY3xFWRlGybKcGrVQuU0psnQwvKfvw8hxI0yY+IozM5Ga2GOXcMGascRQlSSVqdjm7MNrp07kp+Wzq5HHq0z57uEA4cAcO9m+g2ApMKylone8hcADe4YpHKSW1N8sSjjJVWNtPAIruz9u2hg4UfGqR3ntv2dm4mDf1NQFKK3BFKYl6d2JCFELZB67gIATs1rzqRkVcG5XRvQaMiMjCbnarLacYzq2qkzoNdj5e6GlauL2nFMkmGm8PPSwlKIshha6DfyQ2smnReFqEkKtBr6rv4U53atyU2+xs6HppHyz2/C2izhwGEA3Lt3UTlJxaTCshbJTkg0jF/ZYJhUWAo4u3I1AF6D+mHn461umCriNbAf5vZ25KemEV9HB0kWQlStlLNFP04dWzRTOYlxWTg6GLqCJ9ehVpYFOTkkHT4KgEvHmjV8jjEVt7DMjr8iM4ULUQbD+JV1qIW+ELWJhYM9/b5ZiWMLf3ISEtl+73iu7D+odqxqk5N01fC95dY1QOU0FZMKy1okZttOUBSc27epsZMGFA9WLRWW/2rRzB97W9sKHy2alWwZlBUXz6WfNwDQ8ompKiSvHjorS7zvGAwaDSmh5wwto4QQ4lbo8/NJDy865zi1qFstLOHfCruU0PPYolE5jXEkBR9Bn5eHpasLjnXw37yyLBwdsGngBUDK2fMqpxHCNBVfs8j4lULUXJb1nBj44ypcu3QiPy2doPGPcWnD72rHqhaJB4taVzo2b4alcz2V01RM2q3XEoqicOmXjQA0GFZzZ4otHsMy/VIk+vx8tObmKidSX0xsDPvfWFrhdj1fmVPi+dmVq9HnF+DWrTNuXTpVVzxV2Hp74dqlE0mHQojdHoS1p0eNHLNVCKG+tIuX0OcXYG5vh423p9pxjM7G2wur+m7kXEmkn5mN0Y7bsW27Cmcl9/Tw5OjJqm35mZOYRPKxkwDU79UdjaZuVNLeKqcW/mRFx5Jy5lyN6DomhLGl/tNSyVFaWApRo1k6OTHg2y84MOslov7YyoFnXyQ9PILWM59EW4smtozfewAA9x4145wuFZa1ROLBwySFHENrYU6jsaPUjnPLbLw8MbOxpiArm4zLUdK94hZlJyQS/t3PALR65jGV01QP926dyYyMJjv+CjFb/8Lv3lF1ZnZfIUTVSf2n5Zijf9M6WXml0Whw6xJA1O9b6GNmTV5qGhaODtV+3LJm7r7ekMXlr79Zhbm5RP2xFaWwELtGvtj5NazS/ddGTq2aE/vXLq6FnlM7ihAmR1EU0i7808LSX1pYClHTnDt/noHde5RcqCh0t7WgXWYep9//jKTDx+jxwRKs3FzVCVmFFEUhbuduADz791E5TeVIhaUJstZoSQuPwNzWFmsP90q95vQHK4CimaAr+xpTpNFosG/SmGsnT5MWdlEqLG/RqeUfU5idjUvHdnj06al2nGqh0elocOdgwr/9kayYOJKCj+BWA2Y6E0KYln/Hr6y7XYPtmzTC0sUZriZzYc13tH52utqRqlxhXj5Rf2wlLyUVc3t7vIcOqpMV1DerXsvmAKRKhaUQN8i+kkB+WhoanQ57P1+14wghbpJSUMD79z9S6roPV69iaB5c2XeALXfcS4+P3qZ+j65GTli1Us+HkRUbj87Sssa0sJTmSCYkMyaWiJ838IFzQzYPuIuNXQfwe587OPHOB+QkXS3zdfF7/ubK3r/RmJnRYnrNH6vQsXlToGg8LXHzUi+Ec3HdLwB0ePn5Wn1BZuHoiOeAvgAkHAgmKy5e5URCiJqmuIWlUx2bcOd6xa0sAc6uWEVu8jWVE1UtR7Rc/mUjmZHRaMzM8LlrGGbWVmrHqhGcWrUAIPVcGPqCApXTCGFaUs4UVeTbN/ZDZ2WpchohRFX6IzqC72y1JJtpyUlMYse4ycxr2ZEh3bozdmTN7NEat6OodaV7z66YWVurnKZypMLSRCQeCuHSTxvIio4FwNzeDo1WS8blKM58sIJNPYZweN7rpF+6XOJ1aWEX2ffEbKCodWVtmAnauU0rAK6dOqNykppHURSOvbEURa/He9igGjHz1+1ybOGPY/NmoChEb/kLizoyaYQQomqknJMWlgAOzZsSrc8nPz2D0x+uUDtOldDn5xP27Y/MtXIm+0oCOisr/O4dhXX9mtsTxdjsfH3QWVtTmJtLesTlil8gRB1SXGHp1Kq5ykmEEFVNKSjgtYcn0uvxqTi1bokGaJeZxxM5Osz+qbOpaWJ37gHAa2BflZNUnnQJNwEJfx8yzNbk1Lolj+/dxuXINPLS0okP2sfZz1eTfOwkYWt/IOzbH2lwx2Dq9+pGTmISYd/8QH5aGi4BHei44EWV30nVcGrdEoDkU6EqJ6l5ojZtIW7XXrQW5rSfO0vtOEah0WjwHNiXrNg48lPTGGHjqHYkIUQNkZeWbrhRWNy6v67SaDT8lp/Bk5b1CPv6e5pNfFDVLo55qan0N7Nm3/RZZF9JAMCinhP2jXxxbN4Mp+bNcPBvckMLAUVRSAu7SPSWvwj/7ieyYuKw1mixru+O9x2DsaznpMK7qbk0Wi1OLZpx9egJUkLP4SgzIQthkPLPUAnFQycIIWofrbk53kMG4NC0MbF/7SLvWgqjgMPz36Dd889i4VQzrj3zUtNICj4C1JzxK0EqLFWXFnbRUFlZv09PXAM6kLz7TwAsHOxpePcd+Nw1jMRDIZz99CtidwQR/Wcg0X8GGvbh0KwJfb74EDOr2tG9qV7rou5H2XHx5FxNxsrFWeVENYO1RsuR1xYD0PLJRw0zrtcFOktLPPr3IWrTnwy1ciTl7Hmc6nhrKSFExYpb8tv6eGPp5KRuGBNwXp+PR9+exO/eT8grb9Hv6xVGH1YkLy2dhH0HSD13gdHm9kRt3lb2xhoNdg0bYOXqgtbSkoKMDNIvRZKflm7YxMrNhe9jLvHyuOkyMVsZUlNS8HBzK3P9/eb29DSzJuXMOXxHDjdiMiFMW3GFpbSwFKL2s2/kS9Px44gL2kdq6DnCvl5H1OZAOsx7Dr97R5r8MGzRW7ejFBbi6N8UO18fteNUmlRYqig/I5PYv3YB4BLQAdeADqVup9FocO/WGfdunUk5d4GIH34lMyYWpVCP78jhNLhzMFpzc+MFr2bmdrbYN/Yj/eIlrp0OxbNvL7Uj1QgP2TqTk3gV+yaNaPXUo2rHMTqHJo2wb9KI9PAIgucuZPCva+XiVAhRruQTpwGo989QJAI6vf4yW4bdQ3zQPiJ/24zvqBFGO3bq+TBiA3egzy8aK/F8YR73vzoP2wZeoNWSm5RMWthFUs5dIPXcBXKvJpNxOYqMy1El9qO1MKd+r+74jBiG78jhTPdpwHw5H5SpUK8vd8b25BOniNuxW4bqEeI6BTk5pF+8BIBTyxbqhhFCGIXOyooGwwaxNiGKh+t5kBZ2kYOz5xG29gc6vPw8bl06qR2xTJfX/w5Aw9HG+11XFaTCUkVxO3dTmJODlbsb7j27Veo1Ts2b0fHV2tH1uzz1WrcsqrA8eUYqLCsh5ex5ulvaodHp6PbOm+gsLdSOdFNys3Owt7WtcLvs7Jxy13sO6EPChXCuHjlO2Lc/0mz8A1UVUQhRC107WVQB49yutcpJTIdDYz9aPf0Yp5Z9xJGF/6N+z25YublW6zEVRSFh/0FDVyUbL088+vdm5pcf8+rjk8t8XU7SVdLCLpJ7LYXCnFzM7Wyx8fbEsVmTWnUjV23FY34mnziNoigm34pECGNIPXcBRa/H0sUZK/fq/Y4UQpiWOEszhm35hXOfr+H0Byu4euQ42+8dT4M7BtPuxZkm19MxK/4KV/YfBDDqjeiqIBWWKsmMiSU9PAI0GryHDUKr06kdyaTUa9OSyE1/ck3GsaxQTtJV4nYEAdB6xvQyW+qaskJFz/43lla4XYfnni53vbmdHRuyr/GgrQsn/vcuDYYOwrp+2d3chBB1W/KJUwA4t2ujchLT0nL6VKL+2Ebq2fMcmD2Pfms+q7YW64qiEB+0l+RjJwFw7dwR957d0Gi1FXZVBvD08OToyRPVkk0UsXR1oUBRICWVjMtR2Ps1VDuSEKozTLjTsrlU4gtRB+ksLGj11KM0Gjuak8s/IuKHX4ne8hcx23bQcOSdtHr6MRz9TWN89Mjf/gRFwbVLpxo3SbNUWKpAURSu7N4PFFXMyRiNNyrunnfttFRYlic/M5PIjX+gz8vnXH4O9z/9mNqRVLczJ52nevYj+fgpjr65lJ4fvq12JCGECcr7p/IFwLmtdAm/ns7Sgp4fvs22u+4vmvzvs69o+eS0Kj9O8e+h4spKz4H9SrR2rairMsCQxeWvF7dPq9MRoxTgqzEn+cQpqbAUAkg5cxb4d+x9IUTdce78eQZ271FiWT0Xa7qm5eKbW8DlDX9weeNmfO4cQvNpE3AJ6KDajQ1FUYj4aQMAfqPvUiXD7ZAKSxWkh18k+0oCWnMz3Lp3vWF9ZbrHent5c/bC+eqKqLp6bYpmCs+4FEluSopMhlAKfX4+kRs3k5+egUU9Jz4JP8ECM/mTVoDOb71K4MgHiNy4mcbj7sWjd3e1YwkhTEzyP93BbRv61JgZHo3JsXlTOi6Yy+F5r3F8yXs4NGuC95ABVXqMxEMhXD16HACvIQOo17rlTe+jMq0wU1JSbiWeuE6kPh9frTnJx0/JxDtCANdOF1VYOskM4ULUOUpBAe/f/0ip67ITEtnw9Te0M7MiavM2ojZvI9FcyylbCy5am+NYvz4//7bRaFnjd+8j9dwFzGysaXj3HUY7blWR2g0jUxSFpOCjADh3bI+5rc0N21Sme2zPV+ZUSz5TYVnPCYemjUkLu0jSoSN4Dx2odiSTouj1RP8ZSE5CIjprK3xHjSBr2TG1Y5kM53ataTrhAS6s/o6Q+W9wx9b1NW5cTyFE9fq3O7iMX1mWJg/fx7XToYR/+yN/P/MCA9Z9hUuHdlWy7746axL/PgSAR79et1RZCZVrhRnwwrO3tG/xryh90URIxRNVCVGX6fPz/x0Dub0MKSKE+Je1uxurM5LY+PRzXD16nNSzF3DLL2RASg6DsvScSb1MwsHDuHXpZJQJYkM//QqAJg/dVyNv0EuFpZFlRceSfSUBjU6HS4e2ascxaW5dA0gLu0jCoRCpsPyP+D37Sb94CY1OR8O776yRXz7Vre1zzxD1x1bSL17i7MpVtH7mcbUjCSFMSHHFi1RYlk2j0RDw+jwyIqO5smc/ux55nAHffXHbZXbxp/WMsbAHwK17F1w6tq+KuKIaRerzAbh28jT6wkIZe13UaannwijMzcXcwR77Rr5qxxFCmCArVxe8hwykfu8eXDsVyrUTp8lPT6dFHuy4byI2DbzwG3M3fqPvwqFp4wr3N3bkKJITEkpd5+zuXmqrzeTjp0jYfxCNmRn+U8ff9ntSQ/VX6YoSkg4XzYDp1LolZjY3tq4U/3Lr3hmAxAPBKicxLVePnST5aNEEA97DBmHj5alyItNk4ehAh/lFLZHPfLCCjMholRMJIUyFoigkHS7q7eDSsWpaDNZWWnNzeq98D9fOHclPS2PnQ1MNM03eiqg/thL8wqsAuHRsj1u3zlUVVVSjK0ohZjbWFGRlkx4WoXYcIVR19Z9xd53btzVKCykhRM1lZm2NW5dONJvyCH5jR3HWxhwzO1uyomM588EKNg+8m63Dx3J25Wqy4uLL3E9yQgLv3/9IqY/SKjIVvZ4jr/0PAN+Rw7H19qq291id5BvWiDx15kUD/Gs0uAZIa4KKuHUNAODaqVDyMzJVTmMa0i9eIj5oLwDuvbqbzMxjpsp39Ajce3alMDeXI68uQlEUtSMJIUxAxuVIchKT0FqY49JeejtUxNzWln5rPsO1Syfy09IJGv8Y4et+uenv1Kg/A/n72Tkoej1/F2RTv29PmV23hlD4t+trUshRdcMIobKrx4oaDrh0kO7gQojK0Wg02DbwZreTNaOP7KbnR+/gNbAfGjMzrp0K5dibb/Nb98Fsv38SYd/+SO5tjr8d/t3PJB0+ipmtDW3n1NyhcaTC0oj6WxV1f7Jv7IeFo3ThrYitlye2Pg1Q9Hr5cUzRAL5Rm7eBouDUpiWunTuqHcnkaTQaAt6Yj9bcjNgdQcRs26F2JCGECUg8VNTbwbldG3RWliqnqRnM7e3o/+3nnDUDfX4BwXNe5QXvpjR2c8fDza3Eo2Pbkq1WFUXh3Bdfs2/6LPT5BfjcdQc/5qdLZWUN49a1qDVswsEQlZMIoa7kf1pYyg0vIcTNOnf+PEP7D2DSotdZePYoq12s2ONoRayFDhSFxAPBHH7pNTYG9GPf9FnE7tiNvqDgpo6Rej6M44uXAdDuhRnY1uAemTKGpZHkZ2TSw8IOkMGZb4Z7985EREWTcOAwnv16qx1HNfnp6URu/AOloADbhg3wGtBXLvQqybFZE5o/NpnQjz/nyIJF1O/dHXNbW7VjCSFUlHioqMKluCW/qBwzKytWpCfw7aC7Sfj7EJ3NrOhm54hb967Ua90Crbk5AEMWLzS8JiMqhpD5bxC3cw8ATcePo9Nr81B++lqNtyBuQ3H3/cSDwSiKIr9DRJ2Un55B6oVwAJxlPgIhxE0qb4bxed99zdJpj3N5w++knDlnmGXcur47XTJzyL2WgmU9p3L3n34pkp0PTSM/PQPXzh1pOvHBangXxiMtLI3k0q+/Ya3VYlHPCVufBmrHqTGKx7GMD9qnchL1WGk0XN74BwWZWVi6OOMzYhgaGez+prR+9nFsGniRFRvPiSXvqx1HCKEyqbC8dQpF5dbovnuwdHWmMCeX+F17OP/F18Rs3U7yidN00Fly/qu17H1sBn/0uYO4nXvQWlrQccFcAt58Ba2Z3C+viVw7tUNjZkZWbDyZ0bFqxxFCFcknT4OiYOPtibW7m9pxhBC1yJGLYTy1+nOWpsTxs5stJ20tyNZqyL6SQMeMPMLWfMfFH37l6vGT5KWmlRiapyA7mwvfrGPbXfeTk5CIY/Nm9Pnqoxo/SZ78YjQCRVEI++YHoKj7mdyRrjyvAX1Bo+HaqTNkxsbV6ObMt0Kfn8/jdu7kJiVjZmNDw1Ej0FlK98WbZWZtTZfFCwga/zgXVn+Lz/AhuHfvonYsIYQKshMSybgU+c940h3UjlNj2Xh50OSh+7l28gxJR46Rn5pGSug5UkLPMcnCkSMLFxu2rd+nJ50WzsWxWRMVE4vbZWZjg3O71lw9cpzEA8HY+XirHUkIo0sKOQaAS4eSQ1+UN4Nv2PkL1R1LCFELlNb6Ul9YSEbEJbas30BrCxuy4+LJjosnnj3orK2wcHDgnuQMfm3XE31uHgDO7VrT56uPsXRyUuFdVC2psDQCjUZDrxXvMaNbb2a0aq52nBrFytUF184dSQo+QszWHfhPfljtSEajKAohr7xFGwtrNGZmNBw1HAsHe7Vj1Vie/XrT+MGxXPz+Zw4+N587tv0qXcOFqIMS/xl/z6mlPxaODiqnMS2pKSl4uJXfYijlukHgNVotzu3bUK9tKzKjY8iMjCYn6SrHwsPod8cwnFq3xGf4EJxa+FdzcmEs7t06c/XIcRIOhdDovtFqxxHC6BL+DgaKhq26XvEMvqUZtnBetecSQtROWp0Oh6ZN+DLzKr8/OZ3Us+dJv3iJrNh4CrNzyM7OwQ3Qk4eNlwctHp9C0wkP1PiWlcWkwtJIHBr78XPWNWZL67ib1uCOQSQFHyF66/Y6VWF59rOvCP/uJ/SKgu+dQ7Cu7652pBqv4/wXiN+9j8yoaE78710C3pivdiQhhJHF7S4aYsS9R1eVk5ieQr2ewJcWlrtNwAs3zjSp0Wqxa+iDXUMfAKYtXsj8Lz+qjohCZW7dOhP66ZckHghWO4oQRleYl0fS4aKJQN27yzlECGFc5ra2uAZ0xDWgI/r8fHKvpVCQkcmKvTv58JefsG/sV+t688oYlkIVudk52Nvalvto0ayoRYb3kIEAJB48TO61FBVTG0/UH1s5vng5AD9mJePQpJHKiWoHc3s7ui59A4ALa77nyr4DKicSQhiToijE79oLgOeAPiqnEaLmcevSCY2ZGRmXo0iPuKx2HCGM6tqJ0xTm5GDpXA8HfxniQgihHq25Odbubtg39iPKyhyHJo1qXWUlSAtLoZJCRc/+N5aWu03PV+YAYO/XEMcW/qSePU/0n4E0eeg+Y0RUTdKR4xyY+RIAzSY9zPZlb/FGJV5XXAlcnuzsnCpIWLN59OlB00fGEbb2B/5+9kWGbf4Z6/oyaLoQdUFK6DmyrySgs7bGvWvnil8ghCjB3N4Ot64BJOw/SOz2IJpPm6B2JCGM5so/LYvdunWulRUDQghhaqTCUtQIfmPu5viiZYSt/YHGD46ttT8S0i9Fsmfq0xTm5uI1uD8dF7wIy96q1GsrUwnc4bmnqyJmjddh/vMkHgoh9XwY+596jgHff4nW3FztWEKIaha3cw8A9Xt0RWclQ7QIcSu8B/crqrDcsVsqLEWdYhi/sodM3CiEEMYgXcJruRbN/Cvsel0TWt01HjcGraUF106Fknzs5E2/vjLlcH03dDXkJF0laPxj5F5Npl6blvT4cGmtGSzX1JjZ2NBr5fuY2dmSeCiE4/97V+1IQggjiDN0B++tchIhai6vQf0BSDwYTH56hrphhDCSEuNXyhjIQghhFNLCspaLiY2pFa3uLOs50fDuO7n080YufP09Lh3b3dTrK1MO8G83dGPLz8gkaOITZFyOwtbHm76rPpUZrKuZQ2M/ui17i32Pz+Tc52tw6diehncNUzuWEKKa5KakkBRyDADP/jJ+pRC3yr6RL/aN/Ui/eIn4PfvxGT5U7UhCVLvEQ0cozM7Gys0Fx2YyfqUQQhiDtLCsoSozaU1NaT1ZWc0mPABA5KY/yYiKUTlN1SnMy2Pf9JlcO3kaS+d69PtmpYypaAQtmvnTauxotmSnArDziZl0cHQ2mRa3QoiqFb05EKWgAKeW/tj5+qgdR4gazWtQPwCit25XOYkQxhG7fRcAngP7odHKJbQQQhiDtLCsoSozXiHUjNaTleXcvi3uPbuSsP8QJ/73Lj0/fkftSLdN0es59MIrxO/ej87amr6rP8GhsZ/aseqE4la3il5P5G+b4VIk89x8aXTfaKzcXAH1WtwKIare5d82A9Bw5HCVkwhTlJqSgodb+TcLU1JSjBOmBmh41x2c+3wN0X/+Rf6bGZjb26kdSYhqoygKsX/tAsD7n8p6IYQQ1U8qLEWNodFo6PjKHLYOv4/ITX/iP/lhXDt3VDvWbTm+eDmX1/+OxsyM3ivexaXDzXV1F7dPo9XiM2IYl9dvIis2nsvrN+F33z1Y1nNSO5oQoopkxycYJkuQCsvqVxMr/wr1egJfWljuNgEvPGucMDWAc4e22DdpRHp4BFGbt9F43Bi1IwlRbdLDI8i4HIXWwpz6vXuoHUcIIW7K2JGjSE5IKHWds7s7P/+20ciJKk8qLEWNUq91SxqPG8PFdb9w8Pn5DPltHRYO9mrHuiWnP1zB2RWrAOi69HUZU01FWnNzGo4cwaVfNpKTmMTlXzfhN3ak2rGEEFUk8vctoCi4BHTAzsdb7Ti1nlT+1X4ajYZG947ixNL3iPh5o1RYilotdnsQAO7du2BuJ2PMCyFqluSEBN6//5FS1834ca2R09wcGYBD1DjtXpyJjZcH6RcvceDZF9EXFqod6aad/mglJ9/+AICfMpNpN/GhOjEOqTHc6viuOitLfEffhYWTI/np6UT88CsNdRYqvQshRFVRFIWIn9YD4CutK4WoMn733g0aDYkHD5N+KVLtOEJUm+gtfwHgNai/ukGEEKKOkRaWosaxcnGm98oP2H7veGJ3BLHvsRl0f39JjbnjefqjlZxc+j4Av2ZeY+HL81lYzva1aRxSY7id8V3NbG3wGzuayA2/k5N0lRccPYgL2otnv97VEVUIYQSJBw+TEnoenZUVvveMoGPbdsTFx5X7GlPrriyEKbLx9MCzXy/idu3l3Bdr6PzmK2pHEqLKZVyOIinkWNEQQncOUTuOEELc4Nz58wzsXvZwFWHnLxgxTdWSCktRIzm3a02PD5ey/5kXiAncyba77qfdizNpcMdgNBqN2vFKpSgKp9/7lFPvfgxAuzkzefTFmcjPe9NibmeL332jifp9K0RFs3vyUwS8No8mj9xvsp8tIUTZzq8q6urid+9ILJ2ciIuPk+7KQlSRFtOnELdrLxd/+JXWz07H2r38sUuFqGkurd8EgHuv7lh7uKucRgghbqQUFJTZ5Rtg2MJ5RkxTtaTCUtRYDe4YzKAf17Bn2jOkX7zEvsdnYu1RH88BfajXqjm2Pg2wcnHG0sUZC9StaNLn5xP80mtE/FjULbHtnBm0evpReHGmqrlE6XSWljQcPYLV7yyjG3Ycfvl1Eg4E0+V/C2UmVCFqkMzoWGK27gDAf9LDKqcRovZx79EVl4AOXA05xrkvvqbDvOfUjiRElVEUhcvrfwfA7567VE4jhBBVr7zWmabQMlMqLEWN5tKxHcN3bOLs56s5/9VasuOvcPH7n2/Y7mMXX858tBIzG2vMrK2xcK6Htbsbtg28sHR1qdaWc3lp6eybPosre/9Go9XS6Y2XaTb+gWo7nqgaWp2OLzOSeOy1BZxY+j6Rm/4k+cQpen68DOd2rdWOJ4SohDMfrUTR66nfqzuOzZuqHUeIWkej0dDqqUfZM+UpLqz5nmYTHsS2gZfasYSoEsnHTpIecRmdtTUN7hysdhwhhKhy5bXONIWWmVJhKWo8C0cH2j3/LK2ffpwrfx8i8UAwqRfCyY6/Qk5SMrnJyejz8lEKCshPSyc/LZ3sKwmkhp4DwMzOFqcW/ji28K/SXC2a+aO/ksB0Oze8zCzIUfSsSLnCqelTYfpUAJlQx8QpQMvpU3Dr0on9Tz9PxuUoAkc9SPNpE2gz60nMbGzUjiiEKENa2EUu/vArAG1mPalyGiFqL69B/XDt0omk4COEvPoWfb78SIZQEbXChTXfA0W9usxta8ZY+UIIUZtIhaWoNXRWlngN6IPXgD4lliuKQn17ezbPnkdhVjYFWZnkJCWTHXeFzJhYCjIySTp8lKTDR3nF0ZOwb37A9567bmsSH0Wvp9nVNB5x9UUpKMDM1oZWo0aw8j9jO8mEOjWDa0AHhv35M4dfep2oP7ZydsUqIv/YSuc35uM1qJ/a8YQQpTix9H2UwkK8hwzArWuA2nGEqLU0Gg1dFi9g6533EvvXLqI3b8NnxDC1YwlxW7Lir3D5t80ANJ9S9thwQgghqo9W7QBCVDeNRkO2omDp5IiNlwcOTZvg3r0LvvfcRYvpU/AZMQz7Jo3QaLU0NLPk8Muvs7HrAA7Pf4OUczc/bsPVoyfYfu94HrZzQSkowLahD00eul8Goq/hLJ2c6PXpcvp8+RE23p5kRceye/KT7HhgCklHjqsdTwhxnZjAnURv+QuNVku7OTPVjiNErefo35QW06cAcPCFV0i9EK5yIiFuz4U136MUFODWNQDn9m3UjiOEEHWStLAUdZrWzAyHZk1waNaEguwcXlv+Do+27UT6xUuEfb2OsK/X4dY1AN/Rd+E9dECZlY6FeXnE79pL2Lc/ErdzDwA5ih6/AX1xbt9WukbVULnZOdiX0gXIEg132zgxyMqBhP0H+Wv0Q3gPGUCrZx/HpX1bFZIKoY6ObdsRFx9X7jaeHp4cPXnCSIkgN/kawXMXAOA/bYKMXSmEkbSe8QSJwUdJPBDMnilPMfjXtVi5uaodS4iblpeWTvjaHwBo/tgkdcMIIUQdJhWWQvzDzNqK7TlprN/5O1f2HSTsm++J2baTxEMhJB4K4fC813Bo2hjH5s2w8fZEa25Ofmoa6ZciSQo5RmF2NgAarRa/saO4b8UH/NGhncrvStyOQkXP/jeWlrk+Ly2dxIPBJJ8KJSZwJzGBO3EJ6EDzKeNpcOdgtGbyFStqt7j4OAJfWljuNkMWl7++KukLCzk4+2VyEq/i0KwJ7Z5/1mjHFqKu6xzQmbT4eGZbOsPlKFZ27MWneSlcU/SGbYx9A0OIW3Hmo5Xkpabh0LSxDP0jhBAqkqtpYbLKat32X95e3py9cL7KjqvRaPDo3R2P3t3Jiovn8oY/iPx9K9dOniYt7CJpYRdLfZ2Vmwt+Y0bS5OH7sPfz5dqn71ZZJmGaLBzs8R4ykKf3/sXaaU8SuelProYcY3/IMazcXPG95y4a3TsSp5bNq/S4LZr5ExMbU+F2Vf23IYQpUxSFowsXE7sjCK2lBd3fXYzOylLtWELUGcU3MHJTUrn8y2+4p6fzhrMP3ncMxq6hD2DcGxhC3IqMyGjOf/UNAO3nPYdWp1M5kRBC1F1SYSlMVkWt24oFPP9shRWbtzobt42nBy2fmErLJ6aSm5JC0uFjpEdcIvtKIkp+AWZ2Ntg28MalQ1scmzdDo5VhYeuiyMwMhqz8AAeNjn5W9vS3sofEJM6tXM25lauJLsgj3FLHmxt+wrlt69v+nMTExlTqb6PnK3Nu6zhC1BSKXs+xRcuKZnTVaOjx3v9wbtda7VhC1EmWTo40uv8eLm/4ndyryVz+dRPOHdri3r1rlR3DFIejEDWfoigcfX0J+rx83Ht2k9aVQgihMqmwFDVeZSo2q2I2bksnJ7wH97/t/Yja57+fQX1hIRmXIkkNPUd6xCUaYEGDQgi8+wGs3Fxx794F184dcQ3ogFNLf7Tm5pU+lqLX46TVkRUbR15aOvnFj8xM9Ll5FOblos/LR6PTMc/eg9c8m5CqLyS+MI+4wnxiCvOJK8w37E9aYYqaLj89g+C5C4nc9CcAnRbMlRmKhVCZub0djR+4l/igfVw7dYbkYydJPXeBQWY25GdkYm5XcQ+a8pjacBSidrj4w6/EbNuB1tyMTgtelDHohRBCZVJhKcR1KtMNXSp4REW0Oh0OTRrh0KQRBTk5ZFy8xMZNm+ju4k5OYhKRm/40VK5odDpsvL2w9/PBxtsLc1sbdDbW6CwtKczJIT89k/yMDLKvJJIVHUNmTCxv1/Mh4sf1FeZoZGF13bN/P9c6a2tsG3hh69OACb+sreq3L+o4RVHIT0sjPy2DgqwsAnSWRP2xFYt6Ttj5NcTG06NKLgIVRSFux24Ov/ImWdGxaMzM2GReyMwXZ8GLs8p8XUpKym0fWwhRMa25OV6D++Pg35T4XXvITb7G3eZ2bOzSH58Rw2h032jcugZIpZAwCWkXL3FkwWIA2j7/bJUP5yOEEOLmSYWlENepTGtN6WYrboaZlRVOrVqw4vuvWBJ1nqTDR4seIcdICjlOfloamZFRZEZGVXqfhYqClaMD5vb2WDjYY+5gj7m9HVpLS3QWFmgtzFEK9Tz56Qd8MHEa+Wnp5CZfIzf5GjlJVynMzibtQjhpF8JZUs+HLXeMwXvIALwG96+SLuui7snPzCI97CLpEZfIio1Hn5dnWDfewpF9T8w2PDe3t8OhWROcWjbHuV1rnNu3wdG/aaUnqSrMySV623YurPqWpJBjANj6NKD7u4uYMeKOCltdBbwgE/EIcbNSU1LwcHMrd5uybgbYNWxAk0fGkXr2PEe2bKN+ZhYRP64n4sf12Hh7Ur9nN9y6BuDWNQBbH2+ZsE6o4uL3P1OYnY17z660eHyy2nGEEEIgFZZCCGE0OgsL6vfsRv2e3YCiFmI5VxJJvxxJxqVIsuLiKczOoSA7m8KcXHRWVpjb22Jua4ulqwu2Pt7YNvDGu4U/e2dVPIbl8ZwMHP2bllimLygk+8oVMqNiyIyMIiMmjpQz50g5c47T73+Glbsb3oP74zW4P/V7d8fMyqqMvYu6Tp+fTxutBZG/bSY94jIoimGdRqfD3MEeMxsbjkZeolf37uReTSYjMpr89AyuHjnO1SPHCf+2aHudlRX1WrfAsYU/Nt6e2Hp5YuXmitbcjMLcXHKvXiM94hLJx0+RcDCEwuzsotdZWtJs0kO0nvHEbXcxFUKUrVCvv62bARqtFqdWLVi8cR0n/9hCxM8bifx9C1kxcUT8tIGInzYYtrN0dcHGwx0zWxvD65VCPYV5eejz89Hn5jHf0oXzq9ais7REZ2mBzsoKi3pOWLm5YuXqgoWTY1W8bVGHtJ/3HLYNvPAaMkBu3AohhImQCkshhFCJRqPB2sMdaw933Lt1Bio3A/itTiIFoDXTYevtha23F3TvwtBX57J/9VpiAncSv3sfOQmJhH/3E+Hf/YTOygqPPj3wGjIA70H9sHJzveXjitol9UI4Ox+YzDRLJ9IvXgLA2qM+9k0aYefrg5WLM5p/ZlZ9bPFCFvxSNONqYV4e6RGXSTsXRvKpMySfOEXyidMUZGT+0+r4WKWOb+PlQaP77qHpw/dj7eFeHW9RCFFNiltTdlo4l4SDISQeCiHx4GGST5xCn5dPTkIiOQmJ5e7DVasjPzWN/DLW66ytGG/uwMWf1uPRpyc2HvWr/o2IWkWj0dBs4kPlbjN25CiSExJKXRd2/kJ1xBJCiDpNKiyFEMIIKjM+KhRVRh5954Nyt6mKSaSKZSh6Go0dRaOxoyjMzSPhwCFi/9pFTOBOsmLjiQncSUzgToI1GpzbtsK5Q1uc27XBuW0r7Br5SgvMOsreryEA6Yoev86dqNe6BZbOzhW+TmdhgVPzZjg1b0bDkXcCRRNJpV+8xNXjp8i4dJnMmDiyYuPJTbqKotejNTcrGv/StyFOzZvh3qMLji38Zdw7IWqg8rqWawA7tDRxc+fL996nMCf335VaLToLc7QW5mgtLBh1z2jeHz+Vwtw8CnNzKcjKJvfqVXISr5J7NZnC7BwCzKw49Nx8ABxb+OPZvzee/Xvj2rkjOgsLI7xbUdskJyTw/v2PlLpu2MJ5Rk4jhBC1X52qsPzkk094++23iYuLo3Xr1rz33nv06dNH7ViihrmZiichilVmfFSo2srIyijv8+yjs6CdhTVdbB3xVrQknzhN8onTJbax9qiPXcMGWHu4Y1nPCQsnp6L/1nPCwtEBM1vbom7tdnaY29thZmsjF4pVSK3zmtbcnAHff0XLXj3Y2qdnudtWZuy7zIwMbO3sbnsbmVBHCNNWma7lQxYvxHvowHK3uaQvwMbLs9R1SmEhWfFXWPHdtzzYpTvJx0+RevY8qWfPc/azrzCztcG9e5eiG3BtW1OvTUus3FzlJogJMIVrNWlFKYQQpqPOVFj+8MMPzJw5k08++YRevXqxYsUK7rzzTs6cOUPDhg3VjidqEFOteBLiVlR2oqmE8AgSDgaTfPIM106e5trps+SnpZMdf4Xs+Cs3dcx8RSFb0ZOj6P/5r0LOP8+zFD0Zej06B3vWhJ2RcaTKofZ5zdG/KfpKbFfZse/2V9E2Qoi6TaMrGvrkz4JMVv22jtxrKcTv2U/czj3EBe0jN+kqsduDiN0eZHiNzsoK6/puWHvUx8LBHq15UWtOjU7H779tIic7Gy1FrUA1mqL/atEUPQcsLC0ZfPfdWDjYYW5fNBGehaMDlq4uWLk4Y+nijJWbC+aVuOFdV6l9TismrSiFEMJ01JkKy+XLlzN16lSmTZsGwHvvvcfWrVv59NNPWbx4scrphBDCdOVm5+DepNENy201Wtx0ZrhrzbHX6rBRwN7MDDuNDluNFhutFiuNFiuNBmuNFktNUeWjuUaDuUaHA7qyj5mvl8rKCsh5TQhRG93OjOSlsaznhO/I4fiOHI6i13Pt9FkSD4WQfOI0106eJi08gsKcHDIuR5FxOeqG17cBMKtg+JMCuLx+U7mbnDLX8Gb4qUrnrmvknCaEEOK/6kSFZV5eHiEhIcydO7fE8qFDh7J///5SX5Obm0tu7r9j56SmpgKQlpZ2yzkURSEjJ7vi7aDC7SqzjeyrduyrJmeXfdWOfRUoeoJefq3C4/V6+QX2vfV22cfS6xn6ylz+mPsq+rw8lLx8CvPz0efloS/+/9xcCrJy2HbkEPfc4vdt8fe0ct2s1bXNzZ7XquOcBqDX6yv+jFXi3CfbmMY2ppRFtqm72xQUFrJ+1ovlbtN3/pwK95Ny7RruLi7lbpOVmYmDrS0OGi32Gh1OGi0WaNChwQzQaiArJ5un7rgb0IBGU9R1XFP8/4BGw9vrf2L28LspzC06n+nz8ijMyaEwp2h8zcKcbKIz0m/5O7e2n9dM5VoNoKCgoMzPlr6cz291rKsrx5Q8kqemHVPyFH1X3s73baXPa0odEBMTowDKvn37Six/6623FH9//1Jfs2DBAoWia3l5yEMe8pBHDXxERUUZ4xSjips9r8k5TR7ykIc8av6jtp7X5FpNHvKQhzzq5qOi81qdaGFZ7L+DaSuKUuYA2y+99BKzZ882PNfr9SQnJ+Pi4iKDclNUI+7j40NUVBQODg5qxzEZUi5lk7IpnZRL2W61bBRFIT09HS8vr2pMZxoqe14zhXOafNaLSDkUkXIoIuVQRMqhSFnlUFfOa9VxrSafrRtJmZQk5VGSlMeNpExKqoryqOx5rU5UWLq6uqLT6YiPjy+xPCEhgfr165f6GktLSywtLUssc3Jyqq6INZaDg4P80ZZCyqVsUjalk3Ip262UjaOjYzWlMQ03e14zpXOafNaLSDkUkXIoIuVQRMqhSGnlUJvPa8a4VpPP1o2kTEqS8ihJyuNGUiYl3W55VOa8VidmNLCwsCAgIIDAwMASywMDA+nZs6dKqYQQQohbI+c1IYQQtYWc04QQQpSmTrSwBJg9ezbjx4+nc+fO9OjRg5UrVxIZGcn06dPVjiaEEELcNDmvCSGEqC3knCaEEOK/6kyF5bhx47h69Sqvv/46cXFxtGnThs2bN+Pr66t2tBrJ0tKSBQsW3NAVo66TcimblE3ppFzKJmVTvpp2XpN/zyJSDkWkHIpIORSRcihSl8uhus5pdblMyyJlUpKUR0lSHjeSMinJmOWhUZSK5hEXQgghhBBCCCGEEEII46gTY1gKIYQQQgghhBBCCCFqBqmwFEIIIYQQQgghhBBCmAypsBRCCCGEEEIIIYQQQpgMqbAUQgghhBBCCCGEEEKYDKmwFGX69NNPadeuHQ4ODjg4ONCjRw/+/PPPUrd9/PHH0Wg0vPfee8YNqYLKlEtoaCgjR47E0dERe3t7unfvTmRkpEqJjaeissnIyODpp5+mQYMGWFtb07JlSz799FMVE6tj8eLFaDQaZs6caVimKAoLFy7Ey8sLa2tr+vfvz+nTp9ULqYL/lkt+fj4vvvgibdu2xdbWFi8vLyZMmEBsbKy6QUW53nrrLXr27ImNjQ1OTk43rD9+/DgPPvggPj4+hu+B999/v8z9hYWFYW9vX+q+TFlVlMOuXbsYNWoUnp6e2Nra0qFDB7799lsjvYOqUVWfh5MnT9KvXz+sra3x9vbm9ddfpybNG1lROQDMmDGDgIAALC0t6dChQ6nbbN26le7du2Nvb4+bmxv33nsvERER1Re8ilVVOSiKwjvvvIO/vz+Wlpb4+PiwaNGi6gtexaqqHIrV1O9JY/jkk09o1KgRVlZWBAQEsGfPHrUjqWb37t3cfffdeHl5odFo2LBhg9qRVLV48WK6dOmCvb097u7ujB49mnPnzqkdSzU3c+1fF5V2/VbXLFy4EI1GU+Lh4eFRrceUCktRpgYNGvC///2Pw4cPc/jwYQYOHMioUaNuqETZsGEDBw8exMvLS6WkxlVRuYSHh9O7d29atGjBrl27OH78OK+88gpWVlYqJ69+FZXNrFmz2LJlC2vXriU0NJRZs2bxzDPPsHHjRpWTG09wcDArV66kXbt2JZYvXbqU5cuX89FHHxEcHIyHhwdDhgwhPT1dpaTGVVq5ZGVlceTIEV555RWOHDnCr7/+yvnz5xk5cqSKSUVF8vLyuO+++3jiiSdKXR8SEoKbmxtr167l9OnTvPzyy7z00kt89NFHN2ybn5/Pgw8+SJ8+fao7dpWrinLYv38/7dq145dffuHEiRNMmTKFCRMmsGnTJmO9jdtWFeWQlpbGkCFD8PLyIjg4mA8//JB33nmH5cuXG+tt3LaKygGKKuGmTJnCuHHjSl1/8eJFRo0axcCBAzl27Bhbt24lKSmJMWPGVFfsKlcV5QBFlXlffPEF77zzDmfPnmXTpk107dq1OiJXi6oqB6jZ35PV7YcffmDmzJm8/PLLHD16lD59+nDnnXfWiUYEpcnMzKR9+/alnm/roqCgIJ566ikOHDhAYGAgBQUFDB06lMzMTLWjqaKy1/51UVnXb3VR69atiYuLMzxOnjxZvQdUhLgJ9erVU7744gvD8+joaMXb21s5deqU4uvrq7z77rvqhVPR9eUybtw45ZFHHlE5kem4vmxat26tvP766yXWd+rUSZk/f74a0YwuPT1dadasmRIYGKj069dPmTFjhqIoiqLX6xUPDw/lf//7n2HbnJwcxdHRUfnss89USms8ZZVLaQ4dOqQAyuXLl40XUNySVatWKY6OjpXa9sknn1QGDBhww/I5c+YojzzyyE3ty9RURTlcb/jw4crkyZOrIJlx3U45fPLJJ4qjo6OSk5NjWLZ48WLFy8tL0ev1VR21WlWmHBYsWKC0b9/+huU//fSTYmZmphQWFhqW/fbbb4pGo1Hy8vKqOGn1up1yOHPmjGJmZqacPXu2esIZ0e2UQ7Ha8D1ZXbp27apMnz69xLIWLVooc+fOVSmR6QCU9evXqx3DpCQkJCiAEhQUpHYUk/Hfa/+66GauU2q7is5H1UFaWIpKKSwsZN26dWRmZtKjRw8A9Ho948eP54UXXqB169YqJ1THf8tFr9fzxx9/4O/vz7Bhw3B3d6dbt251sstFaZ+Z3r1789tvvxETE4OiKOzcuZPz588zbNgwldMax1NPPcWIESMYPHhwieURERHEx8czdOhQwzJLS0v69evH/v37jR3T6Moql9Kkpqai0Wik21stk5qairOzc4llO3bs4KeffuLjjz9WKZXxlVYOt7JNTfff9/j333/Tr18/LC0tDcuGDRtGbGwsly5dUiGhOjp37oxOp2PVqlUUFhaSmprKN998w9ChQzE3N1c7ntFs2rSJxo0b8/vvv9OoUSP8/PyYNm0aycnJakczurr4PVlZeXl5hISElPhtBTB06NA68dtK3LzU1FSAWn+OrYzSruPqqpu5TqkLLly4gJeXF40aNeKBBx7g4sWL1Xo8s2rdu6jxTp48SY8ePcjJycHOzo7169fTqlUrAJYsWYKZmRnPPvusyimNr6xyiY+PJyMjg//973+8+eabLFmyhC1btjBmzBh27txJv3791I5e7cr7zHzwwQc8+uijNGjQADMzM7RaLV988QW9e/dWOXX1W7duHUeOHCE4OPiGdfHx8QDUr1+/xPL69etz+fJlo+RTS3nl8l85OTnMnTuXhx56CAcHByOkE8bw999/8+OPP/LHH38Yll29epVJkyaxdu3aOvNvXVo5/NfPP/9McHAwK1asMGIy4yqtHOLj4/Hz8yuxXfH3ZXx8PI0aNTJmRNX4+fmxbds27rvvPh5//HEKCwvp0aMHmzdvVjuaUV28eJHLly/z008/8fXXX1NYWMisWbMYO3YsO3bsUDue0dTF78mbkZSURGFhYam/rYp/dwlRTFEUZs+eTe/evWnTpo3acVRT3nVcXXQz1yl1Qbdu3fj666/x9/fnypUrvPnmm/Ts2ZPTp0/j4uJSLceUFpaiXM2bN+fYsWMcOHCAJ554gokTJ3LmzBlCQkJ4//33Wb16NRqNRu2YRldWuej1egBGjRrFrFmz6NChA3PnzuWuu+7is88+Uzm1cZRVNlBUYXngwAF+++03QkJCWLZsGU8++SR//fWXyqmrV1RUFDNmzGDt2rXljmX6378lRVFq9d9XZcsFisboeuCBB9Dr9XzyySdGSiiKlTbI9n8fhw8fvun9nj59mlGjRvHqq68yZMgQw/JHH32Uhx56iL59+1bl27htxi6H6+3atYtJkybx+eefq96rQY1yKO37sbTlxlRd5VCW+Ph4pk2bxsSJEwkODiYoKAgLCwvGjh2r6gRExi4HvV5Pbm4uX3/9NX369KF///58+eWX7Ny5U9UJM4xdDqb6PWlq6tpvK3Frnn76aU6cOMH333+vdhRVlXcdV9fczHVKXXHnnXdy77330rZtWwYPHmy4ubxmzZpqO6a0sBTlsrCwoGnTpkBRV6Tg4GDef/99WrZsSUJCAg0bNjRsW1hYyHPPPcd7771X67tolVUuH374IWZmZjfciWrZsiV79+5VI6rRlVU27733HvPmzWP9+vWMGDECgHbt2nHs2DHeeeedWt3MPiQkhISEBAICAgzLCgsL2b17Nx999JHhAis+Ph5PT0/DNgkJCTe0DKhNKiqX3NxcdDod+fn53H///URERLBjxw5pSaKCp59+mgceeKDcbf7bAq4iZ86cYeDAgTz66KPMnz+/xLodO3bw22+/8c477wBFF5h6vR4zMzNWrlzJlClTbupYVcXY5VAsKCiIu+++m+XLlzNhwoSb2n91MHY5eHh43NAiKiEhAbixZboxVUc5lOfjjz/GwcGBpUuXGpatXbsWHx8fDh48SPfu3avsWDfD2OXg6emJmZkZ/v7+hmUtW7YEIDIykubNm1fZsW6GscvBVL8nTYWrqys6na7U747a/NtK3LxnnnmG3377jd27d9OgQQO146iqrOu42tyzoyyVvU6py2xtbWnbti0XLlyotmNIhaW4KYqikJuby/jx42+oYBo2bBjjx49n8uTJKqVTT3G5WFhY0KVLlxvu8J8/fx5fX1+V0qmruGzy8/PJz89Hqy3ZsFun0xlaptZWgwYNumEGtcmTJ9OiRQtefPFFGjdujIeHB4GBgXTs2BEoGnspKCiIJUuWqBHZKCoql+srKy9cuMDOnTurrbuBKJ+rqyuurq5Vtr/Tp08zcOBAJk6cyFtvvXXD+r///pvCwkLD840bN7JkyRL279+Pt7d3leW4WcYuByhqWXnXXXexZMkSHnvssSo79u0wdjn06NGDefPmkZeXh4WFBQDbtm3Dy8urSiuAblZVl0NFsrKybrg4Kn6u5nnU2OXQq1cvCgoKCA8Pp0mTJkDR7yxA1d9axi4HU/2eNBUWFhYEBAQQGBjIPffcY1geGBjIqFGjVEwmTIWiKDzzzDOsX7+eXbt21ZnhRW5G8XVcXVSZ65S6Ljc3l9DQUPr06VNtx5AKS1GmefPmceedd+Lj40N6ejrr1q1j165dbNmyBRcXlxsqDszNzfHw8FDtzraxlFcuAC+88ALjxo2jb9++DBgwgC1btrBp0yZ27dqlbnAjKK9sHBwc6NevHy+88ALW1tb4+voSFBTE119/zfLly9WOXq3s7e1vGA/H1tYWFxcXw/KZM2eyaNEimjVrRrNmzVi0aBE2NjY89NBDakQ2iorKpaCggLFjx3LkyBF+//13CgsLDS0lnJ2dDRUXwrRERkaSnJxMZGQkhYWFHDt2DICmTZtiZ2fH6dOnGTBgAEOHDmX27NmGf1OdToebmxvwb2upYocPH0ar1daocaWqohx27drFiBEjmDFjBvfee69hGwsLixozKUBVlMNDDz3Ea6+9xqRJk5g3bx4XLlxg0aJFvPrqqzWma2dF5QAQFhZGRkYG8fHxZGdnG7Zp1aoVFhYWjBgxgnfffZfXX3+dBx98kPT0dObNm4evr6/hZpepq4pyGDx4MJ06dWLKlCm899576PV6nnrqKYYMGVKi1aUpq4pyqA3fk9Vt9uzZjB8/ns6dO9OjRw9WrlxJZGQk06dPVzuaKjIyMggLCzM8j4iI4NixYzg7O5foNVdXPPXUU3z33Xds3LgRe3t7w/nH0dERa2trldMZX0XXuHVNZa7f6prnn3+eu+++m4YNG5KQkMCbb75JWloaEydOrL6DGnVOclGjTJkyRfH19VUsLCwUNzc3ZdCgQcq2bdvK3N7X11d59913jRdQJZUply+//FJp2rSpYmVlpbRv317ZsGGDSmmNq6KyiYuLUyZNmqR4eXkpVlZWSvPmzZVly5Yper1exdTq6NevnzJjxgzDc71eryxYsEDx8PBQLC0tlb59+yonT55UL6BKri+XiIgIBSj1sXPnTlVzirJNnDix3H+zBQsWlLre19e3zH2uWrVKcXR0NEr+qlIV5VDWPvr166fKe7oVVfV5OHHihNKnTx/F0tJS8fDwUBYuXFijzh0VlYOiFH3/lbZNRESEYZvvv/9e6dixo2Jra6u4ubkpI0eOVEJDQ43/hm5RVZVDTEyMMmbMGMXOzk6pX7++MmnSJOXq1avGf0O3qKrK4Xo18XvSGD7++GPDb9NOnTopQUFBakdSzc6dO0v9TE2cOFHtaKoo6zfmqlWr1I6mipu99q+L/nv9VteMGzdO8fT0VMzNzRUvLy9lzJgxyunTp6v1mBpFUXGUbiGEEEIIIYQQQgghhLiOzBIuhBBCCCGEEEIIIYQwGVJhKYQQQgghhBBCCCGEMBlSYSmEEEIIIYQQQgghhDAZUmEphBBCCCGEEEIIIYQwGVJhKYQQQgghhBBCCCGEMBlSYSmEEEIIIYQQQgghhDAZUmEphBBCCCGEEEIIIYQwGVJhKUQd4efnx3vvvVct++7fvz8zZ86sln0LIYQQlXXp0iU0Gg3Hjh2rlv1rNBo2bNhQLfsWQghRs+3atQuNRkNKSoraUVRR3edgUfdIhaUQJmjSpEmMHj36ll67evVqnJycblgeHBzMY489ZnguF11CCCGq2u2cv6qCj48PcXFxtGnTBpCLRyGEEP+aNGkSGo2G6dOn37DuySefRKPRMGnSpCo7XnnnoOpsTHKzrly5grm5OWvXri11/eOPP067du2MnEoIqbAUos5wc3PDxsZG7RhCCCFEtdHpdHh4eGBmZqZ2FCGEECbIx8eHdevWkZ2dbViWk5PD999/T8OGDVVMpp769eszYsQIVq1adcO67Oxs1q1bx9SpU1VIJuo6qbAUooZZvnw5bdu2xdbWFh8fH5588kkyMjKAort4kydPJjU1FY1Gg0ajYeHChUDJu3h+fn4A3HPPPWg0GsPz0lrGzJw5k/79+xueZ2ZmMmHCBOzs7PD09GTZsmU3ZMzLy2POnDl4e3tja2tLt27d2LVrVxWWghBCiJomKCiIrl27YmlpiaenJ3PnzqWgoMCwvn///jz77LPMmTMHZ2dnPDw8DOewYmfPnqV3795YWVnRqlUr/vrrrxI9Bq7vjnbp0iUGDBgAQL169Uq0nCmtZUuHDh1KHO/ChQv07dvXcKzAwMAb3lNMTAzjxo2jXr16uLi4MGrUKC5dunS7RSWEEKKadOrUiYYNG/Lrr78alv3666/4+PjQsWNHwzJFUVi6dCmNGzfG2tqa9u3b8/PPP5fY1+bNm/H398fa2poBAwbc1vd/ZGQko0aNws7ODgcHB+6//36uXLliWF+Z67Sff/6Ztm3bYm1tjYuLC4MHDyYzM9OwftWqVbRs2RIrKytatGjBJ598Ylg3depUdu7cecN7+Pnnn8nJyeGRRx5hy5Yt9O7dGycnJ1xcXLjrrrsIDw+/5fcsREWkwlKIGkar1fLBBx9w6tQp1qxZw44dO5gzZw4APXv25L333sPBwYG4uDji4uJ4/vnnb9hHcHAwUHTSiouLMzyvjBdeeIGdO3eyfv16tm3bxq5duwgJCSmxzeTJk9m3bx/r1q3jxIkT3Hfffdxxxx1cuHDhNt65EEKImiomJobhw4fTpUsXjh8/zqeffsqXX37Jm2++WWK7NWvWYGtry8GDB1m6dCmvv/66oaJQr9czevRobGxsOHjwICtXruTll18u85g+Pj788ssvAJw7d464uDjef//9SuXV6/WMGTMGnU7HgQMH+Oyzz3jxxRdLbJOVlcWAAQOws7Nj9+7d7N27Fzs7O+644w7y8vJupniEEEIY0eTJk0u0Jvzqq6+YMmVKiW3mz5/PqlWr+PTTTzl9+jSzZs3ikUceISgoCICoqCjGjBnD8OHDOXbsGNOmTWPu3Lm3lEdRFEaPHk1ycjJBQUEEBgYSHh7OuHHjKr2PuLg4HnzwQaZMmUJoaCi7du1izJgxKIoCwOeff87LL7/MW2+9RWhoKIsWLeKVV15hzZo1AAwfPhwPDw9Wr15dYr9fffUVo0ePxsXFhczMTGbPnk1wcDDbt29Hq9Vyzz33oNfrb+l9C1ER6S8jRA1z/eQ2jRo14o033uCJJ57gk08+wcLCAkdHRzQaDR4eHmXuw83NDQAnJ6dyt/uvjIwMvvzyS77++muGDBkCFF1cNmjQwLBNeHg433//PdHR0Xh5eQHw/PPPs2XLFlatWsWiRYtu5u0KIYSoBT755BN8fHz46KOP0Gg0tGjRgtjYWF588UVeffVVtNqie+jt2rVjwYIFADRr1oyPPvqI7du3M2TIELZt20Z4eDi7du0ynLveeustw/nov3Q6Hc7OzgC4u7uXOr5zWf766y9CQ0O5dOmS4Ry3aNEi7rzzTsM269atQ6vV8sUXX6DRaICiG4FOTk7s2rWLoUOH3lwhCSGEMIrx48fz0ksvGVrlFze0KO4RlpmZyfLly9mxYwc9evQAoHHjxuzdu5cVK1bQr18/Pv30Uxo3bsy7776LRqOhefPmnDx5kiVLltxwvOuvlYplZWUZ/v+vv/7ixIkTRERE4OPjA8A333xD69atCQ4OpkuXLhW+p7i4OAoKChgzZgy+vr4AtG3b1rD+jTfeYNmyZYwZMwYouo48c+YMK1asYOLEieh0OiZMmMDq1atZsGABGo2GiIgIgoKC2LJlCwD33ntviWN++eWXuLu7c+bMGcPY0UJUJamwFKKG2blzJ4sWLeLMmTOkpaVRUFBATk4OmZmZ2NraVuuxw8PDycvLM5y4AZydnWnevLnh+ZEjR1AUBX9//xKvzc3NxcXFpVrzCSGEME2hoaH06NHDULEH0KtXLzIyMoiOjjaMG/bfQf09PT1JSEgAilpJ+vj4lLjR1rVr12rL27BhwxIXmdef+wBCQkIICwvD3t6+xPKcnBzpIieEECbM1dWVESNGsGbNGhRFYcSIEbi6uhrWnzlzhpycnBtuiOXl5Rm6jYeGhtK9e/cS57X/nieK7dmz54ZzxfVduUNDQ/Hx8TFUVgK0atUKJycnQkNDK1Vh2b59ewYNGkTbtm0ZNmwYQ4cOZezYsdSrV4/ExESioqKYOnUqjz76qOE1BQUFODo6Gp5PnTqVJUuWsGPHDgYNGsRXX31FgwYNGDx4MFB0LfjKK69w4MABkpKSDC0rIyMjpcJSVAupsBSiBrl8+TLDhw9n+vTpvPHGGzg7O7N3716mTp1Kfn7+be9fq9Uaug0Uu36//11XGr1ej06nIyQkBJ1OV2KdnZ3dbWcUQghR8yiKUuKirngZUGK5ubl5iW00Go3hgqi0fdyqWznf/ffYer2egIAAvv322xu2Le7JIIQQwjRNmTKFp59+GoCPP/64xLri884ff/yBt7d3iXWWlpZA5a6LijVq1OiGVv7XTw5X1vnt+uUVnbd0Oh2BgYHs37+fbdu28eGHH/Lyyy9z8OBBw8Srn3/+Od26dSuxj+uv15o1a0afPn1YtWoVAwYMYM2aNUyePNnQC+Luu+/Gx8eHzz//HC8vL/R6PW3atJFhUES1kQpLIWqQw4cPU1BQwLJlywwnjh9//LHENhYWFhQWFla4L3Nz8xu2c3Nz49SpUyWWHTt2zHAB2bRpU8zNzTlw4IChNcy1a9c4f/48/fr1A6Bjx44UFhaSkJBAnz59bu2NCiGEqFVatWrFL7/8UuLia//+/djb299wMViWFi1aEBkZyZUrV6hfvz5AhWMwW1hYAJR6vouLizM8T0tLIyIiokTeyMhIYmNjDcOb/P333yX20alTJ3744Qfc3d1xcHCo1HsQQghhGq4fb3jYsGEl1rVq1QpLS0siIyMN1zj/1apVK8OEb8UOHDhwS1mKzzlRUVGGVpZnzpwhNTWVli1bAhVfp0HRjbVevXrRq1cvXn31VXx9fVm/fj2zZ8/G29ubixcv8vDDD5ebZerUqTzxxBOMGjWK6OhoJk+eDMDVq1cJDQ1lxYoVhmu8vXv33tL7FaKyZNIdIUxUamoqx44dK/Fwc3OjoKCADz/8kIsXL/LNN9/w2WeflXidn58fGRkZbN++naSkpBLjo/x3u+3btxMfH8+1a9cAGDhwIIcPH+brr7/mwoULLFiwoMSJ0c7OjqlTp/LCCy+wfft2Tp06xaRJkwyVpwD+/v48/PDDTJgwgV9//ZWIiAiCg4NZsmQJmzdvroaSEkIIYUpKO3899thjREVF8cwzz3D27Fk2btzIggULmD17dolzSHmGDBlCkyZNmDhxIidOnGDfvn2GSXfKannp6+uLRqPh999/JzExkYyMDKDofPfNN9+wZ88eTp06ZRi/q9jgwYNp3rw5EyZM4Pjx4+zZs+eGCX4efvhhXF1dGTVqFHv27DGM9TVjxgyio6NvpeiEEEIYiU6nIzQ0lNDQ0Bt6hdnb2/P8888za9Ys1qxZQ3h4OEePHuXjjz82TFIzffp0wsPDmT17NufOneO77767YcKayho8eDDt2rXj4Ycf5siRIxw6dIgJEybQr18/OnfuDFR8nXbw4EEWLVrE4cOHiYyM5NdffyUxMdFQ4blw4UIWL17M+++/z/nz5zl58iSrVq1i+fLlJbLcd999mJub8/jjjzNo0CD8/PwAqFevHi4uLqxcuZKwsDB27NjB7Nmzb+n9ClFZUmEphInatWsXHTt2LPH46quvWL58OUuWLKFNmzZ8++23LF68uMTrevbsyfTp0xk3bhxubm4sXbq01P0vW7aMwMBAfHx8DGOxDBs2jFdeeYU5c+bQpUsX0tPTmTBhQonXvf322/Tt25eRI0cyePBgevfuTUBAQIltVq1axYQJE3juuedo3rw5I0eO5ODBgyXGZRFCCFE7lXb+WrBgAZs3b+bQoUO0b9+e6dOnM3XqVObPn1/p/ep0OjZs2EBGRgZdunRh2rRphtdbWVmV+hpvb29ee+015s6dS/369Q3d/1566SX69u3LXXfdxfDhwxk9ejRNmjQxvE6r1bJ+/Xpyc3Pp2rUr06ZN46233iqxbxsbG3bv3k3Dhg0ZM2YMLVu2ZMqUKWRnZ0uLSyGEqAEcHBzK/L5+4403ePXVV1m8eDEtW7Zk2LBhbNq0iUaNGgHQsGFDfvnlFzZt2kT79u357LPPbnlyUY1Gw4YNG6hXrx59+/Zl8ODBNG7cmB9++MGwTUXXaQ4ODuzevZvhw4fj7+/P/PnzWbZsmWGyuGnTpvHFF1+wevVq2rZtS79+/Vi9erXh/RSzsbHhgQce4Nq1ayVmTtdqtaxbt46QkBDatGnDrFmzePvtt2/p/QpRWRrlZgZfEEIIIYQQwkTs27eP3r17ExYWVqLCUQghhBBC1GxSYSmEEEIIIWqE9evXY2dnR7NmzQgLC2PGjBnUq1dPxtESQgghhKhlZNIdIYQQQghRI6SnpzNnzhyioqJwdXVl8ODBLFu2TO1YQgghhBCiikkLSyGEEEIIIYQQQgghhMmQSXeEEEIIIYQQQgghhBAmQyoshRBCCCGEEEIIIYQQJkMqLIUQQgghhBBCCCGEECZDKiyFEEIIIYQQQgghhBAmQyoshRBCCCGEEEIIIYQQJkMqLIUQQgghhBBCCCGEECZDKiyFEEIIIYQQQgghhBAmQyoshRBCCCGEEEIIIYQQJkMqLIUQQgghhBBCCCGEECbj/3dRJtDdi2HsAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1600x2000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns ## library more fancy visualization's\n",
+    "\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=3, nrows=3, figsize=(16,20)) ## define canvas\n",
+    "# print(axes.shape)\n",
+    "for i, ax in zip(range(9), axes.flat):\n",
+    "    sns.histplot(data=cal_housing[cal_housing.columns[i]], kde=True, ax=ax, color=\"#a71d2a\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "14afd85d-241b-4bf4-99e6-35dfab342492",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AveRooms</th>\n",
+       "      <th>AveBedrms</th>\n",
+       "      <th>AveOccup</th>\n",
+       "      <th>Population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.429000</td>\n",
+       "      <td>1.096675</td>\n",
+       "      <td>3.070655</td>\n",
+       "      <td>1425.476744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.474173</td>\n",
+       "      <td>0.473911</td>\n",
+       "      <td>10.386050</td>\n",
+       "      <td>1132.462122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.846154</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.692308</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>4.440716</td>\n",
+       "      <td>1.006079</td>\n",
+       "      <td>2.429741</td>\n",
+       "      <td>787.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.229129</td>\n",
+       "      <td>1.048780</td>\n",
+       "      <td>2.818116</td>\n",
+       "      <td>1166.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.052381</td>\n",
+       "      <td>1.099526</td>\n",
+       "      <td>3.282261</td>\n",
+       "      <td>1725.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>141.909091</td>\n",
+       "      <td>34.066667</td>\n",
+       "      <td>1243.333333</td>\n",
+       "      <td>35682.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           AveRooms     AveBedrms      AveOccup    Population\n",
+       "count  20640.000000  20640.000000  20640.000000  20640.000000\n",
+       "mean       5.429000      1.096675      3.070655   1425.476744\n",
+       "std        2.474173      0.473911     10.386050   1132.462122\n",
+       "min        0.846154      0.333333      0.692308      3.000000\n",
+       "25%        4.440716      1.006079      2.429741    787.000000\n",
+       "50%        5.229129      1.048780      2.818116   1166.000000\n",
+       "75%        6.052381      1.099526      3.282261   1725.000000\n",
+       "max      141.909091     34.066667   1243.333333  35682.000000"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_of_interest = [\"AveRooms\", \"AveBedrms\", \"AveOccup\", \"Population\"]\n",
+    "cal_housing[features_of_interest].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e67ec4b-0f55-4c86-a777-95c0f503362a",
+   "metadata": {},
+   "source": [
+    "further information about [color_palettes](https://seaborn.pydata.org/tutorial/color_palettes.html)!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "255e7d1a-a9cc-4da4-b081-3ac787336721",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(\n",
+    "    data=cal_housing,\n",
+    "    x=\"Longitude\",\n",
+    "    y=\"Latitude\",\n",
+    "    size=\"MedHouseVal\",\n",
+    "    hue=\"MedHouseVal\",\n",
+    "    palette=\"flare\", ## see above color palette\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "\n",
+    "plt.legend(title=\"MedHouseVal\", bbox_to_anchor=(1.05, 0.95), loc=\"upper left\")\n",
+    "plt.title(\"Median house value depending of\\n their spatial location\")\n",
+    "plt.savefig('cal_housing_scatter.png', bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "eaff2b02-a3c3-43a0-aa07-4665f31356f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "indices = rng.choice(\n",
+    "    np.arange(cal_housing.shape[0]), size=500, replace=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "107092f6-00e8-493c-bd0b-5c36d575f437",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(\n",
+    "    data=cal_housing.iloc[indices],\n",
+    "    x=\"Longitude\",\n",
+    "    y=\"Latitude\",\n",
+    "    size=\"MedHouseVal\",\n",
+    "    hue=\"MedHouseVal\",\n",
+    "    palette=\"flare\",\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "plt.legend(title=\"MedHouseVal\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
+    "_ = plt.title(\"Median house value depending of\\n their spatial location\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "cd13a20a-914e-4cfd-ac46-d96b078c845f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1604.61x1500 with 42 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Drop unwanted columns\n",
+    "columns_drop = [\"Longitude\", \"Latitude\"]\n",
+    "subset = cal_housing.iloc[indices].drop(columns=columns_drop) ## create subset of original dataframe\n",
+    "\n",
+    "# Quantize the target and keep the midpoint for each interval\n",
+    "subset[\"MedHouseVal\"] = pd.qcut(subset[\"MedHouseVal\"], 6, retbins=False) ## qcut subset the MedHouseVal into 6 quantiles --> Categories\n",
+    "subset[\"MedHouseVal\"] = subset[\"MedHouseVal\"].apply(lambda x: x.mid)\n",
+    "\n",
+    "_ = sns.pairplot(data=subset, hue=\"MedHouseVal\", palette=\"flare\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f20f0730-f457-4169-892e-96169fd8f81e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## standard score of a sample x is calculated as:\n",
+    "## z = (x - u) / s\n",
+    "## u = the mean of the training samples or zero if with_mean=False\n",
+    "## s = the standard deviation of the training samples or one if with_std=False.\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler ## Standardize features by removing the mean and scaling to unit variance\n",
+    "from sklearn.linear_model import LinearRegression ## load LinearModel\n",
+    "\n",
+    "from sklearn.pipeline import make_pipeline ## pipeline construction of different estimators\n",
+    "from sklearn.model_selection import cross_validate \n",
+    "\n",
+    "#alphas = np.logspace(-3, 1, num=30)\n",
+    "model = make_pipeline(StandardScaler(), LinearRegression()) ## \n",
+    "\n",
+    "cv_results = cross_validate(\n",
+    "    model,\n",
+    "    housing.data,\n",
+    "    housing.target,\n",
+    "    return_estimator=True\n",
+    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a6b8e46f-e0d2-4c17-8769-53bf15e72ffb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score: 0.553 ± 0.062\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = cv_results[\"test_score\"]\n",
+    "print(f\"R2 score: {score.mean():.3f} ± {score.std():.3f}\") # correlation coefficient that measures the strength of the relationship between two variables. Range: |-1, 1|\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f454352-5e3f-4669-9f53-1f765f1e467e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/numpy/.ipynb_checkpoints/01_basics-checkpoint.ipynb b/course/numpy/.ipynb_checkpoints/01_basics-checkpoint.ipynb
new file mode 100644
index 0000000..3c2218f
--- /dev/null
+++ b/course/numpy/.ipynb_checkpoints/01_basics-checkpoint.ipynb
@@ -0,0 +1,1168 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b4db2677-a229-40ea-bb5b-d5064390c906",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e6e64f1-5820-4907-90f1-d314cebd8b21",
+   "metadata": {},
+   "source": [
+    "### Creating Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7b612c05-8510-4c75-98b9-aa1dd82638ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5]\n",
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "\n",
+    "print(arr)\n",
+    "\n",
+    "print(type(arr))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee4dfe3a-d0bc-44e8-8c13-c81b669fe029",
+   "metadata": {},
+   "source": [
+    "### Access Array Elements\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "46ed2f18-325e-433f-b8e8-81a7fcb55a68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4])\n",
+    "\n",
+    "print(arr[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "577d8ad4-1ea1-4766-95d0-eca9eaa2e139",
+   "metadata": {},
+   "source": [
+    "### Access 2-D Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f611eb2f-1a51-4922-98cc-e0ca9c23d70c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2nd element on 1st row:  2\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])\n",
+    "\n",
+    "print('2nd element on 1st row: ', arr[0, 1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "039dd714-035a-47ff-9189-4738d0439a25",
+   "metadata": {},
+   "source": [
+    "### Slicing arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d4b7ab6b-337b-43cc-94a3-80286023cb64",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 3 4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
+    "\n",
+    "print(arr[1:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9f4155d9-85be-412b-a60a-d3d501b3f37e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5 6 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(arr[4:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c2a24299-e29a-4ac4-a972-b6c29825974e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(arr[:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1fb6581f-012f-4e76-92f4-e8334e9d72e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# step ->  index 1 to index 5, max 2 elements\n",
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
+    "\n",
+    "print(arr[1:5:2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "68dc5552-2f13-40dd-9bd6-3db60f025063",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[7 8 9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2 dim array\n",
+    "arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])\n",
+    "\n",
+    "print(arr[1, 1:4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbeb9676-d6cf-44f2-83a5-18fc2cfc1999",
+   "metadata": {},
+   "source": [
+    "### Data-Types "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79240bea-6521-4bcb-b630-7bf61e62ce69",
+   "metadata": {},
+   "source": [
+    "#### Python\n",
+    "- strings - used to represent text data, the text is given under quote marks. e.g. \"ABCD\"\n",
+    "- integer - used to represent integer numbers. e.g. -1, -2, -3\n",
+    "- float - used to represent real numbers. e.g. 1.2, 42.42\n",
+    "- boolean - used to represent True or False.\n",
+    "- complex - used to represent complex numbers. e.g. 1.0 + 2.0j, 1.5 + 2.5j\n",
+    "\n",
+    "#### Numpy\n",
+    "- i - integer\n",
+    "- b - boolean\n",
+    "- u - unsigned integer\n",
+    "- f - float\n",
+    "- c - complex float\n",
+    "- m - timedelta\n",
+    "- M - datetime\n",
+    "- O - object\n",
+    "- S - string\n",
+    "- U - unicode string\n",
+    "- V - fixed chunk of memory for other type ( void )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0be7eb7c-1f64-400a-88f4-6104bb778646",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4])\n",
+    "\n",
+    "print(arr.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ccf3a0c3-7132-4800-9dc3-2c7966ddf0e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<U6\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array(['apple', 'banana', 'cherry'])\n",
+    "\n",
+    "print(arr.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7875ec79-b7ed-4b3a-a2b4-139e958994dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[b'1' b'2' b'3' b'4']\n",
+      "|S1\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4], dtype='S')\n",
+    "\n",
+    "print(arr)\n",
+    "print(arr.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e8854d24-35df-44e3-bdb5-383e2baa92b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "int32\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1.1, 2.1, 3.1])\n",
+    "\n",
+    "newarr = arr.astype('i')\n",
+    "\n",
+    "print(newarr)\n",
+    "print(newarr.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c61e36d-f3fd-41a1-8bc0-bd8a7ed114c7",
+   "metadata": {},
+   "source": [
+    "### Copy vs. View\n",
+    "The main difference between a copy and a view of an array is that the copy is a new array, and the view is just a view of the original array.\n",
+    "\n",
+    "The copy owns the data and any changes made to the copy will not affect original array, and any changes made to the original array will not affect the copy.\n",
+    "\n",
+    "The view does not own the data and any changes made to the view will affect the original array, and any changes made to the original array will affect the view."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "15121d39-bed1-4508-a88e-90e931b4c39d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[42  2  3  4  5]\n",
+      "[1 2 3 4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# copy\n",
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "x = arr.copy()\n",
+    "arr[0] = 42\n",
+    "\n",
+    "print(arr)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3f7ee1d4-ab6c-4511-9684-858bf7507250",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[42  2  3  4  5]\n",
+      "[42  2  3  4  5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "x = arr.view()\n",
+    "arr[0] = 42\n",
+    "\n",
+    "print(arr)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cbc20cde-62cd-4114-8c7a-22fad1d98963",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[31  2  3  4  5]\n",
+      "[31  2  3  4  5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "x = arr.view()\n",
+    "x[0] = 31\n",
+    "\n",
+    "print(arr)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "126d79dd-6a6c-4944-91e8-99bca5d71ee4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n",
+      "[1 2 3 4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "\n",
+    "x = arr.copy()\n",
+    "y = arr.view()\n",
+    "\n",
+    "print(x.base)\n",
+    "print(y.base)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f07f77a-4344-4e91-89bb-7b328918eeb3",
+   "metadata": {},
+   "source": [
+    "### Shape of an Array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "10a14265-ab85-4055-b18b-6e922cb5b249",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
+    "\n",
+    "print(arr.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "462f1f64-dda3-4fb9-b24f-5b7bc6f66c00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[[[1 2 3 4]]]]]\n",
+      "shape of array : (1, 1, 1, 1, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4], ndmin=5)\n",
+    "\n",
+    "print(arr)\n",
+    "print('shape of array :', arr.shape)\n",
+    "# at index-4 we have value 4, so we can say that 5th ( 4 + 1 th) dimension has 4 elements."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1c88c47-7707-49a0-a844-211419979186",
+   "metadata": {},
+   "source": [
+    "### Reshape From 1-D to 2-D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c3d02cc8-9b48-478c-9a50-84a68a42c8a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1  2  3]\n",
+      " [ 4  5  6]\n",
+      " [ 7  8  9]\n",
+      " [10 11 12]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
+    "\n",
+    "newarr = arr.reshape(4, 3)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7a700dd-bc22-44d4-85c3-5931418a473a",
+   "metadata": {},
+   "source": [
+    "#### Flattening the arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "0189914c-8a7b-4bba-a7be-73db0f6b3b38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "\n",
+    "newarr = arr.reshape(-1)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc649e80-767d-4570-932a-6e074b17fdd3",
+   "metadata": {},
+   "source": [
+    "### Iterating Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "662af486-9b6b-4bd7-8ade-2e6a3ebb437d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3])\n",
+    "\n",
+    "for x in arr:\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7463237c-c2e5-43a1-97ea-74c970df938a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "[4 5 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "\n",
+    "for x in arr:\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "113a726f-9f1f-49b3-83c4-942fbd6bd981",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2 dim array\n",
+    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "\n",
+    "for x in arr:\n",
+    "    for y in x:\n",
+    "        print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "95202463-8c24-479b-98e3-1104778bbbf0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "#nditer --> n-for loops\n",
+    "arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
+    "\n",
+    "for x in np.nditer(arr):\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "3b2ca7d7-28e7-429d-9c1b-a35ae789f50f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "3\n",
+      "5\n",
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "# different step size\n",
+    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
+    "# Iterate through every scalar element of the 2D array skipping 1 element:\n",
+    "for x in np.nditer(arr[:, ::2]):\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b3257129-9727-4647-84e6-ac985580fe73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0, 0) 1\n",
+      "(0, 1) 2\n",
+      "(0, 2) 3\n",
+      "(0, 3) 4\n",
+      "(1, 0) 5\n",
+      "(1, 1) 6\n",
+      "(1, 2) 7\n",
+      "(1, 3) 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "#ndenumerate --> returns index too\n",
+    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
+    "\n",
+    "for idx, x in np.ndenumerate(arr):\n",
+    "    print(idx, x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "299e02df-ed56-4c1c-a4c8-779a0a71f97a",
+   "metadata": {},
+   "source": [
+    "### Joining Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "72b19370-5292-4987-b79e-bd8772683bea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([1, 2, 3])\n",
+    "\n",
+    "arr2 = np.array([4, 5, 6])\n",
+    "\n",
+    "arr = np.concatenate((arr1, arr2))\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "bb39d7aa-a1ca-41a4-899e-193610ea4bd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 5 6]\n",
+      " [3 4 7 8]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#2 dim array\n",
+    "arr1 = np.array([[1, 2], [3, 4]])\n",
+    "\n",
+    "arr2 = np.array([[5, 6], [7, 8]])\n",
+    "\n",
+    "arr = np.concatenate((arr1, arr2), axis=1)\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "177a8c9a-2cf8-40a9-bed0-39436910c45b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 4]\n",
+      " [2 5]\n",
+      " [3 6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# stack\n",
+    "arr1 = np.array([1, 2, 3])\n",
+    "\n",
+    "arr2 = np.array([4, 5, 6])\n",
+    "\n",
+    "arr = np.stack((arr1, arr2), axis=1)\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "d3519009-19ac-4175-913e-8c5d47b1896f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stacking Along Rows\n",
+    "arr1 = np.array([1, 2, 3])\n",
+    "\n",
+    "arr2 = np.array([4, 5, 6])\n",
+    "\n",
+    "arr = np.hstack((arr1, arr2))\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "d3a2b831-6e4d-4caa-b270-372958b70d49",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [4 5 6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stacking Along Columns\n",
+    "arr1 = np.array([1, 2, 3])\n",
+    "\n",
+    "arr2 = np.array([4, 5, 6])\n",
+    "\n",
+    "arr = np.vstack((arr1, arr2))\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "64a57420-2507-4abd-b573-d9976d2bb385",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1 4]\n",
+      "  [2 5]\n",
+      "  [3 6]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stacking Along Height (depth)\n",
+    "arr1 = np.array([1, 2, 3])\n",
+    "\n",
+    "arr2 = np.array([4, 5, 6])\n",
+    "\n",
+    "arr = np.dstack((arr1, arr2))\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b226ad5-addb-4ee1-93d4-748df1de2e85",
+   "metadata": {},
+   "source": [
+    "### Splitting Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "f357a0b4-7dbf-4cd0-b586-23e06c882f6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([1, 2]), array([3, 4]), array([5, 6])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The return value is a list containing three arrays.\n",
+    "arr = np.array([1, 2, 3, 4, 5, 6])\n",
+    "\n",
+    "newarr = np.array_split(arr, 3)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "437b8f01-1309-48c5-a4ef-13dd26f73025",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([[1, 2],\n",
+      "       [3, 4]]), array([[5, 6],\n",
+      "       [7, 8]]), array([[ 9, 10],\n",
+      "       [11, 12]])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# splitting 2dim arrays\n",
+    "arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])\n",
+    "\n",
+    "newarr = np.array_split(arr, 3)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54416dbe-202c-461c-9417-79043e44207f",
+   "metadata": {},
+   "source": [
+    "### Searching Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "c3dc56a2-9928-40b1-9e89-0f61e22efc11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([3, 5, 6]),)\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5, 4, 4])\n",
+    "\n",
+    "x = np.where(arr == 4)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "b07a60bd-ddb1-45b8-bae8-452b4140133b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([1, 3, 5, 7]),)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find the indexes where the values are even (modulo)\n",
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
+    "\n",
+    "x = np.where(arr%2 == 0)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0400ef9f-09f4-4e8d-b183-56e401fa8a5a",
+   "metadata": {},
+   "source": [
+    "#### Sorting Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "ad6ff4df-0260-46c6-ace8-f716baaa81ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([3, 2, 0, 1])\n",
+    "sorted_array = np.sort(arr)\n",
+    "print(sorted_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "2f51f21d-4ddc-4677-907f-07038c4e3a80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3 2 1 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# reverse\n",
+    "reverse_array = sorted_array[::-1]\n",
+    "print(reverse_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "7264d51f-6d8b-4571-91ef-d8550b539d86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['apple' 'banana' 'cherry']\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array(['banana', 'cherry', 'apple'])\n",
+    "\n",
+    "print(np.sort(arr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "056524c9-a534-4b82-bb7f-4772efb94296",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2 3 4]\n",
+      " [0 1 5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2-dim\n",
+    "arr = np.array([[3, 2, 4], [5, 0, 1]])\n",
+    "\n",
+    "print(np.sort(arr))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5a6b9f1-6a39-4f3b-8a98-a45bb6a803fb",
+   "metadata": {},
+   "source": [
+    "### Filter Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "101256a3-98a7-4ee8-bb6d-5386a42043f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[False False  True  True]\n",
+      "[43 44]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([41, 42, 43, 44])\n",
+    "\n",
+    "filter_arr = arr > 42\n",
+    "\n",
+    "newarr = arr[filter_arr]\n",
+    "\n",
+    "print(filter_arr)\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "79b7bfaf-1056-42f8-948e-bb6c2811a25a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[False  True False  True False  True False]\n",
+      "[2 4 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
+    "\n",
+    "filter_arr = arr % 2 == 0\n",
+    "\n",
+    "newarr = arr[filter_arr]\n",
+    "\n",
+    "print(filter_arr)\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8fcf398-28f3-4380-8aab-7692578ab277",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/numpy/.ipynb_checkpoints/02_random-checkpoint.ipynb b/course/numpy/.ipynb_checkpoints/02_random-checkpoint.ipynb
new file mode 100644
index 0000000..291b63c
--- /dev/null
+++ b/course/numpy/.ipynb_checkpoints/02_random-checkpoint.ipynb
@@ -0,0 +1,250 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "535c30a1-6335-4314-83a1-6e7d23ada40d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7e9842e7-4fb8-404c-b6da-283510f314dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hint: Pseudo Random and True Random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b702eeef-b651-4286-9236-2edca6f0353d",
+   "metadata": {},
+   "source": [
+    "### Random Numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ee62a0e7-e2ce-4570-ae65-ef815526d99b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "88\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.random.randint(100)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ad3c430b-dde2-4202-8782-8cfc2cb434f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6204661862066633\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.random.rand()\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8e033582-3f93-43b6-b254-5dc0d2baad70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[90 98  7 34 80]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=np.random.randint(100, size=(5))\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "bf4cf008-ec0f-4291-a990-eb3cf2b76f14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.random.choice([3, 5, 7, 9])\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "820e0a69-f14d-4fa9-9abf-ba6259de2abf",
+   "metadata": {},
+   "source": [
+    "### Random Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d04aefc6-a1c0-4ee5-822a-bca76cc5bbc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[7 7 3 7 3 7 7 7 7 5 7 7 7 5 7 7 5 5 7 5 5 7 5 7 7 3 7 3 7 3 7 7 7 7 7 7 5\n",
+      " 7 7 3 7 3 7 7 7 5 7 7 5 7 5 7 5 7 5 7 7 7 7 7 3 5 3 7 5 7 7 7 5 5 7 5 7 5\n",
+      " 5 5 7 7 5 7 7 5 7 3 7 5 7 3 5 5 5 7 5 7 7 3 7 5 7 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Probability Density Function: A function that describes a continuous probability. \n",
+    "# i.e. probability of all values in an array.\n",
+    "x = np.random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(100))\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e5627e9-7b03-45f8-a776-89bcd069fc71",
+   "metadata": {},
+   "source": [
+    "### Normal (Gaussian) Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b561d19f-041a-4f10-b6b6-ba1827c79abf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.17858464 4.11191574 1.13382304]\n",
+      " [2.57882466 1.42017738 0.54116511]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# loc - (Mean) where the peak of the bell exists.\n",
+    "# scale - (Standard Deviation) how flat the graph distribution should be.\n",
+    "# size - The shape of the returned array.\n",
+    "x = np.random.normal(loc=1, scale=2, size=(2, 3))\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "1c683c75-bcc2-40ca-8c27-9ed9aa0ccdd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.displot(np.random.normal(size=1000), kind=\"kde\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbfc0beb-9716-48b6-9b08-d3ef4b867f27",
+   "metadata": {},
+   "source": [
+    "\"Some\" other Distributions\n",
+    "- Normal Distribution\n",
+    "- Binomial Distribution\n",
+    "- Poisson Distribution\n",
+    "- Uniform Distribution\n",
+    "- Logistic Distribution\n",
+    "- Multinomial Distribution\n",
+    "- Exponential Distribution\n",
+    "- Chi Square Distribution\n",
+    "- Rayleigh Distribution\n",
+    "- Pareto Distribution\n",
+    "- Zipf Distribution\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7261962-15f5-48db-b2ee-950b2c3c1dc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/numpy/.ipynb_checkpoints/03_ufunc-checkpoint.ipynb b/course/numpy/.ipynb_checkpoints/03_ufunc-checkpoint.ipynb
new file mode 100644
index 0000000..8aee512
--- /dev/null
+++ b/course/numpy/.ipynb_checkpoints/03_ufunc-checkpoint.ipynb
@@ -0,0 +1,854 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f3557a6d-106e-461c-bc75-7ddcef4f81e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80a3b70d-fea8-4a6f-ac02-73e30abc1f66",
+   "metadata": {},
+   "source": [
+    "### ufunc\n",
+    "ufuncs are used to implement vectorization in NumPy which is way faster than iterating over elements.\n",
+    "\n",
+    "They also provide broadcasting and additional methods like reduce, accumulate etc. that are very helpful for computation.\n",
+    "\n",
+    "ufuncs also take additional arguments, like:\n",
+    "\n",
+    "where boolean array or condition defining where the operations should take place.\n",
+    "\n",
+    "dtype defining the return type of elements.\n",
+    "\n",
+    "out output array where the return value should be copied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "60f2d36b-d334-4e19-9f63-36688cca1c20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5, 7, 9, 11]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# python\n",
+    "x = [1, 2, 3, 4]\n",
+    "y = [4, 5, 6, 7]\n",
+    "z = []\n",
+    "\n",
+    "for i, j in zip(x, y):\n",
+    "    z.append(i + j)\n",
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0272feaa-47e7-44b4-a66d-dafdffca4a7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 5  7  9 11]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# numpy\n",
+    "x = [1, 2, 3, 4]\n",
+    "y = [4, 5, 6, 7]\n",
+    "z = np.add(x, y)\n",
+    "\n",
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e52ad13-9186-4866-b1c7-3f8a16901c9f",
+   "metadata": {},
+   "source": [
+    "#### Own function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3d004fc9-f52c-42fb-93a1-f48dd0849ba9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6 8 10 12]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def myadd(x, y):\n",
+    "    return x+y\n",
+    "\n",
+    "myadd = np.frompyfunc(myadd, 2, 1)\n",
+    "\n",
+    "print(myadd([1, 2, 3, 4], [5, 6, 7, 8]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c034cd44-af9c-44ff-8d74-cf0d8d34b018",
+   "metadata": {},
+   "source": [
+    "#### Simple Arithmetic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "428246a1-ea45-4c0b-bc94-b3f1ad27a0ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[30 32 34 36 38 40]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 11, 12, 13, 14, 15])\n",
+    "arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
+    "\n",
+    "newarr = np.add(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae349412-0d47-42b4-8c39-56d0839b3d34",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-10  -1   8  17  26  35]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
+    "\n",
+    "newarr = np.subtract(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f61b2129-e31b-4661-a12e-d7897955d163",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 200  420  660  920 1200 1500]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
+    "\n",
+    "newarr = np.multiply(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67540798-c33e-4d1e-a1d9-47c00a121b36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 3.33333333  4.          3.          5.         25.          1.81818182]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([3, 5, 10, 8, 2, 33])\n",
+    "\n",
+    "newarr = np.divide(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "05c10eef-8c8e-4515-9aea-402821f05a02",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[         1000       3200000     729000000 6553600000000          2500\n",
+      "             0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([3, 5, 6, 8, 2, 33])\n",
+    "\n",
+    "newarr = np.power(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c47b12e8-f0a0-45c9-b1e5-202fd2dfdaed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1  6  3  0  0 27]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([3, 7, 9, 8, 2, 33])\n",
+    "\n",
+    "newarr = np.mod(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7626b9ec-04f0-421b-a35d-8f30bad5a136",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([ 3,  2,  3,  5, 25,  1]), array([ 1,  6,  3,  0,  0, 27]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quotient and Mod\n",
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([3, 7, 9, 8, 2, 33])\n",
+    "\n",
+    "newarr = np.divmod(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e436c31a-a796-4be1-9e7f-ddaf0f026dec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 1 2 3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([-1, -2, 1, 2, 3, -4])\n",
+    "\n",
+    "newarr = np.absolute(arr)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d55a17c4-1f67-4629-8a85-64f9faa32370",
+   "metadata": {},
+   "source": [
+    "### Rounding Decimals\n",
+    "- truncation\n",
+    "- fix\n",
+    "- rounding\n",
+    "- floor\n",
+    "- ceil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "11395b0f-660b-4c28-a2ed-1c722b560a71",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-3.  3.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Truncation\n",
+    "# Remove the decimals, and return the float number closest to zero. Use the trunc() and fix() functions.\n",
+    "\n",
+    "arr = np.trunc([-3.1666, 3.6667])\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9d2c024e-07ca-43cd-978b-206a391e35f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-3.  3.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.fix([-3.1666, 3.6667])\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "59ca3a99-da45-4a5c-8a2f-bcb6b22a5998",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.17\n"
+     ]
+    }
+   ],
+   "source": [
+    "# rounding\n",
+    "arr = np.around(3.1666, 2)\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e213a874-e4b0-4a3e-831a-5e9ff93c3d94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-4.  3.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.floor([-3.1666, 3.6667])\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "fbe93a72-8e92-4b36-bfab-232c1617e4f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-3.  4.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.ceil([-3.1666, 3.6667])\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "225b1cd3-85cf-492d-8f1d-b2c94eb63783",
+   "metadata": {},
+   "source": [
+    "### Logs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "995a441d-e78e-4ace-b6fc-b497cce04268",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6 7 8 9]\n",
+      "[0.         1.         1.5849625  2.         2.32192809 2.5849625\n",
+      " 2.80735492 3.         3.169925  ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# log2() function to perform log at the base 2.\n",
+    "arr = np.arange(1, 10)\n",
+    "print(arr)\n",
+    "print(np.log2(arr))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b386fbe7-54c7-48dd-8b08-10f053e00638",
+   "metadata": {},
+   "source": [
+    "### Summations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "16f39361-d8a9-4d9a-970e-72367f2d4685",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 4 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([1, 2, 3])\n",
+    "arr2 = np.array([1, 2, 3])\n",
+    "\n",
+    "newarr = np.add(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "bd241f37-75bf-4ee5-bcb4-8c72695eb75d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([1, 2, 3])\n",
+    "arr2 = np.array([1, 2, 3])\n",
+    "\n",
+    "newarr = np.sum([arr1, arr2])\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "7309e9d4-c239-4c42-8389-413df30d6abc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# summation over axis\n",
+    "arr1 = np.array([1, 2, 3])\n",
+    "arr2 = np.array([1, 2, 3])\n",
+    "\n",
+    "newarr = np.sum([arr1, arr2], axis=1)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "518cdbe0-417b-4085-9575-729595ae2a52",
+   "metadata": {},
+   "source": [
+    "### Products"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "0211ead9-2b18-4409-a4c6-179688bdd203",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4])\n",
+    "\n",
+    "x = np.prod(arr)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "207af559-db09-48f5-b462-40fd52500bb4",
+   "metadata": {},
+   "source": [
+    "### Differences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "5215ae9f-709b-4206-90f3-95b5dd391f68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  5  10 -20]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([10, 15, 25, 5])\n",
+    "\n",
+    "newarr = np.diff(arr)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "23aeea8b-3f92-490e-927d-a61a8426a365",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  5 -30]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compute discrete difference of the following array twice\n",
+    "# 15-10=5, 25-15=10, and 5-25=-20 AND 10-5=5 and -20-10=-30\n",
+    "arr = np.array([10, 15, 25, 5])\n",
+    "\n",
+    "newarr = np.diff(arr, n=2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0da9fba4-7ae2-4bfe-94b8-378d3d124b57",
+   "metadata": {},
+   "source": [
+    "#### Other"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "69a07fa4-546c-47cd-99b2-a14548b886e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finding LCM (Lowest Common Multiple)\n",
+    "num1 = 4\n",
+    "num2 = 6\n",
+    "# (4*3=12 and 6*2=12)\n",
+    "x = np.lcm(num1, num2)\n",
+    "\n",
+    "print(x) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "9c874e84-1ca6-44f8-871b-0c57d05ac360",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([3, 6, 9])\n",
+    "\n",
+    "x = np.lcm.reduce(arr)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "d7f635b8-7915-452b-8ee1-e963ed5e8f54",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finding GCD (Greatest Common Denominator)\n",
+    "num1 = 6\n",
+    "num2 = 9\n",
+    "\n",
+    "x = np.gcd(num1, num2)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d39331bf-062b-41b8-b7fe-082312dd5d6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# unique\n",
+    "arr = np.array([1, 1, 1, 2, 3, 4, 5, 5, 6, 7])\n",
+    "\n",
+    "x = np.unique(arr)\n",
+    "\n",
+    "print(x)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "126fdebd-0a45-4be7-a65d-b4e119ebef7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([1, 2, 3, 4])\n",
+    "arr2 = np.array([3, 4, 5, 6])\n",
+    "\n",
+    "newarr = np.union1d(arr1, arr2)\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "a84475b1-15c3-4c8f-8535-2913a4b8579d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([1, 2, 3, 4])\n",
+    "arr2 = np.array([3, 4, 5, 6])\n",
+    "\n",
+    "newarr = np.intersect1d(arr1, arr2, assume_unique=True)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "771b4dc1-78e9-4819-af4f-b2bb3bc154e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "set1 = np.array([1, 2, 3, 4])\n",
+    "set2 = np.array([3, 4, 5, 6])\n",
+    "\n",
+    "newarr = np.setdiff1d(set1, set2, assume_unique=True)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "95b8cec1-8b0e-43fb-acae-428d7778c03e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Trigonometric Functions\n",
+    "x = np.sin(np.pi/2)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "e3dbac10-d7bd-45bb-974b-56f8f6082de8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.57079633 3.14159265 4.71238898 6.28318531]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# and sin(), cos() and tan()\n",
+    "# np.deg2rad(arr)\n",
+    "# Hyperbolic: sinh(), cosh() and tanh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67cab045-d1ef-4de1-9aaa-5257d5ba5571",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/numpy/01_basics.ipynb b/course/numpy/01_basics.ipynb
new file mode 100644
index 0000000..e214fd9
--- /dev/null
+++ b/course/numpy/01_basics.ipynb
@@ -0,0 +1,1169 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b4db2677-a229-40ea-bb5b-d5064390c906",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e6e64f1-5820-4907-90f1-d314cebd8b21",
+   "metadata": {},
+   "source": [
+    "### Creating Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7b612c05-8510-4c75-98b9-aa1dd82638ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5]\n",
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "\n",
+    "print(arr)\n",
+    "\n",
+    "print(type(arr))\n",
+    "print"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee4dfe3a-d0bc-44e8-8c13-c81b669fe029",
+   "metadata": {},
+   "source": [
+    "### Access Array Elements\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "46ed2f18-325e-433f-b8e8-81a7fcb55a68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4])\n",
+    "\n",
+    "print(arr[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "577d8ad4-1ea1-4766-95d0-eca9eaa2e139",
+   "metadata": {},
+   "source": [
+    "### Access 2-D Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f611eb2f-1a51-4922-98cc-e0ca9c23d70c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2nd element on 1st row:  2\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])\n",
+    "\n",
+    "print('2nd element on 1st row: ', arr[0, 1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "039dd714-035a-47ff-9189-4738d0439a25",
+   "metadata": {},
+   "source": [
+    "### Slicing arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d4b7ab6b-337b-43cc-94a3-80286023cb64",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 3 4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
+    "\n",
+    "print(arr[1:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9f4155d9-85be-412b-a60a-d3d501b3f37e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5 6 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(arr[4:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c2a24299-e29a-4ac4-a972-b6c29825974e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(arr[:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1fb6581f-012f-4e76-92f4-e8334e9d72e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# step ->  index 1 to index 5, max 2 elements\n",
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
+    "\n",
+    "print(arr[1:5:2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "68dc5552-2f13-40dd-9bd6-3db60f025063",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[7 8 9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2 dim array\n",
+    "arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])\n",
+    "\n",
+    "print(arr[1, 1:4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbeb9676-d6cf-44f2-83a5-18fc2cfc1999",
+   "metadata": {},
+   "source": [
+    "### Data-Types "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79240bea-6521-4bcb-b630-7bf61e62ce69",
+   "metadata": {},
+   "source": [
+    "#### Python\n",
+    "- strings - used to represent text data, the text is given under quote marks. e.g. \"ABCD\"\n",
+    "- integer - used to represent integer numbers. e.g. -1, -2, -3\n",
+    "- float - used to represent real numbers. e.g. 1.2, 42.42\n",
+    "- boolean - used to represent True or False.\n",
+    "- complex - used to represent complex numbers. e.g. 1.0 + 2.0j, 1.5 + 2.5j\n",
+    "\n",
+    "#### Numpy\n",
+    "- i - integer\n",
+    "- b - boolean\n",
+    "- u - unsigned integer\n",
+    "- f - float\n",
+    "- c - complex float\n",
+    "- m - timedelta\n",
+    "- M - datetime\n",
+    "- O - object\n",
+    "- S - string\n",
+    "- U - unicode string\n",
+    "- V - fixed chunk of memory for other type ( void )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0be7eb7c-1f64-400a-88f4-6104bb778646",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4])\n",
+    "\n",
+    "print(arr.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ccf3a0c3-7132-4800-9dc3-2c7966ddf0e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<U6\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array(['apple', 'banana', 'cherry'])\n",
+    "\n",
+    "print(arr.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7875ec79-b7ed-4b3a-a2b4-139e958994dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[b'1' b'2' b'3' b'4']\n",
+      "|S1\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4], dtype='S')\n",
+    "\n",
+    "print(arr)\n",
+    "print(arr.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e8854d24-35df-44e3-bdb5-383e2baa92b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "int32\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1.1, 2.1, 3.1])\n",
+    "\n",
+    "newarr = arr.astype('i')\n",
+    "\n",
+    "print(newarr)\n",
+    "print(newarr.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c61e36d-f3fd-41a1-8bc0-bd8a7ed114c7",
+   "metadata": {},
+   "source": [
+    "### Copy vs. View\n",
+    "The main difference between a copy and a view of an array is that the copy is a new array, and the view is just a view of the original array.\n",
+    "\n",
+    "The copy owns the data and any changes made to the copy will not affect original array, and any changes made to the original array will not affect the copy.\n",
+    "\n",
+    "The view does not own the data and any changes made to the view will affect the original array, and any changes made to the original array will affect the view."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "15121d39-bed1-4508-a88e-90e931b4c39d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[42  2  3  4  5]\n",
+      "[1 2 3 4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# copy\n",
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "x = arr.copy()\n",
+    "arr[0] = 42\n",
+    "\n",
+    "print(arr)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3f7ee1d4-ab6c-4511-9684-858bf7507250",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[42  2  3  4  5]\n",
+      "[42  2  3  4  5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "x = arr.view()\n",
+    "arr[0] = 42\n",
+    "\n",
+    "print(arr)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cbc20cde-62cd-4114-8c7a-22fad1d98963",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[31  2  3  4  5]\n",
+      "[31  2  3  4  5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "x = arr.view()\n",
+    "x[0] = 31\n",
+    "\n",
+    "print(arr)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "126d79dd-6a6c-4944-91e8-99bca5d71ee4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n",
+      "[1 2 3 4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "\n",
+    "x = arr.copy()\n",
+    "y = arr.view()\n",
+    "\n",
+    "print(x.base)\n",
+    "print(y.base)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f07f77a-4344-4e91-89bb-7b328918eeb3",
+   "metadata": {},
+   "source": [
+    "### Shape of an Array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "10a14265-ab85-4055-b18b-6e922cb5b249",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
+    "\n",
+    "print(arr.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "462f1f64-dda3-4fb9-b24f-5b7bc6f66c00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[[[1 2 3 4]]]]]\n",
+      "shape of array : (1, 1, 1, 1, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4], ndmin=5)\n",
+    "\n",
+    "print(arr)\n",
+    "print('shape of array :', arr.shape)\n",
+    "# at index-4 we have value 4, so we can say that 5th ( 4 + 1 th) dimension has 4 elements."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1c88c47-7707-49a0-a844-211419979186",
+   "metadata": {},
+   "source": [
+    "### Reshape From 1-D to 2-D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c3d02cc8-9b48-478c-9a50-84a68a42c8a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1  2  3]\n",
+      " [ 4  5  6]\n",
+      " [ 7  8  9]\n",
+      " [10 11 12]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
+    "\n",
+    "newarr = arr.reshape(4, 3)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7a700dd-bc22-44d4-85c3-5931418a473a",
+   "metadata": {},
+   "source": [
+    "#### Flattening the arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "0189914c-8a7b-4bba-a7be-73db0f6b3b38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "\n",
+    "newarr = arr.reshape(-1)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc649e80-767d-4570-932a-6e074b17fdd3",
+   "metadata": {},
+   "source": [
+    "### Iterating Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "662af486-9b6b-4bd7-8ade-2e6a3ebb437d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3])\n",
+    "\n",
+    "for x in arr:\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7463237c-c2e5-43a1-97ea-74c970df938a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "[4 5 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "\n",
+    "for x in arr:\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "113a726f-9f1f-49b3-83c4-942fbd6bd981",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2 dim array\n",
+    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "\n",
+    "for x in arr:\n",
+    "    for y in x:\n",
+    "        print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "95202463-8c24-479b-98e3-1104778bbbf0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "#nditer --> n-for loops\n",
+    "arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
+    "\n",
+    "for x in np.nditer(arr):\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "3b2ca7d7-28e7-429d-9c1b-a35ae789f50f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "3\n",
+      "5\n",
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "# different step size\n",
+    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
+    "# Iterate through every scalar element of the 2D array skipping 1 element:\n",
+    "for x in np.nditer(arr[:, ::2]):\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b3257129-9727-4647-84e6-ac985580fe73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0, 0) 1\n",
+      "(0, 1) 2\n",
+      "(0, 2) 3\n",
+      "(0, 3) 4\n",
+      "(1, 0) 5\n",
+      "(1, 1) 6\n",
+      "(1, 2) 7\n",
+      "(1, 3) 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "#ndenumerate --> returns index too\n",
+    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
+    "\n",
+    "for idx, x in np.ndenumerate(arr):\n",
+    "    print(idx, x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "299e02df-ed56-4c1c-a4c8-779a0a71f97a",
+   "metadata": {},
+   "source": [
+    "### Joining Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "72b19370-5292-4987-b79e-bd8772683bea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([1, 2, 3])\n",
+    "\n",
+    "arr2 = np.array([4, 5, 6])\n",
+    "\n",
+    "arr = np.concatenate((arr1, arr2))\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "bb39d7aa-a1ca-41a4-899e-193610ea4bd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 5 6]\n",
+      " [3 4 7 8]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#2 dim array\n",
+    "arr1 = np.array([[1, 2], [3, 4]])\n",
+    "\n",
+    "arr2 = np.array([[5, 6], [7, 8]])\n",
+    "\n",
+    "arr = np.concatenate((arr1, arr2), axis=1)\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "177a8c9a-2cf8-40a9-bed0-39436910c45b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 4]\n",
+      " [2 5]\n",
+      " [3 6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# stack\n",
+    "arr1 = np.array([1, 2, 3])\n",
+    "\n",
+    "arr2 = np.array([4, 5, 6])\n",
+    "\n",
+    "arr = np.stack((arr1, arr2), axis=1)\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "d3519009-19ac-4175-913e-8c5d47b1896f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stacking Along Rows\n",
+    "arr1 = np.array([1, 2, 3])\n",
+    "\n",
+    "arr2 = np.array([4, 5, 6])\n",
+    "\n",
+    "arr = np.hstack((arr1, arr2))\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "d3a2b831-6e4d-4caa-b270-372958b70d49",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [4 5 6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stacking Along Columns\n",
+    "arr1 = np.array([1, 2, 3])\n",
+    "\n",
+    "arr2 = np.array([4, 5, 6])\n",
+    "\n",
+    "arr = np.vstack((arr1, arr2))\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "64a57420-2507-4abd-b573-d9976d2bb385",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1 4]\n",
+      "  [2 5]\n",
+      "  [3 6]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stacking Along Height (depth)\n",
+    "arr1 = np.array([1, 2, 3])\n",
+    "\n",
+    "arr2 = np.array([4, 5, 6])\n",
+    "\n",
+    "arr = np.dstack((arr1, arr2))\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b226ad5-addb-4ee1-93d4-748df1de2e85",
+   "metadata": {},
+   "source": [
+    "### Splitting Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "f357a0b4-7dbf-4cd0-b586-23e06c882f6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([1, 2]), array([3, 4]), array([5, 6])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The return value is a list containing three arrays.\n",
+    "arr = np.array([1, 2, 3, 4, 5, 6])\n",
+    "\n",
+    "newarr = np.array_split(arr, 3)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "437b8f01-1309-48c5-a4ef-13dd26f73025",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([[1, 2],\n",
+      "       [3, 4]]), array([[5, 6],\n",
+      "       [7, 8]]), array([[ 9, 10],\n",
+      "       [11, 12]])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# splitting 2dim arrays\n",
+    "arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])\n",
+    "\n",
+    "newarr = np.array_split(arr, 3)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54416dbe-202c-461c-9417-79043e44207f",
+   "metadata": {},
+   "source": [
+    "### Searching Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "c3dc56a2-9928-40b1-9e89-0f61e22efc11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([3, 5, 6]),)\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5, 4, 4])\n",
+    "\n",
+    "x = np.where(arr == 4)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "b07a60bd-ddb1-45b8-bae8-452b4140133b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([1, 3, 5, 7]),)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find the indexes where the values are even (modulo)\n",
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
+    "\n",
+    "x = np.where(arr%2 == 0)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0400ef9f-09f4-4e8d-b183-56e401fa8a5a",
+   "metadata": {},
+   "source": [
+    "#### Sorting Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "ad6ff4df-0260-46c6-ace8-f716baaa81ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([3, 2, 0, 1])\n",
+    "sorted_array = np.sort(arr)\n",
+    "print(sorted_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "2f51f21d-4ddc-4677-907f-07038c4e3a80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3 2 1 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# reverse\n",
+    "reverse_array = sorted_array[::-1]\n",
+    "print(reverse_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "7264d51f-6d8b-4571-91ef-d8550b539d86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['apple' 'banana' 'cherry']\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array(['banana', 'cherry', 'apple'])\n",
+    "\n",
+    "print(np.sort(arr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "056524c9-a534-4b82-bb7f-4772efb94296",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2 3 4]\n",
+      " [0 1 5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2-dim\n",
+    "arr = np.array([[3, 2, 4], [5, 0, 1]])\n",
+    "\n",
+    "print(np.sort(arr))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5a6b9f1-6a39-4f3b-8a98-a45bb6a803fb",
+   "metadata": {},
+   "source": [
+    "### Filter Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "101256a3-98a7-4ee8-bb6d-5386a42043f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[False False  True  True]\n",
+      "[43 44]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([41, 42, 43, 44])\n",
+    "\n",
+    "filter_arr = arr > 42\n",
+    "\n",
+    "newarr = arr[filter_arr]\n",
+    "\n",
+    "print(filter_arr)\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "79b7bfaf-1056-42f8-948e-bb6c2811a25a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[False  True False  True False  True False]\n",
+      "[2 4 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
+    "\n",
+    "filter_arr = arr % 2 == 0\n",
+    "\n",
+    "newarr = arr[filter_arr]\n",
+    "\n",
+    "print(filter_arr)\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8fcf398-28f3-4380-8aab-7692578ab277",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/numpy/02_random.ipynb b/course/numpy/02_random.ipynb
new file mode 100644
index 0000000..1c3f498
--- /dev/null
+++ b/course/numpy/02_random.ipynb
@@ -0,0 +1,250 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "535c30a1-6335-4314-83a1-6e7d23ada40d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7e9842e7-4fb8-404c-b6da-283510f314dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hint: Pseudo Random and True Random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b702eeef-b651-4286-9236-2edca6f0353d",
+   "metadata": {},
+   "source": [
+    "### Random Numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ee62a0e7-e2ce-4570-ae65-ef815526d99b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "88\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.random.randint(100)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ad3c430b-dde2-4202-8782-8cfc2cb434f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6204661862066633\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.random.rand()\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8e033582-3f93-43b6-b254-5dc0d2baad70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[90 98  7 34 80]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=np.random.randint(100, size=(5))\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "bf4cf008-ec0f-4291-a990-eb3cf2b76f14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.random.choice([3, 5, 7, 9])\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "820e0a69-f14d-4fa9-9abf-ba6259de2abf",
+   "metadata": {},
+   "source": [
+    "### Random Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d04aefc6-a1c0-4ee5-822a-bca76cc5bbc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[7 7 3 7 3 7 7 7 7 5 7 7 7 5 7 7 5 5 7 5 5 7 5 7 7 3 7 3 7 3 7 7 7 7 7 7 5\n",
+      " 7 7 3 7 3 7 7 7 5 7 7 5 7 5 7 5 7 5 7 7 7 7 7 3 5 3 7 5 7 7 7 5 5 7 5 7 5\n",
+      " 5 5 7 7 5 7 7 5 7 3 7 5 7 3 5 5 5 7 5 7 7 3 7 5 7 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Probability Density Function: A function that describes a continuous probability. \n",
+    "# i.e. probability of all values in an array.\n",
+    "x = np.random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(100))\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e5627e9-7b03-45f8-a776-89bcd069fc71",
+   "metadata": {},
+   "source": [
+    "### Normal (Gaussian) Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b561d19f-041a-4f10-b6b6-ba1827c79abf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.17858464 4.11191574 1.13382304]\n",
+      " [2.57882466 1.42017738 0.54116511]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# loc - (Mean) where the peak of the bell exists.\n",
+    "# scale - (Standard Deviation) how flat the graph distribution should be.\n",
+    "# size - The shape of the returned array.\n",
+    "x = np.random.normal(loc=1, scale=2, size=(2, 3))\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "1c683c75-bcc2-40ca-8c27-9ed9aa0ccdd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.displot(np.random.normal(size=1000), kind=\"kde\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbfc0beb-9716-48b6-9b08-d3ef4b867f27",
+   "metadata": {},
+   "source": [
+    "\"Some\" other Distributions\n",
+    "- Normal Distribution\n",
+    "- Binomial Distribution\n",
+    "- Poisson Distribution\n",
+    "- Uniform Distribution\n",
+    "- Logistic Distribution\n",
+    "- Multinomial Distribution\n",
+    "- Exponential Distribution\n",
+    "- Chi Square Distribution\n",
+    "- Rayleigh Distribution\n",
+    "- Pareto Distribution\n",
+    "- Zipf Distribution\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7261962-15f5-48db-b2ee-950b2c3c1dc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/numpy/03_ufunc.ipynb b/course/numpy/03_ufunc.ipynb
new file mode 100644
index 0000000..92a3d46
--- /dev/null
+++ b/course/numpy/03_ufunc.ipynb
@@ -0,0 +1,854 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f3557a6d-106e-461c-bc75-7ddcef4f81e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80a3b70d-fea8-4a6f-ac02-73e30abc1f66",
+   "metadata": {},
+   "source": [
+    "### ufunc\n",
+    "ufuncs are used to implement vectorization in NumPy which is way faster than iterating over elements.\n",
+    "\n",
+    "They also provide broadcasting and additional methods like reduce, accumulate etc. that are very helpful for computation.\n",
+    "\n",
+    "ufuncs also take additional arguments, like:\n",
+    "\n",
+    "where boolean array or condition defining where the operations should take place.\n",
+    "\n",
+    "dtype defining the return type of elements.\n",
+    "\n",
+    "out output array where the return value should be copied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "60f2d36b-d334-4e19-9f63-36688cca1c20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5, 7, 9, 11]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# python\n",
+    "x = [1, 2, 3, 4]\n",
+    "y = [4, 5, 6, 7]\n",
+    "z = []\n",
+    "\n",
+    "for i, j in zip(x, y):\n",
+    "    z.append(i + j)\n",
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0272feaa-47e7-44b4-a66d-dafdffca4a7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 5  7  9 11]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# numpy\n",
+    "x = [1, 2, 3, 4]\n",
+    "y = [4, 5, 6, 7]\n",
+    "z = np.add(x, y)\n",
+    "\n",
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e52ad13-9186-4866-b1c7-3f8a16901c9f",
+   "metadata": {},
+   "source": [
+    "#### Own function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3d004fc9-f52c-42fb-93a1-f48dd0849ba9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6 8 10 12]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def myadd(x, y):\n",
+    "    return x+y\n",
+    "\n",
+    "myadd = np.frompyfunc(myadd, 2, 1)\n",
+    "\n",
+    "print(myadd([1, 2, 3, 4], [5, 6, 7, 8]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c034cd44-af9c-44ff-8d74-cf0d8d34b018",
+   "metadata": {},
+   "source": [
+    "#### Simple Arithmetic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "428246a1-ea45-4c0b-bc94-b3f1ad27a0ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[30 32 34 36 38 40]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 11, 12, 13, 14, 15])\n",
+    "arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
+    "\n",
+    "newarr = np.add(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae349412-0d47-42b4-8c39-56d0839b3d34",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-10  -1   8  17  26  35]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
+    "\n",
+    "newarr = np.subtract(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f61b2129-e31b-4661-a12e-d7897955d163",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 200  420  660  920 1200 1500]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
+    "\n",
+    "newarr = np.multiply(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67540798-c33e-4d1e-a1d9-47c00a121b36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 3.33333333  4.          3.          5.         25.          1.81818182]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([3, 5, 10, 8, 2, 33])\n",
+    "\n",
+    "newarr = np.divide(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "05c10eef-8c8e-4515-9aea-402821f05a02",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[         1000       3200000     729000000 6553600000000          2500\n",
+      "             0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([3, 5, 6, 8, 2, 33])\n",
+    "\n",
+    "newarr = np.power(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c47b12e8-f0a0-45c9-b1e5-202fd2dfdaed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1  6  3  0  0 27]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([3, 7, 9, 8, 2, 33])\n",
+    "\n",
+    "newarr = np.mod(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7626b9ec-04f0-421b-a35d-8f30bad5a136",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([ 3,  2,  3,  5, 25,  1]), array([ 1,  6,  3,  0,  0, 27]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quotient and Mod\n",
+    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
+    "arr2 = np.array([3, 7, 9, 8, 2, 33])\n",
+    "\n",
+    "newarr = np.divmod(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e436c31a-a796-4be1-9e7f-ddaf0f026dec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 1 2 3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([-1, -2, 1, 2, 3, -4])\n",
+    "\n",
+    "newarr = np.absolute(arr)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d55a17c4-1f67-4629-8a85-64f9faa32370",
+   "metadata": {},
+   "source": [
+    "### Rounding Decimals\n",
+    "- truncation\n",
+    "- fix\n",
+    "- rounding\n",
+    "- floor\n",
+    "- ceil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "11395b0f-660b-4c28-a2ed-1c722b560a71",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-3.  3.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Truncation\n",
+    "# Remove the decimals, and return the float number closest to zero. Use the trunc() and fix() functions.\n",
+    "\n",
+    "arr = np.trunc([-3.1666, 3.6667])\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9d2c024e-07ca-43cd-978b-206a391e35f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-3.  3.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.fix([-3.1666, 3.6667])\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "59ca3a99-da45-4a5c-8a2f-bcb6b22a5998",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.17\n"
+     ]
+    }
+   ],
+   "source": [
+    "# rounding\n",
+    "arr = np.around(3.1666, 2)\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e213a874-e4b0-4a3e-831a-5e9ff93c3d94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-4.  3.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.floor([-3.1666, 3.6667])\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "fbe93a72-8e92-4b36-bfab-232c1617e4f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-3.  4.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.ceil([-3.1666, 3.6667])\n",
+    "\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "225b1cd3-85cf-492d-8f1d-b2c94eb63783",
+   "metadata": {},
+   "source": [
+    "### Logs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "995a441d-e78e-4ace-b6fc-b497cce04268",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6 7 8 9]\n",
+      "[0.         1.         1.5849625  2.         2.32192809 2.5849625\n",
+      " 2.80735492 3.         3.169925  ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# log2() function to perform log at the base 2.\n",
+    "arr = np.arange(1, 10)\n",
+    "print(arr)\n",
+    "print(np.log2(arr))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b386fbe7-54c7-48dd-8b08-10f053e00638",
+   "metadata": {},
+   "source": [
+    "### Summations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "16f39361-d8a9-4d9a-970e-72367f2d4685",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 4 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([1, 2, 3])\n",
+    "arr2 = np.array([1, 2, 3])\n",
+    "\n",
+    "newarr = np.add(arr1, arr2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "bd241f37-75bf-4ee5-bcb4-8c72695eb75d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([1, 2, 3])\n",
+    "arr2 = np.array([1, 2, 3])\n",
+    "\n",
+    "newarr = np.sum([arr1, arr2])\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "7309e9d4-c239-4c42-8389-413df30d6abc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# summation over axis\n",
+    "arr1 = np.array([1, 2, 3])\n",
+    "arr2 = np.array([1, 2, 3])\n",
+    "\n",
+    "newarr = np.sum([arr1, arr2], axis=1)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "518cdbe0-417b-4085-9575-729595ae2a52",
+   "metadata": {},
+   "source": [
+    "### Products"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "0211ead9-2b18-4409-a4c6-179688bdd203",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4])\n",
+    "\n",
+    "x = np.prod(arr)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "207af559-db09-48f5-b462-40fd52500bb4",
+   "metadata": {},
+   "source": [
+    "### Differences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "5215ae9f-709b-4206-90f3-95b5dd391f68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  5  10 -20]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([10, 15, 25, 5])\n",
+    "\n",
+    "newarr = np.diff(arr)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "23aeea8b-3f92-490e-927d-a61a8426a365",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  5 -30]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compute discrete difference of the following array twice\n",
+    "# 15-10=5, 25-15=10, and 5-25=-20 AND 10-5=5 and -20-10=-30\n",
+    "arr = np.array([10, 15, 25, 5])\n",
+    "\n",
+    "newarr = np.diff(arr, n=2)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0da9fba4-7ae2-4bfe-94b8-378d3d124b57",
+   "metadata": {},
+   "source": [
+    "#### Other"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "69a07fa4-546c-47cd-99b2-a14548b886e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finding LCM (Lowest Common Multiple)\n",
+    "num1 = 4\n",
+    "num2 = 6\n",
+    "# (4*3=12 and 6*2=12)\n",
+    "x = np.lcm(num1, num2)\n",
+    "\n",
+    "print(x) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "9c874e84-1ca6-44f8-871b-0c57d05ac360",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([3, 6, 9])\n",
+    "\n",
+    "x = np.lcm.reduce(arr)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "d7f635b8-7915-452b-8ee1-e963ed5e8f54",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finding GCD (Greatest Common Denominator)\n",
+    "num1 = 6\n",
+    "num2 = 9\n",
+    "\n",
+    "x = np.gcd(num1, num2)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d39331bf-062b-41b8-b7fe-082312dd5d6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# unique\n",
+    "arr = np.array([1, 1, 1, 2, 3, 4, 5, 5, 6, 7])\n",
+    "\n",
+    "x = np.unique(arr)\n",
+    "\n",
+    "print(x)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "126fdebd-0a45-4be7-a65d-b4e119ebef7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([1, 2, 3, 4])\n",
+    "arr2 = np.array([3, 4, 5, 6])\n",
+    "\n",
+    "newarr = np.union1d(arr1, arr2)\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "a84475b1-15c3-4c8f-8535-2913a4b8579d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr1 = np.array([1, 2, 3, 4])\n",
+    "arr2 = np.array([3, 4, 5, 6])\n",
+    "\n",
+    "newarr = np.intersect1d(arr1, arr2, assume_unique=True)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "771b4dc1-78e9-4819-af4f-b2bb3bc154e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "set1 = np.array([1, 2, 3, 4])\n",
+    "set2 = np.array([3, 4, 5, 6])\n",
+    "\n",
+    "newarr = np.setdiff1d(set1, set2, assume_unique=True)\n",
+    "\n",
+    "print(newarr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "95b8cec1-8b0e-43fb-acae-428d7778c03e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Trigonometric Functions\n",
+    "x = np.sin(np.pi/2)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "e3dbac10-d7bd-45bb-974b-56f8f6082de8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.57079633 3.14159265 4.71238898 6.28318531]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# and sin(), cos() and tan()\n",
+    "# np.deg2rad(arr)\n",
+    "# Hyperbolic: sinh(), cosh() and tanh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67cab045-d1ef-4de1-9aaa-5257d5ba5571",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/pandas/.ipynb_checkpoints/01_basics-checkpoint.ipynb b/course/pandas/.ipynb_checkpoints/01_basics-checkpoint.ipynb
new file mode 100644
index 0000000..5f440b3
--- /dev/null
+++ b/course/pandas/.ipynb_checkpoints/01_basics-checkpoint.ipynb
@@ -0,0 +1,631 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bd4b5db9-6439-4519-aef0-c8bb8ffd6a13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3c4bf54-07f7-46bb-9ae5-77c3a4518627",
+   "metadata": {},
+   "source": [
+    "### Basics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "def1dfe3-6afe-4f5e-9714-36b65811094a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    cars  passings\n",
+      "0    BMW         3\n",
+      "1  Volvo         7\n",
+      "2   Ford         2\n"
+     ]
+    }
+   ],
+   "source": [
+    "mydataset = {\n",
+    "  'cars': [\"BMW\", \"Volvo\", \"Ford\"],\n",
+    "  'passings': [3, 7, 2]\n",
+    "}\n",
+    "\n",
+    "myvar = pd.DataFrame(mydataset)\n",
+    "\n",
+    "print(myvar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0785d9bd-e5b1-4221-84e9-1a1a6a2cf37e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    1\n",
+      "1    7\n",
+      "2    2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# series: numpy arrays!\n",
+    "a = [1, 7, 2]\n",
+    "\n",
+    "myvar = pd.Series(a)\n",
+    "\n",
+    "print(myvar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2d7a5f33-79a2-4e2a-9b14-eee43db90662",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x    1\n",
+      "y    7\n",
+      "z    2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# labels\n",
+    "a = [1, 7, 2]\n",
+    "\n",
+    "myvar = pd.Series(a, index = [\"x\", \"y\", \"z\"])\n",
+    "\n",
+    "print(myvar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cba4f2ef-45d1-4c23-94ce-7197c43d4aee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "day1    420\n",
+      "day2    380\n",
+      "day3    390\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# key value objects\n",
+    "calories = {\"day1\": 420, \"day2\": 380, \"day3\": 390}\n",
+    "\n",
+    "myvar = pd.Series(calories)\n",
+    "\n",
+    "print(myvar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e965971f-6a97-42c4-81bf-2550e57cd7e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   calories  duration\n",
+      "0       420        50\n",
+      "1       380        40\n",
+      "2       390        45\n"
+     ]
+    }
+   ],
+   "source": [
+    "# dataframe = multi-dimensional tables\n",
+    "data = {\n",
+    "  \"calories\": [420, 380, 390],\n",
+    "  \"duration\": [50, 40, 45]\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a6650272-40db-4237-b2d6-83d3652184c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calories    420\n",
+      "duration     50\n",
+      "Name: 0, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# locate row\n",
+    "print(df.loc[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "694a5a9b-c280-454c-b7c6-236eda33d8f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   calories  duration\n",
+      "0       420        50\n",
+      "1       380        40\n"
+     ]
+    }
+   ],
+   "source": [
+    "#use a list of indexes:\n",
+    "print(df.loc[[0, 1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e3ff6022-327e-446d-a739-011730d1db8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      calories  duration\n",
+      "day1       420        50\n",
+      "day2       380        40\n",
+      "day3       390        45\n"
+     ]
+    }
+   ],
+   "source": [
+    "# named index\n",
+    "data = {\n",
+    "  \"calories\": [420, 380, 390],\n",
+    "  \"duration\": [50, 40, 45]\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data, index = [\"day1\", \"day2\", \"day3\"])\n",
+    "\n",
+    "print(df) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4d8a50a4-7c1d-4ef7-ab0b-09cbc8085ad6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calories    380\n",
+      "duration     40\n",
+      "Name: day2, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#refer to the named index:\n",
+    "print(df.loc[\"day2\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b18cee34-c5dd-43df-9e2e-bd20ebeed8ed",
+   "metadata": {},
+   "source": [
+    "### Loading Files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "abef3aeb-d82c-4cc5-a2d2-7a70313f0712",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Duration  Pulse  Maxpulse  Calories\n",
+      "0          60    110       130     409.1\n",
+      "1          60    117       145     479.0\n",
+      "2          60    103       135     340.0\n",
+      "3          45    109       175     282.4\n",
+      "4          45    117       148     406.0\n",
+      "5          60    102       127     300.0\n",
+      "6          60    110       136     374.0\n",
+      "7          45    104       134     253.3\n",
+      "8          30    109       133     195.1\n",
+      "9          60     98       124     269.0\n",
+      "10         60    103       147     329.3\n",
+      "11         60    100       120     250.7\n",
+      "12         60    106       128     345.3\n",
+      "13         60    104       132     379.3\n",
+      "14         60     98       123     275.0\n",
+      "15         60     98       120     215.2\n",
+      "16         60    100       120     300.0\n",
+      "17         45     90       112       NaN\n",
+      "18         60    103       123     323.0\n",
+      "19         45     97       125     243.0\n",
+      "20         60    108       131     364.2\n",
+      "21         45    100       119     282.0\n",
+      "22         60    130       101     300.0\n",
+      "23         45    105       132     246.0\n",
+      "24         60    102       126     334.5\n",
+      "25         60    100       120     250.0\n",
+      "26         60     92       118     241.0\n",
+      "27         60    103       132       NaN\n",
+      "28         60    100       132     280.0\n",
+      "29         60    102       129     380.3\n",
+      "30         60     92       115     243.0\n",
+      "31         45     90       112     180.1\n",
+      "32         60    101       124     299.0\n",
+      "33         60     93       113     223.0\n",
+      "34         60    107       136     361.0\n",
+      "35         60    114       140     415.0\n",
+      "36         60    102       127     300.0\n",
+      "37         60    100       120     300.0\n",
+      "38         60    100       120     300.0\n",
+      "39         45    104       129     266.0\n",
+      "40         45     90       112     180.1\n",
+      "41         60     98       126     286.0\n",
+      "42         60    100       122     329.4\n",
+      "43         60    111       138     400.0\n",
+      "44         60    111       131     397.0\n",
+      "45         60     99       119     273.0\n",
+      "46         60    109       153     387.6\n",
+      "47         45    111       136     300.0\n",
+      "48         45    108       129     298.0\n",
+      "49         60    111       139     397.6\n",
+      "50         60    107       136     380.2\n",
+      "51         80    123       146     643.1\n",
+      "52         60    106       130     263.0\n",
+      "53         60    118       151     486.0\n",
+      "54         30    136       175     238.0\n",
+      "55         60    121       146     450.7\n",
+      "56         60    118       121     413.0\n",
+      "57         45    115       144     305.0\n",
+      "58         20    153       172     226.4\n",
+      "59         45    123       152     321.0\n",
+      "60        210    108       160    1376.0\n",
+      "61        160    110       137    1034.4\n",
+      "62        160    109       135     853.0\n",
+      "63         45    118       141     341.0\n",
+      "64         20    110       130     131.4\n",
+      "65        180     90       130     800.4\n",
+      "66        150    105       135     873.4\n",
+      "67        150    107       130     816.0\n",
+      "68         20    106       136     110.4\n",
+      "69        300    108       143    1500.2\n",
+      "70        150     97       129    1115.0\n",
+      "71         60    109       153     387.6\n",
+      "72         90    100       127     700.0\n",
+      "73        150     97       127     953.2\n",
+      "74         45    114       146     304.0\n",
+      "75         90     98       125     563.2\n",
+      "76         45    105       134     251.0\n",
+      "77         45    110       141     300.0\n",
+      "78        120    100       130     500.4\n",
+      "79        270    100       131    1729.0\n",
+      "80         30    159       182     319.2\n",
+      "81         45    149       169     344.0\n",
+      "82         30    103       139     151.1\n",
+      "83        120    100       130     500.0\n",
+      "84         45    100       120     225.3\n",
+      "85         30    151       170     300.0\n",
+      "86         45    102       136     234.0\n",
+      "87        120    100       157    1000.1\n",
+      "88         45    129       103     242.0\n",
+      "89         20     83       107      50.3\n",
+      "90        180    101       127     600.1\n",
+      "91         45    107       137       NaN\n",
+      "92         30     90       107     105.3\n",
+      "93         15     80       100      50.5\n",
+      "94         20    150       171     127.4\n",
+      "95         20    151       168     229.4\n",
+      "96         30     95       128     128.2\n",
+      "97         25    152       168     244.2\n",
+      "98         30    109       131     188.2\n",
+      "99         90     93       124     604.1\n",
+      "100        20     95       112      77.7\n",
+      "101        90     90       110     500.0\n",
+      "102        90     90       100     500.0\n",
+      "103        90     90       100     500.4\n",
+      "104        30     92       108      92.7\n",
+      "105        30     93       128     124.0\n",
+      "106       180     90       120     800.3\n",
+      "107        30     90       120      86.2\n",
+      "108        90     90       120     500.3\n",
+      "109       210    137       184    1860.4\n",
+      "110        60    102       124     325.2\n",
+      "111        45    107       124     275.0\n",
+      "112        15    124       139     124.2\n",
+      "113        45    100       120     225.3\n",
+      "114        60    108       131     367.6\n",
+      "115        60    108       151     351.7\n",
+      "116        60    116       141     443.0\n",
+      "117        60     97       122     277.4\n",
+      "118        60    105       125       NaN\n",
+      "119        60    103       124     332.7\n",
+      "120        30    112       137     193.9\n",
+      "121        45    100       120     100.7\n",
+      "122        60    119       169     336.7\n",
+      "123        60    107       127     344.9\n",
+      "124        60    111       151     368.5\n",
+      "125        60     98       122     271.0\n",
+      "126        60     97       124     275.3\n",
+      "127        60    109       127     382.0\n",
+      "128        90     99       125     466.4\n",
+      "129        60    114       151     384.0\n",
+      "130        60    104       134     342.5\n",
+      "131        60    107       138     357.5\n",
+      "132        60    103       133     335.0\n",
+      "133        60    106       132     327.5\n",
+      "134        60    103       136     339.0\n",
+      "135        20    136       156     189.0\n",
+      "136        45    117       143     317.7\n",
+      "137        45    115       137     318.0\n",
+      "138        45    113       138     308.0\n",
+      "139        20    141       162     222.4\n",
+      "140        60    108       135     390.0\n",
+      "141        60     97       127       NaN\n",
+      "142        45    100       120     250.4\n",
+      "143        45    122       149     335.4\n",
+      "144        60    136       170     470.2\n",
+      "145        45    106       126     270.8\n",
+      "146        60    107       136     400.0\n",
+      "147        60    112       146     361.9\n",
+      "148        30    103       127     185.0\n",
+      "149        60    110       150     409.4\n",
+      "150        60    106       134     343.0\n",
+      "151        60    109       129     353.2\n",
+      "152        60    109       138     374.0\n",
+      "153        30    150       167     275.8\n",
+      "154        60    105       128     328.0\n",
+      "155        60    111       151     368.5\n",
+      "156        60     97       131     270.4\n",
+      "157        60    100       120     270.4\n",
+      "158        60    114       150     382.8\n",
+      "159        30     80       120     240.9\n",
+      "160        30     85       120     250.4\n",
+      "161        45     90       130     260.4\n",
+      "162        45     95       130     270.0\n",
+      "163        45    100       140     280.9\n",
+      "164        60    105       140     290.8\n",
+      "165        60    110       145     300.0\n",
+      "166        60    115       145     310.2\n",
+      "167        75    120       150     320.4\n",
+      "168        75    125       150     330.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "#https://www.w3schools.com/python/pandas/data.csv\n",
+    "df = pd.read_csv('https://www.w3schools.com/python/pandas/data.csv')\n",
+    "\n",
+    "print(df.to_string()) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dba1828-de88-4a61-868b-5a8b638254f0",
+   "metadata": {},
+   "source": [
+    "#### Analyze Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e89c6f79-b957-4c6b-85bc-c76b362d7a2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(169, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "460724ee-1ba6-461c-801e-46632a68c837",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 169 entries, 0 to 168\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column    Non-Null Count  Dtype  \n",
+      "---  ------    --------------  -----  \n",
+      " 0   Duration  169 non-null    int64  \n",
+      " 1   Pulse     169 non-null    int64  \n",
+      " 2   Maxpulse  169 non-null    int64  \n",
+      " 3   Calories  164 non-null    float64\n",
+      "dtypes: float64(1), int64(3)\n",
+      "memory usage: 5.4 KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.info()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "418bbc3e-53ff-4ea2-9728-1b8aa94a140d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Duration  Pulse  Maxpulse  Calories\n",
+      "0        60    110       130     409.1\n",
+      "1        60    117       145     479.0\n",
+      "2        60    103       135     340.0\n",
+      "3        45    109       175     282.4\n",
+      "4        45    117       148     406.0\n",
+      "5        60    102       127     300.0\n",
+      "6        60    110       136     374.0\n",
+      "7        45    104       134     253.3\n",
+      "8        30    109       133     195.1\n",
+      "9        60     98       124     269.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b7424301-9bb4-4200-b2d5-bcc8a22c6427",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Duration  Pulse  Maxpulse  Calories\n",
+      "0        60    110       130     409.1\n",
+      "1        60    117       145     479.0\n",
+      "2        60    103       135     340.0\n",
+      "3        45    109       175     282.4\n",
+      "4        45    117       148     406.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a7649f71-6c14-4158-8040-08019577b1ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Duration  Pulse  Maxpulse  Calories\n",
+      "164        60    105       140     290.8\n",
+      "165        60    110       145     300.0\n",
+      "166        60    115       145     310.2\n",
+      "167        75    120       150     320.4\n",
+      "168        75    125       150     330.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.tail())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "8da3696c-6a3c-4727-8cea-6442d7cedf28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    169.000000\n",
+      "mean     107.461538\n",
+      "std       14.510259\n",
+      "min       80.000000\n",
+      "25%      100.000000\n",
+      "50%      105.000000\n",
+      "75%      111.000000\n",
+      "max      159.000000\n",
+      "Name: Pulse, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Pulse'].describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8689d23c-5d68-4fdb-bcef-9ae080442a27",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/pandas/.ipynb_checkpoints/02_cleaning-checkpoint.ipynb b/course/pandas/.ipynb_checkpoints/02_cleaning-checkpoint.ipynb
new file mode 100644
index 0000000..9305fe3
--- /dev/null
+++ b/course/pandas/.ipynb_checkpoints/02_cleaning-checkpoint.ipynb
@@ -0,0 +1,2206 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b0c0ae08-2fb5-47f5-a5ce-1a66e35791a4",
+   "metadata": {},
+   "source": [
+    "### Cleaning Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f9998a78-ae01-4531-b325-637b6d5ee86d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9516a86a-ed6a-4f79-b631-3195daec258c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('https://gist.githubusercontent.com/maltegrosse/bdfd2c6a5e3bff315d92cd27c2461a48/raw/49d5672953360934601b3d252c9b78121eed10db/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ea25a32c-70d3-479d-8d11-7e487f13f50c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/01'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/02'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/03'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/04'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/05'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/06'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>127</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/07'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>136</td>\n",
+       "      <td>374.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>450</td>\n",
+       "      <td>'2020/12/08'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>134</td>\n",
+       "      <td>253.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30</td>\n",
+       "      <td>'2020/12/09'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>133</td>\n",
+       "      <td>195.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/10'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>124</td>\n",
+       "      <td>269.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/11'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>147</td>\n",
+       "      <td>329.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/13'</td>\n",
+       "      <td>106</td>\n",
+       "      <td>128</td>\n",
+       "      <td>345.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/14'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>132</td>\n",
+       "      <td>379.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/15'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>123</td>\n",
+       "      <td>275.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/16'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>120</td>\n",
+       "      <td>215.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/17'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/18'</td>\n",
+       "      <td>90</td>\n",
+       "      <td>112</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/19'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>123</td>\n",
+       "      <td>323.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/20'</td>\n",
+       "      <td>97</td>\n",
+       "      <td>125</td>\n",
+       "      <td>243.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/21'</td>\n",
+       "      <td>108</td>\n",
+       "      <td>131</td>\n",
+       "      <td>364.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>45</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100</td>\n",
+       "      <td>119</td>\n",
+       "      <td>282.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/23'</td>\n",
+       "      <td>130</td>\n",
+       "      <td>101</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/24'</td>\n",
+       "      <td>105</td>\n",
+       "      <td>132</td>\n",
+       "      <td>246.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/25'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>126</td>\n",
+       "      <td>334.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>60</td>\n",
+       "      <td>20201226</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/27'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>118</td>\n",
+       "      <td>241.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/28'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>132</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/29'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>132</td>\n",
+       "      <td>280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/30'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>129</td>\n",
+       "      <td>380.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/31'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>115</td>\n",
+       "      <td>243.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Duration          Date  Pulse  Maxpulse  Calories\n",
+       "0         60  '2020/12/01'    110       130     409.1\n",
+       "1         60  '2020/12/02'    117       145     479.0\n",
+       "2         60  '2020/12/03'    103       135     340.0\n",
+       "3         45  '2020/12/04'    109       175     282.4\n",
+       "4         45  '2020/12/05'    117       148     406.0\n",
+       "5         60  '2020/12/06'    102       127     300.0\n",
+       "6         60  '2020/12/07'    110       136     374.0\n",
+       "7        450  '2020/12/08'    104       134     253.3\n",
+       "8         30  '2020/12/09'    109       133     195.1\n",
+       "9         60  '2020/12/10'     98       124     269.0\n",
+       "10        60  '2020/12/11'    103       147     329.3\n",
+       "11        60  '2020/12/12'    100       120     250.7\n",
+       "12        60  '2020/12/12'    100       120     250.7\n",
+       "13        60  '2020/12/13'    106       128     345.3\n",
+       "14        60  '2020/12/14'    104       132     379.3\n",
+       "15        60  '2020/12/15'     98       123     275.0\n",
+       "16        60  '2020/12/16'     98       120     215.2\n",
+       "17        60  '2020/12/17'    100       120     300.0\n",
+       "18        45  '2020/12/18'     90       112       NaN\n",
+       "19        60  '2020/12/19'    103       123     323.0\n",
+       "20        45  '2020/12/20'     97       125     243.0\n",
+       "21        60  '2020/12/21'    108       131     364.2\n",
+       "22        45           NaN    100       119     282.0\n",
+       "23        60  '2020/12/23'    130       101     300.0\n",
+       "24        45  '2020/12/24'    105       132     246.0\n",
+       "25        60  '2020/12/25'    102       126     334.5\n",
+       "26        60      20201226    100       120     250.0\n",
+       "27        60  '2020/12/27'     92       118     241.0\n",
+       "28        60  '2020/12/28'    103       132       NaN\n",
+       "29        60  '2020/12/29'    100       132     280.0\n",
+       "30        60  '2020/12/30'    102       129     380.3\n",
+       "31        60  '2020/12/31'     92       115     243.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2baf29d8-cd8f-4dfd-931a-c413a995320e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/01'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/02'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/03'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/04'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/05'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/06'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>127</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/07'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>136</td>\n",
+       "      <td>374.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>450</td>\n",
+       "      <td>'2020/12/08'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>134</td>\n",
+       "      <td>253.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30</td>\n",
+       "      <td>'2020/12/09'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>133</td>\n",
+       "      <td>195.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/10'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>124</td>\n",
+       "      <td>269.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/11'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>147</td>\n",
+       "      <td>329.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/13'</td>\n",
+       "      <td>106</td>\n",
+       "      <td>128</td>\n",
+       "      <td>345.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/14'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>132</td>\n",
+       "      <td>379.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/15'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>123</td>\n",
+       "      <td>275.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/16'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>120</td>\n",
+       "      <td>215.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/17'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/19'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>123</td>\n",
+       "      <td>323.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/20'</td>\n",
+       "      <td>97</td>\n",
+       "      <td>125</td>\n",
+       "      <td>243.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/21'</td>\n",
+       "      <td>108</td>\n",
+       "      <td>131</td>\n",
+       "      <td>364.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/23'</td>\n",
+       "      <td>130</td>\n",
+       "      <td>101</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/24'</td>\n",
+       "      <td>105</td>\n",
+       "      <td>132</td>\n",
+       "      <td>246.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/25'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>126</td>\n",
+       "      <td>334.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>60</td>\n",
+       "      <td>20201226</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/27'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>118</td>\n",
+       "      <td>241.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/29'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>132</td>\n",
+       "      <td>280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/30'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>129</td>\n",
+       "      <td>380.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/31'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>115</td>\n",
+       "      <td>243.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Duration          Date  Pulse  Maxpulse  Calories\n",
+       "0         60  '2020/12/01'    110       130     409.1\n",
+       "1         60  '2020/12/02'    117       145     479.0\n",
+       "2         60  '2020/12/03'    103       135     340.0\n",
+       "3         45  '2020/12/04'    109       175     282.4\n",
+       "4         45  '2020/12/05'    117       148     406.0\n",
+       "5         60  '2020/12/06'    102       127     300.0\n",
+       "6         60  '2020/12/07'    110       136     374.0\n",
+       "7        450  '2020/12/08'    104       134     253.3\n",
+       "8         30  '2020/12/09'    109       133     195.1\n",
+       "9         60  '2020/12/10'     98       124     269.0\n",
+       "10        60  '2020/12/11'    103       147     329.3\n",
+       "11        60  '2020/12/12'    100       120     250.7\n",
+       "12        60  '2020/12/12'    100       120     250.7\n",
+       "13        60  '2020/12/13'    106       128     345.3\n",
+       "14        60  '2020/12/14'    104       132     379.3\n",
+       "15        60  '2020/12/15'     98       123     275.0\n",
+       "16        60  '2020/12/16'     98       120     215.2\n",
+       "17        60  '2020/12/17'    100       120     300.0\n",
+       "19        60  '2020/12/19'    103       123     323.0\n",
+       "20        45  '2020/12/20'     97       125     243.0\n",
+       "21        60  '2020/12/21'    108       131     364.2\n",
+       "23        60  '2020/12/23'    130       101     300.0\n",
+       "24        45  '2020/12/24'    105       132     246.0\n",
+       "25        60  '2020/12/25'    102       126     334.5\n",
+       "26        60      20201226    100       120     250.0\n",
+       "27        60  '2020/12/27'     92       118     241.0\n",
+       "29        60  '2020/12/29'    100       132     280.0\n",
+       "30        60  '2020/12/30'    102       129     380.3\n",
+       "31        60  '2020/12/31'     92       115     243.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop null/NaN\n",
+    "new_df = df.dropna()\n",
+    "new_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "37533007-2851-49da-8fca-2e9d3b74c406",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hint df.dropna(inplace = True) <- manipulates orginal df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e94f0608-1928-4dec-b28c-3f56d72b1867",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fill missing values\n",
+    "# df.fillna(130, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "025cec14-2687-4ec5-9fa9-f10f1da927ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/01'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/02'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/03'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/04'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/05'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/06'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>127</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/07'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>136</td>\n",
+       "      <td>374.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>450</td>\n",
+       "      <td>'2020/12/08'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>134</td>\n",
+       "      <td>253.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30</td>\n",
+       "      <td>'2020/12/09'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>133</td>\n",
+       "      <td>195.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/10'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>124</td>\n",
+       "      <td>269.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/11'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>147</td>\n",
+       "      <td>329.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/13'</td>\n",
+       "      <td>106</td>\n",
+       "      <td>128</td>\n",
+       "      <td>345.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/14'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>132</td>\n",
+       "      <td>379.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/15'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>123</td>\n",
+       "      <td>275.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/16'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>120</td>\n",
+       "      <td>215.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/17'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/18'</td>\n",
+       "      <td>90</td>\n",
+       "      <td>112</td>\n",
+       "      <td>304.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/19'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>123</td>\n",
+       "      <td>323.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/20'</td>\n",
+       "      <td>97</td>\n",
+       "      <td>125</td>\n",
+       "      <td>243.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/21'</td>\n",
+       "      <td>108</td>\n",
+       "      <td>131</td>\n",
+       "      <td>364.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>45</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100</td>\n",
+       "      <td>119</td>\n",
+       "      <td>282.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/23'</td>\n",
+       "      <td>130</td>\n",
+       "      <td>101</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/24'</td>\n",
+       "      <td>105</td>\n",
+       "      <td>132</td>\n",
+       "      <td>246.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/25'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>126</td>\n",
+       "      <td>334.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>60</td>\n",
+       "      <td>20201226</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/27'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>118</td>\n",
+       "      <td>241.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/28'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>132</td>\n",
+       "      <td>304.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/29'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>132</td>\n",
+       "      <td>280.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/30'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>129</td>\n",
+       "      <td>380.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/31'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>115</td>\n",
+       "      <td>243.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Duration          Date  Pulse  Maxpulse  Calories\n",
+       "0         60  '2020/12/01'    110       130    409.10\n",
+       "1         60  '2020/12/02'    117       145    479.00\n",
+       "2         60  '2020/12/03'    103       135    340.00\n",
+       "3         45  '2020/12/04'    109       175    282.40\n",
+       "4         45  '2020/12/05'    117       148    406.00\n",
+       "5         60  '2020/12/06'    102       127    300.00\n",
+       "6         60  '2020/12/07'    110       136    374.00\n",
+       "7        450  '2020/12/08'    104       134    253.30\n",
+       "8         30  '2020/12/09'    109       133    195.10\n",
+       "9         60  '2020/12/10'     98       124    269.00\n",
+       "10        60  '2020/12/11'    103       147    329.30\n",
+       "11        60  '2020/12/12'    100       120    250.70\n",
+       "12        60  '2020/12/12'    100       120    250.70\n",
+       "13        60  '2020/12/13'    106       128    345.30\n",
+       "14        60  '2020/12/14'    104       132    379.30\n",
+       "15        60  '2020/12/15'     98       123    275.00\n",
+       "16        60  '2020/12/16'     98       120    215.20\n",
+       "17        60  '2020/12/17'    100       120    300.00\n",
+       "18        45  '2020/12/18'     90       112    304.68\n",
+       "19        60  '2020/12/19'    103       123    323.00\n",
+       "20        45  '2020/12/20'     97       125    243.00\n",
+       "21        60  '2020/12/21'    108       131    364.20\n",
+       "22        45           NaN    100       119    282.00\n",
+       "23        60  '2020/12/23'    130       101    300.00\n",
+       "24        45  '2020/12/24'    105       132    246.00\n",
+       "25        60  '2020/12/25'    102       126    334.50\n",
+       "26        60      20201226    100       120    250.00\n",
+       "27        60  '2020/12/27'     92       118    241.00\n",
+       "28        60  '2020/12/28'    103       132    304.68\n",
+       "29        60  '2020/12/29'    100       132    280.00\n",
+       "30        60  '2020/12/30'    102       129    380.30\n",
+       "31        60  '2020/12/31'     92       115    243.00"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = df[\"Calories\"].mean()\n",
+    "\n",
+    "df[\"Calories\"].fillna(x, inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d2e87f3b-ef58-4128-b52f-799056e56de8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = df[\"Calories\"].median()\n",
+    "\n",
+    "df[\"Calories\"].fillna(x, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c42df786-aa1b-4174-b436-566421f1683b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-01</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-02</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-03</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-04</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-05</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-06</td>\n",
+       "      <td>102</td>\n",
+       "      <td>127</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-07</td>\n",
+       "      <td>110</td>\n",
+       "      <td>136</td>\n",
+       "      <td>374.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>450</td>\n",
+       "      <td>2020-12-08</td>\n",
+       "      <td>104</td>\n",
+       "      <td>134</td>\n",
+       "      <td>253.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30</td>\n",
+       "      <td>2020-12-09</td>\n",
+       "      <td>109</td>\n",
+       "      <td>133</td>\n",
+       "      <td>195.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-10</td>\n",
+       "      <td>98</td>\n",
+       "      <td>124</td>\n",
+       "      <td>269.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-11</td>\n",
+       "      <td>103</td>\n",
+       "      <td>147</td>\n",
+       "      <td>329.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-12</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-12</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-13</td>\n",
+       "      <td>106</td>\n",
+       "      <td>128</td>\n",
+       "      <td>345.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-14</td>\n",
+       "      <td>104</td>\n",
+       "      <td>132</td>\n",
+       "      <td>379.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-15</td>\n",
+       "      <td>98</td>\n",
+       "      <td>123</td>\n",
+       "      <td>275.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-16</td>\n",
+       "      <td>98</td>\n",
+       "      <td>120</td>\n",
+       "      <td>215.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-17</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-18</td>\n",
+       "      <td>90</td>\n",
+       "      <td>112</td>\n",
+       "      <td>304.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-19</td>\n",
+       "      <td>103</td>\n",
+       "      <td>123</td>\n",
+       "      <td>323.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-20</td>\n",
+       "      <td>97</td>\n",
+       "      <td>125</td>\n",
+       "      <td>243.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-21</td>\n",
+       "      <td>108</td>\n",
+       "      <td>131</td>\n",
+       "      <td>364.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>45</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>100</td>\n",
+       "      <td>119</td>\n",
+       "      <td>282.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-23</td>\n",
+       "      <td>130</td>\n",
+       "      <td>101</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-24</td>\n",
+       "      <td>105</td>\n",
+       "      <td>132</td>\n",
+       "      <td>246.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-25</td>\n",
+       "      <td>102</td>\n",
+       "      <td>126</td>\n",
+       "      <td>334.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-26</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-27</td>\n",
+       "      <td>92</td>\n",
+       "      <td>118</td>\n",
+       "      <td>241.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-28</td>\n",
+       "      <td>103</td>\n",
+       "      <td>132</td>\n",
+       "      <td>304.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-29</td>\n",
+       "      <td>100</td>\n",
+       "      <td>132</td>\n",
+       "      <td>280.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-30</td>\n",
+       "      <td>102</td>\n",
+       "      <td>129</td>\n",
+       "      <td>380.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>92</td>\n",
+       "      <td>115</td>\n",
+       "      <td>243.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Duration       Date  Pulse  Maxpulse  Calories\n",
+       "0         60 2020-12-01    110       130    409.10\n",
+       "1         60 2020-12-02    117       145    479.00\n",
+       "2         60 2020-12-03    103       135    340.00\n",
+       "3         45 2020-12-04    109       175    282.40\n",
+       "4         45 2020-12-05    117       148    406.00\n",
+       "5         60 2020-12-06    102       127    300.00\n",
+       "6         60 2020-12-07    110       136    374.00\n",
+       "7        450 2020-12-08    104       134    253.30\n",
+       "8         30 2020-12-09    109       133    195.10\n",
+       "9         60 2020-12-10     98       124    269.00\n",
+       "10        60 2020-12-11    103       147    329.30\n",
+       "11        60 2020-12-12    100       120    250.70\n",
+       "12        60 2020-12-12    100       120    250.70\n",
+       "13        60 2020-12-13    106       128    345.30\n",
+       "14        60 2020-12-14    104       132    379.30\n",
+       "15        60 2020-12-15     98       123    275.00\n",
+       "16        60 2020-12-16     98       120    215.20\n",
+       "17        60 2020-12-17    100       120    300.00\n",
+       "18        45 2020-12-18     90       112    304.68\n",
+       "19        60 2020-12-19    103       123    323.00\n",
+       "20        45 2020-12-20     97       125    243.00\n",
+       "21        60 2020-12-21    108       131    364.20\n",
+       "22        45        NaT    100       119    282.00\n",
+       "23        60 2020-12-23    130       101    300.00\n",
+       "24        45 2020-12-24    105       132    246.00\n",
+       "25        60 2020-12-25    102       126    334.50\n",
+       "26        60 2020-12-26    100       120    250.00\n",
+       "27        60 2020-12-27     92       118    241.00\n",
+       "28        60 2020-12-28    103       132    304.68\n",
+       "29        60 2020-12-29    100       132    280.00\n",
+       "30        60 2020-12-30    102       129    380.30\n",
+       "31        60 2020-12-31     92       115    243.00"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert into proper data type\n",
+    "df['Date'] = pd.to_datetime(df['Date'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6508edc2-f7f1-469b-a094-1b6c98a155e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove missing value according to a column\n",
+    "# df.dropna(subset=['Date'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "725032e8-c03e-428e-a928-f5c2533a3446",
+   "metadata": {},
+   "source": [
+    "#### Fixing Wrong Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3367d5c9-90f8-4fb1-9c2b-bae2bdaeb7bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# row 7: 450 duration!\n",
+    "df.loc[7, 'Duration'] = 45"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1a9ce891-9275-4539-a23c-4826fb258c1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-01</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-02</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-03</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-04</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-05</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-06</td>\n",
+       "      <td>102</td>\n",
+       "      <td>127</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-07</td>\n",
+       "      <td>110</td>\n",
+       "      <td>136</td>\n",
+       "      <td>374.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-08</td>\n",
+       "      <td>104</td>\n",
+       "      <td>134</td>\n",
+       "      <td>253.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30</td>\n",
+       "      <td>2020-12-09</td>\n",
+       "      <td>109</td>\n",
+       "      <td>133</td>\n",
+       "      <td>195.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-10</td>\n",
+       "      <td>98</td>\n",
+       "      <td>124</td>\n",
+       "      <td>269.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-11</td>\n",
+       "      <td>103</td>\n",
+       "      <td>147</td>\n",
+       "      <td>329.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-12</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-12</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-13</td>\n",
+       "      <td>106</td>\n",
+       "      <td>128</td>\n",
+       "      <td>345.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-14</td>\n",
+       "      <td>104</td>\n",
+       "      <td>132</td>\n",
+       "      <td>379.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-15</td>\n",
+       "      <td>98</td>\n",
+       "      <td>123</td>\n",
+       "      <td>275.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-16</td>\n",
+       "      <td>98</td>\n",
+       "      <td>120</td>\n",
+       "      <td>215.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-17</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-18</td>\n",
+       "      <td>90</td>\n",
+       "      <td>112</td>\n",
+       "      <td>304.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-19</td>\n",
+       "      <td>103</td>\n",
+       "      <td>123</td>\n",
+       "      <td>323.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-20</td>\n",
+       "      <td>97</td>\n",
+       "      <td>125</td>\n",
+       "      <td>243.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-21</td>\n",
+       "      <td>108</td>\n",
+       "      <td>131</td>\n",
+       "      <td>364.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>45</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>100</td>\n",
+       "      <td>119</td>\n",
+       "      <td>282.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-23</td>\n",
+       "      <td>130</td>\n",
+       "      <td>101</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-24</td>\n",
+       "      <td>105</td>\n",
+       "      <td>132</td>\n",
+       "      <td>246.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-25</td>\n",
+       "      <td>102</td>\n",
+       "      <td>126</td>\n",
+       "      <td>334.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-26</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-27</td>\n",
+       "      <td>92</td>\n",
+       "      <td>118</td>\n",
+       "      <td>241.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-28</td>\n",
+       "      <td>103</td>\n",
+       "      <td>132</td>\n",
+       "      <td>304.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-29</td>\n",
+       "      <td>100</td>\n",
+       "      <td>132</td>\n",
+       "      <td>280.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-30</td>\n",
+       "      <td>102</td>\n",
+       "      <td>129</td>\n",
+       "      <td>380.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>92</td>\n",
+       "      <td>115</td>\n",
+       "      <td>243.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Duration       Date  Pulse  Maxpulse  Calories\n",
+       "0         60 2020-12-01    110       130    409.10\n",
+       "1         60 2020-12-02    117       145    479.00\n",
+       "2         60 2020-12-03    103       135    340.00\n",
+       "3         45 2020-12-04    109       175    282.40\n",
+       "4         45 2020-12-05    117       148    406.00\n",
+       "5         60 2020-12-06    102       127    300.00\n",
+       "6         60 2020-12-07    110       136    374.00\n",
+       "7         45 2020-12-08    104       134    253.30\n",
+       "8         30 2020-12-09    109       133    195.10\n",
+       "9         60 2020-12-10     98       124    269.00\n",
+       "10        60 2020-12-11    103       147    329.30\n",
+       "11        60 2020-12-12    100       120    250.70\n",
+       "12        60 2020-12-12    100       120    250.70\n",
+       "13        60 2020-12-13    106       128    345.30\n",
+       "14        60 2020-12-14    104       132    379.30\n",
+       "15        60 2020-12-15     98       123    275.00\n",
+       "16        60 2020-12-16     98       120    215.20\n",
+       "17        60 2020-12-17    100       120    300.00\n",
+       "18        45 2020-12-18     90       112    304.68\n",
+       "19        60 2020-12-19    103       123    323.00\n",
+       "20        45 2020-12-20     97       125    243.00\n",
+       "21        60 2020-12-21    108       131    364.20\n",
+       "22        45        NaT    100       119    282.00\n",
+       "23        60 2020-12-23    130       101    300.00\n",
+       "24        45 2020-12-24    105       132    246.00\n",
+       "25        60 2020-12-25    102       126    334.50\n",
+       "26        60 2020-12-26    100       120    250.00\n",
+       "27        60 2020-12-27     92       118    241.00\n",
+       "28        60 2020-12-28    103       132    304.68\n",
+       "29        60 2020-12-29    100       132    280.00\n",
+       "30        60 2020-12-30    102       129    380.30\n",
+       "31        60 2020-12-31     92       115    243.00"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7888f644-60a5-41e2-bd9f-acf1f5e08f5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0     False\n",
+      "1     False\n",
+      "2     False\n",
+      "3     False\n",
+      "4     False\n",
+      "5     False\n",
+      "6     False\n",
+      "7     False\n",
+      "8     False\n",
+      "9     False\n",
+      "10    False\n",
+      "11    False\n",
+      "12     True\n",
+      "13    False\n",
+      "14    False\n",
+      "15    False\n",
+      "16    False\n",
+      "17    False\n",
+      "18    False\n",
+      "19    False\n",
+      "20    False\n",
+      "21    False\n",
+      "22    False\n",
+      "23    False\n",
+      "24    False\n",
+      "25    False\n",
+      "26    False\n",
+      "27    False\n",
+      "28    False\n",
+      "29    False\n",
+      "30    False\n",
+      "31    False\n",
+      "dtype: bool\n"
+     ]
+    }
+   ],
+   "source": [
+    "# remove duplicates row 11 & 12\n",
+    "print(df.duplicated())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ff4ee9a2-dabb-4015-8b0c-5527f688bb21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop_duplicates(inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "44165eb4-ab0c-4be0-92d6-4c8ccf2ff389",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-01</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-02</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-03</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-04</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-05</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-06</td>\n",
+       "      <td>102</td>\n",
+       "      <td>127</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-07</td>\n",
+       "      <td>110</td>\n",
+       "      <td>136</td>\n",
+       "      <td>374.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-08</td>\n",
+       "      <td>104</td>\n",
+       "      <td>134</td>\n",
+       "      <td>253.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30</td>\n",
+       "      <td>2020-12-09</td>\n",
+       "      <td>109</td>\n",
+       "      <td>133</td>\n",
+       "      <td>195.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-10</td>\n",
+       "      <td>98</td>\n",
+       "      <td>124</td>\n",
+       "      <td>269.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-11</td>\n",
+       "      <td>103</td>\n",
+       "      <td>147</td>\n",
+       "      <td>329.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-12</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-13</td>\n",
+       "      <td>106</td>\n",
+       "      <td>128</td>\n",
+       "      <td>345.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-14</td>\n",
+       "      <td>104</td>\n",
+       "      <td>132</td>\n",
+       "      <td>379.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-15</td>\n",
+       "      <td>98</td>\n",
+       "      <td>123</td>\n",
+       "      <td>275.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-16</td>\n",
+       "      <td>98</td>\n",
+       "      <td>120</td>\n",
+       "      <td>215.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-17</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-18</td>\n",
+       "      <td>90</td>\n",
+       "      <td>112</td>\n",
+       "      <td>304.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-19</td>\n",
+       "      <td>103</td>\n",
+       "      <td>123</td>\n",
+       "      <td>323.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-20</td>\n",
+       "      <td>97</td>\n",
+       "      <td>125</td>\n",
+       "      <td>243.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-21</td>\n",
+       "      <td>108</td>\n",
+       "      <td>131</td>\n",
+       "      <td>364.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>45</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>100</td>\n",
+       "      <td>119</td>\n",
+       "      <td>282.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-23</td>\n",
+       "      <td>130</td>\n",
+       "      <td>101</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>45</td>\n",
+       "      <td>2020-12-24</td>\n",
+       "      <td>105</td>\n",
+       "      <td>132</td>\n",
+       "      <td>246.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-25</td>\n",
+       "      <td>102</td>\n",
+       "      <td>126</td>\n",
+       "      <td>334.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-26</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-27</td>\n",
+       "      <td>92</td>\n",
+       "      <td>118</td>\n",
+       "      <td>241.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-28</td>\n",
+       "      <td>103</td>\n",
+       "      <td>132</td>\n",
+       "      <td>304.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-29</td>\n",
+       "      <td>100</td>\n",
+       "      <td>132</td>\n",
+       "      <td>280.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-30</td>\n",
+       "      <td>102</td>\n",
+       "      <td>129</td>\n",
+       "      <td>380.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>60</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>92</td>\n",
+       "      <td>115</td>\n",
+       "      <td>243.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Duration       Date  Pulse  Maxpulse  Calories\n",
+       "0         60 2020-12-01    110       130    409.10\n",
+       "1         60 2020-12-02    117       145    479.00\n",
+       "2         60 2020-12-03    103       135    340.00\n",
+       "3         45 2020-12-04    109       175    282.40\n",
+       "4         45 2020-12-05    117       148    406.00\n",
+       "5         60 2020-12-06    102       127    300.00\n",
+       "6         60 2020-12-07    110       136    374.00\n",
+       "7         45 2020-12-08    104       134    253.30\n",
+       "8         30 2020-12-09    109       133    195.10\n",
+       "9         60 2020-12-10     98       124    269.00\n",
+       "10        60 2020-12-11    103       147    329.30\n",
+       "11        60 2020-12-12    100       120    250.70\n",
+       "13        60 2020-12-13    106       128    345.30\n",
+       "14        60 2020-12-14    104       132    379.30\n",
+       "15        60 2020-12-15     98       123    275.00\n",
+       "16        60 2020-12-16     98       120    215.20\n",
+       "17        60 2020-12-17    100       120    300.00\n",
+       "18        45 2020-12-18     90       112    304.68\n",
+       "19        60 2020-12-19    103       123    323.00\n",
+       "20        45 2020-12-20     97       125    243.00\n",
+       "21        60 2020-12-21    108       131    364.20\n",
+       "22        45        NaT    100       119    282.00\n",
+       "23        60 2020-12-23    130       101    300.00\n",
+       "24        45 2020-12-24    105       132    246.00\n",
+       "25        60 2020-12-25    102       126    334.50\n",
+       "26        60 2020-12-26    100       120    250.00\n",
+       "27        60 2020-12-27     92       118    241.00\n",
+       "28        60 2020-12-28    103       132    304.68\n",
+       "29        60 2020-12-29    100       132    280.00\n",
+       "30        60 2020-12-30    102       129    380.30\n",
+       "31        60 2020-12-31     92       115    243.00"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3033c2a4-18f1-4fcd-be75-f71f95c9097f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('cleaned.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "549ea6b3-3903-4b74-88ad-74c60e7d862e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/pandas/.ipynb_checkpoints/03_advanced-checkpoint.ipynb b/course/pandas/.ipynb_checkpoints/03_advanced-checkpoint.ipynb
new file mode 100644
index 0000000..f26a342
--- /dev/null
+++ b/course/pandas/.ipynb_checkpoints/03_advanced-checkpoint.ipynb
@@ -0,0 +1,391 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ddb0915a-4364-499f-8224-8af96e00cdf2",
+   "metadata": {},
+   "source": [
+    "#### Advanced"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4f677ea5-7e64-4db2-8d25-c39eec1b1989",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "38eeb7f7-ba40-41da-a029-35a4b5350878",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('https://www.w3schools.com/python/pandas/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ff6a7d3f-d18f-43e8-a03e-257489439289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>60</td>\n",
+       "      <td>105</td>\n",
+       "      <td>140</td>\n",
+       "      <td>290.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>60</td>\n",
+       "      <td>110</td>\n",
+       "      <td>145</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>60</td>\n",
+       "      <td>115</td>\n",
+       "      <td>145</td>\n",
+       "      <td>310.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>75</td>\n",
+       "      <td>120</td>\n",
+       "      <td>150</td>\n",
+       "      <td>320.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>75</td>\n",
+       "      <td>125</td>\n",
+       "      <td>150</td>\n",
+       "      <td>330.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>169 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Duration  Pulse  Maxpulse  Calories\n",
+       "0          60    110       130     409.1\n",
+       "1          60    117       145     479.0\n",
+       "2          60    103       135     340.0\n",
+       "3          45    109       175     282.4\n",
+       "4          45    117       148     406.0\n",
+       "..        ...    ...       ...       ...\n",
+       "164        60    105       140     290.8\n",
+       "165        60    110       145     300.0\n",
+       "166        60    115       145     310.2\n",
+       "167        75    120       150     320.4\n",
+       "168        75    125       150     330.4\n",
+       "\n",
+       "[169 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "59980378-83d8-4ec4-99e4-d29557d367bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Duration</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.155408</td>\n",
+       "      <td>0.009403</td>\n",
+       "      <td>0.922717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pulse</th>\n",
+       "      <td>-0.155408</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.786535</td>\n",
+       "      <td>0.025121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <td>0.009403</td>\n",
+       "      <td>0.786535</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.203813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Calories</th>\n",
+       "      <td>0.922717</td>\n",
+       "      <td>0.025121</td>\n",
+       "      <td>0.203813</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Duration     Pulse  Maxpulse  Calories\n",
+       "Duration  1.000000 -0.155408  0.009403  0.922717\n",
+       "Pulse    -0.155408  1.000000  0.786535  0.025121\n",
+       "Maxpulse  0.009403  0.786535  1.000000  0.203813\n",
+       "Calories  0.922717  0.025121  0.203813  1.000000"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# correlation (1 / -1 --> good, ~0, bad)\n",
+    "df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7c279538-3d8d-4add-a30c-5c426c5c714f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "df.plot()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e3cff924-55c9-48cc-9c8c-6d98b01bcd8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind = 'scatter', x = 'Duration', y = 'Calories')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "75fcadc3-7c9c-4e92-8dcc-6a0551b55bb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind = 'scatter', x = 'Duration', y = 'Maxpulse')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f32fb4d5-9d8a-4ef7-9acf-9322ee98839d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Duration\"].plot(kind = 'hist')\n",
+    "# A histogram shows us the frequency of each interval, \n",
+    "# e.g. how many workouts lasted between 50 and 60 minutes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1af5196-ce21-4120-8483-ccb2cc48adbc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/pandas/.ipynb_checkpoints/cleaned-checkpoint.csv b/course/pandas/.ipynb_checkpoints/cleaned-checkpoint.csv
new file mode 100644
index 0000000..bcbb3e5
--- /dev/null
+++ b/course/pandas/.ipynb_checkpoints/cleaned-checkpoint.csv
@@ -0,0 +1,32 @@
+,Duration,Date,Pulse,Maxpulse,Calories
+0,60,2020-12-01,110,130,409.1
+1,60,2020-12-02,117,145,479.0
+2,60,2020-12-03,103,135,340.0
+3,45,2020-12-04,109,175,282.4
+4,45,2020-12-05,117,148,406.0
+5,60,2020-12-06,102,127,300.0
+6,60,2020-12-07,110,136,374.0
+7,45,2020-12-08,104,134,253.3
+8,30,2020-12-09,109,133,195.1
+9,60,2020-12-10,98,124,269.0
+10,60,2020-12-11,103,147,329.3
+11,60,2020-12-12,100,120,250.7
+13,60,2020-12-13,106,128,345.3
+14,60,2020-12-14,104,132,379.3
+15,60,2020-12-15,98,123,275.0
+16,60,2020-12-16,98,120,215.2
+17,60,2020-12-17,100,120,300.0
+18,45,2020-12-18,90,112,304.68
+19,60,2020-12-19,103,123,323.0
+20,45,2020-12-20,97,125,243.0
+21,60,2020-12-21,108,131,364.2
+22,45,,100,119,282.0
+23,60,2020-12-23,130,101,300.0
+24,45,2020-12-24,105,132,246.0
+25,60,2020-12-25,102,126,334.5
+26,60,2020-12-26,100,120,250.0
+27,60,2020-12-27,92,118,241.0
+28,60,2020-12-28,103,132,304.68
+29,60,2020-12-29,100,132,280.0
+30,60,2020-12-30,102,129,380.3
+31,60,2020-12-31,92,115,243.0
diff --git a/course/pandas/01_basics.ipynb b/course/pandas/01_basics.ipynb
new file mode 100644
index 0000000..130be21
--- /dev/null
+++ b/course/pandas/01_basics.ipynb
@@ -0,0 +1,745 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bd4b5db9-6439-4519-aef0-c8bb8ffd6a13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3c4bf54-07f7-46bb-9ae5-77c3a4518627",
+   "metadata": {},
+   "source": [
+    "### Basics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "def1dfe3-6afe-4f5e-9714-36b65811094a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    cars  passings\n",
+      "0    BMW         3\n",
+      "1  Volvo         7\n",
+      "2   Ford         2\n"
+     ]
+    }
+   ],
+   "source": [
+    "mydataset = {\n",
+    "  'cars': [\"BMW\", \"Volvo\", \"Ford\"],\n",
+    "  'passings': [3, 7, 2]\n",
+    "}\n",
+    "\n",
+    "myvar = pd.DataFrame(mydataset)\n",
+    "\n",
+    "print(myvar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0785d9bd-e5b1-4221-84e9-1a1a6a2cf37e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    1\n",
+      "1    7\n",
+      "2    2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# series: numpy arrays!\n",
+    "a = [1, 7, 2]\n",
+    "\n",
+    "myvar = pd.Series(a)\n",
+    "\n",
+    "print(myvar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2d7a5f33-79a2-4e2a-9b14-eee43db90662",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x    1\n",
+      "y    7\n",
+      "z    2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# labels\n",
+    "a = [1, 7, 2]\n",
+    "\n",
+    "myvar = pd.Series(a, index = [\"x\", \"y\", \"z\"])\n",
+    "\n",
+    "print(myvar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cba4f2ef-45d1-4c23-94ce-7197c43d4aee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "day1    420\n",
+      "day2    380\n",
+      "day3    390\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# key value objects\n",
+    "calories = {\"day1\": 420, \"day2\": 380, \"day3\": 390}\n",
+    "\n",
+    "myvar = pd.Series(calories)\n",
+    "\n",
+    "print(myvar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e965971f-6a97-42c4-81bf-2550e57cd7e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   calories  duration\n",
+      "0       420        50\n",
+      "1       380        40\n",
+      "2       390        45\n"
+     ]
+    }
+   ],
+   "source": [
+    "# dataframe = multi-dimensional tables\n",
+    "data = {\n",
+    "  \"calories\": [420, 380, 390],\n",
+    "  \"duration\": [50, 40, 45]\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a6650272-40db-4237-b2d6-83d3652184c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calories    420\n",
+      "duration     50\n",
+      "Name: 0, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# locate row\n",
+    "print(df.loc[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "694a5a9b-c280-454c-b7c6-236eda33d8f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   calories  duration\n",
+      "0       420        50\n",
+      "1       380        40\n"
+     ]
+    }
+   ],
+   "source": [
+    "#use a list of indexes:\n",
+    "print(df.loc[[0, 1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e3ff6022-327e-446d-a739-011730d1db8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      calories  duration\n",
+      "day1       420        50\n",
+      "day2       380        40\n",
+      "day3       390        45\n"
+     ]
+    }
+   ],
+   "source": [
+    "# named index\n",
+    "data = {\n",
+    "  \"calories\": [420, 380, 390],\n",
+    "  \"duration\": [50, 40, 45]\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data, index = [\"day1\", \"day2\", \"day3\"])\n",
+    "\n",
+    "print(df) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4d8a50a4-7c1d-4ef7-ab0b-09cbc8085ad6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calories    380\n",
+      "duration     40\n",
+      "Name: day2, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#refer to the named index:\n",
+    "print(df.loc[\"day2\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b18cee34-c5dd-43df-9e2e-bd20ebeed8ed",
+   "metadata": {},
+   "source": [
+    "### Loading Files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "abef3aeb-d82c-4cc5-a2d2-7a70313f0712",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Duration  Pulse  Maxpulse  Calories\n",
+      "0          60    110       130     409.1\n",
+      "1          60    117       145     479.0\n",
+      "2          60    103       135     340.0\n",
+      "3          45    109       175     282.4\n",
+      "4          45    117       148     406.0\n",
+      "5          60    102       127     300.0\n",
+      "6          60    110       136     374.0\n",
+      "7          45    104       134     253.3\n",
+      "8          30    109       133     195.1\n",
+      "9          60     98       124     269.0\n",
+      "10         60    103       147     329.3\n",
+      "11         60    100       120     250.7\n",
+      "12         60    106       128     345.3\n",
+      "13         60    104       132     379.3\n",
+      "14         60     98       123     275.0\n",
+      "15         60     98       120     215.2\n",
+      "16         60    100       120     300.0\n",
+      "17         45     90       112       NaN\n",
+      "18         60    103       123     323.0\n",
+      "19         45     97       125     243.0\n",
+      "20         60    108       131     364.2\n",
+      "21         45    100       119     282.0\n",
+      "22         60    130       101     300.0\n",
+      "23         45    105       132     246.0\n",
+      "24         60    102       126     334.5\n",
+      "25         60    100       120     250.0\n",
+      "26         60     92       118     241.0\n",
+      "27         60    103       132       NaN\n",
+      "28         60    100       132     280.0\n",
+      "29         60    102       129     380.3\n",
+      "30         60     92       115     243.0\n",
+      "31         45     90       112     180.1\n",
+      "32         60    101       124     299.0\n",
+      "33         60     93       113     223.0\n",
+      "34         60    107       136     361.0\n",
+      "35         60    114       140     415.0\n",
+      "36         60    102       127     300.0\n",
+      "37         60    100       120     300.0\n",
+      "38         60    100       120     300.0\n",
+      "39         45    104       129     266.0\n",
+      "40         45     90       112     180.1\n",
+      "41         60     98       126     286.0\n",
+      "42         60    100       122     329.4\n",
+      "43         60    111       138     400.0\n",
+      "44         60    111       131     397.0\n",
+      "45         60     99       119     273.0\n",
+      "46         60    109       153     387.6\n",
+      "47         45    111       136     300.0\n",
+      "48         45    108       129     298.0\n",
+      "49         60    111       139     397.6\n",
+      "50         60    107       136     380.2\n",
+      "51         80    123       146     643.1\n",
+      "52         60    106       130     263.0\n",
+      "53         60    118       151     486.0\n",
+      "54         30    136       175     238.0\n",
+      "55         60    121       146     450.7\n",
+      "56         60    118       121     413.0\n",
+      "57         45    115       144     305.0\n",
+      "58         20    153       172     226.4\n",
+      "59         45    123       152     321.0\n",
+      "60        210    108       160    1376.0\n",
+      "61        160    110       137    1034.4\n",
+      "62        160    109       135     853.0\n",
+      "63         45    118       141     341.0\n",
+      "64         20    110       130     131.4\n",
+      "65        180     90       130     800.4\n",
+      "66        150    105       135     873.4\n",
+      "67        150    107       130     816.0\n",
+      "68         20    106       136     110.4\n",
+      "69        300    108       143    1500.2\n",
+      "70        150     97       129    1115.0\n",
+      "71         60    109       153     387.6\n",
+      "72         90    100       127     700.0\n",
+      "73        150     97       127     953.2\n",
+      "74         45    114       146     304.0\n",
+      "75         90     98       125     563.2\n",
+      "76         45    105       134     251.0\n",
+      "77         45    110       141     300.0\n",
+      "78        120    100       130     500.4\n",
+      "79        270    100       131    1729.0\n",
+      "80         30    159       182     319.2\n",
+      "81         45    149       169     344.0\n",
+      "82         30    103       139     151.1\n",
+      "83        120    100       130     500.0\n",
+      "84         45    100       120     225.3\n",
+      "85         30    151       170     300.0\n",
+      "86         45    102       136     234.0\n",
+      "87        120    100       157    1000.1\n",
+      "88         45    129       103     242.0\n",
+      "89         20     83       107      50.3\n",
+      "90        180    101       127     600.1\n",
+      "91         45    107       137       NaN\n",
+      "92         30     90       107     105.3\n",
+      "93         15     80       100      50.5\n",
+      "94         20    150       171     127.4\n",
+      "95         20    151       168     229.4\n",
+      "96         30     95       128     128.2\n",
+      "97         25    152       168     244.2\n",
+      "98         30    109       131     188.2\n",
+      "99         90     93       124     604.1\n",
+      "100        20     95       112      77.7\n",
+      "101        90     90       110     500.0\n",
+      "102        90     90       100     500.0\n",
+      "103        90     90       100     500.4\n",
+      "104        30     92       108      92.7\n",
+      "105        30     93       128     124.0\n",
+      "106       180     90       120     800.3\n",
+      "107        30     90       120      86.2\n",
+      "108        90     90       120     500.3\n",
+      "109       210    137       184    1860.4\n",
+      "110        60    102       124     325.2\n",
+      "111        45    107       124     275.0\n",
+      "112        15    124       139     124.2\n",
+      "113        45    100       120     225.3\n",
+      "114        60    108       131     367.6\n",
+      "115        60    108       151     351.7\n",
+      "116        60    116       141     443.0\n",
+      "117        60     97       122     277.4\n",
+      "118        60    105       125       NaN\n",
+      "119        60    103       124     332.7\n",
+      "120        30    112       137     193.9\n",
+      "121        45    100       120     100.7\n",
+      "122        60    119       169     336.7\n",
+      "123        60    107       127     344.9\n",
+      "124        60    111       151     368.5\n",
+      "125        60     98       122     271.0\n",
+      "126        60     97       124     275.3\n",
+      "127        60    109       127     382.0\n",
+      "128        90     99       125     466.4\n",
+      "129        60    114       151     384.0\n",
+      "130        60    104       134     342.5\n",
+      "131        60    107       138     357.5\n",
+      "132        60    103       133     335.0\n",
+      "133        60    106       132     327.5\n",
+      "134        60    103       136     339.0\n",
+      "135        20    136       156     189.0\n",
+      "136        45    117       143     317.7\n",
+      "137        45    115       137     318.0\n",
+      "138        45    113       138     308.0\n",
+      "139        20    141       162     222.4\n",
+      "140        60    108       135     390.0\n",
+      "141        60     97       127       NaN\n",
+      "142        45    100       120     250.4\n",
+      "143        45    122       149     335.4\n",
+      "144        60    136       170     470.2\n",
+      "145        45    106       126     270.8\n",
+      "146        60    107       136     400.0\n",
+      "147        60    112       146     361.9\n",
+      "148        30    103       127     185.0\n",
+      "149        60    110       150     409.4\n",
+      "150        60    106       134     343.0\n",
+      "151        60    109       129     353.2\n",
+      "152        60    109       138     374.0\n",
+      "153        30    150       167     275.8\n",
+      "154        60    105       128     328.0\n",
+      "155        60    111       151     368.5\n",
+      "156        60     97       131     270.4\n",
+      "157        60    100       120     270.4\n",
+      "158        60    114       150     382.8\n",
+      "159        30     80       120     240.9\n",
+      "160        30     85       120     250.4\n",
+      "161        45     90       130     260.4\n",
+      "162        45     95       130     270.0\n",
+      "163        45    100       140     280.9\n",
+      "164        60    105       140     290.8\n",
+      "165        60    110       145     300.0\n",
+      "166        60    115       145     310.2\n",
+      "167        75    120       150     320.4\n",
+      "168        75    125       150     330.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "#https://www.w3schools.com/python/pandas/data.csv\n",
+    "df = pd.read_csv('https://www.w3schools.com/python/pandas/data.csv')\n",
+    "\n",
+    "print(df.to_string()) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dba1828-de88-4a61-868b-5a8b638254f0",
+   "metadata": {},
+   "source": [
+    "#### Analyze Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e89c6f79-b957-4c6b-85bc-c76b362d7a2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(169, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "460724ee-1ba6-461c-801e-46632a68c837",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 169 entries, 0 to 168\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column    Non-Null Count  Dtype  \n",
+      "---  ------    --------------  -----  \n",
+      " 0   Duration  169 non-null    int64  \n",
+      " 1   Pulse     169 non-null    int64  \n",
+      " 2   Maxpulse  169 non-null    int64  \n",
+      " 3   Calories  164 non-null    float64\n",
+      "dtypes: float64(1), int64(3)\n",
+      "memory usage: 5.4 KB\n",
+      "None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>169.000000</td>\n",
+       "      <td>169.000000</td>\n",
+       "      <td>169.000000</td>\n",
+       "      <td>164.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>63.846154</td>\n",
+       "      <td>107.461538</td>\n",
+       "      <td>134.047337</td>\n",
+       "      <td>375.790244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>42.299949</td>\n",
+       "      <td>14.510259</td>\n",
+       "      <td>16.450434</td>\n",
+       "      <td>266.379919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>50.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>124.000000</td>\n",
+       "      <td>250.925000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>131.000000</td>\n",
+       "      <td>318.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>111.000000</td>\n",
+       "      <td>141.000000</td>\n",
+       "      <td>387.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>300.000000</td>\n",
+       "      <td>159.000000</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>1860.400000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Duration       Pulse    Maxpulse     Calories\n",
+       "count  169.000000  169.000000  169.000000   164.000000\n",
+       "mean    63.846154  107.461538  134.047337   375.790244\n",
+       "std     42.299949   14.510259   16.450434   266.379919\n",
+       "min     15.000000   80.000000  100.000000    50.300000\n",
+       "25%     45.000000  100.000000  124.000000   250.925000\n",
+       "50%     60.000000  105.000000  131.000000   318.600000\n",
+       "75%     60.000000  111.000000  141.000000   387.600000\n",
+       "max    300.000000  159.000000  184.000000  1860.400000"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(df.info()) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c3075b2-8160-4987-a014-050da0c374bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "418bbc3e-53ff-4ea2-9728-1b8aa94a140d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Duration  Pulse  Maxpulse  Calories\n",
+      "0        60    110       130     409.1\n",
+      "1        60    117       145     479.0\n",
+      "2        60    103       135     340.0\n",
+      "3        45    109       175     282.4\n",
+      "4        45    117       148     406.0\n",
+      "5        60    102       127     300.0\n",
+      "6        60    110       136     374.0\n",
+      "7        45    104       134     253.3\n",
+      "8        30    109       133     195.1\n",
+      "9        60     98       124     269.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b7424301-9bb4-4200-b2d5-bcc8a22c6427",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Duration  Pulse  Maxpulse  Calories\n",
+      "0        60    110       130     409.1\n",
+      "1        60    117       145     479.0\n",
+      "2        60    103       135     340.0\n",
+      "3        45    109       175     282.4\n",
+      "4        45    117       148     406.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a7649f71-6c14-4158-8040-08019577b1ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Duration  Pulse  Maxpulse  Calories\n",
+      "164        60    105       140     290.8\n",
+      "165        60    110       145     300.0\n",
+      "166        60    115       145     310.2\n",
+      "167        75    120       150     320.4\n",
+      "168        75    125       150     330.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.tail())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "8da3696c-6a3c-4727-8cea-6442d7cedf28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    169.000000\n",
+      "mean     107.461538\n",
+      "std       14.510259\n",
+      "min       80.000000\n",
+      "25%      100.000000\n",
+      "50%      105.000000\n",
+      "75%      111.000000\n",
+      "max      159.000000\n",
+      "Name: Pulse, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Pulse'].describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8689d23c-5d68-4fdb-bcef-9ae080442a27",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/pandas/02_cleaning.ipynb b/course/pandas/02_cleaning.ipynb
new file mode 100644
index 0000000..74f421a
--- /dev/null
+++ b/course/pandas/02_cleaning.ipynb
@@ -0,0 +1,1259 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b0c0ae08-2fb5-47f5-a5ce-1a66e35791a4",
+   "metadata": {},
+   "source": [
+    "### Cleaning Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f9998a78-ae01-4531-b325-637b6d5ee86d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9516a86a-ed6a-4f79-b631-3195daec258c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('https://gist.githubusercontent.com/maltegrosse/bdfd2c6a5e3bff315d92cd27c2461a48/raw/49d5672953360934601b3d252c9b78121eed10db/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ea25a32c-70d3-479d-8d11-7e487f13f50c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/01'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/02'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/03'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/04'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/05'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/06'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>127</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/07'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>136</td>\n",
+       "      <td>374.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>450</td>\n",
+       "      <td>'2020/12/08'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>134</td>\n",
+       "      <td>253.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30</td>\n",
+       "      <td>'2020/12/09'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>133</td>\n",
+       "      <td>195.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/10'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>124</td>\n",
+       "      <td>269.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/11'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>147</td>\n",
+       "      <td>329.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/13'</td>\n",
+       "      <td>106</td>\n",
+       "      <td>128</td>\n",
+       "      <td>345.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/14'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>132</td>\n",
+       "      <td>379.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/15'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>123</td>\n",
+       "      <td>275.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/16'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>120</td>\n",
+       "      <td>215.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/17'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/18'</td>\n",
+       "      <td>90</td>\n",
+       "      <td>112</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/19'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>123</td>\n",
+       "      <td>323.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/20'</td>\n",
+       "      <td>97</td>\n",
+       "      <td>125</td>\n",
+       "      <td>243.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/21'</td>\n",
+       "      <td>108</td>\n",
+       "      <td>131</td>\n",
+       "      <td>364.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>45</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100</td>\n",
+       "      <td>119</td>\n",
+       "      <td>282.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/23'</td>\n",
+       "      <td>130</td>\n",
+       "      <td>101</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/24'</td>\n",
+       "      <td>105</td>\n",
+       "      <td>132</td>\n",
+       "      <td>246.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/25'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>126</td>\n",
+       "      <td>334.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>60</td>\n",
+       "      <td>20201226</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/27'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>118</td>\n",
+       "      <td>241.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/28'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>132</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/29'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>132</td>\n",
+       "      <td>280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/30'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>129</td>\n",
+       "      <td>380.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/31'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>115</td>\n",
+       "      <td>243.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Duration          Date  Pulse  Maxpulse  Calories\n",
+       "0         60  '2020/12/01'    110       130     409.1\n",
+       "1         60  '2020/12/02'    117       145     479.0\n",
+       "2         60  '2020/12/03'    103       135     340.0\n",
+       "3         45  '2020/12/04'    109       175     282.4\n",
+       "4         45  '2020/12/05'    117       148     406.0\n",
+       "5         60  '2020/12/06'    102       127     300.0\n",
+       "6         60  '2020/12/07'    110       136     374.0\n",
+       "7        450  '2020/12/08'    104       134     253.3\n",
+       "8         30  '2020/12/09'    109       133     195.1\n",
+       "9         60  '2020/12/10'     98       124     269.0\n",
+       "10        60  '2020/12/11'    103       147     329.3\n",
+       "11        60  '2020/12/12'    100       120     250.7\n",
+       "12        60  '2020/12/12'    100       120     250.7\n",
+       "13        60  '2020/12/13'    106       128     345.3\n",
+       "14        60  '2020/12/14'    104       132     379.3\n",
+       "15        60  '2020/12/15'     98       123     275.0\n",
+       "16        60  '2020/12/16'     98       120     215.2\n",
+       "17        60  '2020/12/17'    100       120     300.0\n",
+       "18        45  '2020/12/18'     90       112       NaN\n",
+       "19        60  '2020/12/19'    103       123     323.0\n",
+       "20        45  '2020/12/20'     97       125     243.0\n",
+       "21        60  '2020/12/21'    108       131     364.2\n",
+       "22        45           NaN    100       119     282.0\n",
+       "23        60  '2020/12/23'    130       101     300.0\n",
+       "24        45  '2020/12/24'    105       132     246.0\n",
+       "25        60  '2020/12/25'    102       126     334.5\n",
+       "26        60      20201226    100       120     250.0\n",
+       "27        60  '2020/12/27'     92       118     241.0\n",
+       "28        60  '2020/12/28'    103       132       NaN\n",
+       "29        60  '2020/12/29'    100       132     280.0\n",
+       "30        60  '2020/12/30'    102       129     380.3\n",
+       "31        60  '2020/12/31'     92       115     243.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2baf29d8-cd8f-4dfd-931a-c413a995320e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/01'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/02'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/03'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/04'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/05'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/06'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>127</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/07'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>136</td>\n",
+       "      <td>374.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>450</td>\n",
+       "      <td>'2020/12/08'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>134</td>\n",
+       "      <td>253.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30</td>\n",
+       "      <td>'2020/12/09'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>133</td>\n",
+       "      <td>195.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/10'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>124</td>\n",
+       "      <td>269.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/11'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>147</td>\n",
+       "      <td>329.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/13'</td>\n",
+       "      <td>106</td>\n",
+       "      <td>128</td>\n",
+       "      <td>345.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/14'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>132</td>\n",
+       "      <td>379.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/15'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>123</td>\n",
+       "      <td>275.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/16'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>120</td>\n",
+       "      <td>215.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/17'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/19'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>123</td>\n",
+       "      <td>323.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/20'</td>\n",
+       "      <td>97</td>\n",
+       "      <td>125</td>\n",
+       "      <td>243.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/21'</td>\n",
+       "      <td>108</td>\n",
+       "      <td>131</td>\n",
+       "      <td>364.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/23'</td>\n",
+       "      <td>130</td>\n",
+       "      <td>101</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/24'</td>\n",
+       "      <td>105</td>\n",
+       "      <td>132</td>\n",
+       "      <td>246.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/25'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>126</td>\n",
+       "      <td>334.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>60</td>\n",
+       "      <td>20201226</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/27'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>118</td>\n",
+       "      <td>241.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/29'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>132</td>\n",
+       "      <td>280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/30'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>129</td>\n",
+       "      <td>380.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/31'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>115</td>\n",
+       "      <td>243.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Duration          Date  Pulse  Maxpulse  Calories\n",
+       "0         60  '2020/12/01'    110       130     409.1\n",
+       "1         60  '2020/12/02'    117       145     479.0\n",
+       "2         60  '2020/12/03'    103       135     340.0\n",
+       "3         45  '2020/12/04'    109       175     282.4\n",
+       "4         45  '2020/12/05'    117       148     406.0\n",
+       "5         60  '2020/12/06'    102       127     300.0\n",
+       "6         60  '2020/12/07'    110       136     374.0\n",
+       "7        450  '2020/12/08'    104       134     253.3\n",
+       "8         30  '2020/12/09'    109       133     195.1\n",
+       "9         60  '2020/12/10'     98       124     269.0\n",
+       "10        60  '2020/12/11'    103       147     329.3\n",
+       "11        60  '2020/12/12'    100       120     250.7\n",
+       "12        60  '2020/12/12'    100       120     250.7\n",
+       "13        60  '2020/12/13'    106       128     345.3\n",
+       "14        60  '2020/12/14'    104       132     379.3\n",
+       "15        60  '2020/12/15'     98       123     275.0\n",
+       "16        60  '2020/12/16'     98       120     215.2\n",
+       "17        60  '2020/12/17'    100       120     300.0\n",
+       "19        60  '2020/12/19'    103       123     323.0\n",
+       "20        45  '2020/12/20'     97       125     243.0\n",
+       "21        60  '2020/12/21'    108       131     364.2\n",
+       "23        60  '2020/12/23'    130       101     300.0\n",
+       "24        45  '2020/12/24'    105       132     246.0\n",
+       "25        60  '2020/12/25'    102       126     334.5\n",
+       "26        60      20201226    100       120     250.0\n",
+       "27        60  '2020/12/27'     92       118     241.0\n",
+       "29        60  '2020/12/29'    100       132     280.0\n",
+       "30        60  '2020/12/30'    102       129     380.3\n",
+       "31        60  '2020/12/31'     92       115     243.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop null/NaN\n",
+    "new_df = df.dropna()\n",
+    "new_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "86df1d5f-639e-4f0b-8576-eb4a9dbee188",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 29 entries, 0 to 31\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column    Non-Null Count  Dtype  \n",
+      "---  ------    --------------  -----  \n",
+      " 0   Duration  29 non-null     int64  \n",
+      " 1   Date      29 non-null     object \n",
+      " 2   Pulse     29 non-null     int64  \n",
+      " 3   Maxpulse  29 non-null     int64  \n",
+      " 4   Calories  29 non-null     float64\n",
+      "dtypes: float64(1), int64(3), object(1)\n",
+      "memory usage: 1.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.locnew_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "37533007-2851-49da-8fca-2e9d3b74c406",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hint df.dropna(inplace = True) <- manipulates orginal df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e94f0608-1928-4dec-b28c-3f56d72b1867",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fill missing values\n",
+    "# df.fillna(130, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "025cec14-2687-4ec5-9fa9-f10f1da927ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_733/185300893.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
+      "\n",
+      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
+      "\n",
+      "\n",
+      "  df[\"Calories\"].fillna(x, inplace=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/01'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/02'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/03'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/04'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/05'</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/06'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>127</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/07'</td>\n",
+       "      <td>110</td>\n",
+       "      <td>136</td>\n",
+       "      <td>374.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>450</td>\n",
+       "      <td>'2020/12/08'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>134</td>\n",
+       "      <td>253.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30</td>\n",
+       "      <td>'2020/12/09'</td>\n",
+       "      <td>109</td>\n",
+       "      <td>133</td>\n",
+       "      <td>195.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/10'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>124</td>\n",
+       "      <td>269.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/11'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>147</td>\n",
+       "      <td>329.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/12'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/13'</td>\n",
+       "      <td>106</td>\n",
+       "      <td>128</td>\n",
+       "      <td>345.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/14'</td>\n",
+       "      <td>104</td>\n",
+       "      <td>132</td>\n",
+       "      <td>379.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/15'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>123</td>\n",
+       "      <td>275.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/16'</td>\n",
+       "      <td>98</td>\n",
+       "      <td>120</td>\n",
+       "      <td>215.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/17'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/18'</td>\n",
+       "      <td>90</td>\n",
+       "      <td>112</td>\n",
+       "      <td>304.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/19'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>123</td>\n",
+       "      <td>323.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/20'</td>\n",
+       "      <td>97</td>\n",
+       "      <td>125</td>\n",
+       "      <td>243.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/21'</td>\n",
+       "      <td>108</td>\n",
+       "      <td>131</td>\n",
+       "      <td>364.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>45</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100</td>\n",
+       "      <td>119</td>\n",
+       "      <td>282.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/23'</td>\n",
+       "      <td>130</td>\n",
+       "      <td>101</td>\n",
+       "      <td>300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>45</td>\n",
+       "      <td>'2020/12/24'</td>\n",
+       "      <td>105</td>\n",
+       "      <td>132</td>\n",
+       "      <td>246.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/25'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>126</td>\n",
+       "      <td>334.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>60</td>\n",
+       "      <td>20201226</td>\n",
+       "      <td>100</td>\n",
+       "      <td>120</td>\n",
+       "      <td>250.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/27'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>118</td>\n",
+       "      <td>241.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/28'</td>\n",
+       "      <td>103</td>\n",
+       "      <td>132</td>\n",
+       "      <td>304.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/29'</td>\n",
+       "      <td>100</td>\n",
+       "      <td>132</td>\n",
+       "      <td>280.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/30'</td>\n",
+       "      <td>102</td>\n",
+       "      <td>129</td>\n",
+       "      <td>380.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>60</td>\n",
+       "      <td>'2020/12/31'</td>\n",
+       "      <td>92</td>\n",
+       "      <td>115</td>\n",
+       "      <td>243.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Duration          Date  Pulse  Maxpulse  Calories\n",
+       "0         60  '2020/12/01'    110       130    409.10\n",
+       "1         60  '2020/12/02'    117       145    479.00\n",
+       "2         60  '2020/12/03'    103       135    340.00\n",
+       "3         45  '2020/12/04'    109       175    282.40\n",
+       "4         45  '2020/12/05'    117       148    406.00\n",
+       "5         60  '2020/12/06'    102       127    300.00\n",
+       "6         60  '2020/12/07'    110       136    374.00\n",
+       "7        450  '2020/12/08'    104       134    253.30\n",
+       "8         30  '2020/12/09'    109       133    195.10\n",
+       "9         60  '2020/12/10'     98       124    269.00\n",
+       "10        60  '2020/12/11'    103       147    329.30\n",
+       "11        60  '2020/12/12'    100       120    250.70\n",
+       "12        60  '2020/12/12'    100       120    250.70\n",
+       "13        60  '2020/12/13'    106       128    345.30\n",
+       "14        60  '2020/12/14'    104       132    379.30\n",
+       "15        60  '2020/12/15'     98       123    275.00\n",
+       "16        60  '2020/12/16'     98       120    215.20\n",
+       "17        60  '2020/12/17'    100       120    300.00\n",
+       "18        45  '2020/12/18'     90       112    304.68\n",
+       "19        60  '2020/12/19'    103       123    323.00\n",
+       "20        45  '2020/12/20'     97       125    243.00\n",
+       "21        60  '2020/12/21'    108       131    364.20\n",
+       "22        45           NaN    100       119    282.00\n",
+       "23        60  '2020/12/23'    130       101    300.00\n",
+       "24        45  '2020/12/24'    105       132    246.00\n",
+       "25        60  '2020/12/25'    102       126    334.50\n",
+       "26        60      20201226    100       120    250.00\n",
+       "27        60  '2020/12/27'     92       118    241.00\n",
+       "28        60  '2020/12/28'    103       132    304.68\n",
+       "29        60  '2020/12/29'    100       132    280.00\n",
+       "30        60  '2020/12/30'    102       129    380.30\n",
+       "31        60  '2020/12/31'     92       115    243.00"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = df[\"Calories\"].mean()\n",
+    "\n",
+    "df[\"Calories\"].fillna(x, inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d2e87f3b-ef58-4128-b52f-799056e56de8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_733/2663698494.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
+      "\n",
+      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
+      "\n",
+      "\n",
+      "  df[\"Calories\"].fillna(x, inplace = True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = df[\"Calories\"].median()\n",
+    "\n",
+    "df[\"Calories\"].fillna(x, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c42df786-aa1b-4174-b436-566421f1683b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "time data \"20201226\" doesn't match format \"'%Y/%m/%d'\", at position 26. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# convert into proper data type\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDate\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDate\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m df\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/pandas/core/tools/datetimes.py:1067\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m   1065\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n\u001b[1;32m   1066\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1067\u001b[0m         values \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1068\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_constructor(values, index\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mindex, name\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m   1069\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, (ABCDataFrame, abc\u001b[38;5;241m.\u001b[39mMutableMapping)):\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/pandas/core/tools/datetimes.py:433\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m    431\u001b[0m \u001b[38;5;66;03m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001b[39;00m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 433\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_array_strptime_with_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    435\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m objects_to_datetime64(\n\u001b[1;32m    436\u001b[0m     arg,\n\u001b[1;32m    437\u001b[0m     dayfirst\u001b[38;5;241m=\u001b[39mdayfirst,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    441\u001b[0m     allow_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    442\u001b[0m )\n\u001b[1;32m    444\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    445\u001b[0m     \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m    446\u001b[0m     \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/pandas/core/tools/datetimes.py:467\u001b[0m, in \u001b[0;36m_array_strptime_with_fallback\u001b[0;34m(arg, name, utc, fmt, exact, errors)\u001b[0m\n\u001b[1;32m    456\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_array_strptime_with_fallback\u001b[39m(\n\u001b[1;32m    457\u001b[0m     arg,\n\u001b[1;32m    458\u001b[0m     name,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    462\u001b[0m     errors: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m    463\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Index:\n\u001b[1;32m    464\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    465\u001b[0m \u001b[38;5;124;03m    Call array_strptime, with fallback behavior depending on 'errors'.\u001b[39;00m\n\u001b[1;32m    466\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 467\u001b[0m     result, tz_out \u001b[38;5;241m=\u001b[39m \u001b[43marray_strptime\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    468\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m tz_out \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    469\u001b[0m         unit \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdatetime_data(result\u001b[38;5;241m.\u001b[39mdtype)[\u001b[38;5;241m0\u001b[39m]\n",
+      "File \u001b[0;32mstrptime.pyx:501\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mstrptime.pyx:451\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mstrptime.pyx:583\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime._parse_with_format\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: time data \"20201226\" doesn't match format \"'%Y/%m/%d'\", at position 26. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this."
+     ]
+    }
+   ],
+   "source": [
+    "# convert into proper data type\n",
+    "df['Date'] = pd.to_datetime(df['Date'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6508edc2-f7f1-469b-a094-1b6c98a155e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove missing value according to a column\n",
+    "# df.dropna(subset=['Date'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "725032e8-c03e-428e-a928-f5c2533a3446",
+   "metadata": {},
+   "source": [
+    "#### Fixing Wrong Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3367d5c9-90f8-4fb1-9c2b-bae2bdaeb7bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# row 7: 450 duration!\n",
+    "df.loc[7, 'Duration'] = 45"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a9ce891-9275-4539-a23c-4826fb258c1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7888f644-60a5-41e2-bd9f-acf1f5e08f5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove duplicates row 11 & 12\n",
+    "print(df.duplicated())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff4ee9a2-dabb-4015-8b0c-5527f688bb21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop_duplicates(inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44165eb4-ab0c-4be0-92d6-4c8ccf2ff389",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3033c2a4-18f1-4fcd-be75-f71f95c9097f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('cleaned.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "549ea6b3-3903-4b74-88ad-74c60e7d862e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/pandas/03_advanced.ipynb b/course/pandas/03_advanced.ipynb
new file mode 100644
index 0000000..e935625
--- /dev/null
+++ b/course/pandas/03_advanced.ipynb
@@ -0,0 +1,391 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ddb0915a-4364-499f-8224-8af96e00cdf2",
+   "metadata": {},
+   "source": [
+    "#### Advanced"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4f677ea5-7e64-4db2-8d25-c39eec1b1989",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "38eeb7f7-ba40-41da-a029-35a4b5350878",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('https://www.w3schools.com/python/pandas/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ff6a7d3f-d18f-43e8-a03e-257489439289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>60</td>\n",
+       "      <td>105</td>\n",
+       "      <td>140</td>\n",
+       "      <td>290.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>60</td>\n",
+       "      <td>110</td>\n",
+       "      <td>145</td>\n",
+       "      <td>300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>60</td>\n",
+       "      <td>115</td>\n",
+       "      <td>145</td>\n",
+       "      <td>310.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>75</td>\n",
+       "      <td>120</td>\n",
+       "      <td>150</td>\n",
+       "      <td>320.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>75</td>\n",
+       "      <td>125</td>\n",
+       "      <td>150</td>\n",
+       "      <td>330.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>169 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Duration  Pulse  Maxpulse  Calories\n",
+       "0          60    110       130     409.1\n",
+       "1          60    117       145     479.0\n",
+       "2          60    103       135     340.0\n",
+       "3          45    109       175     282.4\n",
+       "4          45    117       148     406.0\n",
+       "..        ...    ...       ...       ...\n",
+       "164        60    105       140     290.8\n",
+       "165        60    110       145     300.0\n",
+       "166        60    115       145     310.2\n",
+       "167        75    120       150     320.4\n",
+       "168        75    125       150     330.4\n",
+       "\n",
+       "[169 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "59980378-83d8-4ec4-99e4-d29557d367bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Duration</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.155408</td>\n",
+       "      <td>0.009403</td>\n",
+       "      <td>0.922717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pulse</th>\n",
+       "      <td>-0.155408</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.786535</td>\n",
+       "      <td>0.025121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <td>0.009403</td>\n",
+       "      <td>0.786535</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.203813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Calories</th>\n",
+       "      <td>0.922717</td>\n",
+       "      <td>0.025121</td>\n",
+       "      <td>0.203813</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Duration     Pulse  Maxpulse  Calories\n",
+       "Duration  1.000000 -0.155408  0.009403  0.922717\n",
+       "Pulse    -0.155408  1.000000  0.786535  0.025121\n",
+       "Maxpulse  0.009403  0.786535  1.000000  0.203813\n",
+       "Calories  0.922717  0.025121  0.203813  1.000000"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# correlation (1 / -1 --> good, ~0, bad)\n",
+    "df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7c279538-3d8d-4add-a30c-5c426c5c714f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "df.plot()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e3cff924-55c9-48cc-9c8c-6d98b01bcd8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind = 'scatter', x = 'Duration', y = 'Calories')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "75fcadc3-7c9c-4e92-8dcc-6a0551b55bb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind = 'scatter', x = 'Duration', y = 'Maxpulse')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f32fb4d5-9d8a-4ef7-9acf-9322ee98839d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Duration\"].plot(kind = 'hist')\n",
+    "# A histogram shows us the frequency of each interval, \n",
+    "# e.g. how many workouts lasted between 50 and 60 minutes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1af5196-ce21-4120-8483-ccb2cc48adbc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/pandas/cleaned.csv b/course/pandas/cleaned.csv
new file mode 100644
index 0000000..bcbb3e5
--- /dev/null
+++ b/course/pandas/cleaned.csv
@@ -0,0 +1,32 @@
+,Duration,Date,Pulse,Maxpulse,Calories
+0,60,2020-12-01,110,130,409.1
+1,60,2020-12-02,117,145,479.0
+2,60,2020-12-03,103,135,340.0
+3,45,2020-12-04,109,175,282.4
+4,45,2020-12-05,117,148,406.0
+5,60,2020-12-06,102,127,300.0
+6,60,2020-12-07,110,136,374.0
+7,45,2020-12-08,104,134,253.3
+8,30,2020-12-09,109,133,195.1
+9,60,2020-12-10,98,124,269.0
+10,60,2020-12-11,103,147,329.3
+11,60,2020-12-12,100,120,250.7
+13,60,2020-12-13,106,128,345.3
+14,60,2020-12-14,104,132,379.3
+15,60,2020-12-15,98,123,275.0
+16,60,2020-12-16,98,120,215.2
+17,60,2020-12-17,100,120,300.0
+18,45,2020-12-18,90,112,304.68
+19,60,2020-12-19,103,123,323.0
+20,45,2020-12-20,97,125,243.0
+21,60,2020-12-21,108,131,364.2
+22,45,,100,119,282.0
+23,60,2020-12-23,130,101,300.0
+24,45,2020-12-24,105,132,246.0
+25,60,2020-12-25,102,126,334.5
+26,60,2020-12-26,100,120,250.0
+27,60,2020-12-27,92,118,241.0
+28,60,2020-12-28,103,132,304.68
+29,60,2020-12-29,100,132,280.0
+30,60,2020-12-30,102,129,380.3
+31,60,2020-12-31,92,115,243.0
diff --git a/course/python/.ipynb_checkpoints/01-data-types-checkpoint.ipynb b/course/python/.ipynb_checkpoints/01-data-types-checkpoint.ipynb
new file mode 100644
index 0000000..47eeaf0
--- /dev/null
+++ b/course/python/.ipynb_checkpoints/01-data-types-checkpoint.ipynb
@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e29ba0a-8c8d-43a7-8c69-91a45dd510f4",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Python Data-Types "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "985a7730-4e2f-4dd8-8963-0c05929d6537",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### Numbers\n",
+    "some_number = 9\n",
+    "some_decimal_number = 9.123\n",
+    "# some math\n",
+    "result = some_number + 1 \n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bc668dd1-2c00-4428-9792-87fc82268066",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "anna schmidt\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### Text (string)\n",
+    "some_name = \"anna\"\n",
+    "some_last_name= \"schmidt\"\n",
+    "# concat\n",
+    "result = some_name + \" \" + some_last_name\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "15f12de7-b59e-47a5-8493-9ce6dc993b1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ANNA SCHMIDT\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### Text transformation\n",
+    "print(result.upper()) # or .lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef60122f-ff4e-41ba-81c6-03bd425f4182",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### Boolean\n",
+    "wahr = True # 1\n",
+    "falsch = False # 0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5286e63f-d505-4d7f-988d-a4272530edd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[10, 20, 30]\n",
+      "3\n",
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### List\n",
+    "some_list = [10,20,30]\n",
+    "print(some_list)\n",
+    "print(len(some_list)) # length\n",
+    "print(some_list[0]) # first position starts at 0 !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "de268e19-56cb-4b15-9eb4-d2d1c206e816",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['some', 'text', 'going', 'here']\n",
+      "4\n",
+      "['some', 'text', 'going', 'here', 'abit more']\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### More lists\n",
+    "some_text_list = [\"some\",\"text\",\"going\",\"here\"]\n",
+    "print(some_text_list)\n",
+    "print(len(some_text_list))\n",
+    "# add something to the list\n",
+    "some_text_list.append(\"abit more\")\n",
+    "print(some_text_list)\n",
+    "print(len(some_text_list))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0fe7482-edc7-42b8-9f8d-dd54e8fe67f0",
+   "metadata": {},
+   "source": [
+    "#### check type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ec30a547-3e4f-436d-965e-28434dcbb7bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'float'>\n",
+      "<class 'str'>\n",
+      "<class 'bool'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(1.0))\n",
+    "print(type(\"bla\"))\n",
+    "print(type(wahr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee9a0aca-344c-4f1e-adc0-8feaf5fe59ea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/.ipynb_checkpoints/02-logic-operator-checkpoint.ipynb b/course/python/.ipynb_checkpoints/02-logic-operator-checkpoint.ipynb
new file mode 100644
index 0000000..86f0524
--- /dev/null
+++ b/course/python/.ipynb_checkpoints/02-logic-operator-checkpoint.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fd3ba99f-b630-408c-b8f4-e62e189c6057",
+   "metadata": {},
+   "source": [
+    "### Python Logic Operators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "58b1616e-9df5-426c-857b-d3017f052b07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1 == 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c87c73b4-414c-49b2-9e8f-fb3ea64f734a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1 != 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c0785e94-8795-4200-8148-50eea688e381",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1>2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "51a3869d-14f4-4f36-89a5-8b274e6867bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"1\" == 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d77b7f00-7eaf-4812-a2b9-57dc00dfc701",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Add multiple condition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0c4650aa-71f5-43e1-a027-fd68db0c585e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1 < 10 and 1 > 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "97b73046-93fc-4496-bbcc-e4cd83b9e971",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1 < 10 or 1 > 10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "69781d14-9295-42cd-935a-eef3e4e6c1e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(not \"bla\" == \"blup\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2e24ad8-fec6-4399-aada-f09f6f35b992",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/.ipynb_checkpoints/03-loops-checkpoint.ipynb b/course/python/.ipynb_checkpoints/03-loops-checkpoint.ipynb
new file mode 100644
index 0000000..c336ec4
--- /dev/null
+++ b/course/python/.ipynb_checkpoints/03-loops-checkpoint.ipynb
@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3eea86ad-74b7-4d9c-8cac-814d6e9858e9",
+   "metadata": {},
+   "source": [
+    "### Loops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8ba4b151-0dcc-4f65-9b0c-1d242fe63d9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Axel\n",
+      "Elke\n",
+      "Martin\n",
+      "nach der for-Schleife\n"
+     ]
+    }
+   ],
+   "source": [
+    "vornamen = ['Axel', 'Elke', 'Martin']\n",
+    "for einzelwert in vornamen:\n",
+    "    print(einzelwert)\n",
+    "print(\"nach der for-Schleife\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d5d85399-b41a-433f-b6c2-279197a40157",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "nach der Schleife\n"
+     ]
+    }
+   ],
+   "source": [
+    "durchgang = 1\n",
+    "while durchgang < 11:\n",
+    "    print(durchgang)\n",
+    "    durchgang = durchgang + 1\n",
+    "print(\"nach der Schleife\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32f0f630-7392-449b-a33b-ee5250752bb6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/.ipynb_checkpoints/04-functions-checkpoint.ipynb b/course/python/.ipynb_checkpoints/04-functions-checkpoint.ipynb
new file mode 100644
index 0000000..000767a
--- /dev/null
+++ b/course/python/.ipynb_checkpoints/04-functions-checkpoint.ipynb
@@ -0,0 +1,124 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2aa66773-49ef-43bd-8f86-84a9ea68c4d4",
+   "metadata": {},
+   "source": [
+    "### Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "53eaee4f-539c-4a5e-baf2-1f45077eebb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "Funktion ausgabe durchlaufen\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def ausgabe(endwert, anfangswert=1, schrittweite=1):\n",
+    "    summe = 0\n",
+    "    for x in range(anfangswert, endwert, schrittweite):\n",
+    "        print(x)\n",
+    "        summe = summe + x\n",
+    "    print(\"Funktion ausgabe durchlaufen\")\n",
+    "    return summe\n",
+    "\n",
+    "ausgabe(9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e9bc89ad-6b67-4109-b6d6-f74218cfe304",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "Funktion ausgabe durchlaufen\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "66"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ausgabe(12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e207043-c521-48ef-9ee4-b0e6f8ce2164",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/.ipynb_checkpoints/05-classes-checkpoint.ipynb b/course/python/.ipynb_checkpoints/05-classes-checkpoint.ipynb
new file mode 100644
index 0000000..29a6b41
--- /dev/null
+++ b/course/python/.ipynb_checkpoints/05-classes-checkpoint.ipynb
@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bcdc32a0-9b80-472b-a7a6-eda570fb8b86",
+   "metadata": {},
+   "source": [
+    "### classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "03fc3fad-4c9a-4864-8596-c8e9a383fe4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "orange\n"
+     ]
+    }
+   ],
+   "source": [
+    "class BauplanKatzenKlasse():\n",
+    "    \"\"\" Klasse für das Erstellen von Katzen \"\"\"\n",
+    "\n",
+    "    def __init__(self, rufname, farbe, alter):\n",
+    "        self.rufname = rufname\n",
+    "        self.farbe   = farbe\n",
+    "        self.alter   = alter\n",
+    "        self.schlafdauer = 0\n",
+    "    \n",
+    "    def miauzen(self,amount=1):\n",
+    "        out = \"mia\"\n",
+    "        for i in range(amount):\n",
+    "            out = out + \"u\"\n",
+    "        print(out)\n",
+    "\n",
+    "katze_sammy = BauplanKatzenKlasse(\"Sammy\", \"orange\", 3)\n",
+    "print(katze_sammy.farbe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d259947e-1b01-4826-be6b-11909c32bd80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "miau\n"
+     ]
+    }
+   ],
+   "source": [
+    "katze_sammy.miauzen()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e8a7cff4-b60f-48c8-9c1e-8f209516f992",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "miauuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu\n"
+     ]
+    }
+   ],
+   "source": [
+    "katze_sammy.miauzen(amount=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caef4f55-b746-457f-9a88-a6e4c47a56f6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/.ipynb_checkpoints/06-libs-checkpoint.ipynb b/course/python/.ipynb_checkpoints/06-libs-checkpoint.ipynb
new file mode 100644
index 0000000..30c65d0
--- /dev/null
+++ b/course/python/.ipynb_checkpoints/06-libs-checkpoint.ipynb
@@ -0,0 +1,60 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a3795d4d-7bed-4d3b-995c-0a6c05ad8a7c",
+   "metadata": {},
+   "source": [
+    "### libs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4a85fd9b-1e27-4d0b-a323-33a965e37968",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "print(math.sqrt(25))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "621adbab-7608-48fc-9bc8-a8203ee5484a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/01-data-types.ipynb b/course/python/01-data-types.ipynb
new file mode 100644
index 0000000..8407428
--- /dev/null
+++ b/course/python/01-data-types.ipynb
@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e29ba0a-8c8d-43a7-8c69-91a45dd510f4",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Python Data-Types "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "985a7730-4e2f-4dd8-8963-0c05929d6537",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### Numbers\n",
+    "some_number = 9\n",
+    "some_decimal_number = 9.123\n",
+    "# some math\n",
+    "result = some_number + 1 \n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bc668dd1-2c00-4428-9792-87fc82268066",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "anna schmidt\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### Text (string)\n",
+    "some_name = \"anna\"\n",
+    "some_last_name= \"schmidt\"\n",
+    "# concat\n",
+    "result = some_name + \" \" + some_last_name\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "15f12de7-b59e-47a5-8493-9ce6dc993b1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ANNA SCHMIDT\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### Text transformation\n",
+    "print(result.upper()) # or .lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef60122f-ff4e-41ba-81c6-03bd425f4182",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### Boolean\n",
+    "wahr = True # 1\n",
+    "falsch = False # 0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5286e63f-d505-4d7f-988d-a4272530edd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[10, 20, 30]\n",
+      "3\n",
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### List\n",
+    "some_list = [10,20,30]\n",
+    "print(some_list)\n",
+    "print(len(some_list)) # length\n",
+    "print(some_list[0]) # first position starts at 0 !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "de268e19-56cb-4b15-9eb4-d2d1c206e816",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['some', 'text', 'going', 'here']\n",
+      "4\n",
+      "['some', 'text', 'going', 'here', 'abit more']\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### More lists\n",
+    "some_text_list = [\"some\",\"text\",\"going\",\"here\"]\n",
+    "print(some_text_list)\n",
+    "print(len(some_text_list))\n",
+    "# add something to the list\n",
+    "some_text_list.append(\"abit more\")\n",
+    "print(some_text_list)\n",
+    "print(len(some_text_list))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0fe7482-edc7-42b8-9f8d-dd54e8fe67f0",
+   "metadata": {},
+   "source": [
+    "#### check type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ec30a547-3e4f-436d-965e-28434dcbb7bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'float'>\n",
+      "<class 'str'>\n",
+      "<class 'bool'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(1.0))\n",
+    "print(type(\"bla\"))\n",
+    "print(type(wahr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee9a0aca-344c-4f1e-adc0-8feaf5fe59ea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/02-logic-operator.ipynb b/course/python/02-logic-operator.ipynb
new file mode 100644
index 0000000..2a9137e
--- /dev/null
+++ b/course/python/02-logic-operator.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fd3ba99f-b630-408c-b8f4-e62e189c6057",
+   "metadata": {},
+   "source": [
+    "### Python Logic Operators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "58b1616e-9df5-426c-857b-d3017f052b07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1 == 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c87c73b4-414c-49b2-9e8f-fb3ea64f734a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1 != 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c0785e94-8795-4200-8148-50eea688e381",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1>2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "51a3869d-14f4-4f36-89a5-8b274e6867bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"1\" == 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d77b7f00-7eaf-4812-a2b9-57dc00dfc701",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Add multiple condition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0c4650aa-71f5-43e1-a027-fd68db0c585e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1 < 10 and 1 > 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "97b73046-93fc-4496-bbcc-e4cd83b9e971",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1 < 10 or 1 > 10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "69781d14-9295-42cd-935a-eef3e4e6c1e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(not \"bla\" == \"blup\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2e24ad8-fec6-4399-aada-f09f6f35b992",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/03-loops.ipynb b/course/python/03-loops.ipynb
new file mode 100644
index 0000000..23ed7c9
--- /dev/null
+++ b/course/python/03-loops.ipynb
@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3eea86ad-74b7-4d9c-8cac-814d6e9858e9",
+   "metadata": {},
+   "source": [
+    "### Loops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8ba4b151-0dcc-4f65-9b0c-1d242fe63d9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Axel\n",
+      "Elke\n",
+      "Martin\n",
+      "nach der for-Schleife\n"
+     ]
+    }
+   ],
+   "source": [
+    "vornamen = ['Axel', 'Elke', 'Martin']\n",
+    "for einzelwert in vornamen:\n",
+    "    print(einzelwert)\n",
+    "print(\"nach der for-Schleife\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d5d85399-b41a-433f-b6c2-279197a40157",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "nach der Schleife\n"
+     ]
+    }
+   ],
+   "source": [
+    "durchgang = 1\n",
+    "while durchgang < 11:\n",
+    "    print(durchgang)\n",
+    "    durchgang = durchgang + 1\n",
+    "print(\"nach der Schleife\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32f0f630-7392-449b-a33b-ee5250752bb6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/04-functions.ipynb b/course/python/04-functions.ipynb
new file mode 100644
index 0000000..ca238bf
--- /dev/null
+++ b/course/python/04-functions.ipynb
@@ -0,0 +1,124 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2aa66773-49ef-43bd-8f86-84a9ea68c4d4",
+   "metadata": {},
+   "source": [
+    "### Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "53eaee4f-539c-4a5e-baf2-1f45077eebb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "Funktion ausgabe durchlaufen\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def ausgabe(endwert, anfangswert=1, schrittweite=1):\n",
+    "    summe = 0\n",
+    "    for x in range(anfangswert, endwert, schrittweite):\n",
+    "        print(x)\n",
+    "        summe = summe + x\n",
+    "    print(\"Funktion ausgabe durchlaufen\")\n",
+    "    return summe\n",
+    "\n",
+    "ausgabe(9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e9bc89ad-6b67-4109-b6d6-f74218cfe304",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "Funktion ausgabe durchlaufen\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "66"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ausgabe(12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e207043-c521-48ef-9ee4-b0e6f8ce2164",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/05-classes.ipynb b/course/python/05-classes.ipynb
new file mode 100644
index 0000000..29a6b41
--- /dev/null
+++ b/course/python/05-classes.ipynb
@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bcdc32a0-9b80-472b-a7a6-eda570fb8b86",
+   "metadata": {},
+   "source": [
+    "### classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "03fc3fad-4c9a-4864-8596-c8e9a383fe4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "orange\n"
+     ]
+    }
+   ],
+   "source": [
+    "class BauplanKatzenKlasse():\n",
+    "    \"\"\" Klasse für das Erstellen von Katzen \"\"\"\n",
+    "\n",
+    "    def __init__(self, rufname, farbe, alter):\n",
+    "        self.rufname = rufname\n",
+    "        self.farbe   = farbe\n",
+    "        self.alter   = alter\n",
+    "        self.schlafdauer = 0\n",
+    "    \n",
+    "    def miauzen(self,amount=1):\n",
+    "        out = \"mia\"\n",
+    "        for i in range(amount):\n",
+    "            out = out + \"u\"\n",
+    "        print(out)\n",
+    "\n",
+    "katze_sammy = BauplanKatzenKlasse(\"Sammy\", \"orange\", 3)\n",
+    "print(katze_sammy.farbe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d259947e-1b01-4826-be6b-11909c32bd80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "miau\n"
+     ]
+    }
+   ],
+   "source": [
+    "katze_sammy.miauzen()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e8a7cff4-b60f-48c8-9c1e-8f209516f992",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "miauuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu\n"
+     ]
+    }
+   ],
+   "source": [
+    "katze_sammy.miauzen(amount=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caef4f55-b746-457f-9a88-a6e4c47a56f6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/python/06-libs.ipynb b/course/python/06-libs.ipynb
new file mode 100644
index 0000000..6327caa
--- /dev/null
+++ b/course/python/06-libs.ipynb
@@ -0,0 +1,67 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a3795d4d-7bed-4d3b-995c-0a6c05ad8a7c",
+   "metadata": {},
+   "source": [
+    "### libs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4a85fd9b-1e27-4d0b-a323-33a965e37968",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "print(math.sqrt(25))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "621adbab-7608-48fc-9bc8-a8203ee5484a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "var = 6\n",
+    "\n",
+    "\n",
+    "\n",
+    "switch "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/digit_recognition/digit_recognition_DT.ipynb b/digit_recognition/digit_recognition_DT.ipynb
new file mode 100644
index 0000000..20dc81b
--- /dev/null
+++ b/digit_recognition/digit_recognition_DT.ipynb
@@ -0,0 +1,253 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "48214590-414c-4dd0-9d1f-9446c1d751a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f0738b63-6ea5-4bb0-81e3-101ff9d67a63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets, metrics, svm\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8a26a325-096c-45fd-b0ef-cb0166a4833d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### load dataset \n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "\n",
+    "### show training data\n",
+    "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10,3))\n",
+    "for ax, image, label in zip(axes, digits.images, digits.target):\n",
+    "    ax.set_axis_off()\n",
+    "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n",
+    "    ax.set_title(\"Training: %i\" % label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8355cc2f-3128-43a3-8122-1517d11b911a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5 6 1 6 5 7 1 5 3 7 4 1 2 5 4 9 8 2 6 6 0 5 4 9 7 4 8 7 5 6 5 3 8 9 9 6 1\n",
+      " 1 0 6 8 4 8 8 8 1 2 7 1 2 0 1 8 8 9 6 4 6 7 0 4 1 0 5 4 1 7 2 0 6 7 9 1 5\n",
+      " 5 4 0 6 6 2 5 4 1 1 0 9 5 6 5 0 5 1 5 1 3 0 2 5 8 0 1 9 1 1 7 0 6 4 9 8 7\n",
+      " 3 7 4 4 2 7 3 6 2 6 1 9 7 4 8 5 5 1 3 9 4 3 7 8 0 5 8 9 5 5 0 0 5 0 6 1 8\n",
+      " 8 4 0 4 5 0 1 7 6 9 7 9 5 7 5 6 9 5 0 6 9 6 7 6 2 0 3 3 8 4 9 7 1 2 9 5 5\n",
+      " 7 0 9 1 4 6 1 4 0 5 4 9 6 7 1 6 8 1 7 0 7 0 1 5 7 1 6 1 5 1 6 6 7 3 2 7 3\n",
+      " 6 4 2 5 1 2 4 6 4 8 1 2 2 5 6 2 4 8 5 6 3 1 6 8 2 8 3 9 7 1 5 7 3 4 8 4 5\n",
+      " 1 5 4 3 9 0 1 9 5 4 1 4 3 2 2 4 5 7 6 2 0 9 2 6 4 6 8 4 4 5 8 0 4 1 1 3 4\n",
+      " 8 3 3 5 6 3 3 6 7 0 8 3 4 2 8 5 1 0 8 4 7 1 6 3 6 2 7 0 5 8 2 0 0 0 1 3 0\n",
+      " 7 8 8 5 1 0 6 8 2 0 0 5 9 7 5 4 5 7 6 3 5 3 9 3 5 5 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "# gini a criterion to minimize the probability of misclassification\n",
+    "clf_dt = DecisionTreeClassifier(criterion = 'gini')\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, shuffle=True)\n",
+    "\n",
+    "\n",
+    "clf_dt.fit(X_train, y_train)\n",
+    "predicted = clf_dt.predict(X_test)\n",
+    "print(predicted)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a4c53a12-f834-426d-8217-ea98962fda64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8805555555555555"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "accuracy_score(y_test , predicted)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e2b99748-73e4-4e6a-bd3f-4a095eec4c12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10,3))\n",
+    "for ax, image, prediction, t in zip(axes, X_test, predicted, y_test):\n",
+    "    ax.set_axis_off()\n",
+    "    image = image.reshape(8,8)\n",
+    "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n",
+    "    ax.set_title(f\"Prediction: {prediction}, real: {t}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1d010c44-2967-44fa-996f-c1fd733d17f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrix:\n",
+      "[[36  0  0  0  0  1  0  0  0  0]\n",
+      " [ 0 37  1  1  0  0  0  0  2  1]\n",
+      " [ 0  0 21  0  0  0  0  0  2  0]\n",
+      " [ 0  0  4 26  0  2  1  0  1  1]\n",
+      " [ 0  1  0  0 31  0  1  0  0  0]\n",
+      " [ 0  0  0  0  2 43  0  1  0  0]\n",
+      " [ 1  1  0  0  4  0 40  0  0  0]\n",
+      " [ 0  0  0  0  1  1  0 34  1  0]\n",
+      " [ 0  3  0  0  0  1  0  0 27  2]\n",
+      " [ 0  2  0  1  1  2  0  0  0 22]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### confusion matrix\n",
+    "\n",
+    "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted, cmap=\"summer\")\n",
+    "disp.figure_.suptitle(\"Confusion Matrix\")\n",
+    "\n",
+    "print(f\"Confusion Matrix:\\n{disp.confusion_matrix}\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8608e6eb-6dd0-4947-9ee6-dc77f35cecc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for classifier DecisionTreeClassifier():\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.97      0.97        37\n",
+      "           1       0.84      0.88      0.86        42\n",
+      "           2       0.81      0.91      0.86        23\n",
+      "           3       0.93      0.74      0.83        35\n",
+      "           4       0.79      0.94      0.86        33\n",
+      "           5       0.86      0.93      0.90        46\n",
+      "           6       0.95      0.87      0.91        46\n",
+      "           7       0.97      0.92      0.94        37\n",
+      "           8       0.82      0.82      0.82        33\n",
+      "           9       0.85      0.79      0.81        28\n",
+      "\n",
+      "    accuracy                           0.88       360\n",
+      "   macro avg       0.88      0.88      0.88       360\n",
+      "weighted avg       0.89      0.88      0.88       360\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    f\"Classification report for classifier {clf_dt}:\\n\"\n",
+    "    f\"{metrics.classification_report(y_test, predicted)}\\n\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42fdd6a5-af00-4155-bc42-44e037d13c2d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/digit_recognition/digit_recognition_NN.ipynb b/digit_recognition/digit_recognition_NN.ipynb
new file mode 100644
index 0000000..fb56146
--- /dev/null
+++ b/digit_recognition/digit_recognition_NN.ipynb
@@ -0,0 +1,483 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "47d72f0e-bb8b-40f6-9e4f-bfeb1225b1ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting torch\n",
+      "  Using cached torch-2.2.1-cp311-cp311-manylinux1_x86_64.whl.metadata (26 kB)\n",
+      "Collecting torchvision\n",
+      "  Using cached torchvision-0.17.1-cp311-cp311-manylinux1_x86_64.whl.metadata (6.6 kB)\n",
+      "Collecting filelock (from torch)\n",
+      "  Using cached filelock-3.13.1-py3-none-any.whl.metadata (2.8 kB)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.11/site-packages (from torch) (4.9.0)\n",
+      "Requirement already satisfied: sympy in /opt/conda/lib/python3.11/site-packages (from torch) (1.12)\n",
+      "Requirement already satisfied: networkx in /opt/conda/lib/python3.11/site-packages (from torch) (3.2.1)\n",
+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.11/site-packages (from torch) (3.1.3)\n",
+      "Requirement already satisfied: fsspec in /opt/conda/lib/python3.11/site-packages (from torch) (2023.12.2)\n",
+      "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)\n",
+      "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+      "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)\n",
+      "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+      "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)\n",
+      "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
+      "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch)\n",
+      "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
+      "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch)\n",
+      "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+      "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)\n",
+      "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+      "Collecting nvidia-curand-cu12==10.3.2.106 (from torch)\n",
+      "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
+      "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)\n",
+      "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
+      "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)\n",
+      "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
+      "Collecting nvidia-nccl-cu12==2.19.3 (from torch)\n",
+      "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl.metadata (1.8 kB)\n",
+      "Collecting nvidia-nvtx-cu12==12.1.105 (from torch)\n",
+      "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)\n",
+      "Collecting triton==2.2.0 (from torch)\n",
+      "  Using cached triton-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.4 kB)\n",
+      "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)\n",
+      "  Downloading nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+      "Requirement already satisfied: numpy in /opt/conda/lib/python3.11/site-packages (from torchvision) (1.26.3)\n",
+      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.11/site-packages (from torchvision) (10.2.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.11/site-packages (from jinja2->torch) (2.1.5)\n",
+      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.11/site-packages (from sympy->torch) (1.3.0)\n",
+      "Using cached torch-2.2.1-cp311-cp311-manylinux1_x86_64.whl (755.6 MB)\n",
+      "Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
+      "Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
+      "Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
+      "Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
+      "Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
+      "Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
+      "Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
+      "Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
+      "Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
+      "Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
+      "Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
+      "Using cached triton-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (167.9 MB)\n",
+      "Using cached torchvision-0.17.1-cp311-cp311-manylinux1_x86_64.whl (6.9 MB)\n",
+      "Using cached filelock-3.13.1-py3-none-any.whl (11 kB)\n",
+      "Downloading nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hInstalling collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, filelock, triton, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, torchvision\n",
+      "Successfully installed filelock-3.13.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 torch-2.2.1 torchvision-0.17.1 triton-2.2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#!pip3 install torch==1.2.0+cu92 torchvision==0.4.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html\n",
+    "!pip3 install torch torchvision\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3da9ca25-5156-4216-8ac7-b2347e60c34e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d11c9b95-ea6b-457e-a3e7-c5c8de58b450",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## The usual imports\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision\n",
+    "import torchvision.transforms as transforms\n",
+    "\n",
+    "## for printing image\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4aad0f0f-545e-416b-97f7-2678c782215a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.2.1+cu121\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(torch.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "54c98b09-a734-4edf-96c0-20b78bed9d1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## parameter denoting the batch size\n",
+    "BATCH_SIZE = 32\n",
+    "\n",
+    "## transformations\n",
+    "transform = transforms.Compose(\n",
+    "    [transforms.ToTensor()])\n",
+    "\n",
+    "## download and load training dataset\n",
+    "trainset = torchvision.datasets.MNIST(root='./data', train=True,\n",
+    "                                        download=True, transform=transform)\n",
+    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,\n",
+    "                                          shuffle=True, num_workers=2)\n",
+    "\n",
+    "## download and load testing dataset\n",
+    "testset = torchvision.datasets.MNIST(root='./data', train=False,\n",
+    "                                       download=True, transform=transform)\n",
+    "testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE,\n",
+    "                                         shuffle=False, num_workers=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "23880f4e-4790-4055-bbb4-1b0c47755b9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f2b57e46c50>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image = transforms.ToPILImage(mode='L')(torch.randn(1, 96, 96))\n",
+    "plt.imshow(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aadba5ab-c84c-4f80-8c79-d30633343a61",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f2b55422f50>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## dummy transformation\n",
+    "dummy_transform = transforms.Compose(\n",
+    "    [transforms.RandomRotation(45)])\n",
+    "\n",
+    "dummy_result = dummy_transform(image)\n",
+    "\n",
+    "plt.imshow(dummy_result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "11cd3a28-2caa-411e-8091-4f24dc600ba5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f2b55496790>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "## dummy transform \n",
+    "dummy2_transform = transforms.Compose(\n",
+    "    [transforms.RandomRotation(45), transforms.RandomVerticalFlip()])\n",
+    "\n",
+    "dummy2_result = dummy2_transform(image)\n",
+    "\n",
+    "plt.imshow(dummy2_result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9ad649e7-7617-4658-aa61-34348836427a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## functions to show an image\n",
+    "def imshow(img):\n",
+    "    #img = img / 2 + 0.5     # unnormalize\n",
+    "    npimg = img.numpy()\n",
+    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
+    "\n",
+    "## get some random training images\n",
+    "dataiter = iter(trainloader)\n",
+    "images, labels = next(dataiter)\n",
+    "\n",
+    "## show images\n",
+    "imshow(torchvision.utils.make_grid(images))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "21260ecc-202e-40a7-ad12-02d49055bdc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image batch dimensions: torch.Size([32, 1, 28, 28])\n",
+      "Image label dimensions: torch.Size([32])\n"
+     ]
+    }
+   ],
+   "source": [
+    "for images, labels in trainloader:\n",
+    "    print(\"Image batch dimensions:\", images.shape)\n",
+    "    print(\"Image label dimensions:\", labels.shape)\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "00450688-b5a3-4df7-abf5-1428dd7a3792",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## the model\n",
+    "class MyModel(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(MyModel, self).__init__()\n",
+    "        self.d1 = nn.Linear(28 * 28, 128)\n",
+    "        self.dropout = nn.Dropout(p=0.2)\n",
+    "        self.d2 = nn.Linear(128, 10)\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        x = x.flatten(start_dim = 1)\n",
+    "        x = self.d1(x)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.dropout(x)\n",
+    "        logits = self.d2(x)\n",
+    "        out = F.softmax(logits, dim=1)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "20abb31a-584c-4efc-850a-308853300b14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "batch size: torch.Size([32, 1, 28, 28])\n",
+      "torch.Size([32, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "## test the model with 1 batch\n",
+    "model = MyModel()\n",
+    "for images, labels in trainloader:\n",
+    "    print(\"batch size:\", images.shape)\n",
+    "    out = model(images)\n",
+    "    print(out.shape)\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "71f3b1ca-6507-48d2-bde5-3d7a437fb108",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learning_rate = 0.001\n",
+    "num_epochs = 5\n",
+    "\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model = MyModel()\n",
+    "model = model.to(device)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f3aedefc-1e05-4952-a390-2d7fae26bd2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "## utility function to compute accuracy\n",
+    "def get_accuracy(output, target, batch_size):\n",
+    "    ''' Obtain accuracy for training round '''\n",
+    "    corrects = (torch.max(output, 1)[1].view(target.size()).data == target.data).sum()\n",
+    "    accuracy = 100.0 * corrects/batch_size\n",
+    "    return accuracy.item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "498cbc09-c3bd-4e57-9aab-ab192c49198a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## train the model\n",
+    "for epoch in range(num_epochs):\n",
+    "    train_running_loss = 0.0\n",
+    "    train_acc = 0.0\n",
+    "\n",
+    "    ## commence training\n",
+    "    model = model.train()\n",
+    "\n",
+    "    ## training step\n",
+    "    for i, (images, labels) in enumerate(trainloader):\n",
+    "        \n",
+    "        images = images.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        ## forward + backprop + loss\n",
+    "        predictions = model(images)\n",
+    "        loss = criterion(predictions, labels)\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "\n",
+    "        ## update model params\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        train_running_loss += loss.detach().item()\n",
+    "        train_acc += get_accuracy(predictions, labels, BATCH_SIZE)\n",
+    "    \n",
+    "    model.eval()\n",
+    "    print('Epoch: %d | Loss: %.4f | Train Accuracy: %.2f' \\\n",
+    "          %(epoch, train_running_loss / i, train_acc/i)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f4df7d5-b244-4a68-b06a-bb750c975cf3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_acc = 0.0\n",
+    "for i, (images, labels) in enumerate(testloader, 0):\n",
+    "    images = images.to(device)\n",
+    "    labels = labels.to(device)\n",
+    "    outputs = model(images)\n",
+    "    test_acc += get_accuracy(outputs, labels, BATCH_SIZE)\n",
+    "        \n",
+    "print('Test Accuracy: %.2f'%( test_acc/i))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76669cbb-29a0-402e-88ed-9b0ce913945f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/digit_recognition/digit_recognition_SVM.ipynb b/digit_recognition/digit_recognition_SVM.ipynb
new file mode 100644
index 0000000..f5791e0
--- /dev/null
+++ b/digit_recognition/digit_recognition_SVM.ipynb
@@ -0,0 +1,211 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "48214590-414c-4dd0-9d1f-9446c1d751a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f0738b63-6ea5-4bb0-81e3-101ff9d67a63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets, metrics, svm\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8a26a325-096c-45fd-b0ef-cb0166a4833d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### load dataset \n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "\n",
+    "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10,3))\n",
+    "for ax, image, label in zip(axes, digits.images, digits.target):\n",
+    "    ax.set_axis_off()\n",
+    "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n",
+    "    ax.set_title(\"Training: %i\" % label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "801d9ae4-94d0-4ed0-b920-ba9e81f0f843",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### flatten images \n",
+    "n_samples = len(digits.images)\n",
+    "data = digits.images.reshape((n_samples, -1))\n",
+    "clf = svm.SVC(gamma=0.001)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(data, digits.target, test_size=0.5, shuffle=False)\n",
+    "\n",
+    "### learn the digits on the train subset\n",
+    "clf.fit(X_train, y_train)\n",
+    "predicted = clf.predict(X_test)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "adae34b1-517b-4c04-a52a-eb30fc5fa9e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10,3))\n",
+    "for ax, image, prediction, t in zip(axes, X_test, predicted, y_test):\n",
+    "    ax.set_axis_off()\n",
+    "    image = image.reshape(8,8)\n",
+    "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n",
+    "    ax.set_title(f\"Prediction: {prediction}, real: {t}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8f022c19-cdb7-4730-b297-fc58dd49e002",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrix:\n",
+      "[[87  0  0  0  1  0  0  0  0  0]\n",
+      " [ 0 88  1  0  0  0  0  0  1  1]\n",
+      " [ 0  0 85  1  0  0  0  0  0  0]\n",
+      " [ 0  0  0 79  0  3  0  4  5  0]\n",
+      " [ 0  0  0  0 88  0  0  0  0  4]\n",
+      " [ 0  0  0  0  0 88  1  0  0  2]\n",
+      " [ 0  1  0  0  0  0 90  0  0  0]\n",
+      " [ 0  0  0  0  0  1  0 88  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 88  0]\n",
+      " [ 0  0  0  1  0  1  0  0  0 90]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### confusion matrix\n",
+    "\n",
+    "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted)\n",
+    "disp.figure_.suptitle(\"Confusion Matrix\")\n",
+    "print(f\"Confusion Matrix:\\n{disp.confusion_matrix}\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "42fdd6a5-af00-4155-bc42-44e037d13c2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for classifier SVC(gamma=0.001):\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.99      0.99        88\n",
+      "           1       0.99      0.97      0.98        91\n",
+      "           2       0.99      0.99      0.99        86\n",
+      "           3       0.98      0.87      0.92        91\n",
+      "           4       0.99      0.96      0.97        92\n",
+      "           5       0.95      0.97      0.96        91\n",
+      "           6       0.99      0.99      0.99        91\n",
+      "           7       0.96      0.99      0.97        89\n",
+      "           8       0.94      1.00      0.97        88\n",
+      "           9       0.93      0.98      0.95        92\n",
+      "\n",
+      "    accuracy                           0.97       899\n",
+      "   macro avg       0.97      0.97      0.97       899\n",
+      "weighted avg       0.97      0.97      0.97       899\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    f\"Classification report for classifier {clf}:\\n\"\n",
+    "    f\"{metrics.classification_report(y_test, predicted)}\\n\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81db6464-90b6-4fed-92bd-bcbb935eac4e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}