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Technologische_Grundlagen/course/pandas/03_advanced.ipynb

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add examples
2024-09-27 07:00:19 +00:00
{
"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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
"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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
"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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
"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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
"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
}