746 lines
22 KiB
Plaintext
746 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "bd4b5db9-6439-4519-aef0-c8bb8ffd6a13",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a3c4bf54-07f7-46bb-9ae5-77c3a4518627",
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"metadata": {},
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"source": [
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"### Basics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "def1dfe3-6afe-4f5e-9714-36b65811094a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" cars passings\n",
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"0 BMW 3\n",
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"1 Volvo 7\n",
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"2 Ford 2\n"
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]
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}
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],
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"source": [
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"mydataset = {\n",
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" 'cars': [\"BMW\", \"Volvo\", \"Ford\"],\n",
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" 'passings': [3, 7, 2]\n",
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"}\n",
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"\n",
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"myvar = pd.DataFrame(mydataset)\n",
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"\n",
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"print(myvar)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "0785d9bd-e5b1-4221-84e9-1a1a6a2cf37e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0 1\n",
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"1 7\n",
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"2 2\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"# series: numpy arrays!\n",
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"a = [1, 7, 2]\n",
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"\n",
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"myvar = pd.Series(a)\n",
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"\n",
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"print(myvar)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "2d7a5f33-79a2-4e2a-9b14-eee43db90662",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"x 1\n",
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"y 7\n",
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"z 2\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"# labels\n",
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"a = [1, 7, 2]\n",
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"\n",
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"myvar = pd.Series(a, index = [\"x\", \"y\", \"z\"])\n",
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"\n",
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"print(myvar)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "cba4f2ef-45d1-4c23-94ce-7197c43d4aee",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"day1 420\n",
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"day2 380\n",
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"day3 390\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"# key value objects\n",
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"calories = {\"day1\": 420, \"day2\": 380, \"day3\": 390}\n",
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"\n",
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"myvar = pd.Series(calories)\n",
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"\n",
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"print(myvar)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "e965971f-6a97-42c4-81bf-2550e57cd7e9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" calories duration\n",
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"0 420 50\n",
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"1 380 40\n",
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"2 390 45\n"
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]
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}
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],
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"source": [
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"# dataframe = multi-dimensional tables\n",
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"data = {\n",
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" \"calories\": [420, 380, 390],\n",
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" \"duration\": [50, 40, 45]\n",
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"}\n",
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"\n",
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"df = pd.DataFrame(data)\n",
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"\n",
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"print(df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "a6650272-40db-4237-b2d6-83d3652184c2",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"calories 420\n",
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"duration 50\n",
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"Name: 0, dtype: int64\n"
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]
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}
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],
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"source": [
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"# locate row\n",
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"print(df.loc[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "694a5a9b-c280-454c-b7c6-236eda33d8f4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" calories duration\n",
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"0 420 50\n",
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"1 380 40\n"
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]
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}
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],
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"source": [
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"#use a list of indexes:\n",
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"print(df.loc[[0, 1]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "e3ff6022-327e-446d-a739-011730d1db8a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" calories duration\n",
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"day1 420 50\n",
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"day2 380 40\n",
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"day3 390 45\n"
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]
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}
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],
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"source": [
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"# named index\n",
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"data = {\n",
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" \"calories\": [420, 380, 390],\n",
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" \"duration\": [50, 40, 45]\n",
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"}\n",
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"\n",
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"df = pd.DataFrame(data, index = [\"day1\", \"day2\", \"day3\"])\n",
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"\n",
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"print(df) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "4d8a50a4-7c1d-4ef7-ab0b-09cbc8085ad6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"calories 380\n",
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"duration 40\n",
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"Name: day2, dtype: int64\n"
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]
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}
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],
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"source": [
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"#refer to the named index:\n",
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"print(df.loc[\"day2\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b18cee34-c5dd-43df-9e2e-bd20ebeed8ed",
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"metadata": {},
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"source": [
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"### Loading Files"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "abef3aeb-d82c-4cc5-a2d2-7a70313f0712",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Duration Pulse Maxpulse Calories\n",
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"0 60 110 130 409.1\n",
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"1 60 117 145 479.0\n",
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"2 60 103 135 340.0\n",
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"3 45 109 175 282.4\n",
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"4 45 117 148 406.0\n",
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"5 60 102 127 300.0\n",
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"6 60 110 136 374.0\n",
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"7 45 104 134 253.3\n",
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"8 30 109 133 195.1\n",
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"9 60 98 124 269.0\n",
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"10 60 103 147 329.3\n",
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"11 60 100 120 250.7\n",
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"12 60 106 128 345.3\n",
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"13 60 104 132 379.3\n",
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"14 60 98 123 275.0\n",
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"15 60 98 120 215.2\n",
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"16 60 100 120 300.0\n",
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"17 45 90 112 NaN\n",
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"18 60 103 123 323.0\n",
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"19 45 97 125 243.0\n",
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"20 60 108 131 364.2\n",
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"21 45 100 119 282.0\n",
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"22 60 130 101 300.0\n",
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"23 45 105 132 246.0\n",
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"24 60 102 126 334.5\n",
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"25 60 100 120 250.0\n",
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"26 60 92 118 241.0\n",
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"27 60 103 132 NaN\n",
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"28 60 100 132 280.0\n",
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"29 60 102 129 380.3\n",
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"30 60 92 115 243.0\n",
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"31 45 90 112 180.1\n",
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"32 60 101 124 299.0\n",
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"33 60 93 113 223.0\n",
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"34 60 107 136 361.0\n",
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"35 60 114 140 415.0\n",
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"36 60 102 127 300.0\n",
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"37 60 100 120 300.0\n",
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"38 60 100 120 300.0\n",
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"39 45 104 129 266.0\n",
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"40 45 90 112 180.1\n",
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"41 60 98 126 286.0\n",
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"42 60 100 122 329.4\n",
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"43 60 111 138 400.0\n",
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"44 60 111 131 397.0\n",
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"45 60 99 119 273.0\n",
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"46 60 109 153 387.6\n",
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"47 45 111 136 300.0\n",
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"48 45 108 129 298.0\n",
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"49 60 111 139 397.6\n",
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"50 60 107 136 380.2\n",
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"51 80 123 146 643.1\n",
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"52 60 106 130 263.0\n",
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"53 60 118 151 486.0\n",
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"54 30 136 175 238.0\n",
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"55 60 121 146 450.7\n",
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"56 60 118 121 413.0\n",
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"57 45 115 144 305.0\n",
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"58 20 153 172 226.4\n",
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"59 45 123 152 321.0\n",
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"60 210 108 160 1376.0\n",
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"61 160 110 137 1034.4\n",
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"62 160 109 135 853.0\n",
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"63 45 118 141 341.0\n",
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"64 20 110 130 131.4\n",
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"65 180 90 130 800.4\n",
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"66 150 105 135 873.4\n",
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"67 150 107 130 816.0\n",
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"68 20 106 136 110.4\n",
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"69 300 108 143 1500.2\n",
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"70 150 97 129 1115.0\n",
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"71 60 109 153 387.6\n",
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"72 90 100 127 700.0\n",
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"73 150 97 127 953.2\n",
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"74 45 114 146 304.0\n",
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"75 90 98 125 563.2\n",
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"76 45 105 134 251.0\n",
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"77 45 110 141 300.0\n",
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"78 120 100 130 500.4\n",
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"79 270 100 131 1729.0\n",
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"80 30 159 182 319.2\n",
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"81 45 149 169 344.0\n",
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"82 30 103 139 151.1\n",
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"83 120 100 130 500.0\n",
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"84 45 100 120 225.3\n",
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"85 30 151 170 300.0\n",
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"86 45 102 136 234.0\n",
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"87 120 100 157 1000.1\n",
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"88 45 129 103 242.0\n",
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"89 20 83 107 50.3\n",
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"90 180 101 127 600.1\n",
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"91 45 107 137 NaN\n",
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"92 30 90 107 105.3\n",
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"93 15 80 100 50.5\n",
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"94 20 150 171 127.4\n",
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"95 20 151 168 229.4\n",
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"96 30 95 128 128.2\n",
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"97 25 152 168 244.2\n",
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"98 30 109 131 188.2\n",
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"99 90 93 124 604.1\n",
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"100 20 95 112 77.7\n",
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"101 90 90 110 500.0\n",
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"102 90 90 100 500.0\n",
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"103 90 90 100 500.4\n",
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"104 30 92 108 92.7\n",
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"105 30 93 128 124.0\n",
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"106 180 90 120 800.3\n",
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"107 30 90 120 86.2\n",
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"108 90 90 120 500.3\n",
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"109 210 137 184 1860.4\n",
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"110 60 102 124 325.2\n",
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"111 45 107 124 275.0\n",
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"112 15 124 139 124.2\n",
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"113 45 100 120 225.3\n",
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"114 60 108 131 367.6\n",
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"115 60 108 151 351.7\n",
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"116 60 116 141 443.0\n",
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"117 60 97 122 277.4\n",
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"118 60 105 125 NaN\n",
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"119 60 103 124 332.7\n",
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"120 30 112 137 193.9\n",
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"121 45 100 120 100.7\n",
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"122 60 119 169 336.7\n",
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"123 60 107 127 344.9\n",
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"124 60 111 151 368.5\n",
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"125 60 98 122 271.0\n",
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"126 60 97 124 275.3\n",
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"127 60 109 127 382.0\n",
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"128 90 99 125 466.4\n",
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"129 60 114 151 384.0\n",
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"130 60 104 134 342.5\n",
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"131 60 107 138 357.5\n",
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"132 60 103 133 335.0\n",
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"133 60 106 132 327.5\n",
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"134 60 103 136 339.0\n",
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"135 20 136 156 189.0\n",
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"136 45 117 143 317.7\n",
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"137 45 115 137 318.0\n",
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"138 45 113 138 308.0\n",
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"139 20 141 162 222.4\n",
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"140 60 108 135 390.0\n",
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"141 60 97 127 NaN\n",
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"142 45 100 120 250.4\n",
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"143 45 122 149 335.4\n",
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"144 60 136 170 470.2\n",
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"145 45 106 126 270.8\n",
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"146 60 107 136 400.0\n",
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"147 60 112 146 361.9\n",
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"148 30 103 127 185.0\n",
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"149 60 110 150 409.4\n",
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"150 60 106 134 343.0\n",
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"151 60 109 129 353.2\n",
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"152 60 109 138 374.0\n",
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"153 30 150 167 275.8\n",
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"154 60 105 128 328.0\n",
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"155 60 111 151 368.5\n",
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"156 60 97 131 270.4\n",
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"157 60 100 120 270.4\n",
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"158 60 114 150 382.8\n",
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"159 30 80 120 240.9\n",
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"160 30 85 120 250.4\n",
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"161 45 90 130 260.4\n",
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"162 45 95 130 270.0\n",
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"163 45 100 140 280.9\n",
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"164 60 105 140 290.8\n",
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"165 60 110 145 300.0\n",
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"166 60 115 145 310.2\n",
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"167 75 120 150 320.4\n",
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"168 75 125 150 330.4\n"
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]
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}
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],
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"source": [
|
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"#https://www.w3schools.com/python/pandas/data.csv\n",
|
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"df = pd.read_csv('https://www.w3schools.com/python/pandas/data.csv')\n",
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"\n",
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"print(df.to_string()) "
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]
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},
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{
|
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"cell_type": "markdown",
|
|
"id": "1dba1828-de88-4a61-868b-5a8b638254f0",
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"metadata": {},
|
|
"source": [
|
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"#### Analyze Data"
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "e89c6f79-b957-4c6b-85bc-c76b362d7a2a",
|
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"metadata": {},
|
|
"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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|
"text": [
|
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"(169, 4)\n"
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]
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}
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],
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"source": [
|
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"print(df.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "460724ee-1ba6-461c-801e-46632a68c837",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 169 entries, 0 to 168\n",
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"Data columns (total 4 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Duration 169 non-null int64 \n",
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" 1 Pulse 169 non-null int64 \n",
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" 2 Maxpulse 169 non-null int64 \n",
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" 3 Calories 164 non-null float64\n",
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"dtypes: float64(1), int64(3)\n",
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"memory usage: 5.4 KB\n",
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"None\n"
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]
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},
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{
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"data": {
|
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"text/html": [
|
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"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
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" }\n",
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"\n",
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" .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
|
|
}
|