adds exercises for tabular data part
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420
exercises/tabular_split_apply_combine/split_apply_combine.ipynb
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420
exercises/tabular_split_apply_combine/split_apply_combine.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6f6aa857",
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"metadata": {},
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"source": [
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"# Exercise: Compute summary statistics"
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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": 1,
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"id": "8f9bc8b1",
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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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": "1be11d54",
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"metadata": {},
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"source": [
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"# Load the patient data"
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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": 2,
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"id": "d2dfebd3",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv('processed_data_predimed.csv')"
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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": "09554c84",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(6245, 18)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"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": 4,
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"id": "df95a10b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>patient-id</th>\n",
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" <th>location-id</th>\n",
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" <th>sex</th>\n",
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" <th>age</th>\n",
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" <th>smoke</th>\n",
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" <th>bmi</th>\n",
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" <th>waist</th>\n",
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" <th>wth</th>\n",
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" <th>htn</th>\n",
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" <th>diab</th>\n",
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" <th>hyperchol</th>\n",
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" <th>famhist</th>\n",
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" <th>hormo</th>\n",
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" <th>p14</th>\n",
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" <th>toevent</th>\n",
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" <th>event</th>\n",
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" <th>group</th>\n",
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" <th>City</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>Female</td>\n",
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" <td>77</td>\n",
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" <td>Never</td>\n",
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" <td>25.92</td>\n",
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" <td>94</td>\n",
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" <td>0.657343</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>Yes</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>9</td>\n",
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" <td>5.538672</td>\n",
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" <td>No</td>\n",
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" <td>MedDiet + VOO</td>\n",
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" <td>Madrid</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>Female</td>\n",
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" <td>68</td>\n",
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" <td>Never</td>\n",
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" <td>34.85</td>\n",
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" <td>150</td>\n",
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" <td>0.949367</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>Yes</td>\n",
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" <td>Yes</td>\n",
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" <td>NaN</td>\n",
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" <td>10</td>\n",
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" <td>3.063655</td>\n",
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" <td>No</td>\n",
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" <td>MedDiet + Nuts</td>\n",
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" <td>Madrid</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3</td>\n",
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" <td>1</td>\n",
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" <td>Female</td>\n",
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" <td>66</td>\n",
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" <td>Never</td>\n",
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" <td>37.50</td>\n",
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" <td>120</td>\n",
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" <td>0.750000</td>\n",
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" <td>Yes</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>6</td>\n",
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" <td>5.590691</td>\n",
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" <td>No</td>\n",
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" <td>MedDiet + Nuts</td>\n",
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" <td>Madrid</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>1</td>\n",
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" <td>Female</td>\n",
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" <td>77</td>\n",
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" <td>Never</td>\n",
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" <td>29.26</td>\n",
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" <td>93</td>\n",
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" <td>0.628378</td>\n",
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" <td>Yes</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>6</td>\n",
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" <td>5.456537</td>\n",
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" <td>No</td>\n",
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" <td>MedDiet + VOO</td>\n",
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" <td>Madrid</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>1</td>\n",
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" <td>Female</td>\n",
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" <td>60</td>\n",
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" <td>Never</td>\n",
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" <td>30.02</td>\n",
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" <td>104</td>\n",
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" <td>0.662420</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>Yes</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>9</td>\n",
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" <td>2.746064</td>\n",
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" <td>No</td>\n",
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" <td>Control</td>\n",
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" <td>Madrid</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" patient-id location-id sex age smoke bmi waist wth htn \\\n",
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"0 1 1 Female 77 Never 25.92 94 0.657343 Yes \n",
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"1 2 1 Female 68 Never 34.85 150 0.949367 Yes \n",
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"2 3 1 Female 66 Never 37.50 120 0.750000 Yes \n",
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"3 4 1 Female 77 Never 29.26 93 0.628378 Yes \n",
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"4 5 1 Female 60 Never 30.02 104 0.662420 Yes \n",
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"\n",
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" diab hyperchol famhist hormo p14 toevent event group City \n",
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"0 No Yes Yes No 9 5.538672 No MedDiet + VOO Madrid \n",
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"1 No Yes Yes NaN 10 3.063655 No MedDiet + Nuts Madrid \n",
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"2 Yes No No No 6 5.590691 No MedDiet + Nuts Madrid \n",
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"3 Yes No No No 6 5.456537 No MedDiet + VOO Madrid \n",
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"4 No Yes No No 9 2.746064 No Control Madrid "
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0b4f6091",
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"metadata": {},
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"source": [
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"# 1. Did the mediterranean diet help prevent cardiovascular events?\n",
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"\n",
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"To answer this question, we need to compute how many cardiovascular \"events\" occured in each group of participants, separated by the diet they followed.\n",
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"In the data the column `event` contains `Yes` or `No`, indicating if that patient had an cardiovascular event. The column `group` contains which diet they followed.\n",
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"\n",
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"We first convert the column ``event'' to a binary value."
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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": "99b21627-1b48-44ee-bda2-312b0718bd59",
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"metadata": {},
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"outputs": [],
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"source": [
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"df['event'] = df['event'].map({'Yes': 1, 'No': 0})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a63e59c5-fe50-433f-a529-f601c795db67",
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"metadata": {},
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"source": [
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"* Now compute the total number of events by diet group. Compare the numbers and see if you can answer the question."
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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": "00bb9eb1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# your code here:\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b052a40a-ae68-4376-8557-541eafb3face",
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"metadata": {},
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"source": [
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"* Check how many patients had each group"
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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": "db946d0f-8204-43a3-853c-41981a9811f4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# your code here:\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "64339449-d766-4a2a-85d3-8aafac5533b7",
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"metadata": {},
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"source": [
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"There were no equal number in each group, so to be precise we need to put the numbers into perspective of the total. For that:\n",
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"* Calculate how many events occured relative to the amount of patients in each group (in percentage). \n",
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"Do this sepearated by diet group."
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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": "13ad4130-2094-4e7a-a416-f0fd6e810413",
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"metadata": {},
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"outputs": [],
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"source": [
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"# your code here:\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9655c0ac-18e9-4297-9f6e-557bfe95ed5e",
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"metadata": {},
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"source": [
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"It seems that the control group had a higher percentage of events than the other two"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1940d3fe",
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"metadata": {},
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"source": [
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"# 2. Smoking\n",
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"\n",
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"Did smoking make a difference in the outcome of the study?\n",
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"Calculate how many events occured by diet group *and* smoking. The idea is that you arrive to a table like this:\n",
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"\n",
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"| group | Current | Former | Never |\n",
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"|:---------------|----------:|---------:|--------:|\n",
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"| Control | ... | ... | ... |\n",
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"| MedDiet + Nuts | ... | ... | ... |\n",
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"| MedDiet + VOO | ... | ... | ... |\n",
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"\n",
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"where each entry in the table has the percentage of events for each group. \n",
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"\n",
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"Hint: use `pivot_table`"
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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": "5ab4e70e-6261-4a26-8ad9-14eae15be09c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# your code here\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "511c640e-8f0f-449f-af33-85061d89cfd3",
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"metadata": {},
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"source": [
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"# 3. Age differences?"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a04ea5c-8a27-4e67-aafa-ba34580a8d7f",
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"metadata": {},
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"source": [
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"Finally, check that there were no big differences in the age between the groups.\n",
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"* Calculate the mean and standard deviation of the patient's age, separated by diet group.\n",
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"\n",
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"You should be getting a table where diet group are in the rows and gender in columns, like this\n",
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"\n",
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"| group | Female | Male |\n",
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"|:---------------|---------:|-------:|\n",
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"| Control | 68 | 66.4 |\n",
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"| MedDiet + Nuts | 67.4 | 65.8 |\n",
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"| MedDiet + VOO | 67.7 | 66.1 |\n"
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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": "196fd111-72bc-4b87-b8fb-293547a8c83d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# your code here:\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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