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299 lines
7.2 KiB
Plaintext
299 lines
7.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4be81fd7",
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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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" }\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>Gender</th>\n",
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" <th>Height</th>\n",
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" <th>Weight</th>\n",
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" <th>Index</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>Male</td>\n",
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" <td>174</td>\n",
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" <td>96</td>\n",
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" <td>4</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>Male</td>\n",
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" <td>189</td>\n",
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" <td>87</td>\n",
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" <td>2</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>Female</td>\n",
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" <td>185</td>\n",
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" <td>110</td>\n",
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" <td>4</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>Female</td>\n",
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" <td>195</td>\n",
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" <td>104</td>\n",
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" <td>3</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>Male</td>\n",
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" <td>149</td>\n",
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" <td>61</td>\n",
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" <td>3</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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" Gender Height Weight Index\n",
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"0 Male 174 96 4\n",
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"1 Male 189 87 2\n",
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"2 Female 185 110 4\n",
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"3 Female 195 104 3\n",
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"4 Male 149 61 3"
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]
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},
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"execution_count": 1,
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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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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"data = pd.read_csv(\"obese.csv\")\n",
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"data.head()"
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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": "c90a3afb",
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"metadata": {},
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"outputs": [],
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"source": [
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"data['obese'] = (data.Index >= 4).astype('int')\n",
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"data.drop('Index', axis = 1, inplace = True)"
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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": "d0ad044a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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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>Gender</th>\n",
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" <th>Height</th>\n",
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" <th>Weight</th>\n",
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" <th>obese</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>Male</td>\n",
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" <td>174</td>\n",
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" <td>96</td>\n",
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" <td>1</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>Male</td>\n",
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" <td>189</td>\n",
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" <td>87</td>\n",
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" <td>0</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>Female</td>\n",
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" <td>185</td>\n",
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" <td>110</td>\n",
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" <td>1</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>Female</td>\n",
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" <td>195</td>\n",
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" <td>104</td>\n",
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" <td>0</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>Male</td>\n",
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" <td>149</td>\n",
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" <td>61</td>\n",
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" <td>0</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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" Gender Height Weight obese\n",
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"0 Male 174 96 1\n",
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"1 Male 189 87 0\n",
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"2 Female 185 110 1\n",
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"3 Female 195 104 0\n",
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"4 Male 149 61 0"
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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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"data.head()"
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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": "dde37f0d",
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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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" Misclassified when cutting at 100kg: 18 \n",
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" Misclassified when cutting at 80kg: 63\n"
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]
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}
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],
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"source": [
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"print(\n",
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" \" Misclassified when cutting at 100kg:\",\n",
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" data.loc[(data['Weight']>=100) & (data['obese']==0),:].shape[0], \"\\n\",\n",
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" \"Misclassified when cutting at 80kg:\",\n",
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" data.loc[(data['Weight']>=80) & (data['obese']==0),:].shape[0]\n",
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")"
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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": "0d2e9b73",
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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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"Female 0.51\n",
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"Male 0.49\n",
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"Name: Gender, dtype: float64\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"0.4998"
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]
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},
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"execution_count": 8,
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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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"def gini_impurity(y):\n",
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" '''\n",
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" Given a Pandas Series, it calculates the Gini Impurity. \n",
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" y: variable with which calculate Gini Impurity.\n",
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" '''\n",
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" if isinstance(y, pd.Series):\n",
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" p = y.value_counts()/y.shape[0]\n",
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" print(p)\n",
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" gini = 1-np.sum(p**2)\n",
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" return(gini)\n",
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"\n",
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" else:\n",
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" raise('Object must be a Pandas Series.')\n",
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"\n",
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"gini_impurity(data.Gender) "
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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": null,
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"id": "b60120ac",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.7.9"
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