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Created March 28, 2023 21:21
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Análise Exploratória de Dados com Python - Aula do Prof. Neylson Crepalde
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EDA_python.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Análise Exploratória de Dados com Python\n",
"\n",
"<h2>Prof. Dr. <a href=\"https://www.linkedin.com/in/neylsoncrepalde/\" target=”_blank”>Neylson Crepalde</a></h2>\n",
"\n",
"Mais informações sobre mim em <a href=\"http://www.neylsoncrepalde.com\" target=\"_blank\">www.neylsoncrepalde.com</a>\n",
"\n",
"---\n",
"\n",
"Vamos trabalhar com análise exploratória de dados usando uma subamostra do dataset de pessoas da PNAD 2012.\n",
"\n",
"Link dos dados para trabalhar: `https://github.com/neylsoncrepalde/introducao_ao_r/blob/master/dados/pes_2012.csv?raw=true`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"pnad = pd.read_csv(\"https://github.com/neylsoncrepalde/introducao_ao_r/blob/master/dados/pes_2012.csv?raw=true\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>V0101</th>\n",
" <th>UF</th>\n",
" <th>V0302</th>\n",
" <th>V8005</th>\n",
" <th>V0404</th>\n",
" <th>V4803</th>\n",
" <th>V4718</th>\n",
" <th>V4720</th>\n",
" <th>V4729</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Masculino</td>\n",
" <td>48</td>\n",
" <td>Branca</td>\n",
" <td>15 anos ou mais</td>\n",
" <td>3000</td>\n",
" <td>3000</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>48</td>\n",
" <td>Branca</td>\n",
" <td>15 anos ou mais</td>\n",
" <td>3000</td>\n",
" <td>3000</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>23</td>\n",
" <td>Branca</td>\n",
" <td>15 anos ou mais</td>\n",
" <td>1100</td>\n",
" <td>1100</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>21</td>\n",
" <td>Branca</td>\n",
" <td>14 anos</td>\n",
" <td>1100</td>\n",
" <td>1100</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>54</td>\n",
" <td>Branca</td>\n",
" <td>15 anos ou mais</td>\n",
" <td>NaN</td>\n",
" <td>460</td>\n",
" <td>232</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" V0101 UF V0302 V8005 V0404 V4803 V4718 V4720 \\\n",
"0 2012 Rondônia Masculino 48 Branca 15 anos ou mais 3000 3000 \n",
"1 2012 Rondônia Feminino 48 Branca 15 anos ou mais 3000 3000 \n",
"2 2012 Rondônia Feminino 23 Branca 15 anos ou mais 1100 1100 \n",
"3 2012 Rondônia Feminino 21 Branca 14 anos 1100 1100 \n",
"4 2012 Rondônia Feminino 54 Branca 15 anos ou mais NaN 460 \n",
"\n",
" V4729 \n",
"0 232 \n",
"1 232 \n",
"2 232 \n",
"3 232 \n",
"4 232 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Primeiras análises do dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(362451, 9)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Rondônia\n",
"1 Rondônia\n",
"2 Rondônia\n",
"3 Rondônia\n",
"4 Rondônia\n",
" ... \n",
"362446 Distrito Federal\n",
"362447 Distrito Federal\n",
"362448 Distrito Federal\n",
"362449 Distrito Federal\n",
"362450 Distrito Federal\n",
"Name: UF, Length: 362451, dtype: object"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad['UF']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Masculino\n",
"1 Feminino\n",
"2 Feminino\n",
"3 Feminino\n",
"4 Feminino\n",
" ... \n",
"362446 Feminino\n",
"362447 Masculino\n",
"362448 Masculino\n",
"362449 Masculino\n",
"362450 Masculino\n",
"Name: V0302, Length: 362451, dtype: object"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.V0302"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analisando variáveis qualitativas"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Masculino\n",
"1 Feminino\n",
"2 Feminino\n",
"3 Feminino\n",
"4 Feminino\n",
"Name: V0302, dtype: object"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.V0302.head() # primeiros casos"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"362446 Feminino\n",
"362447 Masculino\n",
"362448 Masculino\n",
"362449 Masculino\n",
"362450 Masculino\n",
"Name: V0302, dtype: object"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.V0302.tail() # últimos casos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Feminino 186054\n",
"Masculino 176397\n",
"Name: V0302, dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.V0302.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Feminino 0.513322\n",
"Masculino 0.486678\n",
"Name: V0302, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.V0302.value_counts() / pnad.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Parda 0.479328\n",
"Branca 0.429286\n",
"Preta 0.083101\n",
"Amarela 0.004276\n",
"Indígena 0.003959\n",
"Sem declaração 0.000050\n",
"Name: V0404, dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cor da pele\n",
"pnad.V0404.value_counts() / pnad.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variáveis Quantitativas\n",
"\n",
"Para analisar variáveis numéricas começamos com estatísticas descritivas (média, mediana, desvio padrão, variância...)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 48\n",
"1 48\n",
"2 23\n",
"3 21\n",
"4 54\n",
"Name: V8005, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Idade\n",
"pnad.V8005.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"32.63801175883085"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.V8005.mean() # média"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"32.63801175883085"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(pnad.V8005) # média com numpy"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.V8005.median() # mediana"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"431.4064534606153"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.V8005.var() # variância"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20.77032627236788"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.V8005.std() # desvio padrão"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 362451.000000\n",
"mean 32.638012\n",
"std 20.770326\n",
"min 0.000000\n",
"25% 15.000000\n",
"50% 30.000000\n",
"75% 48.000000\n",
"max 117.000000\n",
"Name: V8005, dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Tirando várias estatísticas descritivas de uma só vez\n",
"pnad.V8005.describe()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<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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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>V0101</th>\n",
" <th>V8005</th>\n",
" <th>V4729</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>362451.0</td>\n",
" <td>362451.000000</td>\n",
" <td>362451.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2012.0</td>\n",
" <td>32.638012</td>\n",
" <td>543.183294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0</td>\n",
" <td>20.770326</td>\n",
" <td>260.364253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2012.0</td>\n",
" <td>0.000000</td>\n",
" <td>174.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2012.0</td>\n",
" <td>15.000000</td>\n",
" <td>334.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2012.0</td>\n",
" <td>30.000000</td>\n",
" <td>522.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2012.0</td>\n",
" <td>48.000000</td>\n",
" <td>705.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2012.0</td>\n",
" <td>117.000000</td>\n",
" <td>1132.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" V0101 V8005 V4729\n",
"count 362451.0 362451.000000 362451.000000\n",
"mean 2012.0 32.638012 543.183294\n",
"std 0.0 20.770326 260.364253\n",
"min 2012.0 0.000000 174.000000\n",
"25% 2012.0 15.000000 334.000000\n",
"50% 2012.0 30.000000 522.000000\n",
"75% 2012.0 48.000000 705.000000\n",
"max 2012.0 117.000000 1132.000000"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vamos investigar os tipos de dados que há em cada coluna"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>V0101</th>\n",
" <th>UF</th>\n",
" <th>V0302</th>\n",
" <th>V8005</th>\n",
" <th>V0404</th>\n",
" <th>V4803</th>\n",
" <th>V4718</th>\n",
" <th>V4720</th>\n",
" <th>V4729</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Masculino</td>\n",
" <td>48</td>\n",
" <td>Branca</td>\n",
" <td>15 anos ou mais</td>\n",
" <td>3000</td>\n",
" <td>3000</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>48</td>\n",
" <td>Branca</td>\n",
" <td>15 anos ou mais</td>\n",
" <td>3000</td>\n",
" <td>3000</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>23</td>\n",
" <td>Branca</td>\n",
" <td>15 anos ou mais</td>\n",
" <td>1100</td>\n",
" <td>1100</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>21</td>\n",
" <td>Branca</td>\n",
" <td>14 anos</td>\n",
" <td>1100</td>\n",
" <td>1100</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>54</td>\n",
" <td>Branca</td>\n",
" <td>15 anos ou mais</td>\n",
" <td>NaN</td>\n",
" <td>460</td>\n",
" <td>232</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" V0101 UF V0302 V8005 V0404 V4803 V4718 V4720 \\\n",
"0 2012 Rondônia Masculino 48 Branca 15 anos ou mais 3000 3000 \n",
"1 2012 Rondônia Feminino 48 Branca 15 anos ou mais 3000 3000 \n",
"2 2012 Rondônia Feminino 23 Branca 15 anos ou mais 1100 1100 \n",
"3 2012 Rondônia Feminino 21 Branca 14 anos 1100 1100 \n",
"4 2012 Rondônia Feminino 54 Branca 15 anos ou mais NaN 460 \n",
"\n",
" V4729 \n",
"0 232 \n",
"1 232 \n",
"2 232 \n",
"3 232 \n",
"4 232 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"V0101 int64\n",
"UF object\n",
"V0302 object\n",
"V8005 int64\n",
"V0404 object\n",
"V4803 object\n",
"V4718 object\n",
"V4720 object\n",
"V4729 int64\n",
"dtype: object"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# Transformar V4718 e V4720 em float\n",
"pnad['V4718'] = pd.to_numeric(pnad['V4718'], errors='coerce')\n",
"pnad['V4720'] = pd.to_numeric(pnad['V4720'], errors='coerce')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>V0101</th>\n",
" <th>V8005</th>\n",
" <th>V4718</th>\n",
" <th>V4720</th>\n",
" <th>V4729</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>362451.0</td>\n",
" <td>362451.000000</td>\n",
" <td>168981.000000</td>\n",
" <td>303634.000000</td>\n",
" <td>362451.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2012.0</td>\n",
" <td>32.638012</td>\n",
" <td>1342.847628</td>\n",
" <td>992.282985</td>\n",
" <td>543.183294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0</td>\n",
" <td>20.770326</td>\n",
" <td>2656.679175</td>\n",
" <td>2492.988418</td>\n",
" <td>260.364253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2012.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>174.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2012.0</td>\n",
" <td>15.000000</td>\n",
" <td>622.000000</td>\n",
" <td>0.000000</td>\n",
" <td>334.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2012.0</td>\n",
" <td>30.000000</td>\n",
" <td>800.000000</td>\n",
" <td>622.000000</td>\n",
" <td>522.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2012.0</td>\n",
" <td>48.000000</td>\n",
" <td>1400.000000</td>\n",
" <td>1100.000000</td>\n",
" <td>705.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2012.0</td>\n",
" <td>117.000000</td>\n",
" <td>350000.000000</td>\n",
" <td>351600.000000</td>\n",
" <td>1132.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" V0101 V8005 V4718 V4720 V4729\n",
"count 362451.0 362451.000000 168981.000000 303634.000000 362451.000000\n",
"mean 2012.0 32.638012 1342.847628 992.282985 543.183294\n",
"std 0.0 20.770326 2656.679175 2492.988418 260.364253\n",
"min 2012.0 0.000000 0.000000 0.000000 174.000000\n",
"25% 2012.0 15.000000 622.000000 0.000000 334.000000\n",
"50% 2012.0 30.000000 800.000000 622.000000 522.000000\n",
"75% 2012.0 48.000000 1400.000000 1100.000000 705.000000\n",
"max 2012.0 117.000000 350000.000000 351600.000000 1132.000000"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Algumas análises\n",
"### Sexo X Renda (V4720)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"sns.set(style='whitegrid')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
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AKC0tte3v7Y80oo5uvG4AFk4bhl4JsVAA6JUQi4XThnGWFwWU2xZKU1MTlixZgqamJiiVSnTv3h0lJSWora3FokWLYDabYbFYMHjwYBQWFgIAlEol1q5di8LCwjZTfP2VRkS3gwoDCAWT24DSs2dPfPDBBw7TysrKnJ43cuRIbNu2LWBpREQUXHxTnoiIZMGAQkREsmBAISIiWTCgEBGRLBhQiIhIFgwoREQkCwYUIiKSBQMKERHJggGFiIhkwYBCRESyYEAhIiJZeLx8PREFBveIp3DDgEIUgqx7xFv3N7HuEQ+AQYVCFru8iEIQ94incMSAQhSCuEc8hSMGFKIQ1DMh1qPjRKGAAYUoBM3O1EIdrWpzTB2twuxMbZByROQeB+WJQpB14J2zvCicMKBQG5yqGjq4RzyFG0ldXs888wymTJmCqVOnIi8vDydO3J5pcvbsWcyYMQMZGRmYMWMGzp07Zzsn0GnkO+tU1Sv1TRD4carqPv3FYGeNiMKApIBSVFSEjz/+GGVlZZgzZw5eeOEFAEBhYSHy8vJQUVGBvLw8FBQU2M4JdBr5jlNVicgXkgJK165dbf++desWFAoFamtrcfz4cWRlZQEAsrKycPz4cdTV1QU8jeTBqapE5AvJYygvvvgiDhw4ACEE3nzzTRgMBvTp0wcq1e2ZKCqVCr1794bBYIAQIqBpiYmJsj6UjqpnQiyuOAgenKpKRFJIDihr1qwBAJSVlWHt2rVYsmSJ3zLlT1VVVcHOgmR6vT6g9xt7Xyds+7IZJrOwHYtWKTD2vk5+y0ugyxgMLGNkYBnd83iW19SpU1FQUIC+ffvi8uXLMJvNUKlUMJvNqKmpgUajgRAioGmeSE5Ohlqt9rTYAafX66HT6QJ6T50OGDQocLO8glHGQGMZI0NHLqPRaJT8RdztGEpDQwMMBoPt5z179qB79+7o0aMHtFotysvLAQDl5eXQarVITEwMeBrJZ7xuAN5eMQkfv5KDt1dM4rRVIpLMbQulqakJS5YsQVNTE5RKJbp3746SkhIoFAr89re/RX5+Pv70pz+hW7duKCoqsp0X6DQiIgoutwGlZ8+e+OCDDxymDR48GJs3bw6JNCIiCi6u5UVERLJgQCEiIlkwoBARkSwYUIiISBYMKEREJAsGFCIikgUDChERyYIBhYiIZMGAQkREsmBAISIiWTCgEBGRLBhQiIhIFgwoREQkCwYUIiKSBQMKERHJggGFiIhkwYBCRESyYEAhIiJZMKAQEZEs3AaU+vp6zJs3DxkZGcjOzsbChQtRV1cHABg6dCiys7ORk5ODnJwcnDx50nbenj17MHnyZEycOBFLly5FU1OTX9OIiCi43AYUhUKBuXPnoqKiAtu2bcOAAQOwfv16W3ppaSm2bt2KrVu3YujQoQCAhoYGvPTSSygpKcEnn3yCzp0746233vJbGhERBZ/bgBIfH4/Ro0fbfh4+fDiqq6tdnvPZZ58hOTkZAwcOBADk5uZi586dfksjIqLgi/LkwxaLBe+//z7S0tJsx2bNmgWz2YyHH34YixYtQkxMDAwGA5KSkmyfSUpKgsFgAAC/pBERUfB5FFBWrVqFuLg4zJw5EwCwb98+aDQa3Lp1C8uXL0dxcTF+85vf+CWjcqmqqgp2FiTT6/XBzoLfsYyRgWWMDL6WUXJAKSoqwvnz51FSUgKl8nZPmUajAQB06dIF06ZNwzvvvGM7fvDgQdu51dXVts/6I80TycnJUKvVHp8XaHq9HjqdLtjZ8CuWMTKwjJHBWRmNRqPkL+KSpg2/+uqrqKqqQnFxMWJiYgAA169fR3NzMwCgtbUVFRUV0Gq1AICxY8fi2LFjOHfuHIDbA/eZmZl+SyMiouBz20I5ffo0SkpKMHDgQOTm5gIA+vfvj7lz56KgoAAKhQKtra0YMWIElixZAuB2i2XlypWYP38+LBYLtFotXnzxRb+lERFR8LkNKPfee2+b90vsbdu2zel5EyZMwIQJEwKWRkREwcU35YmISBYMKEREJAsGFCIikgUDChERyYIBhYiIZMGAQkREsmBAISIiWTCgEBGRLBhQiIhIFh6tNkwUTPv0F/HuzhO4Wt+EngmxmJ2pxXjdAL+fG4zrEoUjBhQKC/v0F7Fh8xEYTWYAwJX6JmzYfAQA3FbgvpwbjOsShSt2eVFYeHfnCVvFbWU0mfHuzhN+PTcY1yUKVwwoFBau1jd5dFyuc4NxXaJwxYBCYaFnQqxHx+U6NxjXJQpXDCgUFmZnaqGOVrU5po5WYXam1q/nBuO6ROGKg/IUFqyD3N7MqPLl3GBclyhcMaBQ2BivG+B1Ze3LucG4LlE4YpcXERHJggGFiIhk4Tag1NfXY968ecjIyEB2djYWLlyIuro6AMDhw4cxZcoUZGRkYM6cOaitrbWdF+g0IiIKLrcBRaFQYO7cuaioqMC2bdswYMAArF+/HkIILF++HAUFBaioqEBKSgrWr18PAAFPIwoX+/QXMWf1bkx5bivmrN6NffqLwc4SkWzcBpT4+HiMHj3a9vPw4cNRXV2NY8eOQa1WIyUlBQCQm5uLXbt2AUDA04jCgXWpliv1TRD4cakWBhWKFB6NoVgsFrz//vtIS0uDwWBAUlKSLS0xMREWiwXXrl0LeBpROOBSLRTpPJo2vGrVKsTFxWHmzJn45JNP/JUnv6qqqgp2FiTT6/XBzoLfdaQyXnGyJMuV+qawfw7hnn8pWEb3JAeUoqIinD9/HiUlJVAqldBoNKiurral19XVQaFQID4+PuBpnkhOToZarfbonGDQ6/XQ6XTBzoZfdbQy9tpZ6zCo9EqIDevn0NF+j5HKWRmNRqPkL+KSurxeffVVVFVVobi4GDExMQBuV8zNzc2orKwEAJSWliIzMzMoaUThgEu1UKRz20I5ffo0SkpKMHDgQOTm5gIA+vfvj+LiYqxduxaFhYUwGo3o168f1q1bBwBQKpUBTSMKB1yqhSKd24By77334uTJkw7TRo4ciW3btoVEGlE44FItFMn4pjwREcmCAYWIiGTBgEJERLLg8vVEYWyf/iIH+SlkMKAQhSnrUi7Wt++tS7kAYFChoGCXF1GY4lIuFGoYUIjC1FUnS7k4O07kb+zyIlmwLz/weibEOlzKpWdCbBByQ8QWCsmAy7IHB5dyoVDDgEI+Y19+cIzXDcDCacPQKyEWCtxeZHLhtGFsGVLQsMuLfMa+/ODhUi4USthCIZ8567NnXz5Rx8KAQj5jXz4RAezyIhlwWfbg4ew6CiUMKCQL9uUHHt+Up1DDLi+iMMXZdRRqGFCIwhRn11GoYUAhClOcXUehhgGFKExxdh2FGkkBpaioCGlpaRg6dChOnTplO56WlobJkycjJycHOTk5+Pzzz21phw8fxpQpU5CRkYE5c+agtrbWr2lEHQ3flKeQIyQ4dOiQqK6uFo888og4efKk7Xj7n60sFouYMGGCOHTokBBCiOLiYpGfn++3NCmam5tFZWWlaG5ulnxOMFVWVgY7C37njzLurbwgnlhVIbKfLRNPrKoQeysvyH4PT/D3GBk6chk9qTsltVBSUlKg0WgkB6ljx45BrVYjJSUFAJCbm4tdu3b5LY0I4CKVRMHm8xjKsmXLkJ2djd/+9re4ceMGAMBgMCApKcn2mcTERFgsFly7ds0vaUQAp9ESBZtPLzZu2rQJGo0GLS0tWLNmDVauXIn169fLlTe/qKqqCnYWJNPr9cHOgt/JWUZHe4NYjwfzWfL3GBlYRvd8CijWbrCYmBjk5eXh6aefth2vrq62fa6urg4KhQLx8fF+SfNEcnIy1Gq1V+UNJL1eD51OF+xs+JXcZey1s9ZhUOmVEBu0Z8nfY2ToyGU0Go2Sv4h73eXV2NiImzdvAgCEENixYwe02tvTFZOTk9Hc3IzKykoAQGlpKTIzM/2WRgRwGi1RsElqoaxevRq7d+/G1atX8cQTTyA+Ph4lJSVYtGgRzGYzLBYLBg8ejMLCQgCAUqnE2rVrUVhYCKPRiH79+mHdunV+SyMCuEglUbAphBAi2JkIBGuzjV1eoYNljAwsY2Rw1+Ulpe7km/JERCQLBhQiIpIFAwoREcmCAYWIiGTBgEJERLJgQCEiIlkwoBARkSwYUIiISBY+reVFRPLap7/IN/0pbDGgkNf8Wfl1xIrVup+LdQl+634uACK+7BQZGFDIK/6s/EK1YvV3kHO1nwsDCoUDjqGQV/y5mVUobpQViN0grzrZz8XZcaJQw4BCXvFn5ReKFWsgglzPhFiPjhOFGgYU8oo/K79QrFgDEeS4nwuFOwYU8oo/K79QrFgDEeTG6wZg4bRh6JUQCwVu7zS5cNowjp9Q2OCgPHnFn5tZheJGWbMztW0mCgD+CXLjdQMYQChsMaCQ1/xZ+YVaxRqKQY4o1DCgEEkUakGOKNRwDIWIiGTBgEJERLJw2+VVVFSEiooKXLp0Cdu2bcOQIUMAAGfPnkV+fj6uXbuG+Ph4FBUVYeDAgUFJI3JEjjfbPbnG6x8exq6DF2CxCCiVCkwefRdGDfJv/vx5PSJPuW2hpKenY9OmTejXr1+b44WFhcjLy0NFRQXy8vJQUFAQtDSi9uR4s92Ta7z+4WHs+Md5WCwCAGCxCOz4x3mUf1nnt/z583pE3nAbUFJSUqDRaNocq62txfHjx5GVlQUAyMrKwvHjx1FXVxfwNCJH5Hiz3ZNr7Dp4weE19Gca/ZY/f16PyBtezfIyGAzo06cPVKrbL5+pVCr07t0bBoMBQoiApiUmJnqU96qqKm+KHBR6vT7YWfA7f5XxipM32K/UN0m+pyfXsLZM2hPCcRldXXvmS+VIH9YN9w/qLCmfnubVH/i3Ghl8LWOHmzacnJwMtVod7Gy4pdfrodPpgp0Nv/JnGXvtrHVYyfZKiJV8z65ll3Gz0XTn8bjoO66hLL3kMKgoFHB4P2f5A4DrjWZsr7yBQYMGSR4DkaO83uLfamRwVkaj0Sj5i7hXs7w0Gg0uX74Ms/l2E9tsNqOmpgYajSbgaUSOBHr5lsmj73J4XDc4zuFxR/mz52l31exMLVRKRZtjKqWC64BRQHkVUHr06AGtVovy8nIAQHl5ObRaLRITEwOeRrRPfxFzVu/GlOe2Ys7q3dinvyjLuli3HLROnB1/+lfD8Yuf3w3lPyt1pVKBX/z8bmSNcvw3ap8/ZzxdeFKhcP0zkb8phBCOO3//afXq1di9ezeuXr2KhIQExMfHY/v27Thz5gzy8/Nx48YNdOvWDUVFRbjnnnsAIOBpUlibbezyCh1ylLH9ZlzA7ZaIHIsqzlm922k30tsrJkm6hpQyynEfOa7hLf6tRgZ3XV5S6k63YygrVqzAihUr7jg+ePBgbN682eE5gU6jjsufuxwGakFIOe4TinvIUMfT4QblKTJYX+JzNrAtR0XqakFIOV8ilGPhyZ4JsQ6fBTfnokBiQKGw46ibqz25KlJHC0L6Y897XxeeDFRrisgVBhQKKm++6Tvq5rLn74rUn91s3uLy+hQKGFAoaI6ebcD2Ss+/6bvqzuoVgIo0VMcruLw+BRtXG6ag+fTIDa+WC3HWnWWd0eTvSjUU97wnCgUMKBQ01xsdd1u5+6Yf7D3ng31/olDFLi8Kmu5xKodBxd03/WCPFwT7/kShigGFgiZ9WDdsr7zh1cykYI8XBPv+RKGIASWChNsGS/cP6oxBgwaFVZ6tAr15F1E4YECJEP54NyIQwvGbvhzPOlx/X0SucFA+QnCDpcAJ1OZdjha9JAplbKFEiFB9NyISyfGsnX32Sn2Tw4Ue2YKhcMAWSoTguxGBI8ezdvVZZ+uTscVJoY4BJULw3YjAkeNZu9tgyxlfWpzedqGx642kYpdXhJDr3QhvZx7Zn9clLhrA7Y2o5Jy9JNesKHfXcZdu/6yv1DdBqVS0aT1IyZOj35ezlok9b1uc3k4C4OQB8gQDSgTxdcaUXJWO/T7sclVAclVs7q4j9T7Wf0v5rLMA1f735WyTLCtfWpzeLmgZigthUuhilxfZeDt7yd3qv3L0/bvKmyddMs6u82rp17aKX+ozkPLZo2cbsGHzEVypb4LAj0HHUR5ddYN5s4WxPW8nEnCyB3mCLRSykbvS8fQz3pxvrUZTsTAAABC/SURBVKCltlycXcdiES73WHF0npTn5WoBzPb58+eSLt5uwMWNu8gTDChkI3el48k1vM2bdfzCnqsuGVd5NZrMUCoVsFiEw/Ps7dNfhEKpgHDzWU8XwHTWbenr+JG3G3A5Og/4cXrz7EwtukrOBUU6n7u80tLSMHnyZOTk5CAnJweff/45AODw4cOYMmUKMjIyMGfOHNTW1trO8Uca+c7b2UvuZiy1v4a1i+q3730vedaQs7w5qvyBOyts6z3dBT6LRbh9BtZxFkf3VikVmJ2ptd3PGU8CrPV+UrrNnBmvG4CF04ahV0IsFJDehWZ/XnvWfBw92yA5HxTZZBlDee2117B161Zs3boVY8eOhRACy5cvR0FBASoqKpCSkoL169cDgF/SSB5yVDoKAF3jotE1LtrhNewrR0B65egsb13/OaOsvS52x9vf0xXrdV09A1djRgoFcOJsrdv7pd7X2+FxR+NBUsd13I0ljdcNwNsrJuHjV3I82jfGep6joGI0mfHpkRuSrkORzy9dXseOHYNarUZKSgoAIDc3F+np6fj973/vlzSSj7czxaSe58usIUf32Fh2zOnnrZWxlEAC/NgScVcWV+NBrWaBXQcvOG05WR36pgZPO8ivo/EgKeM6gZje66zczrr1qOORJaAsW7YMQgjodDo8++yzMBgMSEpKsqUnJibCYrHg2rVrfkmLj4+XnNeqqiofSxs4er0+2Fnw2dGzDfj0yA1cbzQ73f8EuF0B2pe3/Xnpw7rh/kGd7zjPfopy++Ov/fVrmMyuK3Zrnqz36Ioa6PU1Ls/p5qIcANwGE+DO8gLAm2UGh8HWVT6s13B27ptlR9AVrstjz9Vzd1bu7nb5iGQso3s+B5RNmzZBo9GgpaUFa9aswcqVKzFx4kRfL+s3ycnJUKvVwc6GW3q9HjqdLtjZ8Mk+/cU2e8a7qoS7xkXbyuvovO2VNzBo0KA7vm332lnrdLDeXTABgBh1DJ6b6tnLm+6+kTsb2LfXKyH2jt/vjfe2us2DveuNZhTvrMXsTC1uNH7v8DM3Gs2S/47cPfe5uOhwYD99WLew/1t1JxL+P7rjrIxGo1HyF3Gfx1A0Gg0AICYmBnl5efjqq6+g0WhQXV1t+0xdXR0UCgXi4+P9kkbB4a7P3t37Kc548i6Ip4P17Ukdw5E6DqOOVmHy6LvcLqtinSVlf19vZsJZ89/FyViSJ9d099ydjWM5ajlSx+RTC6WxsRFmsxldu3aFEAI7duyAVqtFcnIympubUVlZiZSUFJSWliIzMxMA/JJG8nG0hMrNRpPtW3evf05ZBdy/Je7Juye37LqupLzfYZ9PdcyPlbdCAQDSgomV0WTGK+99hf96/yuIdqcqFEBMlBJGk8XtdRQKID2lP57+1XBoB/VwO35zpb4Jr7z3FV557yv0SohF6n298Wnl9x4HYaPJjJhoJdTRKq92v7SS8twdjS/p9TXcLIwA+BhQamtrsWjRIpjNZlgsFgwePBiFhYVQKpVYu3YtCgsLYTQa0a9fP6xbtw4A/JJG8nC1hIr1G781cMREK90OrktdnwpoOyurS1y0w7ER62fa57O55cd8CAFJlb8j7YOJp9cTAvi08ntoB/WwVbwzXyqXNGh9pb4Jn1Z+j/SU/jj0TY3k52Z1q9GEZ/NG+lSpe/se0tGzDW26yrjeV8flU0AZMGAAysrKHKaNHDkS27ZtC1ga+U5qF5XRZJY088jZS3G+8rYrLRDaB1VPZkAZTWYc+qYGb6+YhOznPBtP6ZkQ6/Nabt6+/OjJagAU2fimfAflqIvC02/Fjth/m7VWJq+895Xb8+y7vG65mLnlaUUbDPZB1dXMNkesYytdnbTSusZFo8nYila7CQdRKoWkri1PVlH2pJXj6WoAFLkYUDoQZ+9lWLsoFArH3T5SWb/Ntq+4nFWO9gSAvJd22P4tJ1/L5ameCbGSZ4Q5cqW+CSqlAlEqRZvAoY5WYeywJFQcvNDm8+3L5ihwANJWRvamleMsaHK9r46HAaWDaD/u0J6vXUjWN9bbt0au1DchSqWASqmA2c3MK3dBxxu3Z1sJr8dVvLlf6n29fe7qM1sEusZFo5M6qk1geHfniTueo9kibN1Lzl5wlDLm5Yy7lk36sG7YXnnDpwkBFBkYUCKQowrA3+MOroJBq1lAHa2EgLSX/uQU6LEWo8mMHf84L8u1bjaacE9SN7y9YpLt2H856T60di85m/ordRXl9n877WeetZ+ZNjtTi/sHdcagQYM4y4sYUCKNp8t32OsUo2ozY0pOgWohRJoj39Yi+7mttsrb3UwsT8fB7LulHP3tuAqO1r+tR1O64Ylf+TYhgCIDA0oE2ae/iFdLv76jFeBqWXZ7/gom5Dtry8BV+pzVu12OF7l7T8WbVqx1cchmHLatYWZ9d6fFZPF6O2hn40COWkF8ByZ0MKBECFdLqgO3u5qkjGNQ+HLXOklP6d9m4UqF4nYX2rs7T/g0y+96Y9tuPvt3d9pvB/3Ke19hY9kxPDX1Z3dU+q4mjfyh9GsoFLBNUrC2jk6crb2jS47vwAQPtwCOEO6+XXaNi5Z99hSFj65x0fi08vs2XziaW8y2/VWkTO2Wy81G0x3L3bhb2sZsEW1mvAG3W0e7Dl7wattq8g+2UEKM/be0rmWXAUjrLnA151+lVMDUagn4gDiFDn/MoAPu7EaTqv0MM28njUjdYI0Cgy2UENL+W9rNRhNuNppc7tJnXaDRVagQ4PgIyc+6OKS37Ct9bwOAUqlweJzvwAQHWyghxN23NPumvCcbR7FlQnLrGhdtm87sbXeZfaXvbt03lVLRZgzFytHftr/egeHgv3sMKCFEyre0K/VNDmdyEQWSHMvgWGemzc7UYnam1uFqz8CPL83ar3rtTC8/VfRcAFMadnmFEKnNdAYTihTWCQH/78vzDoPJsH/pgRaTxTYG5C6YvL1ikl8qeFcLYNKPGFBCiKPNouy527SJKFwd+bbW6XGpg/X+HIjnApjSsMtLRo42p2o/Q+v1D398AUypVGDy6Lvw9K+GA2i72uuV+iZbU9/+GoGc3kkUTrrERUsa5/BmLIQLYEqjECKQ67AGj3VfZH/tKe9u8UV32vf96vV63ERvh7snEpE06miVbSaas4ks1s+4CirvfPiZwwUw3Z0XTtztKS+l7mQLRSa+Lr5ov+jej763/YuBhMhz1u2d3X3G3arLXABTGgYUmbAvlSh82f//ddQl1hWO94rhVOK2GFDcsB/zcLToHVsOROFPAA6nQdsvS9NrZ22bgOFsZW/A+VTiQAegQN8v7MZQzp49i/z8fFy7dg3x8fEoKirCwIED3Z7nzRjK6x8elm1vCyIiZ9TRSrS0WtpMnfb1nRpH47quxn3kGEMJu2nDhYWFyMvLQ0VFBfLy8lBQUOC3ezGYEFEgGE2WO97DcbbcklTONlvz57szYRVQamtrcfz4cWRlZQEAsrKycPz4cdTV1QU5Z0RE8vMlADgb1/XneG9YjaEYDAb06dMHKtXtF/xUKhV69+4Ng8GAxMRESdeoqqryZxaJiGR1pb4Jer3e4/O6OXl3plucyun1vLmPvbAKKHLw6D2U9753/xkiIj/qlRDrcGzDnblwPIYyd+ow6LwYQ5EirAKKRqPB5cuXYTaboVKpYDabUVNTA41GE+ysERHJzpeVk+1X3gjULK+wCig9evSAVqtFeXk5cnJyUF5eDq1WK7m7y1PbXsnxeUVVIiJ7w/6lB1Y//VCbKb0xfpjlBTh+d8afwm7a8JkzZ5Cfn48bN26gW7duKCoqwj333OP2PH8vvSI3Z83PSMIyRgaWMTJ0yKVXBg8ejM2bNwc7G0RE1E5YTRsmIqLQxYBCRESyYEAhIiJZhN0Yirescw9aWlqCnBPpjEZjsLPgdyxjZGAZI4OjMlrrTCnzt8Julpe3bt68iVOnTgU7G0REYWnIkCHo2rWry890mIBisVjQ0NCA6OhoKBSKYGeHiCgsCCFgMpnQuXNnKJWuR0k6TEAhIiL/4qA8ERHJggGFiIhkwYBCRESyYEAhIiJZMKAQEZEsGFCIiEgWDChERCQLBpQgq6+vx7x585CRkYHs7GwsXLgQdXV1AIDDhw9jypQpyMjIwJw5c1BbWxvk3Ppuw4YNGDp0qG3Vgkgqo9FoRGFhISZNmoTs7Gy89NJLAICzZ89ixowZyMjIwIwZM3Du3LngZtQHe/fuxdSpU5GTk4Ps7Gzs3r0bQHiXsaioCGlpaW3+LgHXZQq38joqo6u6B/Dy/6agoKqvrxdffPGF7ef//M//FM8//7ywWCxiwoQJ4tChQ0IIIYqLi0V+fn6wsimLqqoq8eSTT4rx48eLkydPRlwZV61aJdasWSMsFosQQogrV64IIYSYNWuWKCsrE0IIUVZWJmbNmhW0PPrCYrGIlJQUcfLkSSGEECdOnBDDhw8XZrM5rMt46NAhUV1dLR555BFb2YRw/XsLt/I6KqOzukcI4fX/TQaUELNr1y7x61//Whw5ckQ8+uijtuO1tbVi+PDhQcyZb4xGo5g+fbq4cOGC7Y86ksp469YtodPpxK1bt9ocv3r1qtDpdKK1tVUIIURra6vQ6XSitrY2GNn0icViEaNGjRKVlZVCCCG+/PJLMWnSpIgpo31l66pM4Vze9kHTnrXuEUJ4/X+zw6w2HA4sFgvef/99pKWlwWAwICkpyZaWmJgIi8WCa9euIT4+Poi59M5///d/Y8qUKRgw4Mf9rSOpjBcvXkR8fDw2bNiAgwcPonPnzliyZAk6deqEPn36QKVSAQBUKhV69+4Ng8GAxMTEIOfaMwqFAn/4wx/wzDPPIC4uDg0NDfjzn/8Mg8EQMWW0clUmIUTElde+7gG8/7/JMZQQsmrVKsTFxWHmzJnBzoqsvv76axw7dgx5eXnBzorftLa24uLFi/jJT36CLVu2YNmyZVi0aBEaGxuDnTXZtLa24s9//jP+9Kc/Ye/evXj99dfxm9/8JqLK2FHJVfewhRIiioqKcP78eZSUlECpVEKj0aC6utqWXldXB4VCEXbf3AHg0KFD+O6775Ceng4A+OGHH/Dkk09i1qxZEVPGpKQkREVFISsrCwAwbNgwJCQkoFOnTrh8+TLMZjNUKhXMZjNqamqg0WiCnGPPnThxAjU1NdDpdAAAnU6H2NhYqNXqiCmjlUajcVomIURElbd93QPA6/qHLZQQ8Oqrr6KqqgrFxcWIiYkBACQnJ6O5uRmVlZUAgNLSUmRmZgYzm1576qmn8Le//Q179uzBnj170LdvX7z11luYO3duxJQxMTERo0ePxoEDBwDcngVUW1uLgQMHQqvVory8HABQXl4OrVYbll0jffv2xQ8//IDvvvsOAHDmzBlcvXoVd999d8SU0apHjx5Oy+QqLdw4qnsA7+sfLl8fZKdPn0ZWVhYGDhyITp06AQD69++P4uJifPXVVygsLITRaES/fv2wbt069OzZM8g59l1aWhpKSkowZMiQiCrjxYsX8cILL+DatWuIiorC0qVLMW7cOJw5cwb5+fm4ceMGunXrhqKiItxzzz3Bzq5XPv74Y7zxxhu2PYUWL16MCRMmhHUZV69ejd27d+Pq1atISEhAfHw8tm/f7rJM4VZeR2X8wx/+4LTuAeDV/00GFCIikgW7vIiISBYMKEREJAsGFCIikgUDChERyYIBhYiIZMGAQkREsmBAISIiWTCgEBGRLP4/6yFyXD5aG3wAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(pnad.V8005, pnad.V4720)\n",
"plt.title(\"Interação entre idade e renda\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Algumas análises visuais"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# sexo\n",
"sns.countplot(pnad.V0302)\n",
"plt.title(\"Sexo\")\n",
"plt.xlabel(\"\")\n",
"plt.ylabel(\"Frequência\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Cor da pele\n",
"sns.countplot(pnad.V0404)\n",
"plt.title(\"Cor da Pele\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(pnad.V0404, hue=pnad.V0302)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(pnad.V0302, hue=pnad.V0404)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEQCAYAAAB1OJkXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAeWElEQVR4nO3de1TUdf7H8dfMyEXwgmgiKkdXVoikrYDkrLc6I6W4GGAaHDc92apppR1bddUMzFID7bKWRmrHTifLNSwI8pJlnW1bt10tUn7mJe+tqCkXuchtmN8f/pxfpqLyhe+APh9/Od/3fOf7nvHLvOb7+c58vhan0+kUAAAGWN3dAACg5SNMAACGESYAAMMIEwCAYYQJAMAwwgQAYFgrszb0+OOP66effpLVapWPj4+effZZhYWF6dChQ5o1a5aKi4vl5+entLQ09ezZU5IaXAMAmMxpkrNnz7r+vWXLFmdCQoLT6XQ6x4wZ48zKynI6nU5nVlaWc8yYMa77NbQGADCXacNcbdu2df27rKxMFotFZ86c0e7duxUXFydJiouL0+7du1VYWNjgGgDAfKYNc0nSM888o6+//lpOp1OrVq1SQUGBAgICZLPZJEk2m02dO3dWQUGBnE5ng2r+/v5X7aOurk7l5eXy8PCQxWJpuicMADcQp9Opmpoa+fr6ymq9+FjE1DBZsGCBJCkrK0vp6el66qmnzNy8S3l5ufbt2+eWbQNASxcSEnLRaJNkcphckJCQoJSUFHXp0kUnT56Uw+GQzWaTw+HQqVOnFBgYKKfT2aDatfDw8JB0/gXx9PRsyqd6w0tPT5ckzZw5082dABcbP378JctWrVrlhk5uHNXV1dq3b5/rPfSXTAmT8vJynT171vVmv3XrVrVv314dO3ZUWFiYcnNzFR8fr9zcXIWFhbmGqhpau5oLQ1uenp7y8vJqgmd886ioqJAkXkc0O8XFxZcsYz9tHJc7PWBKmJw7d05PPfWUzp07J6vVqvbt2ysjI0MWi0Xz5s3TrFmztHz5crVr105paWmu9RpaAwCYy5Qw6dSpk9atW3fZWnBwsD744INGrQEAzMUv4AEAhhEmAADDCBMAgGGECQDAMMIEAGAYYQIAMIwwAQAYRpgAAAwjTAAAhhEmAADDCBMAgGGECQDAMMIEAGAYYQIAMIwwAQAYRpgAAAwjTAAAhhEmAADDCBMAgGGECQDAMMIEAGAYYQIAMIwwAQAYRpgAAAwjTAAAhhEmAADDCBMAgGGtzNhIUVGRZs6cqaNHj8rT01M9evTQ/Pnz5e/vr9DQUIWEhMhqPZ9r6enpCg0NlSRt3bpV6enpcjgc6tOnjxYtWqTWrVtftQYAMJcpRyYWi0Xjx4/X5s2blZOTo6CgIC1ZssRVX7t2rbKzs5Wdne0KkvLycj377LPKyMjQli1b5Ovrq7feeuuqNQCA+UwJEz8/P0VHR7tu33nnnTp+/Hi96/z9739XeHi4evbsKUlKTk7Wxo0br1oDAJjPlGGuX6qrq9P7778vu93uWjZmzBg5HA4NGjRIU6ZMkaenpwoKCtS1a1fXfbp27aqCggJJqrcGADCf6WHy/PPPy8fHRw8//LAk6csvv1RgYKDKyso0Y8YMLVu2TNOmTTOll/z8fFO2cyMrLS2VJO3YscPNnQBXx37adEwNk7S0NB05ckQZGRmuE+6BgYGSpDZt2mjUqFFavXq1a/k333zjWvf48eOu+9ZXux7h4eHy8vJq8POBlJmZKUmKjIx0cyfA1bGfGlNVVXXFD+GmfTX4lVdeUX5+vpYtWyZPT09JUklJiSorKyVJtbW12rx5s8LCwiRJAwcO1K5du3T48GFJ50/Sx8bGXrUGADCfKUcm+/fvV0ZGhnr27Knk5GRJUvfu3TV+/HilpKTIYrGotrZWd911l5566ilJ549U5s+fr8cee0x1dXUKCwvTM888c9UaAMB8poRJ7969tXfv3svWcnJyrrheTEyMYmJirrsGADAXv4AHABhGmAAADCNMAACGESYAAMMIEwCAYYQJAMAwwgQAYBhhAgAwjDABABhGmAAADCNMAACGESYAAMMIEwCAYYQJAMAwwgQAYBhhAgAwjDABABhGmAAADCNMAACGESYAAMMIEwCAYYQJAMAwwgQAYBhhAgAwjDABABhGmAAADGvl7gYA3Hi2bt2qLVu2uLuNS8yePdst273vvvtkt9vdsm2zcGQCADDMlCOToqIizZw5U0ePHpWnp6d69Oih+fPny9/fX3l5eUpJSVFVVZW6deumxYsXq2PHjpLU4BoA97Lb7W7/JD58+PBLli1atMgNndwcTDkysVgsGj9+vDZv3qycnBwFBQVpyZIlcjqdmjFjhlJSUrR582ZFRUVpyZIlktTgGgBIUp8+fS66ffvtt7upk5uDKWHi5+en6Oho1+0777xTx48f165du+Tl5aWoqChJUnJysjZt2iRJDa4BgCS9+OKLF91euHChmzq5OZh+Ar6urk7vv/++7Ha7CgoK1LVrV1fN399fdXV1Ki4ubnDNz8/vmnvJz89vnCd1EystLZUk7dixw82dAJfy8PBQTU2NevbsyT7axEwPk+eff14+Pj56+OGH3f5tj/DwcHl5ebm1h5YuMzNTkhQZGenmToBLhYaGSuJcSWOpqqq64odwU8MkLS1NR44cUUZGhqxWqwIDA3X8+HFXvbCwUBaLRX5+fg2uAQDMZ9pXg1955RXl5+dr2bJl8vT0lHT+yKCyslLbt2+XJK1du1axsbGGagAA85lyZLJ//35lZGSoZ8+eSk5OliR1795dy5YtU3p6ulJTUy/6iq8kWa3WBtUAAOYzJUx69+6tvXv3XrYWERGhnJycRq0BAMzFL+ABAIYRJgAAwwgTAIBhhAkAwDDCBABgGGECADCMMAEAGEaYAAAMI0wAAIYRJgAAwwgTAIBhhAkAwDDCBABgGGECADDsqlPQHzhwQNnZ2dq/f7/Ky8vl6+ur3r17Kz4+XsHBwWb0CABo5uo9MsnNzVVSUpJOnDihu+++W8OHD1ffvn118uRJJScna8OGDWb1CQBoxuo9Mnn55Zf15ptvKjIy8pLajh07NGPGDA0bNqzJmgMAtAz1HpkUFRWpT58+l63ddtttKioqapKmAAAtS71h0q9fP82ZM0dHjx69aPnRo0c1d+5c9evXr0mbAwC0DPUOcy1cuFDPPfechg0bplatWqlNmzYqLy9XbW2t7r//fi1cuNCsPgEAzVi9YdK+fXu9/PLLOnfunA4fPuz6NlfPnj3VunVrs3oEADRzV/1qsCS1bt1aYWFhTd0LAKCFqvecSV1dnVauXKnJkyfr5ZdfVnFx8UX1iRMnNmlzAICWod4weemll7Rx40ZFR0frwIEDSkhI0I8//uiqb9++vckbBAA0f/UOc33yySdat26dOnfurEceeUTr16/XI488ooyMDIWHh8vpdJrVJwCgGas3TEpLS+Xv7++6/eCDD6p9+/aaOHGili5dKovF0uQNAgCav3rDpEePHvr+++8v+gV8TEyMPD099cQTT6i6urrJGwQANH/1njMZO3as9u/ff8nyQYMG6dVXX1VERMQ1bygtLU12u12hoaHat2+fa7ndbtfQoUMVHx+v+Ph4ffXVV65aXl6eHnjgAQ0ZMkSPPvqozpw5c001AIC56g2ThIQEJScnX7b2+9//Xu+88841b2jw4MFas2aNunXrdklt6dKlys7OVnZ2tgYOHChJcjqdmjFjhlJSUrR582ZFRUVpyZIlV60BAMxXb5hs2bJFlZWVjbKhqKgoBQYGXvP9d+3aJS8vL0VFRUmSkpOTtWnTpqvWAADmq/ecyZQpU+Tr66shQ4ZoxIgRrjfvxjZ9+nQ5nU5FRkbq6aefVrt27VRQUKCuXbu67uPv76+6ujoVFxfXW/Pz82uSHgEAV1ZvmLRu3VpvvPGGPvroI02cOFH+/v5KSEhQfHy8goKCGqWBNWvWKDAwUNXV1VqwYIHmz59v2pBVfn6+Kdu5kZWWlko6f0kCoLlh/zRPvWFisVjUt29f9e3bV6mpqdq0aZOys7P1xhtvKCIiQomJiRoxYoShBi4MfXl6emr06NGaPHmya/nx48dd9yssLJTFYpGfn1+9tesRHh4uLy8vQ/3f7DIzMyXpste8AdyN/bNxVVVVXfFD+DVfA97b21sJCQlavXq1Pv/8c/Xv31+rVq0y1FhFRYXrk4PT6dSGDRtcc4CFh4ersrLS9Sv7tWvXKjY29qo1AID56j0yudIv3Lt06aJJkyZp0qRJ17yhF154QZ9++qlOnz6tcePGyc/PTxkZGZoyZYocDofq6uoUHBys1NRUSZLValV6erpSU1NVVVWlbt26afHixVetAQDMV2+YrFy5stE2NHfuXM2dO/eS5VlZWVdcJyIiQjk5OdddAwCYq95hrit9e+v48ePMywUAcLnmcya/9NBDD3H9dwCAS73DXPfee+9llxcWFiohIUFWq1VffvllE7QFAGhJ6g0Tb29v3XLLLZo4caLrK7ROp1NTp07Vs88+q/bt25vSJACgeat3mCsnJ0f9+vXTiy++qNLSUvXt21fR0dHy8PBQRESE+vbta1afAIBmrN4w8fDw0OTJk5WRkaEPPvhAEyZM0NGjR83qDQDQQtQ7zFVXVyer1aqgoCBlZGRoy5YtmjBhwiXXggcA3NzqDZNBgwbpgQceUEJCgkJCQnTfffdpwIABOnLkiDp06GBWjwCAZq7eMJk3b54+/vhjjRw5UsHBwUpMTFRcXJxuvfVWs/prllauXKmDBw+6u41m4cLrMHv2bDd30jz06tVLEyZMcHcbgOnqDZOYmBjFxMTo7Nmz2rBhg7Kzs7V48WL1799fiYmJstvt8vDwMKvXZuPgwYPK371XNm+mu6+rtUmSfjh40s2duJ+jkuFf3LzqDZML2rVrp+TkZCUnJ+vYsWPKzs7WokWLlJKSom+++aape2yWbN5+8ukx2N1toBmpOPK5u1sA3Oa6fgFfXV2tXbt2aefOnTp9+rRCQkKaqi8AQAtyTUcm27dvV3Z2tjZu3KiOHTvqgQceUGpq6mWv5w4AuPnUGyavvfaasrOzVVJSoqFDh+rNN9/kIjMAgEvUGyZ5eXmaNm2aYmJiuCIhAOCK6g2Tt956y6w+AAAtWIOmoAcA4JcIEwCAYYQJAMAwwgQAYBhhAgAwjDABABhGmAAADCNMAACGESYAAMMIEwCAYYQJAMAwU8IkLS1NdrtdoaGh2rdvn2v5oUOHlJSUpCFDhigpKUmHDx82XAMAmM+UMBk8eLDWrFlzyfVPUlNTNXr0aG3evFmjR49WSkqK4RoAwHymhElUVJQCAwMvWnbmzBnt3r1bcXFxkqS4uDjt3r1bhYWFDa4BANzjmq602BQKCgoUEBAgm80mSbLZbOrcubMKCgrkdDobVPP393fX0wGAm5rbwqQ5yM/Pb9B6paWljdwJbhSlpaXasWOHu9vA/7nwt8r/SdNzW5gEBgbq5MmTcjgcstlscjgcOnXqlAIDA+V0OhtUu17h4eENuoJkZmam9HPFda+HG1/btm25tHUzkpmZKUn8nzSSqqqqK34Id9tXgzt27KiwsDDl5uZKknJzcxUWFiZ/f/8G1wAA7mHKkckLL7ygTz/9VKdPn9a4cePk5+enTz75RPPmzdOsWbO0fPlytWvXTmlpaa51GloDAJjPlDCZO3eu5s6de8ny4OBgffDBB5ddp6E1AID5+AU8AMAwwgQAYBhhAgAwjDABABhGmAAADCNMAACGESYAAMNu6rm5gBvRypUrdfDgQXe30SxceB1mz57t5k6ah169emnChAlN8tiECXCDOXjwoPb/8D/q0oY/79bOOklS6bG9bu7E/U6U1Tbp47O3ATegLm1aadzvmK8O/2/1zqa95hPnTAAAhhEmAADDCBMAgGGECQDAMMIEAGAYYQIAMIyvBjdAUVGRHJXFqjjyubtbQTPiqCxWUZGnu9sA3IIjEwCAYRyZNECHDh10oqhaPj0Gu7sVNCMVRz5Xhw4d3N0G4BYcmQAADCNMAACGESYAAMMIEwCAYYQJAMAwwgQAYBhhAgAwjDABABjWLH60aLfb5enpKS8vL0nS9OnTNXDgQOXl5SklJUVVVVXq1q2bFi9erI4dO0pSvTUAgLmaRZhI0tKlSxUSEuK67XQ6NWPGDC1atEhRUVFavny5lixZokWLFtVbA252RUVFOl1W2+SXaUXLcqKsVrVFRU32+M12mGvXrl3y8vJSVFSUJCk5OVmbNm26ag0AYL5mc2Qyffp0OZ1ORUZG6umnn1ZBQYG6du3qqvv7+6uurk7FxcX11vz8/NzRPtBsdOjQQa3KTmnc7/zd3QqakdU7C9W2CeeOaxZhsmbNGgUGBqq6uloLFizQ/Pnzdd999zX5dvPz8xu0XmlpaSN3ghtFaWmpduzY4fYegMtpyv2zWYRJYGCgJMnT01OjR4/W5MmTNXbsWB0/ftx1n8LCQlksFvn5+SkwMPCKtesRHh7uOul/PTIzM6WfK657Pdz42rZtq8jISLf2kJmZqdJit7aAZsro/llVVXXFD+FuP2dSUVHh+iTldDq1YcMGhYWFKTw8XJWVldq+fbskae3atYqNjZWkemsAAPO5/cjkzJkzmjJlihwOh+rq6hQcHKzU1FRZrValp6crNTX1oq//Sqq3BgAwn9vDJCgoSFlZWZetRUREKCcn57prAABzuX2YCwDQ8hEmAADDCBMAgGGECQDAMMIEAGAYYQIAMIwwAQAYRpgAAAwjTAAAhhEmAADDCBMAgGGECQDAMMIEAGAYYQIAMIwwAQAYRpgAAAwjTAAAhhEmAADDCBMAgGGECQDAMMIEAGAYYQIAMIwwAQAYRpgAAAwjTAAAhhEmAADDCBMAgGGECQDAsBYdJocOHVJSUpKGDBmipKQkHT582N0tAcBNqUWHSWpqqkaPHq3Nmzdr9OjRSklJcXdLAHBTauXuBhrqzJkz2r17t1avXi1JiouL0/PPP6/CwkL5+/vXu67T6ZQkVVdXN2jbPj4+auvllLXo3w1av7HUOaqk2iq39tCstPKS1eblts239XLKx8dHVVXu/T/x8fHRKZuvPjjscFsP5dV1qqipc9v2mxsfD6t8Pd372f2czVcBBvfPC++ZF95Df6nFhklBQYECAgJks9kkSTabTZ07d1ZBQcFVw6SmpkaStG/fvgZte9iwYRrWoDVxM8jPz3fr9ocNGyaxh+IKGmP/rKmpkbe390XLWmyYGOHr66uQkBB5eHjIYrG4ux0AaBGcTqdqamrk6+t7Sa3FhklgYKBOnjwph8Mhm80mh8OhU6dOKTAw8KrrWq1WtW3b1oQuAeDG8usjkgta7An4jh07KiwsTLm5uZKk3NxchYWFXXWICwDQ+CzOy51JaSEOHDigWbNm6ezZs2rXrp3S0tLUq1cvd7cFADedFh0mAIDmocUOcwEAmg/CBABgGGECADCMMAEAGEaY3MDsdrsGDBggh+P/p9VYv369QkND9e6775rWR2hoqMrLyyVJEyZM0NGjR03bNpo3u92uoUOHKj4+XvHx8Vq4cGGjPXZ8fLwqKyuvej/2ycbRYn+0iGtzyy236B//+IfuueceSVJWVpb69Onjtn5Wrlzptm2jeVq6dKlCQkIa/XGzs7Ov6X7sk42DMLnBJSYm6sMPP9Q999yjY8eO6dy5c64/3G3btunVV19VVVWVHA6HJk2apD/84Q+SpNdff125ubny8vKSxWLRO++8o3bt2um7775Tenq660hj5syZGjBggEJDQ/Xtt9+6pln49e0L7Ha7MjIyFBISojFjxig8PFx5eXk6deqUYmNjNX36dEnSkSNHlJKSosLCQrVq1UrTpk3ToEGDzHrZ4EZlZWVatGiR9u7dq6qqKkVHR2v27Nmy2WwaM2aM+vTpo507d+q///2vxo4dq4CAAL377rs6deqUZsyYodjYWEkX74N2u13x8fH65z//qZ9//lmPPvqoHn74YUnsk42FMLnBRUdH67333lNJSYk++ugjJSQkuCZ6u+222/Tee+/JZrPp9OnTGjFihAYMGCBJeuutt7Rt2zZ5e3urrKxM3t7eKi4u1pNPPqnXXntNERERcjgcKisrM9RfQUGB1qxZo/LycsXExGjkyJHq2bOnpk+froceekijRo3Sjz/+qD/+8Y/auHEjMxzcgKZOnSovr/OzPU+fPl2bNm3S3XffrQULFqiurk7Tp0/X+vXr9dBDD0mSTpw4oXfffVc///yz7r//fj3yyCNau3atdu7cqSeffNIVJr9WWVmpv/3tb/rpp580fPhwJSYmXnaOKfbJhiFMbnAWi0WxsbH65JNPtGHDBr3//vuuMCksLNScOXN05MgR2Ww2lZSU6NChQ7r99tv1m9/8RjNmzNDAgQN17733qk2bNsrLy1NwcLAiIiIknZ+puX379ob6Gzp0qGuutODgYB09elSdOnXSDz/8oAcffFCS9Nvf/lZhYWHKy8uT3W439oKg2fn1MNfMmTO1c+dO1+UlKisrFRAQ4Kpf2GcCAgLk5+enmJgYSVKfPn108uRJVVVVucLpl87Ppix1795d7dq104kTJxQcHHzJ/dgnG4YwuQmMGDFCo0aNUt++fdWhQwfX8nnz5slut+v111+XxWLRkCFDVFVVJZvNpnXr1unbb7/Vv/71L40YMUKrVq267DUMLrDZbK769Vwv4Zd/9Bcm7LwSZni+OTidTi1fvlxBQUGXrf96n7lw+8LlKGpray8bJte6r7FPNgzf5roJBAUFadq0aXr88ccvWl5aWqpu3brJYrHo66+/1pEjRySdH7MuLCxU3759NXXqVIWEhGj//v266667dODAAX333XeSJIfDoZKSEtc2du3aJUnKyckx1G+bNm0UFhamjz76SNL5Odj27NmjO+64w9DjomWw2+1asWKF6028sLBQx44dc2tP7JNXx5HJTSIpKemSZX/+85/13HPPaeXKlQoNDVVoaKik82EyZcoUVVZWyul06rbbbtP9998vLy8vvfbaa3rxxRdVUVEhq9Wqv/zlL+rXr5/mzJmjlJQU3XLLLbr33nsN97tkyRKlpKTo7bffVqtWrZSens7Y9E1izpw5Wrx4seLj42WxWOTh4aE5c+Zc8UjFLOyT9WOiRwCAYQxzAQAMI0wAAIYRJgAAwwgTAIBhhAkAwDDCBABgGGECNII//elP+utf/3rJ8s8++0z9+/dXTU2NFi9erOjoaEVHRys9Pd01Y0BhYaGSk5MVHR2tqKgoJSUlaceOHRc9zttvv63+/fsrMjJSs2fPVnV1tSTpzJkzevrppzVgwABFRkYqOTlZ33//fdM/YeBXCBOgESQmJio7O/uSKWc+/vhjDR8+XOvXr9dnn32m7Oxsffzxx/ryyy+1du1aSZKvr68WLlyobdu26T//+Y8mTJigyZMnq7a2VpL01VdfacWKFXr77be1detW/fTTT1q6dKkkqaKiQrfffrs+/PBD/fvf/1ZiYqImTpzomtUZMAthAjSCmJgYlZSUaPv27a5lJSUl+uKLL5SQkKCsrCw9+uij6tKliwICAjRu3DjX1BxeXl7q1auXrFarnE6nrFarSkpKXFPVZGVlaeTIkerdu7fat2+vxx9/3LVuUFCQxo0bp86dO8tmsykpKUk1NTU6dOiQ+S8CbmpMpwI0Am9vb8XGxiorK0t33323JGnjxo3q1auXbr31Vu3fv1+33nqr6/4Xlv3S8OHDdejQIdXU1GjUqFHq2LGjJGn//v0aPHiw636hoaE6ffq0ioqKLpq4U5J++OEH1dTUqEePHk31VIHL4sgEaCQJCQnatGmT61KxWVlZSkxMlHR+OKpNmzau+7Zt21YVFRUXDYvl5ORox44deumllxQZGelafrl1JV0ylFVWVqaZM2fqySefdN0HMAthAjSSqKgo+fv76/PPP9exY8eUn5+vuLg4SZKPj89Fb/5lZWXy8fG5ZApzLy8vxcXFacWKFdqzZ49r3V9ehOzCv395YafKykpNmjRJd9xxhx577LEme47AlRAmQCOKj49XVlaWsrOz1b9/f3Xq1EmS1Lt3b1c4SNKePXvUu3fvKz5ObW2ta9r13r17a+/evRet26lTJ9cQV3V1tZ544gkFBARo/vz5TfG0gKsiTIBGlJCQoG3btmndunVKSEhwLY+Pj9fq1at18uRJnTx5UqtXr3YNgeXl5Wn79u2qrq5WZWWlVqxYodOnT+t3v/uda93MzEz9+OOPKikp0RtvvOFat6amxnXZ27S0NFmt/EnDPTgBDzSi7t2766677tKePXsuOmmenJysY8eOafjw4ZKkkSNHKjk5WdL5I4sXXnhBx44dk4eHh0JCQrRixQrXpWoHDRqk8ePHa+zYsaqsrNSQIUM0depUSdJ3332nL774Qt7e3q4T/5K0cuVKRUVFmfW0Aa5nAgAwjmNiAIBhhAkAwDDCBABgGGECADCMMAEAGEaYAAAMI0wAAIYRJgAAwwgTAIBh/wujslTBmnTe0QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x=pnad.V0302, y=pnad.V4720)\n",
"plt.ylim(0, 3000)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x=pnad.V0404, y=pnad.V4720)\n",
"plt.ylim(0, 3000)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(14,5))\n",
"sns.boxplot(x=pnad.V0302, y=pnad.V4720, hue=pnad.V0404)\n",
"plt.ylim(0, 3000)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tratando a variável renda de todos os trabalhos\n",
"\n",
"Vamos extrair uma subamostra de pessoas entre 18 e 80 anos"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"amostra = pnad.loc[ (pnad.V8005 >= 18) & (pnad.V8005 <= 80) & (pnad.V4720 > 0) ]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"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>V0101</th>\n",
" <th>V8005</th>\n",
" <th>V4718</th>\n",
" <th>V4720</th>\n",
" <th>V4729</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>200443.0</td>\n",
" <td>200443.000000</td>\n",
" <td>157281.000000</td>\n",
" <td>200443.000000</td>\n",
" <td>200443.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2012.0</td>\n",
" <td>42.480511</td>\n",
" <td>1420.021827</td>\n",
" <td>1458.514710</td>\n",
" <td>547.126849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0</td>\n",
" <td>15.657019</td>\n",
" <td>2702.088821</td>\n",
" <td>2937.432182</td>\n",
" <td>261.446124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2012.0</td>\n",
" <td>18.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>174.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2012.0</td>\n",
" <td>30.000000</td>\n",
" <td>622.000000</td>\n",
" <td>622.000000</td>\n",
" <td>334.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2012.0</td>\n",
" <td>40.000000</td>\n",
" <td>850.000000</td>\n",
" <td>802.000000</td>\n",
" <td>523.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2012.0</td>\n",
" <td>54.000000</td>\n",
" <td>1500.000000</td>\n",
" <td>1500.000000</td>\n",
" <td>705.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2012.0</td>\n",
" <td>80.000000</td>\n",
" <td>350000.000000</td>\n",
" <td>351600.000000</td>\n",
" <td>1132.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" V0101 V8005 V4718 V4720 V4729\n",
"count 200443.0 200443.000000 157281.000000 200443.000000 200443.000000\n",
"mean 2012.0 42.480511 1420.021827 1458.514710 547.126849\n",
"std 0.0 15.657019 2702.088821 2937.432182 261.446124\n",
"min 2012.0 18.000000 0.000000 1.000000 174.000000\n",
"25% 2012.0 30.000000 622.000000 622.000000 334.000000\n",
"50% 2012.0 40.000000 850.000000 802.000000 523.000000\n",
"75% 2012.0 54.000000 1500.000000 1500.000000 705.000000\n",
"max 2012.0 80.000000 350000.000000 351600.000000 1132.000000"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amostra.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Estatísticas descritivas agrupadas"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>V4720</th>\n",
" </tr>\n",
" <tr>\n",
" <th>V0302</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Feminino</th>\n",
" <td>1168.730579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Masculino</th>\n",
" <td>1730.798281</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" V4720\n",
"V0302 \n",
"Feminino 1168.730579\n",
"Masculino 1730.798281"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amostra.groupby('V0302').agg({'V4720' : 'mean'})"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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>V4720</th>\n",
" </tr>\n",
" <tr>\n",
" <th>V0404</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Amarela</th>\n",
" <td>2700.308628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Branca</th>\n",
" <td>1887.368195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indígena</th>\n",
" <td>1124.150215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Parda</th>\n",
" <td>1111.447143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Preta</th>\n",
" <td>1085.736557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sem declaração</th>\n",
" <td>622.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" V4720\n",
"V0404 \n",
"Amarela 2700.308628\n",
"Branca 1887.368195\n",
"Indígena 1124.150215\n",
"Parda 1111.447143\n",
"Preta 1085.736557\n",
"Sem declaração 622.000000"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amostra.groupby('V0404').agg({'V4720' : 'mean'})"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>V4720</th>\n",
" </tr>\n",
" <tr>\n",
" <th>V0404</th>\n",
" <th>V0302</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Amarela</th>\n",
" <th>Feminino</th>\n",
" <td>2041.119048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Masculino</th>\n",
" <td>3389.325792</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Branca</th>\n",
" <th>Feminino</th>\n",
" <td>1505.658569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Masculino</th>\n",
" <td>2257.939164</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Indígena</th>\n",
" <th>Feminino</th>\n",
" <td>923.272455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Masculino</th>\n",
" <td>1307.967123</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Parda</th>\n",
" <th>Feminino</th>\n",
" <td>887.403685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Masculino</th>\n",
" <td>1317.552160</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Preta</th>\n",
" <th>Feminino</th>\n",
" <td>869.895289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Masculino</th>\n",
" <td>1277.578274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sem declaração</th>\n",
" <th>Feminino</th>\n",
" <td>622.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" V4720\n",
"V0404 V0302 \n",
"Amarela Feminino 2041.119048\n",
" Masculino 3389.325792\n",
"Branca Feminino 1505.658569\n",
" Masculino 2257.939164\n",
"Indígena Feminino 923.272455\n",
" Masculino 1307.967123\n",
"Parda Feminino 887.403685\n",
" Masculino 1317.552160\n",
"Preta Feminino 869.895289\n",
" Masculino 1277.578274\n",
"Sem declaração Feminino 622.000000"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amostra.groupby(['V0404', 'V0302']).agg({'V4720' : 'mean'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interação entre idade e renda"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(amostra.V8005, amostra.V4720)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"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>V8005</th>\n",
" <th>V4720</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18</td>\n",
" <td>622.172790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>19</td>\n",
" <td>699.414307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20</td>\n",
" <td>767.838549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>21</td>\n",
" <td>808.828689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>22</td>\n",
" <td>847.811980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>76</td>\n",
" <td>1429.906854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>77</td>\n",
" <td>1299.377114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>78</td>\n",
" <td>1470.124408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>79</td>\n",
" <td>1430.717710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>80</td>\n",
" <td>1209.696139</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>63 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" V8005 V4720\n",
"0 18 622.172790\n",
"1 19 699.414307\n",
"2 20 767.838549\n",
"3 21 808.828689\n",
"4 22 847.811980\n",
".. ... ...\n",
"58 76 1429.906854\n",
"59 77 1299.377114\n",
"60 78 1470.124408\n",
"61 79 1430.717710\n",
"62 80 1209.696139\n",
"\n",
"[63 rows x 2 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dado = amostra.groupby('V8005').agg({'V4720' : 'mean'})\n",
"dado.reset_index(inplace=True)\n",
"dado"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,5))\n",
"plt.scatter(x=dado.V8005, y=dado.V4720)\n",
"plt.xlabel('Idade')\n",
"plt.ylabel('Média da renda')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"import statsmodels.formula.api as smf"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: V4720 R-squared: 0.315\n",
"Model: OLS Adj. R-squared: 0.304\n",
"Method: Least Squares F-statistic: 28.05\n",
"Date: Tue, 28 Apr 2020 Prob (F-statistic): 1.71e-06\n",
"Time: 21:24:05 Log-Likelihood: -439.68\n",
"No. Observations: 63 AIC: 883.4\n",
"Df Residuals: 61 BIC: 887.6\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 1010.7813 95.624 10.570 0.000 819.569 1201.993\n",
"V8005 9.6891 1.830 5.296 0.000 6.031 13.348\n",
"==============================================================================\n",
"Omnibus: 8.337 Durbin-Watson: 0.148\n",
"Prob(Omnibus): 0.015 Jarque-Bera (JB): 6.150\n",
"Skew: -0.638 Prob(JB): 0.0462\n",
"Kurtosis: 2.154 Cond. No. 150.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"modelo1 = smf.ols(formula='V4720 ~ V8005', data=dado).fit()\n",
"print(modelo1.summary())"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"yhat1 = modelo1.predict(dado.V8005)\n",
"\n",
"plt.figure(figsize=(10,5))\n",
"plt.scatter(x=dado.V8005, y=dado.V4720)\n",
"plt.plot(dado.V8005, yhat1, c='red')\n",
"plt.xlabel('Idade')\n",
"plt.ylabel('Média da renda')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: V4720 R-squared: 0.935\n",
"Model: OLS Adj. R-squared: 0.933\n",
"Method: Least Squares F-statistic: 431.1\n",
"Date: Tue, 28 Apr 2020 Prob (F-statistic): 2.51e-36\n",
"Time: 21:24:05 Log-Likelihood: -365.52\n",
"No. Observations: 63 AIC: 737.0\n",
"Df Residuals: 60 BIC: 743.5\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"=================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------\n",
"Intercept -720.2850 78.255 -9.204 0.000 -876.818 -563.753\n",
"V8005 91.6297 3.474 26.379 0.000 84.681 98.578\n",
"I(V8005 ** 2) -0.8361 0.035 -23.912 0.000 -0.906 -0.766\n",
"==============================================================================\n",
"Omnibus: 1.393 Durbin-Watson: 1.398\n",
"Prob(Omnibus): 0.498 Jarque-Bera (JB): 0.713\n",
"Skew: 0.106 Prob(JB): 0.700\n",
"Kurtosis: 3.476 Cond. No. 2.48e+04\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 2.48e+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"modelo2 = smf.ols(formula='V4720 ~ V8005 + I(V8005**2)', data=dado).fit()\n",
"print(modelo2.summary())"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"yhat2 = modelo2.predict(dado.V8005)\n",
"\n",
"plt.figure(figsize=(10,5))\n",
"plt.scatter(x=dado.V8005, y=dado.V4720)\n",
"plt.plot(dado.V8005, yhat2, c='red')\n",
"plt.xlabel('Idade')\n",
"plt.ylabel('Média da renda')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"## Escolaridade\n",
"\n",
"Vamos tratar a variável Anos Completos de Escolaridade (V4803) de modo que ela fique uma numérica. Para isso, precisaremos limpá-la primeiro."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"import re"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"11 anos 52721\n",
"15 anos ou mais 22117\n",
"Sem instrução e menos de 1 ano 20014\n",
"8 anos 19748\n",
"4 anos 18552\n",
"5 anos 11224\n",
"3 anos 7985\n",
"7 anos 6845\n",
"6 anos 6560\n",
"2 anos 6003\n",
"10 anos 5942\n",
"12 anos 5551\n",
"9 anos 5144\n",
"1 ano 4019\n",
"14 anos 3871\n",
"13 anos 3544\n",
"Não determinados 603\n",
"Name: V4803, dtype: int64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amostra.V4803.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/neylson/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:965: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" self.obj[item] = s\n"
]
}
],
"source": [
"amostra.loc[amostra.V4803 == 'Sem instrução e menos de 1 ano', 'V4803'] = '0'"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"anos_mais = re.compile(' anos ou mais')\n",
"anos = re.compile(' anos')\n",
"ano = re.compile(' ano')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/neylson/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" \"\"\"Entry point for launching an IPython kernel.\n",
"/home/neylson/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" \n",
"/home/neylson/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
}
],
"source": [
"amostra['V4803'] = amostra['V4803'].apply(lambda linha: anos_mais.sub('', linha))\n",
"amostra['V4803'] = amostra['V4803'].apply(lambda linha: anos.sub('', linha))\n",
"amostra['V4803'] = amostra['V4803'].apply(lambda linha: ano.sub('', linha))"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"11 52721\n",
"15 22117\n",
"0 20014\n",
"8 19748\n",
"4 18552\n",
"5 11224\n",
"3 7985\n",
"7 6845\n",
"6 6560\n",
"2 6003\n",
"10 5942\n",
"12 5551\n",
"9 5144\n",
"1 4019\n",
"14 3871\n",
"13 3544\n",
"Não determinados 603\n",
"Name: V4803, dtype: int64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amostra.V4803.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/neylson/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
],
"source": [
"amostra['V4803'] = pd.to_numeric(amostra['V4803'], errors='coerce')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"603"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.isnull(amostra.V4803).sum()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 199840.000000\n",
"mean 8.005640\n",
"std 4.599891\n",
"min 0.000000\n",
"25% 4.000000\n",
"50% 8.000000\n",
"75% 11.000000\n",
"max 15.000000\n",
"Name: V4803, dtype: float64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amostra.V4803.describe()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"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>V0101</th>\n",
" <th>UF</th>\n",
" <th>V0302</th>\n",
" <th>V8005</th>\n",
" <th>V0404</th>\n",
" <th>V4803</th>\n",
" <th>V4718</th>\n",
" <th>V4720</th>\n",
" <th>V4729</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Masculino</td>\n",
" <td>48</td>\n",
" <td>Branca</td>\n",
" <td>15.0</td>\n",
" <td>3000.0</td>\n",
" <td>3000.0</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>48</td>\n",
" <td>Branca</td>\n",
" <td>15.0</td>\n",
" <td>3000.0</td>\n",
" <td>3000.0</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>23</td>\n",
" <td>Branca</td>\n",
" <td>15.0</td>\n",
" <td>1100.0</td>\n",
" <td>1100.0</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>21</td>\n",
" <td>Branca</td>\n",
" <td>14.0</td>\n",
" <td>1100.0</td>\n",
" <td>1100.0</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>54</td>\n",
" <td>Branca</td>\n",
" <td>15.0</td>\n",
" <td>NaN</td>\n",
" <td>460.0</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362441</th>\n",
" <td>2012</td>\n",
" <td>Distrito Federal</td>\n",
" <td>Masculino</td>\n",
" <td>49</td>\n",
" <td>Branca</td>\n",
" <td>11.0</td>\n",
" <td>3000.0</td>\n",
" <td>3000.0</td>\n",
" <td>290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362442</th>\n",
" <td>2012</td>\n",
" <td>Distrito Federal</td>\n",
" <td>Feminino</td>\n",
" <td>47</td>\n",
" <td>Branca</td>\n",
" <td>11.0</td>\n",
" <td>3000.0</td>\n",
" <td>3000.0</td>\n",
" <td>290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362443</th>\n",
" <td>2012</td>\n",
" <td>Distrito Federal</td>\n",
" <td>Masculino</td>\n",
" <td>22</td>\n",
" <td>Parda</td>\n",
" <td>15.0</td>\n",
" <td>1400.0</td>\n",
" <td>1400.0</td>\n",
" <td>290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362447</th>\n",
" <td>2012</td>\n",
" <td>Distrito Federal</td>\n",
" <td>Masculino</td>\n",
" <td>53</td>\n",
" <td>Branca</td>\n",
" <td>12.0</td>\n",
" <td>20000.0</td>\n",
" <td>20000.0</td>\n",
" <td>290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362450</th>\n",
" <td>2012</td>\n",
" <td>Distrito Federal</td>\n",
" <td>Masculino</td>\n",
" <td>40</td>\n",
" <td>Branca</td>\n",
" <td>15.0</td>\n",
" <td>13000.0</td>\n",
" <td>13000.0</td>\n",
" <td>290</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>200443 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" V0101 UF V0302 V8005 V0404 V4803 V4718 \\\n",
"0 2012 Rondônia Masculino 48 Branca 15.0 3000.0 \n",
"1 2012 Rondônia Feminino 48 Branca 15.0 3000.0 \n",
"2 2012 Rondônia Feminino 23 Branca 15.0 1100.0 \n",
"3 2012 Rondônia Feminino 21 Branca 14.0 1100.0 \n",
"4 2012 Rondônia Feminino 54 Branca 15.0 NaN \n",
"... ... ... ... ... ... ... ... \n",
"362441 2012 Distrito Federal Masculino 49 Branca 11.0 3000.0 \n",
"362442 2012 Distrito Federal Feminino 47 Branca 11.0 3000.0 \n",
"362443 2012 Distrito Federal Masculino 22 Parda 15.0 1400.0 \n",
"362447 2012 Distrito Federal Masculino 53 Branca 12.0 20000.0 \n",
"362450 2012 Distrito Federal Masculino 40 Branca 15.0 13000.0 \n",
"\n",
" V4720 V4729 \n",
"0 3000.0 232 \n",
"1 3000.0 232 \n",
"2 1100.0 232 \n",
"3 1100.0 232 \n",
"4 460.0 232 \n",
"... ... ... \n",
"362441 3000.0 290 \n",
"362442 3000.0 290 \n",
"362443 1400.0 290 \n",
"362447 20000.0 290 \n",
"362450 13000.0 290 \n",
"\n",
"[200443 rows x 9 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amostra"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"# Algumas análises\n",
"\n",
"1) Analisar diferenças de escolaridade entre homens e mulheres."
]
},
{
"cell_type": "code",
"execution_count": 57,
"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>V4803</th>\n",
" </tr>\n",
" <tr>\n",
" <th>V0302</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Feminino</th>\n",
" <td>8.229152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Masculino</th>\n",
" <td>7.795772</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" V4803\n",
"V0302 \n",
"Feminino 8.229152\n",
"Masculino 7.795772"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amostra.groupby('V0302').agg({'V4803': 'mean'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vamos implementar testes estatísticos de hipótese. Para fazer a instalação, utilizamos `!` para executar comandos de terminal no jupyter lab"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"import pingouin as pg"
]
},
{
"cell_type": "code",
"execution_count": 59,
"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>T</th>\n",
" <th>dof</th>\n",
" <th>tail</th>\n",
" <th>p-val</th>\n",
" <th>CI95%</th>\n",
" <th>cohen-d</th>\n",
" <th>BF10</th>\n",
" <th>power</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>T-test</th>\n",
" <td>-21.043</td>\n",
" <td>197634.46</td>\n",
" <td>two-sided</td>\n",
" <td>3.372579e-98</td>\n",
" <td>[-0.47, -0.39]</td>\n",
" <td>0.094</td>\n",
" <td>5.595e+93</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T dof tail p-val CI95% cohen-d \\\n",
"T-test -21.043 197634.46 two-sided 3.372579e-98 [-0.47, -0.39] 0.094 \n",
"\n",
" BF10 power \n",
"T-test 5.595e+93 1.0 "
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fazendo um teste de média para médias de anos completos de escolaridade\n",
"pg.ttest(\n",
" amostra.loc[amostra.V0302 == 'Masculino', 'V4803'],\n",
" amostra.loc[amostra.V0302 == 'Feminino', 'V4803']\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/neylson/anaconda3/lib/python3.7/site-packages/pingouin/bayesian.py:151: RuntimeWarning: divide by zero encountered in double_scalars\n",
" bf10 = 1 / ((1 + t**2 / df)**(-(df + 1) / 2) / integr)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>T</th>\n",
" <th>dof</th>\n",
" <th>tail</th>\n",
" <th>p-val</th>\n",
" <th>CI95%</th>\n",
" <th>cohen-d</th>\n",
" <th>BF10</th>\n",
" <th>power</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>T-test</th>\n",
" <td>43.626</td>\n",
" <td>172697.66</td>\n",
" <td>two-sided</td>\n",
" <td>0.0</td>\n",
" <td>[536.82, 587.32]</td>\n",
" <td>0.192</td>\n",
" <td>inf</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T dof tail p-val CI95% cohen-d BF10 \\\n",
"T-test 43.626 172697.66 two-sided 0.0 [536.82, 587.32] 0.192 inf \n",
"\n",
" power \n",
"T-test 1.0 "
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# O mesmo teste para diferença de renda\n",
"pg.ttest(\n",
" amostra.loc[amostra.V0302 == 'Masculino', 'V4720'],\n",
" amostra.loc[amostra.V0302 == 'Feminino', 'V4720']\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Análise de correlação"
]
},
{
"cell_type": "code",
"execution_count": 61,
"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>n</th>\n",
" <th>r</th>\n",
" <th>CI95%</th>\n",
" <th>r2</th>\n",
" <th>adj_r2</th>\n",
" <th>p-val</th>\n",
" <th>BF10</th>\n",
" <th>power</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>pearson</th>\n",
" <td>199840</td>\n",
" <td>0.25</td>\n",
" <td>[0.25, 0.25]</td>\n",
" <td>0.062</td>\n",
" <td>0.062</td>\n",
" <td>0.0</td>\n",
" <td>inf</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n r CI95% r2 adj_r2 p-val BF10 power\n",
"pearson 199840 0.25 [0.25, 0.25] 0.062 0.062 0.0 inf 1.0"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pg.corr(amostra.V4803, amostra.V4720)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/neylson/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
" keep = (tmp_a >= first_edge)\n",
"/home/neylson/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
" keep &= (tmp_a <= last_edge)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set(style='whitegrid')\n",
"\n",
"plt.hist(amostra.V4803)\n",
"plt.title(\"Distribuição de anos completos de escolaridade\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(amostra.V4720)\n",
"plt.title(\"Renda de todos os trabalhos\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para facilitar a modelagem, vamos trabalhar com o logaritmo natural da renda."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(np.log(amostra.V4720))\n",
"plt.title(\"Logaritmo natural da renda\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"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>n</th>\n",
" <th>r</th>\n",
" <th>CI95%</th>\n",
" <th>r2</th>\n",
" <th>adj_r2</th>\n",
" <th>p-val</th>\n",
" <th>BF10</th>\n",
" <th>power</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>pearson</th>\n",
" <td>199840</td>\n",
" <td>0.417</td>\n",
" <td>[0.41, 0.42]</td>\n",
" <td>0.174</td>\n",
" <td>0.174</td>\n",
" <td>0.0</td>\n",
" <td>inf</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n r CI95% r2 adj_r2 p-val BF10 power\n",
"pearson 199840 0.417 [0.41, 0.42] 0.174 0.174 0.0 inf 1.0"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pg.corr(amostra.V4803, np.log(amostra.V4720))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"# Finalmente, vamos ao nosso modelo estatístico"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/neylson/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
],
"source": [
"amostra['lnrenda'] = np.log(amostra.V4720)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>V0101</th>\n",
" <th>UF</th>\n",
" <th>V0302</th>\n",
" <th>V8005</th>\n",
" <th>V0404</th>\n",
" <th>V4803</th>\n",
" <th>V4718</th>\n",
" <th>V4720</th>\n",
" <th>V4729</th>\n",
" <th>lnrenda</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Masculino</td>\n",
" <td>48</td>\n",
" <td>Branca</td>\n",
" <td>15.0</td>\n",
" <td>3000.0</td>\n",
" <td>3000.0</td>\n",
" <td>232</td>\n",
" <td>8.006368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>48</td>\n",
" <td>Branca</td>\n",
" <td>15.0</td>\n",
" <td>3000.0</td>\n",
" <td>3000.0</td>\n",
" <td>232</td>\n",
" <td>8.006368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>23</td>\n",
" <td>Branca</td>\n",
" <td>15.0</td>\n",
" <td>1100.0</td>\n",
" <td>1100.0</td>\n",
" <td>232</td>\n",
" <td>7.003065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>21</td>\n",
" <td>Branca</td>\n",
" <td>14.0</td>\n",
" <td>1100.0</td>\n",
" <td>1100.0</td>\n",
" <td>232</td>\n",
" <td>7.003065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2012</td>\n",
" <td>Rondônia</td>\n",
" <td>Feminino</td>\n",
" <td>54</td>\n",
" <td>Branca</td>\n",
" <td>15.0</td>\n",
" <td>NaN</td>\n",
" <td>460.0</td>\n",
" <td>232</td>\n",
" <td>6.131226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362441</th>\n",
" <td>2012</td>\n",
" <td>Distrito Federal</td>\n",
" <td>Masculino</td>\n",
" <td>49</td>\n",
" <td>Branca</td>\n",
" <td>11.0</td>\n",
" <td>3000.0</td>\n",
" <td>3000.0</td>\n",
" <td>290</td>\n",
" <td>8.006368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362442</th>\n",
" <td>2012</td>\n",
" <td>Distrito Federal</td>\n",
" <td>Feminino</td>\n",
" <td>47</td>\n",
" <td>Branca</td>\n",
" <td>11.0</td>\n",
" <td>3000.0</td>\n",
" <td>3000.0</td>\n",
" <td>290</td>\n",
" <td>8.006368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362443</th>\n",
" <td>2012</td>\n",
" <td>Distrito Federal</td>\n",
" <td>Masculino</td>\n",
" <td>22</td>\n",
" <td>Parda</td>\n",
" <td>15.0</td>\n",
" <td>1400.0</td>\n",
" <td>1400.0</td>\n",
" <td>290</td>\n",
" <td>7.244228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362447</th>\n",
" <td>2012</td>\n",
" <td>Distrito Federal</td>\n",
" <td>Masculino</td>\n",
" <td>53</td>\n",
" <td>Branca</td>\n",
" <td>12.0</td>\n",
" <td>20000.0</td>\n",
" <td>20000.0</td>\n",
" <td>290</td>\n",
" <td>9.903488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362450</th>\n",
" <td>2012</td>\n",
" <td>Distrito Federal</td>\n",
" <td>Masculino</td>\n",
" <td>40</td>\n",
" <td>Branca</td>\n",
" <td>15.0</td>\n",
" <td>13000.0</td>\n",
" <td>13000.0</td>\n",
" <td>290</td>\n",
" <td>9.472705</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>200443 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" V0101 UF V0302 V8005 V0404 V4803 V4718 \\\n",
"0 2012 Rondônia Masculino 48 Branca 15.0 3000.0 \n",
"1 2012 Rondônia Feminino 48 Branca 15.0 3000.0 \n",
"2 2012 Rondônia Feminino 23 Branca 15.0 1100.0 \n",
"3 2012 Rondônia Feminino 21 Branca 14.0 1100.0 \n",
"4 2012 Rondônia Feminino 54 Branca 15.0 NaN \n",
"... ... ... ... ... ... ... ... \n",
"362441 2012 Distrito Federal Masculino 49 Branca 11.0 3000.0 \n",
"362442 2012 Distrito Federal Feminino 47 Branca 11.0 3000.0 \n",
"362443 2012 Distrito Federal Masculino 22 Parda 15.0 1400.0 \n",
"362447 2012 Distrito Federal Masculino 53 Branca 12.0 20000.0 \n",
"362450 2012 Distrito Federal Masculino 40 Branca 15.0 13000.0 \n",
"\n",
" V4720 V4729 lnrenda \n",
"0 3000.0 232 8.006368 \n",
"1 3000.0 232 8.006368 \n",
"2 1100.0 232 7.003065 \n",
"3 1100.0 232 7.003065 \n",
"4 460.0 232 6.131226 \n",
"... ... ... ... \n",
"362441 3000.0 290 8.006368 \n",
"362442 3000.0 290 8.006368 \n",
"362443 1400.0 290 7.244228 \n",
"362447 20000.0 290 9.903488 \n",
"362450 13000.0 290 9.472705 \n",
"\n",
"[200443 rows x 10 columns]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amostra"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: lnrenda R-squared: 0.353\n",
"Model: OLS Adj. R-squared: 0.353\n",
"Method: Least Squares F-statistic: 1.211e+04\n",
"Date: Tue, 28 Apr 2020 Prob (F-statistic): 0.00\n",
"Time: 21:24:09 Log-Likelihood: -2.2573e+05\n",
"No. Observations: 199840 AIC: 4.515e+05\n",
"Df Residuals: 199830 BIC: 4.516e+05\n",
"Df Model: 9 \n",
"Covariance Type: nonrobust \n",
"===========================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-------------------------------------------------------------------------------------------\n",
"Intercept 4.7341 0.029 165.179 0.000 4.678 4.790\n",
"V0302[T.Masculino] 0.5023 0.003 149.398 0.000 0.496 0.509\n",
"V0404[T.Branca] -0.0644 0.025 -2.569 0.010 -0.114 -0.015\n",
"V0404[T.Indígena] -0.2879 0.038 -7.595 0.000 -0.362 -0.214\n",
"V0404[T.Parda] -0.2927 0.025 -11.662 0.000 -0.342 -0.243\n",
"V0404[T.Preta] -0.2658 0.026 -10.394 0.000 -0.316 -0.216\n",
"V0404[T.Sem declaração] 0.2231 0.749 0.298 0.766 -1.245 1.691\n",
"V8005 0.0363 0.001 59.003 0.000 0.035 0.038\n",
"I(V8005 ** 2) -0.0002 6.63e-06 -28.300 0.000 -0.000 -0.000\n",
"V4803 0.1058 0.000 261.843 0.000 0.105 0.107\n",
"==============================================================================\n",
"Omnibus: 20619.867 Durbin-Watson: 1.643\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 78837.581\n",
"Skew: -0.479 Prob(JB): 0.00\n",
"Kurtosis: 5.924 Cond. No. 1.13e+06\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.13e+06. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"modelo = smf.ols(formula='lnrenda ~ V8005 + I(V8005**2) + V4803 + V0302 + V0404', data=amostra).fit()\n",
"print(modelo.summary())"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"neylson = pd.DataFrame({\n",
" 'V0302':'Masculino',\n",
" 'V8005':33,\n",
" 'V0404': 'Parda',\n",
" 'V4803': 15\n",
"}, index=[0])"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1854.130879\n",
"dtype: float64"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.exp(modelo.predict(neylson))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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