x-004-distribuição.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# O objetivo desta tarefa é aplicar a distribuição de frequência utilizando o atributo age da base de dados do censo\n",
    "\n",
    "# Carregue a base de dados census.csv\n",
    "\n",
    "# Faça testes utilizando o parâmetro bins para visualizar a distribuição dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importação de bibliotecas\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregamento da base de dados em uma variável\n",
    "\n",
    "dados = pd.read_csv('../data/census.csv')\n",
    "# 'census.csv' é um arquivo com alguns dados do census dos estados unidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>final-weight</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loos</th>\n",
       "      <th>hour-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  final-weight   education  education-num  \\\n",
       "0   39          State-gov         77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc         83311   Bachelors             13   \n",
       "2   38            Private        215646     HS-grad              9   \n",
       "3   53            Private        234721        11th              7   \n",
       "4   28            Private        338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loos  hour-per-week  native-country  income  \n",
       "0          2174             0             40   United-States   <=50K  \n",
       "1             0             0             13   United-States   <=50K  \n",
       "2             0             0             40   United-States   <=50K  \n",
       "3             0             0             40   United-States   <=50K  \n",
       "4             0             0             40            Cuba   <=50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualização dos primeiros registros\n",
    "\n",
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# uso da biblioteca seaborn para visualização graficamente dos registos em um histograma\n",
    "\n",
    "sns.distplot(dados['age'], hist = True, kde = True);  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# uso da biblioteca seaborn para visualização graficamente dos registos em um histograma\n",
    "\n",
    "sns.distplot(dados['education-num'], hist = True, kde = True); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# criação das distribuição das classes de idade\n",
    "\n",
    "dados['age'] = pd.cut(dados['age'], bins=[0, 17, 25, 40, 60, 90], \n",
    "                        labels=['Faixa1', \"Faixa2\", \"Faixa3\", \"Faixa4\", \"Faixa5\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>final-weight</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loos</th>\n",
       "      <th>hour-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Faixa3</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Faixa4</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Faixa3</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Faixa4</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Faixa3</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age          workclass  final-weight   education  education-num  \\\n",
       "0  Faixa3          State-gov         77516   Bachelors             13   \n",
       "1  Faixa4   Self-emp-not-inc         83311   Bachelors             13   \n",
       "2  Faixa3            Private        215646     HS-grad              9   \n",
       "3  Faixa4            Private        234721        11th              7   \n",
       "4  Faixa3            Private        338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loos  hour-per-week  native-country  income  \n",
       "0          2174             0             40   United-States   <=50K  \n",
       "1             0             0             13   United-States   <=50K  \n",
       "2             0             0             40   United-States   <=50K  \n",
       "3             0             0             40   United-States   <=50K  \n",
       "4             0             0             40            Cuba   <=50K  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# uso da biblioteca seaborn para visualização graficamente dos registos em um histograma\n",
    "\n",
    "sns.countplot(x='age', data=dados, palette='hls');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "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": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}