diwali_sales_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise de vendas no festival indiano Diwali"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O festival diwali, também conhecido como festival das luzes, é conhecido por sua grande movimentação comercial, por conta das festividades e troca de presentes.\n",
    "\n",
    "O presente relatório tem o objetivo de nos trazer o perfil demográfico de maior impacto durante o festival, além de apresentar as categorias de produtos mais vendidos.\n",
    "\n",
    "Os dados de vendas durante o festival podem ser encontrados na plataforma kaggle: https://www.kaggle.com/datasets/saadharoon27/diwali-sales-dataset\n",
    "\n",
    "Os dados do censo da Índia no ano de 2011 pode ser encontrados no link: https://www.kaggle.com/datasets/shiivvvaam/indian-districts-population-data/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importando bibliotecas e dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "vendas_diwali = pd.read_csv('dataset\\\\diwali_sales.csv')\n",
    "censo_indiano_2011 = pd.read_csv('dataset\\\\census_2011.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conferindo e consertando quaisquer inconsistências - vendas_diwali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11251 entries, 0 to 11250\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        11251 non-null  int64  \n",
      " 1   User_ID           11251 non-null  int64  \n",
      " 2   Cust_name         11251 non-null  object \n",
      " 3   Product_ID        11251 non-null  object \n",
      " 4   Gender            11251 non-null  object \n",
      " 5   Age Group         11251 non-null  object \n",
      " 6   Age               11251 non-null  int64  \n",
      " 7   Marital_Status    11251 non-null  int64  \n",
      " 8   State             11251 non-null  object \n",
      " 9   Zone              11251 non-null  object \n",
      " 10  Occupation        11251 non-null  object \n",
      " 11  Product_Category  11251 non-null  object \n",
      " 12  Orders            11251 non-null  int64  \n",
      " 13  Amount            11239 non-null  float64\n",
      " 14  age_group         11115 non-null  object \n",
      "dtypes: float64(1), int64(5), object(9)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "vendas_diwali.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'Columns Unamed: 0' foi deletada porque era apenas um índice que virou coluna durante o carregamento de dados \n",
    "vendas_diwali = vendas_diwali.drop(columns = 'Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Trocando F e M por Feminino e Masculino\n",
    "vendas_diwali['Gender'] = vendas_diwali['Gender'].replace({'F':'Feminino','M':'Masculino'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11251 entries, 0 to 11250\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   User_ID           11251 non-null  int64  \n",
      " 1   Cust_name         11251 non-null  object \n",
      " 2   Product_ID        11251 non-null  object \n",
      " 3   Gender            11251 non-null  object \n",
      " 4   Age Group         11251 non-null  object \n",
      " 5   Age               11251 non-null  int64  \n",
      " 6   Marital_Status    11251 non-null  int64  \n",
      " 7   State             11251 non-null  object \n",
      " 8   Zone              11251 non-null  object \n",
      " 9   Occupation        11251 non-null  object \n",
      " 10  Product_Category  11251 non-null  object \n",
      " 11  Orders            11251 non-null  int64  \n",
      " 12  Amount            11239 non-null  float64\n",
      " 13  age_group         11115 non-null  object \n",
      "dtypes: float64(1), int64(4), object(9)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#Informação sobre o dataset de venda_diwali\n",
    "vendas_diwali.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['26-35' '0-17' '18-25' '55+' '46-50' '51-55' '36-45']\n",
      "['19-30' '31-40' '11-18' '51-80' '41-50' nan]\n"
     ]
    }
   ],
   "source": [
    "#Conferindo as diferenças entre duas colunas similares.\n",
    "#A coluna 'Age Group' possui mais opções de agrupamento do que a coluna 'age_group', alem de não conter valores ausentes.\n",
    "print(vendas_diwali['Age Group'].unique())\n",
    "print(vendas_diwali['age_group'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remoção da coluna 'age_group'\n",
    "vendas_diwali = vendas_diwali.drop(columns = 'age_group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convertendo dados binários em dados categóricos\n",
    "vendas_diwali['Marital_Status'] = vendas_diwali['Marital_Status'].replace({0:'Solteiro',1:'Casado'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tradução de colunas EN-PTBR\n",
    "vendas_diwali = vendas_diwali.rename(\n",
    "    columns = {\n",
    "        'User_ID':'user_id',\n",
    "        'Cust_name':'cliente_nome',\n",
    "        'Product_ID':'id_do_produto',\n",
    "        'Gender':'genero',\n",
    "        'Age Group':'faixa_etaria',\n",
    "        'Age':'idade',\n",
    "        'Marital_Status':'estado_civil',\n",
    "        'State':'estado',\n",
    "        'Zone':'zona',\n",
    "        'Occupation':'ocupacao',\n",
    "        'Product_Category':'categoria_produto',\n",
    "        'Orders':'pedidos',\n",
    "        'Amount':'total',\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conferindo e consertando quaisquer inconsistências - censo_indiano_2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 610 entries, 0 to 609\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Ranking     610 non-null    int64  \n",
      " 1   District    610 non-null    object \n",
      " 2   State       610 non-null    object \n",
      " 3   Population  610 non-null    object \n",
      " 4   Growth      610 non-null    object \n",
      " 5   Sex-Ratio   610 non-null    int64  \n",
      " 6   Literacy    610 non-null    float64\n",
      "dtypes: float64(1), int64(2), object(4)\n",
      "memory usage: 33.5+ KB\n"
     ]
    }
   ],
   "source": [
    "censo_indiano_2011.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "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>Ranking</th>\n",
       "      <th>District</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Growth</th>\n",
       "      <th>Sex-Ratio</th>\n",
       "      <th>Literacy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>455</td>\n",
       "      <td>Morigaon</td>\n",
       "      <td>Assam</td>\n",
       "      <td>957,423</td>\n",
       "      <td>23.34 %</td>\n",
       "      <td>967</td>\n",
       "      <td>68.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ranking  District  State Population   Growth  Sex-Ratio  Literacy\n",
       "424      455  Morigaon  Assam    957,423  23.34 %        967     68.03"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo_indiano_2011.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retirando vírgulas dos valores de população e transformando a string de população em uma string float\n",
    "censo_indiano_2011['Population'] = censo_indiano_2011['Population'].str.replace(',', '').astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: '36.01 '",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_24668\\3785265291.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Retirando o símbolo de percentual da coluna Growth e transformando em integral\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mcenso_indiano_2011\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Growth'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcenso_indiano_2011\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Growth'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mcenso_indiano_2011\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Growth'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcenso_indiano_2011\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Growth'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Users\\Patricia\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   6641\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6642\u001b[0m             \u001b[1;31m# else, only a single dtype is given\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6643\u001b[1;33m             \u001b[0mnew_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6644\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor_from_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6645\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Patricia\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    428\u001b[0m             \u001b[0mcopy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m         return self.apply(\n\u001b[0m\u001b[0;32m    431\u001b[0m             \u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Patricia\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[0;32m    361\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    362\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 363\u001b[1;33m                 \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    364\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    365\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Patricia\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[0;32m    756\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m# type: ignore[call-overload]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    757\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 758\u001b[1;33m         \u001b[0mnew_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    759\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    760\u001b[0m         \u001b[0mnew_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Patricia\\anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 237\u001b[1;33m         \u001b[0mnew_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    238\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m         \u001b[1;31m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Patricia\\anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py\u001b[0m in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m    180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    181\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 182\u001b[1;33m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_astype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    183\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m     \u001b[1;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Patricia\\anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py\u001b[0m in \u001b[0;36m_astype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m    131\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mobject\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m         \u001b[1;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 133\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    134\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    135\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: '36.01 '"
     ]
    }
   ],
   "source": [
    "#Retirando o símbolo de percentual da coluna Growth e transformando em integral\n",
    "censo_indiano_2011['Growth'] = censo_indiano_2011['Growth'].astype(str)\n",
    "censo_indiano_2011['Growth'] = censo_indiano_2011['Growth'].str.replace('%', '').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "censo_indiano_2011 = censo_indiano_2011.rename(columns={\n",
    "    'Ranking':'rank',\n",
    "    'District':'distrito',\n",
    "    'State':'estado',\n",
    "    'Population':'populacao',\n",
    "    'Growth':'crescimento',\n",
    "    'Sex-Ratio':'razao_de_genero',\n",
    "    'Literacy':'alfabetismo'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rank: A posição do distrito baseada na população\n",
    "#distrito: O nome do distrito\n",
    "#estado: Estado\n",
    "#populacao: População total\n",
    "#crescimento: O percentual de crescimento populacional\n",
    "#razao_de_genero: A razão entre o número de homens para mulheres no distrito - x mulheres para 1000 homens\n",
    "#alfabetismo: Taxa de alfabestismo no distrito"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agrupamento de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criação de agrupamento para identificar total de vendas por faixa etária\n",
    "total_gasto_faixa_etaria = vendas_diwali.groupby(['faixa_etaria','genero'])['total'].sum().reset_index(name = 'total_gasto').sort_values(by = 'total_gasto',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criação de agrupamento para identificar total de vendas por idade\n",
    "total_gasto_idade = vendas_diwali.groupby(['idade','genero'])['total'].sum().reset_index(name = 'total_gasto').sort_values(by = 'total_gasto',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criação de agrupamento para identificar total de vendas por gênero\n",
    "total_gasto_genero = vendas_diwali.groupby(['genero','estado'])['total'].sum().reset_index(name = 'total_gasto').sort_values(by = 'total_gasto',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criação de agrupamento para identificar total de vendas por estado civil\n",
    "total_gasto_estado_civil = vendas_diwali.groupby(['estado_civil'])['total'].sum().reset_index(name = 'total_gasto').sort_values(by = 'total_gasto',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criação de agrupamento para identificar total de vendas por categoria\n",
    "total_gasto_categoria = vendas_diwali.groupby(['categoria_produto'])['total'].sum().reset_index(name = 'total_gasto').sort_values(by = 'total_gasto',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criação de agrupamento para identificar total de vendas por Estado\n",
    "total_gasto_estado = vendas_diwali.groupby(['estado','genero'])['total'].sum().reset_index(name = 'total_gasto').sort_values(by = 'total_gasto',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criação de agrupamento para identificar total de vendas por Ocupação\n",
    "total_gasto_ocupacao = vendas_diwali.groupby(['ocupacao'])['total'].sum().reset_index(name = 'total_gasto').sort_values(by = 'total_gasto',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criação de agrupamento para identificar total de vendas por Zona\n",
    "total_gasto_zona = vendas_diwali.groupby(['zona'])['total'].sum().reset_index(name = 'total_gasto').sort_values(by = 'total_gasto',ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apresentação de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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    "#Alteração do tipo de dado na coluna genero\n",
    "\n",
    "total_gasto_faixa_etaria['genero'] = total_gasto_faixa_etaria['genero'].astype('category')\n",
    "\n",
    "#Criação de gráfico de colunas para identificar total de vendas por faixa etária\n",
    "\n",
    "bar_fig_por_faixa_etaria = px.bar(\n",
    "    total_gasto_faixa_etaria,\n",
    "    x='faixa_etaria',\n",
    "    y='total_gasto',\n",
    "    text_auto=\".2s\",\n",
    "    color='genero',\n",
    "    barmode = 'group'\n",
    ")\n",
    "\n",
    "# Atualização de aspectos visuais do gráfico\n",
    "bar_fig_por_faixa_etaria.update_traces(\n",
    "    textposition='outside'\n",
    ")\n",
    "bar_fig_por_faixa_etaria.update_yaxes(title_text='Total Gasto')\n",
    "bar_fig_por_faixa_etaria.update_xaxes(title_text='Faixa Etária')\n",
    "bar_fig_por_faixa_etaria.update_layout(title_text='Apresentação de total de vendas por faixa etária e gênero')\n",
    "\n",
    "#Impressão do gráfico\n",
    "bar_fig_por_faixa_etaria.show()"
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   "source": [
    "De acordo com os dados representados no gráfico acima, podemos identificar que o público feminino é responsável pela maior parte do total de gastos durante o período do festival. Além disso, o principal grupo de compradores está entre as mulheres e homens entre 26 a 35 anos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abaixo nós temos um gráfico representando a população por gênero de cada Estado Indiano."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Média razão de gênero por estado\n",
    "media_razao_genero = censo_indiano_2011.groupby('estado')['razao_de_genero'].mean().reset_index()\n",
    "\n",
    "#Coluna para separar o gráfico por cores\n",
    "media_razao_genero['cor'] = media_razao_genero['razao_de_genero'] >= 1000"
   ]
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   "source": [
    "#Criação de histograma para identificar a razão da populaçao do sexo feminino por\n",
    "# masculino em cada Estado Indiano.\n",
    "\n",
    "bar_fig_razao_genero = px.histogram(\n",
    "    media_razao_genero,\n",
    "    x='estado',\n",
    "    y='razao_de_genero',\n",
    "    text_auto=\".3s\",\n",
    "    height=600,\n",
    "    width=950,\n",
    "    color='cor',\n",
    "    labels={'cor': 'Razão de Gênero ≥ 1000'}\n",
    ")\n",
    "\n",
    "# Atualização de aspectos visuais do gráfico\n",
    "bar_fig_razao_genero.update_traces(\n",
    "    textposition='outside'\n",
    ")\n",
    "bar_fig_razao_genero.update_yaxes(title_text='Razão de mulheres a cada 1000 homens')\n",
    "bar_fig_razao_genero.update_xaxes(title_text='Estado',tickangle = 45)\n",
    "bar_fig_razao_genero.update_layout(title_text='Razão populacional entre homens e mulheres por Estado')\n",
    "\n",
    "#Impressão do gráfico\n",
    "bar_fig_razao_genero.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acima é possível identificar que apenas 3 estados indianos possuem uma população feminina majoritária, esses seriam Uttarakhand, Puducherry e Kerala, abaixo foi realizada uma análise para verificar se, a presença majoritária da população indiana acarreta em algum impacto significativo no resultado final."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Junção do dataframe de total de gastos por genero (o qual inclui estado)\n",
    "#com o dataframe do censo indiano com a razao de genero entre homens e mulheres em estados indianos\n",
    "total_gasto_genero_razao=total_gasto_genero.merge(media_razao_genero,on='estado')\n",
    "\n",
    "#Renomear coluna de color para identificar se a informação de que o estado possui mais mulheres que homens é\n",
    "#verdadeira ou falsa\n",
    "total_gasto_genero_razao = total_gasto_genero_razao.rename(columns={'cor':'mais_mulheres'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dataframe com o maior valor de gastos totais por cada Estado\n",
    "df_max_total_gasto = total_gasto_genero_razao.loc[total_gasto_genero_razao.groupby('estado')['total_gasto'].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Feminino'], dtype=object)"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identificação de genêros que fazer parte do dataframe com o maior valor de gastos totais por cada Estado\n",
    "df_max_total_gasto['genero'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acima podemos identificar que, em todos os estados, a população feminina foi responsável pela maior quantidade de gastos durante o festival."
   ]
  },
  {
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   "execution_count": null,
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         "text": "Total gasto por Estado - Presença majoritária de população feminina ou masculina"
        },
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          0,
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          "text": "Soma do total de gastos"
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   ],
   "source": [
    "#Criação de histograma para identificar o valor total gasto pela população feminina em cada Estado\n",
    "#O gráfico possui legenda para informar se a população daquele estado é de maioria feminina ou masculina\n",
    "\n",
    "bar_fig_total_gasto = px.histogram(\n",
    "    df_max_total_gasto,\n",
    "    x='estado',\n",
    "    y='total_gasto',\n",
    "    text_auto=\".3s\",\n",
    "    height=600,\n",
    "    width=800,\n",
    "    color='mais_mulheres',\n",
    "    labels={'mais_mulheres': 'População majoritária feminina'}\n",
    ")\n",
    "\n",
    "# Atualização de aspectos visuais do gráfico\n",
    "bar_fig_total_gasto.update_traces(\n",
    "    textposition='outside'\n",
    ")\n",
    "bar_fig_total_gasto.update_yaxes(title_text='Soma do total de gastos')\n",
    "bar_fig_total_gasto.update_xaxes(title_text='Estado')\n",
    "bar_fig_total_gasto.update_layout(title_text='Total gasto por Estado - Presença majoritária de população feminina ou masculina')\n",
    "\n",
    "#Impressão do gráfico\n",
    "bar_fig_total_gasto.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O gráfico acima mostra que os dois Estados com maioria populacional feminina (Kerala e Uttarakhand) não correspondem com os Estados com maior total de vendas, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>distrito</th>\n",
       "      <th>estado</th>\n",
       "      <th>populacao</th>\n",
       "      <th>crescimento</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Thane</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>11060148.0</td>\n",
       "      <td>36.01 %</td>\n",
       "      <td>886</td>\n",
       "      <td>84.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>North Twenty Four Parganas</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>10009781.0</td>\n",
       "      <td>12.04 %</td>\n",
       "      <td>955</td>\n",
       "      <td>84.06</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Bangalore</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>9621551.0</td>\n",
       "      <td>47.18 %</td>\n",
       "      <td>916</td>\n",
       "      <td>87.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>9429408.0</td>\n",
       "      <td>30.37 %</td>\n",
       "      <td>915</td>\n",
       "      <td>86.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Mumbai Suburban</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>9356962.0</td>\n",
       "      <td>8.29 %</td>\n",
       "      <td>860</td>\n",
       "      <td>89.91</td>\n",
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       "      <td>636</td>\n",
       "      <td>Nicobars</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>36842.0</td>\n",
       "      <td>-12.42 %</td>\n",
       "      <td>777</td>\n",
       "      <td>78.06</td>\n",
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       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>637</td>\n",
       "      <td>Upper Siang</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>35320.0</td>\n",
       "      <td>5.87 %</td>\n",
       "      <td>889</td>\n",
       "      <td>59.99</td>\n",
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       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>638</td>\n",
       "      <td>Lahul and Spiti</td>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>31564.0</td>\n",
       "      <td>-5.00 %</td>\n",
       "      <td>903</td>\n",
       "      <td>76.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>639</td>\n",
       "      <td>Anjaw</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>21167.0</td>\n",
       "      <td>14.19 %</td>\n",
       "      <td>839</td>\n",
       "      <td>56.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>640</td>\n",
       "      <td>Dibang Valley</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>8004.0</td>\n",
       "      <td>10.07 %</td>\n",
       "      <td>813</td>\n",
       "      <td>64.10</td>\n",
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      "text/plain": [
       "     rank                    distrito                       estado  \\\n",
       "0       1                       Thane                  Maharashtra   \n",
       "1       2  North Twenty Four Parganas                  West Bengal   \n",
       "2       3                   Bangalore                    Karnataka   \n",
       "3       4                        Pune                  Maharashtra   \n",
       "4       5             Mumbai Suburban                  Maharashtra   \n",
       "..    ...                         ...                          ...   \n",
       "605   636                    Nicobars  Andaman and Nicobar Islands   \n",
       "606   637                 Upper Siang            Arunachal Pradesh   \n",
       "607   638             Lahul and Spiti             Himachal Pradesh   \n",
       "608   639                       Anjaw            Arunachal Pradesh   \n",
       "609   640               Dibang Valley            Arunachal Pradesh   \n",
       "\n",
       "      populacao crescimento  razao_de_genero  alfabetismo  \n",
       "0    11060148.0     36.01 %              886        84.53  \n",
       "1    10009781.0     12.04 %              955        84.06  \n",
       "2     9621551.0     47.18 %              916        87.67  \n",
       "3     9429408.0     30.37 %              915        86.15  \n",
       "4     9356962.0      8.29 %              860        89.91  \n",
       "..          ...         ...              ...          ...  \n",
       "605     36842.0    -12.42 %              777        78.06  \n",
       "606     35320.0      5.87 %              889        59.99  \n",
       "607     31564.0     -5.00 %              903        76.81  \n",
       "608     21167.0     14.19 %              839        56.46  \n",
       "609      8004.0     10.07 %              813        64.10  \n",
       "\n",
       "[610 rows x 7 columns]"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo_indiano_2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "censo_por_estado_populacao = censo_indiano_2011.groupby('estado')['populacao'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 610 entries, 0 to 609\n",
      "Data columns (total 7 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   rank             610 non-null    int64  \n",
      " 1   distrito         610 non-null    object \n",
      " 2   estado           610 non-null    object \n",
      " 3   populacao        610 non-null    float64\n",
      " 4   crescimento      610 non-null    object \n",
      " 5   razao_de_genero  610 non-null    int64  \n",
      " 6   alfabetismo      610 non-null    float64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 33.5+ KB\n"
     ]
    }
   ],
   "source": [
    "censo_indiano_2011.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>rank</th>\n",
       "      <th>distrito</th>\n",
       "      <th>estado</th>\n",
       "      <th>populacao</th>\n",
       "      <th>crescimento</th>\n",
       "      <th>razao_de_genero</th>\n",
       "      <th>alfabetismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Rangareddy</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>5296741.0</td>\n",
       "      <td>48.16 %</td>\n",
       "      <td>961</td>\n",
       "      <td>75.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>East Godavari</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>5154296.0</td>\n",
       "      <td>5.16 %</td>\n",
       "      <td>1006</td>\n",
       "      <td>70.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Guntur</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>4887813.0</td>\n",
       "      <td>9.47 %</td>\n",
       "      <td>1003</td>\n",
       "      <td>67.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>Krishna</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>4517398.0</td>\n",
       "      <td>7.87 %</td>\n",
       "      <td>992</td>\n",
       "      <td>73.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>4290589.0</td>\n",
       "      <td>11.96 %</td>\n",
       "      <td>1006</td>\n",
       "      <td>66.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>Chittoor</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>4174064.0</td>\n",
       "      <td>11.43 %</td>\n",
       "      <td>997</td>\n",
       "      <td>71.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>Anantapur</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>4081148.0</td>\n",
       "      <td>12.10 %</td>\n",
       "      <td>977</td>\n",
       "      <td>63.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>Kurnool</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>4053463.0</td>\n",
       "      <td>14.85 %</td>\n",
       "      <td>988</td>\n",
       "      <td>59.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>Mahbubnagar</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>4053028.0</td>\n",
       "      <td>15.34 %</td>\n",
       "      <td>977</td>\n",
       "      <td>55.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>3943323.0</td>\n",
       "      <td>2.97 %</td>\n",
       "      <td>954</td>\n",
       "      <td>83.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>West Godavari</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>3936966.0</td>\n",
       "      <td>3.51 %</td>\n",
       "      <td>1004</td>\n",
       "      <td>74.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>Karimnagar</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>3776269.0</td>\n",
       "      <td>8.15 %</td>\n",
       "      <td>1008</td>\n",
       "      <td>64.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>Warangal</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>3512576.0</td>\n",
       "      <td>8.21 %</td>\n",
       "      <td>997</td>\n",
       "      <td>65.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>Nalgonda</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>3488809.0</td>\n",
       "      <td>7.41 %</td>\n",
       "      <td>983</td>\n",
       "      <td>64.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>Prakasam</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>3397448.0</td>\n",
       "      <td>11.05 %</td>\n",
       "      <td>981</td>\n",
       "      <td>63.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>165</td>\n",
       "      <td>Nizamabad</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2551335.0</td>\n",
       "      <td>8.77 %</td>\n",
       "      <td>1040</td>\n",
       "      <td>61.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>193</td>\n",
       "      <td>Vizianagaram</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2344474.0</td>\n",
       "      <td>4.23 %</td>\n",
       "      <td>1019</td>\n",
       "      <td>58.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     rank       distrito          estado  populacao crescimento  \\\n",
       "16     17     Rangareddy  Andhra Pradesh  5296741.0     48.16 %   \n",
       "18     19  East Godavari  Andhra Pradesh  5154296.0      5.16 %   \n",
       "21     22         Guntur  Andhra Pradesh  4887813.0      9.47 %   \n",
       "33     34        Krishna  Andhra Pradesh  4517398.0      7.87 %   \n",
       "43     44  Visakhapatnam  Andhra Pradesh  4290589.0     11.96 %   \n",
       "46     47       Chittoor  Andhra Pradesh  4174064.0     11.43 %   \n",
       "51     52      Anantapur  Andhra Pradesh  4081148.0     12.10 %   \n",
       "53     54        Kurnool  Andhra Pradesh  4053463.0     14.85 %   \n",
       "54     55    Mahbubnagar  Andhra Pradesh  4053028.0     15.34 %   \n",
       "59     60      Hyderabad  Andhra Pradesh  3943323.0      2.97 %   \n",
       "61     62  West Godavari  Andhra Pradesh  3936966.0      3.51 %   \n",
       "68     69     Karimnagar  Andhra Pradesh  3776269.0      8.15 %   \n",
       "82     83       Warangal  Andhra Pradesh  3512576.0      8.21 %   \n",
       "87     88       Nalgonda  Andhra Pradesh  3488809.0      7.41 %   \n",
       "97     98       Prakasam  Andhra Pradesh  3397448.0     11.05 %   \n",
       "134   165      Nizamabad  Andhra Pradesh  2551335.0      8.77 %   \n",
       "162   193   Vizianagaram  Andhra Pradesh  2344474.0      4.23 %   \n",
       "\n",
       "     razao_de_genero  alfabetismo  \n",
       "16               961        75.87  \n",
       "18              1006        70.99  \n",
       "21              1003        67.40  \n",
       "33               992        73.74  \n",
       "43              1006        66.91  \n",
       "46               997        71.53  \n",
       "51               977        63.57  \n",
       "53               988        59.97  \n",
       "54               977        55.04  \n",
       "59               954        83.25  \n",
       "61              1004        74.63  \n",
       "68              1008        64.15  \n",
       "82               997        65.11  \n",
       "87               983        64.20  \n",
       "97               981        63.08  \n",
       "134             1040        61.25  \n",
       "162             1019        58.89  "
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo_indiano_2011[censo_indiano_2011['estado']=='Andhra Pradesh']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>estado</th>\n",
       "      <th>populacao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>380581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>67459740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>1383727.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Assam</td>\n",
       "      <td>28381808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>89751364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>1055450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>25545198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>343709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Daman and Diu</td>\n",
       "      <td>243247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>14056012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Goa</td>\n",
       "      <td>1458545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>54816245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>25351462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>6864602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>12541302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>27389394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>55415190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>30596127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Lakshadweep</td>\n",
       "      <td>64473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>72626809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>100887656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Manipur</td>\n",
       "      <td>2855794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>2966889.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mizoram</td>\n",
       "      <td>1097206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Nagaland</td>\n",
       "      <td>1978502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Orissa</td>\n",
       "      <td>41974218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Puducherry</td>\n",
       "      <td>1247953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>27743338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>68548437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sikkim</td>\n",
       "      <td>610577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>69424740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Tripura</td>\n",
       "      <td>3673917.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>194121100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>10086292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>82519780.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         estado    populacao\n",
       "0   Andaman and Nicobar Islands     380581.0\n",
       "1                Andhra Pradesh   67459740.0\n",
       "2             Arunachal Pradesh    1383727.0\n",
       "3                         Assam   28381808.0\n",
       "4                         Bihar   89751364.0\n",
       "5                    Chandigarh    1055450.0\n",
       "6                  Chhattisgarh   25545198.0\n",
       "7        Dadra and Nagar Haveli     343709.0\n",
       "8                 Daman and Diu     243247.0\n",
       "9                         Delhi   14056012.0\n",
       "10                          Goa    1458545.0\n",
       "11                      Gujarat   54816245.0\n",
       "12                      Haryana   25351462.0\n",
       "13             Himachal Pradesh    6864602.0\n",
       "14            Jammu and Kashmir   12541302.0\n",
       "15                    Jharkhand   27389394.0\n",
       "16                    Karnataka   55415190.0\n",
       "17                       Kerala   30596127.0\n",
       "18                  Lakshadweep      64473.0\n",
       "19               Madhya Pradesh   72626809.0\n",
       "20                  Maharashtra  100887656.0\n",
       "21                      Manipur    2855794.0\n",
       "22                    Meghalaya    2966889.0\n",
       "23                      Mizoram    1097206.0\n",
       "24                     Nagaland    1978502.0\n",
       "25                       Orissa   41974218.0\n",
       "26                   Puducherry    1247953.0\n",
       "27                       Punjab   27743338.0\n",
       "28                    Rajasthan   68548437.0\n",
       "29                       Sikkim     610577.0\n",
       "30                   Tamil Nadu   69424740.0\n",
       "31                      Tripura    3673917.0\n",
       "32                Uttar Pradesh  194121100.0\n",
       "33                  Uttarakhand   10086292.0\n",
       "34                  West Bengal   82519780.0"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo_por_estado_populacao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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