3_introducao_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b889b774",
   "metadata": {},
   "source": [
    "# Python para análise de dados (Pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "576f479b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Para instalar:\n",
    "#!pip3 install pandas\n",
    "#Importação da biblioteca\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c776c9a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent  year  lifeExp       pop   gdpPercap\n",
       "0  Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
       "1  Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
       "2  Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
       "3  Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
       "4  Afghanistan      Asia  1972   36.088  13079460  739.981106"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importando um arquivo CSV\n",
    "df = pd.read_csv(\"datasets\\\\Gapminder.csv\", sep=\";\", on_bad_lines=\"skip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3ad22cde",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pais</th>\n",
       "      <th>continente</th>\n",
       "      <th>ano</th>\n",
       "      <th>expDeVida</th>\n",
       "      <th>populacao</th>\n",
       "      <th>rendaPercapta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pais continente   ano  expDeVida  populacao  rendaPercapta\n",
       "0  Afghanistan       Asia  1952     28.801    8425333     779.445314\n",
       "1  Afghanistan       Asia  1957     30.332    9240934     820.853030\n",
       "2  Afghanistan       Asia  1962     31.997   10267083     853.100710\n",
       "3  Afghanistan       Asia  1967     34.020   11537966     836.197138\n",
       "4  Afghanistan       Asia  1972     36.088   13079460     739.981106"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Renomeando colunas:\n",
    "df.rename(columns={\"country\": \"pais\", \"continent\": \"continente\", \"year\": \"ano\", \"lifeExp\": \"expDeVida\", \"pop\": \"populacao\", \"gdpPercap\": \"rendaPercapta\"}, inplace=True)\n",
    "#Visualizando primeiras linhas (parâmetro é o número de linhas a serem listadas)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74d8cde4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3312, 6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total de linhas e colunas no dataframe:\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "94646ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pais', 'continente', 'ano', 'expDeVida', 'populacao', 'rendaPercapta'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificando nome das colunas:\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "edb87c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pais              object\n",
       "continente        object\n",
       "ano                int64\n",
       "expDeVida        float64\n",
       "populacao          int64\n",
       "rendaPercapta    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificando tipo de dados em cada coluna:\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df12a588",
   "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>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3311</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent  year  lifeExp       pop   gdpPercap\n",
       "3307  Zimbabwe    Africa  1987   62.351   9216418  706.157306\n",
       "3308  Zimbabwe    Africa  1992   60.377  10704340  693.420786\n",
       "3309  Zimbabwe    Africa  1997   46.809  11404948  792.449960\n",
       "3310  Zimbabwe    Africa  2002   39.989  11926563  672.038623\n",
       "3311  Zimbabwe    Africa  2007   43.487  12311143  469.709298"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificando últimos linhas (parâmetro é o número de linhas a ser listada):\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce31a331",
   "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>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3312.000000</td>\n",
       "      <td>3312.000000</td>\n",
       "      <td>3.312000e+03</td>\n",
       "      <td>3312.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1980.301630</td>\n",
       "      <td>65.246871</td>\n",
       "      <td>3.161489e+07</td>\n",
       "      <td>11317.115805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.927294</td>\n",
       "      <td>11.768412</td>\n",
       "      <td>1.041193e+08</td>\n",
       "      <td>11369.142739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1950.000000</td>\n",
       "      <td>23.599000</td>\n",
       "      <td>5.941200e+04</td>\n",
       "      <td>241.165876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1967.000000</td>\n",
       "      <td>58.337500</td>\n",
       "      <td>2.678572e+06</td>\n",
       "      <td>2514.625266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1982.000000</td>\n",
       "      <td>69.610000</td>\n",
       "      <td>7.557218e+06</td>\n",
       "      <td>7838.505123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1996.000000</td>\n",
       "      <td>73.657750</td>\n",
       "      <td>1.958522e+07</td>\n",
       "      <td>17357.879170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>82.670000</td>\n",
       "      <td>1.318683e+09</td>\n",
       "      <td>113523.132900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year      lifeExp           pop      gdpPercap\n",
       "count  3312.000000  3312.000000  3.312000e+03    3312.000000\n",
       "mean   1980.301630    65.246871  3.161489e+07   11317.115805\n",
       "std      16.927294    11.768412  1.041193e+08   11369.142739\n",
       "min    1950.000000    23.599000  5.941200e+04     241.165876\n",
       "25%    1967.000000    58.337500  2.678572e+06    2514.625266\n",
       "50%    1982.000000    69.610000  7.557218e+06    7838.505123\n",
       "75%    1996.000000    73.657750  1.958522e+07   17357.879170\n",
       "max    2007.000000    82.670000  1.318683e+09  113523.132900"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificando estatística básica dos dados:\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2b61203e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Asia', 'Europe', 'Africa', 'Americas', nan, 'FSU', 'Oceania'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificando registros únicos da coluna continente:\n",
    "df[\"continente\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2aac74f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pais</th>\n",
       "      <th>continente</th>\n",
       "      <th>ano</th>\n",
       "      <th>expDeVida</th>\n",
       "      <th>populacao</th>\n",
       "      <th>rendaPercapta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1962</td>\n",
       "      <td>56.494</td>\n",
       "      <td>421869</td>\n",
       "      <td>2039.393277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1967</td>\n",
       "      <td>58.607</td>\n",
       "      <td>485000</td>\n",
       "      <td>2170.073285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1972</td>\n",
       "      <td>60.642</td>\n",
       "      <td>544000</td>\n",
       "      <td>2797.759484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1977</td>\n",
       "      <td>62.669</td>\n",
       "      <td>599339</td>\n",
       "      <td>3182.569600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1982</td>\n",
       "      <td>64.683</td>\n",
       "      <td>658906</td>\n",
       "      <td>3209.257468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pais continente   ano  expDeVida  populacao  rendaPercapta\n",
       "975  Fiji    Oceania  1962     56.494     421869    2039.393277\n",
       "976  Fiji    Oceania  1967     58.607     485000    2170.073285\n",
       "977  Fiji    Oceania  1972     60.642     544000    2797.759484\n",
       "978  Fiji    Oceania  1977     62.669     599339    3182.569600\n",
       "979  Fiji    Oceania  1982     64.683     658906    3209.257468"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Buscando todos os registros cujo continente é Oceania:\n",
    "oceania = df.loc[df[\"continente\"] == \"Oceania\"]\n",
    "oceania.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "88f5cee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continente\n",
       "Africa      51\n",
       "Americas    25\n",
       "Asia        41\n",
       "Europe      35\n",
       "FSU          6\n",
       "Oceania      3\n",
       "Name: pais, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Agrupamento utilizando groupby para verificar quantos países distintos em cada continente\n",
    "df.groupby(\"continente\")[\"pais\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b0546d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ano\n",
       "1950    62.002568\n",
       "1951    65.904167\n",
       "1952    49.206867\n",
       "1953    66.674563\n",
       "1954    67.459817\n",
       "1955    67.806757\n",
       "1956    67.950637\n",
       "1957    51.614590\n",
       "1958    68.815936\n",
       "1959    68.226579\n",
       "1960    68.470837\n",
       "1961    68.862480\n",
       "1962    54.035234\n",
       "1963    69.595735\n",
       "1964    70.063105\n",
       "1965    70.259881\n",
       "1966    70.447526\n",
       "1967    56.263629\n",
       "1968    70.689081\n",
       "1969    70.653896\n",
       "1970    70.961141\n",
       "1971    71.103976\n",
       "1972    58.474481\n",
       "1973    71.500338\n",
       "1974    71.778504\n",
       "1975    71.939218\n",
       "1976    72.158050\n",
       "1977    60.429090\n",
       "1978    72.717567\n",
       "1979    73.018717\n",
       "1980    73.064524\n",
       "1981    73.337399\n",
       "1982    62.365871\n",
       "1983    73.787778\n",
       "1984    74.100741\n",
       "1985    74.112222\n",
       "1986    74.452222\n",
       "1987    63.984860\n",
       "1988    74.760000\n",
       "1989    74.924444\n",
       "1990    74.283438\n",
       "1991    74.374848\n",
       "1992    65.008443\n",
       "1993    74.324545\n",
       "1994    74.456667\n",
       "1995    74.552727\n",
       "1996    75.029394\n",
       "1997    65.873799\n",
       "1998    75.569697\n",
       "1999    75.703636\n",
       "2000    76.026364\n",
       "2001    76.257879\n",
       "2002    66.835695\n",
       "2003    76.586667\n",
       "2004    76.921562\n",
       "2005    76.718667\n",
       "2006    77.887778\n",
       "2007    67.868557\n",
       "Name: expDeVida, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Usando groupby e funções matemáticas juntos:\n",
    "df.groupby(\"ano\")[\"expDeVida\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cf55a3b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11317.115804696648"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificando média de valores de uma coluna:\n",
    "df[\"rendaPercapta\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "77384dad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37482287.5451553"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificando soma de valores de uma coluna:\n",
    "df[\"rendaPercapta\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92d65eec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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