pandas_init.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pandas_init.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1l87iS5CmbmDAJjs_BRMqAsdMgZrbfASv",
      "authorship_tag": "ABX9TyOySGdyeuFX5sn95AkRIKnE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luisriego/Analise-de-dados-com-Python-e-Pandas/blob/main/pandas_init.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzW_81bfYZG-"
      },
      "source": [
        "# Importando Pandas\n",
        "import pandas as pd"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kd676COwYpSz"
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Dev/DIO/Carrefour Bank Data Egenieer Bootcamp/Análise de dados com Python e Pandas/Cusro_Python_Pandas_Digital_Innovation-master/datasets/Gapminder.csv\", sep=\";\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "w8nNlAGqZHOq",
        "outputId": "81fde17e-0db8-478d-fbdf-29e1c8ee84da"
      },
      "source": [
        "# Visualizar as 5 primeiras linhas\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZXzijBfaNVI"
      },
      "source": [
        "df = df.rename(columns={\"country\":\"Pais\", \"continent\":\"Continente\", \"year\":\"ano\", \"lifeExp\":\"Exp. vida\", \"pop\":\"Populacao\", \"gdpPercap\":\"PIB\"})"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "aeohm1_Ig_zx",
        "outputId": "d5a098f8-e2b6-4fff-8d6c-017837d026f1"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Pais</th>\n",
              "      <th>Continente</th>\n",
              "      <th>ano</th>\n",
              "      <th>Exp. vida</th>\n",
              "      <th>Populacao</th>\n",
              "      <th>PIB</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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1977</td>\n",
              "      <td>38.438</td>\n",
              "      <td>14880372</td>\n",
              "      <td>786.113360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1982</td>\n",
              "      <td>39.854</td>\n",
              "      <td>12881816</td>\n",
              "      <td>978.011439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1987</td>\n",
              "      <td>40.822</td>\n",
              "      <td>13867957</td>\n",
              "      <td>852.395945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1992</td>\n",
              "      <td>41.674</td>\n",
              "      <td>16317921</td>\n",
              "      <td>649.341395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1997</td>\n",
              "      <td>41.763</td>\n",
              "      <td>22227415</td>\n",
              "      <td>635.341351</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Pais Continente   ano  Exp. vida  Populacao         PIB\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\n",
              "5  Afghanistan       Asia  1977     38.438   14880372  786.113360\n",
              "6  Afghanistan       Asia  1982     39.854   12881816  978.011439\n",
              "7  Afghanistan       Asia  1987     40.822   13867957  852.395945\n",
              "8  Afghanistan       Asia  1992     41.674   16317921  649.341395\n",
              "9  Afghanistan       Asia  1997     41.763   22227415  635.341351"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5AWBxx9i62s",
        "outputId": "885873a4-7d33-4ff7-d92a-056fa3e86114"
      },
      "source": [
        "# Total de linhas e colunas\n",
        "df.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3312, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7VAjSKvkSf3",
        "outputId": "a293c3d2-879f-43c3-fb41-098f6605d197"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Pais', 'Continente', 'ano', 'Exp. vida', 'Populacao', 'PIB'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8DxOnjVkb04",
        "outputId": "9b999f6b-8df5-46ee-e759-7e4d04d09336"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pais           object\n",
              "Continente     object\n",
              "ano             int64\n",
              "Exp. vida     float64\n",
              "Populacao       int64\n",
              "PIB           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "th31rTBykhIq",
        "outputId": "ac981ef9-9e85-411a-de11-3cfef4ad41c1"
      },
      "source": [
        "df.tail(10)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Pais</th>\n",
              "      <th>Continente</th>\n",
              "      <th>ano</th>\n",
              "      <th>Exp. vida</th>\n",
              "      <th>Populacao</th>\n",
              "      <th>PIB</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3302</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1962</td>\n",
              "      <td>52.358</td>\n",
              "      <td>4277736</td>\n",
              "      <td>527.272182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3303</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1967</td>\n",
              "      <td>53.995</td>\n",
              "      <td>4995432</td>\n",
              "      <td>569.795071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3304</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1972</td>\n",
              "      <td>55.635</td>\n",
              "      <td>5861135</td>\n",
              "      <td>799.362176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3305</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1977</td>\n",
              "      <td>57.674</td>\n",
              "      <td>6642107</td>\n",
              "      <td>685.587682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3306</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1982</td>\n",
              "      <td>60.363</td>\n",
              "      <td>7636524</td>\n",
              "      <td>788.855041</td>\n",
              "    </tr>\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": [
              "          Pais Continente   ano  Exp. vida  Populacao         PIB\n",
              "3302  Zimbabwe     Africa  1962     52.358    4277736  527.272182\n",
              "3303  Zimbabwe     Africa  1967     53.995    4995432  569.795071\n",
              "3304  Zimbabwe     Africa  1972     55.635    5861135  799.362176\n",
              "3305  Zimbabwe     Africa  1977     57.674    6642107  685.587682\n",
              "3306  Zimbabwe     Africa  1982     60.363    7636524  788.855041\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"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "_KiLz54Lk4mv",
        "outputId": "e9724cb4-24e6-4d08-8977-94ab15428881"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ano</th>\n",
              "      <th>Exp. vida</th>\n",
              "      <th>Populacao</th>\n",
              "      <th>PIB</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>241.165877</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": [
              "               ano    Exp. vida     Populacao            PIB\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     241.165877\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"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZ8NK2MDk9D0",
        "outputId": "c0a9cae7-4d65-4256-91a7-8d1dcdf51980"
      },
      "source": [
        "df[\"Continente\"].unique()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Asia', 'Europe', 'Africa', 'Americas', nan, 'FSU', 'Oceania'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "ZPcnSpZ7llpy",
        "outputId": "40ff3253-b7e0-4d11-8cb7-f1838ffb4f70"
      },
      "source": [
        "FSU = df.loc[df[\"Continente\"] == \"FSU\"]\n",
        "FSU.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Pais</th>\n",
              "      <th>Continente</th>\n",
              "      <th>ano</th>\n",
              "      <th>Exp. vida</th>\n",
              "      <th>Populacao</th>\n",
              "      <th>PIB</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>FSU</td>\n",
              "      <td>1973</td>\n",
              "      <td>72.71</td>\n",
              "      <td>9236465</td>\n",
              "      <td>4958.608124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>FSU</td>\n",
              "      <td>1990</td>\n",
              "      <td>71.15</td>\n",
              "      <td>10215208</td>\n",
              "      <td>6807.781057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>FSU</td>\n",
              "      <td>1991</td>\n",
              "      <td>70.58</td>\n",
              "      <td>10244639</td>\n",
              "      <td>6693.188400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>FSU</td>\n",
              "      <td>1992</td>\n",
              "      <td>70.17</td>\n",
              "      <td>10306362</td>\n",
              "      <td>6014.406075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>FSU</td>\n",
              "      <td>1993</td>\n",
              "      <td>69.05</td>\n",
              "      <td>10360516</td>\n",
              "      <td>5528.263371</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais Continente   ano  Exp. vida  Populacao          PIB\n",
              "233  Belarus        FSU  1973      72.71    9236465  4958.608124\n",
              "234  Belarus        FSU  1990      71.15   10215208  6807.781057\n",
              "235  Belarus        FSU  1991      70.58   10244639  6693.188400\n",
              "236  Belarus        FSU  1992      70.17   10306362  6014.406075\n",
              "237  Belarus        FSU  1993      69.05   10360516  5528.263371"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVn9LsSfmOi2",
        "outputId": "dfedf1ec-d958-46b0-9200-3e1f9c2b9aad"
      },
      "source": [
        "FSU[\"Pais\"].unique()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Belarus', 'Kazakhstan', 'Latvia', 'Lithuania', 'Russia',\n",
              "       'Ukraine'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i7WzMEUhmdKn",
        "outputId": "ae5a79ca-2983-4393-f31f-059fb77850cb"
      },
      "source": [
        "df.groupby(\"Continente\")[\"Pais\"].nunique()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQv1tJK-nuJ8",
        "outputId": "1b5b4612-a8f2-47bb-d91b-a8fce12e0963"
      },
      "source": [
        "df.groupby(\"ano\")[\"Exp. vida\"].mean()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "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    74.283437\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    76.921563\n",
              "2005    76.718667\n",
              "2006    77.887778\n",
              "2007    67.868557\n",
              "Name: Exp. vida, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6Ik38CqomRz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df95829a-69eb-4acf-b28a-bf3a0d10cd09"
      },
      "source": [
        "df[\"PIB\"].mean()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11317.115804696637"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F90vDNYvcpM3",
        "outputId": "7be59a4f-8466-4a4a-9efa-a1b394a64a98"
      },
      "source": [
        "df[\"PIB\"].sum()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37482287.5451553"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LYNeqH5e-NI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}