Análise_da_Divisão_da_População_por_Sexo.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/Pesquisa Nacional por Amostra de Domicílios.csv - Pesquisa Nacional por Amostra de Domicílios.csv.csv')\n",
        "df"
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              "       UF  Sexo  Idade  Cor  Anos de Estudo   Renda         Altura\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 33183,\n  \"fields\": [\n    {\n      \"column\": \"UF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 11,\n        \"max\": 31,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          11,\n          12,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 13,\n        \"max\": 97,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          39,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anos de Estudo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          12,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Renda\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2574.4748256832295,\n        \"min\": 0.0,\n        \"max\": 200000.0,\n        \"num_unique_values\": 1047,\n        \"samples\": [\n          1892.0,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33179,\n        \"samples\": [\n          \"1.568.525.805\",\n          \"1.649.020.126\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# para conhecer a base:\n",
        "# imprime um resumo conciso do dataframe (a base)\n",
        "# esse método imprime informações sobre o dataframe, incluíndo dtype e colunas do index, valores não nulos e uso de memória.\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9kQwRIOefjM",
        "outputId": "067c1614-8dcf-4cbc-dd40-e2d29cee4d9c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 33183 entries, 0 to 33182\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   UF              33183 non-null  int64  \n",
            " 1   Sexo            33183 non-null  int64  \n",
            " 2   Idade           33183 non-null  int64  \n",
            " 3   Cor             33183 non-null  int64  \n",
            " 4   Anos de Estudo  33183 non-null  int64  \n",
            " 5   Renda           33182 non-null  float64\n",
            " 6   Altura          33182 non-null  object \n",
            "dtypes: float64(1), int64(5), object(1)\n",
            "memory usage: 1.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# trocando os códigos pelos significados:\n",
        "# acessando a coluna sexo do dataframe, eu vou alterar os valores 0 e 1 para masculino e feminino, respectivamente. O inplace=True serve para especificar que a modificação será feita no próprio dataframe, em vez de retornar uma cópia dele com a modificação.\n",
        "df['Sexo'].replace({0:'Masculino', 1: 'Feminino'}, inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "vQaBzOjmei5G",
        "outputId": "a234639f-3d2b-42af-a74d-52144183a778"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       UF       Sexo  Idade  Cor  Anos de Estudo   Renda         Altura\n",
              "0      11  Masculino     23    8              12   800.0  1.603.807.616\n",
              "1      11   Feminino     23    2              12  1150.0  1.739.789.827\n",
              "2      11   Feminino     35    8              15   880.0  1.760.443.822\n",
              "3      11  Masculino     46    2               6  3500.0  1.783.157.945\n",
              "4      11   Feminino     47    8               9   150.0  1.690.630.954\n",
              "...    ..        ...    ...  ...             ...     ...            ...\n",
              "33178  31  Masculino     60    4              12  8000.0  1.649.400.782\n",
              "33179  31  Masculino     33    8              15  2300.0  1.693.094.939\n",
              "33180  31   Feminino     31    8              12  2000.0  1.849.360.141\n",
              "33181  31   Feminino     52    2               9  4000.0  1.785.048.175\n",
              "33182  31  Masculino     36    8               1     NaN            NaN\n",
              "\n",
              "[33183 rows x 7 columns]"
            ],
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              "      <td>800.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>11</td>\n",
              "      <td>Feminino</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1150.0</td>\n",
              "      <td>1.739.789.827</td>\n",
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              "      <th>2</th>\n",
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              "      <td>Feminino</td>\n",
              "      <td>35</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>880.0</td>\n",
              "      <td>1.760.443.822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>46</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>1.783.157.945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>Feminino</td>\n",
              "      <td>47</td>\n",
              "      <td>8</td>\n",
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              "      <td>1.690.630.954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <th>33178</th>\n",
              "      <td>31</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>60</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>8000.0</td>\n",
              "      <td>1.649.400.782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33179</th>\n",
              "      <td>31</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>33</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>2300.0</td>\n",
              "      <td>1.693.094.939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33180</th>\n",
              "      <td>31</td>\n",
              "      <td>Feminino</td>\n",
              "      <td>31</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>1.849.360.141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33181</th>\n",
              "      <td>31</td>\n",
              "      <td>Feminino</td>\n",
              "      <td>52</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>1.785.048.175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33182</th>\n",
              "      <td>31</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>36</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>33183 rows × 7 columns</p>\n",
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              "        const element = document.querySelector('#df-731d4f57-8354-40f3-8550-88f4a1c28149');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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              "\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 33183,\n  \"fields\": [\n    {\n      \"column\": \"UF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 11,\n        \"max\": 31,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          11,\n          12,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Feminino\",\n          \"Masculino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 13,\n        \"max\": 97,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          39,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anos de Estudo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          12,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Renda\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2574.4748256832295,\n        \"min\": 0.0,\n        \"max\": 200000.0,\n        \"num_unique_values\": 1047,\n        \"samples\": [\n          1892.0,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33179,\n        \"samples\": [\n          \"1.568.525.805\",\n          \"1.649.020.126\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['UF'].replace({\n",
        "    11:'Rondônia',\n",
        "    12:'Acre',\n",
        "    13:'Amazonas',\n",
        "    14:'Roraima',\n",
        "    15:'Pará',\n",
        "    16:'Amapá',\n",
        "    17:'Tocatins',\n",
        "    21:'Maranhão',\n",
        "    22:'Piauí',\n",
        "    23:'Ceará',\n",
        "    24:'Rio Grande do Norte',\n",
        "    25:'Paraíba',\n",
        "    26:'Pernambuco',\n",
        "    27:'Alagoas',\n",
        "    28:'Sergipe',\n",
        "    29:'Bahia',\n",
        "    31:'Minas Gerais',\n",
        "    32:'Espírito Santo',\n",
        "    33:'Rio de Janeiro',\n",
        "    35:'São Paulo',\n",
        "    41:'Paraná',\n",
        "    42:'Santa Catarina',\n",
        "    43:'Rio Grande do Sul',\n",
        "    50:'Mato Grosso do Sul',\n",
        "    51:'Mato Grosso',\n",
        "    52:'Goiás',\n",
        "    53:'Distrito Federal'\n",
        "\n",
        "}, inplace=True)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Kr1lv80VelDX",
        "outputId": "0426077c-db2c-486b-cf2f-d684e63939e1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 UF       Sexo  Idade  Cor  Anos de Estudo   Renda  \\\n",
              "0          Rondônia  Masculino     23    8              12   800.0   \n",
              "1          Rondônia   Feminino     23    2              12  1150.0   \n",
              "2          Rondônia   Feminino     35    8              15   880.0   \n",
              "3          Rondônia  Masculino     46    2               6  3500.0   \n",
              "4          Rondônia   Feminino     47    8               9   150.0   \n",
              "...             ...        ...    ...  ...             ...     ...   \n",
              "33178  Minas Gerais  Masculino     60    4              12  8000.0   \n",
              "33179  Minas Gerais  Masculino     33    8              15  2300.0   \n",
              "33180  Minas Gerais   Feminino     31    8              12  2000.0   \n",
              "33181  Minas Gerais   Feminino     52    2               9  4000.0   \n",
              "33182  Minas Gerais  Masculino     36    8               1     NaN   \n",
              "\n",
              "              Altura  \n",
              "0      1.603.807.616  \n",
              "1      1.739.789.827  \n",
              "2      1.760.443.822  \n",
              "3      1.783.157.945  \n",
              "4      1.690.630.954  \n",
              "...              ...  \n",
              "33178  1.649.400.782  \n",
              "33179  1.693.094.939  \n",
              "33180  1.849.360.141  \n",
              "33181  1.785.048.175  \n",
              "33182            NaN  \n",
              "\n",
              "[33183 rows x 7 columns]"
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              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 33183,\n  \"fields\": [\n    {\n      \"column\": \"UF\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Rond\\u00f4nia\",\n          \"Acre\",\n          \"Amap\\u00e1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Feminino\",\n          \"Masculino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 13,\n        \"max\": 97,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          39,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anos de Estudo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          12,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Renda\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2574.4748256832295,\n        \"min\": 0.0,\n        \"max\": 200000.0,\n        \"num_unique_values\": 1047,\n        \"samples\": [\n          1892.0,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33179,\n        \"samples\": [\n          \"1.568.525.805\",\n          \"1.649.020.126\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "anos_de_estudo = {\n",
        "    1:'Sem instrução e menos de 1 ano',\n",
        "    2:'1 ano',\n",
        "    3:'2 anos',\n",
        "    4:'3 anos',\n",
        "    5:'4 anos',\n",
        "    6:'5 anos',\n",
        "    7:'6 anos',\n",
        "    8:'7 anos',\n",
        "    9:'8 anos',\n",
        "    10:'9 anos',\n",
        "    11:'10 anos',\n",
        "    12:'11 anos',\n",
        "    13:'12 anos',\n",
        "    14:'13 anos',\n",
        "    15:'14 anos',\n",
        "    16:'15 anos ou mais',\n",
        "    17:'Não determinados',\n",
        "\n",
        "}\n",
        "\n",
        "df['Anos de Estudo'].replace(anos_de_estudo, inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "erJH5yjAenxv",
        "outputId": "3739fbee-bc10-4aa2-e91c-c0e51a32fbda"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 UF       Sexo  Idade  Cor                  Anos de Estudo  \\\n",
              "0          Rondônia  Masculino     23    8                         11 anos   \n",
              "1          Rondônia   Feminino     23    2                         11 anos   \n",
              "2          Rondônia   Feminino     35    8                         14 anos   \n",
              "3          Rondônia  Masculino     46    2                          5 anos   \n",
              "4          Rondônia   Feminino     47    8                          8 anos   \n",
              "...             ...        ...    ...  ...                             ...   \n",
              "33178  Minas Gerais  Masculino     60    4                         11 anos   \n",
              "33179  Minas Gerais  Masculino     33    8                         14 anos   \n",
              "33180  Minas Gerais   Feminino     31    8                         11 anos   \n",
              "33181  Minas Gerais   Feminino     52    2                          8 anos   \n",
              "33182  Minas Gerais  Masculino     36    8  Sem instrução e menos de 1 ano   \n",
              "\n",
              "        Renda         Altura  \n",
              "0       800.0  1.603.807.616  \n",
              "1      1150.0  1.739.789.827  \n",
              "2       880.0  1.760.443.822  \n",
              "3      3500.0  1.783.157.945  \n",
              "4       150.0  1.690.630.954  \n",
              "...       ...            ...  \n",
              "33178  8000.0  1.649.400.782  \n",
              "33179  2300.0  1.693.094.939  \n",
              "33180  2000.0  1.849.360.141  \n",
              "33181  4000.0  1.785.048.175  \n",
              "33182     NaN            NaN  \n",
              "\n",
              "[33183 rows x 7 columns]"
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              "      <td>14 anos</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 33183,\n  \"fields\": [\n    {\n      \"column\": \"UF\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Rond\\u00f4nia\",\n          \"Acre\",\n          \"Amap\\u00e1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Feminino\",\n          \"Masculino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 13,\n        \"max\": 97,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          39,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anos de Estudo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"11 anos\",\n          \"14 anos\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Renda\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2574.4748256832295,\n        \"min\": 0.0,\n        \"max\": 200000.0,\n        \"num_unique_values\": 1047,\n        \"samples\": [\n          1892.0,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33179,\n        \"samples\": [\n          \"1.568.525.805\",\n          \"1.649.020.126\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cor = {\n",
        "    0:'Indígena',\n",
        "    2:'Branca',\n",
        "    4:'Preta',\n",
        "    6:'Amarela',\n",
        "    8:'Parda',\n",
        "    9:'Sem declaração'\n",
        "}\n",
        "\n",
        "df['Cor'].replace(cor, inplace=True)\n",
        "df\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "wNAIgxAQeq7d",
        "outputId": "4f9db07c-c4a0-4590-caaa-8ac0db16e6d7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 UF       Sexo  Idade     Cor                  Anos de Estudo  \\\n",
              "0          Rondônia  Masculino     23   Parda                         11 anos   \n",
              "1          Rondônia   Feminino     23  Branca                         11 anos   \n",
              "2          Rondônia   Feminino     35   Parda                         14 anos   \n",
              "3          Rondônia  Masculino     46  Branca                          5 anos   \n",
              "4          Rondônia   Feminino     47   Parda                          8 anos   \n",
              "...             ...        ...    ...     ...                             ...   \n",
              "33178  Minas Gerais  Masculino     60   Preta                         11 anos   \n",
              "33179  Minas Gerais  Masculino     33   Parda                         14 anos   \n",
              "33180  Minas Gerais   Feminino     31   Parda                         11 anos   \n",
              "33181  Minas Gerais   Feminino     52  Branca                          8 anos   \n",
              "33182  Minas Gerais  Masculino     36   Parda  Sem instrução e menos de 1 ano   \n",
              "\n",
              "        Renda         Altura  \n",
              "0       800.0  1.603.807.616  \n",
              "1      1150.0  1.739.789.827  \n",
              "2       880.0  1.760.443.822  \n",
              "3      3500.0  1.783.157.945  \n",
              "4       150.0  1.690.630.954  \n",
              "...       ...            ...  \n",
              "33178  8000.0  1.649.400.782  \n",
              "33179  2300.0  1.693.094.939  \n",
              "33180  2000.0  1.849.360.141  \n",
              "33181  4000.0  1.785.048.175  \n",
              "33182     NaN            NaN  \n",
              "\n",
              "[33183 rows x 7 columns]"
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              "      <td>46</td>\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d7683f1c-630e-48d0-96b4-7aad8be3e7c5\">\n",
              "    <style>\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 33183,\n  \"fields\": [\n    {\n      \"column\": \"UF\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Rond\\u00f4nia\",\n          \"Acre\",\n          \"Amap\\u00e1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Feminino\",\n          \"Masculino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 13,\n        \"max\": 97,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          39,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cor\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Branca\",\n          \"Ind\\u00edgena\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anos de Estudo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"11 anos\",\n          \"14 anos\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Renda\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2574.4748256832295,\n        \"min\": 0.0,\n        \"max\": 200000.0,\n        \"num_unique_values\": 1047,\n        \"samples\": [\n          1892.0,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33179,\n        \"samples\": [\n          \"1.568.525.805\",\n          \"1.649.020.126\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-UW4Bq68eth5",
        "outputId": "0e278683-831a-4d65-8754-ade424d79ed7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 33183 entries, 0 to 33182\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   UF              33183 non-null  object \n",
            " 1   Sexo            33183 non-null  object \n",
            " 2   Idade           33183 non-null  int64  \n",
            " 3   Cor             33183 non-null  object \n",
            " 4   Anos de Estudo  33183 non-null  object \n",
            " 5   Renda           33182 non-null  float64\n",
            " 6   Altura          33182 non-null  object \n",
            "dtypes: float64(1), int64(1), object(5)\n",
            "memory usage: 1.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remover os pontos de separação de milhares e converter para números inteiros\n",
        "df['Altura'] = df['Altura'].str.replace('.', '').astype(float).astype(pd.Int64Dtype())\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yb3amQZbeyng",
        "outputId": "139d9778-83d9-4468-ce73-ec25a2d7e151"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 UF       Sexo  Idade     Cor                  Anos de Estudo  \\\n",
            "0          Rondônia  Masculino     23   Parda                         11 anos   \n",
            "1          Rondônia   Feminino     23  Branca                         11 anos   \n",
            "2          Rondônia   Feminino     35   Parda                         14 anos   \n",
            "3          Rondônia  Masculino     46  Branca                          5 anos   \n",
            "4          Rondônia   Feminino     47   Parda                          8 anos   \n",
            "...             ...        ...    ...     ...                             ...   \n",
            "33178  Minas Gerais  Masculino     60   Preta                         11 anos   \n",
            "33179  Minas Gerais  Masculino     33   Parda                         14 anos   \n",
            "33180  Minas Gerais   Feminino     31   Parda                         11 anos   \n",
            "33181  Minas Gerais   Feminino     52  Branca                          8 anos   \n",
            "33182  Minas Gerais  Masculino     36   Parda  Sem instrução e menos de 1 ano   \n",
            "\n",
            "        Renda      Altura  \n",
            "0       800.0  1603807616  \n",
            "1      1150.0  1739789827  \n",
            "2       880.0  1760443822  \n",
            "3      3500.0  1783157945  \n",
            "4       150.0  1690630954  \n",
            "...       ...         ...  \n",
            "33178  8000.0  1649400782  \n",
            "33179  2300.0  1693094939  \n",
            "33180  2000.0  1849360141  \n",
            "33181  4000.0  1785048175  \n",
            "33182     NaN        <NA>  \n",
            "\n",
            "[33183 rows x 7 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kXa1n3UNe5x6",
        "outputId": "62b90d23-76f2-46f3-a815-6ec951221201"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 33183 entries, 0 to 33182\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   UF              33183 non-null  object \n",
            " 1   Sexo            33183 non-null  object \n",
            " 2   Idade           33183 non-null  int64  \n",
            " 3   Cor             33183 non-null  object \n",
            " 4   Anos de Estudo  33183 non-null  object \n",
            " 5   Renda           33182 non-null  float64\n",
            " 6   Altura          33182 non-null  Int64  \n",
            "dtypes: Int64(1), float64(1), int64(1), object(4)\n",
            "memory usage: 1.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# frequencia absoluta de sexo:\n",
        "\n",
        "frequencia_absoluta_sexo = df['Sexo'].value_counts()\n",
        "frequencia_absoluta_sexo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "krlLhOkqfAE8",
        "outputId": "b99775cc-1848-43cf-eef6-7fbed7630d4d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sexo\n",
              "Masculino    22847\n",
              "Feminino     10336\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotar a frequencia absoluta de sexo:\n",
        "\n",
        "frequencia_absoluta_sexo.plot(kind='barh', title='Frequencia Absoluta de Sexo', xlabel='Sexo', ylabel='Frequencia Absoluta')\n",
        "# esse 'barh' quer dizer que o plot será na horizontal"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "YSRxksO4fcn6",
        "outputId": "e13d1a1c-cfc8-4188-a3fe-46350c4b4739"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Frequencia Absoluta de Sexo'}, xlabel='Sexo', ylabel='Frequencia Absoluta'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# esse plot com o kind='bar' vai plotar na vertical.\n",
        "\n",
        "frequencia_absoluta_sexo.plot(kind='bar', title='Frequencia Absoluta de Sexo', xlabel='Sexo', ylabel='Frequencia Absoluta')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "yUMp7ISWgw-Z",
        "outputId": "293f8267-358c-49b0-f6bb-ea7355e98bac"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Frequencia Absoluta de Sexo'}, xlabel='Sexo', ylabel='Frequencia Absoluta'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# criar figura e eixo\n",
        "# figura, eixo = plt.subplots()\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# dados para o gráfico de barras:\n",
        "sexo = ['Masculino', 'Feminino'] # categorias de sexo\n",
        "counts = [frequencia_absoluta_sexo[0], frequencia_absoluta_sexo[1]] # frequência absoluta de cada categoria\n",
        "bar_labels = ['M', 'F'] # rótulos para as barras\n",
        "bar_colors = ['blue', 'brown'] # cor das barras\n",
        "\n",
        "# plotar o gráfico de barras no eixo (subplot)\n",
        "ax.bar(sexo, counts, label=bar_labels, color=bar_colors)\n",
        "# configurar o rótulo do eixo y (eixo das frequências)\n",
        "ax.set_ylabel('Frequencia sexo')\n",
        "# configurar título do gráfico\n",
        "ax.set_title('Divisão da população por sexo')\n",
        "#adicionar legenda ao gráfico\n",
        "ax.legend(title='sexo')\n",
        "# exibir o gráfico\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "HdBIWSNchKs4",
        "outputId": "df0f7f71-822c-405c-8cd2-dabcd8524bad"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "moKZndg2hygJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}