pietra_exs_aula03.ipynb

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      "source": [
        "# Importando o DataSet"
      ],
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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
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      "source": [
        "# Carregar o DataFrame Titanic\n",
        "df_titanic = sns.load_dataset('titanic')"
      ],
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        "> Como visto na aula anterior, uma das etapas mais importantes do **Pré-Processamento** é o que chamamos de **Imputação.**\n",
        "\n",
        "> Nela, tratamos valores vazios e removemos dados indesejados."
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "- Agora, a partir do DataSet titânic, faça a imputação dos a dados a partir das exigências!"
      ],
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      "source": [
        "## Imputação"
      ],
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        "Decisões em cima dos dados vazios:\n",
        "- **Média**\n",
        "  - Desvio Padrão\n",
        "- **Moda**\n",
        "- **Mediana**\n",
        "- **Remoção Total:** Remoção de todos os valores faltantes, importante para colunas inuteis."
      ],
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        "Funções usadas:\n",
        "- `.fillna()` : preenche os valores vazios com algum valor.\n",
        "    - `.fillna(df['exemplo'].mean(), inplace=True)`\n",
        "- `.replace()` : substitui um valor por outro.\n",
        "    - `df['floor'].replace('-', np.nan)`\n",
        "- `.dropna()` : remove linhas que contem valores vazios.\n",
        "    - `dropna(subset=['exemplo'], inplace=True)`\n",
        "- `drop()` : remove uma coluna.\n",
        "    - `.drop(columns=['exemplo_1', 'exemplo_2', ..., 'exemplo_n'], inplace=True)`"
      ],
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        "id": "eVeUJOIoPd2k"
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    {
      "cell_type": "markdown",
      "source": [
        "obs: o uso do `inplace=True`, faz com que os dados alterados, sejam alterados no DataFrame príncipal."
      ],
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        "df_titanic.head()"
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              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
              "3  woman       False    C  Southampton   yes  False  \n",
              "4    man        True  NaN  Southampton    no   True  "
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              "summary": "{\n  \"name\": \"df_titanic\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\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\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_titanic.shape"
      ],
      "metadata": {
        "id": "KBAPM4asJ1pV",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cad8c604-c940-4c09-fdf0-9a576b3f3923"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_titanic.describe()"
      ],
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        "id": "33216dmSKOEU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "a541ca0d-56f3-4e22-d781-ad6c798c077e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         survived      pclass         age       sibsp       parch        fare\n",
              "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
              "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
              "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
              "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
              "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
              "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
              "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
              "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
            ],
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              "      <th>count</th>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_titanic\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.8713661874558,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3838383838383838,\n          1.0,\n          0.4865924542648575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.2523437079694,\n        \"min\": 0.836071240977049,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          2.308641975308642,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 242.9056731818781,\n        \"min\": 0.42,\n        \"max\": 714.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.69911764705882,\n          28.0,\n          714.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.4908277465442,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          0.5230078563411896,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.65971717879,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38159371492704824,\n          6.0,\n          0.8060572211299483\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228578,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.204207968574636,\n          14.4542,\n          891.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Imputação da coluna 'age' com a mediana"
      ],
      "metadata": {
        "id": "cC9NwOXECm11"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# mostrando valores ausentes antes da imputacao\n",
        "print(\"Valores ausentes antes da imputação: \", df_titanic['age'].isnull().sum())"
      ],
      "metadata": {
        "id": "DJyrss4wVF-8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "708cbeb9-3389-4021-84df-e335c39b4bbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valores ausentes antes da imputação:  177\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# printando a coluna 'age' para mostrar valores ausentes\n",
        "print(df_titanic[['age']].tail(15))"
      ],
      "metadata": {
        "id": "qe8HCcMKApfy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdd733b8-0364-42dc-b0ef-06a52520c4a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      age\n",
            "876  20.0\n",
            "877  19.0\n",
            "878   NaN\n",
            "879  56.0\n",
            "880  25.0\n",
            "881  33.0\n",
            "882  22.0\n",
            "883  28.0\n",
            "884  25.0\n",
            "885  39.0\n",
            "886  27.0\n",
            "887  19.0\n",
            "888   NaN\n",
            "889  26.0\n",
            "890  32.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculando a mediana\n",
        "mediana_idade = df_titanic['age'].median()\n",
        "print(\"Mediana da coluna 'age': \", mediana_idade)"
      ],
      "metadata": {
        "id": "kOAVQdzOBSu6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c43ba636-3852-4cb5-81fd-e1ab91c38dd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mediana da coluna 'age':  28.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# imputação dos valores ausentes\n",
        "df_titanic['age'].fillna(mediana_idade, inplace=True)"
      ],
      "metadata": {
        "id": "Ozo-jOUpIWrF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#conferindo os valores ausentes\n",
        "print(\"Valores ausentes apos imputacao: \", df_titanic['age'].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZOOyXjxBaAx",
        "outputId": "09330323-5e47-452d-cdc4-4042a9dac083"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valores ausentes apos imputacao:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dataframe com os valores depois da imputação\n",
        "print(df_titanic[['age']].tail(15))"
      ],
      "metadata": {
        "id": "vld7bVdTy_GF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1df1845-04cd-42ff-a204-5342ecae0869"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      age\n",
            "876  20.0\n",
            "877  19.0\n",
            "878  28.0\n",
            "879  56.0\n",
            "880  25.0\n",
            "881  33.0\n",
            "882  22.0\n",
            "883  28.0\n",
            "884  25.0\n",
            "885  39.0\n",
            "886  27.0\n",
            "887  19.0\n",
            "888  28.0\n",
            "889  26.0\n",
            "890  32.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Decisão de remover valores faltantes na coluna 'embark_town'"
      ],
      "metadata": {
        "id": "T3vZyMx2HeZV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# printando valores da coluna 'embark_town'\n",
        "print(df_titanic[['embark_town']].head())"
      ],
      "metadata": {
        "id": "ShnXM5G-y9XV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1af7ae8d-bd5b-43b2-f660-f52711196186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   embark_town\n",
            "0  Southampton\n",
            "1    Cherbourg\n",
            "2  Southampton\n",
            "3  Southampton\n",
            "4  Southampton\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# printando quantidade de valores faltantes da coluna 'embark_town'\n",
        "print(\"quantidade de valores faltantes: \", df_titanic['embark_town'].isnull().sum())"
      ],
      "metadata": {
        "id": "ZTFMOcHQADGy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "472b74d3-7de9-483b-f990-cdf4cb333719"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "quantidade de valores faltantes:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop das linhas que possuem valores faltantes da coluna 'embark_town' e print para conferir os valores faltantes\n",
        "df_titanic.dropna(subset=['embark_town'], inplace=True)"
      ],
      "metadata": {
        "id": "bEtHdqjL3yF9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"quantidade de valores faltantes: \", df_titanic['embark_town'].isnull().sum())"
      ],
      "metadata": {
        "id": "H2EFhWwxISyF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef65ee29-9d8c-4f07-e60d-4cc116040668"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "quantidade de valores faltantes:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Remover colunas 'Alive', 'Class', 'embarked', 'deck', 'alive', 'adult_male' e 'embark_town'"
      ],
      "metadata": {
        "id": "6j-cVRGvHocl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# drop das colunas selecionadas\n",
        "df_titanic.drop(columns=['alive', 'class', 'embarked', 'deck', 'adult_male', 'embark_town'], inplace=True)"
      ],
      "metadata": {
        "id": "5zxXBThq_wHr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# mostrando o dataframe apos retirada das colunas\n",
        "df_titanic.head()"
      ],
      "metadata": {
        "id": "nSka7BKd_wZ7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "208c17b4-c499-47f7-8bb4-0723f20d80f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch     fare    who  alone\n",
              "0         0       3    male  22.0      1      0   7.2500    man  False\n",
              "1         1       1  female  38.0      1      0  71.2833  woman  False\n",
              "2         1       3  female  26.0      0      0   7.9250  woman   True\n",
              "3         1       1  female  35.0      1      0  53.1000  woman  False\n",
              "4         0       3    male  35.0      0      0   8.0500    man   True"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_titanic",
              "summary": "{\n  \"name\": \"df_titanic\",\n  \"rows\": 889,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\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\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.984932293690775,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.697504316707956,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- A decisão de remover estas colunas, se da pelas redundancias delas."
      ],
      "metadata": {
        "id": "kcHDP0ivQ9bw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificação"
      ],
      "metadata": {
        "id": "EaDmk2cuXMW6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Verifique novamente os valores ausentes\n",
        "df_titanic.isnull().sum()"
      ],
      "metadata": {
        "id": "_jJsRthCaAub",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "7da5d5f9-ef12-4f8c-b7ce-83336539c564"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived    0\n",
              "pclass      0\n",
              "sex         0\n",
              "age         0\n",
              "sibsp       0\n",
              "parch       0\n",
              "fare        0\n",
              "who         0\n",
              "alone       0\n",
              "dtype: int64"
            ],
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              "</div><br><label><b>dtype:</b> int64</label>"
            ]
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          "metadata": {},
          "execution_count": 20
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    {
      "cell_type": "code",
      "source": [
        "# Me de o resumo do DataFrame\n",
        "df_titanic.info()"
      ],
      "metadata": {
        "id": "YlZTICI8bwG2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce27026e-98a4-488e-f93f-5b5d4009951d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 889 entries, 0 to 890\n",
            "Data columns (total 9 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   survived  889 non-null    int64  \n",
            " 1   pclass    889 non-null    int64  \n",
            " 2   sex       889 non-null    object \n",
            " 3   age       889 non-null    float64\n",
            " 4   sibsp     889 non-null    int64  \n",
            " 5   parch     889 non-null    int64  \n",
            " 6   fare      889 non-null    float64\n",
            " 7   who       889 non-null    object \n",
            " 8   alone     889 non-null    bool   \n",
            "dtypes: bool(1), float64(2), int64(4), object(2)\n",
            "memory usage: 63.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_titanic.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZBEb20NJVid",
        "outputId": "d8fba7cd-d1aa-4898-b2d4-bedff856c9a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(889, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_titanic.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ScMO0AiKJfEE",
        "outputId": "4c35b618-22ce-404b-8121-a05858783bb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         survived      pclass         age       sibsp       parch        fare\n",
              "count  889.000000  889.000000  889.000000  889.000000  889.000000  889.000000\n",
              "mean     0.382452    2.311586   29.315152    0.524184    0.382452   32.096681\n",
              "std      0.486260    0.834700   12.984932    1.103705    0.806761   49.697504\n",
              "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
              "25%      0.000000    2.000000   22.000000    0.000000    0.000000    7.895800\n",
              "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
              "75%      1.000000    3.000000   35.000000    1.000000    0.000000   31.000000\n",
              "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
            ],
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              "      <th>mean</th>\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_titanic\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.16434684821394,\n        \"min\": 0.0,\n        \"max\": 889.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38245219347581555,\n          1.0,\n          0.48625968831477334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 313.54516122224857,\n        \"min\": 0.8346997785705754,\n        \"max\": 889.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          889.0,\n          2.3115860517435323,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 304.6995698629917,\n        \"min\": 0.42,\n        \"max\": 889.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.315151856017994,\n          28.0,\n          889.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 313.7836380422493,\n        \"min\": 0.0,\n        \"max\": 889.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          889.0,\n          0.5241844769403825,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 313.9525462229868,\n        \"min\": 0.0,\n        \"max\": 889.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38245219347581555,\n          6.0,\n          0.8067607445174777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.0304547982434,\n        \"min\": 0.0,\n        \"max\": 889.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.09668087739032,\n          14.4542,\n          889.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize novamente a cabeça do DataFrame\n",
        "df_titanic.head()"
      ],
      "metadata": {
        "id": "uPOp819RaED3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "4946f535-2cc2-4495-da19-f1b843e2ca88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch     fare    who  alone\n",
              "0         0       3    male  22.0      1      0   7.2500    man  False\n",
              "1         1       1  female  38.0      1      0  71.2833  woman  False\n",
              "2         1       3  female  26.0      0      0   7.9250  woman   True\n",
              "3         1       1  female  35.0      1      0  53.1000  woman  False\n",
              "4         0       3    male  35.0      0      0   8.0500    man   True"
            ],
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              "      const buttonEl =\n",
              "        document.querySelector('#df-5f7205ba-1333-4dfb-9557-8d030d28ea0d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5f7205ba-1333-4dfb-9557-8d030d28ea0d');\n",
              "        const dataTable =\n",
              "          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",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_titanic",
              "summary": "{\n  \"name\": \"df_titanic\",\n  \"rows\": 889,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\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\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.984932293690775,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.697504316707956,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    }
  ]
}