Wrangling_Montoya.ipynb

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      "name": "Wrangling_Montoya.ipynb",
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        "<a href=\"https://colab.research.google.com/github/jeremontoya155/Wrangling_Montoya/blob/main/Wrangling_Montoya.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "Consigna: Leer los set de datos w_data.csv y p_data.csv, fusionarlos a partir de la variable que tengan en común (ID)y realizar las siguientes ejercicios:\n",
        "\n",
        "1)Cuantos ID tienen en común. Nombrar cuales no tienen en común\n",
        "\n",
        "2)Corregir ‘Hours per week’ a solo dos valores distintos ‘>=40’ y ‘<40’\n",
        "\n",
        "3)Calcular la proporción que hay entre personas con salarios >50k respecto del total para \n",
        "Entre personas de distinto sexo, Raza y Educacion. Por ejemplo cuál es el porcentaje de mujeres con un salario mayor a 50K y compararlo con el de los hombres. Lo mismo para personas de distintas razas y Educación\n"
      ],
      "metadata": {
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    {
      "cell_type": "code",
      "source": [
        "#Importamos la libreias a utilizar\n",
        "import pandas as pd \n",
        "\n"
      ],
      "metadata": {
        "id": "SRY6pqqCXie6"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Leemos los dos csv  de manera directa\n",
        "#debemos agreagar la opcion del separador para  poder hacer de manera correcta la accion\n",
        "df_p=pd.read_csv(\"/content/p_data.csv\",sep=\";\")\n",
        "df_w=pd.read_csv(\"/content/w_data.csv\",sep=\";\")\n"
      ],
      "metadata": {
        "id": "BuWHNpQFXlSq"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Mostamos los primeros  5 elementos de cada uno\n",
        "df_p.head()"
      ],
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          "height": 406
        },
        "id": "AJXBNKBvYR3S",
        "outputId": "2a90d447-e1e8-4135-d02b-1dcfef030567"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Age      Sex      ID    Race         Country   Education  Education-Num  \\\n",
              "0  39.0     Male   49084   White   United-States   Bachelors           13.0   \n",
              "1  50.0     Male   68553   White   United-States   Bachelors           13.0   \n",
              "2  38.0     Male  143654   White   United-States     HS-grad            9.0   \n",
              "3  53.0     Male   41510   Black   United-States        11th            7.0   \n",
              "4  28.0   Female  195025   Black            Cuba   Bachelors           13.0   \n",
              "\n",
              "        Marital Status    Relationship  \n",
              "0        Never-married   Not-in-family  \n",
              "1   Married-civ-spouse         Husband  \n",
              "2             Divorced   Not-in-family  \n",
              "3   Married-civ-spouse         Husband  \n",
              "4   Married-civ-spouse            Wife  "
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              "      <td>53.0</td>\n",
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              "      <td>28.0</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "df_w.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "3VzUMuGAYZ-6",
        "outputId": "518f24e3-1f39-4596-d3f1-3718f35d9e4e"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Workclass    fnlwgt  Capital Gain  Capital Loss  Hours per week  \\\n",
              "0          State-gov   77516.0        2174.0           0.0            40.0   \n",
              "1   Self-emp-not-inc   83311.0           0.0           0.0            13.0   \n",
              "2            Private  215646.0           0.0           0.0            40.0   \n",
              "3            Private  234721.0           0.0           0.0            40.0   \n",
              "4            Private  338409.0           0.0           0.0            40.0   \n",
              "\n",
              "       ID  Target  \n",
              "0   49084   <=50K  \n",
              "1   68553   <=50K  \n",
              "2  143654   <=50K  \n",
              "3   41510   <=50K  \n",
              "4  195025   <=50K  "
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              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>68553</td>\n",
              "      <td>&lt;=50K</td>\n",
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              "      <th>2</th>\n",
              "      <td>Private</td>\n",
              "      <td>215646.0</td>\n",
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              "\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "#Hacemos tambien la descripcion para cada dataFrame\n",
        "df_w.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "_MuLISXkYhet",
        "outputId": "9aca00f7-1ef6-4297-8f51-ee91ad3bc065"
      },
      "execution_count": 5,
      "outputs": [
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          "data": {
            "text/plain": [
              "             fnlwgt  Capital Gain  Capital Loss  Hours per week            ID\n",
              "count  4.884200e+04  48842.000000  48842.000000    48842.000000  4.884300e+04\n",
              "mean   1.896641e+05   1079.067626     87.502314       40.422382  6.775293e+08\n",
              "std    1.056040e+05   7452.019058    403.004552       12.391444  6.496383e+08\n",
              "min    1.228500e+04      0.000000      0.000000        1.000000  3.080000e+03\n",
              "25%    1.175505e+05      0.000000      0.000000       40.000000  1.679978e+08\n",
              "50%    1.781445e+05      0.000000      0.000000       40.000000  4.714461e+08\n",
              "75%    2.376420e+05      0.000000      0.000000       45.000000  1.002107e+09\n",
              "max    1.490400e+06  99999.000000   4356.000000       99.000000  3.229272e+09"
            ],
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              "\n",
              "  <div id=\"df-691739d4-ad92-44dc-bf12-97be03d6d8e4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>Capital Gain</th>\n",
              "      <th>Capital Loss</th>\n",
              "      <th>Hours per week</th>\n",
              "      <th>ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.884200e+04</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>4.884300e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.896641e+05</td>\n",
              "      <td>1079.067626</td>\n",
              "      <td>87.502314</td>\n",
              "      <td>40.422382</td>\n",
              "      <td>6.775293e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.056040e+05</td>\n",
              "      <td>7452.019058</td>\n",
              "      <td>403.004552</td>\n",
              "      <td>12.391444</td>\n",
              "      <td>6.496383e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.228500e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.080000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.175505e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>1.679978e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.781445e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>4.714461e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.376420e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.002107e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.490400e+06</td>\n",
              "      <td>99999.000000</td>\n",
              "      <td>4356.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>3.229272e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "  </style>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-691739d4-ad92-44dc-bf12-97be03d6d8e4');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_p.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "l0B1l20KZWyu",
        "outputId": "31ae3b73-f929-4dc4-b9dc-6ee30a8ca42c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Age            ID  Education-Num\n",
              "count  48841.00000  4.884200e+04   48841.000000\n",
              "mean      38.64366  6.775156e+08      10.078029\n",
              "std       13.71064  6.496379e+08       2.570965\n",
              "min       17.00000  3.080000e+03       1.000000\n",
              "25%       28.00000  1.679894e+08       9.000000\n",
              "50%       37.00000  4.714275e+08      10.000000\n",
              "75%       48.00000  1.002009e+09      12.000000\n",
              "max       90.00000  3.229272e+09      16.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9c72d957-dd8d-4670-8f10-7c64e134624f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "\n",
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              "\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>Age</th>\n",
              "      <th>ID</th>\n",
              "      <th>Education-Num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>48841.00000</td>\n",
              "      <td>4.884200e+04</td>\n",
              "      <td>48841.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.64366</td>\n",
              "      <td>6.775156e+08</td>\n",
              "      <td>10.078029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.71064</td>\n",
              "      <td>6.496379e+08</td>\n",
              "      <td>2.570965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.00000</td>\n",
              "      <td>3.080000e+03</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>28.00000</td>\n",
              "      <td>1.679894e+08</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.00000</td>\n",
              "      <td>4.714275e+08</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>48.00000</td>\n",
              "      <td>1.002009e+09</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.00000</td>\n",
              "      <td>3.229272e+09</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c72d957-dd8d-4670-8f10-7c64e134624f')\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9c72d957-dd8d-4670-8f10-7c64e134624f 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-9c72d957-dd8d-4670-8f10-7c64e134624f');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Unimos  los dos dataFrames\n",
        "inner_df=pd.merge(df_w,df_p,on=\"ID\",how=\"inner\")"
      ],
      "metadata": {
        "id": "AKr_YtQAZZyE"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Actividad Uno \n",
        "#Verifico previo que no sean iguales los elementos\n",
        "df_w[\"ID\"].equals(df_p[\"ID\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6sJFdD4vrUf",
        "outputId": "a3f8ae94-0f93-4791-ff76-73f3a49cd2bc"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Forma uno de muestra\n",
        "lista=[]\n",
        "ban=True\n",
        "\n",
        "listaW=list(df_w[\"ID\"])\n",
        "listaP=list(df_p[\"ID\"])\n",
        "\n",
        "for e in listaW:\n",
        "  if (e not in listaP):\n",
        "    lista.append(e)\n",
        "\n",
        "\n",
        "print(len(lista))\n",
        "print(len(listaW))\n",
        "print(len(listaP))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-XZKLr1wk0h",
        "outputId": "4d9ac97d-1bb5-4e9b-90f3-be07b597da8b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "48843\n",
            "48842\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Caso con apply\n",
        "def casoUno(x):\n",
        "  if x not in df_w[\"ID\"]:\n",
        "    return x\n",
        "\n",
        "df_p[\"ID\"].apply(casoUno)    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PhyXZWmAHEKS",
        "outputId": "1ee81a25-822b-40ad-c517-912f1187fec8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        4.908400e+04\n",
              "1        6.855300e+04\n",
              "2        1.436540e+05\n",
              "3                 NaN\n",
              "4        1.950250e+05\n",
              "             ...     \n",
              "48837    5.971868e+08\n",
              "48838    7.356172e+08\n",
              "48839    1.131807e+09\n",
              "48840    7.814221e+08\n",
              "48841    1.617713e+08\n",
              "Name: ID, Length: 48842, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Actividad dos\n",
        "def CasoDos(x):\n",
        "  if x>40:\n",
        "    return \">40\"\n",
        "  else:\n",
        "    return \"=<40\"\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Sid4Oq16yoIW"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Cambiamos los valores\n",
        "columna=inner_df[\"Hours per week\"].apply(CasoDos)\n",
        "inner_df[\"Horas\"]=columna\n",
        "inner_df.drop([\"Hours per week\"],axis=1)\n",
        "inner_df.rename(columns={'Horas':'Hours per week'},inplace=True)\n",
        "#Mostramos los valores con los cambios\n",
        "inner_df.rename(columns={'Horas':'Hours per week'},inplace=True)\n",
        "inner_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "LV0EsQdxL1pF",
        "outputId": "3a899ec5-687e-433f-b2d3-571cd8d85a01"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Workclass    fnlwgt  Capital Gain  Capital Loss Hours per week  \\\n",
              "0          State-gov   77516.0        2174.0           0.0           40.0   \n",
              "1   Self-emp-not-inc   83311.0           0.0           0.0           13.0   \n",
              "2            Private  215646.0           0.0           0.0           40.0   \n",
              "3            Private  234721.0           0.0           0.0           40.0   \n",
              "4            Private  338409.0           0.0           0.0           40.0   \n",
              "\n",
              "       ID  Target   Age      Sex    Race         Country   Education  \\\n",
              "0   49084   <=50K  39.0     Male   White   United-States   Bachelors   \n",
              "1   68553   <=50K  50.0     Male   White   United-States   Bachelors   \n",
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          },
          "metadata": {},
          "execution_count": 30
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mostramos el df con los cambios\n",
        "inner_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "b2GWwcX_8dEB",
        "outputId": "0a65be9c-8d55-4483-ab69-b00e616d3ad5"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "               Workclass    fnlwgt  Capital Gain  Capital Loss Hours per week  \\\n",
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              "...                  ...       ...           ...           ...            ...   \n",
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              "              Country   Education  Education-Num       Marital Status  \\\n",
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              "48841   United-States   Bachelors           13.0             Divorced   \n",
              "\n",
              "          Relationship Hours per week  \n",
              "0        Not-in-family           =<40  \n",
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              "2        Not-in-family           =<40  \n",
              "3              Husband           =<40  \n",
              "4                 Wife           =<40  \n",
              "...                ...            ...  \n",
              "48837        Own-child           =<40  \n",
              "48838    Not-in-family           =<40  \n",
              "48839   Other-relative           =<40  \n",
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              "      <td>Bachelors</td>\n",
              "      <td>13.0</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>=&lt;40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48839</th>\n",
              "      <td>?</td>\n",
              "      <td>321403.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1131807446</td>\n",
              "      <td>&lt;=50K.</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Black</td>\n",
              "      <td>United-States</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Other-relative</td>\n",
              "      <td>=&lt;40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48840</th>\n",
              "      <td>Private</td>\n",
              "      <td>374983.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>781422087</td>\n",
              "      <td>&lt;=50K.</td>\n",
              "      <td>38.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>White</td>\n",
              "      <td>United-States</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13.0</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Husband</td>\n",
              "      <td>&gt;40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48841</th>\n",
              "      <td>Private</td>\n",
              "      <td>83891.0</td>\n",
              "      <td>5455.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>161771296</td>\n",
              "      <td>&lt;=50K.</td>\n",
              "      <td>44.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Asian-Pac-Islander</td>\n",
              "      <td>United-States</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13.0</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>=&lt;40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>48842 rows × 16 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8929844-6bc3-45f6-9947-0ba9a4852f9e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      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-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e8929844-6bc3-45f6-9947-0ba9a4852f9e 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-e8929844-6bc3-45f6-9947-0ba9a4852f9e');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Veo el tipo de dato para los valores que vamos a usar\n",
        "inner_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFpEXOI0Zwj4",
        "outputId": "04ec0ed8-3b24-4b87-9f9b-a8e922a0a151"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Workclass          object\n",
              "fnlwgt            float64\n",
              "Capital Gain      float64\n",
              "Capital Loss      float64\n",
              "Hours per week    float64\n",
              "ID                  int64\n",
              "Target             object\n",
              "Age               float64\n",
              "Sex                object\n",
              "Race               object\n",
              "Country            object\n",
              "Education          object\n",
              "Education-Num     float64\n",
              "Marital Status     object\n",
              "Relationship       object\n",
              "Hours per week     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Comparacion por sexo\n",
        "inner_df.groupby([\"Sex\",\"Target\"])[\"Sex\"].count()"
      ],
      "metadata": {
        "id": "gNDZqWgn5ux6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52cded8a-8b0e-4579-b580-526ede82647f"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex      Target \n",
              " Female   <=50K      9592\n",
              "          <=50K.     4831\n",
              "          >50K       1179\n",
              "          >50K.       590\n",
              " Male     <=50K     15128\n",
              "          <=50K.     7604\n",
              "          >50K       6662\n",
              "          >50K.      3255\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Comparacion por raza\n",
        "inner_df.groupby([\"Race\",\"Target\"])[\"Race\"].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzdcI0r6iLiB",
        "outputId": "996d7dfe-47e3-4cf1-c21b-8506a666c912"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Race                 Target \n",
              " Amer-Indian-Eskimo   <=50K       275\n",
              "                      <=50K.      140\n",
              "                      >50K         36\n",
              "                      >50K.        19\n",
              " Asian-Pac-Islander   <=50K       763\n",
              "                      <=50K.      347\n",
              "                      >50K        276\n",
              "                      >50K.       133\n",
              " Black                <=50K      2737\n",
              "                      <=50K.     1382\n",
              "                      >50K        387\n",
              "                      >50K.       179\n",
              " Other                <=50K       246\n",
              "                      <=50K.      110\n",
              "                      >50K         25\n",
              "                      >50K.        25\n",
              " White                <=50K     20699\n",
              "                      <=50K.    10456\n",
              "                      >50K       7117\n",
              "                      >50K.      3489\n",
              "Name: Race, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Comparacion por educacion\n",
        "inner_df.groupby([\"Education\",\"Target\"])[\"Education\"].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkpYFSyljs6h",
        "outputId": "3da9c7aa-12f6-41a1-db55-53c734b3e5a6"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Education      Target \n",
              " 10th           <=50K      871\n",
              "                <=50K.     431\n",
              "                >50K        62\n",
              "                >50K.       25\n",
              " 11th           <=50K     1115\n",
              "                          ... \n",
              " Prof-school    >50K.      194\n",
              " Some-college   <=50K     5904\n",
              "                <=50K.    2911\n",
              "                >50K      1387\n",
              "                >50K.      676\n",
              "Name: Education, Length: 63, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_keU8l3UlVBa"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}