Proyecto1_Israel_Fuentes.ipynb

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        "<a href=\"https://colab.research.google.com/github/IsraMasterOne/Ciencia-de-Datos-ConPython/blob/main/Proyecto1_Israel_Fuentes.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": [
        "#Proyecto 1\n",
        "#Israel Fuentes Saavedra"
      ],
      "metadata": {
        "id": "Khsgo8-gH34o"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y9aczw0_kb76"
      },
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.colab import output\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejercicio 1\n"
      ],
      "metadata": {
        "id": "dDWftFwZkp0G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Escribir un programa que pregunte al usuario por las ventas de los últimos 5 días, y muestre por pantalla una serie con los datos de las ventas por producto: Nro de unidades y precio unitario, indexada por los días, antes y después de aplicarles un descuento del 5%."
      ],
      "metadata": {
        "id": "Gkeynqqc6kiR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Creamos un diccionario con la lista de articulos y el precio unitario\n",
        "articulos={\"jeans\":100,\"blusa\":70,\"playera\":50,\"sueter\":200,\"gorra\":90}\n",
        "#Creamos una lista vacía donde añadiremos mas tarde el numero de articulos vendidos,venta total y venta total con descuento\n",
        "ventas=[]\n",
        "#Iteramos dos \"for\" para ir preguntando día por día el numero de ventas de dicho artículo\n",
        "for i in articulos.keys():\n",
        "  for j in range(5):\n",
        "    venta=-1\n",
        "    while(venta<0):\n",
        "      print(\"Introduzca la venta para el día \"+str(j+1)+\": para el articulo: \"+str(i))\n",
        "      venta=int(input())\n",
        "      output.clear()\n",
        "    ventas.append(venta)\n",
        "    ventas.append(venta*articulos[i])\n",
        "    ventas.append(venta*articulos[i]*0.95)"
      ],
      "metadata": {
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      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Con ayuda de numpy, cambiamos la dimensión de la serie de 1x75 a 5x15\n",
        "ventas=np.reshape(ventas, (5, 15))\n",
        "ventas"
      ],
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      "source": [
        "#En una lista creamos las columnas que después separaremos\n",
        "columnas=[\"unidades vendidas_Jeans\",\"SinDescuento_Jeans\",\"Descuento_Jeans\",\"unidades vendidas_Blusa\",\"SinDescuento_Blusa\",\"Descuento_Blusa\",\"unidades vendidas_Playera\",\"SinDescuento_Playera\",\"Descuento_Playera\",\"unidades vendidas_Sueter\",\"SinDescuento_Sueter\",\"Descuento_Sueter\",\"unidades vendidas_Gorra\",\"SinDescuento_Gorra\",\"Descuento_Gorra\"]\n",
        "#Creación del Dataframe\n",
        "df=pd.DataFrame(ventas,columns=columnas)\n",
        "#Separamos las columnas en subcolumnas\n",
        "df.columns = df.columns.str.split('_', expand=True)\n",
        "df.columns = df.columns.swaplevel(1,0)\n",
        "#Creación del index\n",
        "df['Dia']= range(1,6)\n",
        "#Asignamos index\n",
        "df.set_index('Dia',inplace=True)\n",
        "df"
      ],
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              "                Jeans                                    Blusa               \\\n",
              "    unidades vendidas SinDescuento Descuento unidades vendidas SinDescuento   \n",
              "Dia                                                                           \n",
              "1                 2.0        200.0     190.0               3.0        300.0   \n",
              "2                 5.0        350.0     332.5               4.0        280.0   \n",
              "3                 4.0        200.0     190.0               5.0        250.0   \n",
              "4                 8.0       1600.0    1520.0               9.0       1800.0   \n",
              "5                 4.0        360.0     342.0               3.0        270.0   \n",
              "\n",
              "                        Playera                                   Sueter  \\\n",
              "    Descuento unidades vendidas SinDescuento Descuento unidades vendidas   \n",
              "Dia                                                                        \n",
              "1       285.0               4.0        400.0     380.0               5.0   \n",
              "2       266.0               3.0        210.0     199.5               2.0   \n",
              "3       237.5               5.0        250.0     237.5               6.0   \n",
              "4      1710.0               7.0       1400.0    1330.0               6.0   \n",
              "5       256.5               2.0        180.0     171.0               1.0   \n",
              "\n",
              "                                       Gorra                         \n",
              "    SinDescuento Descuento unidades vendidas SinDescuento Descuento  \n",
              "Dia                                                                  \n",
              "1          500.0     475.0               6.0        600.0     570.0  \n",
              "2          140.0     133.0               3.0        210.0     199.5  \n",
              "3          300.0     285.0               7.0        350.0     332.5  \n",
              "4         1200.0    1140.0               5.0       1000.0     950.0  \n",
              "5           90.0      85.5               6.0        540.0     513.0  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ejercicio 2"
      ],
      "metadata": {
        "id": "nueXmQzJ66MK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Escribir un programa que reciba un diccionario con los ingresos mensuales de los clientes de una empresa, y devuelva una serie con el ingreso mínimo, máximo y media."
      ],
      "metadata": {
        "id": "AwQZhWeD66f9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Función creada que tiene como especialidad\n",
        "#recibir un diccionario con la lista de empleados\n",
        "#y su número de ventas mensuales, devolviendo la\n",
        "#cifra mas alta, baja, su respectivo vendedor y \n",
        "#promedio de ventas\n",
        "def Statistic_Employee(Empleados):\n",
        "  for i in range(len(Empleados.keys())):\n",
        "    ventas_m=int(input(\"Ingrese la venta mensual del empleado: \"+str(list(Empleados.keys())[i])+\"--> \"))\n",
        "    #Se modifica en el diccionario el valor de cada llave\n",
        "    Empleados[str(list(Empleados.keys())[i])]=ventas_m\n",
        "  #A partir del diccionario se crea un DataFrame\n",
        "  dfE=pd.DataFrame(Empleados.items(),columns=['Empleado','Ventas mensuales'])\n",
        "  #Se asigna el index\n",
        "  dfE.set_index(['Empleado'],inplace=True)\n",
        "  output.clear()\n",
        "  #La fucnción devuelve la mayor venta, el index de la mayor venta, la menor, el index de la menor venta y el promedio\n",
        "  return pd.Series([dfE['Ventas mensuales'].idxmax(),int(dfE.max()),dfE['Ventas mensuales'].idxmin(),int(dfE.min()),float(dfE.mean())])\n"
      ],
      "metadata": {
        "id": "VALHZTF-j4h4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Prueba de la funcion dos diccionarios distintos\n",
        "Empleados={'Luis Mario':0,'Ivan':0,'Kimberly':0,'Bruce':0,'Nacho':0}\n",
        "Gatos={'Lala':0,'Nacho':0,'Cerezo':0}\n",
        "Eval=Statistic_Employee(Gatos)\n",
        "print(\"Empleado de mayor rendimiento: \"+Eval[0]+\" con ventas: \"+str(Eval[1]))\n",
        "print(\"Empleado de menor rendimiento: \"+Eval[2]+\" con ventas: \"+str(Eval[3]))\n",
        "print(\"Promedio de venta: \"+str(Eval[4]))"
      ],
      "metadata": {
        "id": "jAYx1M7TtHEU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ddaa2dfa-aeb1-4bda-d41a-5ede5f8e154f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empleado de mayor rendimiento: Cerezo con ventas: 30\n",
            "Empleado de menor rendimiento: Nacho con ventas: 10\n",
            "Promedio de venta: 20.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ejercicio 3"
      ],
      "metadata": {
        "id": "1kIZ5mvu7BIK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Escribir programa que genere y muestre por pantalla un DataFrame con los datos de la tabla siguiente:"
      ],
      "metadata": {
        "id": "JsCpKJ1x7Bfo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#En una lista se crea los datos a ingresar al dataframe\n",
        "Datos=[['Enero',30500,22000],['Febrero',35600,23450],['Marzo',28300,18100],['Abril',33900,35700],['Mayo',42500,32450]]"
      ],
      "metadata": {
        "id": "-vaC18AnW4JN"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Se crea el DataFrame\n",
        "df4=pd.DataFrame(Datos,columns=['Mes','Ventas','Gastos'])\n",
        "#Se añade la columna de Gastos sobre Ventas\n",
        "df4['Gastos/Ventas*100']=(df4['Gastos']/df4['Ventas'])*100\n",
        "#Se añade la columna donde si la Venta es mayor a 30000 se coloca Meta Superada, de lo contrario Meta no Superada\n",
        "df4['Meta']=np.where(df4['Ventas']>= 30000, 'Meta Superada', 'Meta no superada')\n",
        "#Se añade la columna donde se calcula el sobregasto, de lo contrario no se calcula\n",
        "df4['Sobre_Gasto']=np.where(df4['Gastos']> df4['Ventas'], abs(df4['Ventas']-df4['Gastos']),\"--\")"
      ],
      "metadata": {
        "id": "PNGbnQXjlYp0"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "_eyPp267lkbt",
        "outputId": "1fef165d-ab34-489c-ab08-4dfd92a94ab2"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Mes  Ventas  Gastos  Gastos/Ventas*100              Meta Sobre_Gasto\n",
              "0    Enero   30500   22000          72.131148     Meta Superada          --\n",
              "1  Febrero   35600   23450          65.870787     Meta Superada          --\n",
              "2    Marzo   28300   18100          63.957597  Meta no superada          --\n",
              "3    Abril   33900   35700         105.309735     Meta Superada        1800\n",
              "4     Mayo   42500   32450          76.352941     Meta Superada          --"
            ],
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              "      <td>Meta Superada</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Febrero</td>\n",
              "      <td>35600</td>\n",
              "      <td>23450</td>\n",
              "      <td>65.870787</td>\n",
              "      <td>Meta Superada</td>\n",
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              "      <th>2</th>\n",
              "      <td>Marzo</td>\n",
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              "      <td>63.957597</td>\n",
              "      <td>Meta no superada</td>\n",
              "      <td>--</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abril</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mayo</td>\n",
              "      <td>42500</td>\n",
              "      <td>32450</td>\n",
              "      <td>76.352941</td>\n",
              "      <td>Meta Superada</td>\n",
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              "          const element = document.querySelector('#df-422bcffa-bd5a-4ba8-ab04-e4078da0fbc9');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Se calcula las ventas de los últimos tres meses\n",
        "df4['Ventas'][-3:].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eib3Sl7Am5fk",
        "outputId": "d2d68c2d-954f-416a-c4ed-d2fe9c7455ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "104700"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Se calcula las gastos de los meses de Febrero a Abril\n",
        "df4['Gastos'][1:4].sum()"
      ],
      "metadata": {
        "id": "-dvuoVaWoY5o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e12ddee1-3f15-45e6-c043-a38ce65b80dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77250"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ejercicio 4"
      ],
      "metadata": {
        "id": "j_pFFAvkF8T_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con el siguiente fichero Data - Riesgos.csv contiene información de riesgo de clientes de un banco. Escribir un programa con los siguientes requisitos:\n",
        "Generar un DataFrame con los datos del fichero.\n",
        "Mostrar por pantalla las dimensiones del DataFrame, el número de datos que contiene, los nombres de sus columnas y filas, los tipos de datos de las columnas, mostrar las 10 primeras filas y las 10 últimas filas, verificar si tiene datos perdidos/nulos.\n",
        "Extraer las 100 primeras columnas y almacenarlas en un contenedor.\n"
      ],
      "metadata": {
        "id": "HQoOwaapyuA6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Se carga el DataFrame a paartir de un archivo CSV\"\n",
        "df5=pd.read_csv ('/content/DataRiesgos.csv')\n",
        "df5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Z_rvJHcYGBIA",
        "outputId": "24135841-9da0-48f0-a7ce-6c8f5a42c748"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID  edad  meses  max_ant   ingreso  score  tipo_vivienda  zona  nivel  \\\n",
              "0     2208  33.0  128.0      6.0   3230.00  192.0              2   3.0      2   \n",
              "1     2211  32.0  185.0      7.0   2930.00  217.0              2   1.0      5   \n",
              "2     2212  28.0    NaN      7.0   1883.70  202.0              2   1.0      2   \n",
              "3     2216  56.0  200.0      0.0    850.00  194.0              2   1.0      5   \n",
              "4     2218  32.0  131.0     65.0   3200.00  200.0              2   1.0      2   \n",
              "...    ...   ...    ...      ...       ...    ...            ...   ...    ...   \n",
              "4495  1494  54.0  510.0     87.0  24000.00  214.0              2   1.0      2   \n",
              "4496  1495  63.0  305.0     99.0   9880.73  219.0              2   5.0      2   \n",
              "4497  1496  48.0  194.0    124.0  23000.00  224.0              4   1.0      2   \n",
              "4498  1497  39.0  245.0    121.0  12450.00  240.0              4   5.0      2   \n",
              "4499  1499  65.0  674.0     38.0  20000.00  238.0              4   5.0      2   \n",
              "\n",
              "      riesgo  \n",
              "0          1  \n",
              "1          1  \n",
              "2          1  \n",
              "3          1  \n",
              "4          1  \n",
              "...      ...  \n",
              "4495       0  \n",
              "4496       0  \n",
              "4497       0  \n",
              "4498       0  \n",
              "4499       0  \n",
              "\n",
              "[4500 rows x 10 columns]"
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              "<p>4500 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-960a5ea2-e065-44ae-9f10-d463154821ab')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert: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",
              "      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-960a5ea2-e065-44ae-9f10-d463154821ab 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-960a5ea2-e065-44ae-9f10-d463154821ab');\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": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Funcion shape para calcular las dimensiones del DataFrame\n",
        "print(df5.shape)\n",
        "#Funcion size para calcular el número de datos del DataFrame\n",
        "print(df5.size)\n",
        "#Funcion columns que regresa todas las columnas del DataFrame\n",
        "print(list(df5.columns))\n",
        "#Funcion dtypes que regresa el tipo de datos por columnas DataFrame\n",
        "print(df5.dtypes)\n",
        "#Funcion head que regresa las diez primeras filas DataFrame\n",
        "print(df5.head(10))\n",
        "#Funcion shape para calcular las dimensiones de2 DataFrame\n",
        "print(df5.tail(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uodV_ptyW-MI",
        "outputId": "2e28ac37-5f0d-4b8d-c9c0-a1d634c78265"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4500, 10)\n",
            "45000\n",
            "['ID', 'edad', 'meses', 'max_ant', 'ingreso', 'score', 'tipo_vivienda', 'zona', 'nivel', 'riesgo']\n",
            "ID                 int64\n",
            "edad             float64\n",
            "meses            float64\n",
            "max_ant          float64\n",
            "ingreso          float64\n",
            "score            float64\n",
            "tipo_vivienda      int64\n",
            "zona             float64\n",
            "nivel              int64\n",
            "riesgo             int64\n",
            "dtype: object\n",
            "     ID  edad  meses  max_ant  ingreso  score  tipo_vivienda  zona  nivel  \\\n",
            "0  2208  33.0  128.0      6.0   3230.0  192.0              2   3.0      2   \n",
            "1  2211  32.0  185.0      7.0   2930.0  217.0              2   1.0      5   \n",
            "2  2212  28.0    NaN      7.0   1883.7  202.0              2   1.0      2   \n",
            "3  2216  56.0  200.0      0.0    850.0  194.0              2   1.0      5   \n",
            "4  2218  32.0  131.0     65.0   3200.0  200.0              2   1.0      2   \n",
            "5  2219  34.0  145.0      6.0   7000.0  196.0              2   5.0      2   \n",
            "6  2226   NaN  114.0      0.0   2171.5  178.0              2   2.0      1   \n",
            "7  2227  27.0  116.0      0.0    800.0  169.0              2   1.0      5   \n",
            "8  2228  26.0  182.0      6.0   3150.0  190.0              4   3.0      5   \n",
            "9  2230  25.0  154.0      1.0   2600.0  189.0              2   1.0      2   \n",
            "\n",
            "   riesgo  \n",
            "0       1  \n",
            "1       1  \n",
            "2       1  \n",
            "3       1  \n",
            "4       1  \n",
            "5       1  \n",
            "6       1  \n",
            "7       1  \n",
            "8       1  \n",
            "9       1  \n",
            "        ID  edad  meses  max_ant   ingreso  score  tipo_vivienda  zona  nivel  \\\n",
            "4490  1486  59.0  288.0      0.0  30000.00  207.0              2   1.0      2   \n",
            "4491  1487  55.0  294.0     57.0   9300.00  238.0              4   1.0      2   \n",
            "4492  1488  53.0  148.0    106.0  30000.00  219.0              4   1.0      2   \n",
            "4493  1491  29.0  198.0     94.0   5250.00  214.0              2   1.0      2   \n",
            "4494  1493  32.0  195.0     41.0   7500.00  219.0              2   1.0      2   \n",
            "4495  1494  54.0  510.0     87.0  24000.00  214.0              2   1.0      2   \n",
            "4496  1495  63.0  305.0     99.0   9880.73  219.0              2   5.0      2   \n",
            "4497  1496  48.0  194.0    124.0  23000.00  224.0              4   1.0      2   \n",
            "4498  1497  39.0  245.0    121.0  12450.00  240.0              4   5.0      2   \n",
            "4499  1499  65.0  674.0     38.0  20000.00  238.0              4   5.0      2   \n",
            "\n",
            "      riesgo  \n",
            "4490       0  \n",
            "4491       0  \n",
            "4492       0  \n",
            "4493       0  \n",
            "4494       0  \n",
            "4495       0  \n",
            "4496       0  \n",
            "4497       0  \n",
            "4498       0  \n",
            "4499       0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df5.isnull().values.any())\n",
        "print(df5.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqIrVTHnzbFY",
        "outputId": "c7b982fe-cd36-4274-a787-d93ee3a00d6c"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "ID                0\n",
            "edad             13\n",
            "meses             6\n",
            "max_ant           4\n",
            "ingreso           5\n",
            "score             5\n",
            "tipo_vivienda     0\n",
            "zona              6\n",
            "nivel             0\n",
            "riesgo            0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfE4=df5[:][0:100]\n",
        "dfE4"
      ],
      "metadata": {
        "id": "_1ln8jtx2hiJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "35c2a6e1-6138-4bb6-d563-0e13143a01f6"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ID  edad  meses  max_ant  ingreso  score  tipo_vivienda  zona  nivel  \\\n",
              "0   2208  33.0  128.0      6.0  3230.00  192.0              2   3.0      2   \n",
              "1   2211  32.0  185.0      7.0  2930.00  217.0              2   1.0      5   \n",
              "2   2212  28.0    NaN      7.0  1883.70  202.0              2   1.0      2   \n",
              "3   2216  56.0  200.0      0.0   850.00  194.0              2   1.0      5   \n",
              "4   2218  32.0  131.0     65.0  3200.00  200.0              2   1.0      2   \n",
              "..   ...   ...    ...      ...      ...    ...            ...   ...    ...   \n",
              "95  2387  41.0  141.0      0.0  3420.36  214.0              4   2.0      1   \n",
              "96  2389  29.0  159.0     17.0  1157.06  163.0              2   4.0      2   \n",
              "97  2391  27.0  122.0      NaN  2000.00  188.0              2   1.0      2   \n",
              "98  2392  35.0  111.0      0.0   812.53  177.0              2   1.0      5   \n",
              "99  2394  28.0  137.0      0.0  2000.00  175.0              3   1.0      5   \n",
              "\n",
              "    riesgo  \n",
              "0        1  \n",
              "1        1  \n",
              "2        1  \n",
              "3        1  \n",
              "4        1  \n",
              "..     ...  \n",
              "95       1  \n",
              "96       1  \n",
              "97       1  \n",
              "98       1  \n",
              "99       1  \n",
              "\n",
              "[100 rows x 10 columns]"
            ],
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              "      <td>1</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>2387</td>\n",
              "      <td>41.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3420.36</td>\n",
              "      <td>214.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>17.0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2392</td>\n",
              "      <td>35.0</td>\n",
              "      <td>111.0</td>\n",
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              "      <td>1</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0cf2defe-e0b1-4d88-b616-06f45459c50b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display: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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0cf2defe-e0b1-4d88-b616-06f45459c50b 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-0cf2defe-e0b1-4d88-b616-06f45459c50b');\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": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "SdCUei7BmCz4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}