ETL_streamlit.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Vevz6MGruh_"
      },
      "source": [
        "# **PROBLEMA DE NEGOCIO**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I879QRj7i8ab"
      },
      "source": [
        "### Una tienda online de moda, con presencia en todo Brasil, necesita impulsar su rendimiento utilizando sus datos de manera estratégica. Como científico de datos, has sido convocado para analizar estos datos y ofrecer insights que guíen sus decisiones y respondan a las siguientes preguntas clave:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gq88Mm5mNlU"
      },
      "source": [
        "### **Preguntas**\n",
        "\n",
        "1. ¿Cual es el Top 5 productos más vendidos históricamente?\n",
        "\n",
        "2. ¿Cual es la evolución histórica de las ingresos netos?\n",
        "\n",
        "3. ¿Cuáles son los ingresos netos por vendedor por año?\n",
        "\n",
        "4. ¿Cuáles son las ciudades que proporcionan mayores ingresos netos?\n",
        "\n",
        "5. ¿Existe otro insight que puedas proporcionar?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jUHpF5smRvN"
      },
      "source": [
        "### **1. Configuración del Ambiente**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 246,
      "metadata": {
        "id": "6LQQAaHMpVGz"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lQO7FHssJhr"
      },
      "source": [
        "### **2. Obtención, Tratamiento y Análisis Exploratório (EDA)**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MdQXCMggsM0m"
      },
      "source": [
        "#### **2.1 Cargando las bases de datos**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Base de datos con el detalle de los pedidos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 247,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "x19qgbjpsKJx",
        "outputId": "340a64dc-a6a7-4f1b-a1b3-e6bdf87bfc60"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_recibo</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>cantidad</th>\n",
              "      <th>valor_unitario</th>\n",
              "      <th>valor_total</th>\n",
              "      <th>ciudad</th>\n",
              "      <th>costo_envio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41518</td>\n",
              "      <td>341</td>\n",
              "      <td>3</td>\n",
              "      <td>260</td>\n",
              "      <td>780</td>\n",
              "      <td>BR-BA</td>\n",
              "      <td>156.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4307</td>\n",
              "      <td>1174</td>\n",
              "      <td>5</td>\n",
              "      <td>6175</td>\n",
              "      <td>30875</td>\n",
              "      <td>BR-RJ</td>\n",
              "      <td>6175.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>22407</td>\n",
              "      <td>1399</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>600</td>\n",
              "      <td>BR-PB</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3820</td>\n",
              "      <td>1652</td>\n",
              "      <td>6</td>\n",
              "      <td>139</td>\n",
              "      <td>834</td>\n",
              "      <td>BR-DF</td>\n",
              "      <td>166.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>29012</td>\n",
              "      <td>2470</td>\n",
              "      <td>3</td>\n",
              "      <td>525</td>\n",
              "      <td>1575</td>\n",
              "      <td>BR-BA</td>\n",
              "      <td>315.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id_recibo  producto_id  pedido_id  cantidad  valor_unitario  valor_total  \\\n",
              "0          1        41518        341         3             260          780   \n",
              "1          2         4307       1174         5            6175        30875   \n",
              "2          3        22407       1399         3             200          600   \n",
              "3          4         3820       1652         6             139          834   \n",
              "4          5        29012       2470         3             525         1575   \n",
              "\n",
              "  ciudad  costo_envio  \n",
              "0  BR-BA        156.0  \n",
              "1  BR-RJ       6175.0  \n",
              "2  BR-PB        120.0  \n",
              "3  BR-DF        166.8  \n",
              "4  BR-BA        315.0  "
            ]
          },
          "execution_count": 247,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_itens_pedidos = pd.read_csv('https://raw.githubusercontent.com/ElProfeAlejo/Bootcamp_Databases/main/itens_pedidos.csv')\n",
        "df_itens_pedidos.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 248,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 24527 entries, 0 to 24526\n",
            "Data columns (total 8 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   id_recibo       24527 non-null  int64  \n",
            " 1   producto_id     24527 non-null  int64  \n",
            " 2   pedido_id       24527 non-null  int64  \n",
            " 3   cantidad        24527 non-null  int64  \n",
            " 4   valor_unitario  24527 non-null  int64  \n",
            " 5   valor_total     24527 non-null  int64  \n",
            " 6   ciudad          24527 non-null  object \n",
            " 7   costo_envio     24527 non-null  float64\n",
            "dtypes: float64(1), int64(6), object(1)\n",
            "memory usage: 1.5+ MB\n"
          ]
        }
      ],
      "source": [
        "df_itens_pedidos.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "se verifican duplicados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 249,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id_recibo         0\n",
              "producto_id       0\n",
              "pedido_id         0\n",
              "cantidad          0\n",
              "valor_unitario    0\n",
              "valor_total       0\n",
              "ciudad            0\n",
              "costo_envio       0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 249,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_itens_pedidos.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Base de datos general de los pedidos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 250,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VpUPaj5ktEy6",
        "outputId": "054557bd-d96c-4df2-e74f-646501a8cc6f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>vendedor_id</th>\n",
              "      <th>fecha_compra</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>341</td>\n",
              "      <td>41518</td>\n",
              "      <td>6</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1174</td>\n",
              "      <td>4307</td>\n",
              "      <td>3</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>30875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1399</td>\n",
              "      <td>22407</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1652</td>\n",
              "      <td>3820</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2470</td>\n",
              "      <td>29012</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>1575</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   pedido_id  producto_id  vendedor_id fecha_compra  total\n",
              "0        341        41518            6   2019-01-05    780\n",
              "1       1174         4307            3   2019-01-05  30875\n",
              "2       1399        22407            1   2019-01-05    600\n",
              "3       1652         3820            4   2019-01-05    834\n",
              "4       2470        29012            2   2019-01-05   1575"
            ]
          },
          "execution_count": 250,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_pedidos = pd.read_csv('https://raw.githubusercontent.com/ElProfeAlejo/Bootcamp_Databases/main/pedidos.csv')\n",
        "df_pedidos.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 251,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 24527 entries, 0 to 24526\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   pedido_id     24527 non-null  int64 \n",
            " 1   producto_id   24527 non-null  int64 \n",
            " 2   vendedor_id   24527 non-null  int64 \n",
            " 3   fecha_compra  24527 non-null  object\n",
            " 4   total         24527 non-null  int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 958.2+ KB\n"
          ]
        }
      ],
      "source": [
        "df_pedidos.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se observa que el tipo de dato de la fecha de compra no es el correcto, se transforma en datetime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 252,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_pedidos['fecha_compra'] = pd.to_datetime(df_pedidos['fecha_compra'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Base de datos de los productos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 253,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JloX5KfQmSOC",
        "outputId": "b1bf23af-62be-410a-c65b-d39ce0b86b65"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "\n",
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              "        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>producto_id</th>\n",
              "      <th>producto</th>\n",
              "      <th>precio</th>\n",
              "      <th>marca</th>\n",
              "      <th>sku</th>\n",
              "      <th>condicion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21244</td>\n",
              "      <td>Oculos Lente Azulada</td>\n",
              "      <td>1120</td>\n",
              "      <td>D&amp;g Dolce &amp; Gabbana</td>\n",
              "      <td>209297.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9981</td>\n",
              "      <td>Bolsa Coral Saco</td>\n",
              "      <td>4000</td>\n",
              "      <td>Givenchy</td>\n",
              "      <td>278612.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84176</td>\n",
              "      <td>Camisa Xadrez Verde</td>\n",
              "      <td>310</td>\n",
              "      <td>Joe Fresh</td>\n",
              "      <td>322482.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47475</td>\n",
              "      <td>Calca Alfaiataria Preta</td>\n",
              "      <td>490</td>\n",
              "      <td>Mixed</td>\n",
              "      <td>263658.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>74864</td>\n",
              "      <td>Vestido Jeans Babados</td>\n",
              "      <td>130</td>\n",
              "      <td>Zara</td>\n",
              "      <td>219248.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   producto_id                 producto  precio                marca  \\\n",
              "0        21244     Oculos Lente Azulada    1120  D&g Dolce & Gabbana   \n",
              "1         9981         Bolsa Coral Saco    4000             Givenchy   \n",
              "2        84176      Camisa Xadrez Verde     310            Joe Fresh   \n",
              "3        47475  Calca Alfaiataria Preta     490                Mixed   \n",
              "4        74864    Vestido Jeans Babados     130                 Zara   \n",
              "\n",
              "        sku condicion  \n",
              "0  209297.0     Usado  \n",
              "1  278612.0     Usado  \n",
              "2  322482.0     Usado  \n",
              "3  263658.0     Usado  \n",
              "4  219248.0     Usado  "
            ]
          },
          "execution_count": 253,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_productos = pd.read_csv('https://raw.githubusercontent.com/ElProfeAlejo/Bootcamp_Databases/main/productos.csv')\n",
        "df_productos.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 254,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 207 entries, 0 to 206\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   producto_id  207 non-null    int64  \n",
            " 1   producto     205 non-null    object \n",
            " 2   precio       207 non-null    int64  \n",
            " 3   marca        207 non-null    object \n",
            " 4   sku          205 non-null    float64\n",
            " 5   condicion    207 non-null    object \n",
            "dtypes: float64(1), int64(2), object(3)\n",
            "memory usage: 9.8+ KB\n"
          ]
        }
      ],
      "source": [
        "df_productos.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "se verifican cuales son los valores nulos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 255,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>producto_id</th>\n",
              "      <th>producto</th>\n",
              "      <th>precio</th>\n",
              "      <th>marca</th>\n",
              "      <th>sku</th>\n",
              "      <th>condicion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>41598</td>\n",
              "      <td>NaN</td>\n",
              "      <td>260</td>\n",
              "      <td>Banana Republic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Nuevo con etiqueta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>32926</td>\n",
              "      <td>NaN</td>\n",
              "      <td>210</td>\n",
              "      <td>Limone By Jade Seba</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Nuevo sin etiqueta</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     producto_id producto  precio                marca  sku  \\\n",
              "205        41598      NaN     260      Banana Republic  NaN   \n",
              "206        32926      NaN     210  Limone By Jade Seba  NaN   \n",
              "\n",
              "              condicion  \n",
              "205  Nuevo con etiqueta  \n",
              "206  Nuevo sin etiqueta  "
            ]
          },
          "execution_count": 255,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_productos[df_productos.isna().any(axis=1)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "se borran estos valores nulos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 256,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_productos.dropna(axis=0, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Base de datos de los vendedores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 257,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qaB_aJ8YuaMf",
        "outputId": "581a092f-3558-4c5b-a4ba-5d1466250964"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vendedor_id</th>\n",
              "      <th>nombre_vendedor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Ana Duarte</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Daniel Siqueira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Nadia Oliveira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Millena Pereira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Paulo Calanca</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vendedor_id  nombre_vendedor\n",
              "0            1       Ana Duarte\n",
              "1            2  Daniel Siqueira\n",
              "2            3   Nadia Oliveira\n",
              "3            4  Millena Pereira\n",
              "4            5    Paulo Calanca"
            ]
          },
          "execution_count": 257,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_vendedores = pd.read_csv('https://raw.githubusercontent.com/ElProfeAlejo/Bootcamp_Databases/main/vendedores.csv')\n",
        "df_vendedores.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 258,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6 entries, 0 to 5\n",
            "Data columns (total 2 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   vendedor_id      6 non-null      int64 \n",
            " 1   nombre_vendedor  6 non-null      object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 228.0+ bytes\n"
          ]
        }
      ],
      "source": [
        "df_vendedores.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJs3S7WQuuTK"
      },
      "source": [
        "## **2.2 Feature engineering**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### DataFrame df_itens_pedidos\n",
        "Se verifica la columna ciudad, y se reemplaza por otra columna con el nombre de la ciudad"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 259,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "NvLcmA4o8DFv",
        "outputId": "afe5e85e-e6df-48d6-aae4-fd55962bb145"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_recibo</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>cantidad</th>\n",
              "      <th>valor_unitario</th>\n",
              "      <th>valor_total</th>\n",
              "      <th>ciudad</th>\n",
              "      <th>costo_envio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41518</td>\n",
              "      <td>341</td>\n",
              "      <td>3</td>\n",
              "      <td>260</td>\n",
              "      <td>780</td>\n",
              "      <td>BR-BA</td>\n",
              "      <td>156.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4307</td>\n",
              "      <td>1174</td>\n",
              "      <td>5</td>\n",
              "      <td>6175</td>\n",
              "      <td>30875</td>\n",
              "      <td>BR-RJ</td>\n",
              "      <td>6175.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>22407</td>\n",
              "      <td>1399</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>600</td>\n",
              "      <td>BR-PB</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3820</td>\n",
              "      <td>1652</td>\n",
              "      <td>6</td>\n",
              "      <td>139</td>\n",
              "      <td>834</td>\n",
              "      <td>BR-DF</td>\n",
              "      <td>166.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>29012</td>\n",
              "      <td>2470</td>\n",
              "      <td>3</td>\n",
              "      <td>525</td>\n",
              "      <td>1575</td>\n",
              "      <td>BR-BA</td>\n",
              "      <td>315.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>72621</td>\n",
              "      <td>2980</td>\n",
              "      <td>3</td>\n",
              "      <td>179</td>\n",
              "      <td>537</td>\n",
              "      <td>BR-MG</td>\n",
              "      <td>107.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>86243</td>\n",
              "      <td>3443</td>\n",
              "      <td>4</td>\n",
              "      <td>350</td>\n",
              "      <td>1400</td>\n",
              "      <td>BR-PR</td>\n",
              "      <td>280.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>29012</td>\n",
              "      <td>3557</td>\n",
              "      <td>5</td>\n",
              "      <td>525</td>\n",
              "      <td>2625</td>\n",
              "      <td>BR-MS</td>\n",
              "      <td>525.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>61071</td>\n",
              "      <td>3585</td>\n",
              "      <td>3</td>\n",
              "      <td>450</td>\n",
              "      <td>1350</td>\n",
              "      <td>BR-SP</td>\n",
              "      <td>270.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>100961</td>\n",
              "      <td>3687</td>\n",
              "      <td>2</td>\n",
              "      <td>480</td>\n",
              "      <td>960</td>\n",
              "      <td>BR-GO</td>\n",
              "      <td>192.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id_recibo  producto_id  pedido_id  cantidad  valor_unitario  valor_total  \\\n",
              "0          1        41518        341         3             260          780   \n",
              "1          2         4307       1174         5            6175        30875   \n",
              "2          3        22407       1399         3             200          600   \n",
              "3          4         3820       1652         6             139          834   \n",
              "4          5        29012       2470         3             525         1575   \n",
              "5          6        72621       2980         3             179          537   \n",
              "6          7        86243       3443         4             350         1400   \n",
              "7          8        29012       3557         5             525         2625   \n",
              "8          9        61071       3585         3             450         1350   \n",
              "9         10       100961       3687         2             480          960   \n",
              "\n",
              "  ciudad  costo_envio  \n",
              "0  BR-BA        156.0  \n",
              "1  BR-RJ       6175.0  \n",
              "2  BR-PB        120.0  \n",
              "3  BR-DF        166.8  \n",
              "4  BR-BA        315.0  \n",
              "5  BR-MG        107.4  \n",
              "6  BR-PR        280.0  \n",
              "7  BR-MS        525.0  \n",
              "8  BR-SP        270.0  \n",
              "9  BR-GO        192.0  "
            ]
          },
          "execution_count": 259,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_itens_pedidos.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "se verifican todos los valores unicos que hay en la columna ciudades"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 260,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['BR-BA', 'BR-RJ', 'BR-PB', 'BR-DF', 'BR-MG', 'BR-PR', 'BR-MS',\n",
              "       'BR-SP', 'BR-GO', 'BR-AM', 'BR-CE', 'BR-RS', 'BR-AC', 'BR-RO',\n",
              "       'BR-MT', 'BR-RR', 'BR-PE', 'BR-MA', 'BR-PA', 'BR-SC', 'BR-SE',\n",
              "       'BR-TO', 'BR-AP', 'BR-PI', 'BR-ES', 'BR-AL', 'BR-RN'], dtype=object)"
            ]
          },
          "execution_count": 260,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_itens_pedidos['ciudad'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se crea un diccionario con las ciudades"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 261,
      "metadata": {},
      "outputs": [],
      "source": [
        "ciudades_dict = {\n",
        "    'BR-BA': 'Bahia',\n",
        "    'BR-RJ': 'Rio de Janeiro',\n",
        "    'BR-PB': 'Paraíba',\n",
        "    'BR-DF': 'Distrito Federal',\n",
        "    'BR-MG': 'Minas Gerais',\n",
        "    'BR-PR': 'Paraná',\n",
        "    'BR-MS': 'Mato Grosso do Sul',\n",
        "    'BR-SP': 'São Paulo',\n",
        "    'BR-GO': 'Goiás',\n",
        "    'BR-AM': 'Amazonas',\n",
        "    'BR-CE': 'Ceará',\n",
        "    'BR-RS': 'Rio Grande do Sul',\n",
        "    'BR-AC': 'Acre',\n",
        "    'BR-RO': 'Rondônia',\n",
        "    'BR-MT': 'Mato Grosso',\n",
        "    'BR-RR': 'Roraima',\n",
        "    'BR-PE': 'Pernambuco',\n",
        "    'BR-MA': 'Maranhão',\n",
        "    'BR-PA': 'Pará',\n",
        "    'BR-SC': 'Santa Catarina',\n",
        "    'BR-SE': 'Sergipe',\n",
        "    'BR-TO': 'Tocantins',\n",
        "    'BR-AP': 'Amapá',\n",
        "    'BR-PI': 'Piauí',\n",
        "    'BR-ES': 'Espírito Santo',\n",
        "    'BR-AL': 'Alagoas',\n",
        "    'BR-RN': 'Rio Grande do Norte'\n",
        "}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se mapea y se crea la columna nueva"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 262,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_itens_pedidos['ciudad'] = df_itens_pedidos['ciudad'].map(ciudades_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se visualiza el cambio de la columna ciudad"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 263,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_recibo</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>cantidad</th>\n",
              "      <th>valor_unitario</th>\n",
              "      <th>valor_total</th>\n",
              "      <th>ciudad</th>\n",
              "      <th>costo_envio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41518</td>\n",
              "      <td>341</td>\n",
              "      <td>3</td>\n",
              "      <td>260</td>\n",
              "      <td>780</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>156.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4307</td>\n",
              "      <td>1174</td>\n",
              "      <td>5</td>\n",
              "      <td>6175</td>\n",
              "      <td>30875</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>6175.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>22407</td>\n",
              "      <td>1399</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>600</td>\n",
              "      <td>Paraíba</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3820</td>\n",
              "      <td>1652</td>\n",
              "      <td>6</td>\n",
              "      <td>139</td>\n",
              "      <td>834</td>\n",
              "      <td>Distrito Federal</td>\n",
              "      <td>166.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>29012</td>\n",
              "      <td>2470</td>\n",
              "      <td>3</td>\n",
              "      <td>525</td>\n",
              "      <td>1575</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>315.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id_recibo  producto_id  pedido_id  cantidad  valor_unitario  valor_total  \\\n",
              "0          1        41518        341         3             260          780   \n",
              "1          2         4307       1174         5            6175        30875   \n",
              "2          3        22407       1399         3             200          600   \n",
              "3          4         3820       1652         6             139          834   \n",
              "4          5        29012       2470         3             525         1575   \n",
              "\n",
              "             ciudad  costo_envio  \n",
              "0             Bahia        156.0  \n",
              "1    Rio de Janeiro       6175.0  \n",
              "2           Paraíba        120.0  \n",
              "3  Distrito Federal        166.8  \n",
              "4             Bahia        315.0  "
            ]
          },
          "execution_count": 263,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_itens_pedidos.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se crea las columnas latitud y longitud para cada ciudad"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 264,
      "metadata": {},
      "outputs": [],
      "source": [
        "coordenadas = {\n",
        "    'Bahia': {'latitud': -12.9704, 'longitud': -38.5124},\n",
        "    'Rio de Janeiro': {'latitud': -22.9068, 'longitud': -43.1729},\n",
        "    'Paraíba': {'latitud': -7.2398, 'longitud': -36.7819},\n",
        "    'Distrito Federal': {'latitud': -15.7939, 'longitud': -47.8828},\n",
        "    'Minas Gerais': {'latitud': -19.9286, 'longitud': -43.9386},\n",
        "    'Paraná': {'latitud': -25.2521, 'longitud': -52.0215},\n",
        "    'Mato Grosso do Sul': {'latitud': -20.4428, 'longitud': -54.6464},\n",
        "    'São Paulo': {'latitud': -23.5505, 'longitud': -46.6333},\n",
        "    'Goiás': {'latitud': -16.6869, 'longitud': -49.2648},\n",
        "    'Amazonas': {'latitud': -3.1190, 'longitud': -60.0217},\n",
        "    'Ceará': {'latitud': -3.7172, 'longitud': -38.5434},\n",
        "    'Rio Grande do Sul': {'latitud': -30.0346, 'longitud': -51.2177},\n",
        "    'Acre': {'latitud': -9.0238, 'longitud': -70.8120},\n",
        "    'Rondônia': {'latitud': -8.7612, 'longitud': -63.9039},\n",
        "    'Mato Grosso': {'latitud': -15.6014, 'longitud': -56.0979},\n",
        "    'Roraima': {'latitud': 2.8235, 'longitud': -60.6758},\n",
        "    'Pernambuco': {'latitud': -8.0476, 'longitud': -34.8770},\n",
        "    'Maranhão': {'latitud': -2.5387, 'longitud': -44.2829},\n",
        "    'Pará': {'latitud': -1.4558, 'longitud': -48.4902},\n",
        "    'Santa Catarina': {'latitud': -27.5954, 'longitud': -48.5480},\n",
        "    'Sergipe': {'latitud': -10.9472, 'longitud': -37.0731},\n",
        "    'Tocantins': {'latitud': -10.2527, 'longitud': -48.2530},\n",
        "    'Amapá': {'latitud': 0.0340, 'longitud': -51.0694},\n",
        "    'Piauí': {'latitud': -5.0892, 'longitud': -42.8016},\n",
        "    'Espírito Santo': {'latitud': -20.3155, 'longitud': -40.3128},\n",
        "    'Alagoas': {'latitud': -9.5713, 'longitud': -36.7820},\n",
        "    'Rio Grande do Norte': {'latitud': -5.7945, 'longitud': -35.2110}\n",
        "}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se crea la columna de latitud y longitud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 265,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_itens_pedidos['latitud'] = df_itens_pedidos['ciudad'].map(lambda x: coordenadas[x]['latitud'])\n",
        "df_itens_pedidos['longitud'] = df_itens_pedidos['ciudad'].map(lambda x: coordenadas[x]['longitud'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se crea las columnas de mes y año"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 266,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>vendedor_id</th>\n",
              "      <th>fecha_compra</th>\n",
              "      <th>total</th>\n",
              "      <th>anio</th>\n",
              "      <th>mes</th>\n",
              "      <th>num_mes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>341</td>\n",
              "      <td>41518</td>\n",
              "      <td>6</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>780</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   pedido_id  producto_id  vendedor_id fecha_compra  total  anio      mes  \\\n",
              "0        341        41518            6   2019-01-05    780  2019  January   \n",
              "\n",
              "   num_mes  \n",
              "0        1  "
            ]
          },
          "execution_count": 266,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_pedidos['anio'] = df_pedidos['fecha_compra'].dt.year\n",
        "df_pedidos['mes'] = df_pedidos['fecha_compra'].dt.month_name()\n",
        "df_pedidos['num_mes'] = df_pedidos['fecha_compra'].dt.month\n",
        "\n",
        "df_pedidos.head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### DataFrame productos\n",
        "se crea otra columna con el tipo de producto, para ello se extrae la primera palabra de la columna producto"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 267,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8vl4If_89nO",
        "outputId": "8ef76141-e868-41c5-a126-2d926c746d4b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>producto_id</th>\n",
              "      <th>producto</th>\n",
              "      <th>precio</th>\n",
              "      <th>marca</th>\n",
              "      <th>sku</th>\n",
              "      <th>condicion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21244</td>\n",
              "      <td>Oculos Lente Azulada</td>\n",
              "      <td>1120</td>\n",
              "      <td>D&amp;g Dolce &amp; Gabbana</td>\n",
              "      <td>209297.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9981</td>\n",
              "      <td>Bolsa Coral Saco</td>\n",
              "      <td>4000</td>\n",
              "      <td>Givenchy</td>\n",
              "      <td>278612.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84176</td>\n",
              "      <td>Camisa Xadrez Verde</td>\n",
              "      <td>310</td>\n",
              "      <td>Joe Fresh</td>\n",
              "      <td>322482.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47475</td>\n",
              "      <td>Calca Alfaiataria Preta</td>\n",
              "      <td>490</td>\n",
              "      <td>Mixed</td>\n",
              "      <td>263658.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>74864</td>\n",
              "      <td>Vestido Jeans Babados</td>\n",
              "      <td>130</td>\n",
              "      <td>Zara</td>\n",
              "      <td>219248.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   producto_id                 producto  precio                marca  \\\n",
              "0        21244     Oculos Lente Azulada    1120  D&g Dolce & Gabbana   \n",
              "1         9981         Bolsa Coral Saco    4000             Givenchy   \n",
              "2        84176      Camisa Xadrez Verde     310            Joe Fresh   \n",
              "3        47475  Calca Alfaiataria Preta     490                Mixed   \n",
              "4        74864    Vestido Jeans Babados     130                 Zara   \n",
              "\n",
              "        sku condicion  \n",
              "0  209297.0     Usado  \n",
              "1  278612.0     Usado  \n",
              "2  322482.0     Usado  \n",
              "3  263658.0     Usado  \n",
              "4  219248.0     Usado  "
            ]
          },
          "execution_count": 267,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_productos.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 268,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KzA9MC1P9HX8",
        "outputId": "2f8ec542-7802-43b5-e6ad-0424efdbdbb3"
      },
      "outputs": [],
      "source": [
        "df_productos['tipo_producto'] = df_productos['producto'].str.split().str[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "insertamos la nueva columna en la segunda posicion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 269,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_productos.insert(2, 'tipo_producto', df_productos.pop('tipo_producto'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 270,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>producto_id</th>\n",
              "      <th>producto</th>\n",
              "      <th>tipo_producto</th>\n",
              "      <th>precio</th>\n",
              "      <th>marca</th>\n",
              "      <th>sku</th>\n",
              "      <th>condicion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21244</td>\n",
              "      <td>Oculos Lente Azulada</td>\n",
              "      <td>Oculos</td>\n",
              "      <td>1120</td>\n",
              "      <td>D&amp;g Dolce &amp; Gabbana</td>\n",
              "      <td>209297.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9981</td>\n",
              "      <td>Bolsa Coral Saco</td>\n",
              "      <td>Bolsa</td>\n",
              "      <td>4000</td>\n",
              "      <td>Givenchy</td>\n",
              "      <td>278612.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84176</td>\n",
              "      <td>Camisa Xadrez Verde</td>\n",
              "      <td>Camisa</td>\n",
              "      <td>310</td>\n",
              "      <td>Joe Fresh</td>\n",
              "      <td>322482.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47475</td>\n",
              "      <td>Calca Alfaiataria Preta</td>\n",
              "      <td>Calca</td>\n",
              "      <td>490</td>\n",
              "      <td>Mixed</td>\n",
              "      <td>263658.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>74864</td>\n",
              "      <td>Vestido Jeans Babados</td>\n",
              "      <td>Vestido</td>\n",
              "      <td>130</td>\n",
              "      <td>Zara</td>\n",
              "      <td>219248.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   producto_id                 producto tipo_producto  precio  \\\n",
              "0        21244     Oculos Lente Azulada        Oculos    1120   \n",
              "1         9981         Bolsa Coral Saco         Bolsa    4000   \n",
              "2        84176      Camisa Xadrez Verde        Camisa     310   \n",
              "3        47475  Calca Alfaiataria Preta         Calca     490   \n",
              "4        74864    Vestido Jeans Babados       Vestido     130   \n",
              "\n",
              "                 marca       sku condicion  \n",
              "0  D&g Dolce & Gabbana  209297.0     Usado  \n",
              "1             Givenchy  278612.0     Usado  \n",
              "2            Joe Fresh  322482.0     Usado  \n",
              "3                Mixed  263658.0     Usado  \n",
              "4                 Zara  219248.0     Usado  "
            ]
          },
          "execution_count": 270,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_productos.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Una vez tratados los datos se unen los cuatro DataFrames"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 271,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_recibo</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>cantidad</th>\n",
              "      <th>valor_unitario</th>\n",
              "      <th>valor_total</th>\n",
              "      <th>ciudad</th>\n",
              "      <th>costo_envio</th>\n",
              "      <th>latitud</th>\n",
              "      <th>longitud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41518</td>\n",
              "      <td>341</td>\n",
              "      <td>3</td>\n",
              "      <td>260</td>\n",
              "      <td>780</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>156.0</td>\n",
              "      <td>-12.9704</td>\n",
              "      <td>-38.5124</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id_recibo  producto_id  pedido_id  cantidad  valor_unitario  valor_total  \\\n",
              "0          1        41518        341         3             260          780   \n",
              "\n",
              "  ciudad  costo_envio  latitud  longitud  \n",
              "0  Bahia        156.0 -12.9704  -38.5124  "
            ]
          },
          "execution_count": 271,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_itens_pedidos.head(1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 272,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>vendedor_id</th>\n",
              "      <th>fecha_compra</th>\n",
              "      <th>total</th>\n",
              "      <th>anio</th>\n",
              "      <th>mes</th>\n",
              "      <th>num_mes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>341</td>\n",
              "      <td>41518</td>\n",
              "      <td>6</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>780</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   pedido_id  producto_id  vendedor_id fecha_compra  total  anio      mes  \\\n",
              "0        341        41518            6   2019-01-05    780  2019  January   \n",
              "\n",
              "   num_mes  \n",
              "0        1  "
            ]
          },
          "execution_count": 272,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_pedidos.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 273,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>producto_id</th>\n",
              "      <th>producto</th>\n",
              "      <th>tipo_producto</th>\n",
              "      <th>precio</th>\n",
              "      <th>marca</th>\n",
              "      <th>sku</th>\n",
              "      <th>condicion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21244</td>\n",
              "      <td>Oculos Lente Azulada</td>\n",
              "      <td>Oculos</td>\n",
              "      <td>1120</td>\n",
              "      <td>D&amp;g Dolce &amp; Gabbana</td>\n",
              "      <td>209297.0</td>\n",
              "      <td>Usado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   producto_id              producto tipo_producto  precio  \\\n",
              "0        21244  Oculos Lente Azulada        Oculos    1120   \n",
              "\n",
              "                 marca       sku condicion  \n",
              "0  D&g Dolce & Gabbana  209297.0     Usado  "
            ]
          },
          "execution_count": 273,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_productos.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 274,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vendedor_id</th>\n",
              "      <th>nombre_vendedor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Ana Duarte</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Daniel Siqueira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Nadia Oliveira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Millena Pereira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Paulo Calanca</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vendedor_id  nombre_vendedor\n",
              "0            1       Ana Duarte\n",
              "1            2  Daniel Siqueira\n",
              "2            3   Nadia Oliveira\n",
              "3            4  Millena Pereira\n",
              "4            5    Paulo Calanca\n",
              "5            6          Unknown"
            ]
          },
          "execution_count": 274,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_vendedores.head(6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 275,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "45803930"
            ]
          },
          "execution_count": 275,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_itens_pedidos['valor_total'].sum() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 276,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_final = df_itens_pedidos.merge(df_pedidos, on=['producto_id', 'pedido_id']).merge(df_productos, on= 'producto_id').merge(df_vendedores, on='vendedor_id')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 277,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_recibo</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>cantidad</th>\n",
              "      <th>valor_unitario</th>\n",
              "      <th>valor_total</th>\n",
              "      <th>ciudad</th>\n",
              "      <th>costo_envio</th>\n",
              "      <th>latitud</th>\n",
              "      <th>longitud</th>\n",
              "      <th>...</th>\n",
              "      <th>anio</th>\n",
              "      <th>mes</th>\n",
              "      <th>num_mes</th>\n",
              "      <th>producto</th>\n",
              "      <th>tipo_producto</th>\n",
              "      <th>precio</th>\n",
              "      <th>marca</th>\n",
              "      <th>sku</th>\n",
              "      <th>condicion</th>\n",
              "      <th>nombre_vendedor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41518</td>\n",
              "      <td>341</td>\n",
              "      <td>3</td>\n",
              "      <td>260</td>\n",
              "      <td>780</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>156.0</td>\n",
              "      <td>-12.9704</td>\n",
              "      <td>-38.5124</td>\n",
              "      <td>...</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "      <td>Calca Jeans Basica</td>\n",
              "      <td>Calca</td>\n",
              "      <td>260</td>\n",
              "      <td>Banana Republic</td>\n",
              "      <td>255410.0</td>\n",
              "      <td>Nuevo con etiqueta</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4307</td>\n",
              "      <td>1174</td>\n",
              "      <td>5</td>\n",
              "      <td>6175</td>\n",
              "      <td>30875</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>6175.0</td>\n",
              "      <td>-22.9068</td>\n",
              "      <td>-43.1729</td>\n",
              "      <td>...</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "      <td>Bolsa Intrecciato Caramelo</td>\n",
              "      <td>Bolsa</td>\n",
              "      <td>6175</td>\n",
              "      <td>Bottega Veneta</td>\n",
              "      <td>301305.0</td>\n",
              "      <td>Usado</td>\n",
              "      <td>Nadia Oliveira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>22407</td>\n",
              "      <td>1399</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>600</td>\n",
              "      <td>Paraíba</td>\n",
              "      <td>120.0</td>\n",
              "      <td>-7.2398</td>\n",
              "      <td>-36.7819</td>\n",
              "      <td>...</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "      <td>Cropped Listras Florzinhas</td>\n",
              "      <td>Cropped</td>\n",
              "      <td>200</td>\n",
              "      <td>Topshop</td>\n",
              "      <td>327426.0</td>\n",
              "      <td>Usado</td>\n",
              "      <td>Ana Duarte</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   id_recibo  producto_id  pedido_id  cantidad  valor_unitario  valor_total  \\\n",
              "0          1        41518        341         3             260          780   \n",
              "1          2         4307       1174         5            6175        30875   \n",
              "2          3        22407       1399         3             200          600   \n",
              "\n",
              "           ciudad  costo_envio  latitud  longitud  ...  anio      mes  \\\n",
              "0           Bahia        156.0 -12.9704  -38.5124  ...  2019  January   \n",
              "1  Rio de Janeiro       6175.0 -22.9068  -43.1729  ...  2019  January   \n",
              "2         Paraíba        120.0  -7.2398  -36.7819  ...  2019  January   \n",
              "\n",
              "   num_mes                    producto tipo_producto  precio            marca  \\\n",
              "0        1          Calca Jeans Basica         Calca     260  Banana Republic   \n",
              "1        1  Bolsa Intrecciato Caramelo         Bolsa    6175   Bottega Veneta   \n",
              "2        1  Cropped Listras Florzinhas       Cropped     200          Topshop   \n",
              "\n",
              "        sku           condicion nombre_vendedor  \n",
              "0  255410.0  Nuevo con etiqueta         Unknown  \n",
              "1  301305.0               Usado  Nadia Oliveira  \n",
              "2  327426.0               Usado      Ana Duarte  \n",
              "\n",
              "[3 rows x 23 columns]"
            ]
          },
          "execution_count": 277,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_final.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "se borran columnas que se repiten, precio y total"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 278,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_final.drop(columns=['precio', 'total'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 279,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 24527 entries, 0 to 24526\n",
            "Data columns (total 21 columns):\n",
            " #   Column           Non-Null Count  Dtype         \n",
            "---  ------           --------------  -----         \n",
            " 0   id_recibo        24527 non-null  int64         \n",
            " 1   producto_id      24527 non-null  int64         \n",
            " 2   pedido_id        24527 non-null  int64         \n",
            " 3   cantidad         24527 non-null  int64         \n",
            " 4   valor_unitario   24527 non-null  int64         \n",
            " 5   valor_total      24527 non-null  int64         \n",
            " 6   ciudad           24527 non-null  object        \n",
            " 7   costo_envio      24527 non-null  float64       \n",
            " 8   latitud          24527 non-null  float64       \n",
            " 9   longitud         24527 non-null  float64       \n",
            " 10  vendedor_id      24527 non-null  int64         \n",
            " 11  fecha_compra     24527 non-null  datetime64[ns]\n",
            " 12  anio             24527 non-null  int32         \n",
            " 13  mes              24527 non-null  object        \n",
            " 14  num_mes          24527 non-null  int32         \n",
            " 15  producto         24527 non-null  object        \n",
            " 16  tipo_producto    24527 non-null  object        \n",
            " 17  marca            24527 non-null  object        \n",
            " 18  sku              24527 non-null  float64       \n",
            " 19  condicion        24527 non-null  object        \n",
            " 20  nombre_vendedor  24527 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(4), int32(2), int64(7), object(7)\n",
            "memory usage: 3.7+ MB\n"
          ]
        }
      ],
      "source": [
        "df_final.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# EDA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 280,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_recibo</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>cantidad</th>\n",
              "      <th>valor_unitario</th>\n",
              "      <th>valor_total</th>\n",
              "      <th>costo_envio</th>\n",
              "      <th>latitud</th>\n",
              "      <th>longitud</th>\n",
              "      <th>vendedor_id</th>\n",
              "      <th>fecha_compra</th>\n",
              "      <th>anio</th>\n",
              "      <th>num_mes</th>\n",
              "      <th>sku</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527.00000</td>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527</td>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527.000000</td>\n",
              "      <td>24527.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12405.414278</td>\n",
              "      <td>44627.641171</td>\n",
              "      <td>69931.383088</td>\n",
              "      <td>3.497126</td>\n",
              "      <td>535.68867</td>\n",
              "      <td>1867.490113</td>\n",
              "      <td>373.498023</td>\n",
              "      <td>-12.332892</td>\n",
              "      <td>-47.389147</td>\n",
              "      <td>2.975782</td>\n",
              "      <td>2020-04-02 12:10:46.406001664</td>\n",
              "      <td>2019.741020</td>\n",
              "      <td>6.676887</td>\n",
              "      <td>302916.864802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2706.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.00000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>-30.034600</td>\n",
              "      <td>-70.812000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2019-01-05 00:00:00</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>101494.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6132.500000</td>\n",
              "      <td>17258.000000</td>\n",
              "      <td>34487.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>210.00000</td>\n",
              "      <td>520.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>-20.315500</td>\n",
              "      <td>-52.021500</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2019-11-12 00:00:00</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>222028.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12264.000000</td>\n",
              "      <td>42913.000000</td>\n",
              "      <td>69945.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>336.00000</td>\n",
              "      <td>1050.000000</td>\n",
              "      <td>210.000000</td>\n",
              "      <td>-10.252700</td>\n",
              "      <td>-47.882800</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2020-04-09 00:00:00</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>263899.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>18689.500000</td>\n",
              "      <td>70673.000000</td>\n",
              "      <td>105355.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>450.00000</td>\n",
              "      <td>1920.000000</td>\n",
              "      <td>384.000000</td>\n",
              "      <td>-5.089200</td>\n",
              "      <td>-38.543400</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2020-09-21 00:00:00</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>304828.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>25055.000000</td>\n",
              "      <td>100983.000000</td>\n",
              "      <td>139993.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6500.00000</td>\n",
              "      <td>39000.000000</td>\n",
              "      <td>7800.000000</td>\n",
              "      <td>2.823500</td>\n",
              "      <td>-34.877000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2021-03-10 00:00:00</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>952821.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7242.697484</td>\n",
              "      <td>29347.824879</td>\n",
              "      <td>40560.769565</td>\n",
              "      <td>1.706170</td>\n",
              "      <td>839.21405</td>\n",
              "      <td>3379.681887</td>\n",
              "      <td>675.936377</td>\n",
              "      <td>8.829049</td>\n",
              "      <td>9.068160</td>\n",
              "      <td>1.442412</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.583029</td>\n",
              "      <td>3.698035</td>\n",
              "      <td>186964.667915</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          id_recibo    producto_id      pedido_id      cantidad  \\\n",
              "count  24527.000000   24527.000000   24527.000000  24527.000000   \n",
              "mean   12405.414278   44627.641171   69931.383088      3.497126   \n",
              "min        1.000000    2706.000000       1.000000      1.000000   \n",
              "25%     6132.500000   17258.000000   34487.000000      2.000000   \n",
              "50%    12264.000000   42913.000000   69945.000000      4.000000   \n",
              "75%    18689.500000   70673.000000  105355.000000      5.000000   \n",
              "max    25055.000000  100983.000000  139993.000000      6.000000   \n",
              "std     7242.697484   29347.824879   40560.769565      1.706170   \n",
              "\n",
              "       valor_unitario   valor_total   costo_envio       latitud      longitud  \\\n",
              "count     24527.00000  24527.000000  24527.000000  24527.000000  24527.000000   \n",
              "mean        535.68867   1867.490113    373.498023    -12.332892    -47.389147   \n",
              "min          45.00000     45.000000      9.000000    -30.034600    -70.812000   \n",
              "25%         210.00000    520.000000    104.000000    -20.315500    -52.021500   \n",
              "50%         336.00000   1050.000000    210.000000    -10.252700    -47.882800   \n",
              "75%         450.00000   1920.000000    384.000000     -5.089200    -38.543400   \n",
              "max        6500.00000  39000.000000   7800.000000      2.823500    -34.877000   \n",
              "std         839.21405   3379.681887    675.936377      8.829049      9.068160   \n",
              "\n",
              "        vendedor_id                   fecha_compra          anio  \\\n",
              "count  24527.000000                          24527  24527.000000   \n",
              "mean       2.975782  2020-04-02 12:10:46.406001664   2019.741020   \n",
              "min        1.000000            2019-01-05 00:00:00   2019.000000   \n",
              "25%        2.000000            2019-11-12 00:00:00   2019.000000   \n",
              "50%        3.000000            2020-04-09 00:00:00   2020.000000   \n",
              "75%        4.000000            2020-09-21 00:00:00   2020.000000   \n",
              "max        6.000000            2021-03-10 00:00:00   2021.000000   \n",
              "std        1.442412                            NaN      0.583029   \n",
              "\n",
              "            num_mes            sku  \n",
              "count  24527.000000   24527.000000  \n",
              "mean       6.676887  302916.864802  \n",
              "min        1.000000  101494.000000  \n",
              "25%        3.000000  222028.000000  \n",
              "50%        7.000000  263899.000000  \n",
              "75%       10.000000  304828.000000  \n",
              "max       12.000000  952821.000000  \n",
              "std        3.698035  186964.667915  "
            ]
          },
          "execution_count": 280,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_final.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Graficas\n",
        "distribucion de los productos vendidos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 281,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_recibo</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>cantidad</th>\n",
              "      <th>valor_unitario</th>\n",
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              "      <th>latitud</th>\n",
              "      <th>longitud</th>\n",
              "      <th>...</th>\n",
              "      <th>fecha_compra</th>\n",
              "      <th>anio</th>\n",
              "      <th>mes</th>\n",
              "      <th>num_mes</th>\n",
              "      <th>producto</th>\n",
              "      <th>tipo_producto</th>\n",
              "      <th>marca</th>\n",
              "      <th>sku</th>\n",
              "      <th>condicion</th>\n",
              "      <th>nombre_vendedor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41518</td>\n",
              "      <td>341</td>\n",
              "      <td>3</td>\n",
              "      <td>260</td>\n",
              "      <td>780</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>156.0</td>\n",
              "      <td>-12.9704</td>\n",
              "      <td>-38.5124</td>\n",
              "      <td>...</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "      <td>Calca Jeans Basica</td>\n",
              "      <td>Calca</td>\n",
              "      <td>Banana Republic</td>\n",
              "      <td>255410.0</td>\n",
              "      <td>Nuevo con etiqueta</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4307</td>\n",
              "      <td>1174</td>\n",
              "      <td>5</td>\n",
              "      <td>6175</td>\n",
              "      <td>30875</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>6175.0</td>\n",
              "      <td>-22.9068</td>\n",
              "      <td>-43.1729</td>\n",
              "      <td>...</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "      <td>Bolsa Intrecciato Caramelo</td>\n",
              "      <td>Bolsa</td>\n",
              "      <td>Bottega Veneta</td>\n",
              "      <td>301305.0</td>\n",
              "      <td>Usado</td>\n",
              "      <td>Nadia Oliveira</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   id_recibo  producto_id  pedido_id  cantidad  valor_unitario  valor_total  \\\n",
              "0          1        41518        341         3             260          780   \n",
              "1          2         4307       1174         5            6175        30875   \n",
              "\n",
              "           ciudad  costo_envio  latitud  longitud  ...  fecha_compra  anio  \\\n",
              "0           Bahia        156.0 -12.9704  -38.5124  ...    2019-01-05  2019   \n",
              "1  Rio de Janeiro       6175.0 -22.9068  -43.1729  ...    2019-01-05  2019   \n",
              "\n",
              "       mes num_mes                    producto tipo_producto            marca  \\\n",
              "0  January       1          Calca Jeans Basica         Calca  Banana Republic   \n",
              "1  January       1  Bolsa Intrecciato Caramelo         Bolsa   Bottega Veneta   \n",
              "\n",
              "        sku           condicion nombre_vendedor  \n",
              "0  255410.0  Nuevo con etiqueta         Unknown  \n",
              "1  301305.0               Usado  Nadia Oliveira  \n",
              "\n",
              "[2 rows x 21 columns]"
            ]
          },
          "execution_count": 281,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_final.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 282,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "39"
            ]
          },
          "execution_count": 282,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_final['tipo_producto'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 283,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tipo_producto</th>\n",
              "      <th>cantidad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Vestido</td>\n",
              "      <td>18483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Blusa</td>\n",
              "      <td>9822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Calca</td>\n",
              "      <td>9674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Saia</td>\n",
              "      <td>7181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Camisa</td>\n",
              "      <td>4792</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  tipo_producto  cantidad\n",
              "0       Vestido     18483\n",
              "1         Blusa      9822\n",
              "2         Calca      9674\n",
              "3          Saia      7181\n",
              "4        Camisa      4792"
            ]
          },
          "execution_count": 283,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_vendidos = df_final.groupby('tipo_producto')['cantidad'].sum().sort_values(ascending=False).reset_index().head(10)\n",
        "df_vendidos.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 284,
      "metadata": {},
      "outputs": [],
      "source": [
        "def grafico_barras(x, y, titulo, subtitulo, x_label, y_label):\n",
        "    fig, ax = plt.subplots()\n",
        "    plt.subplots_adjust(top=0.90) \n",
        "    ax.bar(x, y,color='#4CAF50', edgecolor='black')\n",
        "    ax.grid(axis = 'y', linestyle='--', linewidth=1.2)\n",
        "    fig.patch.set_facecolor('lightgrey')\n",
        "    fig.suptitle(titulo, fontsize=16, fontweight='bold', y=1.05)\n",
        "    fig.text(0.5, 0.95, subtitulo, ha='center', fontsize=10)\n",
        "    ax.set_xlabel(x_label, fontsize=12, fontweight='bold')\n",
        "    ax.set_ylabel(y_label, fontsize=12, fontweight='bold')\n",
        "    plt.xticks(rotation=90);\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 285,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "grafico_barras(df_vendidos['tipo_producto'], df_vendidos['cantidad'], 'Distribucion de productos mas vendidos', 'Periodo: Enero 2019 - Marzo 2021', 'Tipos de producto', 'Cantidad Vendida' )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Distribución de ciudades con mas productos vendidos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 286,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ciudad</th>\n",
              "      <th>valor_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alagoas</td>\n",
              "      <td>1902520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Pernambuco</td>\n",
              "      <td>1886497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Santa Catarina</td>\n",
              "      <td>1846203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Amapá</td>\n",
              "      <td>1844672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ceará</td>\n",
              "      <td>1835101</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ciudad  valor_total\n",
              "0         Alagoas      1902520\n",
              "1      Pernambuco      1886497\n",
              "2  Santa Catarina      1846203\n",
              "3           Amapá      1844672\n",
              "4           Ceará      1835101"
            ]
          },
          "execution_count": 286,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ciudades = df_final.groupby('ciudad')['valor_total'].sum().sort_values(ascending=False).reset_index()\n",
        "df_ciudades.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 287,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "grafico_barras(df_ciudades['ciudad'], df_ciudades['valor_total'], 'distribución de ventas en dinero por ciudad', 'Periodo: Enero 2019 - Marzo 2021' , 'Ciudad', 'Cantidad recaudada')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Condición de los articulos que se venden"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 288,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>condicion</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Usado</td>\n",
              "      <td>21050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Nuevo con etiqueta</td>\n",
              "      <td>2606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nuevo sin etiqueta</td>\n",
              "      <td>871</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            condicion  count\n",
              "0               Usado  21050\n",
              "1  Nuevo con etiqueta   2606\n",
              "2  Nuevo sin etiqueta    871"
            ]
          },
          "execution_count": 288,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "condicion = df_final['condicion'].value_counts().reset_index()\n",
        "condicion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Grafico de pie"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 289,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Datos (asumiendo que tienes un DataFrame llamado df)\n",
        "etiquetas = condicion['condicion']\n",
        "valores = condicion['count']\n",
        "\n",
        "# Colores personalizados\n",
        "colores = ['#1f77b4', '#ff7f0e', '#2ca02c']  # Azul, naranja, verde\n",
        "\n",
        "# Crear la figura y los ejes\n",
        "fig, ax = plt.subplots(figsize=(8, 6))  # Ajustar el tamaño del gráfico\n",
        "\n",
        "# Crear el gráfico de pie\n",
        "wedges, texts, autotexts = ax.pie(valores, labels=etiquetas, autopct='%1.1f%%', startangle=90, colors=colores,\n",
        "                                 wedgeprops=dict(width=1), textprops=dict(color=\"w\"))\n",
        "\n",
        "# Título y subtítulo\n",
        "ax.set_title('Distribución de la condición de los Productos vendidos', fontsize=14, fontweight='bold')\n",
        "fig.text(0.5, 0.85, 'Periodo: Enero 2019 - Marzo 2021', ha='center', fontsize=10)\n",
        "ax.text(0.2, -0.05, 'Fuente: Datos de ventas internas', ha='center', fontsize=10, fontdict= dict(color=\"w\"))\n",
        "\n",
        "# Ajustar el tamaño del texto de los porcentajes\n",
        "plt.setp(autotexts, size=10, weight=\"bold\")\n",
        "\n",
        "ax.legend(wedges, etiquetas, title=\"Condición\", loc=\"center left\", bbox_to_anchor=(1, 0, 0.5, 1))\n",
        "\n",
        "# Mostrar el gráfico\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### **Preguntas**\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "1. ¿Cual es el Top 5 productos más vendidos históricamente?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 290,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>producto</th>\n",
              "      <th>cantidad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Vestido Recortes Cores</td>\n",
              "      <td>795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Calca Jeans Costuras</td>\n",
              "      <td>755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Saia Pregas Preta</td>\n",
              "      <td>751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Calca Alfaiataria Preta</td>\n",
              "      <td>750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Saia Midi Cinto</td>\n",
              "      <td>549</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  producto  cantidad\n",
              "0   Vestido Recortes Cores       795\n",
              "1     Calca Jeans Costuras       755\n",
              "2        Saia Pregas Preta       751\n",
              "3  Calca Alfaiataria Preta       750\n",
              "4          Saia Midi Cinto       549"
            ]
          },
          "execution_count": 290,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "df_prod_mas_vendidos = df_final.groupby('producto')['cantidad'].sum().sort_values(ascending=False).reset_index().head(5)\n",
        "df_prod_mas_vendidos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "grafica de dichos productos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 291,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "grafico_barras(df_prod_mas_vendidos['producto'], df_prod_mas_vendidos['cantidad'], 'Top 5 de los pructos mas vendidos históricamente', 'Periodo: Enero 2019 - Marzo 2021', 'productos', 'cantidad')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "2. ¿Cual es la evolución histórica de las ingresos netos?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se crea otra columna con los ingresos netos, la cual resulta de restar al valor total el costo de envio del producto"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 292,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_final['ingreso_neto'] = df_final['valor_total'] - df_final['costo_envio']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Creamos un DataFrame agrupando la suma de todas las ganancias netas por mes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 293,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fecha_compra</th>\n",
              "      <th>ingreso_neto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2020-09-30</td>\n",
              "      <td>1942581.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2020-10-31</td>\n",
              "      <td>1663932.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2020-11-30</td>\n",
              "      <td>1664667.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>2763424.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fecha_compra  ingreso_neto\n",
              "20   2020-09-30     1942581.6\n",
              "21   2020-10-31     1663932.0\n",
              "22   2020-11-30     1664667.2\n",
              "23   2020-12-31     2763424.8"
            ]
          },
          "execution_count": 293,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ingreso_neto = df_final[['fecha_compra', 'ingreso_neto']].groupby(pd.Grouper(key= 'fecha_compra', freq='ME')).sum().reset_index()\n",
        "# debido a que los datos del 2021 parecen estar viciados para esta consulta se filtran y no se toman en cuenta\n",
        "df_ingreso_neto = df_ingreso_neto [df_ingreso_neto['fecha_compra'].dt.year != 2021]\n",
        "df_ingreso_neto.tail(4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Grafico de linea"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 294,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns # type: ignore\n",
        "sns.set_palette('Accent')\n",
        "sns.set_style('darkgrid')\n",
        "ax =sns.lineplot(x = 'fecha_compra', y = 'ingreso_neto', data = df_ingreso_neto)\n",
        "ax.figure.set_size_inches(15,6)\n",
        "ax.set_title('Comportamiento de las ventas a lo largo de los meses (2019-2021)', loc='center', fontsize = 16,  fontweight='bold')\n",
        "ax.set_xlabel('Meses', fontsize = 14, fontweight='bold')\n",
        "ax.set_ylabel('ingresos netos', fontsize = 14, fontweight='bold')\n",
        "ax = ax"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "3. ¿Cuáles son los ingresos netos por vendedor por año?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 304,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_recibo</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>cantidad</th>\n",
              "      <th>valor_unitario</th>\n",
              "      <th>valor_total</th>\n",
              "      <th>ciudad</th>\n",
              "      <th>costo_envio</th>\n",
              "      <th>latitud</th>\n",
              "      <th>longitud</th>\n",
              "      <th>...</th>\n",
              "      <th>anio</th>\n",
              "      <th>mes</th>\n",
              "      <th>num_mes</th>\n",
              "      <th>producto</th>\n",
              "      <th>tipo_producto</th>\n",
              "      <th>marca</th>\n",
              "      <th>sku</th>\n",
              "      <th>condicion</th>\n",
              "      <th>nombre_vendedor</th>\n",
              "      <th>ingreso_neto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41518</td>\n",
              "      <td>341</td>\n",
              "      <td>3</td>\n",
              "      <td>260</td>\n",
              "      <td>780</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>156.0</td>\n",
              "      <td>-12.9704</td>\n",
              "      <td>-38.5124</td>\n",
              "      <td>...</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "      <td>Calca Jeans Basica</td>\n",
              "      <td>Calca</td>\n",
              "      <td>Banana Republic</td>\n",
              "      <td>255410.0</td>\n",
              "      <td>Nuevo con etiqueta</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>624.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   id_recibo  producto_id  pedido_id  cantidad  valor_unitario  valor_total  \\\n",
              "0          1        41518        341         3             260          780   \n",
              "\n",
              "  ciudad  costo_envio  latitud  longitud  ...  anio      mes  num_mes  \\\n",
              "0  Bahia        156.0 -12.9704  -38.5124  ...  2019  January        1   \n",
              "\n",
              "             producto  tipo_producto            marca       sku  \\\n",
              "0  Calca Jeans Basica          Calca  Banana Republic  255410.0   \n",
              "\n",
              "            condicion  nombre_vendedor ingreso_neto  \n",
              "0  Nuevo con etiqueta          Unknown        624.0  \n",
              "\n",
              "[1 rows x 22 columns]"
            ]
          },
          "execution_count": 304,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_final.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 314,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nombre_vendedor</th>\n",
              "      <th>anio</th>\n",
              "      <th>ingreso_neto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nadia Oliveira</td>\n",
              "      <td>2021</td>\n",
              "      <td>810020.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ana Duarte</td>\n",
              "      <td>2021</td>\n",
              "      <td>547470.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Paulo Calanca</td>\n",
              "      <td>2021</td>\n",
              "      <td>455401.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Daniel Siqueira</td>\n",
              "      <td>2021</td>\n",
              "      <td>419702.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Millena Pereira</td>\n",
              "      <td>2021</td>\n",
              "      <td>398896.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Daniel Siqueira</td>\n",
              "      <td>2020</td>\n",
              "      <td>5266932.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ana Duarte</td>\n",
              "      <td>2020</td>\n",
              "      <td>5054009.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nadia Oliveira</td>\n",
              "      <td>2020</td>\n",
              "      <td>4046902.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Millena Pereira</td>\n",
              "      <td>2020</td>\n",
              "      <td>3804385.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Paulo Calanca</td>\n",
              "      <td>2020</td>\n",
              "      <td>3609530.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Paulo Calanca</td>\n",
              "      <td>2019</td>\n",
              "      <td>3702835.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Nadia Oliveira</td>\n",
              "      <td>2019</td>\n",
              "      <td>2296986.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Ana Duarte</td>\n",
              "      <td>2019</td>\n",
              "      <td>2154261.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Millena Pereira</td>\n",
              "      <td>2019</td>\n",
              "      <td>2054682.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Daniel Siqueira</td>\n",
              "      <td>2019</td>\n",
              "      <td>2020503.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Unknown</td>\n",
              "      <td>2019</td>\n",
              "      <td>624.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    nombre_vendedor  anio  ingreso_neto\n",
              "0    Nadia Oliveira  2021      810020.0\n",
              "1        Ana Duarte  2021      547470.4\n",
              "2     Paulo Calanca  2021      455401.6\n",
              "3   Daniel Siqueira  2021      419702.4\n",
              "4   Millena Pereira  2021      398896.0\n",
              "5   Daniel Siqueira  2020     5266932.8\n",
              "6        Ana Duarte  2020     5054009.6\n",
              "7    Nadia Oliveira  2020     4046902.4\n",
              "8   Millena Pereira  2020     3804385.6\n",
              "9     Paulo Calanca  2020     3609530.4\n",
              "10    Paulo Calanca  2019     3702835.2\n",
              "11   Nadia Oliveira  2019     2296986.4\n",
              "12       Ana Duarte  2019     2154261.6\n",
              "13  Millena Pereira  2019     2054682.4\n",
              "14  Daniel Siqueira  2019     2020503.2\n",
              "15          Unknown  2019         624.0"
            ]
          },
          "execution_count": 314,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ingresos_x_vendedor = df_final.groupby(['nombre_vendedor','anio'])['ingreso_neto'].sum().sort_values(ascending=False).reset_index()\n",
        "df_ingresos_x_vendedor = df_ingresos_x_vendedor.sort_values(by=['anio', 'ingreso_neto'], ascending=False).reset_index(drop=True)\n",
        "df_ingresos_x_vendedor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "4. ¿Cuáles son las ciudades que proporcionan mayores ingresos netos?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 296,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_recibo</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>cantidad</th>\n",
              "      <th>valor_unitario</th>\n",
              "      <th>valor_total</th>\n",
              "      <th>ciudad</th>\n",
              "      <th>costo_envio</th>\n",
              "      <th>latitud</th>\n",
              "      <th>longitud</th>\n",
              "      <th>...</th>\n",
              "      <th>anio</th>\n",
              "      <th>mes</th>\n",
              "      <th>num_mes</th>\n",
              "      <th>producto</th>\n",
              "      <th>tipo_producto</th>\n",
              "      <th>marca</th>\n",
              "      <th>sku</th>\n",
              "      <th>condicion</th>\n",
              "      <th>nombre_vendedor</th>\n",
              "      <th>ingreso_neto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41518</td>\n",
              "      <td>341</td>\n",
              "      <td>3</td>\n",
              "      <td>260</td>\n",
              "      <td>780</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>156.0</td>\n",
              "      <td>-12.9704</td>\n",
              "      <td>-38.5124</td>\n",
              "      <td>...</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "      <td>Calca Jeans Basica</td>\n",
              "      <td>Calca</td>\n",
              "      <td>Banana Republic</td>\n",
              "      <td>255410.0</td>\n",
              "      <td>Nuevo con etiqueta</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>624.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4307</td>\n",
              "      <td>1174</td>\n",
              "      <td>5</td>\n",
              "      <td>6175</td>\n",
              "      <td>30875</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>6175.0</td>\n",
              "      <td>-22.9068</td>\n",
              "      <td>-43.1729</td>\n",
              "      <td>...</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "      <td>Bolsa Intrecciato Caramelo</td>\n",
              "      <td>Bolsa</td>\n",
              "      <td>Bottega Veneta</td>\n",
              "      <td>301305.0</td>\n",
              "      <td>Usado</td>\n",
              "      <td>Nadia Oliveira</td>\n",
              "      <td>24700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>22407</td>\n",
              "      <td>1399</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>600</td>\n",
              "      <td>Paraíba</td>\n",
              "      <td>120.0</td>\n",
              "      <td>-7.2398</td>\n",
              "      <td>-36.7819</td>\n",
              "      <td>...</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "      <td>Cropped Listras Florzinhas</td>\n",
              "      <td>Cropped</td>\n",
              "      <td>Topshop</td>\n",
              "      <td>327426.0</td>\n",
              "      <td>Usado</td>\n",
              "      <td>Ana Duarte</td>\n",
              "      <td>480.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   id_recibo  producto_id  pedido_id  cantidad  valor_unitario  valor_total  \\\n",
              "0          1        41518        341         3             260          780   \n",
              "1          2         4307       1174         5            6175        30875   \n",
              "2          3        22407       1399         3             200          600   \n",
              "\n",
              "           ciudad  costo_envio  latitud  longitud  ...  anio      mes  \\\n",
              "0           Bahia        156.0 -12.9704  -38.5124  ...  2019  January   \n",
              "1  Rio de Janeiro       6175.0 -22.9068  -43.1729  ...  2019  January   \n",
              "2         Paraíba        120.0  -7.2398  -36.7819  ...  2019  January   \n",
              "\n",
              "   num_mes                    producto  tipo_producto            marca  \\\n",
              "0        1          Calca Jeans Basica          Calca  Banana Republic   \n",
              "1        1  Bolsa Intrecciato Caramelo          Bolsa   Bottega Veneta   \n",
              "2        1  Cropped Listras Florzinhas        Cropped          Topshop   \n",
              "\n",
              "        sku           condicion  nombre_vendedor ingreso_neto  \n",
              "0  255410.0  Nuevo con etiqueta          Unknown        624.0  \n",
              "1  301305.0               Usado   Nadia Oliveira      24700.0  \n",
              "2  327426.0               Usado       Ana Duarte        480.0  \n",
              "\n",
              "[3 rows x 22 columns]"
            ]
          },
          "execution_count": 296,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_final.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 297,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ciudad</th>\n",
              "      <th>ingreso_neto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alagoas</td>\n",
              "      <td>1522016.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Pernambuco</td>\n",
              "      <td>1509197.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Santa Catarina</td>\n",
              "      <td>1476962.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Amapá</td>\n",
              "      <td>1475737.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ceará</td>\n",
              "      <td>1468080.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ciudad  ingreso_neto\n",
              "0         Alagoas     1522016.0\n",
              "1      Pernambuco     1509197.6\n",
              "2  Santa Catarina     1476962.4\n",
              "3           Amapá     1475737.6\n",
              "4           Ceará     1468080.8"
            ]
          },
          "execution_count": 297,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ciudades_ingresos_netos= df_final.groupby('ciudad')['ingreso_neto'].sum().sort_values(ascending=False).reset_index()\n",
        "df_ciudades_ingresos_netos.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "5. ¿Existe otro insight que puedas proporcionar? marca de ropa con mas ingresos netos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 298,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>marca</th>\n",
              "      <th>ingreso_neto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mixed</td>\n",
              "      <td>3671312.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Christian Dior</td>\n",
              "      <td>2529200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bottega Veneta</td>\n",
              "      <td>1773460.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Christian Louboutin</td>\n",
              "      <td>1457040.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Barbara Bela</td>\n",
              "      <td>1451520.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 marca  ingreso_neto\n",
              "0                Mixed     3671312.8\n",
              "1       Christian Dior     2529200.0\n",
              "2       Bottega Veneta     1773460.0\n",
              "3  Christian Louboutin     1457040.0\n",
              "4         Barbara Bela     1451520.0"
            ]
          },
          "execution_count": 298,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_marca_ropa_ingresos_netos = df_final.groupby('marca')['ingreso_neto'].sum().sort_values(ascending=False).reset_index()\n",
        "df_marca_ropa_ingresos_netos.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 299,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_final.to_csv(\"D:/bootcamp_experience/visualizacion/df_final.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 300,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_recibo</th>\n",
              "      <th>producto_id</th>\n",
              "      <th>pedido_id</th>\n",
              "      <th>cantidad</th>\n",
              "      <th>valor_unitario</th>\n",
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              "      <th>latitud</th>\n",
              "      <th>longitud</th>\n",
              "      <th>...</th>\n",
              "      <th>anio</th>\n",
              "      <th>mes</th>\n",
              "      <th>num_mes</th>\n",
              "      <th>producto</th>\n",
              "      <th>tipo_producto</th>\n",
              "      <th>marca</th>\n",
              "      <th>sku</th>\n",
              "      <th>condicion</th>\n",
              "      <th>nombre_vendedor</th>\n",
              "      <th>ingreso_neto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41518</td>\n",
              "      <td>341</td>\n",
              "      <td>3</td>\n",
              "      <td>260</td>\n",
              "      <td>780</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>156.0</td>\n",
              "      <td>-12.9704</td>\n",
              "      <td>-38.5124</td>\n",
              "      <td>...</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
              "      <td>1</td>\n",
              "      <td>Calca Jeans Basica</td>\n",
              "      <td>Calca</td>\n",
              "      <td>Banana Republic</td>\n",
              "      <td>255410.0</td>\n",
              "      <td>Nuevo con etiqueta</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>624.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   id_recibo  producto_id  pedido_id  cantidad  valor_unitario  valor_total  \\\n",
              "0          1        41518        341         3             260          780   \n",
              "\n",
              "  ciudad  costo_envio  latitud  longitud  ...  anio      mes  num_mes  \\\n",
              "0  Bahia        156.0 -12.9704  -38.5124  ...  2019  January        1   \n",
              "\n",
              "             producto  tipo_producto            marca       sku  \\\n",
              "0  Calca Jeans Basica          Calca  Banana Republic  255410.0   \n",
              "\n",
              "            condicion  nombre_vendedor ingreso_neto  \n",
              "0  Nuevo con etiqueta          Unknown        624.0  \n",
              "\n",
              "[1 rows x 22 columns]"
            ]
          },
          "execution_count": 300,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_final.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 303,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2763424.8"
            ]
          },
          "execution_count": 303,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "resultado = df_final['ingreso_neto'][(df_final['anio']==2020) & (df_final['mes']== 'December')].sum()\n",
        "resultado"
      ]
    }
  ],
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