Proyecto3TripleTen.DataWrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4295c6a0",
   "metadata": {},
   "source": [
    "¡Hola!\n",
    "\n",
    "Mi nombre es Tonatiuh Cruz. Me complace revisar tu proyecto hoy.\n",
    "\n",
    "Al identificar cualquier error inicialmente, simplemente los destacaré. Te animo a localizar y abordar los problemas de forma independiente como parte de tu preparación para un rol como data-scientist. En un entorno profesional, tu líder de equipo seguiría un enfoque similar. Si encuentras la tarea desafiante, proporcionaré una pista más específica en la próxima iteración.\n",
    "\n",
    "Encontrarás mis comentarios a continuación - **por favor no los muevas, modifiques o elimines**.\n",
    "\n",
    "Puedes encontrar mis comentarios en cajas verdes, amarillas o rojas como esta:\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Comentario del revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Éxito. Todo está hecho correctamente.\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Comentario del revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Observaciones. Algunas recomendaciones.\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Comentario del revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Necesita corrección. El bloque requiere algunas correcciones. El trabajo no puede ser aceptado con comentarios en rojo.\n",
    "</div>\n",
    "\n",
    "Puedes responderme utilizando esto:\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Respuesta del estudiante.</b> <a class=\"tocSkip\"></a>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7147a2c",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Respuesta del estudiante.</b> <a class=\"tocSkip\"></a>\n",
    "</div> # Mucho gusto Tonatiuh, a continuación te envío las modificaciones que solicitaste, muchas gracias "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3Xg_RQfnafXz",
   "metadata": {
    "id": "3Xg_RQfnafXz"
   },
   "source": [
    "# ¡Llena ese carrito!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mhIvmmkW414q",
   "metadata": {
    "id": "mhIvmmkW414q"
   },
   "source": [
    "# Introducción\n",
    "\n",
    "Instacart es una plataforma de entregas de comestibles donde la clientela puede registrar un pedido y hacer que se lo entreguen, similar a Uber Eats y Door Dash.\n",
    "El conjunto de datos que te hemos proporcionado tiene modificaciones del original. Redujimos el tamaño del conjunto para que tus cálculos se hicieran más rápido e introdujimos valores ausentes y duplicados. Tuvimos cuidado de conservar las distribuciones de los datos originales cuando hicimos los cambios.\n",
    "\n",
    "Debes completar tres pasos. Para cada uno de ellos, escribe una breve introducción que refleje con claridad cómo pretendes resolver cada paso, y escribe párrafos explicatorios que justifiquen tus decisiones al tiempo que avanzas en tu solución.  También escribe una conclusión que resuma tus hallazgos y elecciones.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3MbyzpOQZ5Or",
   "metadata": {
    "id": "3MbyzpOQZ5Or"
   },
   "source": [
    "## Diccionario de datos\n",
    "\n",
    "Hay cinco tablas en el conjunto de datos, y tendrás que usarlas todas para hacer el preprocesamiento de datos y el análisis exploratorio de datos. A continuación se muestra un diccionario de datos que enumera las columnas de cada tabla y describe los datos que contienen.\n",
    "\n",
    "- `instacart_orders.csv`: cada fila corresponde a un pedido en la aplicación Instacart.\n",
    "    - `'order_id'`: número de ID que identifica de manera única cada pedido.\n",
    "    - `'user_id'`: número de ID que identifica de manera única la cuenta de cada cliente.\n",
    "    - `'order_number'`: el número de veces que este cliente ha hecho un pedido.\n",
    "    - `'order_dow'`: día de la semana en que se hizo el pedido (0 si es domingo).\n",
    "    - `'order_hour_of_day'`: hora del día en que se hizo el pedido.\n",
    "    - `'days_since_prior_order'`: número de días transcurridos desde que este cliente hizo su pedido anterior.\n",
    "- `products.csv`: cada fila corresponde a un producto único que pueden comprar los clientes.\n",
    "    - `'product_id'`: número ID que identifica de manera única cada producto.\n",
    "    - `'product_name'`: nombre del producto.\n",
    "    - `'aisle_id'`: número ID que identifica de manera única cada categoría de pasillo de víveres.\n",
    "    - `'department_id'`: número ID que identifica de manera única cada departamento de víveres.\n",
    "- `order_products.csv`: cada fila corresponde a un artículo pedido en un pedido.\n",
    "    - `'order_id'`: número de ID que identifica de manera única cada pedido.\n",
    "    - `'product_id'`: número ID que identifica de manera única cada producto.\n",
    "    - `'add_to_cart_order'`: el orden secuencial en el que se añadió cada artículo en el carrito.\n",
    "    - `'reordered'`: 0 si el cliente nunca ha pedido este producto antes, 1 si lo ha pedido.\n",
    "- `aisles.csv`\n",
    "    - `'aisle_id'`: número ID que identifica de manera única cada categoría de pasillo de víveres.\n",
    "    - `'aisle'`: nombre del pasillo.\n",
    "- `departments.csv`\n",
    "    - `'department_id'`: número ID que identifica de manera única cada departamento de víveres.\n",
    "    - `'department'`: nombre del departamento."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "n3Ha_cNSZ8lK",
   "metadata": {
    "id": "n3Ha_cNSZ8lK"
   },
   "source": [
    "# Paso 1. Descripción de los datos\n",
    "\n",
    "Lee los archivos de datos (`/datasets/instacart_orders.csv`, `/datasets/products.csv`, `/datasets/aisles.csv`, `/datasets/departments.csv` y `/datasets/order_products.csv`) con `pd.read_csv()` usando los parámetros adecuados para leer los datos correctamente. Verifica la información para cada DataFrame creado.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OmlQPLlyaAfR",
   "metadata": {
    "id": "OmlQPLlyaAfR"
   },
   "source": [
    "## Plan de solución\n",
    "\n",
    "Escribe aquí tu plan de solución para el Paso 1. Descripción de los datos. \n",
    "#Para poder describir los datos, se procede a importar la liberia pandas y posteriormente se usa su herramienta pd.read_csv para poder leer los archivos solicitados. por último usamos el método .info() para conocer la información general del dataframe, usando \"sep=;\" ya que por defecto \"pd.read_csv\" considera que los valores estan separados por comas, mientras que en los df se utiliza \";\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fbf1adab",
   "metadata": {
    "id": "fbf1adab"
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt # importar librerías"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad36396d",
   "metadata": {
    "id": "ad36396d"
   },
   "outputs": [],
   "source": [
    "orders= pd.read_csv(\"/datasets/instacart_orders.csv\", sep=\";\")\n",
    "products= pd.read_csv(\"/datasets/products.csv\", sep=\";\" )\n",
    "aisles= pd.read_csv(\"/datasets/aisles.csv\", sep=\";\")\n",
    "departments= pd.read_csv(\"/datasets/departments.csv\", sep=\";\")\n",
    "order_products= pd.read_csv(\"/datasets/order_products.csv\", sep=\";\")\n",
    "# leer conjuntos de datos en los DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad6a1efa",
   "metadata": {
    "id": "ad6a1efa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 478967 entries, 0 to 478966\n",
      "Data columns (total 6 columns):\n",
      " #   Column                  Non-Null Count   Dtype  \n",
      "---  ------                  --------------   -----  \n",
      " 0   order_id                478967 non-null  int64  \n",
      " 1   user_id                 478967 non-null  int64  \n",
      " 2   order_number            478967 non-null  int64  \n",
      " 3   order_dow               478967 non-null  int64  \n",
      " 4   order_hour_of_day       478967 non-null  int64  \n",
      " 5   days_since_prior_order  450148 non-null  float64\n",
      "dtypes: float64(1), int64(5)\n",
      "memory usage: 21.9 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(orders.info())# mostrar información del DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5130e05e",
   "metadata": {
    "id": "5130e05e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 49694 entries, 0 to 49693\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   product_id     49694 non-null  int64 \n",
      " 1   product_name   48436 non-null  object\n",
      " 2   aisle_id       49694 non-null  int64 \n",
      " 3   department_id  49694 non-null  int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 1.5+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(products.info())# mostrar información del DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8370c05",
   "metadata": {
    "id": "d8370c05"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 134 entries, 0 to 133\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   aisle_id  134 non-null    int64 \n",
      " 1   aisle     134 non-null    object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 2.2+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(aisles.info())# mostrar información del DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59e2090f",
   "metadata": {
    "id": "59e2090f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21 entries, 0 to 20\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   department_id  21 non-null     int64 \n",
      " 1   department     21 non-null     object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 464.0+ bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(departments.info())# mostrar información del DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6fc81694",
   "metadata": {
    "id": "6fc81694"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4545007 entries, 0 to 4545006\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count    Dtype  \n",
      "---  ------             --------------    -----  \n",
      " 0   order_id           4545007 non-null  int64  \n",
      " 1   product_id         4545007 non-null  int64  \n",
      " 2   add_to_cart_order  4544171 non-null  float64\n",
      " 3   reordered          4545007 non-null  int64  \n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 138.7 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(order_products.info(show_counts=True))# mostrar información del DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t75BD19oaR5f",
   "metadata": {
    "id": "t75BD19oaR5f"
   },
   "source": [
    "## Conclusiones\n",
    "\n",
    "Escribe aquí tus conclusiones intermedias sobre el Paso 1. Descripción de los datos.\n",
    "#lo primero que se realizó fue la importación de pandas como pd para posteriormente leer los archivos .csv con la herramienta de pandas pd.read_csv(). Por ultimo llamamos al método info() para poder conocer algunas generalidades de cada uno de los archivos.\n",
    "Es interesante notar que existe una gran disparidad en el tamaño de los archivos, mientras que todos solo manifiestan tener 1 columna y existen diferentes filas donde existen algunas coincidencias. todos los dtypes son objects.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8RJHqxVmaVsF",
   "metadata": {
    "id": "8RJHqxVmaVsF"
   },
   "source": [
    "# Paso 2. Preprocesamiento de los datos\n",
    "\n",
    "Preprocesa los datos de la siguiente manera:\n",
    "\n",
    "- Verifica y corrige los tipos de datos (por ejemplo, asegúrate de que las columnas de ID sean números enteros).\n",
    "- Identifica y completa los valores ausentes.\n",
    "- Identifica y elimina los valores duplicados.\n",
    "\n",
    "Asegúrate de explicar qué tipos de valores ausentes y duplicados encontraste, cómo los completaste o eliminaste y por qué usaste esos métodos. ¿Por qué crees que estos valores ausentes y duplicados pueden haber estado presentes en el conjunto de datos?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p6poABZQaZYb",
   "metadata": {
    "id": "p6poABZQaZYb"
   },
   "source": [
    "## Plan de solución\n",
    "\n",
    "Escribe aquí tu plan para el Paso 2. Preprocesamiento de los datos.\n",
    "#para poder preprocesar los datos de forma correcta es necesario que los datos sean congruentes para el tipo de categoria para el que son requeridos, de igual forma es necesario conocer si existen valores duplicados, o si es que existen valores ausentes para poder corregir antes de analizar la información. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07357848-dc64-4156-9cc3-01ff4365226d",
   "metadata": {
    "id": "07357848-dc64-4156-9cc3-01ff4365226d"
   },
   "source": [
    "## Encuentra y elimina los valores duplicados (y describe cómo tomaste tus decisiones)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f179f0a9",
   "metadata": {
    "id": "f179f0a9"
   },
   "source": [
    "### `orders` data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b99297a5-405a-463d-8535-9adc3da4ad74",
   "metadata": {
    "id": "b99297a5-405a-463d-8535-9adc3da4ad74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    },
    {
     "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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145574</th>\n",
       "      <td>794638</td>\n",
       "      <td>50898</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223105</th>\n",
       "      <td>2160484</td>\n",
       "      <td>107525</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230807</th>\n",
       "      <td>1918001</td>\n",
       "      <td>188546</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266232</th>\n",
       "      <td>1782114</td>\n",
       "      <td>106752</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273805</th>\n",
       "      <td>1112182</td>\n",
       "      <td>202304</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284038</th>\n",
       "      <td>2845099</td>\n",
       "      <td>31189</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311713</th>\n",
       "      <td>1021560</td>\n",
       "      <td>53767</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321100</th>\n",
       "      <td>408114</td>\n",
       "      <td>68324</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323900</th>\n",
       "      <td>1919531</td>\n",
       "      <td>191501</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345917</th>\n",
       "      <td>2232988</td>\n",
       "      <td>82565</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371905</th>\n",
       "      <td>391768</td>\n",
       "      <td>57671</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394347</th>\n",
       "      <td>467134</td>\n",
       "      <td>63189</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411408</th>\n",
       "      <td>1286742</td>\n",
       "      <td>183220</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415163</th>\n",
       "      <td>2282673</td>\n",
       "      <td>86751</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441599</th>\n",
       "      <td>2125197</td>\n",
       "      <td>14050</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_id  user_id  order_number  order_dow  order_hour_of_day  \\\n",
       "145574    794638    50898            24          3                  2   \n",
       "223105   2160484   107525            16          3                  2   \n",
       "230807   1918001   188546            14          3                  2   \n",
       "266232   1782114   106752             1          3                  2   \n",
       "273805   1112182   202304            84          3                  2   \n",
       "284038   2845099    31189            11          3                  2   \n",
       "311713   1021560    53767             3          3                  2   \n",
       "321100    408114    68324             4          3                  2   \n",
       "323900   1919531   191501            32          3                  2   \n",
       "345917   2232988    82565             1          3                  2   \n",
       "371905    391768    57671            19          3                  2   \n",
       "394347    467134    63189            21          3                  2   \n",
       "411408   1286742   183220            48          3                  2   \n",
       "415163   2282673    86751            49          3                  2   \n",
       "441599   2125197    14050            48          3                  2   \n",
       "\n",
       "        days_since_prior_order  \n",
       "145574                     2.0  \n",
       "223105                    30.0  \n",
       "230807                    16.0  \n",
       "266232                     NaN  \n",
       "273805                     6.0  \n",
       "284038                     7.0  \n",
       "311713                     9.0  \n",
       "321100                    18.0  \n",
       "323900                     7.0  \n",
       "345917                     NaN  \n",
       "371905                    10.0  \n",
       "394347                     2.0  \n",
       "411408                     4.0  \n",
       "415163                     2.0  \n",
       "441599                     3.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(orders.duplicated().sum())\n",
    "orders[orders.duplicated()]\n",
    "# Revisa si hay pedidos duplicados\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "PEkwUX_gvt5A",
   "metadata": {
    "id": "PEkwUX_gvt5A"
   },
   "source": [
    "¿Tienes líneas duplicadas? #Si, ¿qué tienen en común?# existen 15 ordenes duplicadas, todas ellas realizadas el dia miercoles a las 2am (considerando que la hora esté en formato de 24hrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d44de5d",
   "metadata": {
    "id": "0d44de5d"
   },
   "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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4838</th>\n",
       "      <td>2766110</td>\n",
       "      <td>162084</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5156</th>\n",
       "      <td>2190225</td>\n",
       "      <td>138285</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15506</th>\n",
       "      <td>553049</td>\n",
       "      <td>58599</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18420</th>\n",
       "      <td>382357</td>\n",
       "      <td>120200</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24691</th>\n",
       "      <td>690242</td>\n",
       "      <td>77357</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457013</th>\n",
       "      <td>3384021</td>\n",
       "      <td>14881</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458816</th>\n",
       "      <td>910166</td>\n",
       "      <td>164782</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459635</th>\n",
       "      <td>1680532</td>\n",
       "      <td>106435</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468324</th>\n",
       "      <td>222962</td>\n",
       "      <td>54979</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477526</th>\n",
       "      <td>2592344</td>\n",
       "      <td>46860</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_id  user_id  order_number  order_dow  order_hour_of_day  \\\n",
       "4838     2766110   162084            41          3                  2   \n",
       "5156     2190225   138285            18          3                  2   \n",
       "15506     553049    58599            13          3                  2   \n",
       "18420     382357   120200            19          3                  2   \n",
       "24691     690242    77357             2          3                  2   \n",
       "...          ...      ...           ...        ...                ...   \n",
       "457013   3384021    14881             6          3                  2   \n",
       "458816    910166   164782            18          3                  2   \n",
       "459635   1680532   106435             6          3                  2   \n",
       "468324    222962    54979            59          3                  2   \n",
       "477526   2592344    46860            38          3                  2   \n",
       "\n",
       "        days_since_prior_order  \n",
       "4838                      16.0  \n",
       "5156                      11.0  \n",
       "15506                      7.0  \n",
       "18420                     11.0  \n",
       "24691                      9.0  \n",
       "...                        ...  \n",
       "457013                    30.0  \n",
       "458816                     4.0  \n",
       "459635                    21.0  \n",
       "468324                     3.0  \n",
       "477526                     3.0  \n",
       "\n",
       "[121 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[(orders[\"order_dow\"]==3) & (orders[\"order_hour_of_day\"]==2)] \n",
    "\n",
    "# Basándote en tus hallazgos,\n",
    "# Verifica todos los pedidos que se hicieron el miércoles a las 2:00 a.m.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vu2OWyoqwOx5",
   "metadata": {
    "id": "vu2OWyoqwOx5"
   },
   "source": [
    "¿Qué sugiere este resultado?\n",
    "#existen 121 ordenes el miercoles a las 2 de la mañana, los valores parecen aleatorios y no conozco la distribución normal de ventas en linea, ni conozco el espacio de tiempo que corresponde a este df  para asegurar si este número es normal o no, sin embargo, en conjunto con la tabla anterior podemos decir que más del 10% de las ordenes del dia miercoles a las 2am están duplicadas, lo que pudiera pensarse como un error en la plataforma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "480563c7",
   "metadata": {
    "id": "480563c7"
   },
   "outputs": [],
   "source": [
    "orders.drop_duplicates(inplace=True)\n",
    "\n",
    "# Elimina los pedidos duplicados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0b2396a2",
   "metadata": {
    "id": "0b2396a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(orders.duplicated().sum())\n",
    "# Vuelve a verificar si hay filas duplicadas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6113ef1",
   "metadata": {
    "id": "f6113ef1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[\"order_id\"].duplicated().sum() # Vuelve a verificar únicamente si hay IDs duplicados de pedidos\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75e6f23",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Comentario del revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Muy buen trabajo!! Desarrollaste de manera excelente el análisis de duplicados y eliminaste esos casos. \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8905ae",
   "metadata": {
    "id": "5e8905ae"
   },
   "source": [
    "Describe brevemente tus hallazgos y lo que hiciste con ellos. #Al existir duplicados en las ordenes y poderlos identificar facilmente podemos observar que todos ellos sucedieron el dia miercoles a las 2am, al ser duplicados de \"order_id\" que no deberia tenerlos, se proceden a eliminar usando el método .drop_duplicates() usando (inplace=True) para poder incluir los cambios en el df. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e6bc46",
   "metadata": {
    "id": "45e6bc46"
   },
   "source": [
    "### `products` data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f526b5b-8175-46fa-a0fd-441767d50e64",
   "metadata": {
    "id": "5f526b5b-8175-46fa-a0fd-441767d50e64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "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>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [product_id, product_name, aisle_id, department_id]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(products.duplicated().sum())\n",
    "products[products.duplicated()]\n",
    "# Verifica si hay filas totalmente duplicadas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88daa4f4",
   "metadata": {
    "id": "88daa4f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49673"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products[\"department_id\"].duplicated().sum()\n",
    "# Revisa únicamente si hay ID de departamentos duplicados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c773f0bf",
   "metadata": {
    "id": "c773f0bf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1361"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products['product_name'].str.upper().duplicated().sum()\n",
    "# Revisa únicamente si hay nombres duplicados de productos (convierte los nombres a letras mayúsculas para compararlos mejor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "01efd02e",
   "metadata": {
    "id": "01efd02e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products[products[\"product_name\"].notna()][\"product_name\"].str.upper().duplicated().sum()\n",
    "# Revisa si hay nombres duplicados de productos no faltantes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da8d293",
   "metadata": {
    "id": "9da8d293"
   },
   "source": [
    "Describe brevemente tus hallazgos y lo que hiciste con ellos.# en el primer requerimiento podemos observar que no hay filas completamente duplicadas, sin embargo si hay duplicados en \"department_id\" y en \"product_name\"(este ultimo a pesar de usar el método .str.upper() para convertir todo a mayusculas) y existen nombres duplicados en productos no faltantes. esto se puede explicar por que pueden existir 2 productos con el mismo nombre, pero diferente product_id, por lo cual se sugeriría ser mas especifico con la descripción de los productos. \n",
    "\n",
    "#2Respecto a los 104 productos duplicados podemos asumir que no son productos con el mismo nombre aunque tengan diferente product_id, por lo cual se sugeriría hacer descripciones más específicas de los productos. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7dd7d5a",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Comentario del revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Muy buen trabajo!! Desarrollaste de manera excelente el análisis de duplicados. Para complementar el análisis, qué podríamos decir de estos 104 productos duplicados? \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60f30db1",
   "metadata": {
    "id": "60f30db1"
   },
   "source": [
    "### `departments` data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fb410ad4-0fbf-4b80-bb09-23fdea79afe3",
   "metadata": {
    "id": "fb410ad4-0fbf-4b80-bb09-23fdea79afe3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "    department_id       department\n",
      "0               1           frozen\n",
      "1               2            other\n",
      "2               3           bakery\n",
      "3               4          produce\n",
      "4               5          alcohol\n",
      "5               6    international\n",
      "6               7        beverages\n",
      "7               8             pets\n",
      "8               9  dry goods pasta\n",
      "9              10             bulk\n",
      "10             11    personal care\n",
      "11             12     meat seafood\n",
      "12             13           pantry\n",
      "13             14        breakfast\n",
      "14             15     canned goods\n",
      "15             16       dairy eggs\n",
      "16             17        household\n",
      "17             18           babies\n",
      "18             19           snacks\n",
      "19             20             deli\n"
     ]
    }
   ],
   "source": [
    "print(departments.duplicated().sum())\n",
    "departments[departments.duplicated()]\n",
    "print(departments.head(20))# Revisa si hay filas totalmente duplicadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "332b12bb",
   "metadata": {
    "id": "332b12bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments[\"department_id\"].duplicated().sum()# Revisa únicamente si hay IDs duplicadas de productos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd30d78e",
   "metadata": {
    "id": "bd30d78e"
   },
   "source": [
    "Describe brevemente tus hallazgos y lo que hiciste con ellos.#El Df Departments no cuenta con elementos repetidos, al llamar al método info nos damos cuenta de que solo cuenta con 20 filas, y al hacer la revisión manual y en código observamos que no existen duplicados. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889349c1",
   "metadata": {
    "id": "889349c1"
   },
   "source": [
    "### `aisles` data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9da89fb9-4679-40f5-ad0d-c34df753a3f8",
   "metadata": {
    "id": "9da89fb9-4679-40f5-ad0d-c34df753a3f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "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>aisle_id</th>\n",
       "      <th>aisle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [aisle_id, aisle]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(aisles.duplicated().sum())\n",
    "aisles[aisles.duplicated()]# Revisa si hay filas totalmente duplicadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da7c2822",
   "metadata": {
    "id": "da7c2822"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aisles[\"aisle_id\"].duplicated().sum()# Revisa únicamente si hay IDs duplicadas de productos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac63826f",
   "metadata": {
    "id": "ac63826f"
   },
   "source": [
    "Describe brevemente tus hallazgos y lo que hiciste con ellos.# En ambas pruebas no se encontraron duplicados, por lo tanto no es necesario realizar ningun manejo adicional. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274cd06a",
   "metadata": {
    "id": "274cd06a"
   },
   "source": [
    "### `order_products` data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52326689-84a8-4b8f-a881-7c68780f62c9",
   "metadata": {
    "id": "52326689-84a8-4b8f-a881-7c68780f62c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [order_id, product_id, add_to_cart_order, reordered]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(order_products.duplicated().sum())\n",
    "order_products[order_products.duplicated()]# Revisa si hay filas totalmente duplicadas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7b861391",
   "metadata": {
    "id": "7b861391"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products.duplicated(subset=[\"order_id\", \"product_id\"]).sum()# Vuelve a verificar si hay cualquier otro duplicado engañosoor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b890492",
   "metadata": {
    "id": "0b890492"
   },
   "source": [
    "Describe brevemente tus hallazgos y lo que hiciste con ellos. #Al parecer no se encuentran filas duplicadas en el dataframe, ni tampoco en la combinación de columnas order_id y product_id, por lo tanto no es necesario ningun manejo adicional.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e46de76",
   "metadata": {
    "id": "1e46de76"
   },
   "source": [
    "## Encuentra y elimina los valores ausentes\n",
    "\n",
    "Al trabajar con valores duplicados, pudimos observar que también nos falta investigar valores ausentes:\n",
    "\n",
    "* La columna `'product_name'` de la tabla products.\n",
    "* La columna `'days_since_prior_order'` de la tabla orders.\n",
    "* La columna `'add_to_cart_order'` de la tabla order_productos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0aebd78",
   "metadata": {
    "id": "c0aebd78"
   },
   "source": [
    "### `products` data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fe4a8b0f",
   "metadata": {
    "id": "fe4a8b0f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1258"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products[\"product_name\"].isna().sum()# Encuentra los valores ausentes en la columna 'product_name'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "-A6x1AbJhjkp",
   "metadata": {
    "id": "-A6x1AbJhjkp"
   },
   "source": [
    "Describe brevemente cuáles son tus hallazgos.#Al parecer existen 1258 valores ausentes en la columna \"product_name\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e96167bc",
   "metadata": {
    "id": "e96167bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products[products[\"product_name\"].isna()][\"aisle_id\"].unique()\n",
    "#  ¿Todos los nombres de productos ausentes están relacionados con el pasillo con ID 100?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "WRpIfXRphpSL",
   "metadata": {
    "id": "WRpIfXRphpSL"
   },
   "source": [
    "Describe brevemente cuáles son tus hallazgos.#todos los valores ausentes corresponden al pasillo con ID100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8fd0a541",
   "metadata": {
    "id": "8fd0a541"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products[products[\"product_name\"].isna()][\"department_id\"].unique()\n",
    "# ¿Todos los nombres de productos ausentes están relacionados con el departamento con ID 21?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qDpdYn1Fht_I",
   "metadata": {
    "id": "qDpdYn1Fht_I"
   },
   "source": [
    "Describe brevemente cuáles son tus hallazgos.#todos los valores ausentes corresponden al departamento id 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "325c3464",
   "metadata": {
    "id": "325c3464"
   },
   "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>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>297</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49552</th>\n",
       "      <td>49553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49574</th>\n",
       "      <td>49575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49640</th>\n",
       "      <td>49641</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49663</th>\n",
       "      <td>49664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49668</th>\n",
       "      <td>49669</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1258 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_id product_name  aisle_id  department_id\n",
       "37             38          NaN       100             21\n",
       "71             72          NaN       100             21\n",
       "109           110          NaN       100             21\n",
       "296           297          NaN       100             21\n",
       "416           417          NaN       100             21\n",
       "...           ...          ...       ...            ...\n",
       "49552       49553          NaN       100             21\n",
       "49574       49575          NaN       100             21\n",
       "49640       49641          NaN       100             21\n",
       "49663       49664          NaN       100             21\n",
       "49668       49669          NaN       100             21\n",
       "\n",
       "[1258 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.query(\"aisle_id == 100\" and \"department_id == 21\")\n",
    "\n",
    "\n",
    "\n",
    "# Usa las tablas department y aisle para revisar los datos del pasillo con ID 100 y el departamento con ID 21.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nqhSTUPhhvEJ",
   "metadata": {
    "id": "nqhSTUPhhvEJ"
   },
   "source": [
    "Describe brevemente cuáles son tus hallazgos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6b295120",
   "metadata": {
    "id": "6b295120"
   },
   "outputs": [],
   "source": [
    "products[\"product_name\"].fillna(\"Unkown\", inplace=True)# Completa los nombres de productos ausentes con 'Unknown'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc70e5ad",
   "metadata": {
    "id": "fc70e5ad"
   },
   "source": [
    "Describe brevemente tus hallazgos y lo que hiciste con ellos.#se sustituyeron los valores ausentes con \"Unknown\" y posteriormente se usa la función inplace=True para guardar los resultados en el dataframe. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c524f660",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Comentario del revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Muy buen trabajo!! Desarrollaste de manera excelente el análisis de valores faltantes y los llenaste con \"unknown\".\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb45c86",
   "metadata": {
    "id": "7eb45c86"
   },
   "source": [
    "### `orders` data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "93e41d96",
   "metadata": {
    "id": "93e41d96"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                      0\n",
       "user_id                       0\n",
       "order_number                  0\n",
       "order_dow                     0\n",
       "order_hour_of_day             0\n",
       "days_since_prior_order    28817\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.isna().sum()# Encuentra los valores ausentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "166e5a86",
   "metadata": {
    "id": "166e5a86"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                  0\n",
       "user_id                   0\n",
       "order_number              0\n",
       "order_dow                 0\n",
       "order_hour_of_day         0\n",
       "days_since_prior_order    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders[\"order_number\"]>1].isna().sum()# ¿Hay algún valor ausente que no sea el primer pedido del cliente?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e23d089",
   "metadata": {
    "id": "4e23d089"
   },
   "source": [
    "Describe brevemente tus hallazgos y lo que hiciste con ellos.#Al parecer no existen ningun pedido ausente que no corresponda al primer pedido del cliente, ya que al colocar un filtro para excluir las primeras compras, dejan de existir estos valores ausentes en la columna \"days_since_prior_order\". "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69dc9ca4",
   "metadata": {
    "id": "69dc9ca4"
   },
   "source": [
    "### `order_products` data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b8240253",
   "metadata": {
    "id": "b8240253"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   order_id  product_id  add_to_cart_order  reordered\n",
      "0   2141543       11440               17.0          0\n",
      "1    567889        1560                1.0          1\n",
      "2   2261212       26683                1.0          1\n",
      "3    491251        8670               35.0          1\n",
      "4   2571142        1940                5.0          1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "order_id               0\n",
       "product_id             0\n",
       "add_to_cart_order    836\n",
       "reordered              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(order_products.head())\n",
    "order_products.isna().sum()# Encuentra los valores ausentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9a78e5ee",
   "metadata": {
    "id": "9a78e5ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "64.0\n"
     ]
    }
   ],
   "source": [
    "print(order_products[\"add_to_cart_order\"].min())# ¿Cuáles son los valores mínimos y máximos en esta columna?\n",
    "print(order_products[\"add_to_cart_order\"].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "-Z2Ja215jW78",
   "metadata": {
    "id": "-Z2Ja215jW78"
   },
   "source": [
    "Describe brevemente cuáles son tus hallazgos.#Es interesante notar que el valor máximo corresponde con 64, lo cual puede significar que nadie ha comprado mas de 64 productos o que el sistema ya no registra a partir de ese número. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ab9d0c2b",
   "metadata": {
    "id": "ab9d0c2b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "737        2449164\n",
      "9926       1968313\n",
      "14394      2926893\n",
      "16418      1717990\n",
      "30114      1959075\n",
      "            ...   \n",
      "4505662    1800005\n",
      "4511400    1633337\n",
      "4517562     404157\n",
      "4534112    1673227\n",
      "4535739    1832957\n",
      "Name: order_id, Length: 836, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "id_va= order_products[order_products[\"add_to_cart_order\"].isna()][\"order_id\"]# Guarda todas las IDs de pedidos que tengan un valor ausente en 'add_to_cart_order'\n",
    "print(id_va)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "54c31695",
   "metadata": {
    "id": "54c31695"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order_id\n",
      "9310        65\n",
      "61355      127\n",
      "102236      95\n",
      "129627      69\n",
      "165801      70\n",
      "          ... \n",
      "2999801     70\n",
      "3125735     86\n",
      "3308010    115\n",
      "3347453     71\n",
      "3383594     69\n",
      "Name: product_id, Length: 70, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "or_va=order_products[order_products[\"order_id\"].isin(id_va)].groupby(\"order_id\").count()\n",
    "\n",
    "prod_min= or_va[\"product_id\"]\n",
    "\n",
    "print(prod_min)\n",
    "\n",
    "# ¿Todos los pedidos con valores ausentes tienen más de 64 productos?\n",
    "# Agrupa todos los pedidos con datos ausentes por su ID de pedido.\n",
    "# Cuenta el número de 'product_id' en cada pedido y revisa el valor mínimo del conteo.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p9V6pddtjglj",
   "metadata": {
    "id": "p9V6pddtjglj"
   },
   "source": [
    "Describe brevemente cuáles son tus hallazgos.#Al parecer todos los pedidos con valores ausentes tienen mas de 64 productos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7a987d08",
   "metadata": {
    "id": "7a987d08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          17\n",
       "1           1\n",
       "2           1\n",
       "3          35\n",
       "4           5\n",
       "           ..\n",
       "4545002    12\n",
       "4545003     9\n",
       "4545004     4\n",
       "4545005     8\n",
       "4545006     9\n",
       "Name: add_to_cart_order, Length: 4545007, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products[\"add_to_cart_order\"].fillna(999).astype(int)# Remplaza los valores ausentes en la columna 'add_to_cart? con 999 y convierte la columna al tipo entero.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea4bcc7",
   "metadata": {
    "id": "fea4bcc7"
   },
   "source": [
    "Describe brevemente tus hallazgos y lo que hiciste con ellos.#como se solicita, se reemplazaron los valores ausentes en la columna \"add_to_cart_order\" con 999 y se convirtió la columna a tipo entero con la función .astype(int)\n",
    "\n",
    "#2 Respecto a los pedidos que contienen más de 64 productos, pareciera que el sistema los coloca como valores ausentes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db441059",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Comentario revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "\n",
    "Muy buena conclusión de esta base. Pero qué podríamos decir de los pedidos que tienen más de 64 productos?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "RnplPFY1gSrb",
   "metadata": {
    "id": "RnplPFY1gSrb"
   },
   "source": [
    "## Conclusiones\n",
    "\n",
    "Escribe aquí tus conclusiones intermedias sobre el Paso 2. Preprocesamiento de los datos\n",
    "#En esta etapa pudimos revisar el tema de los duplicados y valores ausentes, corrigiendo en ambos casos para poder realizar un analisi de estos, vale la pena mencionar los hallazgos respecto al pasillo 100 y al departamento 21, así como la aparente restricción sobre ingresar mas de 64 articulos en el carrito de compra. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "UjrOIwTcz3ri",
   "metadata": {
    "id": "UjrOIwTcz3ri"
   },
   "source": [
    "# Paso 3. Análisis de los datos\n",
    "\n",
    "Una vez los datos estén procesados y listos, haz el siguiente análisis:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "turkish-kidney",
   "metadata": {
    "id": "turkish-kidney"
   },
   "source": [
    "# [A] Fácil (deben completarse todos para aprobar)\n",
    "\n",
    "1. Verifica que los valores en las columnas `'order_hour_of_day'` y `'order_dow'` en la tabla orders sean razonables (es decir, `'order_hour_of_day'` oscile entre 0 y 23 y `'order_dow'` oscile entre 0 y 6).\n",
    "2. Crea un gráfico que muestre el número de personas que hacen pedidos dependiendo de la hora del día.\n",
    "3. Crea un gráfico que muestre qué día de la semana la gente hace sus compras.\n",
    "4. Crea un gráfico que muestre el tiempo que la gente espera hasta hacer su siguiente pedido, y comenta sobre los valores mínimos y máximos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "short-capability",
   "metadata": {
    "id": "short-capability"
   },
   "source": [
    "### [A1] Verifica que los valores sean sensibles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "growing-fruit",
   "metadata": {
    "id": "growing-fruit"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "el valor mínimo de la columna order_hour_of_day es\n",
      "0\n",
      "el valor máximo de la columna order_hour_of_day es\n",
      "23\n",
      "el valor mínimo de la columna order_dow es\n",
      "0\n",
      "el valor máximo de la columna order_dow es\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "print(\"el valor mínimo de la columna order_hour_of_day es\")\n",
    "print(orders[\"order_hour_of_day\"].min())\n",
    "print(\"el valor máximo de la columna order_hour_of_day es\")\n",
    "print(orders[\"order_hour_of_day\"].max())\n",
    "\n",
    "print(\"el valor mínimo de la columna order_dow es\")\n",
    "print(orders[\"order_dow\"].min())\n",
    "print(\"el valor máximo de la columna order_dow es\")\n",
    "print(orders[\"order_dow\"].max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "accessory-malaysia",
   "metadata": {
    "id": "accessory-malaysia"
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db63582b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98309b91",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "hmR2GNQ6lCsX",
   "metadata": {
    "id": "hmR2GNQ6lCsX"
   },
   "source": [
    "Escribe aquí tus conclusiones# Los valores de las columnas order_hour_of_day y order_dow son razonables y se encuentran dentro de los parametros solicitados. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6743a70",
   "metadata": {
    "id": "a6743a70"
   },
   "source": [
    "### [A2] Para cada hora del día, ¿cuántas personas hacen órdenes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a36cca27",
   "metadata": {
    "id": "a36cca27"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_hour_of_day'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders.groupby(\"order_hour_of_day\")[\"order_id\"].count().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4431c893",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Comentario del revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Muy buen trabajo!! Buen uso de la gráfica de barras para mostrar la distribución por horas.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11270eed",
   "metadata": {
    "id": "11270eed"
   },
   "source": [
    "Escribe aquí tus conclusiones# 2.- Podemos observar que la mayor cantidad de ordenes se encuentran entre las 9am y 4pm "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "documented-command",
   "metadata": {
    "id": "documented-command"
   },
   "source": [
    "### [A3] ¿Qué día de la semana compran víveres las personas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "chief-digit",
   "metadata": {
    "id": "chief-digit"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_dow'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders.groupby(\"order_dow\")[\"order_id\"].count().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dominant-lightning",
   "metadata": {
    "id": "dominant-lightning"
   },
   "source": [
    "Escribe aquí tus conclusiones #El dia de la semana que mas compras se realizan es el dia domingo, seguido por el día lunes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cutting-concert",
   "metadata": {
    "id": "cutting-concert"
   },
   "source": [
    "### [A4] ¿Cuánto tiempo esperan las personas hasta hacer otro pedido? Comenta sobre los valores mínimos y máximos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c231f523",
   "metadata": {
    "id": "c231f523"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='days_since_prior_order'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders.groupby(\"days_since_prior_order\")[\"order_id\"].count().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f4e8aaf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='days_since_prior_order'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders_filtered = orders[orders[\"days_since_prior_order\"] < 30]\n",
    "grouped_orders = orders_filtered.groupby(\"days_since_prior_order\")[\"order_id\"].count()\n",
    "grouped_orders.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "discrete-vertex",
   "metadata": {
    "id": "discrete-vertex"
   },
   "source": [
    "Escribe aquí tus conclusiones# La mayoría de las personas esperan una semana para hacer siguiente pedido, sin embargo es notorio que a partir de ese dato, la frecuencia disminuye hasta el día 29 y súbitamente se eleva en el dia 30, lo que podria indicar que valores posteriores (31 en adelante) se agrupan en la columna 30. \n",
    "\n",
    "#2 Se presenta la gráfica excluyendo los valores del día 30, donde se observa una disminución desde el día 7, y teniendo un breve repunte en el día 14 que no parece llegar a ser significativa.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68072a0",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Comentario Revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Muy buen trabajo, pero pareciera que las personas que tardan 30 días puede ser un error. Entonces te sugiero complementar este análisis con una gráfica donde se muestren los valores sin contar los de 30\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tight-staff",
   "metadata": {
    "id": "tight-staff"
   },
   "source": [
    "# [B] Intermedio (deben completarse todos para aprobar)\n",
    "\n",
    "1. ¿Existe alguna diferencia entre las distribuciones `'order_hour_of_day'` de los miércoles y los sábados? Traza gráficos de barra de `'order_hour_of_day'` para ambos días en la misma figura y describe las diferencias que observes.\n",
    "2. Grafica la distribución para el número de órdenes que hacen los clientes (es decir, cuántos clientes hicieron solo 1 pedido, cuántos hicieron 2, cuántos 3, y así sucesivamente...).\n",
    "3. ¿Cuáles son los 20 principales productos que se piden con más frecuencia (muestra su identificación y nombre)?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "packed-classic",
   "metadata": {
    "id": "packed-classic"
   },
   "source": [
    "### [B1] Diferencia entre miércoles y sábados para  `'order_hour_of_day'`. Traza gráficos de barra para los dos días y describe las diferencias que veas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d89b8403",
   "metadata": {
    "id": "d89b8403"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_hour_of_day'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAeCUlEQVR4nO3de5QV5Znv8e8jAh1BAbFFxyY2maCojIAioNLEyxzE4Ih4wGhcERg5rCgxjpM5Sk5yDmhkQk5YeIuXwSigE+MtXki8hYCOOArSXFQQGVpFaQ4qcvOCYAPP+aNeSNHsvXt3sy/du36ftWp11Vv1VL17995P1X6r6i1zd0REJBkOKnYFRESkcJT0RUQSRElfRCRBlPRFRBJESV9EJEGU9EVEEuTgYlcgkyOOOMIrKyuLXQ0RkRZl8eLFn7p7eap5zTrpV1ZWUl1dXexqiIi0KGb2Qbp5at4REUkQJX0RkQRR0hcRSZBm3aYvItJYdXV11NbWsn379mJXJe/KysqoqKigdevWWcco6YtISamtreXQQw+lsrISMyt2dfLG3dm4cSO1tbV069Yt6zg174hISdm+fTudO3cu6YQPYGZ07ty50b9olPRFpOSUesLfoymvU0lfRCRBlPRFSsGkDn8dJCcmTZrE1KlTc77eyspKPv3005yvN1tK+iKSeO7O7t27D2gdO3fuzFFt8ktJX6Q50RF73kybNo2ePXvSs2dPbr31VtasWcPxxx/PFVdcQc+ePVm7di2TJ0/muOOOY+DAgaxatWpv7LvvvsuQIUM49dRTqaqq4p133gFg9OjR/PCHP6R///5cf/31Kbe7ceNGBg8ezEknncTYsWOJP6K2fp0Afv3rX3P77bcDcN1113HOOecAMG/ePC6//PIDfh+U9EWk5C1evJgZM2awcOFCFixYwL333svmzZtZvXo1V199NStWrODTTz/l4YcfZtmyZTz77LMsWrRob/y4ceO44447WLx4MVOnTuXqq6/eO6+2tpZXX32VadOmpdz2jTfeyMCBA1mxYgXDhw/nww8/TFunpUuXUlVVxfz58wGorq7miy++oK6ujvnz5zNo0KADfi90nb6IlLxXXnmF4cOH065dOwAuvvhi5s+fz7HHHsuAAQMAmD9/PsOHD+eQQw4B4MILLwTgiy++4NVXX2XkyJF717djx4694yNHjqRVq1Zpt/3yyy/zxBNPADB06FA6deqUsU5XXXUVixcv5rPPPqNt27accsopVFdXM3/+/L2/AA6Ekr6IJNaehJvJ7t276dixI8uWLWvyOhqjdevWdOvWjZkzZ3LGGWdw8skn8+KLL1JTU8MJJ5xwwOtX846IlLyqqiqeeuoptm3bxpdffsmTTz5JVVXVPssMGjSIp556iq+++orPP/+cP/7xjwAcdthhdOvWjcceewyITvq+8cYbWW970KBBPPTQQwA899xzbN68ucE6VVVVMXXqVAYNGkRVVRX33HMPffr0ycn9BzrSF0my+AnjSVuLV488O+WUUxg9ejT9+vUDYOzYsXubWeLLfO9736NXr14ceeSRnHbaaXvn/e53v+Oqq67i5ptvpq6ujksvvZRevXplte2JEydy2WWXcdJJJ3HGGWfwzW9+M22d+vTpA0RJf/LkyZx++um0a9eOsrKy/XZSTWXxM8nNTd++fV0PUZFEaWoSzjKucsIz+0yvKft+07bXjK1cuTInzSAtRarXa2aL3b1vquV1pC+SDwk5gpaWR0lfJBMlb8nSjBkzuO222/YpO/PMM7nzzjuLVKPUskr6ZrYG+BzYBex0975mdjjwCFAJrAEucffNFp1puA34LrANGO3uS8J6RgE/D6u92d1n5e6liIgUz5gxYxgzZkyxq9Ggxly9c7a79461E00A5rp7d2BumAY4H+gehnHA3QBhJzER6A/0Ayaa2b5nUkREJK8OpHlnGHBWGJ8FvATcEMof8OgM8QIz62hmR4dl57j7JgAzmwMMAX5/AHUQkWJQs1eLlW3Sd+DPZubAv7n7dKCLu68P8z8CuoTxY4C1sdjaUJauXCTn9rtKZcrQpsWV5axKIs1Ctkl/oLuvM7MjgTlm9k58prt72CEcMDMbR9QstPd6VhEpLu0Mc2/79u0MGjSIHTt2sHPnTkaMGMGNN96Y9+1mlfTdfV34+4mZPUnUJv+xmR3t7utD880nYfF1QNdYeEUoW8dfm4P2lL+UYlvTgekQXaffmBcjItIU9XdqByqbX5Zt27Zl3rx5tG/fnrq6OgYOHMj555+/ty+gfGnwRK6ZtTOzQ/eMA4OB5cBsYFRYbBTwdBifDVxhkQHA1tAM9AIw2Mw6hRO4g0OZiEjimBnt27cHoK6ujrq6uoI85jGbI/0uwJOhMgcDD7n782a2CHjUzK4EPgAuCcs/S3S5Zg3RJZtjANx9k5n9AtjTX+lNe07qiuSdTjxKM7Rr1y5OPfVUampqGD9+PP3798/7NhtM+u7+HrBfJxPuvhE4N0W5A+PTrOt+4P7GV1NEpPS0atWKZcuWsWXLFoYPH87y5cvp2bNnXrepXjZFRIqsY8eOnH322Tz//PN535aSvohIEWzYsIEtW7YA8NVXXzFnzhx69OiR9+2q7x0RkSJYv349o0aNYteuXezevZtLLrmECy64IO/bVdIXaYF03XxuZXvzXi6dfPLJLF26tODbVfOOiEiCKOmLiCSIkr6ISIKoTV8kB5raxq62eSk0HemLiCSIkr6ISIIo6YuIFMmWLVsYMWIEPXr04IQTTuC1117L+zbVpi8iEu+QLyfry65Tv2uvvZYhQ4bw+OOP8/XXX7Nt27bc1iMFJX0RkSLYunUrL7/8MjNnzgSgTZs2tGnTJu/bVfOOiEgRvP/++5SXlzNmzBj69OnD2LFj+fLLL/O+XSV9adYqJzyzzyBSKnbu3MmSJUu46qqrWLp0Ke3atWPKlCl5366SvohIEVRUVFBRUbH3wSkjRoxgyZIled+ukr6ISBEcddRRdO3alVWrVgEwd+5cTjzxxLxvVydyRUSK5I477uDyyy/n66+/5lvf+hYzZszI+zaV9EVEivTc5N69e1NdXV3Qbap5R0QkQZT0RUQSRM070rLE75ws0k9ykZZMSV9ECqN+Vwd53Gm7O2aWt/U3F+7e6Bg174hISSkrK2Pjxo1NSogtibuzceNGysoa9xAGHemLSEmpqKigtraWDRs2FLsqeVdWVkZFRUWjYpT0RaSktG7dmm7duhW7Gs2Wkr6I5E28vyQ9CrJ5UJu+iEiCKOmLiCSIkr6ISIJknfTNrJWZLTWzP4Xpbma20MxqzOwRM2sTytuG6ZowvzK2jp+G8lVmdl7OX42IiGTUmCP9a4GVselfAbe4+7eBzcCVofxKYHMovyUsh5mdCFwKnAQMAe4ys1YHVn0REWmMrJK+mVUAQ4HfhmkDzgEeD4vMAi4K48PCNGH+uWH5YcDD7r7D3d8HaoB+OXgNIiKSpWyP9G8Frgd2h+nOwBZ33xmma4FjwvgxwFqAMH9rWH5veYqYvcxsnJlVm1l1Em6uEBEppAaTvpldAHzi7osLUB/cfbq793X3vuXl5YXYpIhIYmRzc9aZwIVm9l2gDDgMuA3oaGYHh6P5CmBdWH4d0BWoNbODgQ7Axlj5HvEYEREpgAaP9N39p+5e4e6VRCdi57n75cCLwIiw2Cjg6TA+O0wT5s/zqOej2cCl4eqebkB34PWcvRIREWnQgXTDcAPwsJndDCwF7gvl9wEPmlkNsIloR4G7rzCzR4G3gZ3AeHffdQDbFxGRRmpU0nf3l4CXwvh7pLj6xt23AyPTxE8GJje2kiIikhu6I1dEJEGU9EVEEkRJX0QkQZT0RUQSRElfRCRBlPRFRBJESV9EJEGU9EVEEkRJX0QkQZT0RUQSRElfRCRBlPRFRBJESV9EJEGU9EVEEkRJX0QkQQ7kISoiInlROeGZveNrpgwtYk1Kj470RUQSRElfRCRBlPRFRBJESV9EJEF0IldEmrdJHepNby1OPUqEjvRFRBJESV9EJEHUvCPFEf/Jrp/rIgWjI30RkQRR0hcRSRAlfRGRBFHSFxFJEJ3IlYKId6AFsKasSBURSbgGj/TNrMzMXjezN8xshZndGMq7mdlCM6sxs0fMrE0obxuma8L8yti6fhrKV5nZeXl7VSIiklI2zTs7gHPcvRfQGxhiZgOAXwG3uPu3gc3AlWH5K4HNofyWsBxmdiJwKXASMAS4y8xa5fC1iIhIAxpM+h75Iky2DoMD5wCPh/JZwEVhfFiYJsw/18wslD/s7jvc/X2gBuiXixchIiLZyepErpm1MrNlwCfAHOBdYIu77wyL1ALHhPFjgLUAYf5WoHO8PEWMiIgUQFZJ3913uXtvoILo6LxHvipkZuPMrNrMqjds2JCvzYiIJFKjLtl09y3Ai8DpQEcz23P1TwWwLoyvA7oChPkdgI3x8hQx8W1Md/e+7t63vLy8MdUTEZEGZHP1TrmZdQzj3wD+G7CSKPmPCIuNAp4O47PDNGH+PHf3UH5puLqnG9AdeD1Hr0NERLKQzXX6RwOzwpU2BwGPuvufzOxt4GEzuxlYCtwXlr8PeNDMaoBNRFfs4O4rzOxR4G1gJzDe3Xfl9uWIiEgmDSZ9d38T6JOi/D1SXH3j7tuBkWnWNRmY3PhqiohILqgbBhGRBFHSFxFJECV9EZEEUdIXEUkQJX0RkQRR0hcRSRAlfRGRBFHSFxFJED05S0RKRvwJbWumDC1iTZovHemLiCSIjvRFpDRN6lBvemtx6tHM6EhfRCRBlPRFRBJESV9EJEGU9EVEEkRJX0QkQZT0RUQSRElfRCRBlPRFRBJESV9EJEGU9EVEEkRJX0QkQZT0RUQSRElfRCRBlPRFRBJESV9EJEGU9EVEEkRJX0QkQZT0RUQSRElfRCRBGkz6ZtbVzF40s7fNbIWZXRvKDzezOWa2OvztFMrNzG43sxoze9PMTomta1RYfrWZjcrfyxIRkVSyOdLfCfzE3U8EBgDjzexEYAIw1927A3PDNMD5QPcwjAPuhmgnAUwE+gP9gIl7dhQiIlIYDSZ9d1/v7kvC+OfASuAYYBgwKyw2C7gojA8DHvDIAqCjmR0NnAfMcfdN7r4ZmAMMyeWLERGRzBrVpm9mlUAfYCHQxd3Xh1kfAV3C+DHA2lhYbShLV15/G+PMrNrMqjds2NCY6omISAOyTvpm1h74A/BP7v5ZfJ67O+C5qJC7T3f3vu7et7y8PBerFBGRIKukb2atiRL+79z9iVD8cWi2Ifz9JJSvA7rGwitCWbpyEREpkGyu3jHgPmClu0+LzZoN7LkCZxTwdKz8inAVzwBga2gGegEYbGadwgncwaFMRKSoKic8s3codQdnscyZwA+At8xsWSj7X8AU4FEzuxL4ALgkzHsW+C5QA2wDxgC4+yYz+wWwKCx3k7tvysWLEBGR7DSY9N39FcDSzD43xfIOjE+zrvuB+xtTQRERyZ1sjvRFRJJjUod601uLU488UTcMIiIJoqQvIpIgSvoiIgmiNn1plPqXtK2ZMrRINRGRptCRvohIgijpi4gkiJK+iEiCKOmLiCSITuTKgYnfyFJiN7GIlCId6YuIJIiSvohIgijpi4gkiJK+iEiC6ESuiEgutJDeOZX0RUSaKN4tyZqyIlakEdS8IyKSIEr6IiIJoqQvIpIgSvoiIgmipC8ikiBK+iIiCaKkLyKSIEr6IiIJoqQvIpIguiNXRKSYCtx9g470RUQSREf6IiIFVsw+e3SkLyKSIA0mfTO738w+MbPlsbLDzWyOma0OfzuFcjOz282sxszeNLNTYjGjwvKrzWxUfl6OiIhkks2R/kxgSL2yCcBcd+8OzA3TAOcD3cMwDrgbop0EMBHoD/QDJu7ZUYiISOE0mPTd/WVgU73iYcCsMD4LuChW/oBHFgAdzexo4DxgjrtvcvfNwBz235GIiEieNbVNv4u7rw/jHwFdwvgxwNrYcrWhLF25iIgU0AGfyHV3BzwHdQHAzMaZWbWZVW/YsCFXqxUREZqe9D8OzTaEv5+E8nVA19hyFaEsXfl+3H26u/d1977l5eVNrJ6IiKTS1KQ/G9hzBc4o4OlY+RXhKp4BwNbQDPQCMNjMOoUTuINDmYiIFFCDN2eZ2e+Bs4AjzKyW6CqcKcCjZnYl8AFwSVj8WeC7QA2wDRgD4O6bzOwXwKKw3E3uXv/ksIiI5FmDSd/dL0sz69wUyzowPs167gfub1TtREQktSb22aM7ckVEEkRJX0QkQZT0RUQSRElfRCRBlPRFRBJE/emLiLQQueiHX0f6IiIJoqQvIpIgat5JsH1+Kk4ZWsSaiEih6EhfRCRBlPRFRBJESV9EJEGU9EVEEkRJX0QkQZT0RUQSpOVestnEvqRFRJKs5SZ9ya34TlQ7UJGSpeYdEZEEUdIXEUmQFtW8k4se5kREkkxH+iIiCaKkLyKSIEr6IiIJ0qLa9HNC1/eLSILpSF9EJEEScaSvq35ERCKJSPo5oWYhESkBSvolYN9fMt/fd6Z2TiISo6Sfb/qFICLNiJJ+Bk09F5CbOB2xi0juFfzqHTMbYmarzKzGzCYUevsiIklW0KRvZq2AO4HzgROBy8zsxELWQUQkyQp9pN8PqHH399z9a+BhYFiB6yAikljm7oXbmNkIYIi7jw3TPwD6u/uPYsuMA8aFyeOBVWlWdwTwaROqoTjFKa44cS2hjqUSd6y7l6ec4+4FG4ARwG9j0z8AftPEdVUrTnGKazlxLaGOSYgrdPPOOqBrbLoilImISAEUOukvArqbWTczawNcCswucB1ERBKroNfpu/tOM/sR8ALQCrjf3Vc0cXXTFac4xbWouJZQx5KPK+iJXBERKS51rSwikiBK+iIiCaKkLyKSIC2mwzUz60F09+4xoWgdMNvdV+Zxe8cAC939i1j5EHd/PkNcP8DdfVHoYmII8I67P9vI7T/g7lc0MmYg0V3Py939zxmW6w+sdPfPzOwbwATgFOBt4F/dPWXvbmb2Y+BJd1/byHrtuVLr/7n7X8zs+8AZwEpgurvXpYn7FnAx0WW+u4D/Ah5y988as30R+asWcaRvZjcQddlgwOthMOD3Te20zczGZJj3Y+Bp4BpguZnFu4r41wxxE4HbgbvN7JfAb4B2wAQz+1mGuNn1hj8CF++ZzhD3emz8f4TtHQpMbOB9uR/YFsZvAzoAvwplMzLE/QJYaGbzzexqM0t9x9/+ZgBDgWvN7EFgJLAQOA34baqA8D+4BygLy7UlSv4LzOysLLebGGZ2ZIG317mQ28sXM+tgZlPM7B0z22RmG81sZSjr2MR1Ppdh3mFm9kszezAc/MTn3ZUh7igzu9vM7jSzzmY2yczeMrNHzezoRlWwKXd0FXogOsJrnaK8DbC6iev8MMO8t4D2YbwSqAauDdNLG4hrBRwCfAYcFsq/AbyZIW4J8O/AWcB3wt/1Yfw7GeKWxsYXAeVhvB3wVoa4lfFt15u3LNP2iA4UBgP3ARuA54FRwKEZ4t4Mfw8GPgZahWlL977seS/D+CHAS2H8m5n+B2GZDsAU4B1gE7CR6FfFFKBjEz8vz2WYdxjwS+BB4Pv15t2VIe4o4G6iTgg7A5PC634UODpD3OH1hs7AGqATcHiGuCH13qP7gDeBh4AuGeKmAEeE8b7Ae0AN8EEDn88lwM+Bv23ke90XeDF8J7oCc4Ct4TPeJ01Me+AmYEVYdgOwABjdwLZeAG4Ajqr3f7kB+HOGuFPSDKcC6zPE/SG8nxcR3aP0B6Btqu9ivbjniQ5CJ4T/2Q3hvbkGeLpR729TvgCFHsKX99gU5ccCqzLEvZlmeAvYkSFuRYoP1PPANBpIiqnGw3SmuIOA68KHu3coey+L9+WN8EXvTL1bsutvv968x4AxYXwG0DeMHwcsyhBXfwfRGrgQ+D2wIUPccqIddCfgc0JiIjqKX5km5q3Yl6FT/PURNV9lel9K+osM7AberzfUhb9pPzfxuhD9wro5fIeuA57KEPdWbPxF4LTY5yVtVwChPlOBD4l+nV8H/E0Wn+vXiXrivQxYC4wI5ecCr6WJeRoYTXSX/z8D/xvoDswiarJMt61M+SPTvF3AvPB+1B++yhC3rN70z4D/JPoOZ/qsLI2Nf5hpnQ2+v41ZuFgDUbt4DfAc0Q0J08MXpobY0UuKuI+B3uGDHR8qidqX08XNIyTfWNnBwAPArgxxC4FDwvhBsfIOmf6hseUqiBLyb+r/Y9Msv4boqOv98PfoUN4+0wch1Gcm8G6oc12I/w+gVzYfvBTzDskw77qw/g+AHwNzgXuJEvvENDHXEiXCe4l2+nt2UuXAyw28LyX9RQZ+Ej7/fxcrez+Lz8uSdOtvYHsrgYPD+IJ68zL9ooxvrwq4C/govJ/jmvi+pPwMAm/Um14U/h5EdE4t3bb+DFxP7JcO0IVoB/yXDHHLge5p5q1t4L08qF7ZaKJfKB9kiHsjNn5ztv+DlOtqzMLFHMI/bwDw38MwgPDzP0PMfcDANPMeyhBXQewosd68MzPEtU1TfkT8C5rFax1KhqOTLOIPAbplsdxhQC+iI9m0P+9jyx93AHX6G8JRHtCRqPO9fg3EnBSW69HIbZX8F5m/HiBMIzqPk80vw1qio+CfEO2ELTYvU/PjNeE9PYeoCeo2oqbHG4EHM8Ttt8Mjav4cAszIEPcaURPiSKIDhYtC+XdI88sCeHXPd53o1+cLsXmZdvSdiM5nvQNsJmoOXBnKMjWVjQCOTzPvogxx/xf4+xTlQ8jQVE3UdNU+Rfm3gcez+V7sjWnMwho0tISh3hd5U70vcqcMcS3uixwS3ALgoyyWnVhv2HMO6CjggQZizwIeITqv8xbwLFEX6AdniHm4if+/XkRNdM8BPcJOZgvRTvSMNDEnEzULbQZeIRygEP0y/HED2+sB/H39/wUZWhFicefmMO78fGxvv/U05Z+iQUNLHQjNRKUUR3ShQM/mXs9ixWWKIWpuXAU8RdRcOiw2L1PTXFPjrilkXMp1NeWN16ChpQ5kca5EcaUVlymGA7tSr9nHpRpazM1ZItkyszfTzSJq21dcicU1dVtE52K+AHD3NeEekMfN7NgQ29Lj9qOkL6WoC3AeUftunBGd8FNc6cU1dVsfm1lvd18G4O5fmNkFRDcw/l0JxO1HSV9K0Z+Ifgovqz/DzF5SXEnGNXVbVwA74wXuvhO4wsz+rQTi9qP+9EVEEqRF9L0jIiK5oaQvIpIgSvoiIgmipC8tUuha9l+ay3pyUI9yM1toZkvNrCqL5Ueb2W8KUTcpLUr60uxZ5IA+q2ZW0CvVmrC9c4n62+nj7vPzUScRUNKXZsLM/tnMlofhn8ys0sxWmdkDRB2hdTWzn5nZf5nZK8Dxsdi/NbPnzWxxeMBLj1A+08zuMbOFRP3jpHOimb1kZu+Fh7ekrFMoqzSz5bFl/sXMJoXxl8zsVjOrJuolNNXrrDSzeWb2ppnNNbNvmlnvUL9hZrbMoqeZpYodE17/68CZsfJ/iP1K+IuZdTGzg8xstYUH3YTpGsv+wTdSonSdvhSdmZ0KjAH6E91Ms5Com+fuwCh3XxCWuZSoq+yDiR7QsTisYjrwQ3dfbdGjIO8i6hESot4oz3D3XRmq0AM4m6i3ylVmdjdRB1771MnM/oP9b/6pr427980w/w5glrvPMrN/BG5394vM7P8QPdfgR6mCwtORbiTqEXUrUffES8PsV4AB7u5mNha43t1/Ymb/DlwO3ErUodgb7r6hgfpLiVPSl+ZgINGzd78EMLMniPpf/8DdF4RlqsIy28Iys8Pf9kTP233MbO/d6G1j636sgYQP8Iy77wB2mNknRHd3pqtT2sdXBo80MP90ouf+QvSkrUy/QOL6Ez09bEOozyNEDzGBaMf2SNgxtCF6vgJEd2s+TZT0/5HMj8KUhFDSl+bsyyyWOQjY4u69D2AdO2Lju8j8vdjJvs2iZU3YXq7dAUxz99mhT5ZJAO6+1sw+NrNzgH5ER/2ScGrTl+ZgPnCRmR1iZu2A4aEs7uWwzDfM7FDgHwDc/TPgfTMbCXtP+vbKY50+Bo606OHUbYELGrneV4maqSBKwtmetF0IfCdstzXRA0b26ACsC+Oj6sX9luhZs9n84pEE0JG+FJ27LzGzmUQPwYAoUW1OscwjRM8F/oToIdl7XA7cbWY/J3pu78NhuZzWyd2XApjZTaF8HdGDWhrjGmCGmf1Pood3j8myPuvDCePXiB4osiw2exJR89Zmosc9dovNm03UrKOmHQHU945ISTOzvsAt7t7gtf+SDDrSFylRZjYBuAq15UuMjvQlEcxsDPtfO/+f7j4+T9v7Gfu2u0PUrj45i9iF7HsFEsAP3P2tXNVPkktJX0QkQXT1johIgijpi4gkiJK+iEiCKOmLiCSIkr6ISIL8f7pzAWDZvr/CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "(orders.query(\"(order_dow == 3) or (order_dow == 6)\")).pivot_table(index=\"order_hour_of_day\", columns=\"order_dow\", values=\"order_id\", aggfunc=\"count\").plot(kind=\"bar\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94e54e8a",
   "metadata": {
    "id": "94e54e8a"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "797c90a8",
   "metadata": {
    "id": "797c90a8"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35bcea30",
   "metadata": {
    "id": "35bcea30"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "charitable-congo",
   "metadata": {
    "id": "charitable-congo"
   },
   "source": [
    "Escribe aquí tus conclusiones# Ambas distribuciones son muy parecidas entre si, se puede observar una diferencia minima en favor del dia sabado entre las 12 y 14 hrs, que no me parece significativa. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3891143",
   "metadata": {
    "id": "e3891143"
   },
   "source": [
    "### [B2] ¿Cuál es la distribución para el número de pedidos por cliente?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d8c26c23",
   "metadata": {
    "id": "d8c26c23"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "opc=(orders.groupby(\"user_id\")[\"order_id\"]).count()\n",
    "opc.plot(kind=\"hist\")\n",
    "plt.xlabel(\"Número de pedidos por usuario\")\n",
    "plt.ylabel(\"Frecuencia\")\n",
    "plt.title(\"Histograma del número de pedidos por usuario\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00e69a66",
   "metadata": {
    "id": "00e69a66"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d2eff7d5",
   "metadata": {
    "id": "d2eff7d5"
   },
   "source": [
    "Escribe aquí tus conclusiones#la mayoria de los usuarios hacen menos de 5 pedidos, lo que puede indicar que la plataforma es nueva. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a0319c",
   "metadata": {
    "id": "54a0319c"
   },
   "source": [
    "### [B3] ¿Cuáles son los 20 productos más populares (muestra su ID y nombre)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "exterior-arizona",
   "metadata": {
    "id": "exterior-arizona",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product_id  product_name            \n",
      "24852       Banana                      66050\n",
      "13176       Bag of Organic Bananas      53297\n",
      "21137       Organic Strawberries        37039\n",
      "21903       Organic Baby Spinach        33971\n",
      "47209       Organic Hass Avocado        29773\n",
      "47766       Organic Avocado             24689\n",
      "47626       Large Lemon                 21495\n",
      "16797       Strawberries                20018\n",
      "26209       Limes                       19690\n",
      "27845       Organic Whole Milk          19600\n",
      "27966       Organic Raspberries         19197\n",
      "22935       Organic Yellow Onion        15898\n",
      "24964       Organic Garlic              15292\n",
      "45007       Organic Zucchini            14584\n",
      "39275       Organic Blueberries         13879\n",
      "49683       Cucumber Kirby              13675\n",
      "28204       Organic Fuji Apple          12544\n",
      "5876        Organic Lemon               12232\n",
      "8277        Apple Honeycrisp Organic    11993\n",
      "40706       Organic Grape Tomatoes      11781\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "order_products_id= pd.merge(order_products, products, on=\"product_id\")\n",
    "order_products_pop=order_products_id[[\"product_id\", \"product_name\"]].value_counts()\n",
    "print(order_products_pop.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358ac8b0",
   "metadata": {
    "id": "universal-facial"
   },
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Comentario del revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Muy buen desarrollo de la sección. Desarrollaste de buena manera todos los análisis y lo complementaste con una gráfica. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbcdd20b",
   "metadata": {
    "id": "bbcdd20b"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7df00f47",
   "metadata": {
    "id": "7df00f47"
   },
   "source": [
    "Escribe aquí tus conclusiones#Es interesante ver que casi todos los 20 productos mas vendidos tienen la denominación de organico."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gross-theme",
   "metadata": {
    "id": "gross-theme"
   },
   "source": [
    "# [C] Difícil (deben completarse todos para aprobar)\n",
    "\n",
    "1. ¿Cuántos artículos suelen comprar las personas en un pedido? ¿Cómo es la distribución?\n",
    "2. ¿Cuáles son los 20 principales artículos que vuelven a pedirse con mayor frecuencia (muestra sus nombres e IDs de los productos)?\n",
    "3. Para cada producto, ¿cuál es la tasa de repetición del pedido (número de repeticiones de pedido/total de pedidos?\n",
    "4. Para cada cliente, ¿qué proporción de los productos que pidió ya los había pedido? Calcula la tasa de repetición de pedido para cada usuario en lugar de para cada producto.\n",
    "5. ¿Cuáles son los 20 principales artículos que la gente pone primero en sus carritos (muestra las IDs de los productos, sus nombres, y el número de veces en que fueron el primer artículo en añadirse al carrito)?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "romantic-deposit",
   "metadata": {
    "id": "romantic-deposit"
   },
   "source": [
    "### [C1] ¿Cuántos artículos compran normalmente las personas en un pedido? ¿Cómo es la distribución?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "virtual-punch",
   "metadata": {
    "id": "virtual-punch"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,5,10,15,20,25,30,35,40,45,50]\n",
    "(order_products.groupby(\"order_id\")[\"product_id\"].count()).plot(kind=\"hist\", bins=bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sensitive-breathing",
   "metadata": {
    "id": "sensitive-breathing"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "completed-frank",
   "metadata": {
    "id": "completed-frank"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "designed-mortality",
   "metadata": {
    "id": "designed-mortality"
   },
   "source": [
    "Escribe aquí tus conclusiones#Podemos observar que la mayoría de las personas compran entre 5 y 10 articulos por pedido. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e6b5537",
   "metadata": {
    "id": "5e6b5537"
   },
   "source": [
    "### [C2] ¿Cuáles son los 20 principales artículos que vuelven a pedirse con mayor frecuencia (muestra sus nombres e IDs de los productos)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9374e55a",
   "metadata": {
    "id": "9374e55a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product_id  product_name            \n",
      "24852       Banana                      55763\n",
      "13176       Bag of Organic Bananas      44450\n",
      "21137       Organic Strawberries        28639\n",
      "21903       Organic Baby Spinach        26233\n",
      "47209       Organic Hass Avocado        23629\n",
      "47766       Organic Avocado             18743\n",
      "27845       Organic Whole Milk          16251\n",
      "47626       Large Lemon                 15044\n",
      "27966       Organic Raspberries         14748\n",
      "16797       Strawberries                13945\n",
      "26209       Limes                       13327\n",
      "22935       Organic Yellow Onion        11145\n",
      "24964       Organic Garlic              10411\n",
      "45007       Organic Zucchini            10076\n",
      "49683       Cucumber Kirby               9538\n",
      "28204       Organic Fuji Apple           8989\n",
      "8277        Apple Honeycrisp Organic     8836\n",
      "39275       Organic Blueberries          8799\n",
      "5876        Organic Lemon                8412\n",
      "49235       Organic Half & Half          8389\n",
      "Name: reordered, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "reordered_products= order_products_id.groupby([\"product_id\", \"product_name\"])['reordered'].sum().sort_values(ascending=False)\n",
    "print(reordered_products.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "possible-change",
   "metadata": {
    "id": "possible-change",
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "professional-surfing",
   "metadata": {
    "id": "professional-surfing",
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48dd3658",
   "metadata": {
    "id": "48dd3658"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "80e70c74",
   "metadata": {
    "id": "80e70c74"
   },
   "source": [
    "Escribe aquí tus conclusiones#podemos obsevar que existen varias coincidencias respecto a los productos más populares, lo cual hace bastante sentido, ya que todos son biens de consumo que requieren una recompra constante. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d312b14",
   "metadata": {
    "id": "9d312b14"
   },
   "source": [
    "### [C3] Para cada producto, ¿cuál es la proporción de las veces que se pide y que se vuelve a pedir?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8ae9f4b1",
   "metadata": {
    "id": "8ae9f4b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product_name\n",
      "Organic Classic Kombucha Maqui Berry Mint             1.0\n",
      "Plant Based Mango Cashew Lucuma Paleo Print Cookie    1.0\n",
      "Burger Veggie Curry                                   1.0\n",
      "G Series Fierce Strawberry Sports Drink               1.0\n",
      "Burgers- Chile Relleno                                1.0\n",
      "Burrito- Pinto Bean Rice & Cheese                     1.0\n",
      "Unscented Men's Anti-Perspirant & Deodorant Gel       1.0\n",
      "Fuzzy Navel Cooler                                    1.0\n",
      "Double Chocolate Sundae Dream Syrup                   1.0\n",
      "Unscented Liken Plant Natural Deodorant               1.0\n",
      "Hershey's Chocolate Chunk Cookie Mix                  1.0\n",
      "Double Concentrated Tomato Paste                      1.0\n",
      "Sugar Sweetened Peach Iced Tea Mix                    1.0\n",
      "Hi-Chew Fruit Chews Green Apple                       1.0\n",
      "Organic Acai Berry Juice Blend                        1.0\n",
      "Butter Flavor Instant Grits                           1.0\n",
      "Plant Gel Toothpaste                                  1.0\n",
      "Almond Sea Salt 72% Cocoa Square                      1.0\n",
      "Hibiscus Tea Squeeze                                  1.0\n",
      "Butter Pecan                                          1.0\n",
      "Name: reordered, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "proporc_reorder= order_products_id.groupby(\"product_name\")[\"reordered\"].mean().sort_values(ascending=False)\n",
    "print(proporc_reorder.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "103e7fe1",
   "metadata": {
    "id": "103e7fe1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product_name\n",
      "Happy Pho Zesty Ginger Brown Rice Noodle Soup              0.0\n",
      "Hand Wash- Pink Grapefruit                                 0.0\n",
      "Happy Hips Dog Food Lamb & Sweet Potato Stew               0.0\n",
      "Happy Go Snacky Non-GMO Chocolate Croissants               0.0\n",
      "Happy Birthday Candles                                     0.0\n",
      "Signature Mint Face Lotion & Post Shave                    0.0\n",
      "Signature Mint Scent Body Wash                             0.0\n",
      "Signature Pineapple Supreme Cake Mix                       0.0\n",
      "Signature Pink Velvet Cake Mix                             0.0\n",
      "Hanging Moisture Absorber                                  0.0\n",
      "Signature Ripe Tomato Extra Hot Gourmet Salsa              0.0\n",
      "Signature Seasoned Salmon                                  0.0\n",
      "Handsaver Flex Strong Small Gloves                         0.0\n",
      "Hands & Face Citrus Scent Antibacterial Wipes              0.0\n",
      "Signature Spice Cake Cake Mix                              0.0\n",
      "Signature Spicy Chicken                                    0.0\n",
      "Handi Foil Stuffing Pan                                    0.0\n",
      "Handcrafted Organic Original Digestive Bitters             0.0\n",
      "Handcrafted Organic Deodorant - Ylang Ylang + Calendula    0.0\n",
      "Medium Diced Tomatoes with Green Chilis                    0.0\n",
      "Name: reordered, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(proporc_reorder.tail(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "social-individual",
   "metadata": {
    "id": "social-individual"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product_name\n",
      "Tucks Medicated Cooling Pads                                   0.142857\n",
      "Simply Blue Corn Tortilla Chips                                0.491228\n",
      "Sliced Natural Cheddar Cheese                                  0.000000\n",
      "Fresh Cut Sweet Peas                                           0.384615\n",
      "Grape White/Green Seedless                                     0.665928\n",
      "Green Pickled Sliced Jalapeno Peppers                          0.333333\n",
      "Crispix Rice & Corn Cereal                                     0.682857\n",
      "Magnesium 400 Mg Vegetarian                                    0.000000\n",
      "Men's Pro Edge Multivitamin/Multimineral Supplement Tablets    0.000000\n",
      "Coconut Flakes                                                 0.316222\n",
      "Organic Sea Salt 70% Dark Chocolate                            0.484848\n",
      "Fragrance Free Olive & Aloe- Deep Moisturizing  Lotion         0.000000\n",
      "Mocha Chocolate Protein Bar                                    0.458333\n",
      "Strawberry Poppyseed Vinaigrette                               0.000000\n",
      "Stems & Pieces Mushrooms                                       0.708333\n",
      "Original Steak & Chop Marinade                                 0.250000\n",
      "White Chocolate Candy Bar                                      0.500000\n",
      "Organic Dark Red Kidney Beans                                  0.400000\n",
      "Advanced Hand Sanitizer Refreshing Aloe                        0.333333\n",
      "Berry Colossal Crunch Cereal                                   1.000000\n",
      "Name: reordered, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(proporc_reorder.sample(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "I10dEIAyldcO",
   "metadata": {
    "id": "I10dEIAyldcO"
   },
   "source": [
    "Escribe aquí tus conclusiones#la proporción se toma con base en que la columna reordered es binaria, de forma que hacer un promedio de esta nos da la proporción entre las reordenes que existen de un determinado producto, asi podemos observar que los primeros productos obtenidos por el método head() nos da una proporción de 1(siempre se reordenan) mientas que los productos del final obtenidos por el método tail() nos da 0, donde ninguno se vuelve a reordenar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subject-boxing",
   "metadata": {
    "id": "subject-boxing"
   },
   "source": [
    "### [C4] Para cada cliente, ¿qué proporción de sus productos ya los había pedido?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "medical-couple",
   "metadata": {
    "id": "medical-couple",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "2         0.038462\n",
       "4         0.000000\n",
       "5         0.666667\n",
       "6         0.000000\n",
       "7         0.928571\n",
       "            ...   \n",
       "206203    0.222222\n",
       "206206    0.714286\n",
       "206207    0.891304\n",
       "206208    0.696000\n",
       "206209    0.320000\n",
       "Name: reordered, Length: 149626, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clients_reorder_p = pd.merge(order_products, orders, on=\"order_id\")\n",
    "clients_reorder_p.groupby(\"user_id\")[\"reordered\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8892e4b4",
   "metadata": {
    "id": "8892e4b4"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "BgJSvXDsleXz",
   "metadata": {
    "id": "BgJSvXDsleXz"
   },
   "source": [
    "Escribe aquí tus conclusiones#la proporción de reordered se calcula como en el ejercicio anterior con un promedio sobre la columna, lo que nos da la proporción de reorden, en este caso de los usuarios. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d35137",
   "metadata": {
    "id": "92d35137"
   },
   "source": [
    "### [C5] ¿Cuáles son los 20 principales artículos que las personas ponen primero en sus carritos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0ae57274",
   "metadata": {
    "id": "0ae57274",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product_id  product_name            \n",
      "24852       Banana                      6642\n",
      "13176       Bag of Organic Bananas      5355\n",
      "21137       Organic Strawberries        3634\n",
      "21903       Organic Baby Spinach        3297\n",
      "47209       Organic Hass Avocado        2996\n",
      "47766       Organic Avocado             2459\n",
      "47626       Large Lemon                 2125\n",
      "26209       Limes                       1991\n",
      "16797       Strawberries                1984\n",
      "27845       Organic Whole Milk          1973\n",
      "27966       Organic Raspberries         1850\n",
      "22935       Organic Yellow Onion        1540\n",
      "45007       Organic Zucchini            1478\n",
      "24964       Organic Garlic              1450\n",
      "39275       Organic Blueberries         1338\n",
      "49683       Cucumber Kirby              1319\n",
      "28204       Organic Fuji Apple          1247\n",
      "8277        Apple Honeycrisp Organic    1219\n",
      "5876        Organic Lemon               1211\n",
      "40706       Organic Grape Tomatoes      1206\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "x=order_products_id[order_products[\"add_to_cart_order\"] == 1].groupby([\"product_id\", \"product_name\"]).size().sort_values(ascending=False)\n",
    "print(x.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1250f5cf",
   "metadata": {
    "id": "forty-netherlands",
    "scrolled": true
   },
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Comentario del revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Muy buena utilización  del filtro. Solamente te recomiendo hacer la agrupación por las variables ['product_id', 'product_name'] y posteriormente hacer el conteo de la variable ['product_id'] para saber cuáles son los 20 principales artículos que las personas ponen primero en sus carritos\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "41a4694d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product_id  product_name                                                     \n",
      "1           Chocolate Sandwich Cookies                                           27\n",
      "2           All-Seasons Salt                                                      1\n",
      "3           Robust Golden Unsweetened Oolong Tea                                  7\n",
      "4           Smart Ones Classic Favorites Mini Rigatoni With Vodka Cream Sauce     6\n",
      "8           Cut Russet Potatoes Steam N' Mash                                     1\n",
      "9           Light Strawberry Blueberry Yogurt                                     4\n",
      "10          Sparkling Orange Juice & Prickly Pear Beverage                       32\n",
      "12          Chocolate Fudge Layer Cake                                            3\n",
      "13          Saline Nasal Mist                                                     1\n",
      "18          Pizza for One Suprema  Frozen Pizza                                   2\n",
      "23          Organic Turkey Burgers                                               19\n",
      "25          Salted Caramel Lean Protein & Fiber Bar                              36\n",
      "26          Fancy Feast Trout Feast Flaked Wet Cat Food                           4\n",
      "28          Wheat Chex Cereal                                                     6\n",
      "29          Fresh Cut Golden Sweet No Salt Added Whole Kernel Corn                5\n",
      "31          White Pearl Onions                                                    1\n",
      "32          Nacho Cheese White Bean Chips                                         3\n",
      "34          Peanut Butter Cereal                                                 94\n",
      "35          Italian Herb Porcini Mushrooms Chicken Sausage                        2\n",
      "36          Traditional Lasagna with Meat Sauce Savory Italian Recipes            4\n",
      "Name: product_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "x = order_products_id[order_products[\"add_to_cart_order\"] == 1].groupby([\"product_id\", \"product_name\"])\n",
    "y = x[\"product_id\"].count()\n",
    "print(y.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2157de4e",
   "metadata": {
    "id": "08e41ec2"
   },
   "source": [
    "#2 Se hacen las correcciones al código solicitadas, en el código original se agrupan por \"product_id\" y por \"product_name\" sin embargo al hacer el conteo sugerido sobre \"product_id\" me parece que el resultado no es el mas adecuado, por esa razón lo puse en un código aparte. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ce9872",
   "metadata": {
    "id": "28ce9872"
   },
   "source": [
    "Escribe aquí tus conclusiones#de nuevo podemos observar que muchos de los productos más vendidos y mas reordenados se encuentran entre los productos que las personas ponen primero en sus carritos de compra."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uITbFCEoDosC",
   "metadata": {
    "id": "uITbFCEoDosC"
   },
   "source": [
    "### Conclusion general del proyecto:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "Nk-4GS7mDwqY",
   "metadata": {
    "id": "Nk-4GS7mDwqY"
   },
   "outputs": [],
   "source": [
    "# Es un ejercicio interesante poder extraer nueva información a partir de dataframes y su correcto procesamiento y\n",
    "# análisis, en lo personal me pareció extraordinariamente complejo por la falta de práctica que tengo respecto al \n",
    "# uso de estas plataformas, sin embargo, puedo imaginar el potencial de aprender a hacerlo de forma correcta para \n",
    "# una gran variedad de datos.\n",
    "\n",
    "# tambien me pareció interesante la capacidad de nuestros códigos para poder encontrar algunos problemas en la\n",
    "# programacion de la plataforma, como la agrupación en el dia 30 de las reordenes, los duplicados del dia miercoles\n",
    "# a las 2am o los problemas en el pasillo 100 departamento 21 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d67596",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Comentario revisor</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "En general creo que hiciste un muy buen trabajo con el proyecto, pudiste limpiar y trabajar las bases de datos de beuna manera, análisis de duplicados, y de valores faltantes. Además dearrollaste de buena manera los diferentes anális que se solicitaban y cuando podías los complementabas con gráficas. No obstante, recuerda que siempre podemos mejorar y te menciono algunos puntos que debes considerar:\n",
    "\n",
    "* Revisar que los filtros sean correctos en algunos apartados con base en la indicación\n",
    "    \n",
    "*  Realizar análisis complementarios eliminando los valores que parecen ser atípicos. \n",
    "    \n",
    "*  Realizar gráficas de barras en algunos análisis para complementarlos.\n",
    "   \n",
    "    \n",
    "*  profundizar en los resultados intermedio y finales \n",
    "    \n",
    "    \n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "vscode": {
   "interpreter": {
    "hash": "01a7be9ec63e704a62cefc5fe7a4756944464ee731be31632bdf42a4cb4688cf"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}