{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3fb09efb17ad6eba",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### Según los datos revisados en SQL, podríamos obtener resultados de:\n",
    "- Pizzas con mayor y menor venta\n",
    "- Horas en las que ocurren la mayor cantidad de ventas\n",
    "- Días de la semana en los que ocurren la mayor cantidad de ventas\n",
    "- Categoría de pizza mas comprada"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Análisis de un restaurante de Pizzas"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cd223a3fe5b723d6"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Importamos las librerias necesarias"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d3323399517cf439"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\users\\bryan\\documents\\portafolio\\pycharm\\pythonproject\\.venv\\lib\\site-packages (5.20.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\bryan\\documents\\portafolio\\pycharm\\pythonproject\\.venv\\lib\\site-packages (from plotly) (8.2.3)\n",
      "Requirement already satisfied: packaging in c:\\users\\bryan\\documents\\portafolio\\pycharm\\pythonproject\\.venv\\lib\\site-packages (from plotly) (23.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.2.1 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install plotly"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:15.613207Z",
     "start_time": "2024-06-06T03:20:08.802404Z"
    }
   },
   "id": "8026b9cad75a506b",
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7c9115bea80333f",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:15.622968Z",
     "start_time": "2024-06-06T03:20:15.617216Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84cf80d2f1626f66",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Importación de CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7b83a435fb885365",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:15.793826Z",
     "start_time": "2024-06-06T03:20:15.624977Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/Bryan/Documents/Portafolio/Pizza-Sales/pizza_sales.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f3df0076ff33eb",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Revisión de columnas con datos nulos o duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "498764376a8c733",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:15.823909Z",
     "start_time": "2024-06-06T03:20:15.795836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "pizza_id             0\norder_id             0\npizza_name_id        0\nquantity             0\norder_date           0\norder_time           0\nunit_price           0\ntotal_price          0\npizza_size           0\npizza_category       0\npizza_ingredients    0\npizza_name           0\ndtype: int64"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "361a2d757ad84b08",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:15.885180Z",
     "start_time": "2024-06-06T03:20:15.824921Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123a55938dd897b",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Escaneo general de los datos del CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e18814f8f4b1dc9b",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:15.940051Z",
     "start_time": "2024-06-06T03:20:15.887191Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "           pizza_id      order_id      quantity    unit_price   total_price\ncount  48620.000000  48620.000000  48620.000000  48620.000000  48620.000000\nmean   24310.500000  10701.479761      1.019622     16.494132     16.821474\nstd    14035.529381   6180.119770      0.143077      3.621789      4.437398\nmin        1.000000      1.000000      1.000000      9.750000      9.750000\n25%    12155.750000   5337.000000      1.000000     12.750000     12.750000\n50%    24310.500000  10682.500000      1.000000     16.500000     16.500000\n75%    36465.250000  16100.000000      1.000000     20.250000     20.500000\nmax    48620.000000  21350.000000      4.000000     35.950000     83.000000",
      "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>pizza_id</th>\n      <th>order_id</th>\n      <th>quantity</th>\n      <th>unit_price</th>\n      <th>total_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>48620.000000</td>\n      <td>48620.000000</td>\n      <td>48620.000000</td>\n      <td>48620.000000</td>\n      <td>48620.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>24310.500000</td>\n      <td>10701.479761</td>\n      <td>1.019622</td>\n      <td>16.494132</td>\n      <td>16.821474</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>14035.529381</td>\n      <td>6180.119770</td>\n      <td>0.143077</td>\n      <td>3.621789</td>\n      <td>4.437398</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>9.750000</td>\n      <td>9.750000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>12155.750000</td>\n      <td>5337.000000</td>\n      <td>1.000000</td>\n      <td>12.750000</td>\n      <td>12.750000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>24310.500000</td>\n      <td>10682.500000</td>\n      <td>1.000000</td>\n      <td>16.500000</td>\n      <td>16.500000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>36465.250000</td>\n      <td>16100.000000</td>\n      <td>1.000000</td>\n      <td>20.250000</td>\n      <td>20.500000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>48620.000000</td>\n      <td>21350.000000</td>\n      <td>4.000000</td>\n      <td>35.950000</td>\n      <td>83.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a0fd3de4eb66e05b",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:16.019215Z",
     "start_time": "2024-06-06T03:20:15.942061Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "       pizza_name_id  order_date order_time pizza_size pizza_category  \\\ncount          48620       48620      48620      48620          48620   \nunique            91         358      16382          5              4   \ntop       big_meat_s  26-11-2015   12:32:00          L        Classic   \nfreq            1811         261         26      18526          14579   \n\n                                        pizza_ingredients  \\\ncount                                               48620   \nunique                                                 32   \ntop     Pepperoni, Mushrooms, Red Onions, Red Peppers,...   \nfreq                                                 2416   \n\n                      pizza_name  \ncount                      48620  \nunique                        32  \ntop     The Classic Deluxe Pizza  \nfreq                        2416  ",
      "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>pizza_name_id</th>\n      <th>order_date</th>\n      <th>order_time</th>\n      <th>pizza_size</th>\n      <th>pizza_category</th>\n      <th>pizza_ingredients</th>\n      <th>pizza_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>48620</td>\n      <td>48620</td>\n      <td>48620</td>\n      <td>48620</td>\n      <td>48620</td>\n      <td>48620</td>\n      <td>48620</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>91</td>\n      <td>358</td>\n      <td>16382</td>\n      <td>5</td>\n      <td>4</td>\n      <td>32</td>\n      <td>32</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>big_meat_s</td>\n      <td>26-11-2015</td>\n      <td>12:32:00</td>\n      <td>L</td>\n      <td>Classic</td>\n      <td>Pepperoni, Mushrooms, Red Onions, Red Peppers,...</td>\n      <td>The Classic Deluxe Pizza</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>1811</td>\n      <td>261</td>\n      <td>26</td>\n      <td>18526</td>\n      <td>14579</td>\n      <td>2416</td>\n      <td>2416</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a23df84e537d9b96",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:16.040958Z",
     "start_time": "2024-06-06T03:20:16.020225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   pizza_id  order_id  pizza_name_id  quantity order_date order_time  \\\n0       1.0       1.0     hawaiian_m       1.0   1/1/2015   11:38:36   \n1       2.0       2.0  classic_dlx_m       1.0   1/1/2015   11:57:40   \n2       3.0       2.0  five_cheese_l       1.0   1/1/2015   11:57:40   \n3       4.0       2.0    ital_supr_l       1.0   1/1/2015   11:57:40   \n4       5.0       2.0     mexicana_m       1.0   1/1/2015   11:57:40   \n\n   unit_price  total_price pizza_size pizza_category  \\\n0       13.25        13.25          M        Classic   \n1       16.00        16.00          M        Classic   \n2       18.50        18.50          L         Veggie   \n3       20.75        20.75          L        Supreme   \n4       16.00        16.00          M         Veggie   \n\n                                   pizza_ingredients  \\\n0           Sliced Ham, Pineapple, Mozzarella Cheese   \n1  Pepperoni, Mushrooms, Red Onions, Red Peppers,...   \n2  Mozzarella Cheese, Provolone Cheese, Smoked Go...   \n3  Calabrese Salami, Capocollo, Tomatoes, Red Oni...   \n4  Tomatoes, Red Peppers, Jalapeno Peppers, Red O...   \n\n                  pizza_name  \n0         The Hawaiian Pizza  \n1   The Classic Deluxe Pizza  \n2      The Five Cheese Pizza  \n3  The Italian Supreme Pizza  \n4         The Mexicana Pizza  ",
      "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>pizza_id</th>\n      <th>order_id</th>\n      <th>pizza_name_id</th>\n      <th>quantity</th>\n      <th>order_date</th>\n      <th>order_time</th>\n      <th>unit_price</th>\n      <th>total_price</th>\n      <th>pizza_size</th>\n      <th>pizza_category</th>\n      <th>pizza_ingredients</th>\n      <th>pizza_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>hawaiian_m</td>\n      <td>1.0</td>\n      <td>1/1/2015</td>\n      <td>11:38:36</td>\n      <td>13.25</td>\n      <td>13.25</td>\n      <td>M</td>\n      <td>Classic</td>\n      <td>Sliced Ham, Pineapple, Mozzarella Cheese</td>\n      <td>The Hawaiian Pizza</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>classic_dlx_m</td>\n      <td>1.0</td>\n      <td>1/1/2015</td>\n      <td>11:57:40</td>\n      <td>16.00</td>\n      <td>16.00</td>\n      <td>M</td>\n      <td>Classic</td>\n      <td>Pepperoni, Mushrooms, Red Onions, Red Peppers,...</td>\n      <td>The Classic Deluxe Pizza</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>five_cheese_l</td>\n      <td>1.0</td>\n      <td>1/1/2015</td>\n      <td>11:57:40</td>\n      <td>18.50</td>\n      <td>18.50</td>\n      <td>L</td>\n      <td>Veggie</td>\n      <td>Mozzarella Cheese, Provolone Cheese, Smoked Go...</td>\n      <td>The Five Cheese Pizza</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>ital_supr_l</td>\n      <td>1.0</td>\n      <td>1/1/2015</td>\n      <td>11:57:40</td>\n      <td>20.75</td>\n      <td>20.75</td>\n      <td>L</td>\n      <td>Supreme</td>\n      <td>Calabrese Salami, Capocollo, Tomatoes, Red Oni...</td>\n      <td>The Italian Supreme Pizza</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>mexicana_m</td>\n      <td>1.0</td>\n      <td>1/1/2015</td>\n      <td>11:57:40</td>\n      <td>16.00</td>\n      <td>16.00</td>\n      <td>M</td>\n      <td>Veggie</td>\n      <td>Tomatoes, Red Peppers, Jalapeno Peppers, Red O...</td>\n      <td>The Mexicana Pizza</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f7499a420581a3a9",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:16.065144Z",
     "start_time": "2024-06-06T03:20:16.042976Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                pizza_id    order_id  quantity  unit_price  \\\npizza_name                                                                   \nThe Barbecue Chicken Pizza    56500574.0  24871757.0    2432.0    41683.00   \nThe Big Meat Pizza            44373219.0  19533329.0    1914.0    21732.00   \nThe Brie Carre Pizza          11717887.0   5158921.0     490.0    11352.00   \nThe Calabrese Pizza           22835268.0  10052080.0     937.0    15763.75   \nThe California Chicken Pizza  56230910.0  24753723.0    2370.0    40166.50   \n\n                              total_price  \npizza_name                                 \nThe Barbecue Chicken Pizza       42768.00  \nThe Big Meat Pizza               22968.00  \nThe Brie Carre Pizza             11588.50  \nThe Calabrese Pizza              15934.25  \nThe California Chicken Pizza     41409.50  ",
      "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>pizza_id</th>\n      <th>order_id</th>\n      <th>quantity</th>\n      <th>unit_price</th>\n      <th>total_price</th>\n    </tr>\n    <tr>\n      <th>pizza_name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>The Barbecue Chicken Pizza</th>\n      <td>56500574.0</td>\n      <td>24871757.0</td>\n      <td>2432.0</td>\n      <td>41683.00</td>\n      <td>42768.00</td>\n    </tr>\n    <tr>\n      <th>The Big Meat Pizza</th>\n      <td>44373219.0</td>\n      <td>19533329.0</td>\n      <td>1914.0</td>\n      <td>21732.00</td>\n      <td>22968.00</td>\n    </tr>\n    <tr>\n      <th>The Brie Carre Pizza</th>\n      <td>11717887.0</td>\n      <td>5158921.0</td>\n      <td>490.0</td>\n      <td>11352.00</td>\n      <td>11588.50</td>\n    </tr>\n    <tr>\n      <th>The Calabrese Pizza</th>\n      <td>22835268.0</td>\n      <td>10052080.0</td>\n      <td>937.0</td>\n      <td>15763.75</td>\n      <td>15934.25</td>\n    </tr>\n    <tr>\n      <th>The California Chicken Pizza</th>\n      <td>56230910.0</td>\n      <td>24753723.0</td>\n      <td>2370.0</td>\n      <td>40166.50</td>\n      <td>41409.50</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.groupby(\"pizza_name\").sum(\"total_price\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Transformación de datos a información"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6c8664a43601e60e"
  },
  {
   "cell_type": "markdown",
   "id": "a8c98da6a532c94c",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Graficamos los ingresos por tipo de pizza y las cantidades compradas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "274c6772a760b2c7",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:16.813721Z",
     "start_time": "2024-06-06T03:20:16.067154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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WHA6vzK8na4u+YuzYsRo9erT27duniRMnyt/fXx999JFKly6t4cOHu7PIm4oxRl5eqZ9Eh8O9H2Tw8vKSt3fmN0h6SpXyvqH5kbe4W2vZgVqzF2oNnkKtwVOoNXgKtQZPyelacysQ+M9//qN33303VVu/fv2yoz83hcDAQO3bt8+6HRcXp3vvvTcXewQAAAAAQPZyO25Ys2aNnnzySdWsWVMPPvigwsLCtHTp0uzsW655+OGHFRERoXPnzunMmTPavn27atWqldvdAgAAAAAg27g1QuDTTz/VtGnT1LlzZ/Xs2VMul0u//PKLxo0bJ5fLpSeffDK7++lRZcqUUffu3dWhQwclJyerV69eCggIyO1uAQAAAACQbbyMyfrlMJo0aaLevXsrNDQ0VftXX32ld999V2vWrMmu/gEAAAAAgBzg1ikDp06dUlBQUJr2Bx98UP/8888NdwoAAAAAAOQstwKBatWqafny5Wnaly9frkqVKt1wpwAAAAAAQM5y65SByMhIPffcc6pataruv/9+SdLu3bv1+++/Kzw8XLVr1872jgIAAAAAgOzjViAgSQcPHtSXX36pgwcPqlChQrrrrrvUqVMn3XrrrdndRwAAAAAAkM3cDgSulpCQoP3796tChQoqXrx4dvQLAAAAAADkILeuIXD48GF16NBBv/zyi86fP6/HH39cTz31lBo2bKidO3dmdx8BAAAAAEA2cysQGDt2rAIDA3XnnXdq6dKlOnXqlH744Qc9//zzmjRpUnb3EQAAAAAAZDO3AoFffvlFr7/+ugICAvT999+rSZMmuvXWWxUaGqo//vgju/sIAAAAAACymVuBQJEiRRQfH69Tp07pl19+UYMGDSRJf/75p0qWLJmd/UM6hg4dqvbt26tZs2aqWbOm2rdvr/bt22v//v3q0qWL9uzZk+Vlzpo1S+3bt1dISIjuv/9+a5kREREaPHiwvv322yz178CBA2nao6Oj1atXL2s9Y8eOVWJioiRl2O+MlpWyvDZt2mS6X5nRpUsXtWjRwnr8Tz31lKKiov61L3lFTtSOJDVu3Fht27ZV+/bt1bp1aw0ePFiXL1/O9Pzbtm3Tyy+/7Na6s8vevXv1wgsvWI/hrbfeUsolVho3bqxTp06lmefFF19UfHx8usvLiceU8jyHhoaqXbt26tKli44dO/avfckNOVVrkvTpp5/qscces16j27dvv+70c+bM0fz58yVJHTp0kCTt2bNHDRs2VK9evdzuR8pyxo0bl+npnU6n3n77bev56NKli/bt2ydJWrZsmcaMGZNmnu+//15z587NcJk3+nxea9myZapbt67at2+v0NBQhYSEaN68eZnqy80oN2oxo23pjs8//1xfffVVpqa9+j2sTZs26tKly78eqMnOvl5r8ODBaty4sfWch4WFKSIiQtKVzx1btmzJkfXejHKqDo8dO6bu3burXbt2at26tcaNG6ekpKTrzpOyH3TX1f198cUXrzvt1fWVHe9Tc+bMUb169aznr23bttbrIyuvlZvVzVQnGUnZ5jnxOfxax44dy3D/xPeKbGTcMGLECNOsWTPTpk0b8+ijj5qEhATzf//3f+aRRx4xb775pjuLhBu2bt1qXnrppVRtnTt3Nrt373Z7mceOHTOtW7dO1TZo0CCzevVqt5dpjDHnzp0zzZs3N5s3bzbGGJOcnGyGDh1qRo0aZYxxr9/p9fVGXduPiIgI89hjj2XrOm4G2V07jRo1MnFxcdbtAQMGmE8++eSG+uNJf//9t2nSpIn57bffjDHGXLp0yXTv3t28//77xpi0jy8zcuIxXduPRYsWmd69e2frOrJbdtfaRx99ZHr06GHOnz9vjDFm37595pFHHjHR0dEZzjN79mwzb968VG1z5swx7777rlt9uBETJkwwI0aMMAkJCcYYY7Zs2WKCg4PN+fPnzdKlS83o0aOzvMwb3e9f69p+XLx40TRr1szs378/29aRGzxZi+5uyxt17eP54YcfTKNGjczly5cznCcn+3rt54cDBw6YRx991LhcrhxZX16Q3XX4/PPPmw0bNhhjrny26t+/f5r9XXa7ur8PPvjgdafN7vq6dn9+8uRJU7duXXPmzJlsW8fN4Gauk5RtnhOfw6+V0WcpvldkLx93QoThw4frk08+0V9//aUOHTqoQIECSkxMVM+ePfXMM89kd2aBLPr444/1xx9/6OLFi5o8ebIefPBBHTx4UKNGjdL58+cVEBCgcePGqUyZMple5po1azR37lydPXtWQ4YMUZMmTfTbb79pwoQJunjxoi5cuKAJEyaoVq1a6tKliwYOHKj77rvPmv+bb75RUFCQHn30UUmSt7e3BgwYoMjIyOv2O2VZd999t4YPH649e/aoUKFCGjZsmP7zn/9Y837++ef69ttvNXfuXEVHR6f7WJs1a6aQkBDr6MTMmTNVrly56z7uWrVq6dChQ5Jk9eWbb76xjmrExcWpbNmyGjBggMaOHSvpyhHAP/74Q99++60uXryY7nN0s8qO2klOTtalS5dUqlQpSdLmzZs1Z84cXbp0ScYYzZw5UxUrVlSzZs1UrVo1HT58WAMHDtSJEyf07LPP6uTJk2rXrp1efvllJSUlacKECfr1119ljFGPHj3UsmVLnTp1SoMHD9Zff/2lokWLauLEidq1a5eioqI0YsQISVKbNm303nvv6dZbb013GVdbtGiR2rdvr3vuuUeSVKhQIY0cOVL//POPNc2bb76pqKgouVwuvfXWWypfvrwaN26sJUuWSFKa/lxt6tSpiomJ0ZQpUxQZGakpU6YoISFB5cqV07hx4+Tn56dGjRqpXr16ioyMVMmSJTVr1iwFBARcd3vVqlVLn3zyiSRZfRk/fryVOB8/flzBwcFq0qSJwsPDJUmXL1/WX3/9pe3bt+uXX35Jd9t4gju1ZozRvHnz9Nlnn6lo0aKSpCpVqmjatGny8fFRYmKihg0bpj///FOxsbFq3bq1BgwYkGq9QUFBevvtt/X555/L4XCoSJEiqlu3rkaMGKELFy7o1ltv1fjx43XrrbemqdGPP/5YDodDhw4dUu3atTVu3Dht27ZNH3zwgcLDw/X111/rk08+0YULF1S8eHHNmTNHgYGB1rrPnz+v5cuXa9OmTSpQoIAk6eGHH9bo0aOVnJwsSTpw4IA6deqk48ePKzQ0VH369NGyZcus2l6+fLnmzp0rLy8vNW/eXP369bOWf+zYMb344ouaOnWqqlatmm7dz5kzR7GxsTp06JD+/vtv9ejRQ0899dR1t1XhwoV1zz336PDhw4qKilJUVJR1NEZKvc8bMWKEdQTw2LFj6tKli1599dV/3S65KSdq8Wrp1YW/v78GDRqkw4cPy8vLS6+++qqaNGmi999/XytXrpS3t7eaNGmiXr16ac6cOSpSpIheeOEFzZ07V0uXLpW3t7c6d+6sTp06Xfex1a9fXxUrVlRERISaNm2qL774QosWLZLT6VTDhg312muvpZo+KCjIel+eP3++Ll68qIYNG6pPnz5auXKl4uLi9Nxzz2nJkiVKTk7W8OHDFRsbq8KFC2vkyJGqUqXKdftTsWJFuVwunTlzRpMnT1bDhg11/PhxLV++XJJ07tw5JSUl6dNPP1Xv3r2t+fbv36/58+erfPnyGjZsmM6dO6fTp09r4MCBafbneZW7778nT57UxYsXJf3/z1Ypr8HGjRurcePG2rZtW6r3lZTtfOzYMQ0aNEjnzp1TyZIlNX36dPXu3VuDBg1SUFCQzp8/r9DQUK1Zs0be3t5p+jxhwgRdvnxZzz33nBYsWKCJEycqMjJSJ0+eVM2aNTVlypRU06e8T128eDHd7Th48GAVL15ce/bsUWxsrIYNG6ZGjRpd93m75ZZbVLZsWR07dkwLFy5UkSJFVKZMmXTf8zp27GjNd+jQIQ0fPlwtWrTQoEGDFBcXp5MnT+r5559Xly5dsr4BPSQn6mT79u2aNGmSkpKSVKlSJY0dO1ZnzpxRjx49tGrVKknSmDFjVL16de3bt8/a5uPGjdOFCxf06quv6tChQypXrpzmzJmj3r176/nnn1edOnU0YMAAlSxZUsOGDdOaNWu0fft29e/fXyNGjNCff/4ph8OhgQMHqnbt2vrqq6/04YcfyuFw6L777tOYMWM0ceJEHTt2TNOmTUv1vsH3imz+XnEjaYLT6TRHjx41SUlJ1tEOeE5G6eHUqVONMVdS2ZSjh08//bQ5ePCgMcaY9evXm1deeSXdZWY0QqB///7WOlOSrTFjxphff/3VGGPM6tWrzcsvv2z14dpUbvTo0ebDDz/M8LFk1O+UZc2dO9cMHTrUGGPM/v37TdeuXa2+rl692nTo0ME6SpPRY23UqJFZsmSJMcaYWbNmmQkTJqTbj6v7/sUXX5hnn3023fvOnDljWrVqZfbs2ZNqGePHjzeTJk267nOU27K7dho1amTatGlj2rVrZ+rWrWtCQkLMuXPnjDHG9OrVy/z111/GGGPmzZtnxowZY83z7bffWv2pWbOmOX78uLl8+bJp3769iYqKMp988omZNWuWMeZKGhwSEmJiY2PNyJEjzdtvv22MMWbjxo1m8ODBaY5CtG7d2hw7dizDZVztpZdeMmvXrs3w+WrUqJH57LPPjDFXjk6kbN+UI/bp9SflOf7ggw/Mq6++au0nQ0NDrfV//PHHZvz48cYYYypXrmx++uknY4wxAwcONB999FG6/UgZIeByucyMGTPMkCFD0txnjDFHjx41zZo1S3PkvFevXubjjz++7rbJTtlZa3FxcaZGjRoZruunn34ykydPNsZcGeVRt25dExcXl+qIUsqRjavbQkNDzZYtW4wxV17zr776qjEmbY3Wrl3bxMXFmYSEBNO8eXOzd+9e6/E5nU7TrVs3q+5Hjx5t5s+fn6p/u3fvNm3bts2w/0uXLjXNmjUz586dM+fOnTO1atUy586ds2r7+PHjpkGDBiYuLs4kJyebZ5991hw4cMB07tzZbNy40bRt29bs2LHDGGMyrPvZs2ebrl27muTkZHP06FFTu3btdPtx9Wvpr7/+Mg0aNDBHjx5N92jf1fu8FLt27TIhISEmPj4+w+3iaZ6sxZTnKaO62LZtm+nTp48x5sp72rRp00xSUpJp0KCBcTqd5vLly+a1114ziYmJVq3u2rXLtG3b1ly4cMFcvHjRhIaGmrNnz6Z5PNe+/06ePNmEh4ebffv2mW7dupnExETjdDpNnz59zHfffZdqm159tHfevHlm9uzZxhhjJk2aZCZOnGg6d+5sHfHv3bu3tc/au3evCQsLS/M8XDtCICIiwjRv3jzd+xISEszTTz9tvv/++1TLWLBggbVdwsPDrdfk7t27TZs2bTLcBjer7H7//f77702NGjVMkyZNzPDhw619mTFX9mELFiwwxhjz1ltvmZEjRxpj/v927t69u/nqq6+MMcZ89tlnZtasWWbRokXWe8HSpUvTvLZT+nvtCIFDhw6ZAQMGGJfLZZKTk03btm3Nnj17UtVXyvtURttx0KBBZtCgQcaYK/vz0NDQNOu+doTA3r17TXBwsDl37ly6o8Gufs9L8d1335mnn37aJCYmmq+++sp6jmJjY6/7uvYkT9VJQkKCadSokTlw4IAxxpjp06ebCRMmpPk+MHr0aLN06VJjTOoRAvfcc4/Zt2+fcblcpnPnzmbDhg3m448/tvYd7dq1M48//rgxxphhw4aZjRs3mkmTJlmfx48fP26aNWtmEhMTrfc/l8tlhg4damJjYzMcIcD3iuz9XuHWCIGkpCRNnz5dn3zyiZxOp9asWWOl4mPHjlWRIkVuPKmA25o2bSpJqlSpkpYvX67z58/rt99+U//+/SVdObLh5eWVpWU2adJEknT33Xfr9OnTkqQ33nhDGzdu1FtvvaWdO3fK6XRmOL+Xl5d8fX2z1O+r7dy5U88995wkqXLlyvroo48UHR2t48eP6/XXX9fEiRNVtGjRf32sKUni3XffrU2bNqXbjwEDBqhQoUJyOBwqV66cJkyYkGYal8ulAQMGqGvXrqpevbrVvmrVKv3222/66KOPsvwc3QxupHY++ugjBQQEyOVyafr06Ro/frwmTpyoKVOmaP369Vq8eLF+/PFHVapUyZrn/vvvt/6uW7eubr31VklXjiLs3LlTO3fu1O+//67vv/9eknTp0iUdOnRIO3fu1LvvvitJatCggRo0aKBly5al26+tW7emu4yUEQzSlfpMOWKbmefm2tpJrz/btm3Trl27tGnTJn322Wfy8fHR/v37deTIEb3wwguSrqS+ZcuWlST5+vqqTp06kq7UZ0bnWT777LNyOBzy9vZW1apVNXDgwDTTXL58WX379tXQoUN1++23W+3h4eHy9fVV586dJem62yanuVNr/7ad6tatqxIlSuijjz7SH3/8ocuXL+vSpUvX7cf58+d1+vRpPfzww5KksLAwvfnmm9b9V9dotWrVrFEb5cuXT7WNHA6HZsyYobVr1+rQoUPaunWrQkJCstT/lMdQrFgxSVJgYKDOnj1r3RcZGanatWtbfViwYIF13xtvvKFq1apZRwoyqntJqlOnjry9vXXHHXdY51te65tvvtHPP/8sLy8vFS5cWEOHDtUdd9yhHTt2pJru2n2eJJ06dUqvv/66Zs6cqeLFi7u1XTwpJ2oxRUZ1cffdd+u3337Tyy+/rEaNGunll1+Wj4+PqlatqieffFLNmjVT//79U71v7tixQ02aNLE+Y137PpkRLy8vFSpUSNu3b9fvv/+uJ554QtKV/cQ999yj0qVL/+sy+vbtq9atW+uee+6xjshv27ZNR44csaY5ffq0EhMT0zwvU6ZM0bvvviuHw6HSpUtrzpw56a5j3Lhxeuihh9S4cWOrbefOnfryyy+1aNEiSVfOXd68ebPCw8O1Z88eXbhwIVPPQV7g7vtv48aNFRERoS1btmjz5s3q27evunfvrpdeeknSlX2aJLVr1049e/ZMNe8vv/xivXelHD2Pj4/Xe++9pyFDhmjlypUaNGhQpvpfoUIF9erVS5999pkOHDigEydOWEekr3W97ZiyL65cuXKq/d/VFixYoBUrVsjLy0t+fn6aMWOGtd+82rXvedKVkQGTJ0/Wxx9/LF9fX7Vv314///yz5s+fr/37999U+6b0ZHedNGjQQP/5z3+s0YGPP/64+vTpk+lREnfccYc1MqhSpUo6ffq06tWrp6FDh6pdu3a68847dfjwYV24cEE7d+7U0KFDNWvWLEVERGjhwoWSpISEBB0/flz16tXTk08+qebNm6tr164qVaqUDh48mO56+V6Rvd8r3AoEZs+erc2bN2v+/PnWRbO6dOmi4cOHa9KkSTl2cRpkTsqwrpSCNcaoRIkS+vrrryVdGdJ95swZt5Z5td69e+u2225T/fr1Vb16deuiXempWrVqqmE80pUPDyNHjtSsWbPS7fe167+6/eDBgypYsKB8fHw0ffp0jR49Wo0bN/7Xx5ryQcXLy8u6YNy1pk2blup0h/SkDPm8+sI8+/fv18yZM/Xpp59aQ0az8hzdDLKjdhwOh9q0aaP//ve/MsbomWeeUYMGDVSnTh3ddttt+uWXX6xpCxYsmGbdKev18fGRy+XS8OHDFRwcLOnKkLeSJUummfbQoUNptmnKxXIyWsbVqlatqqioKOsCqZL0+++/a9GiRdYpCFc/N9fWTnr9kaSSJUtq+PDhGj9+vBYvXixjjCpXrmx9uL36i5Gvr6/1vF+vPlOCl+sZMWKEFUyk+PHHH7Vq1Spr3f+2bXKaO7Xm7++vggUL6u+//9Ztt91mtc+bN0+VKlVSYmKi5s6dq65du6pbt27auXNnhs/j1a6exhhjDd+XUtfo1X9fu43Onz+vp556Sk8++aQaNmyoggULpll3xYoVdezYMV2+fFmFChWy2idPnqxWrVpJUqrh5teuw8fHJ9V+MCYmxhqu3rdvXy1ZskTr169X48aNM6z7bdu2pXocGQkJCbFqPyPp7fOcTqf69eunF198UdWqVZMkrV271q3t4ik5UYspMqoLf39/rVq1Sps3b9a6dev06aefauXKlXr33Xf1888/a8OGDerYsaP1ek3p59XbPzo6WqVKlUpVS+nZv3+/6tatq0OHDumxxx6zThM4e/asChQooNWrV1vTXr1drn4dnD59WsYY/fnnn9aXfmOMvvzyS+t99fjx4+mGJJkZ1r9kyRIdPnw4VcgVExOjwYMH65133rG+7I0fP16nTp1SSEiIgoODU51WkNe5U4dnzpzR3LlzNXDgQDVt2lRNmzZV+/btNXToUCsQSFluel8Ur97fXL58WSdPnlTZsmX1wAMPaPXq1Tp16pSqVq2aqf7v3r1bgwcPVrdu3fTUU0/pyJEjGb7Or7cdM7N/eu6556xgPSPXvudJ0oULF9S3b1+NGjXKGh6+YMECRURE6IknnlDz5s1TvR5uRtldJ/Xr1081rTFGTqczzfvP1fuDq6X3nlW+fHmdPn1amzZtUu3atVWyZEktX75ct99+uwoVKiSXy6U5c+aoQoUKkq681kuXLq3hw4frt99+04YNG/TSSy9p5syZGT4PfK/I3u8Vbv3KwP/93/9p1KhRql27ttVWq1YtTZgwQd999122dAzZp3jx4goICNCGDRskSV988YWGDRt2w8vdsWOH+vXrpwYNGmj9+vXXTalatWqlbdu2WefIJCYmasqUKQoICMjUaIVatWpZv3Rw8OBBvfLKK5KkUqVKqX79+mrQoIHmzJmTY4/1auvXr9f69es1evRoqy0+Pl79+vWzzj1OkZXn6Gbk7vO5detW3XPPPTp79qxOnDihV199VQ899JDWrVuX4XOwY8cOxcXF6dKlS1q3bp1q1aqlhx56SF9++aVcLpdiYmLUrl07nT59OlU9bN68WePGjZO/v79+//13SdK+ffsUHR0tSRku42pPPfWUlixZor1790q68qFh8uTJmb7ORnr9ka4cRW7durVuueUWff7556pQoYL+/vtvaz2zZ8/W22+/nal1ZNYnn3yiuLi4VB+w/vrrLw0fPlwzZ860vkBmZdt4QmZrrWvXrho7dqx15CkqKkoLFixQxYoVtX37dusXGE6fPq2jR4/K5XJdd73FihWTv7+/fvrpJ0lXjrq6cz7ekSNHVLRoUXXv3l3VqlXThg0b0jyfhQsXVps2baxzNSXphx9+0DfffKO77rrrX9dx33336eeff9bZs2fldDrVv39/7d+/X5J0zz33WOHTpUuXMlX3NyKjfd60adN0xx13WEehJbm1XXJTdtRiiozqYsOGDRoxYoSaNm2qESNGKDY21rp+SvXq1fX666+rYsWKOnr0qLWsmjVrauPGjUpISNClS5f0wgsv/Os2/eGHHxQdHa1HHnlEtWrV0po1axQfH6+kpCT16NHDOvc1ReHChXXkyBElJSVp48aNVvuoUaPUp08f1alTR++8847Vn8WLF1vr+berzWckKipK4eHhmjFjhvUBPikpSX379lXv3r1VuXJla9pt27apR48eatq0qSIiIvLce2pWZKYOixcvrrVr12rdunVW2++//57qOUt5b1qxYoV19D1FUFCQ1q5dK0n66quv9N5770mSQkNDNWXKlCxdcf3nn39WgwYN9OSTT6pAgQLatWtXhq/znN6O6b3nSVeOrrZq1co6qitd+bzSuXNntWrVSr/++qsSExPzVF3daJ1UqFBB//zzj3UkfunSpXrooYfk5+en2NhYnT17VufPn9fWrVuz1K86deroo48+Uq1atVSnTh2999571kGKWrVqWfuO/fv36/HHH1dycrJatWqlMmXKqFevXnr00Ud14MABeXt7pxtG8L0ie79XuDVC4PTp07rlllvStBcuXDhLPzUGz5k6dapGjRql6dOny8/PT1OnTr3hZb788ssKCwtTsWLFVKNGjVQXYLtWsWLFFB4ervHjx2vSpElKTExUcHBwusOd0/PMM89o1Kh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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=0.7)\n",
    "fig, axs = plt.subplots(nrows=2, figsize=[12,6], dpi=100)\n",
    "sns.barplot(data=df[\"total_price\"].nlargest(6), color=\"blue\", ax=axs[0])\n",
    "sns.barplot(data=df[\"total_price\"].nsmallest(6).sort_values(ascending=False), color=\"red\", ax=axs[1])\n",
    "\n",
    "axs[0].set(xlabel=None, ylabel=None)\n",
    "axs[1].set(xlabel=None, ylabel=None)\n",
    "fig.supylabel(\"Ingresos\", x=0.06)\n",
    "fig.supxlabel(\"Tipo de pizza\")\n",
    "fig.suptitle(\"Ingreso por tipo de pizza\", y=0.92)\n",
    "plt.savefig(\"graficos/barra_tipo_pizza.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "995e62359d56f81",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:17.714788Z",
     "start_time": "2024-06-06T03:20:16.815731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=0.7)\n",
    "fig, axs = plt.subplots(nrows=2, figsize=[12,6], dpi=100)\n",
    "sns.barplot(data=df[\"quantity\"].nlargest(6), color=\"blue\", ax=axs[0])\n",
    "sns.barplot(data=df[\"quantity\"].nsmallest(6).sort_values(ascending=False), color=\"red\", ax=axs[1])\n",
    "\n",
    "axs[0].set(xlabel=None, ylabel=None)\n",
    "axs[1].set(xlabel=None, ylabel=None)\n",
    "fig.supylabel(\"Cantidad\", x=0.06)\n",
    "fig.supxlabel(\"Tipo de pizza\")\n",
    "fig.suptitle(\"Cantidades compradas por tipo de pizza\", y=0.92)\n",
    "plt.savefig(\"graficos/barra_cantidad_compras_pizza.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e6e62c01ddae3a",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Ordenamos los datos para separarlos en años, meses y días, esto nos servirá para realizar gráficos más detallados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30c788f59aa24ebd",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:20.038331Z",
     "start_time": "2024-06-06T03:20:17.718802Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   pizza_id  order_id  pizza_name_id  quantity  unit_price  total_price  \\\n0       1.0       1.0     hawaiian_m       1.0       13.25        13.25   \n1       2.0       2.0  classic_dlx_m       1.0       16.00        16.00   \n2       3.0       2.0  five_cheese_l       1.0       18.50        18.50   \n3       4.0       2.0    ital_supr_l       1.0       20.75        20.75   \n4       5.0       2.0     mexicana_m       1.0       16.00        16.00   \n\n  pizza_size pizza_category  \\\n0          M        Classic   \n1          M        Classic   \n2          L         Veggie   \n3          L        Supreme   \n4          M         Veggie   \n\n                                   pizza_ingredients  \\\n0           Sliced Ham, Pineapple, Mozzarella Cheese   \n1  Pepperoni, Mushrooms, Red Onions, Red Peppers,...   \n2  Mozzarella Cheese, Provolone Cheese, Smoked Go...   \n3  Calabrese Salami, Capocollo, Tomatoes, Red Oni...   \n4  Tomatoes, Red Peppers, Jalapeno Peppers, Red O...   \n\n                  pizza_name                date  \n0         The Hawaiian Pizza 2015-01-01 11:38:36  \n1   The Classic Deluxe Pizza 2015-01-01 11:57:40  \n2      The Five Cheese Pizza 2015-01-01 11:57:40  \n3  The Italian Supreme Pizza 2015-01-01 11:57:40  \n4         The Mexicana Pizza 2015-01-01 11:57:40  ",
      "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>pizza_id</th>\n      <th>order_id</th>\n      <th>pizza_name_id</th>\n      <th>quantity</th>\n      <th>unit_price</th>\n      <th>total_price</th>\n      <th>pizza_size</th>\n      <th>pizza_category</th>\n      <th>pizza_ingredients</th>\n      <th>pizza_name</th>\n      <th>date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>hawaiian_m</td>\n      <td>1.0</td>\n      <td>13.25</td>\n      <td>13.25</td>\n      <td>M</td>\n      <td>Classic</td>\n      <td>Sliced Ham, Pineapple, Mozzarella Cheese</td>\n      <td>The Hawaiian Pizza</td>\n      <td>2015-01-01 11:38:36</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>classic_dlx_m</td>\n      <td>1.0</td>\n      <td>16.00</td>\n      <td>16.00</td>\n      <td>M</td>\n      <td>Classic</td>\n      <td>Pepperoni, Mushrooms, Red Onions, Red Peppers,...</td>\n      <td>The Classic Deluxe Pizza</td>\n      <td>2015-01-01 11:57:40</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>five_cheese_l</td>\n      <td>1.0</td>\n      <td>18.50</td>\n      <td>18.50</td>\n      <td>L</td>\n      <td>Veggie</td>\n      <td>Mozzarella Cheese, Provolone Cheese, Smoked Go...</td>\n      <td>The Five Cheese Pizza</td>\n      <td>2015-01-01 11:57:40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>ital_supr_l</td>\n      <td>1.0</td>\n      <td>20.75</td>\n      <td>20.75</td>\n      <td>L</td>\n      <td>Supreme</td>\n      <td>Calabrese Salami, Capocollo, Tomatoes, Red Oni...</td>\n      <td>The Italian Supreme Pizza</td>\n      <td>2015-01-01 11:57:40</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>mexicana_m</td>\n      <td>1.0</td>\n      <td>16.00</td>\n      <td>16.00</td>\n      <td>M</td>\n      <td>Veggie</td>\n      <td>Tomatoes, Red Peppers, Jalapeno Peppers, Red O...</td>\n      <td>The Mexicana Pizza</td>\n      <td>2015-01-01 11:57:40</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = data\n",
    "df2[\"date\"] = pd.to_datetime(df2[\"order_date\"] + \" \" + df2[\"order_time\"], format=\"mixed\")\n",
    "df2 = df2.drop([\"order_date\", \"order_time\"], axis=1)\n",
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "993cf02038729d9b",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:20.091201Z",
     "start_time": "2024-06-06T03:20:20.039337Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   pizza_id  order_id  pizza_name_id  quantity  unit_price  total_price  \\\n0       1.0       1.0     hawaiian_m       1.0       13.25        13.25   \n1       2.0       2.0  classic_dlx_m       1.0       16.00        16.00   \n2       3.0       2.0  five_cheese_l       1.0       18.50        18.50   \n3       4.0       2.0    ital_supr_l       1.0       20.75        20.75   \n4       5.0       2.0     mexicana_m       1.0       16.00        16.00   \n\n  pizza_size pizza_category  \\\n0          M        Classic   \n1          M        Classic   \n2          L         Veggie   \n3          L        Supreme   \n4          M         Veggie   \n\n                                   pizza_ingredients  \\\n0           Sliced Ham, Pineapple, Mozzarella Cheese   \n1  Pepperoni, Mushrooms, Red Onions, Red Peppers,...   \n2  Mozzarella Cheese, Provolone Cheese, Smoked Go...   \n3  Calabrese Salami, Capocollo, Tomatoes, Red Oni...   \n4  Tomatoes, Red Peppers, Jalapeno Peppers, Red O...   \n\n                  pizza_name                date  year    month       day  \n0         The Hawaiian Pizza 2015-01-01 11:38:36  2015  January  Thursday  \n1   The Classic Deluxe Pizza 2015-01-01 11:57:40  2015  January  Thursday  \n2      The Five Cheese Pizza 2015-01-01 11:57:40  2015  January  Thursday  \n3  The Italian Supreme Pizza 2015-01-01 11:57:40  2015  January  Thursday  \n4         The Mexicana Pizza 2015-01-01 11:57:40  2015  January  Thursday  ",
      "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>pizza_id</th>\n      <th>order_id</th>\n      <th>pizza_name_id</th>\n      <th>quantity</th>\n      <th>unit_price</th>\n      <th>total_price</th>\n      <th>pizza_size</th>\n      <th>pizza_category</th>\n      <th>pizza_ingredients</th>\n      <th>pizza_name</th>\n      <th>date</th>\n      <th>year</th>\n      <th>month</th>\n      <th>day</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>hawaiian_m</td>\n      <td>1.0</td>\n      <td>13.25</td>\n      <td>13.25</td>\n      <td>M</td>\n      <td>Classic</td>\n      <td>Sliced Ham, Pineapple, Mozzarella Cheese</td>\n      <td>The Hawaiian Pizza</td>\n      <td>2015-01-01 11:38:36</td>\n      <td>2015</td>\n      <td>January</td>\n      <td>Thursday</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>classic_dlx_m</td>\n      <td>1.0</td>\n      <td>16.00</td>\n      <td>16.00</td>\n      <td>M</td>\n      <td>Classic</td>\n      <td>Pepperoni, Mushrooms, Red Onions, Red Peppers,...</td>\n      <td>The Classic Deluxe Pizza</td>\n      <td>2015-01-01 11:57:40</td>\n      <td>2015</td>\n      <td>January</td>\n      <td>Thursday</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>five_cheese_l</td>\n      <td>1.0</td>\n      <td>18.50</td>\n      <td>18.50</td>\n      <td>L</td>\n      <td>Veggie</td>\n      <td>Mozzarella Cheese, Provolone Cheese, Smoked Go...</td>\n      <td>The Five Cheese Pizza</td>\n      <td>2015-01-01 11:57:40</td>\n      <td>2015</td>\n      <td>January</td>\n      <td>Thursday</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>ital_supr_l</td>\n      <td>1.0</td>\n      <td>20.75</td>\n      <td>20.75</td>\n      <td>L</td>\n      <td>Supreme</td>\n      <td>Calabrese Salami, Capocollo, Tomatoes, Red Oni...</td>\n      <td>The Italian Supreme Pizza</td>\n      <td>2015-01-01 11:57:40</td>\n      <td>2015</td>\n      <td>January</td>\n      <td>Thursday</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>mexicana_m</td>\n      <td>1.0</td>\n      <td>16.00</td>\n      <td>16.00</td>\n      <td>M</td>\n      <td>Veggie</td>\n      <td>Tomatoes, Red Peppers, Jalapeno Peppers, Red O...</td>\n      <td>The Mexicana Pizza</td>\n      <td>2015-01-01 11:57:40</td>\n      <td>2015</td>\n      <td>January</td>\n      <td>Thursday</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"year\"] = df2[\"date\"].dt.year\n",
    "df2[\"month\"] = df2[\"date\"].dt.month_name()\n",
    "df2[\"day\"] = df2[\"date\"].dt.day_name()\n",
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a85b5f5f21c94f",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Ahora podemos conocer el estado de las ventas en el año 2015 (único año) y en los meses y/o días"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "427066f8ef934eb8",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:20.641489Z",
     "start_time": "2024-06-06T03:20:20.094209Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<function matplotlib.pyplot.show(close=None, block=None)>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 999x444 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "months = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \n",
    "          \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n",
    "x = df2.groupby(\"month\").count()\n",
    "x = x.reindex(months, axis=0)\n",
    "\n",
    "plt.figure(figsize=[9,4], dpi=111)\n",
    "plt.ylabel(\"Cantidad de compras\")\n",
    "plt.xlabel(\"Mes, Año 2015\")\n",
    "plt.suptitle(\"Compras por mes durante el Año 2015\")\n",
    "plt.plot(x.index, x[\"quantity\"], color=\"blue\")\n",
    "plt.savefig(\"graficos/lineas_compras_mes.png\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3f00dacb2765bc91",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:21.290044Z",
     "start_time": "2024-06-06T03:20:20.643500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<function matplotlib.pyplot.show(close=None, block=None)>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 999x444 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "days = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \n",
    "          \"Sunday\"]\n",
    "x = df2.groupby([\"month\",\"day\"]).count()\n",
    "x = x.groupby(\"day\").mean()\n",
    "x = x.reindex(days, axis=0)\n",
    "\n",
    "plt.figure(figsize=[9,4], dpi=111)\n",
    "plt.ylabel(\"Cantidad de compras\")\n",
    "plt.xlabel(\"Dia, Año 2015\")\n",
    "plt.suptitle(\"Promedio de compras al mes por dia\")\n",
    "sns.barplot(x=x.index, y=\"quantity\", data=x, hue=x.index)\n",
    "plt.savefig(\"graficos/barras_promedio_compras_mes.png\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9976a1c5e7b8c966",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Ordenamos los datos por su respectiva hora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1d401149762dc4d1",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:22.927262Z",
     "start_time": "2024-06-06T03:20:21.291052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 999x444 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data\n",
    "x[\"order_time\"] = pd.to_datetime(data[\"order_time\"], format=\"%H:%M:%S\")\n",
    "x[\"hour\"] = x[\"order_time\"].dt.hour\n",
    "\n",
    "plt.figure(figsize=[9,4], dpi=111)\n",
    "sns.kdeplot(x[\"hour\"], color=\"blue\")\n",
    "plt.suptitle(\"Compras realizadas en X hora del dia\")\n",
    "plt.savefig(\"graficos/lineas_compras_hora.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3aec5cb39ff39849",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:23.347656Z",
     "start_time": "2024-06-06T03:20:22.929273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 999x444 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data.groupby(\"pizza_size\").count()\n",
    "\n",
    "plt.figure(figsize=[9,4], dpi=111)\n",
    "plt.ylabel(\"Cantidad de compras\")\n",
    "plt.xlabel(\"Tamaño de Pizza\")\n",
    "sns.barplot(x=x.index, y=\"pizza_id\", data=x, hue=x.index)\n",
    "plt.suptitle(\"Tamaño de Pizzas Compradas\")\n",
    "plt.savefig(\"graficos/barra_pizza_size.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c91a631ffab21708",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:23.546593Z",
     "start_time": "2024-06-06T03:20:23.350582Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 710.4x532.8 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data.groupby(\"pizza_category\").count()\n",
    "\n",
    "plt.figure(dpi=111)\n",
    "plt.pie(x[\"pizza_id\"], labels=x.index, autopct=\"%1.1f%%\")\n",
    "plt.suptitle(\"Categoria de Pizzas Compradas\")\n",
    "plt.savefig(\"graficos/barra_categoria_pizza.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "473ce8f7a9c73f99",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:23.580600Z",
     "start_time": "2024-06-06T03:20:23.548605Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                              unit_price\npizza_name                        pizza_size            \nThe Barbecue Chicken Pizza        L                20.75\n                                  M                16.75\n                                  S                12.75\nThe Big Meat Pizza                S                12.00\nThe Brie Carre Pizza              S                23.65\n...                                                  ...\nThe Thai Chicken Pizza            M                16.75\n                                  S                12.75\nThe Vegetables + Vegetables Pizza L                20.25\n                                  M                16.00\n                                  S                12.00\n\n[91 rows x 1 columns]",
      "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></th>\n      <th>unit_price</th>\n    </tr>\n    <tr>\n      <th>pizza_name</th>\n      <th>pizza_size</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">The Barbecue Chicken Pizza</th>\n      <th>L</th>\n      <td>20.75</td>\n    </tr>\n    <tr>\n      <th>M</th>\n      <td>16.75</td>\n    </tr>\n    <tr>\n      <th>S</th>\n      <td>12.75</td>\n    </tr>\n    <tr>\n      <th>The Big Meat Pizza</th>\n      <th>S</th>\n      <td>12.00</td>\n    </tr>\n    <tr>\n      <th>The Brie Carre Pizza</th>\n      <th>S</th>\n      <td>23.65</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">The Thai Chicken Pizza</th>\n      <th>M</th>\n      <td>16.75</td>\n    </tr>\n    <tr>\n      <th>S</th>\n      <td>12.75</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">The Vegetables + Vegetables Pizza</th>\n      <th>L</th>\n      <td>20.25</td>\n    </tr>\n    <tr>\n      <th>M</th>\n      <td>16.00</td>\n    </tr>\n    <tr>\n      <th>S</th>\n      <td>12.00</td>\n    </tr>\n  </tbody>\n</table>\n<p>91 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"pizza_name\", \"pizza_size\", \"unit_price\"]].groupby([\"pizza_name\", \"pizza_size\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "633e27c01469265d",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:24.172407Z",
     "start_time": "2024-06-06T03:20:23.583613Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 710.4x532.8 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=111)\n",
    "plt.ylabel(\"Precio Unitario\")\n",
    "sns.boxplot(y=\"unit_price\", data=data)\n",
    "plt.suptitle(\"Pizzas compradas por su precio unitario\")\n",
    "plt.savefig(\"graficos/caja_pizza_precio_unidad.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "748ebc0b5af6b2be",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Separamos los ingredientes para ver la cantidad de estos usados durante el año"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b4ef3ce50d6f44ca",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:26.173421Z",
     "start_time": "2024-06-06T03:20:24.175420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data[\"pizza_ingredients\"]\n",
    "x = data[\"pizza_ingredients\"].str.split(\", \")\n",
    "x2 = [ingredient for sublist in x for ingredient in sublist]\n",
    "x3 = pd.Series(x2).value_counts()\n",
    "\n",
    "sns.set(font_scale=0.7)\n",
    "fig, axs = plt.subplots(nrows=2, figsize=[12,6], dpi=100)\n",
    "sns.barplot(data=x3.nlargest(6), color=\"blue\", ax=axs[0])\n",
    "sns.barplot(data=x3.nsmallest(6).sort_values(ascending=False), color=\"red\", ax=axs[1])\n",
    "\n",
    "axs[0].set(xlabel=None, ylabel=None)\n",
    "axs[1].set(xlabel=None, ylabel=None)\n",
    "fig.supylabel(\"Cantidad\", x=0.06)\n",
    "fig.supxlabel(\"Ingredientes\")\n",
    "fig.suptitle(\"Ingredientes utilizados en 2015\", y=0.92)\n",
    "plt.savefig(\"graficos/barra_top_ingredientes.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Predicción\n",
    "\n",
    "Realizaremos las predicciones con el objetivo de saber las ventas en el próximo mes, para esto primero realizaremos una predicción de prueba para ver si coincide con los datos"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2b7421050a322d20"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6fc7fbc009f00a9c",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:28.292745Z",
     "start_time": "2024-06-06T03:20:26.177429Z"
    }
   },
   "outputs": [],
   "source": [
    "from pmdarima.arima import auto_arima\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8b1541eadc5303",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:28.341959Z",
     "start_time": "2024-06-06T03:20:28.294924Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "            total_price\ndate                   \n2015-01-01      2713.85\n2015-01-02      3189.20\n2015-01-03      1598.55\n2015-01-04      2176.85\n2015-01-05      2571.95\n...                 ...\n2015-12-27      1419.00\n2015-12-28      1637.20\n2015-12-29      1353.25\n2015-12-30      1337.80\n2015-12-31      2916.00\n\n[365 rows x 1 columns]",
      "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>total_price</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2015-01-01</th>\n      <td>2713.85</td>\n    </tr>\n    <tr>\n      <th>2015-01-02</th>\n      <td>3189.20</td>\n    </tr>\n    <tr>\n      <th>2015-01-03</th>\n      <td>1598.55</td>\n    </tr>\n    <tr>\n      <th>2015-01-04</th>\n      <td>2176.85</td>\n    </tr>\n    <tr>\n      <th>2015-01-05</th>\n      <td>2571.95</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2015-12-27</th>\n      <td>1419.00</td>\n    </tr>\n    <tr>\n      <th>2015-12-28</th>\n      <td>1637.20</td>\n    </tr>\n    <tr>\n      <th>2015-12-29</th>\n      <td>1353.25</td>\n    </tr>\n    <tr>\n      <th>2015-12-30</th>\n      <td>1337.80</td>\n    </tr>\n    <tr>\n      <th>2015-12-31</th>\n      <td>2916.00</td>\n    </tr>\n  </tbody>\n</table>\n<p>365 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"date\"] = pd.to_datetime(data[\"order_date\"], format=\"mixed\")\n",
    "x = data[[\"total_price\", \"date\"]].groupby(\"date\").sum()\n",
    "x = x.asfreq('D', fill_value=0)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff5b52981d066c1",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Separaremos las ventas en la tendencia, estacionalidad y residuos para estudiar como adaptar los datos al modelo ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [
    {
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     "end_time": "2024-06-06T05:32:53.914589Z",
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                       {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('c3ee0de6-0492-46d0-9a1b-88704f011335');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dcx = seasonal_decompose(x, model=\"additive\")\n",
    "\n",
    "trx = dcx.trend\n",
    "ssx = dcx.seasonal\n",
    "rdx = dcx.resid\n",
    "\n",
    "fig = px.line(trx, title=\"Tendencia\")\n",
    "fig.show()\n",
    "\n",
    "fig = px.line(ssx, title=\"Estacionalidad\")\n",
    "fig.show()\n",
    "\n",
    "fig = px.line(rdx, title=\"Residuos\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c7b7932dc1856623",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:30.754059Z",
     "start_time": "2024-06-06T03:20:30.594294Z"
    }
   },
   "outputs": [],
   "source": [
    "fig = px.line(x, title=\"Ventas Diarias\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b025c402c09daaaf",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Observamos un patrón en la tendencia de altos y bajos, los bajos corresponden días en los que no se vendió o no fueron capturados en la base de datos y un patrón en la estacionalidad de 1 semana\n",
    "Ahora vamos a comenzar con la predicción configurando algunos parámetros para que se adapte lo mejor posible a los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "adef836cb7cce612",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:20:30.789065Z",
     "start_time": "2024-06-06T03:20:30.757069Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "((335, 1), (335,), (30, 1), (30,))"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_size = 30\n",
    "train, test = x.iloc[:-test_size], x.iloc[-test_size:]\n",
    "x_train, x_test = np.array(range(train.shape[0])), np.array(range(train.shape[0], x.shape[0]))\n",
    "train.shape, x_train.shape, test.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2e68b28774b2d12e",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:21:02.108182Z",
     "start_time": "2024-06-06T03:20:30.797122Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(2,1,2)(1,0,1)[7] intercept   : AIC=inf, Time=1.07 sec\n",
      " ARIMA(0,1,0)(0,0,0)[7] intercept   : AIC=5268.497, Time=0.02 sec\n",
      " ARIMA(1,1,0)(1,0,0)[7] intercept   : AIC=5214.638, Time=0.08 sec\n",
      " ARIMA(0,1,1)(0,0,1)[7] intercept   : AIC=inf, Time=0.26 sec\n",
      " ARIMA(0,1,0)(0,0,0)[7]             : AIC=5266.504, Time=0.01 sec\n",
      " ARIMA(1,1,0)(0,0,0)[7] intercept   : AIC=5220.037, Time=0.03 sec\n",
      " ARIMA(1,1,0)(2,0,0)[7] intercept   : AIC=5214.708, Time=0.15 sec\n",
      " ARIMA(1,1,0)(1,0,1)[7] intercept   : AIC=inf, Time=0.63 sec\n",
      " ARIMA(1,1,0)(0,0,1)[7] intercept   : AIC=5215.661, Time=0.08 sec\n",
      " ARIMA(1,1,0)(2,0,1)[7] intercept   : AIC=inf, Time=1.16 sec\n",
      " ARIMA(0,1,0)(1,0,0)[7] intercept   : AIC=5267.921, Time=0.08 sec\n",
      " ARIMA(2,1,0)(1,0,0)[7] intercept   : AIC=5165.612, Time=0.10 sec\n",
      " ARIMA(2,1,0)(0,0,0)[7] intercept   : AIC=5174.968, Time=0.05 sec\n",
      " ARIMA(2,1,0)(2,0,0)[7] intercept   : AIC=5165.330, Time=0.54 sec\n",
      " ARIMA(2,1,0)(2,0,1)[7] intercept   : AIC=inf, Time=1.44 sec\n",
      " ARIMA(2,1,0)(1,0,1)[7] intercept   : AIC=inf, Time=0.83 sec\n",
      " ARIMA(3,1,0)(2,0,0)[7] intercept   : AIC=5163.481, Time=0.61 sec\n",
      " ARIMA(3,1,0)(1,0,0)[7] intercept   : AIC=5163.792, Time=0.15 sec\n",
      " ARIMA(3,1,0)(2,0,1)[7] intercept   : AIC=inf, Time=2.35 sec\n",
      " ARIMA(3,1,0)(1,0,1)[7] intercept   : AIC=inf, Time=0.89 sec\n",
      " ARIMA(4,1,0)(2,0,0)[7] intercept   : AIC=5154.513, Time=0.29 sec\n",
      " ARIMA(4,1,0)(1,0,0)[7] intercept   : AIC=5155.092, Time=0.14 sec\n",
      " ARIMA(4,1,0)(2,0,1)[7] intercept   : AIC=inf, Time=1.92 sec\n",
      " ARIMA(4,1,0)(1,0,1)[7] intercept   : AIC=inf, Time=1.12 sec\n",
      " ARIMA(5,1,0)(2,0,0)[7] intercept   : AIC=5136.024, Time=0.34 sec\n",
      " ARIMA(5,1,0)(1,0,0)[7] intercept   : AIC=5135.350, Time=0.22 sec\n",
      " ARIMA(5,1,0)(0,0,0)[7] intercept   : AIC=5139.374, Time=0.10 sec\n",
      " ARIMA(5,1,0)(1,0,1)[7] intercept   : AIC=inf, Time=1.33 sec\n",
      " ARIMA(5,1,0)(0,0,1)[7] intercept   : AIC=5136.138, Time=0.19 sec\n",
      " ARIMA(5,1,0)(2,0,1)[7] intercept   : AIC=inf, Time=1.99 sec\n",
      " ARIMA(6,1,0)(1,0,0)[7] intercept   : AIC=5098.527, Time=0.95 sec\n",
      " ARIMA(6,1,0)(0,0,0)[7] intercept   : AIC=5112.250, Time=0.15 sec\n",
      " ARIMA(6,1,0)(2,0,0)[7] intercept   : AIC=5094.379, Time=0.87 sec\n",
      " ARIMA(6,1,0)(2,0,1)[7] intercept   : AIC=5082.623, Time=2.31 sec\n",
      " ARIMA(6,1,0)(1,0,1)[7] intercept   : AIC=5080.794, Time=1.43 sec\n",
      " ARIMA(6,1,0)(0,0,1)[7] intercept   : AIC=5079.548, Time=1.03 sec\n",
      " ARIMA(6,1,0)(0,0,2)[7] intercept   : AIC=5080.863, Time=1.58 sec\n",
      " ARIMA(6,1,0)(1,0,2)[7] intercept   : AIC=inf, Time=1.12 sec\n",
      " ARIMA(6,1,1)(0,0,1)[7] intercept   : AIC=inf, Time=1.28 sec\n",
      " ARIMA(5,1,1)(0,0,1)[7] intercept   : AIC=inf, Time=1.19 sec\n",
      " ARIMA(6,1,0)(0,0,1)[7]             : AIC=inf, Time=1.10 sec\n",
      "\n",
      "Best model:  ARIMA(6,1,0)(0,0,1)[7] intercept\n",
      "Total fit time: 31.232 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "ARIMA(order=(6, 1, 0), scoring_args={}, seasonal_order=(0, 0, 1, 7),\n      suppress_warnings=True)",
      "text/html": "<style>#sk-container-id-1 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-1 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n  padding: 0;\n}\n\n#sk-container-id-1 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-1 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-1 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-1 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-1 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-1 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-1 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-1 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-1 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-1 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-1 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre> ARIMA(6,1,0)(0,0,1)[7] intercept</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ARIMA<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre> ARIMA(6,1,0)(0,0,1)[7] intercept</pre></div> </div></div></div></div>"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_auto = auto_arima(train, seasonal=True, max_p=8,max_d=8, max_q=8, information_criterion=\"aic\", trace=True, m=7, d=1)\n",
    "model_auto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b07394fc8beda603",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:21:02.146358Z",
     "start_time": "2024-06-06T03:21:02.109194Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                                      SARIMAX Results                                      \n===========================================================================================\nDep. Variable:                                   y   No. Observations:                  335\nModel:             SARIMAX(6, 1, 0)x(0, 0, [1], 7)   Log Likelihood               -2530.774\nDate:                             Wed, 05 Jun 2024   AIC                           5079.548\nTime:                                     23:21:02   BIC                           5113.848\nSample:                                 01-01-2015   HQIC                          5093.224\n                                      - 12-01-2015                                         \nCovariance Type:                               opg                                         \n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nintercept      1.6189      4.037      0.401      0.688      -6.293       9.531\nar.L1         -0.9712      0.020    -48.779      0.000      -1.010      -0.932\nar.L2         -0.9853      0.019    -52.100      0.000      -1.022      -0.948\nar.L3         -0.9687      0.029    -33.662      0.000      -1.025      -0.912\nar.L4         -0.9755      0.026    -37.685      0.000      -1.026      -0.925\nar.L5         -0.9804      0.018    -53.021      0.000      -1.017      -0.944\nar.L6         -0.9689      0.033    -28.974      0.000      -1.034      -0.903\nma.S.L7       -0.9228      0.055    -16.820      0.000      -1.030      -0.815\nsigma2      2.446e+05   1.14e+04     21.407      0.000    2.22e+05    2.67e+05\n===================================================================================\nLjung-Box (L1) (Q):                   2.99   Jarque-Bera (JB):              1151.85\nProb(Q):                              0.08   Prob(JB):                         0.00\nHeteroskedasticity (H):               2.66   Skew:                            -0.58\nProb(H) (two-sided):                  0.00   Kurtosis:                        12.02\n===================================================================================\n\nWarnings:\n[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n\"\"\"",
      "text/html": "<table class=\"simpletable\">\n<caption>SARIMAX Results</caption>\n<tr>\n  <th>Dep. Variable:</th>                  <td>y</td>                <th>  No. Observations:  </th>    <td>335</td>   \n</tr>\n<tr>\n  <th>Model:</th>           <td>SARIMAX(6, 1, 0)x(0, 0, [1], 7)</td> <th>  Log Likelihood     </th> <td>-2530.774</td>\n</tr>\n<tr>\n  <th>Date:</th>                   <td>Wed, 05 Jun 2024</td>         <th>  AIC                </th> <td>5079.548</td> \n</tr>\n<tr>\n  <th>Time:</th>                       <td>23:21:02</td>             <th>  BIC                </th> <td>5113.848</td> \n</tr>\n<tr>\n  <th>Sample:</th>                    <td>01-01-2015</td>            <th>  HQIC               </th> <td>5093.224</td> \n</tr>\n<tr>\n  <th></th>                          <td>- 12-01-2015</td>           <th>                     </th>     <td> </td>    \n</tr>\n<tr>\n  <th>Covariance Type:</th>               <td>opg</td>               <th>                     </th>     <td> </td>    \n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n</tr>\n<tr>\n  <th>intercept</th> <td>    1.6189</td> <td>    4.037</td> <td>    0.401</td> <td> 0.688</td> <td>   -6.293</td> <td>    9.531</td>\n</tr>\n<tr>\n  <th>ar.L1</th>     <td>   -0.9712</td> <td>    0.020</td> <td>  -48.779</td> <td> 0.000</td> <td>   -1.010</td> <td>   -0.932</td>\n</tr>\n<tr>\n  <th>ar.L2</th>     <td>   -0.9853</td> <td>    0.019</td> <td>  -52.100</td> <td> 0.000</td> <td>   -1.022</td> <td>   -0.948</td>\n</tr>\n<tr>\n  <th>ar.L3</th>     <td>   -0.9687</td> <td>    0.029</td> <td>  -33.662</td> <td> 0.000</td> <td>   -1.025</td> <td>   -0.912</td>\n</tr>\n<tr>\n  <th>ar.L4</th>     <td>   -0.9755</td> <td>    0.026</td> <td>  -37.685</td> <td> 0.000</td> <td>   -1.026</td> <td>   -0.925</td>\n</tr>\n<tr>\n  <th>ar.L5</th>     <td>   -0.9804</td> <td>    0.018</td> <td>  -53.021</td> <td> 0.000</td> <td>   -1.017</td> <td>   -0.944</td>\n</tr>\n<tr>\n  <th>ar.L6</th>     <td>   -0.9689</td> <td>    0.033</td> <td>  -28.974</td> <td> 0.000</td> <td>   -1.034</td> <td>   -0.903</td>\n</tr>\n<tr>\n  <th>ma.S.L7</th>   <td>   -0.9228</td> <td>    0.055</td> <td>  -16.820</td> <td> 0.000</td> <td>   -1.030</td> <td>   -0.815</td>\n</tr>\n<tr>\n  <th>sigma2</th>    <td> 2.446e+05</td> <td> 1.14e+04</td> <td>   21.407</td> <td> 0.000</td> <td> 2.22e+05</td> <td> 2.67e+05</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Ljung-Box (L1) (Q):</th>     <td>2.99</td> <th>  Jarque-Bera (JB):  </th> <td>1151.85</td>\n</tr>\n<tr>\n  <th>Prob(Q):</th>                <td>0.08</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n</tr>\n<tr>\n  <th>Heteroskedasticity (H):</th> <td>2.66</td> <th>  Skew:              </th>  <td>-0.58</td> \n</tr>\n<tr>\n  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>12.02</td> \n</tr>\n</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).",
      "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}          &                y                & \\textbf{  No. Observations:  } &    335      \\\\\n\\textbf{Model:}                  & SARIMAX(6, 1, 0)x(0, 0, [1], 7) & \\textbf{  Log Likelihood     } & -2530.774   \\\\\n\\textbf{Date:}                   &         Wed, 05 Jun 2024        & \\textbf{  AIC                } &  5079.548   \\\\\n\\textbf{Time:}                   &             23:21:02            & \\textbf{  BIC                } &  5113.848   \\\\\n\\textbf{Sample:}                 &            01-01-2015           & \\textbf{  HQIC               } &  5093.224   \\\\\n\\textbf{}                        &           - 12-01-2015          & \\textbf{                     } &             \\\\\n\\textbf{Covariance Type:}        &               opg               & \\textbf{                     } &             \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{intercept} &       1.6189  &        4.037     &     0.401  &         0.688        &       -6.293    &        9.531     \\\\\n\\textbf{ar.L1}     &      -0.9712  &        0.020     &   -48.779  &         0.000        &       -1.010    &       -0.932     \\\\\n\\textbf{ar.L2}     &      -0.9853  &        0.019     &   -52.100  &         0.000        &       -1.022    &       -0.948     \\\\\n\\textbf{ar.L3}     &      -0.9687  &        0.029     &   -33.662  &         0.000        &       -1.025    &       -0.912     \\\\\n\\textbf{ar.L4}     &      -0.9755  &        0.026     &   -37.685  &         0.000        &       -1.026    &       -0.925     \\\\\n\\textbf{ar.L5}     &      -0.9804  &        0.018     &   -53.021  &         0.000        &       -1.017    &       -0.944     \\\\\n\\textbf{ar.L6}     &      -0.9689  &        0.033     &   -28.974  &         0.000        &       -1.034    &       -0.903     \\\\\n\\textbf{ma.S.L7}   &      -0.9228  &        0.055     &   -16.820  &         0.000        &       -1.030    &       -0.815     \\\\\n\\textbf{sigma2}    &    2.446e+05  &     1.14e+04     &    21.407  &         0.000        &     2.22e+05    &     2.67e+05     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Ljung-Box (L1) (Q):}     & 2.99 & \\textbf{  Jarque-Bera (JB):  } & 1151.85  \\\\\n\\textbf{Prob(Q):}                & 0.08 & \\textbf{  Prob(JB):          } &   0.00   \\\\\n\\textbf{Heteroskedasticity (H):} & 2.66 & \\textbf{  Skew:              } &  -0.58   \\\\\n\\textbf{Prob(H) (two-sided):}    & 0.00 & \\textbf{  Kurtosis:          } &  12.02   \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{SARIMAX Results}\n\\end{center}\n\nWarnings: \\newline\n [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_auto.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ea1f65b808a71efd",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:21:02.170891Z",
     "start_time": "2024-06-06T03:21:02.152366Z"
    }
   },
   "outputs": [],
   "source": [
    "predict, confint = model_auto.predict(n_periods=test_size, return_conf_int=True)\n",
    "cf= pd.DataFrame(confint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e5fe094d1dbf49ea",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:21:02.219949Z",
     "start_time": "2024-06-06T03:21:02.173909Z"
    }
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   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=x.index, \n",
    "                         y=x[\"total_price\"], \n",
    "                         name='Ventas',\n",
    "                         line=dict(color='#3a419b',\n",
    "                                   width=1.5\n",
    "                                   )))\n",
    "fig.add_trace(go.Scatter(x=predict.index, \n",
    "                         y=predict, \n",
    "                         name='Predicción',\n",
    "                         line=dict(color='#fa636e',\n",
    "                                   width=1.5\n",
    "                                   )))\n",
    "\n",
    "fig.add_trace(go.Scatter(x=predict.index, \n",
    "                         y=cf[0], \n",
    "                         name='Predict Min',\n",
    "                         fill='tonexty',\n",
    "                         line=dict(color='#d4d4d9', \n",
    "                                   width=1.5)))\n",
    "\n",
    "fig.add_trace(go.Scatter(x=predict.index, \n",
    "                         y=cf[1], \n",
    "                         name='Predict Max',\n",
    "                         fill='tonexty',\n",
    "                         line=dict(color='#d4d4d9', \n",
    "                                   width=1.5)))\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Test de Predicción\",\n",
    "#    xaxis_title=\"X Axis Title\",\n",
    "#    yaxis_title=\"Y Axis Title\",\n",
    "#    legend_title=\"Legend Title\",\n",
    "#    font=dict(\n",
    "#        family=\"Courier New, monospace\",\n",
    "#        size=18,\n",
    "#        color=\"RebeccaPurple\"\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Podemos observar una cercanía de la predicción a las ventas ocurridas en el ultimo mes, las caídas extremas no fueron predecidas ya que es complicado para el modelo predecir esas caídas repentinas\n",
    "Ahora continuaremos con una predicción real para el próximo mes"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "106bbf01cdbc33b1"
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "39410a216f50f03b",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:21:22.797660Z",
     "start_time": "2024-06-06T03:21:02.221958Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(2,1,2)(1,0,1)[7] intercept   : AIC=inf, Time=1.42 sec\n",
      " ARIMA(0,1,0)(0,0,0)[7] intercept   : AIC=5772.715, Time=0.02 sec\n",
      " ARIMA(1,1,0)(1,0,0)[7] intercept   : AIC=5706.684, Time=0.09 sec\n",
      " ARIMA(0,1,1)(0,0,1)[7] intercept   : AIC=inf, Time=0.26 sec\n",
      " ARIMA(0,1,0)(0,0,0)[7]             : AIC=5770.716, Time=0.01 sec\n",
      " ARIMA(1,1,0)(0,0,0)[7] intercept   : AIC=5710.285, Time=0.04 sec\n",
      " ARIMA(1,1,0)(2,0,0)[7] intercept   : AIC=5708.669, Time=0.13 sec\n",
      " ARIMA(1,1,0)(1,0,1)[7] intercept   : AIC=5708.606, Time=0.23 sec\n",
      " ARIMA(1,1,0)(0,0,1)[7] intercept   : AIC=5706.624, Time=0.10 sec\n",
      " ARIMA(1,1,0)(0,0,2)[7] intercept   : AIC=5708.620, Time=0.15 sec\n",
      " ARIMA(1,1,0)(1,0,2)[7] intercept   : AIC=5710.624, Time=0.19 sec\n",
      " ARIMA(0,1,0)(0,0,1)[7] intercept   : AIC=5772.845, Time=0.07 sec\n",
      " ARIMA(2,1,0)(0,0,1)[7] intercept   : AIC=5656.194, Time=0.12 sec\n",
      " ARIMA(2,1,0)(0,0,0)[7] intercept   : AIC=5663.056, Time=0.06 sec\n",
      " ARIMA(2,1,0)(1,0,1)[7] intercept   : AIC=5658.028, Time=0.24 sec\n",
      " ARIMA(2,1,0)(0,0,2)[7] intercept   : AIC=5658.046, Time=0.19 sec\n",
      " ARIMA(2,1,0)(1,0,0)[7] intercept   : AIC=5656.031, Time=0.14 sec\n",
      " ARIMA(2,1,0)(2,0,0)[7] intercept   : AIC=5658.028, Time=0.20 sec\n",
      " ARIMA(2,1,0)(2,0,1)[7] intercept   : AIC=5660.026, Time=0.25 sec\n",
      " ARIMA(3,1,0)(1,0,0)[7] intercept   : AIC=5651.836, Time=0.17 sec\n",
      " ARIMA(3,1,0)(0,0,0)[7] intercept   : AIC=5657.445, Time=0.08 sec\n",
      " ARIMA(3,1,0)(2,0,0)[7] intercept   : AIC=5653.836, Time=0.24 sec\n",
      " ARIMA(3,1,0)(1,0,1)[7] intercept   : AIC=5653.836, Time=0.27 sec\n",
      " ARIMA(3,1,0)(0,0,1)[7] intercept   : AIC=5651.969, Time=0.12 sec\n",
      " ARIMA(3,1,0)(2,0,1)[7] intercept   : AIC=5655.835, Time=0.28 sec\n",
      " ARIMA(4,1,0)(1,0,0)[7] intercept   : AIC=5643.818, Time=0.20 sec\n",
      " ARIMA(4,1,0)(0,0,0)[7] intercept   : AIC=5648.161, Time=0.09 sec\n",
      " ARIMA(4,1,0)(2,0,0)[7] intercept   : AIC=5645.808, Time=0.29 sec\n",
      " ARIMA(4,1,0)(1,0,1)[7] intercept   : AIC=5645.801, Time=0.36 sec\n",
      " ARIMA(4,1,0)(0,0,1)[7] intercept   : AIC=5643.967, Time=0.14 sec\n",
      " ARIMA(4,1,0)(2,0,1)[7] intercept   : AIC=inf, Time=3.01 sec\n",
      " ARIMA(5,1,0)(1,0,0)[7] intercept   : AIC=5630.620, Time=0.22 sec\n",
      " ARIMA(5,1,0)(0,0,0)[7] intercept   : AIC=5633.382, Time=0.09 sec\n",
      " ARIMA(5,1,0)(2,0,0)[7] intercept   : AIC=5632.576, Time=0.31 sec\n",
      " ARIMA(5,1,0)(1,0,1)[7] intercept   : AIC=5632.575, Time=0.41 sec\n",
      " ARIMA(5,1,0)(0,0,1)[7] intercept   : AIC=5630.579, Time=0.15 sec\n",
      " ARIMA(5,1,0)(0,0,2)[7] intercept   : AIC=5632.574, Time=0.24 sec\n",
      " ARIMA(5,1,0)(1,0,2)[7] intercept   : AIC=5634.577, Time=0.26 sec\n",
      " ARIMA(6,1,0)(0,0,1)[7] intercept   : AIC=5573.705, Time=1.00 sec\n",
      " ARIMA(6,1,0)(0,0,0)[7] intercept   : AIC=5608.895, Time=0.14 sec\n",
      " ARIMA(6,1,0)(1,0,1)[7] intercept   : AIC=inf, Time=1.45 sec\n",
      " ARIMA(6,1,0)(0,0,2)[7] intercept   : AIC=inf, Time=1.59 sec\n",
      " ARIMA(6,1,0)(1,0,0)[7] intercept   : AIC=5598.776, Time=0.39 sec\n",
      " ARIMA(6,1,0)(1,0,2)[7] intercept   : AIC=inf, Time=1.80 sec\n",
      " ARIMA(6,1,1)(0,0,1)[7] intercept   : AIC=inf, Time=1.38 sec\n",
      " ARIMA(5,1,1)(0,0,1)[7] intercept   : AIC=inf, Time=1.06 sec\n",
      " ARIMA(6,1,0)(0,0,1)[7]             : AIC=inf, Time=0.83 sec\n",
      "\n",
      "Best model:  ARIMA(6,1,0)(0,0,1)[7] intercept\n",
      "Total fit time: 20.510 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "ARIMA(order=(6, 1, 0), scoring_args={}, seasonal_order=(0, 0, 1, 7),\n      suppress_warnings=True)",
      "text/html": "<style>#sk-container-id-2 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-2 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-2 pre {\n  padding: 0;\n}\n\n#sk-container-id-2 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-2 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-2 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-2 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-2 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-2 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-2 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-2 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-2 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-2 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n#sk-container-id-2 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-2 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-2 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-2 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-2 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-2 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-2 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre> ARIMA(6,1,0)(0,0,1)[7] intercept</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ARIMA<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre> ARIMA(6,1,0)(0,0,1)[7] intercept</pre></div> </div></div></div></div>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_auto = auto_arima(x, seasonal=True, max_p=8,max_d=8, max_q=8, information_criterion=\"aic\", trace=True, m=7, d=1)\n",
    "model_auto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2c11d4b1f2764bad",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-06T03:21:22.831992Z",
     "start_time": "2024-06-06T03:21:22.798670Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                                      SARIMAX Results                                      \n===========================================================================================\nDep. Variable:                                   y   No. Observations:                  365\nModel:             SARIMAX(6, 1, 0)x(0, 0, [1], 7)   Log Likelihood               -2777.853\nDate:                             Wed, 05 Jun 2024   AIC                           5573.705\nTime:                                     23:21:22   BIC                           5608.779\nSample:                                 01-01-2015   HQIC                          5587.645\n                                      - 12-31-2015                                         \nCovariance Type:                               opg                                         \n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nintercept     -8.2946      3.978     -2.085      0.037     -16.090      -0.499\nar.L1         -0.9626      0.023    -41.699      0.000      -1.008      -0.917\nar.L2         -0.9833      0.021    -47.773      0.000      -1.024      -0.943\nar.L3         -0.9663      0.029    -33.186      0.000      -1.023      -0.909\nar.L4         -0.9682      0.030    -32.361      0.000      -1.027      -0.910\nar.L5         -0.9767      0.023    -42.857      0.000      -1.021      -0.932\nar.L6         -0.9591      0.035    -27.149      0.000      -1.028      -0.890\nma.S.L7       -0.9306      0.051    -18.169      0.000      -1.031      -0.830\nsigma2      2.734e+05   1.27e+04     21.554      0.000    2.48e+05    2.98e+05\n===================================================================================\nLjung-Box (L1) (Q):                   2.77   Jarque-Bera (JB):              1111.62\nProb(Q):                              0.10   Prob(JB):                         0.00\nHeteroskedasticity (H):               3.17   Skew:                            -0.88\nProb(H) (two-sided):                  0.00   Kurtosis:                        11.38\n===================================================================================\n\nWarnings:\n[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n\"\"\"",
      "text/html": "<table class=\"simpletable\">\n<caption>SARIMAX Results</caption>\n<tr>\n  <th>Dep. Variable:</th>                  <td>y</td>                <th>  No. Observations:  </th>    <td>365</td>   \n</tr>\n<tr>\n  <th>Model:</th>           <td>SARIMAX(6, 1, 0)x(0, 0, [1], 7)</td> <th>  Log Likelihood     </th> <td>-2777.853</td>\n</tr>\n<tr>\n  <th>Date:</th>                   <td>Wed, 05 Jun 2024</td>         <th>  AIC                </th> <td>5573.705</td> \n</tr>\n<tr>\n  <th>Time:</th>                       <td>23:21:22</td>             <th>  BIC                </th> <td>5608.779</td> \n</tr>\n<tr>\n  <th>Sample:</th>                    <td>01-01-2015</td>            <th>  HQIC               </th> <td>5587.645</td> \n</tr>\n<tr>\n  <th></th>                          <td>- 12-31-2015</td>           <th>                     </th>     <td> </td>    \n</tr>\n<tr>\n  <th>Covariance Type:</th>               <td>opg</td>               <th>                     </th>     <td> </td>    \n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n</tr>\n<tr>\n  <th>intercept</th> <td>   -8.2946</td> <td>    3.978</td> <td>   -2.085</td> <td> 0.037</td> <td>  -16.090</td> <td>   -0.499</td>\n</tr>\n<tr>\n  <th>ar.L1</th>     <td>   -0.9626</td> <td>    0.023</td> <td>  -41.699</td> <td> 0.000</td> <td>   -1.008</td> <td>   -0.917</td>\n</tr>\n<tr>\n  <th>ar.L2</th>     <td>   -0.9833</td> <td>    0.021</td> <td>  -47.773</td> <td> 0.000</td> <td>   -1.024</td> <td>   -0.943</td>\n</tr>\n<tr>\n  <th>ar.L3</th>     <td>   -0.9663</td> <td>    0.029</td> <td>  -33.186</td> <td> 0.000</td> <td>   -1.023</td> <td>   -0.909</td>\n</tr>\n<tr>\n  <th>ar.L4</th>     <td>   -0.9682</td> <td>    0.030</td> <td>  -32.361</td> <td> 0.000</td> <td>   -1.027</td> <td>   -0.910</td>\n</tr>\n<tr>\n  <th>ar.L5</th>     <td>   -0.9767</td> <td>    0.023</td> <td>  -42.857</td> <td> 0.000</td> <td>   -1.021</td> <td>   -0.932</td>\n</tr>\n<tr>\n  <th>ar.L6</th>     <td>   -0.9591</td> <td>    0.035</td> <td>  -27.149</td> <td> 0.000</td> <td>   -1.028</td> <td>   -0.890</td>\n</tr>\n<tr>\n  <th>ma.S.L7</th>   <td>   -0.9306</td> <td>    0.051</td> <td>  -18.169</td> <td> 0.000</td> <td>   -1.031</td> <td>   -0.830</td>\n</tr>\n<tr>\n  <th>sigma2</th>    <td> 2.734e+05</td> <td> 1.27e+04</td> <td>   21.554</td> <td> 0.000</td> <td> 2.48e+05</td> <td> 2.98e+05</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Ljung-Box (L1) (Q):</th>     <td>2.77</td> <th>  Jarque-Bera (JB):  </th> <td>1111.62</td>\n</tr>\n<tr>\n  <th>Prob(Q):</th>                <td>0.10</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n</tr>\n<tr>\n  <th>Heteroskedasticity (H):</th> <td>3.17</td> <th>  Skew:              </th>  <td>-0.88</td> \n</tr>\n<tr>\n  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>11.38</td> \n</tr>\n</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).",
      "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}          &                y                & \\textbf{  No. Observations:  } &    365      \\\\\n\\textbf{Model:}                  & SARIMAX(6, 1, 0)x(0, 0, [1], 7) & \\textbf{  Log Likelihood     } & -2777.853   \\\\\n\\textbf{Date:}                   &         Wed, 05 Jun 2024        & \\textbf{  AIC                } &  5573.705   \\\\\n\\textbf{Time:}                   &             23:21:22            & \\textbf{  BIC                } &  5608.779   \\\\\n\\textbf{Sample:}                 &            01-01-2015           & \\textbf{  HQIC               } &  5587.645   \\\\\n\\textbf{}                        &           - 12-31-2015          & \\textbf{                     } &             \\\\\n\\textbf{Covariance Type:}        &               opg               & \\textbf{                     } &             \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{intercept} &      -8.2946  &        3.978     &    -2.085  &         0.037        &      -16.090    &       -0.499     \\\\\n\\textbf{ar.L1}     &      -0.9626  &        0.023     &   -41.699  &         0.000        &       -1.008    &       -0.917     \\\\\n\\textbf{ar.L2}     &      -0.9833  &        0.021     &   -47.773  &         0.000        &       -1.024    &       -0.943     \\\\\n\\textbf{ar.L3}     &      -0.9663  &        0.029     &   -33.186  &         0.000        &       -1.023    &       -0.909     \\\\\n\\textbf{ar.L4}     &      -0.9682  &        0.030     &   -32.361  &         0.000        &       -1.027    &       -0.910     \\\\\n\\textbf{ar.L5}     &      -0.9767  &        0.023     &   -42.857  &         0.000        &       -1.021    &       -0.932     \\\\\n\\textbf{ar.L6}     &      -0.9591  &        0.035     &   -27.149  &         0.000        &       -1.028    &       -0.890     \\\\\n\\textbf{ma.S.L7}   &      -0.9306  &        0.051     &   -18.169  &         0.000        &       -1.031    &       -0.830     \\\\\n\\textbf{sigma2}    &    2.734e+05  &     1.27e+04     &    21.554  &         0.000        &     2.48e+05    &     2.98e+05     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Ljung-Box (L1) (Q):}     & 2.77 & \\textbf{  Jarque-Bera (JB):  } & 1111.62  \\\\\n\\textbf{Prob(Q):}                & 0.10 & \\textbf{  Prob(JB):          } &   0.00   \\\\\n\\textbf{Heteroskedasticity (H):} & 3.17 & \\textbf{  Skew:              } &  -0.88   \\\\\n\\textbf{Prob(H) (two-sided):}    & 0.00 & \\textbf{  Kurtosis:          } &  11.38   \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{SARIMAX Results}\n\\end{center}\n\nWarnings: \\newline\n [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_auto.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c744dea5df64ec0b",
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    "ExecuteTime": {
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     "start_time": "2024-06-06T03:21:22.834002Z"
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   "outputs": [
    {
     "data": {
      "text/plain": "2016-01-01    2242.393756\n2016-01-02    2127.494299\n2016-01-03    1952.273921\n2016-01-04    1883.559692\n2016-01-05    2027.336546\n2016-01-06    2029.557629\n2016-01-07    2143.732866\n2016-01-08    2232.279581\n2016-01-09    2120.302244\n2016-01-10    1945.719476\n2016-01-11    1879.406885\n2016-01-12    2015.451016\n2016-01-13    2022.542359\n2016-01-14    2131.195093\n2016-01-15    2221.990465\n2016-01-16    2113.102132\n2016-01-17    1939.205229\n2016-01-18    1875.028565\n2016-01-19    2003.902751\n2016-01-20    2015.242381\n2016-01-21    2118.954395\n2016-01-22    2211.556240\n2016-01-23    2105.879872\n2016-01-24    1932.730448\n2016-01-25    1870.441235\n2016-01-26    1992.661676\n2016-01-27    2007.692335\n2016-01-28    2106.976911\n2016-01-29    2201.002752\n2016-01-30    2098.624110\nFreq: D, dtype: float64"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict, confint = model_auto.predict(n_periods=30, return_conf_int=True)\n",
    "\n",
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cd807436613faf4d",
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     "start_time": "2024-06-06T03:21:22.851971Z"
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   "outputs": [],
   "source": [
    "cf= pd.DataFrame(confint)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8fc2864e37b74d04",
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   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=x.index, \n",
    "                         y=x[\"total_price\"], \n",
    "                         name='Ventas',\n",
    "                         line=dict(color='#3a419b',\n",
    "                                   width=1.5\n",
    "                                   )))\n",
    "fig.add_trace(go.Scatter(x=predict.index, \n",
    "                         y=predict, \n",
    "                         name='Predicción',\n",
    "                         line=dict(color='#fa636e',\n",
    "                                   width=1.5\n",
    "                                   )))\n",
    "\n",
    "fig.add_trace(go.Scatter(x=predict.index, \n",
    "                         y=cf[0], \n",
    "                         name='Predict Min',\n",
    "                         fill='tonexty',\n",
    "                         line=dict(color='#d4d4d9', \n",
    "                                   width=1.5)))\n",
    "\n",
    "fig.add_trace(go.Scatter(x=predict.index, \n",
    "                         y=cf[1], \n",
    "                         name='Predict Max',\n",
    "                         fill='tonexty',\n",
    "                         line=dict(color='#d4d4d9', \n",
    "                                   width=1.5)))\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Predicción Enero 2016\"\n",
    "    )"
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    "Podemos observar que las ventas mantendrán un valor estable, esto quiere decir que la empresa no muestra un crecimiento o decrecimiento de sus ventas por lo que si la empresa busca un crecimiento, debería buscar alguna forma de innovar para atraer más clientes y ventas."
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "#### Resumen del estudio\n",
    "1. Las pizzas que generaron más ingresos fueron la Thai Chicken Pizza, Barbecue Chicken Pizza y California Chicken Pizza, y las que generaron menos fueron Spinach Supreme Pizza, Green Garden Pizza, Brie Carre Pizza.\n",
    "2. La pizza Classic Deluxe fue la más comprada y la Brie Carre Pizza fue la menos comprada.\n",
    "3. Un decrecimiento en las ventas del mes de diciembre.\n",
    "4. Los días jueves, viernes y sábado son los días en que los clientes más compran.\n",
    "5. Los clientes suelen comprar más desde el mediodía a las 1 de la tarde y desde las 4 de la tarde hasta las 6. \n",
    "6. La mayoría de las pizzas compradas son de tamaño L, M y S (en orden), pocas compras para las pizzas XL y prácticamente nada de XXL.\n",
    "7. Hay una preferencia por las pizzas clásicas.\n",
    "8. Los clientes suelen gastar entre 12,5 a 20 dólares en una pizza.\n",
    "9. Los ingredientes más utilizados son Ajo, Tomate y Cebolla roja, los menos utilizados son Cebolla caramelizada, Pera y Tomillo.\n",
    "10. La predicción muestra que la empresa se encuentra estancada, esto quiere decir que en el futuro la empresa no espera un crecimiento ni un decrecimiento."
   ]
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   "id": "48e168f810a46064",
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   "source": [
    "#### Algunas recomendaciones para la empresa\n",
    "- Reforzar la venta de las Pizzas de Thai Chicken Pizza, Barbecue Chicken Pizza, California Chicken Pizza y Classic Deluxe ofreciendo descuentos para clientes nuevos con la intención de atraer más clientela, por otro lado repensar sobre la situación de la Brie Carre Pizza y quitarla del menú.\n",
    "- Hubo un decrecimiento en el ultimo mes. Quizás alguna reforma a la empresa provoco esto, cambio de personal o algún factor externo, se podría buscar atraer mas clientes, fidelizar a los actuales y si hubo una reforma, intentar volver a la antigua forma de operar.\n",
    "- Hacer descuentos los días lunes, martes y domingo para estabilizar las compras en estos días.\n",
    "- Aumentar el personal entre las 12pm a 1pm y 4pm a 6pm, y reducirlo en las demás horas.\n",
    "- Mantener los precios entre 12,5 a 20 dólares, ya que es lo que están dispuesto a pagar los clientes.\n",
    "- Incluir en el menú más pizzas con el uso de Ajo, Tomate y Cebolla roja.\n",
    "- Realizar alguno de estos cambios podría ayudar en el estancamiento actual de la empresa, provocando un crecimiento en el mes que viene."
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