data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "65baddee-def3-4cd0-8ed6-dc270f7c03ef",
   "metadata": {},
   "source": [
    "# Elabastecedor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a56afa-a0c1-4c0c-a008-0dc8a65035f6",
   "metadata": {},
   "source": [
    "## Prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fd7b34f-da75-42f2-be75-d91fb109e174",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from datetime import date\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34f3261b-cc2a-467c-b68c-001ece068101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('data/20231231_products.csv'),\n",
       " PosixPath('data/20240104_products.csv'),\n",
       " PosixPath('data/20240107_products.csv'),\n",
       " PosixPath('data/20240111_products.csv'),\n",
       " PosixPath('data/20240114_products.csv'),\n",
       " PosixPath('data/20240117_products.csv'),\n",
       " PosixPath('data/20240121_products.csv'),\n",
       " PosixPath('data/20240124_products.csv'),\n",
       " PosixPath('data/20240128_products.csv'),\n",
       " PosixPath('data/20240131_products.csv'),\n",
       " PosixPath('data/20240204_products.csv'),\n",
       " PosixPath('data/20240207_products.csv'),\n",
       " PosixPath('data/20240211_products.csv'),\n",
       " PosixPath('data/20240214_products.csv'),\n",
       " PosixPath('data/20240218_products.csv'),\n",
       " PosixPath('data/20240221_products.csv'),\n",
       " PosixPath('data/20240225_products.csv'),\n",
       " PosixPath('data/20240228_products.csv'),\n",
       " PosixPath('data/20240303_products.csv'),\n",
       " PosixPath('data/20240306_products.csv'),\n",
       " PosixPath('data/20240310_products.csv'),\n",
       " PosixPath('data/20240313_products.csv'),\n",
       " PosixPath('data/20240317_products.csv'),\n",
       " PosixPath('data/20240320_products.csv'),\n",
       " PosixPath('data/20240324_products.csv'),\n",
       " PosixPath('data/20240327_products.csv'),\n",
       " PosixPath('data/20240331_products.csv'),\n",
       " PosixPath('data/20240403_products.csv'),\n",
       " PosixPath('data/20240407_products.csv'),\n",
       " PosixPath('data/20240410_products.csv'),\n",
       " PosixPath('data/20240414_products.csv'),\n",
       " PosixPath('data/20240417_products.csv'),\n",
       " PosixPath('data/20240421_products.csv'),\n",
       " PosixPath('data/20240424_products.csv'),\n",
       " PosixPath('data/20240428_products.csv'),\n",
       " PosixPath('data/20240501_products.csv'),\n",
       " PosixPath('data/20240505_products.csv'),\n",
       " PosixPath('data/20240508_products.csv'),\n",
       " PosixPath('data/20240512_products.csv'),\n",
       " PosixPath('data/20240515_products.csv'),\n",
       " PosixPath('data/20240519_products.csv'),\n",
       " PosixPath('data/20240522_products.csv'),\n",
       " PosixPath('data/20240526_products.csv'),\n",
       " PosixPath('data/20240529_products.csv'),\n",
       " PosixPath('data/20240602_products.csv'),\n",
       " PosixPath('data/20240605_products.csv'),\n",
       " PosixPath('data/20240609_products.csv'),\n",
       " PosixPath('data/20240612_products.csv'),\n",
       " PosixPath('data/20240616_products.csv'),\n",
       " PosixPath('data/20240619_products.csv'),\n",
       " PosixPath('data/20240623_products.csv'),\n",
       " PosixPath('data/20240626_products.csv'),\n",
       " PosixPath('data/20240630_products.csv'),\n",
       " PosixPath('data/20240703_products.csv'),\n",
       " PosixPath('data/20240707_products.csv'),\n",
       " PosixPath('data/20240710_products.csv'),\n",
       " PosixPath('data/20240714_products.csv'),\n",
       " PosixPath('data/20240717_products.csv'),\n",
       " PosixPath('data/20240721_products.csv'),\n",
       " PosixPath('data/20240724_products.csv'),\n",
       " PosixPath('data/20240728_products.csv'),\n",
       " PosixPath('data/20240731_products.csv'),\n",
       " PosixPath('data/20240804_products.csv'),\n",
       " PosixPath('data/20240807_products.csv'),\n",
       " PosixPath('data/20240811_products.csv'),\n",
       " PosixPath('data/20240814_products.csv'),\n",
       " PosixPath('data/20240818_products.csv'),\n",
       " PosixPath('data/20240821_products.csv'),\n",
       " PosixPath('data/20240825_products.csv'),\n",
       " PosixPath('data/20240828_products.csv'),\n",
       " PosixPath('data/20240901_products.csv'),\n",
       " PosixPath('data/20240904_products.csv'),\n",
       " PosixPath('data/20240908_products.csv'),\n",
       " PosixPath('data/20240911_products.csv'),\n",
       " PosixPath('data/20240915_products.csv'),\n",
       " PosixPath('data/20240918_products.csv'),\n",
       " PosixPath('data/20240922_products.csv'),\n",
       " PosixPath('data/20240925_products.csv'),\n",
       " PosixPath('data/20240929_products.csv'),\n",
       " PosixPath('data/20241002_products.csv'),\n",
       " PosixPath('data/20241006_products.csv'),\n",
       " PosixPath('data/20241009_products.csv'),\n",
       " PosixPath('data/20241013_products.csv'),\n",
       " PosixPath('data/20241016_products.csv'),\n",
       " PosixPath('data/20241020_products.csv'),\n",
       " PosixPath('data/20241023_products.csv'),\n",
       " PosixPath('data/20241027_products.csv'),\n",
       " PosixPath('data/20241030_products.csv'),\n",
       " PosixPath('data/20241103_products.csv'),\n",
       " PosixPath('data/20241106_products.csv'),\n",
       " PosixPath('data/20241110_products.csv'),\n",
       " PosixPath('data/20241113_products.csv')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get datasets\n",
    "\n",
    "path = Path(\"data\")\n",
    "entries = [path / entry for entry in os.listdir(path) if entry.endswith(\".csv\")]\n",
    "entries.sort()\n",
    "list(entries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdd268d5-807a-447a-a980-722957d78599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load datasets\n",
    "\n",
    "# Get date from filename\n",
    "def get_date(filename):\n",
    "    year = filename[0:4]\n",
    "    month = filename[4:6]\n",
    "    day = filename[6:8]\n",
    "    return f\"{year}-{month}-{day}\"\n",
    "\n",
    "# Append datasets in a list\n",
    "dfs = []\n",
    "for file in entries:\n",
    "    date = get_date(file.name)\n",
    "    df = pd.read_csv(file, index_col=\"id\")\n",
    "    df.drop_duplicates(inplace=True)\n",
    "    df.rename(columns={\"precio\":date}, inplace=True)\n",
    "    dfs.append(df)\n",
    "\n",
    "len(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9eef9b6-ff26-4ecc-b5ab-b09306283667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 233838 entries, 44 to 61\n",
      "Data columns (total 96 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   codigo      233838 non-null  int64  \n",
      " 1   marca       233838 non-null  object \n",
      " 2   nombre      233838 non-null  object \n",
      " 3   2023-12-31  75 non-null      float64\n",
      " 4   2024-01-04  73 non-null      float64\n",
      " 5   2024-01-07  84 non-null      float64\n",
      " 6   2024-01-11  3124 non-null    float64\n",
      " 7   categoria   233606 non-null  object \n",
      " 8   2024-01-14  3103 non-null    float64\n",
      " 9   2024-01-17  3033 non-null    float64\n",
      " 10  2024-01-21  3067 non-null    float64\n",
      " 11  2024-01-24  2978 non-null    float64\n",
      " 12  2024-01-28  3034 non-null    float64\n",
      " 13  2024-01-31  2946 non-null    float64\n",
      " 14  2024-02-04  3082 non-null    float64\n",
      " 15  2024-02-07  3062 non-null    float64\n",
      " 16  2024-02-11  3102 non-null    float64\n",
      " 17  2024-02-14  2988 non-null    float64\n",
      " 18  2024-02-18  3080 non-null    float64\n",
      " 19  2024-02-21  3026 non-null    float64\n",
      " 20  2024-02-25  1361 non-null    float64\n",
      " 21  2024-02-28  2995 non-null    float64\n",
      " 22  2024-03-03  3071 non-null    float64\n",
      " 23  2024-03-06  3047 non-null    float64\n",
      " 24  2024-03-10  3156 non-null    float64\n",
      " 25  2024-03-13  3083 non-null    float64\n",
      " 26  2024-03-17  3152 non-null    float64\n",
      " 27  2024-03-20  3117 non-null    float64\n",
      " 28  2024-03-24  3120 non-null    float64\n",
      " 29  2024-03-27  3034 non-null    float64\n",
      " 30  2024-03-31  3032 non-null    float64\n",
      " 31  2024-04-03  2881 non-null    float64\n",
      " 32  2024-04-07  2667 non-null    float64\n",
      " 33  2024-04-10  2850 non-null    float64\n",
      " 34  2024-04-14  2846 non-null    float64\n",
      " 35  2024-04-17  2768 non-null    float64\n",
      " 36  2024-04-21  2782 non-null    float64\n",
      " 37  2024-04-24  2684 non-null    float64\n",
      " 38  2024-04-28  2718 non-null    float64\n",
      " 39  2024-05-01  2671 non-null    float64\n",
      " 40  2024-05-05  2635 non-null    float64\n",
      " 41  2024-05-08  2634 non-null    float64\n",
      " 42  2024-05-12  2681 non-null    float64\n",
      " 43  2024-05-15  2648 non-null    float64\n",
      " 44  2024-05-19  2658 non-null    float64\n",
      " 45  2024-05-22  2586 non-null    float64\n",
      " 46  2024-05-26  2588 non-null    float64\n",
      " 47  2024-05-29  2542 non-null    float64\n",
      " 48  2024-06-02  2565 non-null    float64\n",
      " 49  2024-06-05  2532 non-null    float64\n",
      " 50  2024-06-09  2568 non-null    float64\n",
      " 51  2024-06-12  2526 non-null    float64\n",
      " 52  2024-06-16  2550 non-null    float64\n",
      " 53  2024-06-19  2486 non-null    float64\n",
      " 54  2024-06-23  2503 non-null    float64\n",
      " 55  2024-06-26  2453 non-null    float64\n",
      " 56  2024-06-30  2503 non-null    float64\n",
      " 57  2024-07-03  2418 non-null    float64\n",
      " 58  2024-07-07  2435 non-null    float64\n",
      " 59  2024-07-10  2354 non-null    float64\n",
      " 60  2024-07-14  2391 non-null    float64\n",
      " 61  2024-07-17  2333 non-null    float64\n",
      " 62  2024-07-21  2374 non-null    float64\n",
      " 63  2024-07-24  2423 non-null    float64\n",
      " 64  2024-07-28  2411 non-null    float64\n",
      " 65  2024-07-31  2353 non-null    float64\n",
      " 66  2024-08-04  2447 non-null    float64\n",
      " 67  2024-08-07  2384 non-null    float64\n",
      " 68  2024-08-11  2422 non-null    float64\n",
      " 69  2024-08-14  2352 non-null    float64\n",
      " 70  2024-08-18  2460 non-null    float64\n",
      " 71  2024-08-21  2398 non-null    float64\n",
      " 72  2024-08-25  2476 non-null    float64\n",
      " 73  2024-08-28  2431 non-null    float64\n",
      " 74  2024-09-01  2431 non-null    float64\n",
      " 75  2024-09-04  2066 non-null    float64\n",
      " 76  2024-09-08  2422 non-null    float64\n",
      " 77  2024-09-11  2363 non-null    float64\n",
      " 78  2024-09-15  2420 non-null    float64\n",
      " 79  2024-09-18  2395 non-null    float64\n",
      " 80  2024-09-22  2450 non-null    float64\n",
      " 81  2024-09-25  2410 non-null    float64\n",
      " 82  2024-09-29  2465 non-null    float64\n",
      " 83  2024-10-02  2394 non-null    float64\n",
      " 84  2024-10-06  2370 non-null    float64\n",
      " 85  2024-10-09  2318 non-null    float64\n",
      " 86  2024-10-13  2372 non-null    float64\n",
      " 87  2024-10-16  2332 non-null    float64\n",
      " 88  2024-10-20  2367 non-null    float64\n",
      " 89  2024-10-23  2358 non-null    float64\n",
      " 90  2024-10-27  2413 non-null    float64\n",
      " 91  2024-10-30  2381 non-null    float64\n",
      " 92  2024-11-03  2426 non-null    float64\n",
      " 93  2024-11-06  2396 non-null    float64\n",
      " 94  2024-11-10  2468 non-null    float64\n",
      " 95  2024-11-13  2409 non-null    float64\n",
      "dtypes: float64(92), int64(1), object(3)\n",
      "memory usage: 173.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Merge datasets\n",
    "\n",
    "df = pd.concat(dfs)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8bbbbef-6d52-4e34-a3d4-01f0db637f1e",
   "metadata": {},
   "source": [
    "## Analyze"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f782db76-6b80-4ab5-8865-0e5855d85cfe",
   "metadata": {},
   "source": [
    "### Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1452369-6e25-4207-974c-ed42aa64a36e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>categoria</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codigo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114746</th>\n",
       "      <td>ACEITES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112222</th>\n",
       "      <td>ACEITES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103100</th>\n",
       "      <td>ACEITES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112537</th>\n",
       "      <td>ACEITES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114573</th>\n",
       "      <td>ACEITES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119965</th>\n",
       "      <td>BUDINES Y MAGDALENAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103129</th>\n",
       "      <td>BUDINES Y MAGDALENAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91000</th>\n",
       "      <td>HAMBURGUESAS Y MEDALLONES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74300</th>\n",
       "      <td>QUESOS SEMIDUROS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3015</th>\n",
       "      <td>FRUTAS Y VERDURAS ENVASADAS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4203 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          categoria\n",
       "codigo                             \n",
       "114746                      ACEITES\n",
       "112222                      ACEITES\n",
       "103100                      ACEITES\n",
       "112537                      ACEITES\n",
       "114573                      ACEITES\n",
       "...                             ...\n",
       "119965         BUDINES Y MAGDALENAS\n",
       "103129         BUDINES Y MAGDALENAS\n",
       "91000     HAMBURGUESAS Y MEDALLONES\n",
       "74300              QUESOS SEMIDUROS\n",
       "3015    FRUTAS Y VERDURAS ENVASADAS\n",
       "\n",
       "[4203 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get categories\n",
    "\n",
    "categories = df[[\"codigo\", \"categoria\"]].dropna().drop_duplicates([\"codigo\"]).set_index(\"codigo\")\n",
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "494231c1-f9fd-4322-a734-220f67c952b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "categoria                \n",
       "GALLETITAS DULCES            171\n",
       "YOGUR                        154\n",
       "VINOS TINTOS                 136\n",
       "LIMPIEZA DE COCINA Y BANO    121\n",
       "CUIDADO DEL CABELLO          116\n",
       "                            ... \n",
       "GRASAS                         2\n",
       "GRANADINA                      2\n",
       "LEVADURAS                      2\n",
       "PESCADOS Y MARISCOS            2\n",
       "SALSA GOLF                     2\n",
       "Name: count, Length: 141, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd3b0ea-a7c0-482b-a2aa-9890d8a3ca3a",
   "metadata": {},
   "source": [
    "### Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ebac015d-2c98-4123-a2ba-faa70ce9ae26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4206 entries, 59 to 3015\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   nombre     4206 non-null   object\n",
      " 1   marca      4206 non-null   object\n",
      " 2   categoria  4203 non-null   object\n",
      "dtypes: object(3)\n",
      "memory usage: 131.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Get products\n",
    "\n",
    "products = df[[\"codigo\", \"nombre\", \"marca\"]].dropna().drop_duplicates([\"codigo\"]).set_index(\"codigo\")\n",
    "products[\"categoria\"] = categories\n",
    "products.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "039fe9d6-d405-4926-b003-c4c51e4b4695",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nombre</th>\n",
       "      <th>marca</th>\n",
       "      <th>categoria</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codigo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BOLA DE LOMO XKG</td>\n",
       "      <td>A BALANZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123525</th>\n",
       "      <td>TIERNITOS CARNE X1.5KG</td>\n",
       "      <td>INVENTARIABLES</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123523</th>\n",
       "      <td>TIERNITOS CACHORROS C/L/C X1.5KG</td>\n",
       "      <td>EL ABASTECEDOR</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  nombre           marca categoria\n",
       "codigo                                                            \n",
       "27                      BOLA DE LOMO XKG       A BALANZA       NaN\n",
       "123525            TIERNITOS CARNE X1.5KG  INVENTARIABLES       NaN\n",
       "123523  TIERNITOS CACHORROS C/L/C X1.5KG  EL ABASTECEDOR       NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show products with missing category\n",
    "products[products.categoria.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b497733f-b906-40bc-ab7d-668dba1ecc4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4203 entries, 59 to 3015\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   nombre     4203 non-null   object\n",
      " 1   marca      4203 non-null   object\n",
      " 2   categoria  4203 non-null   object\n",
      "dtypes: object(3)\n",
      "memory usage: 131.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Drop products with missing category\n",
    "\n",
    "products.dropna(inplace=True)\n",
    "products.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe5ba35-5e81-4c5e-8bed-e01b93753bb9",
   "metadata": {},
   "source": [
    "### Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95d89e62-4a03-463c-a3b0-63b6763f1a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2023-12-31</th>\n",
       "      <th>2024-01-04</th>\n",
       "      <th>2024-01-07</th>\n",
       "      <th>2024-01-11</th>\n",
       "      <th>2024-01-14</th>\n",
       "      <th>2024-01-17</th>\n",
       "      <th>2024-01-21</th>\n",
       "      <th>2024-01-24</th>\n",
       "      <th>2024-01-28</th>\n",
       "      <th>2024-01-31</th>\n",
       "      <th>...</th>\n",
       "      <th>2024-10-13</th>\n",
       "      <th>2024-10-16</th>\n",
       "      <th>2024-10-20</th>\n",
       "      <th>2024-10-23</th>\n",
       "      <th>2024-10-27</th>\n",
       "      <th>2024-10-30</th>\n",
       "      <th>2024-11-03</th>\n",
       "      <th>2024-11-06</th>\n",
       "      <th>2024-11-10</th>\n",
       "      <th>2024-11-13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codigo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5150.0</td>\n",
       "      <td>4099.0</td>\n",
       "      <td>4099.0</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>4849.0</td>\n",
       "      <td>5449.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5899.0</td>\n",
       "      <td>5899.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>5899.0</td>\n",
       "      <td>5899.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>5899.0</td>\n",
       "      <td>5899.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5499.0</td>\n",
       "      <td>5499.0</td>\n",
       "      <td>7199.0</td>\n",
       "      <td>7199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5850.0</td>\n",
       "      <td>4799.0</td>\n",
       "      <td>4799.0</td>\n",
       "      <td>4399.0</td>\n",
       "      <td>4399.0</td>\n",
       "      <td>4399.0</td>\n",
       "      <td>4399.0</td>\n",
       "      <td>4399.0</td>\n",
       "      <td>4999.0</td>\n",
       "      <td>4999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6499.0</td>\n",
       "      <td>6499.0</td>\n",
       "      <td>5499.0</td>\n",
       "      <td>5499.0</td>\n",
       "      <td>6499.0</td>\n",
       "      <td>6499.0</td>\n",
       "      <td>6899.0</td>\n",
       "      <td>6899.0</td>\n",
       "      <td>6499.0</td>\n",
       "      <td>6499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7300.0</td>\n",
       "      <td>5899.0</td>\n",
       "      <td>5399.0</td>\n",
       "      <td>5399.0</td>\n",
       "      <td>5399.0</td>\n",
       "      <td>5399.0</td>\n",
       "      <td>5399.0</td>\n",
       "      <td>5399.0</td>\n",
       "      <td>6799.0</td>\n",
       "      <td>7299.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>8399.0</td>\n",
       "      <td>8399.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>8399.0</td>\n",
       "      <td>8399.0</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>7999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9800.0</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>7999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9449.0</td>\n",
       "      <td>9449.0</td>\n",
       "      <td>7299.0</td>\n",
       "      <td>7299.0</td>\n",
       "      <td>9449.0</td>\n",
       "      <td>9449.0</td>\n",
       "      <td>9449.0</td>\n",
       "      <td>9449.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9449.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150853</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150856</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>6199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150889</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>845.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>845.0</td>\n",
       "      <td>845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150925</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>1299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150931</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2499.0</td>\n",
       "      <td>2380.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4203 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        2023-12-31  2024-01-04  2024-01-07  2024-01-11  2024-01-14  \\\n",
       "codigo                                                               \n",
       "2           5150.0      4099.0      4099.0      3699.0         NaN   \n",
       "3           5600.0         NaN         NaN         NaN         NaN   \n",
       "4           5850.0      4799.0      4799.0      4399.0      4399.0   \n",
       "7           7300.0      5899.0      5399.0      5399.0      5399.0   \n",
       "11          9800.0      7999.0      7999.0      7999.0      7999.0   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "150853         NaN         NaN         NaN         NaN         NaN   \n",
       "150856         NaN         NaN         NaN         NaN         NaN   \n",
       "150889         NaN         NaN         NaN         NaN         NaN   \n",
       "150925         NaN         NaN         NaN         NaN         NaN   \n",
       "150931         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "        2024-01-17  2024-01-21  2024-01-24  2024-01-28  2024-01-31  ...  \\\n",
       "codigo                                                              ...   \n",
       "2           3699.0      3699.0      3699.0      4849.0      5449.0  ...   \n",
       "3              NaN         NaN         NaN         NaN         NaN  ...   \n",
       "4           4399.0      4399.0      4399.0      4999.0      4999.0  ...   \n",
       "7           5399.0      5399.0      5399.0      6799.0      7299.0  ...   \n",
       "11          7999.0      7999.0         NaN      7999.0      7999.0  ...   \n",
       "...            ...         ...         ...         ...         ...  ...   \n",
       "150853         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "150856         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "150889         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "150925         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "150931         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "\n",
       "        2024-10-13  2024-10-16  2024-10-20  2024-10-23  2024-10-27  \\\n",
       "codigo                                                               \n",
       "2           5899.0      5899.0      6199.0      6199.0      5899.0   \n",
       "3              NaN         NaN         NaN         NaN         NaN   \n",
       "4           6499.0      6499.0      5499.0      5499.0      6499.0   \n",
       "7           7999.0      7999.0      8399.0      8399.0         NaN   \n",
       "11          9449.0      9449.0      7299.0      7299.0      9449.0   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "150853         NaN         NaN      1090.0      1090.0      1090.0   \n",
       "150856         NaN         NaN         NaN         NaN      6199.0   \n",
       "150889         NaN         NaN         NaN         NaN       845.0   \n",
       "150925         NaN         NaN         NaN         NaN         NaN   \n",
       "150931         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "        2024-10-30  2024-11-03  2024-11-06  2024-11-10  2024-11-13  \n",
       "codigo                                                              \n",
       "2           5899.0      6199.0      6199.0      5899.0      5899.0  \n",
       "3              NaN      5499.0      5499.0      7199.0      7199.0  \n",
       "4           6499.0      6899.0      6899.0      6499.0      6499.0  \n",
       "7           7999.0      8399.0      8399.0      7999.0      7999.0  \n",
       "11          9449.0      9449.0      9449.0         NaN      9449.0  \n",
       "...            ...         ...         ...         ...         ...  \n",
       "150853      1090.0      1090.0      1090.0      1090.0      1200.0  \n",
       "150856      6199.0      6199.0      6199.0      6199.0      6199.0  \n",
       "150889       845.0       845.0         NaN       845.0       845.0  \n",
       "150925         NaN         NaN         NaN      1299.0      1299.0  \n",
       "150931         NaN         NaN         NaN      2499.0      2380.0  \n",
       "\n",
       "[4203 rows x 92 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get prices\n",
    "prices = (df[df[\"codigo\"].isin(categories.index)]\n",
    "           .groupby(\"codigo\")\n",
    "           .sum()\n",
    "           .replace(0, np.nan)\n",
    "           .drop(columns=products.columns))\n",
    "prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "41f3b933-a335-46a4-b70b-c14f90b8d657",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2024-01-11</th>\n",
       "      <th>2024-01-14</th>\n",
       "      <th>2024-01-17</th>\n",
       "      <th>2024-01-21</th>\n",
       "      <th>2024-01-24</th>\n",
       "      <th>2024-01-28</th>\n",
       "      <th>2024-01-31</th>\n",
       "      <th>...</th>\n",
       "      <th>2024-10-13</th>\n",
       "      <th>2024-10-16</th>\n",
       "      <th>2024-10-20</th>\n",
       "      <th>2024-10-23</th>\n",
       "      <th>2024-10-27</th>\n",
       "      <th>2024-10-30</th>\n",
       "      <th>2024-11-03</th>\n",
       "      <th>2024-11-06</th>\n",
       "      <th>2024-11-10</th>\n",
       "      <th>2024-11-13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>3124.000000</td>\n",
       "      <td>3103.000000</td>\n",
       "      <td>3033.000000</td>\n",
       "      <td>3067.000000</td>\n",
       "      <td>2978.000000</td>\n",
       "      <td>3034.000000</td>\n",
       "      <td>2946.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2372.000000</td>\n",
       "      <td>2332.000000</td>\n",
       "      <td>2367.000000</td>\n",
       "      <td>2358.000000</td>\n",
       "      <td>2413.000000</td>\n",
       "      <td>2381.000000</td>\n",
       "      <td>2426.000000</td>\n",
       "      <td>2396.000000</td>\n",
       "      <td>2468.000000</td>\n",
       "      <td>2409.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2999.040541</td>\n",
       "      <td>2872.684932</td>\n",
       "      <td>2488.621951</td>\n",
       "      <td>2496.435915</td>\n",
       "      <td>2530.728073</td>\n",
       "      <td>2621.589149</td>\n",
       "      <td>2618.719677</td>\n",
       "      <td>2688.513227</td>\n",
       "      <td>2632.066882</td>\n",
       "      <td>2671.545815</td>\n",
       "      <td>...</td>\n",
       "      <td>3782.235919</td>\n",
       "      <td>3804.600600</td>\n",
       "      <td>3781.043629</td>\n",
       "      <td>3791.761934</td>\n",
       "      <td>3765.307082</td>\n",
       "      <td>3789.277190</td>\n",
       "      <td>3816.177894</td>\n",
       "      <td>3826.926674</td>\n",
       "      <td>3821.175976</td>\n",
       "      <td>3832.833093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2793.892931</td>\n",
       "      <td>2641.349344</td>\n",
       "      <td>2474.422560</td>\n",
       "      <td>3496.646721</td>\n",
       "      <td>3448.710021</td>\n",
       "      <td>3734.677710</td>\n",
       "      <td>3655.598256</td>\n",
       "      <td>3700.222567</td>\n",
       "      <td>3641.347483</td>\n",
       "      <td>3677.161676</td>\n",
       "      <td>...</td>\n",
       "      <td>5722.266292</td>\n",
       "      <td>5758.878928</td>\n",
       "      <td>5722.021621</td>\n",
       "      <td>5731.457508</td>\n",
       "      <td>5660.825056</td>\n",
       "      <td>5719.982701</td>\n",
       "      <td>5740.365569</td>\n",
       "      <td>5769.555951</td>\n",
       "      <td>5711.647280</td>\n",
       "      <td>5784.985990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>59.990000</td>\n",
       "      <td>59.990000</td>\n",
       "      <td>59.990000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>126.990000</td>\n",
       "      <td>126.990000</td>\n",
       "      <td>126.990000</td>\n",
       "      <td>126.990000</td>\n",
       "      <td>126.990000</td>\n",
       "      <td>126.990000</td>\n",
       "      <td>126.990000</td>\n",
       "      <td>126.990000</td>\n",
       "      <td>126.990000</td>\n",
       "      <td>126.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>724.000000</td>\n",
       "      <td>799.000000</td>\n",
       "      <td>711.500000</td>\n",
       "      <td>987.250000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1027.500000</td>\n",
       "      <td>1079.000000</td>\n",
       "      <td>1045.000000</td>\n",
       "      <td>1069.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1430.000000</td>\n",
       "      <td>1423.500000</td>\n",
       "      <td>1437.500000</td>\n",
       "      <td>1439.250000</td>\n",
       "      <td>1425.000000</td>\n",
       "      <td>1425.000000</td>\n",
       "      <td>1425.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1449.000000</td>\n",
       "      <td>1449.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1599.500000</td>\n",
       "      <td>1719.000000</td>\n",
       "      <td>1345.000000</td>\n",
       "      <td>1536.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1690.000000</td>\n",
       "      <td>1725.500000</td>\n",
       "      <td>1710.000000</td>\n",
       "      <td>1749.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2282.500000</td>\n",
       "      <td>2299.000000</td>\n",
       "      <td>2295.000000</td>\n",
       "      <td>2299.000000</td>\n",
       "      <td>2299.000000</td>\n",
       "      <td>2299.000000</td>\n",
       "      <td>2299.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>2299.000000</td>\n",
       "      <td>2299.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5112.250000</td>\n",
       "      <td>4799.000000</td>\n",
       "      <td>4146.500000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2822.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2990.000000</td>\n",
       "      <td>2839.000000</td>\n",
       "      <td>2915.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3945.000000</td>\n",
       "      <td>3955.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9999.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>71415.000000</td>\n",
       "      <td>71415.000000</td>\n",
       "      <td>90483.000000</td>\n",
       "      <td>79839.000000</td>\n",
       "      <td>79839.000000</td>\n",
       "      <td>79839.000000</td>\n",
       "      <td>79839.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>129999.000000</td>\n",
       "      <td>129999.000000</td>\n",
       "      <td>129999.000000</td>\n",
       "      <td>129999.000000</td>\n",
       "      <td>129999.000000</td>\n",
       "      <td>129999.000000</td>\n",
       "      <td>129999.000000</td>\n",
       "      <td>129999.000000</td>\n",
       "      <td>129999.000000</td>\n",
       "      <td>129999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        2023-12-31   2024-01-04    2024-01-07    2024-01-11    2024-01-14  \\\n",
       "count    74.000000    73.000000     82.000000   3124.000000   3103.000000   \n",
       "mean   2999.040541  2872.684932   2488.621951   2496.435915   2530.728073   \n",
       "std    2793.892931  2641.349344   2474.422560   3496.646721   3448.710021   \n",
       "min      90.000000    90.000000    120.000000     59.990000     59.990000   \n",
       "25%     724.000000   799.000000    711.500000    987.250000    999.000000   \n",
       "50%    1599.500000  1719.000000   1345.000000   1536.000000   1599.000000   \n",
       "75%    5112.250000  4799.000000   4146.500000   2700.000000   2822.000000   \n",
       "max    9999.000000  9999.000000  12000.000000  71415.000000  71415.000000   \n",
       "\n",
       "         2024-01-17    2024-01-21    2024-01-24    2024-01-28    2024-01-31  \\\n",
       "count   3033.000000   3067.000000   2978.000000   3034.000000   2946.000000   \n",
       "mean    2621.589149   2618.719677   2688.513227   2632.066882   2671.545815   \n",
       "std     3734.677710   3655.598256   3700.222567   3641.347483   3677.161676   \n",
       "min       59.990000    110.000000    110.000000    110.000000    120.000000   \n",
       "25%     1030.000000   1027.500000   1079.000000   1045.000000   1069.000000   \n",
       "50%     1650.000000   1690.000000   1725.500000   1710.000000   1749.000000   \n",
       "75%     2930.000000   2889.000000   2990.000000   2839.000000   2915.000000   \n",
       "max    90483.000000  79839.000000  79839.000000  79839.000000  79839.000000   \n",
       "\n",
       "       ...     2024-10-13     2024-10-16     2024-10-20     2024-10-23  \\\n",
       "count  ...    2372.000000    2332.000000    2367.000000    2358.000000   \n",
       "mean   ...    3782.235919    3804.600600    3781.043629    3791.761934   \n",
       "std    ...    5722.266292    5758.878928    5722.021621    5731.457508   \n",
       "min    ...     126.990000     126.990000     126.990000     126.990000   \n",
       "25%    ...    1430.000000    1423.500000    1437.500000    1439.250000   \n",
       "50%    ...    2282.500000    2299.000000    2295.000000    2299.000000   \n",
       "75%    ...    3999.000000    3999.000000    3999.000000    3999.000000   \n",
       "max    ...  129999.000000  129999.000000  129999.000000  129999.000000   \n",
       "\n",
       "          2024-10-27     2024-10-30     2024-11-03     2024-11-06  \\\n",
       "count    2413.000000    2381.000000    2426.000000    2396.000000   \n",
       "mean     3765.307082    3789.277190    3816.177894    3826.926674   \n",
       "std      5660.825056    5719.982701    5740.365569    5769.555951   \n",
       "min       126.990000     126.990000     126.990000     126.990000   \n",
       "25%      1425.000000    1425.000000    1425.000000    1440.000000   \n",
       "50%      2299.000000    2299.000000    2299.000000    2300.000000   \n",
       "75%      3945.000000    3955.000000    4000.000000    3999.000000   \n",
       "max    129999.000000  129999.000000  129999.000000  129999.000000   \n",
       "\n",
       "          2024-11-10     2024-11-13  \n",
       "count    2468.000000    2409.000000  \n",
       "mean     3821.175976    3832.833093  \n",
       "std      5711.647280    5784.985990  \n",
       "min       126.990000     126.990000  \n",
       "25%      1449.000000    1449.000000  \n",
       "50%      2299.000000    2299.000000  \n",
       "75%      4000.000000    3999.000000  \n",
       "max    129999.000000  129999.000000  \n",
       "\n",
       "[8 rows x 92 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show prices' statistics per date\n",
    "prices.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8e1493c4-533b-4007-b4d6-a9f2ac9eb90f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>codigo</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>7</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>...</th>\n",
       "      <th>150829</th>\n",
       "      <th>150835</th>\n",
       "      <th>150842</th>\n",
       "      <th>150844</th>\n",
       "      <th>150847</th>\n",
       "      <th>150853</th>\n",
       "      <th>150856</th>\n",
       "      <th>150889</th>\n",
       "      <th>150925</th>\n",
       "      <th>150931</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5753.455556</td>\n",
       "      <td>6253.636364</td>\n",
       "      <td>6245.714286</td>\n",
       "      <td>7727.420455</td>\n",
       "      <td>8863.379310</td>\n",
       "      <td>6390.025641</td>\n",
       "      <td>11349.0</td>\n",
       "      <td>3081.954545</td>\n",
       "      <td>2313.117647</td>\n",
       "      <td>6506.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>3789.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1103.750000</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>2439.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>593.191845</td>\n",
       "      <td>623.375051</td>\n",
       "      <td>771.601643</td>\n",
       "      <td>784.168946</td>\n",
       "      <td>742.336134</td>\n",
       "      <td>692.480183</td>\n",
       "      <td>0.0</td>\n",
       "      <td>927.637165</td>\n",
       "      <td>557.022899</td>\n",
       "      <td>781.822463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.890873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.145707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3699.000000</td>\n",
       "      <td>5499.000000</td>\n",
       "      <td>4399.000000</td>\n",
       "      <td>5399.000000</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>4999.000000</td>\n",
       "      <td>11349.0</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1399.000000</td>\n",
       "      <td>4299.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>3789.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1090.000000</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>2380.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5649.000000</td>\n",
       "      <td>5799.500000</td>\n",
       "      <td>5999.000000</td>\n",
       "      <td>7374.250000</td>\n",
       "      <td>8399.000000</td>\n",
       "      <td>5999.000000</td>\n",
       "      <td>11349.0</td>\n",
       "      <td>2299.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>6049.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>3789.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1090.000000</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>2409.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5899.000000</td>\n",
       "      <td>6199.000000</td>\n",
       "      <td>6499.000000</td>\n",
       "      <td>7999.000000</td>\n",
       "      <td>9449.000000</td>\n",
       "      <td>6899.000000</td>\n",
       "      <td>11349.0</td>\n",
       "      <td>2999.000000</td>\n",
       "      <td>2199.000000</td>\n",
       "      <td>6499.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>3789.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1090.000000</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>2439.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6199.000000</td>\n",
       "      <td>6699.000000</td>\n",
       "      <td>6899.000000</td>\n",
       "      <td>7999.000000</td>\n",
       "      <td>9449.000000</td>\n",
       "      <td>6899.000000</td>\n",
       "      <td>11349.0</td>\n",
       "      <td>4199.000000</td>\n",
       "      <td>2999.000000</td>\n",
       "      <td>7199.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>3789.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1090.000000</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>2469.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6199.000000</td>\n",
       "      <td>7199.000000</td>\n",
       "      <td>6899.000000</td>\n",
       "      <td>8399.000000</td>\n",
       "      <td>9800.000000</td>\n",
       "      <td>6899.000000</td>\n",
       "      <td>11349.0</td>\n",
       "      <td>4199.000000</td>\n",
       "      <td>2999.000000</td>\n",
       "      <td>7199.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>3789.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>2499.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 4203 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "codigo       2            3            4            7            11      \\\n",
       "count     90.000000    11.000000    91.000000    88.000000    87.000000   \n",
       "mean    5753.455556  6253.636364  6245.714286  7727.420455  8863.379310   \n",
       "std      593.191845   623.375051   771.601643   784.168946   742.336134   \n",
       "min     3699.000000  5499.000000  4399.000000  5399.000000  6999.000000   \n",
       "25%     5649.000000  5799.500000  5999.000000  7374.250000  8399.000000   \n",
       "50%     5899.000000  6199.000000  6499.000000  7999.000000  9449.000000   \n",
       "75%     6199.000000  6699.000000  6899.000000  7999.000000  9449.000000   \n",
       "max     6199.000000  7199.000000  6899.000000  8399.000000  9800.000000   \n",
       "\n",
       "codigo       12       13           15           16           17      ...  \\\n",
       "count     78.000000     21.0    88.000000    85.000000    90.000000  ...   \n",
       "mean    6390.025641  11349.0  3081.954545  2313.117647  6506.800000  ...   \n",
       "std      692.480183      0.0   927.637165   557.022899   781.822463  ...   \n",
       "min     4999.000000  11349.0  1599.000000  1399.000000  4299.000000  ...   \n",
       "25%     5999.000000  11349.0  2299.000000  1999.000000  6049.000000  ...   \n",
       "50%     6899.000000  11349.0  2999.000000  2199.000000  6499.000000  ...   \n",
       "75%     6899.000000  11349.0  4199.000000  2999.000000  7199.000000  ...   \n",
       "max     6899.000000  11349.0  4199.000000  2999.000000  7199.000000  ...   \n",
       "\n",
       "codigo  150829  150835  150842  150844  150847       150853  150856  150889  \\\n",
       "count      2.0     8.0     8.0     8.0     9.0     8.000000     6.0     5.0   \n",
       "mean     999.0  4490.0  1765.0  3789.0   899.0  1103.750000  6199.0   845.0   \n",
       "std        0.0     0.0     0.0     0.0     0.0    38.890873     0.0     0.0   \n",
       "min      999.0  4490.0  1765.0  3789.0   899.0  1090.000000  6199.0   845.0   \n",
       "25%      999.0  4490.0  1765.0  3789.0   899.0  1090.000000  6199.0   845.0   \n",
       "50%      999.0  4490.0  1765.0  3789.0   899.0  1090.000000  6199.0   845.0   \n",
       "75%      999.0  4490.0  1765.0  3789.0   899.0  1090.000000  6199.0   845.0   \n",
       "max      999.0  4490.0  1765.0  3789.0   899.0  1200.000000  6199.0   845.0   \n",
       "\n",
       "codigo  150925       150931  \n",
       "count      2.0     2.000000  \n",
       "mean    1299.0  2439.500000  \n",
       "std        0.0    84.145707  \n",
       "min     1299.0  2380.000000  \n",
       "25%     1299.0  2409.750000  \n",
       "50%     1299.0  2439.500000  \n",
       "75%     1299.0  2469.250000  \n",
       "max     1299.0  2499.000000  \n",
       "\n",
       "[8 rows x 4203 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show price's statistics per product\n",
    "prices_per_product = prices.T\n",
    "prices_per_product.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "733a58d3-3893-4ba8-ab97-421f80276a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>codigo</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>7</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
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       "      <th>17</th>\n",
       "      <th>...</th>\n",
       "      <th>150829</th>\n",
       "      <th>150835</th>\n",
       "      <th>150842</th>\n",
       "      <th>150844</th>\n",
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       "      <th>150853</th>\n",
       "      <th>150856</th>\n",
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       "      <th>150925</th>\n",
       "      <th>150931</th>\n",
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       "  </thead>\n",
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       "      <th>2023-12-31</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2024-01-04</th>\n",
       "      <td>-20.407767</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2024-01-07</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.303571</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-11</th>\n",
       "      <td>-9.758478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.335070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.418837</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2024-01-14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2024-10-30</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-03</th>\n",
       "      <td>5.085608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.154793</td>\n",
       "      <td>5.000625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.027211</td>\n",
       "      <td>50.025013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-06</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-10</th>\n",
       "      <td>-4.839490</td>\n",
       "      <td>30.914712</td>\n",
       "      <td>-5.797942</td>\n",
       "      <td>-4.762472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.914712</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-13</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.091743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.761905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 4203 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "codigo         2          3          4          7          11      12      \\\n",
       "2023-12-31        NaN        NaN        NaN        NaN        NaN     NaN   \n",
       "2024-01-04 -20.407767        NaN -17.965812 -19.191781 -18.377551     NaN   \n",
       "2024-01-07   0.000000        NaN   0.000000  -8.476013   0.000000     NaN   \n",
       "2024-01-11  -9.758478        NaN  -8.335070   0.000000   0.000000     NaN   \n",
       "2024-01-14        NaN        NaN   0.000000   0.000000   0.000000     0.0   \n",
       "...               ...        ...        ...        ...        ...     ...   \n",
       "2024-10-30   0.000000        NaN   0.000000        NaN   0.000000     NaN   \n",
       "2024-11-03   5.085608        NaN   6.154793   5.000625   0.000000     NaN   \n",
       "2024-11-06   0.000000   0.000000   0.000000   0.000000   0.000000     NaN   \n",
       "2024-11-10  -4.839490  30.914712  -5.797942  -4.762472        NaN     NaN   \n",
       "2024-11-13   0.000000   0.000000   0.000000   0.000000        NaN     NaN   \n",
       "\n",
       "codigo      13         15         16         17      ...  150829  150835  \\\n",
       "2023-12-31     NaN        NaN        NaN        NaN  ...     NaN     NaN   \n",
       "2024-01-04     NaN        NaN        NaN   0.000000  ...     NaN     NaN   \n",
       "2024-01-07     NaN        NaN        NaN -14.303571  ...     NaN     NaN   \n",
       "2024-01-11     NaN        NaN        NaN -10.418837  ...     NaN     NaN   \n",
       "2024-01-14     NaN   0.000000   0.000000   0.000000  ...     NaN     NaN   \n",
       "...            ...        ...        ...        ...  ...     ...     ...   \n",
       "2024-10-30     NaN   0.000000   0.000000   0.000000  ...     NaN     0.0   \n",
       "2024-11-03     NaN  68.027211  50.025013   0.000000  ...     NaN     0.0   \n",
       "2024-11-06     NaN   0.000000   0.000000   0.000000  ...     NaN     0.0   \n",
       "2024-11-10     NaN   0.000000   0.000000  30.914712  ...     NaN     0.0   \n",
       "2024-11-13     NaN   0.000000        NaN   0.000000  ...     NaN     0.0   \n",
       "\n",
       "codigo      150842  150844  150847     150853  150856  150889  150925  \\\n",
       "2023-12-31     NaN     NaN     NaN        NaN     NaN     NaN     NaN   \n",
       "2024-01-04     NaN     NaN     NaN        NaN     NaN     NaN     NaN   \n",
       "2024-01-07     NaN     NaN     NaN        NaN     NaN     NaN     NaN   \n",
       "2024-01-11     NaN     NaN     NaN        NaN     NaN     NaN     NaN   \n",
       "2024-01-14     NaN     NaN     NaN        NaN     NaN     NaN     NaN   \n",
       "...            ...     ...     ...        ...     ...     ...     ...   \n",
       "2024-10-30     0.0     0.0     0.0   0.000000     0.0     0.0     NaN   \n",
       "2024-11-03     0.0     0.0     0.0   0.000000     0.0     0.0     NaN   \n",
       "2024-11-06     0.0     0.0     0.0   0.000000     0.0     NaN     NaN   \n",
       "2024-11-10     0.0     0.0     0.0   0.000000     0.0     NaN     NaN   \n",
       "2024-11-13     0.0     0.0     0.0  10.091743     0.0     0.0     0.0   \n",
       "\n",
       "codigo        150931  \n",
       "2023-12-31       NaN  \n",
       "2024-01-04       NaN  \n",
       "2024-01-07       NaN  \n",
       "2024-01-11       NaN  \n",
       "2024-01-14       NaN  \n",
       "...              ...  \n",
       "2024-10-30       NaN  \n",
       "2024-11-03       NaN  \n",
       "2024-11-06       NaN  \n",
       "2024-11-10       NaN  \n",
       "2024-11-13 -4.761905  \n",
       "\n",
       "[92 rows x 4203 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# price change per date\n",
    "change_per_date = prices_per_product.pct_change(fill_method=None) * 100\n",
    "change_per_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f37a9765-d205-4dfc-aab4-d015b2b68d5b",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>2023-12-31</th>\n",
       "      <th>2024-01-04</th>\n",
       "      <th>2024-01-07</th>\n",
       "      <th>2024-01-11</th>\n",
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       "      <th>2024-01-31</th>\n",
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       "      <th>2024-10-16</th>\n",
       "      <th>2024-10-20</th>\n",
       "      <th>2024-10-23</th>\n",
       "      <th>2024-10-27</th>\n",
       "      <th>2024-10-30</th>\n",
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       "      <th>2024-11-06</th>\n",
       "      <th>2024-11-10</th>\n",
       "      <th>2024-11-13</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>3049.000000</td>\n",
       "      <td>3002.000000</td>\n",
       "      <td>2948.000000</td>\n",
       "      <td>2969.000000</td>\n",
       "      <td>2901.000000</td>\n",
       "      <td>2922.000000</td>\n",
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       "      <td>2286.000000</td>\n",
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       "      <td>2284.000000</td>\n",
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       "      <td>2330.000000</td>\n",
       "      <td>2371.000000</td>\n",
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       "      <td>2367.000000</td>\n",
       "      <td>2371.000000</td>\n",
       "      <td>2390.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.378456</td>\n",
       "      <td>-0.785705</td>\n",
       "      <td>8.438657</td>\n",
       "      <td>3.891930</td>\n",
       "      <td>2.225437</td>\n",
       "      <td>2.088244</td>\n",
       "      <td>2.612687</td>\n",
       "      <td>-0.185708</td>\n",
       "      <td>0.781613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.176806</td>\n",
       "      <td>0.592999</td>\n",
       "      <td>0.852061</td>\n",
       "      <td>0.348690</td>\n",
       "      <td>0.494281</td>\n",
       "      <td>0.155329</td>\n",
       "      <td>0.676078</td>\n",
       "      <td>0.313529</td>\n",
       "      <td>0.839783</td>\n",
       "      <td>-0.003076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.987697</td>\n",
       "      <td>17.008046</td>\n",
       "      <td>22.316726</td>\n",
       "      <td>16.749631</td>\n",
       "      <td>10.859529</td>\n",
       "      <td>14.541099</td>\n",
       "      <td>10.953936</td>\n",
       "      <td>10.865165</td>\n",
       "      <td>8.816803</td>\n",
       "      <td>...</td>\n",
       "      <td>8.608734</td>\n",
       "      <td>6.799962</td>\n",
       "      <td>9.742348</td>\n",
       "      <td>4.238803</td>\n",
       "      <td>9.807264</td>\n",
       "      <td>3.511615</td>\n",
       "      <td>10.558948</td>\n",
       "      <td>3.967689</td>\n",
       "      <td>7.705256</td>\n",
       "      <td>3.942960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.407767</td>\n",
       "      <td>-42.424242</td>\n",
       "      <td>-42.892857</td>\n",
       "      <td>-42.074776</td>\n",
       "      <td>-40.016006</td>\n",
       "      <td>-50.050000</td>\n",
       "      <td>-35.714286</td>\n",
       "      <td>-64.428571</td>\n",
       "      <td>-63.660899</td>\n",
       "      <td>...</td>\n",
       "      <td>-54.206219</td>\n",
       "      <td>-42.768496</td>\n",
       "      <td>-39.299750</td>\n",
       "      <td>-43.314438</td>\n",
       "      <td>-56.312195</td>\n",
       "      <td>-41.025641</td>\n",
       "      <td>-57.514379</td>\n",
       "      <td>-57.171429</td>\n",
       "      <td>-42.857143</td>\n",
       "      <td>-53.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.846154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.809986</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>61.616162</td>\n",
       "      <td>105.444444</td>\n",
       "      <td>174.413457</td>\n",
       "      <td>124.068966</td>\n",
       "      <td>353.805073</td>\n",
       "      <td>177.570093</td>\n",
       "      <td>168.168168</td>\n",
       "      <td>204.554697</td>\n",
       "      <td>...</td>\n",
       "      <td>126.859504</td>\n",
       "      <td>94.444444</td>\n",
       "      <td>118.370264</td>\n",
       "      <td>70.972424</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>59.980000</td>\n",
       "      <td>138.722644</td>\n",
       "      <td>45.859873</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>41.701418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       2023-12-31  2024-01-04  2024-01-07  2024-01-11   2024-01-14  \\\n",
       "count         0.0   67.000000   53.000000   78.000000  3049.000000   \n",
       "mean          NaN    3.378456   -0.785705    8.438657     3.891930   \n",
       "std           NaN   16.987697   17.008046   22.316726    16.749631   \n",
       "min           NaN  -20.407767  -42.424242  -42.892857   -42.074776   \n",
       "25%           NaN    0.000000   -7.846154    0.000000     0.000000   \n",
       "50%           NaN    0.000000    0.000000    0.000000     0.000000   \n",
       "75%           NaN    0.000000    0.000000   17.809986     0.000000   \n",
       "max           NaN   55.833333   61.616162  105.444444   174.413457   \n",
       "\n",
       "        2024-01-17   2024-01-21   2024-01-24   2024-01-28   2024-01-31  ...  \\\n",
       "count  3002.000000  2948.000000  2969.000000  2901.000000  2922.000000  ...   \n",
       "mean      2.225437     2.088244     2.612687    -0.185708     0.781613  ...   \n",
       "std      10.859529    14.541099    10.953936    10.865165     8.816803  ...   \n",
       "min     -40.016006   -50.050000   -35.714286   -64.428571   -63.660899  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "max     124.068966   353.805073   177.570093   168.168168   204.554697  ...   \n",
       "\n",
       "        2024-10-13   2024-10-16   2024-10-20   2024-10-23   2024-10-27  \\\n",
       "count  2286.000000  2309.000000  2284.000000  2315.000000  2330.000000   \n",
       "mean      0.176806     0.592999     0.852061     0.348690     0.494281   \n",
       "std       8.608734     6.799962     9.742348     4.238803     9.807264   \n",
       "min     -54.206219   -42.768496   -39.299750   -43.314438   -56.312195   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max     126.859504    94.444444   118.370264    70.972424   125.000000   \n",
       "\n",
       "        2024-10-30   2024-11-03   2024-11-06   2024-11-10   2024-11-13  \n",
       "count  2371.000000  2348.000000  2367.000000  2371.000000  2390.000000  \n",
       "mean      0.155329     0.676078     0.313529     0.839783    -0.003076  \n",
       "std       3.511615    10.558948     3.967689     7.705256     3.942960  \n",
       "min     -41.025641   -57.514379   -57.171429   -42.857143   -53.400000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "max      59.980000   138.722644    45.859873   100.000000    41.701418  \n",
       "\n",
       "[8 rows x 92 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show prices' statistics per date\n",
    "change_per_date.T.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "38af1a40-36e0-4538-8a60-c9bac7bdefe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pct_change(key, values, df):\n",
    "    \"Group by dataset, get pct_change from mean\"\n",
    "    return (df\n",
    "     .assign(**{key: values})\n",
    "     .groupby(key)\n",
    "     .mean()\n",
    "     .pct_change(fill_method=None) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bc736954-00e7-49e7-adff-b043a077110d",
   "metadata": {},
   "outputs": [
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       "      <th>codigo</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>33.191157</td>\n",
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       "      <td>19.535476</td>\n",
       "      <td>24.527113</td>\n",
       "      <td>3.125391</td>\n",
       "      <td>10.502100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.571915</td>\n",
       "      <td>12.385951</td>\n",
       "      <td>23.907633</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>9.403411</td>\n",
       "      <td>3.602029</td>\n",
       "      <td>4.512331</td>\n",
       "      <td>10.610266</td>\n",
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       "      <td>13.668372</td>\n",
       "      <td>14.003463</td>\n",
       "      <td>7.672135</td>\n",
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       "      <td>5.178204</td>\n",
       "      <td>5.541880</td>\n",
       "      <td>6.569257</td>\n",
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       "      <td>30.398646</td>\n",
       "      <td>15.186707</td>\n",
       "      <td>6.866352</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05</th>\n",
       "      <td>-1.903070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.535744</td>\n",
       "      <td>2.391301</td>\n",
       "      <td>-6.105677</td>\n",
       "      <td>1.855691</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.196884</td>\n",
       "      <td>2.326664</td>\n",
       "      <td>3.636893</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06</th>\n",
       "      <td>4.215286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.364735</td>\n",
       "      <td>1.099037</td>\n",
       "      <td>1.162360</td>\n",
       "      <td>-2.010353</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.052779</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07</th>\n",
       "      <td>1.332941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.077397</td>\n",
       "      <td>-0.077649</td>\n",
       "      <td>9.328628</td>\n",
       "      <td>6.154793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.011671</td>\n",
       "      <td>31.832651</td>\n",
       "      <td>-6.482382</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.985520</td>\n",
       "      <td>-0.874233</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.704618</td>\n",
       "      <td>3.449465</td>\n",
       "      <td>3.960984</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09</th>\n",
       "      <td>-1.496843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.644216</td>\n",
       "      <td>0.960338</td>\n",
       "      <td>-1.117108</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.698798</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10</th>\n",
       "      <td>-1.105156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.429106</td>\n",
       "      <td>-0.776492</td>\n",
       "      <td>-3.983779</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-26.990553</td>\n",
       "      <td>-22.229632</td>\n",
       "      <td>-5.547307</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11</th>\n",
       "      <td>1.396882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.726735</td>\n",
       "      <td>1.056468</td>\n",
       "      <td>5.325671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.968577</td>\n",
       "      <td>28.583679</td>\n",
       "      <td>-1.295538</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.522936</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 4203 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "codigo      2       3          4          7          11         12      \\\n",
       "Mes                                                                      \n",
       "2023-12        NaN     NaN        NaN        NaN        NaN        NaN   \n",
       "2024-01 -19.194175     NaN -21.004748 -20.257230 -18.377551        NaN   \n",
       "2024-02  33.191157     NaN  19.535476  24.527113   3.125391  10.502100   \n",
       "2024-03   2.217612     NaN   9.403411   3.602029   4.512331  10.610266   \n",
       "2024-04   4.339001     NaN   7.744847   5.178204   5.541880   6.569257   \n",
       "2024-05  -1.903070     NaN  -1.535744   2.391301  -6.105677   1.855691   \n",
       "2024-06   4.215286     NaN   1.364735   1.099037   1.162360  -2.010353   \n",
       "2024-07   1.332941     NaN   3.077397  -0.077649   9.328628   6.154793   \n",
       "2024-08   0.000000     NaN   2.985520  -0.874233   0.000000   0.000000   \n",
       "2024-09  -1.496843     NaN  -0.644216   0.960338  -1.117108   0.000000   \n",
       "2024-10  -1.105156     NaN  -8.429106  -0.776492  -3.983779   0.000000   \n",
       "2024-11   1.396882     NaN   6.726735   1.056468   5.325671        NaN   \n",
       "\n",
       "codigo   13         15         16         17      ...  150829  150835  150842  \\\n",
       "Mes                                               ...                           \n",
       "2023-12     NaN        NaN        NaN        NaN  ...     NaN     NaN     NaN   \n",
       "2024-01     NaN        NaN        NaN -13.904762  ...     NaN     NaN     NaN   \n",
       "2024-02     NaN  13.571915  12.385951  23.907633  ...     NaN     NaN     NaN   \n",
       "2024-03     NaN  13.668372  14.003463   7.672135  ...     NaN     NaN     NaN   \n",
       "2024-04     NaN  30.398646  15.186707   6.866352  ...     NaN     NaN     NaN   \n",
       "2024-05     0.0   6.196884   2.326664   3.636893  ...     NaN     NaN     NaN   \n",
       "2024-06     0.0   0.000000   0.000000   1.052779  ...     NaN     NaN     NaN   \n",
       "2024-07     NaN  35.011671  31.832651  -6.482382  ...     NaN     NaN     NaN   \n",
       "2024-08     NaN   3.704618   3.449465   3.960984  ...     NaN     NaN     NaN   \n",
       "2024-09     0.0   0.000000   0.000000  -2.698798  ...     NaN     NaN     NaN   \n",
       "2024-10     0.0 -26.990553 -22.229632  -5.547307  ...     NaN     NaN     NaN   \n",
       "2024-11     NaN  36.968577  28.583679  -1.295538  ...     NaN     0.0     0.0   \n",
       "\n",
       "codigo   150844  150847    150853  150856  150889  150925  150931  \n",
       "Mes                                                                \n",
       "2023-12     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-01     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-02     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-03     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-04     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-05     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-06     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-07     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-08     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-09     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-10     NaN     NaN       NaN     NaN     NaN     NaN     NaN  \n",
       "2024-11     0.0     0.0  2.522936     0.0     0.0     NaN     NaN  \n",
       "\n",
       "[12 rows x 4203 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change per month\n",
    "\n",
    "key = \"Mes\"\n",
    "values = [column[0:7] for column in prices.columns]\n",
    "change_per_month = pct_change(key, values, prices_per_product)\n",
    "change_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a176c78c-438e-465d-9959-15be9f4588d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Mes</th>\n",
       "      <th>2023-12</th>\n",
       "      <th>2024-01</th>\n",
       "      <th>2024-02</th>\n",
       "      <th>2024-03</th>\n",
       "      <th>2024-04</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>3116.000000</td>\n",
       "      <td>3165.000000</td>\n",
       "      <td>3054.000000</td>\n",
       "      <td>2845.000000</td>\n",
       "      <td>2652.000000</td>\n",
       "      <td>2548.000000</td>\n",
       "      <td>2524.000000</td>\n",
       "      <td>2541.000000</td>\n",
       "      <td>2526.000000</td>\n",
       "      <td>2436.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.666119</td>\n",
       "      <td>8.348681</td>\n",
       "      <td>6.641454</td>\n",
       "      <td>5.329718</td>\n",
       "      <td>2.399522</td>\n",
       "      <td>2.514801</td>\n",
       "      <td>2.727310</td>\n",
       "      <td>3.276916</td>\n",
       "      <td>3.489117</td>\n",
       "      <td>2.312708</td>\n",
       "      <td>1.686112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>27.031032</td>\n",
       "      <td>21.276038</td>\n",
       "      <td>13.862161</td>\n",
       "      <td>12.220355</td>\n",
       "      <td>11.753905</td>\n",
       "      <td>10.050030</td>\n",
       "      <td>9.409572</td>\n",
       "      <td>11.462903</td>\n",
       "      <td>10.061676</td>\n",
       "      <td>9.489604</td>\n",
       "      <td>8.262957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-42.546784</td>\n",
       "      <td>-60.025447</td>\n",
       "      <td>-47.826087</td>\n",
       "      <td>-37.061256</td>\n",
       "      <td>-60.534195</td>\n",
       "      <td>-41.818182</td>\n",
       "      <td>-73.179104</td>\n",
       "      <td>-65.006906</td>\n",
       "      <td>-59.452177</td>\n",
       "      <td>-62.707345</td>\n",
       "      <td>-54.580153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.326777</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.490870</td>\n",
       "      <td>3.544087</td>\n",
       "      <td>3.592085</td>\n",
       "      <td>2.009306</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.682136</td>\n",
       "      <td>1.254216</td>\n",
       "      <td>1.791534</td>\n",
       "      <td>1.912617</td>\n",
       "      <td>1.141957</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>29.171531</td>\n",
       "      <td>12.872996</td>\n",
       "      <td>10.809463</td>\n",
       "      <td>9.239517</td>\n",
       "      <td>4.575163</td>\n",
       "      <td>5.155639</td>\n",
       "      <td>6.128217</td>\n",
       "      <td>5.698692</td>\n",
       "      <td>6.493238</td>\n",
       "      <td>4.870251</td>\n",
       "      <td>4.272780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>87.945434</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>258.333333</td>\n",
       "      <td>190.019002</td>\n",
       "      <td>123.027068</td>\n",
       "      <td>300.667780</td>\n",
       "      <td>113.952325</td>\n",
       "      <td>137.603306</td>\n",
       "      <td>81.340670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Mes    2023-12    2024-01      2024-02      2024-03      2024-04      2024-05  \\\n",
       "count      0.0  72.000000  3116.000000  3165.000000  3054.000000  2845.000000   \n",
       "mean       NaN   9.666119     8.348681     6.641454     5.329718     2.399522   \n",
       "std        NaN  27.031032    21.276038    13.862161    12.220355    11.753905   \n",
       "min        NaN -42.546784   -60.025447   -47.826087   -37.061256   -60.534195   \n",
       "25%        NaN  -9.326777     0.000000     0.000000     0.000000     0.000000   \n",
       "50%        NaN   0.490870     3.544087     3.592085     2.009306     0.000000   \n",
       "75%        NaN  29.171531    12.872996    10.809463     9.239517     4.575163   \n",
       "max        NaN  87.945434   275.000000   168.000000   150.000000   258.333333   \n",
       "\n",
       "Mes        2024-06      2024-07      2024-08      2024-09      2024-10  \\\n",
       "count  2652.000000  2548.000000  2524.000000  2541.000000  2526.000000   \n",
       "mean      2.514801     2.727310     3.276916     3.489117     2.312708   \n",
       "std      10.050030     9.409572    11.462903    10.061676     9.489604   \n",
       "min     -41.818182   -73.179104   -65.006906   -59.452177   -62.707345   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.682136     1.254216     1.791534     1.912617     1.141957   \n",
       "75%       5.155639     6.128217     5.698692     6.493238     4.870251   \n",
       "max     190.019002   123.027068   300.667780   113.952325   137.603306   \n",
       "\n",
       "Mes        2024-11  \n",
       "count  2436.000000  \n",
       "mean      1.686112  \n",
       "std       8.262957  \n",
       "min     -54.580153  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       4.272780  \n",
       "max      81.340670  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show prices' statistics per month\n",
    "change_per_month.T.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2b3ce332-e5b4-4ac4-b894-1b2618f507e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>codigo</th>\n",
       "      <th>2</th>\n",
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       "      <th>150925</th>\n",
       "      <th>150931</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trimestre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2023-4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-1</th>\n",
       "      <td>-0.097087</td>\n",
       "      <td>12.482143</td>\n",
       "      <td>-7.840894</td>\n",
       "      <td>-6.230769</td>\n",
       "      <td>-15.275510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.386676</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-2</th>\n",
       "      <td>15.028781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.100732</td>\n",
       "      <td>17.755402</td>\n",
       "      <td>5.419728</td>\n",
       "      <td>16.947241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.086437</td>\n",
       "      <td>30.691686</td>\n",
       "      <td>23.324501</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-3</th>\n",
       "      <td>2.919272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.241461</td>\n",
       "      <td>1.097464</td>\n",
       "      <td>7.499334</td>\n",
       "      <td>5.519461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.943401</td>\n",
       "      <td>35.955399</td>\n",
       "      <td>-3.337576</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-4</th>\n",
       "      <td>-1.636715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.982819</td>\n",
       "      <td>-0.055779</td>\n",
       "      <td>-3.387564</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-17.745492</td>\n",
       "      <td>-15.773509</td>\n",
       "      <td>-6.266389</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4203 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "codigo        2          3          4          7          11         12      \\\n",
       "Trimestre                                                                     \n",
       "2023-4           NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2024-1     -0.097087  12.482143  -7.840894  -6.230769 -15.275510        NaN   \n",
       "2024-2     15.028781        NaN  20.100732  17.755402   5.419728  16.947241   \n",
       "2024-3      2.919272        NaN   5.241461   1.097464   7.499334   5.519461   \n",
       "2024-4     -1.636715        NaN  -5.982819  -0.055779  -3.387564   0.000000   \n",
       "\n",
       "codigo     13         15         16         17      ...  150829  150835  \\\n",
       "Trimestre                                           ...                   \n",
       "2023-4        NaN        NaN        NaN        NaN  ...     NaN     NaN   \n",
       "2024-1        NaN        NaN        NaN   2.386676  ...     NaN     NaN   \n",
       "2024-2        NaN  52.086437  30.691686  23.324501  ...     NaN     NaN   \n",
       "2024-3        0.0  40.943401  35.955399  -3.337576  ...     NaN     NaN   \n",
       "2024-4        0.0 -17.745492 -15.773509  -6.266389  ...     NaN     NaN   \n",
       "\n",
       "codigo     150842  150844  150847  150853  150856  150889  150925  150931  \n",
       "Trimestre                                                                  \n",
       "2023-4        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2024-1        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2024-2        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2024-3        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2024-4        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 4203 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change per quarter\n",
    "\n",
    "def calc_quarter(date):\n",
    "    y = date[0:4]\n",
    "    m = date[5:7]\n",
    "    q = ((int(m) - 1) // 3) + 1\n",
    "    return f\"{y}-{q}\"\n",
    "\n",
    "key = \"Trimestre\"\n",
    "values = [calc_quarter(date) for date in prices.columns]\n",
    "change_per_quarter = pct_change(key, values, prices_per_product)\n",
    "change_per_quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9f612e44-4260-432c-a7dc-d0432748c4d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Trimestre</th>\n",
       "      <th>2023-4</th>\n",
       "      <th>2024-1</th>\n",
       "      <th>2024-2</th>\n",
       "      <th>2024-3</th>\n",
       "      <th>2024-4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>2733.000000</td>\n",
       "      <td>2578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38.316990</td>\n",
       "      <td>14.339887</td>\n",
       "      <td>8.316215</td>\n",
       "      <td>6.149102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>56.364365</td>\n",
       "      <td>24.413136</td>\n",
       "      <td>13.752499</td>\n",
       "      <td>13.708022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-39.742105</td>\n",
       "      <td>-62.812400</td>\n",
       "      <td>-47.122152</td>\n",
       "      <td>-65.428440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.647929</td>\n",
       "      <td>1.718361</td>\n",
       "      <td>1.097464</td>\n",
       "      <td>0.525895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.049410</td>\n",
       "      <td>9.595403</td>\n",
       "      <td>7.290880</td>\n",
       "      <td>5.247917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>61.807808</td>\n",
       "      <td>22.579153</td>\n",
       "      <td>13.836056</td>\n",
       "      <td>10.519834</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>223.645546</td>\n",
       "      <td>389.141068</td>\n",
       "      <td>152.732910</td>\n",
       "      <td>239.051868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Trimestre  2023-4      2024-1       2024-2       2024-3       2024-4\n",
       "count         0.0   73.000000  3200.000000  2733.000000  2578.000000\n",
       "mean          NaN   38.316990    14.339887     8.316215     6.149102\n",
       "std           NaN   56.364365    24.413136    13.752499    13.708022\n",
       "min           NaN  -39.742105   -62.812400   -47.122152   -65.428440\n",
       "25%           NaN   -0.647929     1.718361     1.097464     0.525895\n",
       "50%           NaN   18.049410     9.595403     7.290880     5.247917\n",
       "75%           NaN   61.807808    22.579153    13.836056    10.519834\n",
       "max           NaN  223.645546   389.141068   152.732910   239.051868"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show prices' statistics per quarter\n",
    "change_per_quarter.T.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "236219db-1357-409b-9514-c2732c339153",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>codigo</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>7</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
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       "      <th>150829</th>\n",
       "      <th>150835</th>\n",
       "      <th>150842</th>\n",
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       "      <th>150853</th>\n",
       "      <th>150856</th>\n",
       "      <th>150889</th>\n",
       "      <th>150925</th>\n",
       "      <th>150931</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Año</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>11.849242</td>\n",
       "      <td>12.839286</td>\n",
       "      <td>6.839506</td>\n",
       "      <td>5.922374</td>\n",
       "      <td>-9.668486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.374799</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 4203 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "codigo     2          3         4         7         11      12      13      \\\n",
       "Año                                                                          \n",
       "2023          NaN        NaN       NaN       NaN       NaN     NaN     NaN   \n",
       "2024    11.849242  12.839286  6.839506  5.922374 -9.668486     NaN     NaN   \n",
       "\n",
       "codigo  15      16         17      ...  150829  150835  150842  150844  \\\n",
       "Año                                ...                                   \n",
       "2023       NaN     NaN        NaN  ...     NaN     NaN     NaN     NaN   \n",
       "2024       NaN     NaN  16.374799  ...     NaN     NaN     NaN     NaN   \n",
       "\n",
       "codigo  150847  150853  150856  150889  150925  150931  \n",
       "Año                                                     \n",
       "2023       NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2024       NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[2 rows x 4203 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change per year\n",
    "\n",
    "key = \"Año\"\n",
    "values = [date[0:4] for date in prices.columns]\n",
    "change_per_year = pct_change(key, values, prices_per_product)\n",
    "change_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "861c088c-106e-404e-aa90-7845c6b50acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Año</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>NaN</td>\n",
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       "      <th>50%</th>\n",
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       "      <th>75%</th>\n",
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      "text/plain": [
       "Año    2023        2024\n",
       "count   0.0   74.000000\n",
       "mean    NaN   90.892787\n",
       "std     NaN  122.609159\n",
       "min     NaN  -12.873882\n",
       "25%     NaN   13.067468\n",
       "50%     NaN   46.788987\n",
       "75%     NaN  115.898631\n",
       "max     NaN  656.363636"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show prices' statistics per year\n",
    "change_per_year.T.describe()"
   ]
  },
  {
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       "Mes                      2023-12  2024-01     2024-02     2024-03     2024-04  \\\n",
       "Categoria                                                                       \n",
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       "\n",
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       "Categoria                                                                   \n",
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       "\n",
       "Mes                          2024-09     2024-10      2024-11  \n",
       "Categoria                                                      \n",
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       "\n",
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       "Trimestre                2023-4  2024-1       2024-2      2024-3        2024-4\n",
       "Categoria                                                                     \n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOGUR</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Año                      2023  2024\n",
       "Categoria                          \n",
       "ACCESORIOS DE VIDRIO      NaN   NaN\n",
       "ACCESORIOS PARA EL MATE   NaN   NaN\n",
       "ACCESORIOS PLÁSTICOS      NaN   NaN\n",
       "ACEITES                   NaN   NaN\n",
       "ACHURAS                   NaN   NaN\n",
       "...                       ...   ...\n",
       "VINOS ROSADOS             NaN   NaN\n",
       "VINOS TINTOS              NaN   NaN\n",
       "WHISKYS                   NaN   NaN\n",
       "YERBAS                    NaN   NaN\n",
       "YOGUR                     NaN   NaN\n",
       "\n",
       "[141 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pct_change(\"Categoria\", categories, change_per_year.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23b758b3-db3a-4cea-8b5e-ffd264f46c76",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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