task2_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2 - Alkemy\n",
    "\n",
    "_Lorenzo Antolini, Enrico Grandi, Simone Lu, Enrico Romano_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from scipy.stats import shapiro\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sd = pd.read_csv('sales_cleaned.csv')\n",
    "# # df_cr = pd.read_csv('clicksreg_cleaned.csv')\n",
    "df_pc = pd.read_csv('product_catalog.csv')\n",
    "# df_cb = pd.read_csv('clicks_bidding.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale_date</th>\n",
       "      <th>product_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>sales_price_tax</th>\n",
       "      <th>regular_price_tax</th>\n",
       "      <th>sales_price</th>\n",
       "      <th>regular_price</th>\n",
       "      <th>purchase_price</th>\n",
       "      <th>profit</th>\n",
       "      <th>revenue</th>\n",
       "      <th>promotion_perc</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>109751</td>\n",
       "      <td>1</td>\n",
       "      <td>2499.0</td>\n",
       "      <td>2777.0</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>2313.8917</td>\n",
       "      <td>1499.4000</td>\n",
       "      <td>582.6000</td>\n",
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       "      <td>47.0000</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>39.246018</td>\n",
       "      <td>1</td>\n",
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       "      <td>792.6000</td>\n",
       "      <td>4992.0</td>\n",
       "      <td>22.969600</td>\n",
       "      <td>1</td>\n",
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       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>114010</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184647</td>\n",
       "      <td>1</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>2878.0</td>\n",
       "      <td>1658.0</td>\n",
       "      <td>2398.1481</td>\n",
       "      <td>1638.0000</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>1658.0</td>\n",
       "      <td>30.863319</td>\n",
       "      <td>12</td>\n",
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       "      <td>4620.3704</td>\n",
       "      <td>3600.0000</td>\n",
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       "      <td>508.0000</td>\n",
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       "      <td>12</td>\n",
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       "<p>114014 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "         sale_date  product_id  quantity  sales_price_tax  regular_price_tax  \\\n",
       "0       2021-01-02      109751         1           2499.0             2777.0   \n",
       "1       2021-01-02      110853         1           3990.0             8322.0   \n",
       "2       2021-01-02      131410         1           2288.0             3767.0   \n",
       "3       2021-01-02      134294         1           4990.0             6666.0   \n",
       "4       2021-01-02      134298         1           5990.0             7777.0   \n",
       "...            ...         ...       ...              ...                ...   \n",
       "114009  2021-12-31      184595         2           3290.0             4433.0   \n",
       "114010  2021-12-31      184647         1           1990.0             2878.0   \n",
       "114011  2021-12-31      184749         1           4990.0             5544.0   \n",
       "114012  2021-12-31      184809         1           2590.0             2878.0   \n",
       "114013  2021-12-31      184860         3          29990.0            38878.0   \n",
       "\n",
       "        sales_price  regular_price  purchase_price     profit  revenue  \\\n",
       "0            2082.0      2313.8917       1499.4000   582.6000   2082.0   \n",
       "1            3325.0      6935.1852       2833.7000   491.3000   3325.0   \n",
       "2            1907.0      3138.8889       1860.0000    47.0000   1907.0   \n",
       "3            4158.0      5554.6296       3599.4024   558.5976   4158.0   \n",
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       "...             ...            ...             ...        ...      ...   \n",
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       "114010       1658.0      2398.1481       1638.0000    20.0000   1658.0   \n",
       "114011       4158.0      4620.3704       3600.0000   558.0000   4158.0   \n",
       "114012       2158.0      2398.1481       1650.0000   508.0000   2158.0   \n",
       "114013      24992.0     32398.1500      22800.0001  2191.9999  74976.0   \n",
       "\n",
       "        promotion_perc  month  \n",
       "0            10.021718      1  \n",
       "1            52.056075      1  \n",
       "2            39.246018      1  \n",
       "3            25.143524      1  \n",
       "4            22.969600      1  \n",
       "...                ...    ...  \n",
       "114009       25.780450     12  \n",
       "114010       30.863319     12  \n",
       "114011       10.007215     12  \n",
       "114012       10.013898     12  \n",
       "114013       22.859793     12  \n",
       "\n",
       "[114014 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sd['sale_date'] = pd.to_datetime(df_sd['sale_date'],errors = 'coerce',format = '%Y-%m-%d')\n",
    "df_sd['profit'] = df_sd['profit']*df_sd['quantity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sd_1 = df_sd.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABC analysis \n",
    "ABC analysis is an inventory management technique that determines the value of inventory items based on their importance to the business. ABC ranks items on demand, cost and risk data, and inventory mangers group items into classes based on those criteria. This helps business leaders understand which products or services are most critical to the financial success of their organization.\n",
    "The most important stock keeping units (SKUs), based on either sales volume or profitability, are \"Class A\" items, the next-most important are Class B and the least important are Class C. Some companies may choose a classification system that breaks products into more than just those three groups (A-F, for example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4158.0</td>\n",
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       "      <td>1650.0000</td>\n",
       "      <td>508.0000</td>\n",
       "      <td>2158.0</td>\n",
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       "      <td>2021-12-31</td>\n",
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       "      <td>6575.9997</td>\n",
       "      <td>74976.0</td>\n",
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      ],
      "text/plain": [
       "        sale_date  product_id  quantity  sales_price_tax  regular_price_tax  \\\n",
       "0      2021-01-02      109751         1           2499.0             2777.0   \n",
       "1      2021-01-02      110853         1           3990.0             8322.0   \n",
       "2      2021-01-02      131410         1           2288.0             3767.0   \n",
       "3      2021-01-02      134294         1           4990.0             6666.0   \n",
       "4      2021-01-02      134298         1           5990.0             7777.0   \n",
       "...           ...         ...       ...              ...                ...   \n",
       "114009 2021-12-31      184595         2           3290.0             4433.0   \n",
       "114010 2021-12-31      184647         1           1990.0             2878.0   \n",
       "114011 2021-12-31      184749         1           4990.0             5544.0   \n",
       "114012 2021-12-31      184809         1           2590.0             2878.0   \n",
       "114013 2021-12-31      184860         3          29990.0            38878.0   \n",
       "\n",
       "        sales_price  regular_price  purchase_price     profit  revenue  \\\n",
       "0            2082.0      2313.8917       1499.4000   582.6000   2082.0   \n",
       "1            3325.0      6935.1852       2833.7000   491.3000   3325.0   \n",
       "2            1907.0      3138.8889       1860.0000    47.0000   1907.0   \n",
       "3            4158.0      5554.6296       3599.4024   558.5976   4158.0   \n",
       "4            4992.0      6480.5583       4199.4000   792.6000   4992.0   \n",
       "...             ...            ...             ...        ...      ...   \n",
       "114009       2742.0      3694.4444       2474.2500   535.5000   5484.0   \n",
       "114010       1658.0      2398.1481       1638.0000    20.0000   1658.0   \n",
       "114011       4158.0      4620.3704       3600.0000   558.0000   4158.0   \n",
       "114012       2158.0      2398.1481       1650.0000   508.0000   2158.0   \n",
       "114013      24992.0     32398.1500      22800.0001  6575.9997  74976.0   \n",
       "\n",
       "        promotion_perc  month  \n",
       "0            10.021718      1  \n",
       "1            52.056075      1  \n",
       "2            39.246018      1  \n",
       "3            25.143524      1  \n",
       "4            22.969600      1  \n",
       "...                ...    ...  \n",
       "114009       25.780450     12  \n",
       "114010       30.863319     12  \n",
       "114011       10.007215     12  \n",
       "114012       10.013898     12  \n",
       "114013       22.859793     12  \n",
       "\n",
       "[114014 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sd_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_withprodcat = pd.merge(df_sd_1,df_pc, how='left', on=['product_id'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale_date</th>\n",
       "      <th>product_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>sales_price_tax</th>\n",
       "      <th>regular_price_tax</th>\n",
       "      <th>sales_price</th>\n",
       "      <th>regular_price</th>\n",
       "      <th>purchase_price</th>\n",
       "      <th>profit</th>\n",
       "      <th>revenue</th>\n",
       "      <th>promotion_perc</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>109751</td>\n",
       "      <td>1</td>\n",
       "      <td>2499.0</td>\n",
       "      <td>2777.0</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>2313.8917</td>\n",
       "      <td>1499.4000</td>\n",
       "      <td>582.6000</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>10.021718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>110853</td>\n",
       "      <td>1</td>\n",
       "      <td>3990.0</td>\n",
       "      <td>8322.0</td>\n",
       "      <td>3325.0</td>\n",
       "      <td>6935.1852</td>\n",
       "      <td>2833.7000</td>\n",
       "      <td>491.3000</td>\n",
       "      <td>3325.0</td>\n",
       "      <td>52.056075</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>131410</td>\n",
       "      <td>1</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>3767.0</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>3138.8889</td>\n",
       "      <td>1860.0000</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>39.246018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>134294</td>\n",
       "      <td>1</td>\n",
       "      <td>4990.0</td>\n",
       "      <td>6666.0</td>\n",
       "      <td>4158.0</td>\n",
       "      <td>5554.6296</td>\n",
       "      <td>3599.4024</td>\n",
       "      <td>558.5976</td>\n",
       "      <td>4158.0</td>\n",
       "      <td>25.143524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>134298</td>\n",
       "      <td>1</td>\n",
       "      <td>5990.0</td>\n",
       "      <td>7777.0</td>\n",
       "      <td>4992.0</td>\n",
       "      <td>6480.5583</td>\n",
       "      <td>4199.4000</td>\n",
       "      <td>792.6000</td>\n",
       "      <td>4992.0</td>\n",
       "      <td>22.969600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sale_date  product_id  quantity  sales_price_tax  regular_price_tax  \\\n",
       "0 2021-01-02      109751         1           2499.0             2777.0   \n",
       "1 2021-01-02      110853         1           3990.0             8322.0   \n",
       "2 2021-01-02      131410         1           2288.0             3767.0   \n",
       "3 2021-01-02      134294         1           4990.0             6666.0   \n",
       "4 2021-01-02      134298         1           5990.0             7777.0   \n",
       "\n",
       "   sales_price  regular_price  purchase_price    profit  revenue  \\\n",
       "0       2082.0      2313.8917       1499.4000  582.6000   2082.0   \n",
       "1       3325.0      6935.1852       2833.7000  491.3000   3325.0   \n",
       "2       1907.0      3138.8889       1860.0000   47.0000   1907.0   \n",
       "3       4158.0      5554.6296       3599.4024  558.5976   4158.0   \n",
       "4       4992.0      6480.5583       4199.4000  792.6000   4992.0   \n",
       "\n",
       "   promotion_perc  month  \n",
       "0       10.021718      1  \n",
       "1       52.056075      1  \n",
       "2       39.246018      1  \n",
       "3       25.143524      1  \n",
       "4       22.969600      1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sd_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables that we need for ABC Analysis :\n",
    "- Product_id;\n",
    "- Quantity;\n",
    "- Revenue;\n",
    "- Stock;\n",
    "- Profit;\n",
    "- Promotion Percentage.\n",
    "\n",
    "So, we need to consider also stock's dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_st = pd.read_csv('stock.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_st['stock_date'] = pd.to_datetime(df_st['stock_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_withstock = pd.merge(df_withprodcat ,df_st, how='inner', left_on = ['sale_date','product_id'],right_on = ['stock_date','product_id'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sale_date            0.0\n",
       "product_id           0.0\n",
       "quantity             0.0\n",
       "sales_price_tax      0.0\n",
       "regular_price_tax    0.0\n",
       "sales_price          0.0\n",
       "regular_price        0.0\n",
       "purchase_price       0.0\n",
       "profit               0.0\n",
       "revenue              0.0\n",
       "promotion_perc       0.0\n",
       "month                0.0\n",
       "coded_cat1           0.0\n",
       "coded_cat2           0.0\n",
       "coded_cat3           0.0\n",
       "coded_brand          0.0\n",
       "coded_name           0.0\n",
       "stock_date           0.0\n",
       "total_stock          0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_withstock.isnull().sum()/ len(df_withstock) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check for duplicate values: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "113204    False\n",
       "113205    False\n",
       "113206    False\n",
       "113207    False\n",
       "113208    False\n",
       "Length: 113209, dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_withstock.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113209, 19)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_withstock.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sale_date', 'product_id', 'quantity', 'sales_price_tax',\n",
       "       'regular_price_tax', 'sales_price', 'regular_price', 'purchase_price',\n",
       "       'profit', 'revenue', 'promotion_perc', 'month', 'coded_cat1',\n",
       "       'coded_cat2', 'coded_cat3', 'coded_brand', 'coded_name', 'stock_date',\n",
       "       'total_stock'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_withstock.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale_date</th>\n",
       "      <th>product_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>profit</th>\n",
       "      <th>revenue</th>\n",
       "      <th>promotion_perc</th>\n",
       "      <th>total_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>109751</td>\n",
       "      <td>1</td>\n",
       "      <td>582.6000</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>10.021718</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>110853</td>\n",
       "      <td>1</td>\n",
       "      <td>491.3000</td>\n",
       "      <td>3325.0</td>\n",
       "      <td>52.056075</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>131410</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>39.246018</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>134294</td>\n",
       "      <td>1</td>\n",
       "      <td>558.5976</td>\n",
       "      <td>4158.0</td>\n",
       "      <td>25.143524</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>134298</td>\n",
       "      <td>1</td>\n",
       "      <td>792.6000</td>\n",
       "      <td>4992.0</td>\n",
       "      <td>22.969600</td>\n",
       "      <td>10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113204</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184592</td>\n",
       "      <td>2</td>\n",
       "      <td>951.5000</td>\n",
       "      <td>6650.0</td>\n",
       "      <td>28.036073</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113205</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184595</td>\n",
       "      <td>2</td>\n",
       "      <td>535.5000</td>\n",
       "      <td>5484.0</td>\n",
       "      <td>25.780450</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113206</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184647</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>1658.0</td>\n",
       "      <td>30.863319</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113207</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184749</td>\n",
       "      <td>1</td>\n",
       "      <td>558.0000</td>\n",
       "      <td>4158.0</td>\n",
       "      <td>10.007215</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113208</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184860</td>\n",
       "      <td>3</td>\n",
       "      <td>6575.9997</td>\n",
       "      <td>74976.0</td>\n",
       "      <td>22.859793</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113209 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sale_date  product_id  quantity     profit  revenue  promotion_perc  \\\n",
       "0      2021-01-02      109751         1   582.6000   2082.0       10.021718   \n",
       "1      2021-01-02      110853         1   491.3000   3325.0       52.056075   \n",
       "2      2021-01-02      131410         1    47.0000   1907.0       39.246018   \n",
       "3      2021-01-02      134294         1   558.5976   4158.0       25.143524   \n",
       "4      2021-01-02      134298         1   792.6000   4992.0       22.969600   \n",
       "...           ...         ...       ...        ...      ...             ...   \n",
       "113204 2021-12-31      184592         2   951.5000   6650.0       28.036073   \n",
       "113205 2021-12-31      184595         2   535.5000   5484.0       25.780450   \n",
       "113206 2021-12-31      184647         1    20.0000   1658.0       30.863319   \n",
       "113207 2021-12-31      184749         1   558.0000   4158.0       10.007215   \n",
       "113208 2021-12-31      184860         3  6575.9997  74976.0       22.859793   \n",
       "\n",
       "        total_stock  \n",
       "0                12  \n",
       "1               302  \n",
       "2                50  \n",
       "3                54  \n",
       "4                10  \n",
       "...             ...  \n",
       "113204           73  \n",
       "113205           79  \n",
       "113206           30  \n",
       "113207          106  \n",
       "113208           31  \n",
       "\n",
       "[113209 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ABC = df_withstock[['sale_date','product_id','quantity','profit','revenue','promotion_perc','total_stock']]\n",
    "df_ABC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's divide the dataset in time windows, in order to analyze the popularity of products in different periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ABC['month'] = df_ABC['sale_date'].dt.month\n",
    "# black_friday = df_ABC[(df_ABC[\"sale_date\"]>=\"2021-11-01\") & (df_ABC['sale_date']<=\"2021-11-30\")]\n",
    "df_ABC[\"BLACKFRIDAY\"] = [0 if x != 11 else 1 for x in df_ABC['sale_date'].dt.month]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale_date</th>\n",
       "      <th>product_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>profit</th>\n",
       "      <th>revenue</th>\n",
       "      <th>promotion_perc</th>\n",
       "      <th>total_stock</th>\n",
       "      <th>month</th>\n",
       "      <th>BLACKFRIDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>109751</td>\n",
       "      <td>1</td>\n",
       "      <td>582.6000</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>10.021718</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>110853</td>\n",
       "      <td>1</td>\n",
       "      <td>491.3000</td>\n",
       "      <td>3325.0</td>\n",
       "      <td>52.056075</td>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>131410</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>39.246018</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>134294</td>\n",
       "      <td>1</td>\n",
       "      <td>558.5976</td>\n",
       "      <td>4158.0</td>\n",
       "      <td>25.143524</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>134298</td>\n",
       "      <td>1</td>\n",
       "      <td>792.6000</td>\n",
       "      <td>4992.0</td>\n",
       "      <td>22.969600</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113204</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184592</td>\n",
       "      <td>2</td>\n",
       "      <td>951.5000</td>\n",
       "      <td>6650.0</td>\n",
       "      <td>28.036073</td>\n",
       "      <td>73</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113205</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184595</td>\n",
       "      <td>2</td>\n",
       "      <td>535.5000</td>\n",
       "      <td>5484.0</td>\n",
       "      <td>25.780450</td>\n",
       "      <td>79</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113206</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184647</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>1658.0</td>\n",
       "      <td>30.863319</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113207</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184749</td>\n",
       "      <td>1</td>\n",
       "      <td>558.0000</td>\n",
       "      <td>4158.0</td>\n",
       "      <td>10.007215</td>\n",
       "      <td>106</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113208</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>184860</td>\n",
       "      <td>3</td>\n",
       "      <td>6575.9997</td>\n",
       "      <td>74976.0</td>\n",
       "      <td>22.859793</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113209 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sale_date  product_id  quantity     profit  revenue  promotion_perc  \\\n",
       "0      2021-01-02      109751         1   582.6000   2082.0       10.021718   \n",
       "1      2021-01-02      110853         1   491.3000   3325.0       52.056075   \n",
       "2      2021-01-02      131410         1    47.0000   1907.0       39.246018   \n",
       "3      2021-01-02      134294         1   558.5976   4158.0       25.143524   \n",
       "4      2021-01-02      134298         1   792.6000   4992.0       22.969600   \n",
       "...           ...         ...       ...        ...      ...             ...   \n",
       "113204 2021-12-31      184592         2   951.5000   6650.0       28.036073   \n",
       "113205 2021-12-31      184595         2   535.5000   5484.0       25.780450   \n",
       "113206 2021-12-31      184647         1    20.0000   1658.0       30.863319   \n",
       "113207 2021-12-31      184749         1   558.0000   4158.0       10.007215   \n",
       "113208 2021-12-31      184860         3  6575.9997  74976.0       22.859793   \n",
       "\n",
       "        total_stock  month  BLACKFRIDAY  \n",
       "0                12      1            0  \n",
       "1               302      1            0  \n",
       "2                50      1            0  \n",
       "3                54      1            0  \n",
       "4                10      1            0  \n",
       "...             ...    ...          ...  \n",
       "113204           73     12            0  \n",
       "113205           79     12            0  \n",
       "113206           30     12            0  \n",
       "113207          106     12            0  \n",
       "113208           31     12            0  \n",
       "\n",
       "[113209 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ABC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ABC = df_ABC.groupby(['product_id','month']).agg(\n",
    "                                                                    #orders done every day \n",
    "                                                                    orders_x_day=('sale_date', 'count'),\n",
    "                                                                    #stock level per product\n",
    "                                                                    total_stock=('total_stock', 'sum'),\n",
    "                                                                    #sum of quantity sold totally by product\n",
    "                                                                    quantity=('quantity', 'sum'),\n",
    "                                                                    #sum of revenue \n",
    "                                                                    revenue=('revenue', 'sum'),\n",
    "                                                                    #promotion percentage, reg price -sale price / reg price\n",
    "                                                                    promotion_perc = ('promotion_perc', 'mean'), \n",
    "                                                                    #profit \n",
    "                                                                    profit = ('profit', 'sum')).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis per quarters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>month</th>\n",
       "      <th>orders_x_day</th>\n",
       "      <th>total_stock</th>\n",
       "      <th>quantity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>promotion_perc</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100043</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "      <td>1</td>\n",
       "      <td>382.0</td>\n",
       "      <td>10.117647</td>\n",
       "      <td>112.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100043</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>764.0</td>\n",
       "      <td>10.117647</td>\n",
       "      <td>224.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100043</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>1</td>\n",
       "      <td>408.0</td>\n",
       "      <td>10.073469</td>\n",
       "      <td>138.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100044</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>491.0</td>\n",
       "      <td>9.968834</td>\n",
       "      <td>141.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100044</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>437.0</td>\n",
       "      <td>19.870972</td>\n",
       "      <td>87.0000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38798</th>\n",
       "      <td>184596</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>311</td>\n",
       "      <td>9</td>\n",
       "      <td>29925.0</td>\n",
       "      <td>28.036073</td>\n",
       "      <td>4281.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38799</th>\n",
       "      <td>184647</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>210</td>\n",
       "      <td>7</td>\n",
       "      <td>11606.0</td>\n",
       "      <td>30.863319</td>\n",
       "      <td>140.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38800</th>\n",
       "      <td>184749</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>372</td>\n",
       "      <td>5</td>\n",
       "      <td>20790.0</td>\n",
       "      <td>10.007215</td>\n",
       "      <td>2790.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38801</th>\n",
       "      <td>184809</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "      <td>12</td>\n",
       "      <td>25056.0</td>\n",
       "      <td>12.015442</td>\n",
       "      <td>5256.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38802</th>\n",
       "      <td>184860</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>680</td>\n",
       "      <td>21</td>\n",
       "      <td>528998.0</td>\n",
       "      <td>22.216855</td>\n",
       "      <td>50197.9985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38803 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_id  month  orders_x_day  total_stock  quantity   revenue  \\\n",
       "0          100043      1             1          194         1     382.0   \n",
       "1          100043      3             2          300         2     764.0   \n",
       "2          100043     12             1          157         1     408.0   \n",
       "3          100044      2             1          135         1     491.0   \n",
       "4          100044      3             1          120         1     437.0   \n",
       "...           ...    ...           ...          ...       ...       ...   \n",
       "38798      184596     12             4          311         9   29925.0   \n",
       "38799      184647     12             4          210         7   11606.0   \n",
       "38800      184749     12             3          372         5   20790.0   \n",
       "38801      184809     12             5           44        12   25056.0   \n",
       "38802      184860     12            10          680        21  528998.0   \n",
       "\n",
       "       promotion_perc      profit  \n",
       "0           10.117647    112.0000  \n",
       "1           10.117647    224.0000  \n",
       "2           10.073469    138.0000  \n",
       "3            9.968834    141.0000  \n",
       "4           19.870972     87.0000  \n",
       "...               ...         ...  \n",
       "38798       28.036073   4281.7500  \n",
       "38799       30.863319    140.0000  \n",
       "38800       10.007215   2790.0000  \n",
       "38801       12.015442   5256.0000  \n",
       "38802       22.216855  50197.9985  \n",
       "\n",
       "[38803 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ABC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a product dataset that includes total revenue we can create the product ABC classification. We’ll construct a Python function to handle this for us. \n",
    "\n",
    "The function works by sorting the Pandas dataframe of customers by the metric column (i.e. revenue) so that the product that is sold the most are at the top of the list. It then calculates the cumulative sum of the total revenue and the running percentage total. It assigns:\n",
    "- <b>Class A</b>, _to all customers where the cumulative percentage is up to 80%_\n",
    "- <b>Class B</b>, _to those where the cumulative percentage contribution is between 80% and 90%_\n",
    "- <b>Class C</b>, _to the rest._\n",
    "\n",
    "The function returns both the ABC class for each customer and its rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def abc(df, metric_column, abc_class_name='class'):\n",
    "    \"\"\"Assign an ABC class and rank to a metric based on cumulative percentage contribution. \n",
    "    \n",
    "    Args:\n",
    "        df: Pandas dataframe containing data. \n",
    "        metric_column (string): Name of column containing metric to calculate. \n",
    "        abc_class_name (string, optional): Name to assign to class column. \n",
    "    \n",
    "    Return:\n",
    "        Pandas dataframe containing original data, plus the metric class and rank. \n",
    "    \"\"\"\n",
    "    \n",
    "    def _abc_segment(percentage):\n",
    "        \"\"\"Assign an ABC segment based on cumulative percentage contribution.\n",
    "        Args:\n",
    "            percentage (float): Cumulative percentage of ranked metric.\n",
    "        Returns:\n",
    "            segments: Pandas DataFrame\n",
    "        \"\"\"\n",
    "\n",
    "        if 0 < percentage <= 80:\n",
    "            return 'A'\n",
    "        elif 80 < percentage <= 90:\n",
    "            return 'B'\n",
    "        else:\n",
    "            return 'C'    \n",
    "    \n",
    "    data = df.sort_values(by=metric_column, ascending=False)\n",
    "    data[metric_column+'_sum'] = data[metric_column].sum()\n",
    "    data[metric_column+'_cumsum'] = data[metric_column].cumsum()\n",
    "    data[metric_column+'_running_pc'] = (data[metric_column+'_cumsum'] / data[metric_column+'_sum']) * 100\n",
    "    data[abc_class_name] = data[metric_column+'_running_pc'].apply(_abc_segment)\n",
    "    data[abc_class_name+'_rank'] = data[metric_column+'_running_pc'].rank().astype(int)\n",
    "    data.drop([metric_column+'_sum', metric_column+'_cumsum', metric_column+'_running_pc'], axis=1, inplace=True)\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can use the function to segment our products. We ’ll pass in the Pandas dataframe containing the products and we’ll define the metric column as the revenue, and we’ll call the ABC class abc_class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ABC_metric = pd.DataFrame()\n",
    "for month in set(df_ABC['month']):\n",
    "    df_metric_1 = pd.DataFrame()\n",
    "    df_metric_2 = pd.DataFrame()\n",
    "    df_classrev = pd.DataFrame()\n",
    "    df_classprof = pd.DataFrame()\n",
    "    df_classqt = pd.DataFrame()\n",
    "    #select quarter\n",
    "    df_quarter = df_ABC[df_ABC[\"month\"]==month]\n",
    "    #perform abc analysis revenues\n",
    "    df_classrev = abc(df_quarter, 'revenue', 'abc_class')[['product_id',\"total_stock\",\"quantity\",\"revenue\",'month','abc_class','abc_class_rank']]\n",
    "    df_classrev.columns = ['product_id',\"total_stock\",\"quantity\",\"revenue\",'month','class_revenue', 'class_rank_revenue']\n",
    "    #perform abc analysis profit\n",
    "    df_classprof = abc(df_quarter, 'profit', 'abc_class')[['product_id',\"total_stock\",\"quantity\",\"revenue\",'month','abc_class','abc_class_rank']]\n",
    "    df_classprof.columns = ['product_id',\"total_stock\",\"quantity\",\"revenue\",'month','class_profit', 'class_rank_profit']\n",
    "    #perform abc analysis quantity\n",
    "    df_classqt = abc(df_quarter, 'quantity', 'abc_class')[['product_id',\"total_stock\",\"quantity\",\"revenue\",'month','abc_class','abc_class_rank']]\n",
    "    df_classqt.columns = ['product_id',\"total_stock\",\"quantity\",\"revenue\",'month','class_quantity', 'class_rank_quantity']\n",
    "    #merge the metrics\n",
    "    df_metric_1 = pd.merge(df_classprof,df_classrev, how = 'left', on = ['product_id','month',])\n",
    "    df_metric_2 = pd.merge(df_metric_1,df_classqt, how = 'left', on = ['product_id','month',])\n",
    "    #we transformed the class rank profit with a power of -1, in order to give high value to rank 1 (best product) and fewer value to the last product(rank>5000)\n",
    "    df_metric_2['P_rank_norm'] = (((df_metric_2['class_rank_profit']).astype(float))**(-1))*100\n",
    "    df_metric_2['R_rank_norm'] = (((df_metric_2['class_rank_revenue']).astype(float))**(-1))*100\n",
    "    df_metric_2['Q_rank_norm'] = (((df_metric_2['class_rank_quantity']).astype(float))**(-1))*100\n",
    "    #so then we combine the three value with the formula 0,35*profit+0,05*revenue+0,6*quantity, in order to take information from margin and demand\n",
    "    df_metric_2['Pop_Index'] = ((0.35*df_metric_2['P_rank_norm'])+(0.05*df_metric_2['R_rank_norm'])+(0.60*df_metric_2['Q_rank_norm']))\n",
    "\n",
    "\n",
    "    df_ABC_metric = df_ABC_metric.append(df_metric_2)\n",
    "    df_ABC_metric = df_ABC_metric[['product_id', 'month',\n",
    "       'class_profit', 'class_rank_profit',  \n",
    "       'class_revenue', 'class_rank_revenue', 'total_stock',\n",
    "       'quantity', 'revenue', 'class_quantity', 'class_rank_quantity',\n",
    "       'P_rank_norm', 'R_rank_norm', 'Q_rank_norm', 'Pop_Index']]\n",
    "    #df_ABC_metric = df_ABC_metric.groupby([\"product_id\",'quarter']).first().reset_index().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>month</th>\n",
       "      <th>class_profit</th>\n",
       "      <th>class_rank_profit</th>\n",
       "      <th>class_revenue</th>\n",
       "      <th>class_rank_revenue</th>\n",
       "      <th>total_stock</th>\n",
       "      <th>quantity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>class_quantity</th>\n",
       "      <th>class_rank_quantity</th>\n",
       "      <th>P_rank_norm</th>\n",
       "      <th>R_rank_norm</th>\n",
       "      <th>Q_rank_norm</th>\n",
       "      <th>Pop_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>177925</td>\n",
       "      <td>12</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
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       "      <td>2059522.0</td>\n",
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       "      <td>50.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>78.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110853</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
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       "      <td>482125.0</td>\n",
       "      <td>A</td>\n",
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       "      <td>6.250000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>69.062500</td>\n",
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       "      <th>0</th>\n",
       "      <td>160649</td>\n",
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       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>67.500000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110853</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>6</td>\n",
       "      <td>A</td>\n",
       "      <td>13</td>\n",
       "      <td>9127</td>\n",
       "      <td>128</td>\n",
       "      <td>402171.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>7.692308</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>66.217949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3271</th>\n",
       "      <td>112582</td>\n",
       "      <td>6</td>\n",
       "      <td>C</td>\n",
       "      <td>857</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>17346</td>\n",
       "      <td>244</td>\n",
       "      <td>4504284.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.116686</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>65.040840</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2985</th>\n",
       "      <td>153830</td>\n",
       "      <td>12</td>\n",
       "      <td>C</td>\n",
       "      <td>3417</td>\n",
       "      <td>C</td>\n",
       "      <td>3551</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>332.0</td>\n",
       "      <td>C</td>\n",
       "      <td>3585</td>\n",
       "      <td>0.029265</td>\n",
       "      <td>0.028161</td>\n",
       "      <td>0.027894</td>\n",
       "      <td>0.028387</td>\n",
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       "    <tr>\n",
       "      <th>3073</th>\n",
       "      <td>159960</td>\n",
       "      <td>12</td>\n",
       "      <td>C</td>\n",
       "      <td>3564</td>\n",
       "      <td>C</td>\n",
       "      <td>3323</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1242.0</td>\n",
       "      <td>C</td>\n",
       "      <td>3520</td>\n",
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       "      <td>0.030093</td>\n",
       "      <td>0.028409</td>\n",
       "      <td>0.028371</td>\n",
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       "    <tr>\n",
       "      <th>3112</th>\n",
       "      <td>154108</td>\n",
       "      <td>12</td>\n",
       "      <td>C</td>\n",
       "      <td>3567</td>\n",
       "      <td>C</td>\n",
       "      <td>3336</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1182.0</td>\n",
       "      <td>C</td>\n",
       "      <td>3521</td>\n",
       "      <td>0.028035</td>\n",
       "      <td>0.029976</td>\n",
       "      <td>0.028401</td>\n",
       "      <td>0.028352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3157</th>\n",
       "      <td>155850</td>\n",
       "      <td>11</td>\n",
       "      <td>C</td>\n",
       "      <td>3561</td>\n",
       "      <td>C</td>\n",
       "      <td>3498</td>\n",
       "      <td>281</td>\n",
       "      <td>1</td>\n",
       "      <td>516.0</td>\n",
       "      <td>C</td>\n",
       "      <td>3520</td>\n",
       "      <td>0.028082</td>\n",
       "      <td>0.028588</td>\n",
       "      <td>0.028409</td>\n",
       "      <td>0.028304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>135000</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>3557</td>\n",
       "      <td>C</td>\n",
       "      <td>3399</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>832.0</td>\n",
       "      <td>C</td>\n",
       "      <td>3553</td>\n",
       "      <td>0.028114</td>\n",
       "      <td>0.029420</td>\n",
       "      <td>0.028145</td>\n",
       "      <td>0.028198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38803 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      product_id  month class_profit  class_rank_profit class_revenue  \\\n",
       "1         177925     12            A                  2             A   \n",
       "3         110853      1            A                  4             A   \n",
       "0         160649      7            A                  1             A   \n",
       "5         110853      2            A                  6             A   \n",
       "3271      112582      6            C                857             A   \n",
       "...          ...    ...          ...                ...           ...   \n",
       "2985      153830     12            C               3417             C   \n",
       "3073      159960     12            C               3564             C   \n",
       "3112      154108     12            C               3567             C   \n",
       "3157      155850     11            C               3561             C   \n",
       "2983      135000      3            C               3557             C   \n",
       "\n",
       "      class_rank_revenue  total_stock  quantity    revenue class_quantity  \\\n",
       "1                      4        28317       264  2059522.0              A   \n",
       "3                     16         3792       145   482125.0              A   \n",
       "0                      2         2536       184  2595634.0              A   \n",
       "5                     13         9127       128   402171.0              A   \n",
       "3271                   1        17346       244  4504284.0              A   \n",
       "...                  ...          ...       ...        ...            ...   \n",
       "2985                3551           30         1      332.0              C   \n",
       "3073                3323            3         1     1242.0              C   \n",
       "3112                3336           31         1     1182.0              C   \n",
       "3157                3498          281         1      516.0              C   \n",
       "2983                3399            6         1      832.0              C   \n",
       "\n",
       "      class_rank_quantity  P_rank_norm  R_rank_norm  Q_rank_norm  Pop_Index  \n",
       "1                       1    50.000000    25.000000   100.000000  78.750000  \n",
       "3                       1    25.000000     6.250000   100.000000  69.062500  \n",
       "0                       2   100.000000    50.000000    50.000000  67.500000  \n",
       "5                       1    16.666667     7.692308   100.000000  66.217949  \n",
       "3271                    1     0.116686   100.000000   100.000000  65.040840  \n",
       "...                   ...          ...          ...          ...        ...  \n",
       "2985                 3585     0.029265     0.028161     0.027894   0.028387  \n",
       "3073                 3520     0.028058     0.030093     0.028409   0.028371  \n",
       "3112                 3521     0.028035     0.029976     0.028401   0.028352  \n",
       "3157                 3520     0.028082     0.028588     0.028409   0.028304  \n",
       "2983                 3553     0.028114     0.029420     0.028145   0.028198  \n",
       "\n",
       "[38803 rows x 15 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ABC_metric = df_ABC_metric.sort_values(by='Pop_Index',ascending=False)\n",
    "df_ABC_metric\n",
    "\n",
    "# how do they vary based on the quarter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
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       "      <th>class_rank_revenue</th>\n",
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       "      <th>revenue</th>\n",
       "      <th>class_rank_quantity</th>\n",
       "      <th>P_rank_norm</th>\n",
       "      <th>R_rank_norm</th>\n",
       "      <th>Q_rank_norm</th>\n",
       "      <th>Pop_Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_profit</th>\n",
       "      <th>class_quantity</th>\n",
       "      <th>class_revenue</th>\n",
       "      <th>month</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 rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>6</th>\n",
       "      <td>148187.972851</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>240.194570</td>\n",
       "      <td>1076.493213</td>\n",
       "      <td>13.339367</td>\n",
       "      <td>237972.613122</td>\n",
       "      <td>444.438914</td>\n",
       "      <td>2.684000</td>\n",
       "      <td>1.434274</td>\n",
       "      <td>1.247218</td>\n",
       "      <td>1.759444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>147551.888136</td>\n",
       "      <td>169.437288</td>\n",
       "      <td>248.559322</td>\n",
       "      <td>848.427119</td>\n",
       "      <td>15.684746</td>\n",
       "      <td>232470.633898</td>\n",
       "      <td>442.030508</td>\n",
       "      <td>2.088654</td>\n",
       "      <td>1.440903</td>\n",
       "      <td>1.350359</td>\n",
       "      <td>1.613289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141819.716088</td>\n",
       "      <td>195.690852</td>\n",
       "      <td>312.851735</td>\n",
       "      <td>1390.728707</td>\n",
       "      <td>16.340694</td>\n",
       "      <td>198276.255521</td>\n",
       "      <td>506.466877</td>\n",
       "      <td>1.935504</td>\n",
       "      <td>1.477651</td>\n",
       "      <td>1.386872</td>\n",
       "      <td>1.583432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>154715.591463</td>\n",
       "      <td>188.381098</td>\n",
       "      <td>293.817073</td>\n",
       "      <td>1850.454268</td>\n",
       "      <td>19.506098</td>\n",
       "      <td>268618.455793</td>\n",
       "      <td>484.012195</td>\n",
       "      <td>1.910869</td>\n",
       "      <td>1.658393</td>\n",
       "      <td>1.357075</td>\n",
       "      <td>1.565969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>140961.910082</td>\n",
       "      <td>213.354223</td>\n",
       "      <td>245.422343</td>\n",
       "      <td>979.051771</td>\n",
       "      <td>9.942779</td>\n",
       "      <td>144726.519074</td>\n",
       "      <td>436.425068</td>\n",
       "      <td>1.737238</td>\n",
       "      <td>1.426300</td>\n",
       "      <td>1.429217</td>\n",
       "      <td>1.536879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\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",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">C</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">C</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">C</th>\n",
       "      <th>8</th>\n",
       "      <td>140650.538375</td>\n",
       "      <td>2323.024831</td>\n",
       "      <td>2459.041761</td>\n",
       "      <td>79.959368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5134.545147</td>\n",
       "      <td>2720.981941</td>\n",
       "      <td>0.048219</td>\n",
       "      <td>0.043610</td>\n",
       "      <td>0.037291</td>\n",
       "      <td>0.041432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>136832.476239</td>\n",
       "      <td>2450.486350</td>\n",
       "      <td>2561.348837</td>\n",
       "      <td>85.849343</td>\n",
       "      <td>1.033367</td>\n",
       "      <td>4613.139535</td>\n",
       "      <td>2740.380182</td>\n",
       "      <td>0.044737</td>\n",
       "      <td>0.041478</td>\n",
       "      <td>0.037176</td>\n",
       "      <td>0.040038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>140493.313926</td>\n",
       "      <td>2466.564910</td>\n",
       "      <td>2651.142408</td>\n",
       "      <td>149.579072</td>\n",
       "      <td>1.276161</td>\n",
       "      <td>6511.381589</td>\n",
       "      <td>2851.623131</td>\n",
       "      <td>0.046375</td>\n",
       "      <td>0.040718</td>\n",
       "      <td>0.035898</td>\n",
       "      <td>0.039806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135251.328056</td>\n",
       "      <td>2540.194371</td>\n",
       "      <td>2669.497801</td>\n",
       "      <td>114.800352</td>\n",
       "      <td>1.279683</td>\n",
       "      <td>5789.156552</td>\n",
       "      <td>2876.104661</td>\n",
       "      <td>0.044507</td>\n",
       "      <td>0.040106</td>\n",
       "      <td>0.035497</td>\n",
       "      <td>0.038881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>141029.370464</td>\n",
       "      <td>2535.388186</td>\n",
       "      <td>2671.875949</td>\n",
       "      <td>104.111392</td>\n",
       "      <td>1.174684</td>\n",
       "      <td>6340.896203</td>\n",
       "      <td>2886.943460</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.040253</td>\n",
       "      <td>0.035351</td>\n",
       "      <td>0.038755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>318 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    product_id  \\\n",
       "class_profit class_quantity class_revenue month                  \n",
       "A            A              A             6      148187.972851   \n",
       "                                          7      147551.888136   \n",
       "                                          3      141819.716088   \n",
       "                                          12     154715.591463   \n",
       "                                          1      140961.910082   \n",
       "...                                                        ...   \n",
       "C            C              C             8      140650.538375   \n",
       "                                          4      136832.476239   \n",
       "                                          12     140493.313926   \n",
       "                                          3      135251.328056   \n",
       "                                          11     141029.370464   \n",
       "\n",
       "                                                 class_rank_profit  \\\n",
       "class_profit class_quantity class_revenue month                      \n",
       "A            A              A             6             118.000000   \n",
       "                                          7             169.437288   \n",
       "                                          3             195.690852   \n",
       "                                          12            188.381098   \n",
       "                                          1             213.354223   \n",
       "...                                                            ...   \n",
       "C            C              C             8            2323.024831   \n",
       "                                          4            2450.486350   \n",
       "                                          12           2466.564910   \n",
       "                                          3            2540.194371   \n",
       "                                          11           2535.388186   \n",
       "\n",
       "                                                 class_rank_revenue  \\\n",
       "class_profit class_quantity class_revenue month                       \n",
       "A            A              A             6              240.194570   \n",
       "                                          7              248.559322   \n",
       "                                          3              312.851735   \n",
       "                                          12             293.817073   \n",
       "                                          1              245.422343   \n",
       "...                                                             ...   \n",
       "C            C              C             8             2459.041761   \n",
       "                                          4             2561.348837   \n",
       "                                          12            2651.142408   \n",
       "                                          3             2669.497801   \n",
       "                                          11            2671.875949   \n",
       "\n",
       "                                                 total_stock   quantity  \\\n",
       "class_profit class_quantity class_revenue month                           \n",
       "A            A              A             6      1076.493213  13.339367   \n",
       "                                          7       848.427119  15.684746   \n",
       "                                          3      1390.728707  16.340694   \n",
       "                                          12     1850.454268  19.506098   \n",
       "                                          1       979.051771   9.942779   \n",
       "...                                                      ...        ...   \n",
       "C            C              C             8        79.959368   1.000000   \n",
       "                                          4        85.849343   1.033367   \n",
       "                                          12      149.579072   1.276161   \n",
       "                                          3       114.800352   1.279683   \n",
       "                                          11      104.111392   1.174684   \n",
       "\n",
       "                                                       revenue  \\\n",
       "class_profit class_quantity class_revenue month                  \n",
       "A            A              A             6      237972.613122   \n",
       "                                          7      232470.633898   \n",
       "                                          3      198276.255521   \n",
       "                                          12     268618.455793   \n",
       "                                          1      144726.519074   \n",
       "...                                                        ...   \n",
       "C            C              C             8        5134.545147   \n",
       "                                          4        4613.139535   \n",
       "                                          12       6511.381589   \n",
       "                                          3        5789.156552   \n",
       "                                          11       6340.896203   \n",
       "\n",
       "                                                 class_rank_quantity  \\\n",
       "class_profit class_quantity class_revenue month                        \n",
       "A            A              A             6               444.438914   \n",
       "                                          7               442.030508   \n",
       "                                          3               506.466877   \n",
       "                                          12              484.012195   \n",
       "                                          1               436.425068   \n",
       "...                                                              ...   \n",
       "C            C              C             8              2720.981941   \n",
       "                                          4              2740.380182   \n",
       "                                          12             2851.623131   \n",
       "                                          3              2876.104661   \n",
       "                                          11             2886.943460   \n",
       "\n",
       "                                                 P_rank_norm  R_rank_norm  \\\n",
       "class_profit class_quantity class_revenue month                             \n",
       "A            A              A             6         2.684000     1.434274   \n",
       "                                          7         2.088654     1.440903   \n",
       "                                          3         1.935504     1.477651   \n",
       "                                          12        1.910869     1.658393   \n",
       "                                          1         1.737238     1.426300   \n",
       "...                                                      ...          ...   \n",
       "C            C              C             8         0.048219     0.043610   \n",
       "                                          4         0.044737     0.041478   \n",
       "                                          12        0.046375     0.040718   \n",
       "                                          3         0.044507     0.040106   \n",
       "                                          11        0.044378     0.040253   \n",
       "\n",
       "                                                 Q_rank_norm  Pop_Index  \n",
       "class_profit class_quantity class_revenue month                          \n",
       "A            A              A             6         1.247218   1.759444  \n",
       "                                          7         1.350359   1.613289  \n",
       "                                          3         1.386872   1.583432  \n",
       "                                          12        1.357075   1.565969  \n",
       "                                          1         1.429217   1.536879  \n",
       "...                                                      ...        ...  \n",
       "C            C              C             8         0.037291   0.041432  \n",
       "                                          4         0.037176   0.040038  \n",
       "                                          12        0.035898   0.039806  \n",
       "                                          3         0.035497   0.038881  \n",
       "                                          11        0.035351   0.038755  \n",
       "\n",
       "[318 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ABC_metric_quarter = df_ABC_metric.groupby(['class_profit','class_quantity','class_revenue',\"month\"]).mean().sort_values(by='Pop_Index',ascending=False)\n",
    "df_ABC_metric_quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 318 entries, ('A', 'A', 'A', 6) to ('C', 'C', 'C', 11)\n",
      "Data columns (total 11 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   product_id           318 non-null    float64\n",
      " 1   class_rank_profit    318 non-null    float64\n",
      " 2   class_rank_revenue   318 non-null    float64\n",
      " 3   total_stock          318 non-null    float64\n",
      " 4   quantity             318 non-null    float64\n",
      " 5   revenue              318 non-null    float64\n",
      " 6   class_rank_quantity  318 non-null    float64\n",
      " 7   P_rank_norm          318 non-null    float64\n",
      " 8   R_rank_norm          318 non-null    float64\n",
      " 9   Q_rank_norm          318 non-null    float64\n",
      " 10  Pop_Index            318 non-null    float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 29.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_ABC_metric_quarter.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# top10 = df_ABC_metric_quarter.head(10)\n",
    "# top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# top10 = top10.reset_index()\n",
    "# top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_profit</th>\n",
       "      <th>class_quantity</th>\n",
       "      <th>class_revenue</th>\n",
       "      <th>month</th>\n",
       "      <th>product_id</th>\n",
       "      <th>total_stock</th>\n",
       "      <th>quantity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>P_rank_norm</th>\n",
       "      <th>R_rank_norm</th>\n",
       "      <th>Q_rank_norm</th>\n",
       "      <th>Pop_Index</th>\n",
       "      <th>CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>367</td>\n",
       "      <td>359312</td>\n",
       "      <td>3649</td>\n",
       "      <td>53114632.5</td>\n",
       "      <td>1.737238</td>\n",
       "      <td>1.426300</td>\n",
       "      <td>1.429217</td>\n",
       "      <td>1.536879</td>\n",
       "      <td>AAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>386</td>\n",
       "      <td>313450</td>\n",
       "      <td>3097</td>\n",
       "      <td>49788811.5</td>\n",
       "      <td>1.658606</td>\n",
       "      <td>1.503681</td>\n",
       "      <td>1.192628</td>\n",
       "      <td>1.371273</td>\n",
       "      <td>AAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>317</td>\n",
       "      <td>440861</td>\n",
       "      <td>5180</td>\n",
       "      <td>62853573.0</td>\n",
       "      <td>1.935504</td>\n",
       "      <td>1.477651</td>\n",
       "      <td>1.386872</td>\n",
       "      <td>1.583432</td>\n",
       "      <td>AAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>428</td>\n",
       "      <td>428704</td>\n",
       "      <td>4592</td>\n",
       "      <td>69810775.0</td>\n",
       "      <td>1.521091</td>\n",
       "      <td>1.426610</td>\n",
       "      <td>1.117514</td>\n",
       "      <td>1.274221</td>\n",
       "      <td>AAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>407</td>\n",
       "      <td>276715</td>\n",
       "      <td>3119</td>\n",
       "      <td>49084995.5</td>\n",
       "      <td>1.571406</td>\n",
       "      <td>1.479061</td>\n",
       "      <td>0.947415</td>\n",
       "      <td>1.192394</td>\n",
       "      <td>AAA</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>8</td>\n",
       "      <td>886</td>\n",
       "      <td>70844</td>\n",
       "      <td>886</td>\n",
       "      <td>4549207.0</td>\n",
       "      <td>0.048219</td>\n",
       "      <td>0.043610</td>\n",
       "      <td>0.037291</td>\n",
       "      <td>0.041432</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>9</td>\n",
       "      <td>932</td>\n",
       "      <td>79753</td>\n",
       "      <td>932</td>\n",
       "      <td>5225839.0</td>\n",
       "      <td>0.049658</td>\n",
       "      <td>0.044288</td>\n",
       "      <td>0.038048</td>\n",
       "      <td>0.042424</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>10</td>\n",
       "      <td>785</td>\n",
       "      <td>81488</td>\n",
       "      <td>785</td>\n",
       "      <td>3261137.0</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.043793</td>\n",
       "      <td>0.038938</td>\n",
       "      <td>0.041983</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>11</td>\n",
       "      <td>1185</td>\n",
       "      <td>123372</td>\n",
       "      <td>1392</td>\n",
       "      <td>7513962.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.040253</td>\n",
       "      <td>0.035351</td>\n",
       "      <td>0.038755</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>12</td>\n",
       "      <td>1271</td>\n",
       "      <td>190115</td>\n",
       "      <td>1622</td>\n",
       "      <td>8275966.0</td>\n",
       "      <td>0.046375</td>\n",
       "      <td>0.040718</td>\n",
       "      <td>0.035898</td>\n",
       "      <td>0.039806</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>318 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    class_profit class_quantity class_revenue  month  product_id  total_stock  \\\n",
       "0              A              A             A      1         367       359312   \n",
       "1              A              A             A      2         386       313450   \n",
       "2              A              A             A      3         317       440861   \n",
       "3              A              A             A      4         428       428704   \n",
       "4              A              A             A      5         407       276715   \n",
       "..           ...            ...           ...    ...         ...          ...   \n",
       "313            C              C             C      8         886        70844   \n",
       "314            C              C             C      9         932        79753   \n",
       "315            C              C             C     10         785        81488   \n",
       "316            C              C             C     11        1185       123372   \n",
       "317            C              C             C     12        1271       190115   \n",
       "\n",
       "     quantity     revenue  P_rank_norm  R_rank_norm  Q_rank_norm  Pop_Index  \\\n",
       "0        3649  53114632.5     1.737238     1.426300     1.429217   1.536879   \n",
       "1        3097  49788811.5     1.658606     1.503681     1.192628   1.371273   \n",
       "2        5180  62853573.0     1.935504     1.477651     1.386872   1.583432   \n",
       "3        4592  69810775.0     1.521091     1.426610     1.117514   1.274221   \n",
       "4        3119  49084995.5     1.571406     1.479061     0.947415   1.192394   \n",
       "..        ...         ...          ...          ...          ...        ...   \n",
       "313       886   4549207.0     0.048219     0.043610     0.037291   0.041432   \n",
       "314       932   5225839.0     0.049658     0.044288     0.038048   0.042424   \n",
       "315       785   3261137.0     0.046946     0.043793     0.038938   0.041983   \n",
       "316      1392   7513962.0     0.044378     0.040253     0.035351   0.038755   \n",
       "317      1622   8275966.0     0.046375     0.040718     0.035898   0.039806   \n",
       "\n",
       "    CLASS  \n",
       "0     AAA  \n",
       "1     AAA  \n",
       "2     AAA  \n",
       "3     AAA  \n",
       "4     AAA  \n",
       "..    ...  \n",
       "313   CCC  \n",
       "314   CCC  \n",
       "315   CCC  \n",
       "316   CCC  \n",
       "317   CCC  \n",
       "\n",
       "[318 rows x 13 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ABC_metric_quarter = df_ABC_metric.groupby(['class_profit','class_quantity','class_revenue',\"month\"]).agg(\n",
    "    {\"product_id\":\"count\",\n",
    "    \"total_stock\":\"sum\",\n",
    "    \"quantity\":\"sum\",\n",
    "    \"revenue\":\"sum\",\n",
    "    \"P_rank_norm\":\"mean\",\n",
    "    \"R_rank_norm\":\"mean\",\n",
    "    \"Q_rank_norm\":\"mean\",\n",
    "    \"Pop_Index\":\"mean\"})\n",
    "df_ABC_metric_quarter.sort_values(by='Pop_Index',ascending=False)\n",
    "df_ABC_metric_quarter = df_ABC_metric_quarter.reset_index()\n",
    "df_ABC_metric_quarter[\"CLASS\"] = (df_ABC_metric_quarter[\"class_profit\"]).astype(str)+ (df_ABC_metric_quarter[\"class_quantity\"]).astype(str) +(df_ABC_metric_quarter[\"class_revenue\"]).astype(str)\n",
    "df_ABC_metric_quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "_=sns.lineplot(data=df_ABC_metric_quarter\n",
    "            , x=\"month\",\n",
    "             y=\"Pop_Index\",\n",
    "             hue = \"CLASS\")\n",
    "\n",
    "_=plt.legend(bbox_to_anchor = (1.03, 1.02), fontsize = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.lineplot(data=df_ABC_metric_quarter[(df_ABC_metric_quarter[\"class_profit\"]== \"A\")& (df_ABC_metric_quarter[\"class_quantity\"]== \"A\")& (df_ABC_metric_quarter[\"class_revenue\"]== \"A\")]\n",
    "# , x=\"month\", y=\"Pop_Index\",color='green').set(title='Popularity index for products of class A-A-A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax=sns.lineplot(data=df_ABC_metric_quarter[(df_ABC_metric_quarter[\"class_profit\"]== \"A\")& (df_ABC_metric_quarter[\"class_quantity\"]== \"A\")& (df_ABC_metric_quarter[\"class_revenue\"]== \"A\")]\n",
    "            , x=\"month\", y=\"Pop_Index\",color='green').set(title='Popularity index for products of class A-A-A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax=sns.lineplot(data=df_ABC_metric_quarter[(df_ABC_metric_quarter[\"class_profit\"]== \"B\")& (df_ABC_metric_quarter[\"class_quantity\"]== \"A\")& (df_ABC_metric_quarter[\"class_revenue\"]== \"B\")]\n",
    "            , x=\"month\", y=\"Pop_Index\",color='blue').set(title='Popularity index for products of class A-A-B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kl1jcRH5ENCdDEgRIT7aBYc9uQJKUjkREXsbiJvIjojkZrrLlIDVvDvHcWehSTysdiYi8jMVN5EdEczKclSrD1eBRAODhciI/xOIm8iO6K8WNhx+GKySUxU3kh1jcRP7CYoH4TxpclSoDej0cdesVnOcmIr/C4ibyE2KKGQAK9rgB2GPjIR75A0JWpoKpiMjbWNxEfuLqUDBnTCUAgD2uAQRJgn7/PgVTEZG3sbiJ/IS7uK/ucdepB0kUYdj7i5KxiMjLWNxEfkI0n4QrPAJSZFTBEyEhcDz0MAx79ygbjIi8isVN5CfEFLN7b/sqe1w8DL/uB+x2hVIRkbexuIn8hHsoWCGO2HgIViv0h39TKBUReRuLm8gfOBwQT58qGApWiD02HgBvxELkT1jcRH5AdyYVgsNx3R63q2w5OCvG8Dw3kR9hcRP5gf9eUV6YvX4c9Ht+4YQjRH6CxU3kB4ot7th4iP+kQXflBi1EpG0sbiI/IJqTIRmNcJUtd91rPM9N5F9Y3ER+QDQnw1kxBtBd/7+08/4H4AoL533LifwEi5vID4hFDAX790URjnr1YdjH4ibyByxuIq2TpCLHcBdmj42H/ugRCBnpPgxGRHJgcRNpnHDxInSWnOvGcBdmj2sAADDs3+urWEQkExY3kcaJKTe+ovwq+yN1Ien1PM9N5AdY3EQa9+9QsCo3fpPJBMfDNaHnleVEmsfiJtI40ZwMSRAKriovhj22AQwHDwA2m4+SEZEcWNxEGieakwvGbwcGFvs+e2w8hLw86H9L8k0wIpIFi5tI44odClbIvzdi4X3LibSMxU2kcTcbCnaVVKYMnJUq8w5qRBrH4ibSMosF4j9pxQ4FK8weGw/DXk44QqRlLG4iDROvTBzijKnk0fvtsfHQXbwIMfmEfKGISFYsbiINK25WsKJcvRGLnue5iTSLxU2kYbda3M57q8MVEQHDnl/kjEVEMmJxE2mYmJIMV3gEpMgozz6g08FeP44XqBFpGIubSMM8HQpWmD2uAfTH/oZw6ZJMqYhITixuIg3zdChYYY6r47n38Tw3kRaxuIm0yuGAePqUx0PBrrLXegSSwcDD5UQaxeIm0ijdmVQIDsct73EjKAiOmrVZ3EQaxeIm0qhbvaK8MHtcA+iTfgXy8rwdi4hkJltxHzp0CH369Lnu+e3bt6Nr165ISEjA2rVr5Vo9kd+7o+KOjYdgs0F/KMnLqYhIbno5Frpo0SJs2rQJQUFB1zxvt9sxdepUrFu3DkFBQejRoweaNWuG6OhoOWIQ+TXRnAzJaCyYGewW2evHAQAMe3fDERfv7WhEJCNZ9rgrVqyIOXPmXPf8iRMnULFiRYSHh8NoNKJu3brYv3+/HBGI/J5oTi6Yg1t36/8bS9HRcFStBsM+nucm0hpZ9rhbt26N1NTU657PyclBaGio+3FwcDBycnJuujxRFBARYfJqRrURRZ3ff0dv4Hb6l/50CqR7qxW5PTzZTrpGjSB+uRkR4UGAIMgVU9X48+QZbqeb8+U2kqW4byQkJAQWi8X92GKxXFPkN+J0SsjIsMoZTXERESa//47ewO10hSSh1IkTyIuNh6WI7eHJdgqsXQ+hy5Yie/8hOO+tLldSVePPk2e4nW5Ojm0UHV10P/r0qvKqVasiJSUFGRkZsNls2L9/Px555BFfRiDyC8KlS9BZcm55DHdhVycc4bAwIm3xyR735s2bYbVakZCQgBEjRuCZZ56BJEno2rUrypQp44sIRH5FNJ8EcHtXlF/lrFoNrlKlYNjzC/J69fVWNCKSmWzFXb58efdwr/bt27ufb968OZo3by7XaolKhH+HglW5/YUIAuz146DnHjeRpvAGLEQaJJqTIQlCwVXld8Ae2wD6kycgXLjgpWREJDcWN5EGiebkgvHbgYF3tBz71QlHuNdNpBksbiINup3pPIviqFUbUkAAi5tIQ1jcRBp0O9N5FikgAI7adVjcRBrC4ibSGosF4j9pcMVU8sri7LHx0P+WBOTmemV5RCQvFjeRxogpZgB3NhSsMHtsPAS7HYakX72yPCKSF4ubSGPuZFawotjrxwIAh4URaQSLm0hjvF3cUlQpOKrfB8OeX7yyPCKSF4ubSGPElGS4wiMgRUZ5bZn22HgY9u0FXC6vLZOI5MHiJtIYbw0FK8weGw9dZgbEv//y6nKJyPtY3EQa47WhYIXwRixE2sHiJtIShwPi6VN3NCtYUVyVq8BVOprnuYk0gMVNpCG6M6kQHA6v73FDEArOc3OPm0j1WNxEGuLtK8oLs8c1gJhihi7tvNeXTUTew+Im0hBZizs2DgDHcxOpHYubSENEczIkoxGuu8t6fdmOh2tBCgzk4XIilWNxE2mIaE4umINbFL2/cKMR9jr1WNxEKsfiJtIQOcZwF1Yw4cghwGKRbR1EdGdY3ERaIUmyjOEuzBEbB8HphOHgAdnWQUR3hsVNpBHCpUvQWXK8Poa7MHu9WEiCwMPlRCrG4ibSCNF8EoA8V5RfJUVEwnn/A7wRC5GKsbiJNOLfoWBVZF2PvX489Pv3AU6nrOshotvD4ibSCNGcDEkQCq4ql5E9Lh667CyIR4/Iuh4iuj0sbiKNEM3JcJUtBwQGyroe94QjPFxOpEosbiKNkHso2FWuijFwlrmbF6gRqRSLm0gj5B4K5iYIsMc1gGHfHvnXRUS3jMVNpAUWC8R/0uCKqeST1Tli4yCePgXd2TM+WR8ReY7FTaQBYooZgLxDwQpzn+fm4XIi1WFxE2mAnLOCFcXx0MOQTCbOFEakQixuIg3wdXHDYIC9bn0Y9vI8N5HasLiJNEBMSYYrPAJSZJTP1mmvHwf94d8g5GT7bJ1EdHMsbiIN8NVQsMLssfEQXC7oD+z36XqJqHgsbiIN8NlQsEIc9WMh6XS8QI1IZVjcRGrncEA8fUrWWcGKIoWGwfnAQzDsYXETqQmLm0jldGdSITgcPt/jBgB7bBz0B/YBDofP101ERWNxE6mcz68oL8Qe1wA6Sw70R/7w+bqJqGgsbiKVcxe3j+6aVtjVG7HoOeEIkWqwuIlUTjQnQzIaC2YG8zFX+QpwlruHF6gRqQiLm0jlRHNywRzcoqjI+u1x8QUXqEmSIusnomuxuIlUTokx3IXZY+MhnjsLXeppxTIQ0b9Y3ERqJkmKjOEuzMEJR4hUhcVNpGLCpUvQWXJ8Poa7MMcDD8EVEsriJlIJFjeRionmkwCUGQrmptfDUbceb8RCpBIsbiIV+3cMdxVFc9hj4yEe+QNCVqaiOYiIxU2kaqI5GZIgFFxVriB7XAMIkgT9/n2K5iAiFjeRqonm5ILx24GBiuaw16kHSRRh2MsbsRApjcVNpGJKDwVzCwmB46GHYdi7R+kkRCUei5tIxXTmZEVudVoUe2wcDL/uB+x2paMQlWgsbiK1slgg/pOm6FCwwhxxDSBYrdD/8bvSUYhKNBY3kUqJKWYACg8FK+TqhCMGTjhCpChZitvlcmHs2LFISEhAnz59kJKScs3rmzZtQufOndG1a1esXLlSjghEmqfkdJ5FcZUtB2eFijzPTaQwvRwL3bp1K2w2G9asWYOkpCRMmzYN8+fPd7/+zjvv4Msvv4TJZELbtm3Rtm1bhIeHyxGFSLPUtscNFOx1G3btLJhwRBCUjkNUIsmyx33gwAE0btwYAFC7dm0cPnz4mtfvu+8+ZGdnw2azQZIkCPwLgOg6ovkkXOERkCKjlI7iZo+Nh5h2Hrorv1QQke/Jssedk5ODkJAQ92NRFOFwOKDXF6zu3nvvRdeuXREUFISWLVsiLCys2OWJooCICJMcUVVDFHV+/x29oSRtJ/HMKaBq1dv6vrJtpxZNAQDhhw9Cqv2Q95fvYyXp5+lOcDvdnC+3kSzFHRISAovF4n7scrncpX306FF8//332LZtG0wmE4YPH46vv/4aTz755A2X53RKyMiwyhFVNSIiTH7/Hb2hJG2nyGPH4ahZG9m38X1l2073VEapsHDYv9+JnHZdvL98HytJP093gtvp5uTYRtHRoUU+L8uh8jp16mDnzp0AgKSkJFSvXt39WmhoKAIDAxEQEABRFBEVFYWsrCw5YhBpl8MB8fQp1QwFcxNFOOrV5x3UiBQkyx53y5Yt8dNPPyExMRGSJGHKlCnYvHkzrFYrEhISkJCQgJ49e8JgMKBixYro3LmzHDGINEt3JhWCw6GqC9OussfGI3jaJAgZ6ZAiIpWOQ1TiyFLcOp0OEyZMuOa5qlWruv/co0cP9OjRQ45VE/kFtQ0FK8we1wAAYNi/F7bHWyuchqjk4Q1YiFTIXdwqud1pYfZH6kLS6zk/N5FCWNxEKiSakyEZjQUzg6mNyQTHwzWh38viJlICi5tIhURzcsEc3KKodJQi2WMbwHDwAGCzKR2FqMRhcROpkGqm87wBe2w8hLw86H9LUjoKUYnD4iZSG0mCLsWs+uIGwPuWEymAxU2kMsKlS9DlZKtvDHchUpkycFaqDAPPcxP5HIubSGVE80kA6hwKVpg9Nr7gRiySpHQUohKFxU2kMv+O4a6icJLi2WPjobt4EWLyCaWjEJUoLG4ilRHNyZAEoeCqchW7ep5bz/PcRD7F4iZSGdGcXDB+OzBQ6SjFcla/D66ICJ7nJvIxFjeRyojmZFXeMe06Oh3s9eNg2MMJR4h8icVNpDI6lY/hLsweGw/9sb8hXL6kdBSiEoPFTaQmFgvEf9JUPRSsMMfVCUf27VU4CVHJweImUhExxQxA/UPBrrLXegSSwcDD5UQ+xOImUhE1T+dZpKAgOGrW5gVqRD7E4iZSEa3tcQMF83Prk34F8vKUjkJUIrC4iVRENJ+EKzwCUmSU0lE8Zo+Nh2CzQX8oSekoRCUCi5tIRdQ+K1hR7PXjAICHy4l8hMVNpCJaGgp2lRQdDUfVajDsY3ET+QKLm0gtHA6Ip09pZihYYQUTjuzmhCNEPuBRcR89evSax9u3b5clDFFJpjuTCsHh0NweNwA4YuOhu3wZ4vFjSkch8nseFffIkSPx2WefwWazYeLEiVi+fLncuYhKHPdQMC3c7vQ/7FdvxMLz3ESy86i4V65ciR9//BHNmjVDdHQ0Pv74Y7lzEZU4mhvDXYizajW4SpXijViIfMCj4t68eTOSk5PRr18/fPPNNzhw4IDcuYhKHNGcDMloLJgZTGsEAfb6cdBzj5tIdh4V965du7By5UoMHDgQc+bMwfTp0+XORVTiiObkgjm4RVHpKLfFHtsA+pMnIFy4oHQUIr/mUXHPmjULly5dwg8//ACDwYBPPvlE7lxEJY4Wx3AXZo+NB8Dz3ERy03vypk8//RT/93//h8zMTHTq1AmnTp3C2LFj5c5GVHJIEnQpZtgaPKp0ktvmqFUbUkAADHt3w9a2vdJxiPyWR3vcX331FZYuXYrQ0FD0798fhw4dkjsXUYkiXLoEXU62JsdwuwUEwFHrEe5xE8nMo+KWrtxUQRAEAIDRaJQvEVEJJJpPAtDmFeWF2eMaQP9bEpCbq3QUIr/lUXG3a9cOvXr1wqlTp/Dcc8/h8ccflzsXUYny71CwKgonuTP22HgIdjsMSb8qHYXIb3l0jrt3795o0KAB/v77b1SuXBn333+/3LmIShR3cVeMUTjJnbHXjwUA6Pfuhr1BQ4XTEPmnYot77ty51z134sQJbN26FS+99JJsoYhKGtGcDGfZckBgoNJR7ogUVQqO6vfBsHc3eLCcSB7FHiovXbo0SpcujaSkJFy8eBEVK1ZEZmbmdfcuJ6I7o/WhYIUVTDiyB3C5lI5C5JeKLe7ExEQkJiZCkiSMHz8eHTp0wKhRo2CxWHyVj6hE0OJ0njdij42HLjMD4t9/KR2FyC95dHFaeno6Tp06BQA4efIkcnJyZA1FVKJYLBD/SdP2ULBCeCMWInl5dHHayJEjMXToUKSlpSE6Opq3PCXyIjHFDED7Q8GuclWuAlfpaBj2/IK8vgOUjkPkdzwq7nr16mHdunVyZyEqkbQ8K1iRBOHKeW7ucRPJwaPi/uKLL7Bw4ULk5+e7n9u2bZtsoYhKEn/b4wYKbsQSsGUzdGnn4Spzt9JxiPyKR8W9aNEizJ8/H2XLlpU7D1GJI5pPwhUeASkySukoXmOPjQNQMJ7b1r6TsmGI/IxHF6dVqFABMTExMBqN7n+IyDv8aSjYVY6Ha0EKDOThciIZeLTHHRgYiGeffRYPPPCA+37lQ4cOlTUYUUmhMyfDUbO20jG8y2iEvU49FjeRDDwq7iZNmsidg6hkcjggnj4FW4fOSifxOntsPExz3gMsFiA4WOk4RH6j2OJOTi642rV27dq+yEJU4ujOpEJwOOCMqaR0FK9zxMZBcDphOHgA9kaPKR2HyG8UW9xjx44t8nlBEPDJJ5/IEoioJPG7oWCF2OvFQhIEGPbuZnETeVGxxb18+fJiP7x161ZO8Ul0B/y5uKWISDjvf4DnuYm8zKOrym+Ee91Ed0Y0J0MyGuEqW07pKLKw14+Hft9ewOlUOgqR37ij4pYkyVs5iEok0ZxcMAe3KCodRRb22DjosrMgHj2idBQiv3FHxX11aBgR3R5ditkvD5NfZY9rAIATjhB50x0VNxHdAUnyy5uvFOaqGANnmbth2POL0lGI/AYPlRMpRLh0CbqcbL+ZzrNIggBHbDwM+/YonYTIb3hc3EeOHMGWLVvw119/uZ8bMKDoKftcLhfGjh2LhIQE9OnTBykpKde8/ttvv6Fnz57o0aMHhgwZcs3kJUQlhWg+CcA/rygvzB4XD/H0KejOnlE6CpFf8Ki433vvPbz11ltISkrCuHHj8NFHHwEAmjdvXuT7t27dCpvNhjVr1mDYsGGYNm2a+zVJkjBmzBhMnToVq1atQuPGjXHmDP+HppLn36FgVRROIi97bDwAnucm8haPbnn6448/Yt26ddDpdHA6nUhISMCzzz57w/cfOHAAjRs3BlBw17XDhw+7X0tOTkZERASWLVuGv//+G02aNEGVKv79FxdRUdzFXTFG4STyctSoCckUDOP2rcjv1FXpOESa51Fx33333bBYLAgNDYXD4UDp0qWLfX9OTg5CQkLcj0VRhMPhgF6vR3p6Og4ePIgxY8YgJiYGgwYNQo0aNdCgQYMbLk8UBUREmDz8Stokijq//47e4E/bSTx7GtI99yDibu9P56m27eTq3x8BHy6AOHoUUL260nHc1Lad1Irb6eZ8uY08Ku5//vkHrVu3xv3334/jx4/DYDAgMTERALB69err3h8SEgKLxeJ+7HK5oNcXrCoiIgIxMTGoVq0aAKBx48Y4fPhwscXtdErIyLB6/q00KCLC5Pff0Rv8aTtF/H0MUsVKyJTh+6htOwkvDkXUsmVwvTkSWYvVc+MmtW0nteJ2ujk5tlF0dGiRz3tU3LNmzQJQMG7bkyvJ69Spgx07dqBNmzZISkpC9UK/YVeoUAEWiwUpKSmIiYnB/v370a1bN09iEPkVnTkZthYtlY7hE1J0NHL/NxjB06dC/+t+OOrUUzoSkWZ5VNyiKGLKlCk4ceIEKlWqhDfffBPly5e/4ftbtmyJn376CYmJiZAkCVOmTMHmzZthtVqRkJCAyZMnY9iwYZAkCY888giaNm3qre9DpA0WC8R/0vx7KNh/5L7wEoI+XoTgieOQuf5LgDdwIrotHhX36NGj0aNHD9SvXx979+7FqFGjsGzZshu+X6fTYcKECdc8V7VqVfefGzRogHXr1t1mZCLtE1PMAPx/KFhhUkgoLMPeQOibw2HYsRX25iXjaAORt3k0HCw/Px8tWrRAWFgYHn/8cTgcDrlzEfm1kljcAJDXZwCcMZUQMnE84HIpHYdIkzwqbqfT6b7xyl9//cV7lBPdIX+ezrNYRiMsI8dC/8fvCFj/mdJpiDTppofKc3JyMHToUIwcORIXLlzAXXfdhUmTJvkiG5HfEs0n4QqPgBTp/aFgapffsQvsc2cheNok5LfvBAQEKB2JSFOKLe5PP/0US5YsgV6vx+jRo/HYY4/5KheRX/P3yUWKpdPBMuYtRHTvhKBli5E78H9KJyLSlGIPlX/55Zf45ptvsHr1anzyiXrGXhJpna4kFzcAe9PmsD3WDKb3pkPIzlI6DpGmFFvcRqMRRqMRUVFRsNvtvspE5N8cDoinT8EVU0npJIqyjBkP3aVLCPpgttJRiDTF49nBOIUnkXfozqRCcDhK9B43ADhqPYK8Tl1gWjAXQlqa0nGINKPYc9zHjx933yjl6p+vmjlzpuzhiPxRib2ivAiWEWMQ8OUmBM+chpx33lM6DpEmFFvc77//vvvPV+9NTkR3hsX9L1eVqsjrOwCBy5Ygd9CLcFappnQkItUrtrhjY2N9lYOoxBDNyZCMRrjKllM6iipYhr6BwNUrYZo6CdmLliodh0j1PD7HTUTeIZqTC+bgFkWlo6iCdNddsL7wEgI3rof+4AGl4xCpHoubyMd0KWYeJv+P3P8Nhqt0aQRPHAfwQliiYrG4iXxJkkr2zVduQAoNg2Xo6zDu2gnDjm1KxyFSNRY3kQ8Jly5Bl5Ndoqbz9FRe36fhrFgJwZPGcwISomKwuIl8SDSfBMAryotkNMLy5mgYDv+GgA2c9pfoRljcRD7071CwKgonUaf8zt1gr1ETwVMnATab0nGIVInFTeRD7uKuUFHhJCp1ZQIS8ZQZgZ8sUToNkSqxuIl8SDQnw1m2HBAUpHQU1bI3bQ5b4yYInvk2JyAhKgKLm8iHeEW5BwQBltFXJiCZN0fpNESqw+Im8qGSPp2npxyP1EVexy4wzZ8L4Z9/lI5DpCosbiJfsVgg/pPGoWAesr45GrDlI/jdt5WOQqQqLG4iHxFTzAA4FMxTzirVkNe7HwI/+Ri6kyeUjkOkGixuIh9hcd86y7ARgNGI4GkTlY5CpBosbiIf4XSet04qUwbWQS8h8Iv10B86qHQcIlVgcRP5iGg+CVd4BKTIKKWjaErui0PgKlUKwRPHKx2FSBVY3EQ+wqFgt0cKDYP11eEw7twBw/fblY5DpDgWN5GPcCjY7cvt9wycFWMKpv3kBCRUwrG4iXzB4YB4+hRcMZWUTqJNAQGwjBgNw++HELBxvdJpiBTF4ibyAd2ZVAgOB/e470B+l6fgeOhhBE+ZwAlIqERjcRP5AK8o9wKdDjljxkNMMSNw+cdKpyFSDIubyAdY3N5hb/Y4bI0eK5iAJCdb6ThEimBxE/mAaE6GZDTCVbac0lG07eoEJBcvImj+XKXTECmCxU3kA6I5Gc6KMYAoKh1F8xx16iG/fScEzZvDCUioRGJxE/mALsXMw+ReZBk5BkJeLoLfe0fpKEQ+x+Imkpsk8eYrXuasei/yevdH4LIl0CWfVDoOkU+xuIlkJly6BF1ONqfz9DLra28UTEDy9iSloxD5FIubSGaiuWCPkHvc3uUqczesz/8PgevXQf9bktJxiHyGxU0kM/dQsBgWt7flvvgyXFFRBbdCJSohWNxEMnMXd8UYhZP4HyksvGACkh92wPDDDqXjEPkEi5tIZqI5Gc6y5YCgIKWj+KXc/s/CWaEigieN5wQkVCKwuIlkxivKZRYQAMsbo2A4dBABmzYonYZIdixuIplxOk/55XftDseDNTgBCZUILG4iOVksEP9J41AwuYkiLKPHQTQnI/DTZUqnIZIVi5tIRmKKGQCHgvmCrUUr2B5thOAZ04CcHKXjEMmGxU0kIxa3DwkCLGPegu7iBZgWcAIS8l8sbiIZcTpP33LUrY/8dh0R9MFsCBcuKB2HSBYsbiIZieaTcIVHQIqMUjpKiWEZORZCXi5M709XOgqRLFjcRDLiUDDfc1a7F3k9+yJo6WLorhzxIPInLG4iGenMyXDGVFI6RoljHT4C0OsRPI0TkJD/YXETycXhgHj6FIeCKcB1d1lYn38Rges/g/73Q0rHIfIqWYrb5XJh7NixSEhIQJ8+fZCSklLk+8aMGYMZM2bIEYFIcbozqRAcDh4qV0juSy/DFRlZcCtUIj8iS3Fv3boVNpsNa9aswbBhwzBt2rTr3rN69Wr8/fffcqyeSBV4RbmypLBwWF8ZDuOObTDs/F7pOEReI0txHzhwAI0bNwYA1K5dG4cPH77m9YMHD+LQoUNISEiQY/VEqsDiVl7ugGfhLF+hYNpPTkBCfkIvx0JzcnIQEhLifiyKIhwOB/R6Pf755x/MnTsXc+fOxddff+3R8kRRQESESY6oqiGKOr//jt6gpe2kO3caktGIsAeqAaLo03VraTvJywRp/FswPPs0Ird9Dempp655ldvJM9xON+fLbSRLcYeEhMBisbgfu1wu6PUFq/rmm2+Qnp6OgQMH4sKFC8jLy0OVKlXQpUuXGy7P6ZSQkWGVI6pqRESY/P47eoOWtlPY38chVoxBRna+z9etpe0ku7adEfnADGD0KGQ0bQUYDO6XuJ08w+10c3Jso+jo0CKfl+VQeZ06dbBz504AQFJSEqpXr+5+rW/fvli/fj2WL1+OgQMHol27dsWWNpFWcVYwlRBFWEaPhz75JCcgIb8gS3G3bNkSRqMRiYmJmDp1Kt58801s3rwZa9askWN1ROojSbz5iorYHm8NW4OGnICE/IIsh8p1Oh0mTJhwzXNVq1a97n3c0yZ/JVy6BF1ONsdwq8WVCUgi2zwO08J5sA59XelERLeNN2AhkoFoPgmAV5SriaNeLPLbtEfQ3FkQLl5UOg7RbWNxE8nAPRQshsWtJpZR4yBYLZyAhDSNxU0kA3dxV4xROAkV5ry3OvJ69UXQxx9Bd2WudCKtYXETyUA0J8NZthwQFKR0FPoP62sjAFFE8NuTlY5CdFtY3EQy4BXl6uUqWw65A/+HgM/XAklJSschumUsbiIZcAy3ulkHvwIpPBzimFFKRyG6ZSxuIm+zWCD+k8ahYComhUfA+vJr0H37LQy7diodh+iWsLiJvEw8VTCNLfe41S33mYGQypdH8KRxgCQpHYfIYyxuIi/jrGAaERgI57jxMPx6AMYvNymdhshjLG4iL2Nxa4fUuw8c992P4ClvAQ6H0nGIPMLiJvIy0XwSrvAISJFRSkehmxFFWEaNh/7EcQSuXK50GiKPsLiJvEw0J8MZU0npGOQhW+snYY+Nh2n6VMDKqStJ/VjcRF7GoWAaIwjIGf0WxLTzCFo0X+k0RDfF4ibyJocD4ulTHAqmMY74Bshv/SRMs9+DcPmS0nGIisXiJvIi3ZlUCA4H97g1yDJyHARLDkyz3lU6ClGxWNxEXsQryrXL+cCDyO/eA0FLFkKXelrpOEQ3xOIm8iIWt7ZZXh8JAAh+Z4rCSYhujMVN5EWiORmS0QhX2XJKR6Hb4CpfAblPD0TA2lUQj/ypdByiIrG4ibxITDEXzMEtikpHodtkfXkopOCQgpuyEKkQi5vIizgUTPukqFKwDnkVAd9+Df3uX5SOQ3QdFjeRt0gS5+H2E7nPvQBnmbsRMnEsJyAh1WFxE3mJcOkSdDnZHMPtD0wmWIe/CcO+PTB++7XSaYiuweIm8hLRfBIA4IxhcfuDvJ594KhaDcGTxwNOp9JxiNxY3ERewqFgfkavh2XkOOj/OoqAtauUTkPkxuIm8hJ3cVeMUTgJeYutXQfY69RF8NuTgdxcpeMQAWBxE3mNaE6Gs2w5IChI6SjkLYIAy5gJEM+eQdCSRUqnIQIA6JUOUGLY7dCdOwvx7BnoUk9Dd/YMxNTT0J1JhXjmDESXE8YRY2Br007ppHSbeEW5f7I3bAxb88dhmjUDeb37QgqPUDoSlXAsbm+QJAiXLkE8cxq6M2cK/p2a+m85nz0D3flzEP4zrMQVGQlXufJwVqgA8Uwqwvv3RF63BORMeQdSRKRCX4Zul86cDFuLlkrHIBnkjH4LkS0awTTnfVhGj1c6DpVwLG5PWCzuPWXxTOqVveRU6M6cge7MaYhnz0DIy7vmI1JAAJz3lIfrngqwN2l25c/lC/5dvgKc5e4BgoPd748INsA27i2Y3p8Bw66dyHlvDmwtWvn6m6pXfj6CPpwHMdUMsf9AOB98SOlE17JYIP6TxqFgfspZ42Hkd3kKQYvmI/eZgbylLSlKkCT1313AbnciI8Mqz8IdDujOn4MuNRXi2dR//30mFWJqKnRnU6FLT7/mI5IgwHV3WbjK3QNn+Qpw3VMernvugfOeCu5/S6VKAYLgcYyICBMyMqzQ/5aE0Jeeh/7oEeT26gvLhCmQQsO8/a01xfDzLoQMfwX6Y39DCgwE8vOR36kLrMNHwlntXqXjAQDEI38iqkk8sj5cgvzO3ZSO4/55ouLdynbSpZgR9Whd5CX2Qs7M2TInUxf+PN2cHNsoOjq0yOdL3B63kJ2F4PGjoT96BLozqQWHsF2ua97jiogoOIRdvjzs9WMLyrncPQV7yveUh+vusoDBIEs+R83aSP+/nQiePhVBc9+H8YcdyH7/A9gfayrL+tRMuHgRIW+NRuCalXBWrITMlZ/B1LwJbNPegWnhfARs3IC8hJ6wDnsDLoWv5OZQMP/niqmE3P7PIGjJIuQOegnOe6srHYlKqBK3xy1cvIjw/j0hBQQU7DEXPoR9Zc9ZCin6txw5FfXbmn7/XoQOHgT9iePIHfAscsZMAEJCfJ7N51wuBK5cjuAJYyBYLLC++DKsr7wGmEzu7SRcuADT7HcRtPQjwOVCXq++sL46XLFDmEHz5yJk3Ehc/MsMKTJKkQyFcQ/JM7e6nYQLFxAVWwv2ps2R9fGnMiZTB92pFARPHg99QgLSm7W+paOIJY0v97hLXHGr1Q3/o1utCJ46EUEL58FVMQbZcxbAHv+o7wP6iPjnHwgd/goM+/bA1qAhct55D8777ne//t/tpDt3Fqb3piNwxSeATofc/s/COmQopOhon+YOeWMoAtavw6Vjp3y63hthcXvmdraTacY0BL8zBelbtsJRL1amZMoT0i8jol0r6I/9DQCw13oEljfHwN6sBQu8CL4sbo7jVjuTCZaJU5H5xRYAQHjHJxE8dqT/3QzCYkHwW2MQ2aIRxJPHkTV7PjK/2HJNaRfFVbYcct55D5d/PoC8KxcPlapfE8GT34KQftlH4a8MBYup5LP1kXKsg16Cq3Q0gieN998JSPLyENavJ8QUMzI+3wzHR0ugu3wJEYldEN6pDfR7diudsERjcWuEvUFDXN7xM/L6PwPTgrmIbNEI+gP7lI7lFcZvtiCqcSxMH8xCXo/euPzTfuQn9rql3+pdMZWQM2se0nftQ/4TTyJo9ruIqlcTpplvQ8jOkjF9AU7nWYKEhMAy7A0Yf94F4/b/UzqN97lcCB08CMbdPxcc4WvcBFLfvrj88wFkT50B/fFjiGzfCmE9u0H/+yGl05ZILG4tCQlBztvvIuOzjRBycxHRtiWCJ78F5Ocrney26FJPI6xvD4T3TYQUGor0zd8h5905kKJK3fYyndXuRfaCJUjf8TPsjZsg+O3JiKpfE0FzZwFW+UYmiKdPcShYCZLXpz+cMZUQPHE88J+LW7UueMJYBG5cj5wxE64dIREQgLxnBuLS3kPIGf0WDPv3IrJFY4Q+1x/iiWPKBS6BWNwaZG/SDOk//IK8xF4wzZqJyFZNtfWbr92OoHlzENUoFsYftiNnzASkb/0Rjrh4r63C+eBDyFq6AunffQ/7I3URMmEMStWvicCPFnj9Fx3dmVQIDgf3uEsSoxGWkWOh//MwAj5fq3Qarwlc/CFM82Yjd8CzyH3p5aLfFByM3CGv4vK+32AZOhwB//ctIhvFIuTVl6BLPe3bwCUUi1ujpLBw5Lz/ATJXrIVw+RIiWjeDafpUwG5XOlqx9Pv2ILJlE4SMHwVbo8a4/ONe5A5+Rb7hdbXrIGvV50jf/B0c1e9D6MjXERX/CAKXL/XatuJQsJIpv2MX2B+uheBpkzR71Ksw45YvETLydeQ/0RY5U6bf9FSVFB4B64gxuLTvN+Q++zwCP1uNqPhHEDz6DQgXLvgodcnE4tY4W8snkL5zN/I7dkHw9KmIeLIFxCN/Kh3rOkL6ZYQMexmRbVtCyEhH5tKVyFq+xmfjrx1x8chc/yUy1m2C6+6yCB02BFGP1i2YrvEO51pmcZdQOh0sY96CePoUgpYtVjrNHdHv34uwQU/DUacushYsBkTR489K0dGwTJyGy7sPIu+pRAQtXohS9WvCNHUChMwM+UKXYCxuPyBFRiF7/kfI/HgFxLOpiGz5GIJmvws4HEpHAyQJAWtXIaphPQSu/ATWQS/h8q59BZOp+HpIiSDA/lhTZGzZiswVa+EKDUPYS88jskk8jJs23Pa5StGcDMlo5G0wSyB70+awNW4K03vTfXIRpBx0J08gvE8CXHeXRebytYDJdFvLcZWvgJz35iL9x73Ib9Uawe/NQFS9mgV/F1ksXk5dsrG4/YitbXtc3rkXttZtEDJpPCLat4J4XLmLRsRjfyO8a3uEvfQ8nDGVkP7dD7BMmKL8TWQEAbaWTyBj605kLl4OCALCn+2HiMcfg/G7r295iI+YYi6Yg/sW9lLIf1jGjIfu0iUEfaC926AKFy8iIrELACBz9eeQSpe+42U6q92L7IVLcXnbLthj4xAyaTyi4mojcPFCwGa74+UTi9vvSKVLI+ujZcj6cAnEkycQ2bwhghbM9e2Vr7m5ME2biMimDaD//TdkT38fGV9thfPhmr7L4AmdDrb2HZH+/S/ImrcIupxshPdOQESbFjD8sMPjAudQsJLNUbsO8jp2gWnBBxDS0pSO4zmrFeF9ukN3/hwyl6+Bs0o1ry7e+XBNZK34DOmbv4OzajWEvvlawemp1Svu+PRUScfi9keCgPzO3ZC+cw9sTZohZOxIhHdqA13ySdlXbdi+FVFN4hH87nTkd+iMyz/tR16/pwGdin/URBH53RJw+af9yH53DnTnzyPiqY4I79wW+t2/FP9ZSeI83ATrm6MBWz6C331b6SiecToRNugZ6H89gKz5i2W9A5wjLh6ZX2xBxpoNcEVGIWzICwWnpzZv9N8b2MhMxX+b0p1ylbkbWZ+sRtbs+dD/+QeimjVE4JJFsux969LOI3Rgf0QkdoEkishYtwnZ8z+CdNddXl+XbAwG5PXuh8u7DyJ76nSIx48hskNrhCd0hv7ggSI/Ily6BF1ONly8a1qJ5qxSDXm9+yFw+VLoTp5QOk7xJAkho15HwDdfIWfy27C1bS//OgUB9mYtkPHd98hcUnCP9/Bn+iCiVVMYtm9lgd8iFre/EwTkJ/ZC+s7dsMfGIXTEMIR37+y98ZZOJwIXf4jIR+sh4OuvYHl9JNK//0Xbs5kFBCDvmedxee8h5IybBP2hg4hs3QxhfXtA/POPa94qmguOYjgrVVEiKamIZdgIwGhE8LSJSkcpVtAHsxG0ZBGsLwxG3rODfLtyQYCtXQek/7AbWXMWQJd+mbdRvQ0s7hLCVe4eZK7ZgOwZs6A/sA+RTRogcOXyO/pNV3/oICKeaI7QN4fDUacu0n/4BdbXRgABAV5MriCTCbkvDsHl/b/DMmI0DD/vQmSzRxH6/AD3RX8cCkZXSWXKwDroRQR+sR76QweVjlOkgA3rEDJhDPI6doFlnIK/YIgi8hN6FtxGddpMiCeO8zaqt4Czg6mEL2dz0qWYEfrKizD+9CPyH2+FnHfnFMwx7iEhOwumaZMQtHghpFKlkTNpGvI7dfXJ8C4lZ70S0i8jaP5cmBbOB/Jykd+9BySDAUHLl+JCShoQFKRIrqJwdjDPeHs7CdlZiKpfE44atZC5bqPXlusNhp93Ibx7J9jr1EPm2i+AwECPPyv7z5PViqDFC2Ga8y50GRnI69gF1jdGwVntXvnW6WWcHYxk5YqphMzPNyN7yjsw/vQjIh+LQ8C6NTff+5YkGDdtQOSj9RD00YfI6/8MLv+8v+B+xiVgmj8pMgrWkWML7hQ18H8I+OJzBC1fCmfZcqoqbVKOFBoG66vDYdy5A4bvtysdx0386yjC+vWEM6YSspatvKXS9gmTCbmDXyk4ujX09Su3Ua2PkFde5G1Ui8A9bpVQag9JPHkcoS8NgmH/XuS3aY/s6e8XOZe1zpyM0BHDYNy+FfaHayFnxvtwPFLX53nVtCepO3cWQR/MgqtceeT+b7DSca6hpu2kZrJsp/x8RD1aF67IKGR8973iIyp0aecR8WQLwGZDxpatt3W3Ql//PAkXLsA0+10ELf0IkCTk9n8G1pdfK/LvJrXgHjf5jLNKNWRs/hY5YyfCuO07RD0WC+PmL/59Q34+TO9NR9RjcdDv2Y2cSdOQ8e0ORUpbbVxly8Ey6W3VlTYpLCAAljdGwfBbEgI2bVA0ipCTjbCeT0F3+TKyVn7ms1sM36mC26hOLbiNakJP3kb1P2TZ43a5XBg/fjz++usvGI1GTJo0CTEx//7AfPnll1i2bBlEUUT16tUxfvx46Ir5rZR73L4h/nUUoYOfhyHpIPI6d0V+l+4InjAG+mN/I799J+RMmqb4bT3VsJ20gNvJM7JtJ6cTkc0bQci14vKufYDR6P113IzdjvA+CTD8sANZn66BrUWr216U0j9P4snjML0zBYHr18EVHoHcZ56D6667/z1FJwjX//k/j6XCj//776I+X8RrkvvP179uqnE/Mip69yY2N9rjlqW4v/vuO2zfvh3Tpk1DUlISPvzwQ8yfPx8AkJeXh3bt2mHz5s0ICgrC0KFD0bZtW7Ro0eKGy2Nx+5DDAdPsd2Ga+TYEux3OijHImTYDtsdbK50MgIq2k8pxO3lGzu1k/L9vEN6rO7KnzUTe08/Jso4bkiSEDB2MoBWfIPvdOcjr3e+OFqeWnyfx8O8IfnsSAr79Wuko15GionDxqNmry7xRceu9upYrDhw4gMaNGwMAateujcOHD7tfMxqNWL16NYKuXMzjcDgQ4C/Dh/yBXg/r0NeR37oNDL/sQl7Pvrc96QBRSWZ7vDVsDRoieObbyOvew6f36De9+w6CVnwCy9Dhd1zaauKs8TCylq8pOFxuuzItryQBkgQB0jWP3f/897kbPBZQ+DE8//yV50LvrSTnV7+GLMWdk5ODkEI/pKIowuFwQK/XQ6fTofSVG9kvX74cVqsVDRs2LHZ5oiggIsK/y0MUder6jg1jgYaxUNm1p+rbTirF7eQZubeT8M7b0DVuhKhlC+EaNVq29Vyzzk+WQf/2ZLh694Fx6hQYvTDiQ3U/T2rKcoUo6hDh9M2cELIUd0hICCyFpnFzuVzQ6/XXPJ4+fTqSk5MxZ84cCDf5wXI6JVUcppGTWg5FqR23k2e4nTwj+3a6rybC2rSHYeZMZCb09crsW8Ux7NiG8EHPw/ZYM2ROew/IzPXKcvnzdHOav6q8Tp062LlzJwAgKSkJ1atXv+b1sWPHIj8/H/PmzXMfMici8keWkWMhWC0wvT9d1vWIh39H2DN94bz3PmQt+USZC+LIJ2S9qvzvv/+GJEmYMmUK/vzzT1itVtSoUQNdu3ZFvXr13Hvaffv2RcuWLW+4PF6cRldxO3mG28kzvtpOIUMHI3DNSlz++YAsE9LozqQWjNUWBGR8vQ2ucvd4dfn8ebo5X+5x8wYsKsH/MTzD7eQZbifP+Go76c6dRVRcbeS364jseYu8umwhMwMR7VtDd+YMMjZ/C+eDD3l1+QB/njyh+UPlRET0L1fZcsh97gUEfL4W4uHfvbfg/HyEDegN8cRxZC1dIUtpk/qwuImIfMA6+BVIYeEInjzeOwuUpILJgnbtRPb7H8DeuIl3lkuqx+ImIvIBKSIS1peHIWDb/8Hw0493vLzgKRMQ+PlaWEaORf5TiV5ISFrB4iYi8pHcZwbCWe4eBE8ce/PZ+IoRuGwJTLNmIrfPAFhfHubFhKQFLG4iIl8JCoJ1+Jsw/HoAxq8239YijN99jZA3hiK/ZWvkvD2zREypS9dicRMR+VBeQk84qt+H4ClvAQ7HLX1Wf/AAwgYOgOPhWsj68GNAL8s9tEjlWNxERL6k18Mychz0x48hcNWnHn9MZ05GeK/ucJWORuana31673NSFxY3EZGP2Z5sC3u9WJimTwWsNx/7K1y+hPAeXQGHHZmrPodUpowPUpJasbiJiHxNEGAZOwHi+XMI+mhB8e/NzUV4n0SIqaeR+ckaOO+tXvz7ye+xuImIFGCPfxT5rZ6AafZ7ENIvF/0mpxNhLw6Efv9eZH2wEI74Br4NSarE4iYiUohl5DgI2VkwzXq3yNeDx49CwJcbYXlrMmwdOvs4HakVi5uISCHOBx9CfvceCFr8IXRnUq95LejDD2D6cB6sA19A7qCXFEpIasTiJiJSkOX1kYAkwfTOFPdzxs0bETx2JPLbdoDlrSnFfJpKIhY3EZGCXBUqInfAcwhcsxLi0SPQ79mNsP89C0e9WGTNWwSIotIRSWVY3ERECrO+8hqk4BCEDn8F4X0T4LynPDI/WQ0EBSkdjVSIxU1EpDCpVCnkvvQyDHt+AUSxYKx2qVJKxyKV4v3yiIhUwDrwf9ClnUdezz5wVa6idBxSMRY3EZEaBAcjZ9pMpVOQBvBQORERkYawuImIiDSExU1ERKQhLG4iIiINYXETERFpCIubiIhIQ1jcREREGsLiJiIi0hAWNxERkYawuImIiDSExU1ERKQhLG4iIiINYXETERFpiCBJkqR0CCIiIvIM97iJiIg0hMVNRESkISxuIiIiDWFxExERaQiLm4iISENY3ERERBrC4laY3W7H8OHD0bNnT3Tr1g3btm1TOpJqXbp0CU2aNMGJEyeUjqJqH374IRISEtClSxd89tlnSsdRHbvdjmHDhiExMRE9e/bkz1MRDh06hD59+gAAUlJS0KNHD/Ts2RPjxo2Dy+VSOJ16FN5OR44cQc+ePdGnTx8888wzuHjxomzrZXErbNOmTYiIiMDKlSuxaNEiTJw4UelIqmS32zF27FgEBgYqHUXV9uzZg4MHD2LVqlVYvnw5zp8/r3Qk1fnhhx/gcDiwevVqvPjii3j//feVjqQqixYtwujRo5Gfnw8AmDp1Kl555RWsXLkSkiRx5+KK/26nyZMnY8yYMVi+fDlatmyJRYsWybZuFrfCnnjiCbz88svux6IoKphGvd5++20kJibirrvuUjqKqu3atQvVq1fHiy++iEGDBqFp06ZKR1KdypUrw+l0wuVyIScnB3q9XulIqlKxYkXMmTPH/fiPP/5AbGwsAOCxxx7Dzz//rFQ0Vfnvdnr33XfxwAMPAACcTicCAgJkWzd/YhUWHBwMAMjJycGQIUPwyiuvKBtIhdavX4+oqCg0btwYCxcuVDqOqqWnp+Ps2bNYsGABUlNT8cILL+Cbb76BIAhKR1MNk8mEM2fO4Mknn0R6ejoWLFigdCRVad26NVJTU92PJUly//wEBwcjOztbqWiq8t/tdHWn4tdff8Wnn36KFStWyLZu7nGrwLlz59C3b1907NgR7du3VzqO6nz++ef4+eef0adPHxw5cgRvvPEGLly4oHQsVYqIiECjRo1gNBpRpUoVBAQE4PLly0rHUpWlS5eiUaNG+Pbbb7Fx40aMGDHCfbiTrqfT/VsTFosFYWFhCqZRty1btmDcuHFYuHAhoqKiZFsPi1thFy9exNNPP43hw4ejW7duSsdRpRUrVuDTTz/F8uXL8cADD+Dtt99GdHS00rFUqW7duvjxxx8hSRLS0tKQm5uLiIgIpWOpSlhYGEJDQwEA4eHhcDgccDqdCqdSrwcffBB79uwBAOzcuRP16tVTOJE6bdy40f33VIUKFWRdFw+VK2zBggXIysrCvHnzMG/ePAAFFz3wIiy6Hc2aNcO+ffvQrVs3SJKEsWPH8rqJ/+jfvz9GjhyJnj17wm6349VXX4XJZFI6lmq98cYbGDNmDN59911UqVIFrVu3VjqS6jidTkyePBlly5bF4MGDAQD169fHkCFDZFkfZwcjIiLSEB4qJyIi0hAWNxERkYawuImIiDSExU1ERKQhLG4iIiINYXET0S3766+/sG/fPgBA8+bNeQMTIh9icRPRLfvuu+9w/PhxpWMQlUi8AQtRCbF+/Xrs2LEDeXl5uHDhAvr27Ytt27bh2LFjeP3112G1WrFs2TIYjUZUqlQJEyZMwObNm/HDDz8gLy8Pp06dwnPPPYeGDRtiw4YNMBgMeOihhwAA48ePd9+3ee7cuQgPD1fyqxL5NRY3UQlisViwZMkSfPXVV1i6dCnWrl2LPXv2YOnSpThx4gQ2bNiAkJAQTJkyBWvWrIHJZEJOTg4WL14Ms9mMQYMGoUuXLujcuTNKly6NmjVrAgC6du2KevXqYcSIEfjpp5/Qpk0bhb8pkf/ioXKiEuTqtIOhoaGoWrUqBEFAeHg4cnNzUa1aNYSEhAAouF3jsWPHAAD3338/AKBs2bKw2WxFLrdGjRoAgNKlSyMvL0/ur0FUorG4iUqQG03vKQgCTpw4AavVCgDYu3cvKleufMPPCIIAl8t10+USkfexuIkIoihi8ODB6Nu3L7p374709HT06NHjhu+vUaMGVqxYgd27d/swJREBnGSEiIhIU7jHTUREpCEsbiIiIg1hcRMREWkIi5uIiEhDWNxEREQawuImIiLSEBY3ERGRhrC4iYiINOT/AVb4M9yCtW3QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax=sns.lineplot(data=df_ABC_metric_quarter[(df_ABC_metric_quarter[\"class_profit\"]== \"A\")& (df_ABC_metric_quarter[\"class_quantity\"]== \"A\")& (df_ABC_metric_quarter[\"class_revenue\"]== \"C\")]\n",
    "            , x=\"month\", y=\"Pop_Index\",color='red').set(title='Popularity index for products of class A-A-C')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax=sns.lineplot(data=df_ABC_metric_quarter[(df_ABC_metric_quarter[\"class_profit\"]== \"A\")& (df_ABC_metric_quarter[\"class_quantity\"]== \"A\")& (df_ABC_metric_quarter[\"class_revenue\"]== \"A\")]\n",
    "            , x=\"month\", y=\"P_rank_norm\",color='green').set(title='Trends of profit metric for products of class A-A-A')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax=sns.lineplot(data=df_ABC_metric_quarter[(df_ABC_metric_quarter[\"class_profit\"]== \"B\")& (df_ABC_metric_quarter[\"class_quantity\"]== \"A\")& (df_ABC_metric_quarter[\"class_revenue\"]== \"B\")]\n",
    "            , x=\"month\", y=\"P_rank_norm\",color='blue').set(title='Trends of profit metric for products of class A-A-B')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax=sns.lineplot(data=df_ABC_metric_quarter[(df_ABC_metric_quarter[\"class_profit\"]== \"C\")& (df_ABC_metric_quarter[\"class_quantity\"]== \"A\")& (df_ABC_metric_quarter[\"class_revenue\"]== \"C\")]\n",
    "            , x=\"month\", y=\"P_rank_norm\",color='red').set(title='Trends of profit metric for products of class A-A-C')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax=sns.lineplot(data=df_ABC_metric_quarter[(df_ABC_metric_quarter[\"class_profit\"]== \"A\")& (df_ABC_metric_quarter[\"class_quantity\"]== \"A\")& (df_ABC_metric_quarter[\"class_revenue\"]== \"A\")]\n",
    "            , x=\"month\", y=\"R_rank_norm\",color='green').set(title='Trends of revenue metric for products of class A-A-A')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax=sns.lineplot(data=df_ABC_metric_quarter[(df_ABC_metric_quarter[\"class_profit\"]== \"B\")& (df_ABC_metric_quarter[\"class_quantity\"]== \"A\")& (df_ABC_metric_quarter[\"class_revenue\"]== \"B\")]\n",
    "            , x=\"month\", y=\"R_rank_norm\",color='blue').set(title='Trends of revenue metric for products of class A-A-B')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax=sns.lineplot(data=df_ABC_metric_quarter[(df_ABC_metric_quarter[\"class_profit\"]== \"C\")& (df_ABC_metric_quarter[\"class_quantity\"]== \"A\")& (df_ABC_metric_quarter[\"class_revenue\"]== \"C\")]\n",
    "            , x=\"month\", y=\"R_rank_norm\",color='red').set(title='Trends of revenue metric for products of class A-A-C')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PjmbNmkW7du3ioIMOioULF64XgEtLS2P06NHRs2fP2GeffeKYY46peu3mm2+OcePGRd++faO8vLzqF23XrFkTM2fOjD59+sSuu+4ae+65Z4wbN26jNaprn9SmdevWcfjhh8cpp5yy3i9rbqhz585xzjnnxMiRI6NQKMRuu+0WP/vZz6pdDa5LXfNcvXp1DBo0KFauXBlDhw6NTp06RUTEZZddFuecc060atUqevXqVa3fPfbYI84999w49dRTY9ddd402bdpEhw4dYuHChbHvvvtudD4HHXRQ7LTTTjFo0KD43e9+F4VCIUpKSmLatGkN+ssvF1xwQYwdOzZOPvnkaNWqVbRr167qtY3t93vvvTfef//9uOWWW6Jp06bRuXPnuOaaa2r8xWBg+1RIdV36AL72rrzyyjj44IPj7LPPzj0VvmZKS0vjzDPPrDFsby0VFRVx/vnnR79+/aJ3797Z5gHsmNymAgAb8e6770anTp2iZcuWWb8QADsuV8YBACATV8YBACATYRwAADIRxgEAIJNa/7RhZWVlrFnT8FvKi4oKjdIP9aP+eal/Xuqfl/rno/Z5qX9e22L9mzYtqvH5WsP4mjUpli9f1eDBW7TYtVH6oX7UPy/1z0v981L/fNQ+L/XPa1usf+vW1f835wi3qQAAQDbCOAAAZCKMAwBAJsI4AABkIowDAEAmwjgAAGQijAMAQCbCOAAAZCKMAwBAJsI4AABkIowDAEAmwjgAAGQijAMAQCbCOAAAZCKMAwBAJsI4AABkIowDAEAmwjgAAGQijAMAQCbCOAAAZCKMAwBAJsI4AABkIowDAEAmwjgAAGQijAMAQCbCOAAAZCKMAwBAJsI4AABkIowDAEAmxbknsCX9+tf/EQsWzKtX2+XLP4mIiBYtWjbmlOrlgAMOjO9+95zc0wAAoJHt0GF8wYJ58cZf3o41u7ba7LZFqz6OiIhFn61p7Glt5jyWZR0fAIAtZ4cO4xERa3ZtFZ8f0nuz2+3y1rSIiHq1bUxr5wEAwI7HPeMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJls02H8j3+cEX/844zc02Ab5NgAAHYExbknUJsZMx6PiIiuXbtnngnbGscGALAj2KavjAMAwI5MGAcAgEyEcQAAyEQYBwCATIRxAADIRBgHAIBMhHEAAMhEGAcAgEyEcQAAyEQYBwCATIRxAADIRBgHAIBMhHEAAMhEGAcAgEyEcQAAyEQYBwCATIRxAADIRBgHAIBMhHEAAMhEGAcAgEyEcQAAyEQYBwCATIRxAADIRBgHAIBMhHEAAMhEGAcAgEyEcQAAyEQYBwCATIRxAADIRBgHAIBMhHEAAMhEGAcAgEyEcQAAyEQYBwCATIRxAADIRBgHAIBMhHEAAMhEGAcAgEyEcQAAyEQYBwCATIRxAADIRBgHAIBMhHEAAMhEGAcAgEyEcQAAyEQYBwCATIRxAADIRBgHAIBMhHEAAMhEGAcAgEyEcQAAyEQYBwCATIRxAADIRBgHAIBMhHEAAMhEGAcAgEyEcQAAyEQYBwCATIRxAADIRBgHAIBMhHEAAMhEGGeH9Mkny+IHP7gyPvnkk/Wenz9/XgwbdnosWDC/0fqsa9uPP/54k9vVNM7ax/Pnz4+rrhodV1116Xp9rbv9J58sq3Gbhqylvm0bMkZd/a67xo2Ns/b5uXPnbpF5NIYtVSMaX037qjH2X2191Kf/zW1T1/Y5jtGv8/uivp8zDemnPttvb7b19Qnj7JCmTJkcb73155g6dfJ6z996683x+eer4tZbb260Puva9t57J21yu5rGWfv4tttujnfe+Uu8887b6/W17vZTpkyucZuGrKW+bRsyRl39rrvGjY2z9vmJEydskXk0hi1VIxpfTfuqMfZfbX3Up//NbVPX9jmO0a/z+6K+nzMN6ac+229vtvX1CePscD75ZFk8+eT0SCnFk08+UfVNeP78ebFkyeKIiFi8eNFmXR3fWJ91bTtjxuPx2GOPbVK7msZZsGBe1ePFixdVbTdjxuNVV4XXHWvGjCeqbdOQtdS3bUPGqHv8/1vj9On/E08++US1cdYdf+HCBY0+j8awpWpE46tpXzXG/qutj/r0v2GbZcuW1Xv8+s6hob7O74v6fs409NjZ0Wu+PayvOPcEarN8+fJYvnxZjB07pl7tFyyYF4XKpo08q62r8OXnsWDBvHrXICKiuLgoKirWNOKs8luwYF60aNGqxtemTJkcKVVGRERlZWVMnTo5vve9f6l2NfzWW2+OH//49k0ab2N91rVtRUVF1fN1tatpnFtu+VHV43VVVFTE1KmTI6W03lgppWrbbDje5qylrvltrG1Dxqhr/HVrWlFREYVCodo4646/VmPOozFsqRrR+GraV+u+9+q7/2o7BupzfGzY5je/mRTDhp1Tr/HrO4eG+jq/L+r7OdPQY2dHr/n2sD5XxtnhzJr1VFVgq6ioiJkzn4yIqLoqvta6V5rr22dd2371gZ02qV1N4yxZsmi98LlWSilmznyy2lg1bdOQtdS3bUPGqGv8mta54Tjrjr9WY86jMcyatWVqROObNav6vqrpucbod1Ne29T+pk9/YrO233CM+syhoXKMua2YNat+nzMNPXY2d/vtzaxZ2/76tukr4y1atIgWLVrED384sV7tx44dE68t/qiRZ7V1paa7xAH77lvvGkREtGixayxfvqoRZ5VfbT8p6NKlW8yY8XhUVFREcXFxHHfc8RERsc8++64XyPfdd79NHm9jfda17dqrtimlOtvVNM7/+3//X/ztb+9VC5aFQiGOO+74qttT1o61blBdu01D1lLftg0Zo67xH3/8sWrr3LC+646/VmPOozFsqRrR+GraV+u+9+q7/2o7BupzfGzY5oQTetR7/PrOoaG+zu+L+n7ONPTY2dFrvj2sz5VxdjiDBg2JQuGrQ7tJkyYxcOCQiIi48MLR62234eP69FnXtsXFxVFcXLxJ7WoaZ9SoS6ser6u4uDgGDhxSw1hNq23TkLXUt21Dxqhr/LX1jNh4fdcdf63GnEdj2FI1ovHVtK8aY//V1kd9+t+wzVlnldZ7/PrOoaG+zu+L+n7ONPTY2dFrvj2sTxhnh9OyZas4/vgTolAoxPHH94iWLVtGRETbtgfGPvvsGxFfXRU/4IC2De6zrm27dz8xevXqtUntahrngAMOrHq87pX87t1PjJYtW1Ybq3v3HtW2acha6tu2IWPUPf7/rfGEE06K44/vUW2cdcfff/8DGn0ejWFL1YjGV9O+aoz9V1sf9el/wzatWtX8ezWbOkaOY/Tr/L6o7+dMQ4+dHb3m28P6tunbVKC+Bg0aEosXL6r2DfjCC0fH2LFjNuuqeF191rXtHnvsHO++O2+Tv41vOM7ax9/97j/Hf/zH7RGRql0F+b/tU8yfP6/aNg1ZS33bNmSMuvpdf42pxnHWjj9q1Ki45ZZbtsmrIVuqRjS+mvZVY+y/2vqoT/+b26au7XMco1/n90V9P2ca0k99tt/ebOvrK6QNfxtqHV9+uaZR7jWu7z3La+8Lbug9458f0nuz2+7y1rSIiHq1bUy7vDUtDt93L/eMb6Chx8bWtCPWf3ui/nmpfz5qn5f657Ut1r91691rfN5tKgAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJBJce4J1KZ79xNzT4FtlGMDANgRbNNhvGvX7rmnwDbKsQEA7AjcpgIAAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJCJMA4AAJkI4wAAkElx7glsaUWrlsUub02rR7uPIyLq1bYxFa1aFhF7ZZ0DAABbxg4dxg844MB6t12+vCgiIlq0aNlY06mnvRq0DgAAtl07dBj/7nfPyT0FAADYKPeMAwBAJsI4AABkIowDAEAmwjgAAGQijAMAQCbCOAAAZCKMAwBAJsI4AABkIowDAEAmwjgAAGQijAMAQCbCOAAAZCKMAwBAJsI4AABkIowDAEAmwjgAAGQijAMAQCbCOAAAZCKMAwBAJsI4AABkIowDAEAmwjgAAGQijAMAQCbCOAAAZCKMAwBAJsI4AABkIowDAEAmwjgAAGQijAMAQCbCOAAAZCKMAwBAJoWUUso9CQAA+DpyZRwAADIRxgEAIBNhHAAAMhHGAQAgE2EcAAAyEcYBACCT4oZ2UFlZGddee2385S9/iWbNmsX1118f+++/f9XrM2bMiNtvvz2Ki4tj4MCBcdppp9XZhk1XVy0ffvjhuPvuu6OoqCjatWsX1157bTRp0iQGDBgQu+++e0RE7LPPPjFx4sRcS9iu1VX/X//61zFlypRo1apVRET88Ic/jAMOOMDx3whqq/2HH34Yl1xySdW2b775Zlx66aVxxhlnOPYb2auvvho333xzTJo0ab3nnfu3jo3V37l/69hY/Z37t7yaar/dnvtTA/3hD39IV1xxRUoppZdffjmdd955Va+Vl5enHj16pOXLl6cvvvgilZSUpA8++KDWNmye2mr5+eefpxNOOCGtWrUqpZTSxRdfnJ544om0evXq1L9//xzT3eHUdSxfeumlac6cOZvVhk2zqXV86aWXUmlpaaqoqHDsN7Jf/vKXqU+fPmnw4MHrPe/cv3VsrP7O/VvHxuqfknP/llZb7dfans79Db5N5cUXX4wuXbpERET79u3j9ddfr3pt7ty5sd9++8Wee+4ZzZo1i29/+9vxwgsv1NqGzVNbLZs1axaTJ0+OXXbZJSIiKioqYqeddoq33norPv/88xg5cmQMGzYsXnnllRxT3yHUdSy/8cYb8ctf/jLOOOOMuPPOOzepDZtmU+qYUopx48bFtddeG0VFRY79RrbffvvFbbfdVu155/6tY2P1d+7fOjZW/wjn/i2tttpHbH/n/gbfplJWVha77bZb1eOioqKoqKiI4uLiKCsrq/qRQERE8+bNo6ysrNY2bJ7aatmkSZPYa6+9IiJi0qRJsWrVqvjOd74Tb7/9dpx99tkxePDgWLBgQZxzzjnx2GOPqX891HUsn3LKKTF06NDYbbfd4vzzz48nn3zS8d9INqWOM2bMiIMPPjgOPPDAiIjYeeedHfuNqGfPnrFkyZJqzzv3bx0bq79z/9axsfpHOPdvabXVPmL7O/c3eBa77bZbrFy5supxZWVl1eI2fG3lypWx++6719qGzVNXLSsrK+Omm26K+fPnx2233RaFQiHatm0b+++/f9W/W7RoER9++GHsvffeOZawXaut/imlGD58eFUo6dq1a/z5z392/DeSTanjQw89FMOGDat67NjfOpz783Puz8e5P7/t7dzf4NtUOnToEDNnzoyIiFdeeSXatWtX9do//MM/xMKFC2P58uVRXl4eL7zwQhxxxBG1tmHz1FXLH/zgB/HFF1/Ez3/+86ofWU6ZMiVuuOGGiIhYunRplJWVRevWrbfuxHcQtdW/rKws+vTpEytXroyUUjz77LNx2GGHOf4byabU8Y033ogOHTpUPXbsbx3O/fk59+fj3J/f9nbub/BXshNPPDH+9Kc/xZAhQyKlFBMmTIjf//73sWrVqjj99NPjyiuvjLPPPjtSSjFw4MBo06ZNjW2on9rqf9hhh8WUKVPiyCOPjOHDh0dExLBhw2LQoEExZsyYOOOMM6JQKMSECRN8O6+nuo7/iy++OIYNGxbNmjWLTp06RdeuXaOystLx3wjqqv2yZcuiefPmUSgUqto49rcs5/68nPvzcu7PZ3s/9xdSSin3JAAA4OvIf/oDAACZCOMAAJCJMA4AAJkI4wAAkIkwDgAAmQjjAFvBs88+G506dYrS0tIoLS2N0047LSZNmtQofZeWlsbcuXM3adv77ruv1v9GGoCta9v5I4sAO7hjjjkmfvKTn0RERHl5efTq1Sv69+8fe+yxR+aZAZCLMA6QQVlZWTRp0iTefvvt+NGPfhRFRUWx0047xbhx46KysjJGjRoVrVu3jqVLl8Zxxx0XF198cZ193n///fHHP/4xVq9eHYsWLYpzzjknSkpK4oUXXogJEybEnnvuGU2aNIn27dtHRMSkSZPi4YcfjkKhEL17945hw4bFhRdeGN/5zneiX79+MXTo0Bg/fnx885vf3MLVAPj6EsYBtpLZs2dHaWlpFAqFaNq0aVxzzTUxYcKEGD9+fBx66KHxxBNPxA033BCXX355/PWvf4277rordt999xg6dGi88cYb8a1vfavOMcrKyuKuu+6KBQsWxHnnnRclJSUxceLE+NGPfhRt27aNsWPHRkTEu+++G9OmTYv/+q//ikKhECNGjIjOnTvH9ddfH0OHDo2nn346Tj/9dEEcYAtzzzjAVnLMMcfEpEmT4p577om77rorunbtGh988EEceuihERHRsWPHeOeddyIi4pBDDokWLVpEUVFRHH744TF//vxNGuOQQw6JiIi99947ysvLIyJi6dKl0bZt24iI6NChQ0REvP322/Hee+/FiBEjYvjw4bF8+fJYtGhR7LHHHtGvX7944YUX4tRTT23U9QNQnTAOkNHf//3fx1tvvRUREc8//3wccMABERExd+7c+Pzzz2PNmjXx2muvxUEHHbRJ/RUKhWrPtW7duuoXPOfMmRMREQceeGAcdNBBcc8998SkSZOipKQk2rVrF4sXL45HHnkkSktL49///d8bYYUA1MZtKgAZXX/99TFu3LhIKUVRUVFMmDAhIiKaNm0ao0aNio8++ih69epVdcW7Pm666aa44ooronnz5tG8efPYc88945BDDolOnTrFGWecEeXl5XH44YfHXnvtFaWlpXH11VfHkUceGSNGjIgnnngievTo0VjLBWADhZRSyj0JAP7PkiVL4pJLLonf/va3uacCwBbmyjjAdmD69Onxn//5n9WeHzZsWJx44olbf0IANApXxgEAIBO/wAkAAJkI4wAAkIkwDgAAmQjjAACQiTAOAACZCOMAAJDJ/w8o8RdFio9tNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pop_Index's boxplot\n",
    "plt.figure(figsize=(13,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "_=sns.boxplot(data=df_ABC_metric_quarter, x='Pop_Index').set(title='Boxplot of score values for the Popularity index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_profit</th>\n",
       "      <th>class_quantity</th>\n",
       "      <th>class_revenue</th>\n",
       "      <th>month</th>\n",
       "      <th>product_id</th>\n",
       "      <th>total_stock</th>\n",
       "      <th>quantity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>P_rank_norm</th>\n",
       "      <th>R_rank_norm</th>\n",
       "      <th>Q_rank_norm</th>\n",
       "      <th>Pop_Index</th>\n",
       "      <th>CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>367</td>\n",
       "      <td>359312</td>\n",
       "      <td>3649</td>\n",
       "      <td>53114632.5</td>\n",
       "      <td>1.737238</td>\n",
       "      <td>1.426300</td>\n",
       "      <td>1.429217</td>\n",
       "      <td>1.536879</td>\n",
       "      <td>AAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>386</td>\n",
       "      <td>313450</td>\n",
       "      <td>3097</td>\n",
       "      <td>49788811.5</td>\n",
       "      <td>1.658606</td>\n",
       "      <td>1.503681</td>\n",
       "      <td>1.192628</td>\n",
       "      <td>1.371273</td>\n",
       "      <td>AAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>317</td>\n",
       "      <td>440861</td>\n",
       "      <td>5180</td>\n",
       "      <td>62853573.0</td>\n",
       "      <td>1.935504</td>\n",
       "      <td>1.477651</td>\n",
       "      <td>1.386872</td>\n",
       "      <td>1.583432</td>\n",
       "      <td>AAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>428</td>\n",
       "      <td>428704</td>\n",
       "      <td>4592</td>\n",
       "      <td>69810775.0</td>\n",
       "      <td>1.521091</td>\n",
       "      <td>1.426610</td>\n",
       "      <td>1.117514</td>\n",
       "      <td>1.274221</td>\n",
       "      <td>AAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>407</td>\n",
       "      <td>276715</td>\n",
       "      <td>3119</td>\n",
       "      <td>49084995.5</td>\n",
       "      <td>1.571406</td>\n",
       "      <td>1.479061</td>\n",
       "      <td>0.947415</td>\n",
       "      <td>1.192394</td>\n",
       "      <td>AAA</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>8</td>\n",
       "      <td>886</td>\n",
       "      <td>70844</td>\n",
       "      <td>886</td>\n",
       "      <td>4549207.0</td>\n",
       "      <td>0.048219</td>\n",
       "      <td>0.043610</td>\n",
       "      <td>0.037291</td>\n",
       "      <td>0.041432</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>9</td>\n",
       "      <td>932</td>\n",
       "      <td>79753</td>\n",
       "      <td>932</td>\n",
       "      <td>5225839.0</td>\n",
       "      <td>0.049658</td>\n",
       "      <td>0.044288</td>\n",
       "      <td>0.038048</td>\n",
       "      <td>0.042424</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>10</td>\n",
       "      <td>785</td>\n",
       "      <td>81488</td>\n",
       "      <td>785</td>\n",
       "      <td>3261137.0</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.043793</td>\n",
       "      <td>0.038938</td>\n",
       "      <td>0.041983</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>11</td>\n",
       "      <td>1185</td>\n",
       "      <td>123372</td>\n",
       "      <td>1392</td>\n",
       "      <td>7513962.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.040253</td>\n",
       "      <td>0.035351</td>\n",
       "      <td>0.038755</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>12</td>\n",
       "      <td>1271</td>\n",
       "      <td>190115</td>\n",
       "      <td>1622</td>\n",
       "      <td>8275966.0</td>\n",
       "      <td>0.046375</td>\n",
       "      <td>0.040718</td>\n",
       "      <td>0.035898</td>\n",
       "      <td>0.039806</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>318 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    class_profit class_quantity class_revenue  month  product_id  total_stock  \\\n",
       "0              A              A             A      1         367       359312   \n",
       "1              A              A             A      2         386       313450   \n",
       "2              A              A             A      3         317       440861   \n",
       "3              A              A             A      4         428       428704   \n",
       "4              A              A             A      5         407       276715   \n",
       "..           ...            ...           ...    ...         ...          ...   \n",
       "313            C              C             C      8         886        70844   \n",
       "314            C              C             C      9         932        79753   \n",
       "315            C              C             C     10         785        81488   \n",
       "316            C              C             C     11        1185       123372   \n",
       "317            C              C             C     12        1271       190115   \n",
       "\n",
       "     quantity     revenue  P_rank_norm  R_rank_norm  Q_rank_norm  Pop_Index  \\\n",
       "0        3649  53114632.5     1.737238     1.426300     1.429217   1.536879   \n",
       "1        3097  49788811.5     1.658606     1.503681     1.192628   1.371273   \n",
       "2        5180  62853573.0     1.935504     1.477651     1.386872   1.583432   \n",
       "3        4592  69810775.0     1.521091     1.426610     1.117514   1.274221   \n",
       "4        3119  49084995.5     1.571406     1.479061     0.947415   1.192394   \n",
       "..        ...         ...          ...          ...          ...        ...   \n",
       "313       886   4549207.0     0.048219     0.043610     0.037291   0.041432   \n",
       "314       932   5225839.0     0.049658     0.044288     0.038048   0.042424   \n",
       "315       785   3261137.0     0.046946     0.043793     0.038938   0.041983   \n",
       "316      1392   7513962.0     0.044378     0.040253     0.035351   0.038755   \n",
       "317      1622   8275966.0     0.046375     0.040718     0.035898   0.039806   \n",
       "\n",
       "    CLASS  \n",
       "0     AAA  \n",
       "1     AAA  \n",
       "2     AAA  \n",
       "3     AAA  \n",
       "4     AAA  \n",
       "..    ...  \n",
       "313   CCC  \n",
       "314   CCC  \n",
       "315   CCC  \n",
       "316   CCC  \n",
       "317   CCC  \n",
       "\n",
       "[318 rows x 13 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ABC_metric_quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>product_id</th>\n",
       "      <th>total_stock</th>\n",
       "      <th>quantity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>P_rank_norm</th>\n",
       "      <th>R_rank_norm</th>\n",
       "      <th>Q_rank_norm</th>\n",
       "      <th>Pop_Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLASS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAA</th>\n",
       "      <td>78</td>\n",
       "      <td>4316</td>\n",
       "      <td>4175761</td>\n",
       "      <td>48015</td>\n",
       "      <td>756091045.0</td>\n",
       "      <td>21.719749</td>\n",
       "      <td>16.597332</td>\n",
       "      <td>13.911934</td>\n",
       "      <td>16.778939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAB</th>\n",
       "      <td>78</td>\n",
       "      <td>457</td>\n",
       "      <td>768093</td>\n",
       "      <td>4007</td>\n",
       "      <td>13901147.5</td>\n",
       "      <td>3.713521</td>\n",
       "      <td>1.257150</td>\n",
       "      <td>7.381738</td>\n",
       "      <td>5.791632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAC</th>\n",
       "      <td>78</td>\n",
       "      <td>92</td>\n",
       "      <td>192401</td>\n",
       "      <td>710</td>\n",
       "      <td>1447077.0</td>\n",
       "      <td>3.113188</td>\n",
       "      <td>0.866379</td>\n",
       "      <td>5.108946</td>\n",
       "      <td>4.198302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABA</th>\n",
       "      <td>78</td>\n",
       "      <td>605</td>\n",
       "      <td>48371</td>\n",
       "      <td>1285</td>\n",
       "      <td>40766970.0</td>\n",
       "      <td>4.668608</td>\n",
       "      <td>2.549508</td>\n",
       "      <td>0.737935</td>\n",
       "      <td>2.204249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABB</th>\n",
       "      <td>78</td>\n",
       "      <td>136</td>\n",
       "      <td>11796</td>\n",
       "      <td>259</td>\n",
       "      <td>4126603.0</td>\n",
       "      <td>3.569387</td>\n",
       "      <td>1.264687</td>\n",
       "      <td>0.728677</td>\n",
       "      <td>1.749726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABC</th>\n",
       "      <td>55</td>\n",
       "      <td>21</td>\n",
       "      <td>3662</td>\n",
       "      <td>37</td>\n",
       "      <td>351738.0</td>\n",
       "      <td>1.815021</td>\n",
       "      <td>0.592403</td>\n",
       "      <td>0.495274</td>\n",
       "      <td>0.962042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACA</th>\n",
       "      <td>78</td>\n",
       "      <td>206</td>\n",
       "      <td>5639</td>\n",
       "      <td>258</td>\n",
       "      <td>9730085.0</td>\n",
       "      <td>4.321185</td>\n",
       "      <td>1.928656</td>\n",
       "      <td>0.509383</td>\n",
       "      <td>1.914477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACB</th>\n",
       "      <td>78</td>\n",
       "      <td>218</td>\n",
       "      <td>7175</td>\n",
       "      <td>231</td>\n",
       "      <td>6739397.0</td>\n",
       "      <td>3.266785</td>\n",
       "      <td>1.268293</td>\n",
       "      <td>0.467113</td>\n",
       "      <td>1.487057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC</th>\n",
       "      <td>60</td>\n",
       "      <td>26</td>\n",
       "      <td>725</td>\n",
       "      <td>26</td>\n",
       "      <td>424120.0</td>\n",
       "      <td>2.434619</td>\n",
       "      <td>0.729105</td>\n",
       "      <td>0.410444</td>\n",
       "      <td>1.134838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAA</th>\n",
       "      <td>78</td>\n",
       "      <td>637</td>\n",
       "      <td>306156</td>\n",
       "      <td>4243</td>\n",
       "      <td>58167947.5</td>\n",
       "      <td>2.147253</td>\n",
       "      <td>4.034000</td>\n",
       "      <td>3.579671</td>\n",
       "      <td>3.101041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAB</th>\n",
       "      <td>78</td>\n",
       "      <td>443</td>\n",
       "      <td>436541</td>\n",
       "      <td>3329</td>\n",
       "      <td>12917494.5</td>\n",
       "      <td>2.105326</td>\n",
       "      <td>1.202719</td>\n",
       "      <td>4.760629</td>\n",
       "      <td>3.653377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAC</th>\n",
       "      <td>78</td>\n",
       "      <td>264</td>\n",
       "      <td>414147</td>\n",
       "      <td>1673</td>\n",
       "      <td>3960692.5</td>\n",
       "      <td>2.050555</td>\n",
       "      <td>0.840356</td>\n",
       "      <td>4.406503</td>\n",
       "      <td>3.403614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBA</th>\n",
       "      <td>78</td>\n",
       "      <td>256</td>\n",
       "      <td>15841</td>\n",
       "      <td>521</td>\n",
       "      <td>15239699.0</td>\n",
       "      <td>2.131798</td>\n",
       "      <td>2.236342</td>\n",
       "      <td>0.725493</td>\n",
       "      <td>1.293242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBB</th>\n",
       "      <td>78</td>\n",
       "      <td>174</td>\n",
       "      <td>17642</td>\n",
       "      <td>330</td>\n",
       "      <td>4964400.0</td>\n",
       "      <td>2.099848</td>\n",
       "      <td>1.189181</td>\n",
       "      <td>0.703480</td>\n",
       "      <td>1.216494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBC</th>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "      <td>10567</td>\n",
       "      <td>168</td>\n",
       "      <td>1261786.0</td>\n",
       "      <td>2.021946</td>\n",
       "      <td>0.846829</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>1.189584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCA</th>\n",
       "      <td>78</td>\n",
       "      <td>113</td>\n",
       "      <td>2921</td>\n",
       "      <td>141</td>\n",
       "      <td>5102290.0</td>\n",
       "      <td>2.114286</td>\n",
       "      <td>1.727780</td>\n",
       "      <td>0.489088</td>\n",
       "      <td>1.119842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCB</th>\n",
       "      <td>78</td>\n",
       "      <td>305</td>\n",
       "      <td>7828</td>\n",
       "      <td>319</td>\n",
       "      <td>9118117.0</td>\n",
       "      <td>2.072644</td>\n",
       "      <td>1.229198</td>\n",
       "      <td>0.467200</td>\n",
       "      <td>1.067205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCC</th>\n",
       "      <td>78</td>\n",
       "      <td>99</td>\n",
       "      <td>4977</td>\n",
       "      <td>102</td>\n",
       "      <td>1516924.0</td>\n",
       "      <td>2.038360</td>\n",
       "      <td>0.854525</td>\n",
       "      <td>0.461362</td>\n",
       "      <td>1.032970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAA</th>\n",
       "      <td>78</td>\n",
       "      <td>2323</td>\n",
       "      <td>1603311</td>\n",
       "      <td>22192</td>\n",
       "      <td>327741916.5</td>\n",
       "      <td>0.928622</td>\n",
       "      <td>11.131212</td>\n",
       "      <td>9.865250</td>\n",
       "      <td>6.800729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAB</th>\n",
       "      <td>78</td>\n",
       "      <td>1349</td>\n",
       "      <td>930633</td>\n",
       "      <td>9255</td>\n",
       "      <td>40429200.5</td>\n",
       "      <td>1.019611</td>\n",
       "      <td>1.184893</td>\n",
       "      <td>3.241708</td>\n",
       "      <td>2.361134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAC</th>\n",
       "      <td>78</td>\n",
       "      <td>6721</td>\n",
       "      <td>4083752</td>\n",
       "      <td>32823</td>\n",
       "      <td>57837298.0</td>\n",
       "      <td>0.782530</td>\n",
       "      <td>0.638923</td>\n",
       "      <td>2.267267</td>\n",
       "      <td>1.666192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CBA</th>\n",
       "      <td>78</td>\n",
       "      <td>791</td>\n",
       "      <td>52359</td>\n",
       "      <td>1726</td>\n",
       "      <td>48583464.0</td>\n",
       "      <td>1.004078</td>\n",
       "      <td>2.160957</td>\n",
       "      <td>0.730603</td>\n",
       "      <td>0.897837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CBB</th>\n",
       "      <td>78</td>\n",
       "      <td>664</td>\n",
       "      <td>54284</td>\n",
       "      <td>1314</td>\n",
       "      <td>20040828.0</td>\n",
       "      <td>0.974479</td>\n",
       "      <td>1.201929</td>\n",
       "      <td>0.709122</td>\n",
       "      <td>0.826638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CBC</th>\n",
       "      <td>78</td>\n",
       "      <td>4995</td>\n",
       "      <td>1006413</td>\n",
       "      <td>10153</td>\n",
       "      <td>32401622.5</td>\n",
       "      <td>0.661314</td>\n",
       "      <td>0.574702</td>\n",
       "      <td>0.712413</td>\n",
       "      <td>0.687643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCA</th>\n",
       "      <td>78</td>\n",
       "      <td>363</td>\n",
       "      <td>8775</td>\n",
       "      <td>469</td>\n",
       "      <td>17523352.0</td>\n",
       "      <td>0.972313</td>\n",
       "      <td>1.817838</td>\n",
       "      <td>0.499230</td>\n",
       "      <td>0.730739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCB</th>\n",
       "      <td>78</td>\n",
       "      <td>1617</td>\n",
       "      <td>46047</td>\n",
       "      <td>1732</td>\n",
       "      <td>47711026.0</td>\n",
       "      <td>0.941182</td>\n",
       "      <td>1.177898</td>\n",
       "      <td>0.465288</td>\n",
       "      <td>0.667481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCC</th>\n",
       "      <td>78</td>\n",
       "      <td>11528</td>\n",
       "      <td>1189568</td>\n",
       "      <td>12510</td>\n",
       "      <td>60793419.0</td>\n",
       "      <td>0.585106</td>\n",
       "      <td>0.533297</td>\n",
       "      <td>0.466276</td>\n",
       "      <td>0.511218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  product_id  total_stock  quantity      revenue  P_rank_norm  \\\n",
       "CLASS                                                                       \n",
       "AAA       78        4316      4175761     48015  756091045.0    21.719749   \n",
       "AAB       78         457       768093      4007   13901147.5     3.713521   \n",
       "AAC       78          92       192401       710    1447077.0     3.113188   \n",
       "ABA       78         605        48371      1285   40766970.0     4.668608   \n",
       "ABB       78         136        11796       259    4126603.0     3.569387   \n",
       "ABC       55          21         3662        37     351738.0     1.815021   \n",
       "ACA       78         206         5639       258    9730085.0     4.321185   \n",
       "ACB       78         218         7175       231    6739397.0     3.266785   \n",
       "ACC       60          26          725        26     424120.0     2.434619   \n",
       "BAA       78         637       306156      4243   58167947.5     2.147253   \n",
       "BAB       78         443       436541      3329   12917494.5     2.105326   \n",
       "BAC       78         264       414147      1673    3960692.5     2.050555   \n",
       "BBA       78         256        15841       521   15239699.0     2.131798   \n",
       "BBB       78         174        17642       330    4964400.0     2.099848   \n",
       "BBC       78          84        10567       168    1261786.0     2.021946   \n",
       "BCA       78         113         2921       141    5102290.0     2.114286   \n",
       "BCB       78         305         7828       319    9118117.0     2.072644   \n",
       "BCC       78          99         4977       102    1516924.0     2.038360   \n",
       "CAA       78        2323      1603311     22192  327741916.5     0.928622   \n",
       "CAB       78        1349       930633      9255   40429200.5     1.019611   \n",
       "CAC       78        6721      4083752     32823   57837298.0     0.782530   \n",
       "CBA       78         791        52359      1726   48583464.0     1.004078   \n",
       "CBB       78         664        54284      1314   20040828.0     0.974479   \n",
       "CBC       78        4995      1006413     10153   32401622.5     0.661314   \n",
       "CCA       78         363         8775       469   17523352.0     0.972313   \n",
       "CCB       78        1617        46047      1732   47711026.0     0.941182   \n",
       "CCC       78       11528      1189568     12510   60793419.0     0.585106   \n",
       "\n",
       "       R_rank_norm  Q_rank_norm  Pop_Index  \n",
       "CLASS                                       \n",
       "AAA      16.597332    13.911934  16.778939  \n",
       "AAB       1.257150     7.381738   5.791632  \n",
       "AAC       0.866379     5.108946   4.198302  \n",
       "ABA       2.549508     0.737935   2.204249  \n",
       "ABB       1.264687     0.728677   1.749726  \n",
       "ABC       0.592403     0.495274   0.962042  \n",
       "ACA       1.928656     0.509383   1.914477  \n",
       "ACB       1.268293     0.467113   1.487057  \n",
       "ACC       0.729105     0.410444   1.134838  \n",
       "BAA       4.034000     3.579671   3.101041  \n",
       "BAB       1.202719     4.760629   3.653377  \n",
       "BAC       0.840356     4.406503   3.403614  \n",
       "BBA       2.236342     0.725493   1.293242  \n",
       "BBB       1.189181     0.703480   1.216494  \n",
       "BBC       0.846829     0.732603   1.189584  \n",
       "BCA       1.727780     0.489088   1.119842  \n",
       "BCB       1.229198     0.467200   1.067205  \n",
       "BCC       0.854525     0.461362   1.032970  \n",
       "CAA      11.131212     9.865250   6.800729  \n",
       "CAB       1.184893     3.241708   2.361134  \n",
       "CAC       0.638923     2.267267   1.666192  \n",
       "CBA       2.160957     0.730603   0.897837  \n",
       "CBB       1.201929     0.709122   0.826638  \n",
       "CBC       0.574702     0.712413   0.687643  \n",
       "CCA       1.817838     0.499230   0.730739  \n",
       "CCB       1.177898     0.465288   0.667481  \n",
       "CCC       0.533297     0.466276   0.511218  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_ABC_metric_quarter.groupby(\"CLASS\").sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "pie = df_ABC_metric_quarter.groupby(\"CLASS\").sum()[\"revenue\"].reset_index().sort_values(\"revenue\",ascending=False)[0:7]\n",
    "plt.pie(pie[\"revenue\"],labels=pie[\"CLASS\"], autopct='%.0f%%')\n",
    "plt.title('revenue of first seven classes',fontsize='15')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "pie = df_ABC_metric_quarter.groupby(\"CLASS\").sum()[\"quantity\"].reset_index().sort_values(\"quantity\",ascending=False)[0:7]\n",
    "plt.pie(pie[\"quantity\"],labels=pie[\"CLASS\"], autopct='%.0f%%')\n",
    "plt.title('quantity of first seven classes',fontsize='15')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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