data_WRONG.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rw_sls = pd.read_csv('sales.csv')\n",
    "rw_sku = pd.read_csv('sku.csv')\n",
    "rw_test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SKU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SKU</th>\n",
       "      <th>Category</th>\n",
       "      <th>Type</th>\n",
       "      <th>brandId</th>\n",
       "      <th>lagerUnitQuantity</th>\n",
       "      <th>Units</th>\n",
       "      <th>trademark</th>\n",
       "      <th>countryOfOrigin</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>Pomegranate</td>\n",
       "      <td>Tropical fruit — Pomegranate— Plain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tropical fruits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>208</td>\n",
       "      <td>Water, sparkling</td>\n",
       "      <td>Therapeutic-table water — PET — from 1 to 2 L</td>\n",
       "      <td>1241.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3670.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mineral water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1008</td>\n",
       "      <td>Water, import, sparkling</td>\n",
       "      <td>Therapeutic-table water — Import — Glass</td>\n",
       "      <td>1241.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4970.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Mineral water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16649</td>\n",
       "      <td>Water, still</td>\n",
       "      <td>Table water — PET — from 1 to 2 L — Still</td>\n",
       "      <td>1241.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1323.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mineral water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20872</td>\n",
       "      <td>Water, sparkling</td>\n",
       "      <td>Therapeutic-table water — PET — from 1 to 2 L</td>\n",
       "      <td>2693.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4384.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mineral water</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SKU                  Category  \\\n",
       "0     24               Pomegranate   \n",
       "1    208          Water, sparkling   \n",
       "2   1008  Water, import, sparkling   \n",
       "3  16649              Water, still   \n",
       "4  20872          Water, sparkling   \n",
       "\n",
       "                                            Type  brandId  lagerUnitQuantity  \\\n",
       "0            Tropical fruit — Pomegranate— Plain      NaN                1.0   \n",
       "1  Therapeutic-table water — PET — from 1 to 2 L   1241.0                1.5   \n",
       "2       Therapeutic-table water — Import — Glass   1241.0                0.5   \n",
       "3      Table water — PET — from 1 to 2 L — Still   1241.0                1.5   \n",
       "4  Therapeutic-table water — PET — from 1 to 2 L   2693.0                1.5   \n",
       "\n",
       "   Units  trademark  countryOfOrigin            Group  \n",
       "0    NaN        NaN              NaN  Tropical fruits  \n",
       "1    NaN     3670.0              1.0    Mineral water  \n",
       "2    NaN     4970.0             14.0    Mineral water  \n",
       "3    NaN     1323.0              1.0    Mineral water  \n",
       "4    NaN     4384.0              1.0    Mineral water  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rw_sku.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SKU</th>\n",
       "      <th>Category</th>\n",
       "      <th>Type</th>\n",
       "      <th>brandId</th>\n",
       "      <th>lagerUnitQuantity</th>\n",
       "      <th>Units</th>\n",
       "      <th>trademark</th>\n",
       "      <th>countryOfOrigin</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Semi-hard coarse-pored cheese</td>\n",
       "      <td>semi-hard cheese — More expensive — National —...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>386478.483333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3599.625000</td>\n",
       "      <td>129.254167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7092.285714</td>\n",
       "      <td>6.878788</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>288483.152114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2648.397674</td>\n",
       "      <td>169.380298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4753.617202</td>\n",
       "      <td>10.361460</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>967.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>63214.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1330.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2781.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>406352.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2737.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6133.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>639679.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4677.500000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9666.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>838137.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8314.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15156.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  SKU                       Category  \\\n",
       "count       60.000000                             55   \n",
       "unique            NaN                             39   \n",
       "top               NaN  Semi-hard coarse-pored cheese   \n",
       "freq              NaN                              4   \n",
       "mean    386478.483333                            NaN   \n",
       "std     288483.152114                            NaN   \n",
       "min         24.000000                            NaN   \n",
       "25%      63214.000000                            NaN   \n",
       "50%     406352.500000                            NaN   \n",
       "75%     639679.750000                            NaN   \n",
       "max     838137.000000                            NaN   \n",
       "\n",
       "                                                     Type      brandId  \\\n",
       "count                                                  55    32.000000   \n",
       "unique                                                 38          NaN   \n",
       "top     semi-hard cheese — More expensive — National —...          NaN   \n",
       "freq                                                    5          NaN   \n",
       "mean                                                  NaN  3599.625000   \n",
       "std                                                   NaN  2648.397674   \n",
       "min                                                   NaN   967.000000   \n",
       "25%                                                   NaN  1330.000000   \n",
       "50%                                                   NaN  2737.000000   \n",
       "75%                                                   NaN  4677.500000   \n",
       "max                                                   NaN  8314.000000   \n",
       "\n",
       "        lagerUnitQuantity  Units     trademark  countryOfOrigin   Group  \n",
       "count           60.000000    0.0     49.000000        33.000000      60  \n",
       "unique                NaN    NaN           NaN              NaN       5  \n",
       "top                   NaN    NaN           NaN              NaN  Bakery  \n",
       "freq                  NaN    NaN           NaN              NaN      21  \n",
       "mean           129.254167    NaN   7092.285714         6.878788     NaN  \n",
       "std            169.380298    NaN   4753.617202        10.361460     NaN  \n",
       "min              0.500000    NaN    297.000000         1.000000     NaN  \n",
       "25%              1.000000    NaN   2781.000000         1.000000     NaN  \n",
       "50%              2.000000    NaN   6133.000000         1.000000     NaN  \n",
       "75%            300.000000    NaN   9666.000000        14.000000     NaN  \n",
       "max            550.000000    NaN  15156.000000        37.000000     NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rw_sku.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SALES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>geoCluster</th>\n",
       "      <th>SKU</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RR27956459</td>\n",
       "      <td>92</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>38.29</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RR27956474</td>\n",
       "      <td>92</td>\n",
       "      <td>32549</td>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>89.39</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RR27956489</td>\n",
       "      <td>112</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-05-27</td>\n",
       "      <td>45.39</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RR27956490</td>\n",
       "      <td>112</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RR27956491</td>\n",
       "      <td>112</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "           ID  geoCluster    SKU        date  price  sales\n",
       "0  RR27956459          92  32485  2021-07-05  38.29    4.0\n",
       "1  RR27956474          92  32549  2021-07-05  89.39    0.5\n",
       "2  RR27956489         112  32485  2021-05-27  45.39    3.7\n",
       "3  RR27956490         112  32485  2021-05-28    NaN    NaN\n",
       "4  RR27956491         112  32485  2021-05-29    NaN    NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rw_sls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>geoCluster</th>\n",
       "      <th>SKU</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>sales</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4605985</td>\n",
       "      <td>4.605985e+06</td>\n",
       "      <td>4.605985e+06</td>\n",
       "      <td>4605985</td>\n",
       "      <td>778366.000000</td>\n",
       "      <td>778366.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4605985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>RR27956459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14361</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.220031e+03</td>\n",
       "      <td>3.671353e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.490666</td>\n",
       "      <td>3.593748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.459249e+02</td>\n",
       "      <td>3.005135e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.631611</td>\n",
       "      <td>4.627158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.200000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.049000e+03</td>\n",
       "      <td>3.946500e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.190000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.158000e+03</td>\n",
       "      <td>3.637130e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.490000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.269000e+03</td>\n",
       "      <td>6.427000e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.890000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.230000e+03</td>\n",
       "      <td>8.381370e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7246.890000</td>\n",
       "      <td>801.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID    geoCluster           SKU        date          price  \\\n",
       "count      4605985  4.605985e+06  4.605985e+06     4605985  778366.000000   \n",
       "unique     4605985           NaN           NaN         552            NaN   \n",
       "top     RR27956459           NaN           NaN  2021-07-05            NaN   \n",
       "freq             1           NaN           NaN       14361            NaN   \n",
       "mean           NaN  2.220031e+03  3.671353e+05         NaN      67.490666   \n",
       "std            NaN  2.459249e+02  3.005135e+05         NaN      85.631611   \n",
       "min            NaN  9.200000e+01  2.400000e+01         NaN       0.000000   \n",
       "25%            NaN  2.049000e+03  3.946500e+04         NaN      24.190000   \n",
       "50%            NaN  2.158000e+03  3.637130e+05         NaN      38.490000   \n",
       "75%            NaN  2.269000e+03  6.427000e+05         NaN      61.890000   \n",
       "max            NaN  3.230000e+03  8.381370e+05         NaN    7246.890000   \n",
       "\n",
       "                sales  \n",
       "count   778366.000000  \n",
       "unique            NaN  \n",
       "top               NaN  \n",
       "freq              NaN  \n",
       "mean         3.593748  \n",
       "std          4.627158  \n",
       "min          0.001000  \n",
       "25%          1.000000  \n",
       "50%          2.500000  \n",
       "75%          4.000000  \n",
       "max        801.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rw_sls.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>geoCluster</th>\n",
       "      <th>SKU</th>\n",
       "      <th>date</th>\n",
       "      <th>price_filled</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RR27956447</td>\n",
       "      <td>21</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-07-08</td>\n",
       "      <td>39.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RR27956448</td>\n",
       "      <td>21</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-07-09</td>\n",
       "      <td>39.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RR27956449</td>\n",
       "      <td>21</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-07-10</td>\n",
       "      <td>39.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RR27956450</td>\n",
       "      <td>21</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-07-11</td>\n",
       "      <td>39.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RR27956451</td>\n",
       "      <td>21</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-07-12</td>\n",
       "      <td>39.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID  geoCluster    SKU        date  price_filled  sales\n",
       "0  RR27956447          21  32485  2021-07-08         39.69    NaN\n",
       "1  RR27956448          21  32485  2021-07-09         39.69    NaN\n",
       "2  RR27956449          21  32485  2021-07-10         39.69    NaN\n",
       "3  RR27956450          21  32485  2021-07-11         39.69    NaN\n",
       "4  RR27956451          21  32485  2021-07-12         39.69    NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rw_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>geoCluster</th>\n",
       "      <th>SKU</th>\n",
       "      <th>date</th>\n",
       "      <th>price_filled</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>202737</td>\n",
       "      <td>202737.000000</td>\n",
       "      <td>202737.000000</td>\n",
       "      <td>202737</td>\n",
       "      <td>202737.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>202737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>RR27956447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2244.779517</td>\n",
       "      <td>367044.202449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.675751</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>359.694670</td>\n",
       "      <td>300460.823849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.742848</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2052.000000</td>\n",
       "      <td>39465.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.690000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2184.000000</td>\n",
       "      <td>363713.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.690000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2310.000000</td>\n",
       "      <td>642700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.090000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3230.000000</td>\n",
       "      <td>838137.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>424.690000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID     geoCluster            SKU        date   price_filled  \\\n",
       "count       202737  202737.000000  202737.000000      202737  202737.000000   \n",
       "unique      202737            NaN            NaN          14            NaN   \n",
       "top     RR27956447            NaN            NaN  2021-07-19            NaN   \n",
       "freq             1            NaN            NaN       14589            NaN   \n",
       "mean           NaN    2244.779517  367044.202449         NaN      79.675751   \n",
       "std            NaN     359.694670  300460.823849         NaN      98.742848   \n",
       "min            NaN      21.000000      24.000000         NaN       0.000000   \n",
       "25%            NaN    2052.000000   39465.000000         NaN      23.690000   \n",
       "50%            NaN    2184.000000  363713.000000         NaN      39.690000   \n",
       "75%            NaN    2310.000000  642700.000000         NaN      68.090000   \n",
       "max            NaN    3230.000000  838137.000000         NaN     424.690000   \n",
       "\n",
       "        sales  \n",
       "count     0.0  \n",
       "unique    NaN  \n",
       "top       NaN  \n",
       "freq      NaN  \n",
       "mean      NaN  \n",
       "std       NaN  \n",
       "min       NaN  \n",
       "25%       NaN  \n",
       "50%       NaN  \n",
       "75%       NaN  \n",
       "max       NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rw_test.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='sales'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rw_sls.plot.scatter(x=\"price\", y=\"sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                  0\n",
       "geoCluster          0\n",
       "SKU                 0\n",
       "date                0\n",
       "price         3827619\n",
       "sales         3827619\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rw_sls.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='sales'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1lxRiX00bNlTWSuYljgFWfGUmXvvopEf3td/851UwGnR4dOM+NLVHJvvZoNfAaNDhwuIs2fHs9AnYXCUvY12QnSy9lyvO44FgNOhQMjVLlljjKiel+xMEkfiQUvCCa79i4HzZiAlJGo9J2GjQ4aVtxyOmEDiWkcpPeBtnIKt2Kk1BEIQ/SCm48fTrn+PwqW7otA4b/pDVt9N1/Ts1+GtVDUbskRvTlWW5GLDYpMS1STkpOHK6EyfP9mFyXgrSUpJgswtI0nLSZO+skVScb0SKQYuPDzVjy25HH2lRBK6+KB856XrkZhgwKSdFMjE5//+9/Y2obzbj4mmZuPyCfJkica+VRAqGIMYPVPvIxTyy+rfbwzmsmFA+rxAi5A12/CWvAZB2Pt7OlXpSi8CGyho43Rgcy2BNRakqk9eoro9/SEa+icfaR7RTGOXp1z+P9RDCwnaFbmuB6Eun78TbuVa7gE1VdRAFAa55frwgYhN1XiOIcYOqQ1JdcW9LSXjCMs7ENs/jzsQ3giASG1IKo5QVZcR6CHGPIContgkiKHmNIMYJpBRG+f43Lon1EMICA2DhHLl9n2UYvwlsznIf3s7TaVisWjIbqyvK4Jrnx7EMVlHyGkGMG8in4MLGteVBRx9FG2+OYI5xZDKvrijDFWUTceNlU2XRR119wzAPjKDubC/SU3TQ6zTot1hDij5SCtUlCGJ8QNFHY/T87zh0Di+9f2JM9wgVvY6DIIi49cvTUTTR6JF/EEhegq9zKHLENyQf/5CMfEPRR+MMs8WKV330NwgHGo4BxzIYcWv5qdey+I8vTcPELIPiat3ZJ4KBCJsg4rpLCnHTwmLZec5zwt3uk5LkCCJxiYpSWLduHd5//300Nzdjy5YtmDlzJgCgvLwcOp0OSUlJAICHHnoIixYtisaQZLgmY20/eBafn+qO+hi8kazXoDA7GcebemWhoMM2Aa98WA+OcRS5nj8rB8sXTkNBdjJaOgexsapWVhPpo+pmfFTdjKsvzENW2gTkZUzApnfrZOds2FqLKbmpSDFosWHLMZxuNePikizcfdOFsjG510A6dKIDbV0WzJuZgy7zMDZV1TmcGyKwaik14CGIRCIq5qODBw+isLAQd9xxB1544QWZUnD9HCpjMR9t+bheloyV6JQVZeDE2V7ZZB8MDOPIeHZn49pyAPLdhdXG+5UbyzD4wwNXj8sdA5lG/EMy8k08mo+iEn20YMEC5OfnR+NRQdE3MIJNVbXjRiEAjnyLUBUCoKwQAODFLUdhtlilTnVDVv8KAQAEUUTdGcoBIYhEIeY+hYceegiiKGL+/Pl48MEHYTQag76HN43njxNNPWA5FuB992EmgMOnuvH161loNKysSmwg8GCQk6Nc0C/RGa/vFU5IRr6JN/nEVCm8/PLLyM/Ph9Vqxa9//Ws89thjePLJJ4O+T6jmo7xMAwQ+kl0Qxg8Xl2SCEwXYg1QIADA1J3lcmhDINOIfkpFvVGs+8obTpKTT6bBixQpUV1dH9flpKUlYtbQUCdB0LWiSNCwCadTGMZB1WfPW3e3umy6E0aCTOtVN0HGKcstI0co+l88rDKqPA0EQsSVmOwWLxQKe55GamgpRFFFVVYXS0tKoj8O9b0K8RR+5kqRh8PVrp8OYrENjmxnv7T+reJ5Ww+K7X78IU/JSYeqyYOfhZvQNWpGWokPxRCPMFpus6xoAWTIaAK/RR+79GwB59NGMyemyst2kEAgisYhK9NETTzyBbdu2obOzExkZGUhPT8cLL7yABx54ADzPQxAElJSU4Gc/+xlyc3ODvn+0k9fMFit+9OxuqbJotNBpWPzu/qukSJ59NW3YXFUHURRh40VoNSwYIGz5BrT19w3Jxz8kI9/Eo/mIMppDVQrP7Ip61NI3rivB7CkZsh7JrqUpRmx8WBPG6Aftm0SUT7QTCxNRRtEkHpVCzKOPYkVL5yAON/TAoGGg0bDIStPD1GXBhq01aO+NzzLQr+845cwJk2AAzJqcBpEBLp11fpfV0NyH6vpOzJuRjZwMA2oau8HzAgaGbBgYssHOC0g16DBi5WHjeXT0DWP2lAxcN28SjAYdXtxyFIdPdaNsajpuvKLIZwmNAYsNNY3dMCbrMHtqxrjMSRgPRCqDnRhfqHKn8NK247LOZM4fCeEdZ+e1K8omyiaXEZsAweWfEAPgnmVlqphsEmkVbLZY8fDze2ThxO7myEiQSDKKBfG4U1Bd6eyWzkGZQgBACiEArHYBm6vq0NI5KEtgE9zWFCKAjVtrYbZYYzNQQpGuvmGpPLoTjmWoORLhgeqUQkOrOdZDSFg4lkFDq9ljcnGHAXViizey0vQeix9eEKk5EuGB6pRCcX7wGdOEA14QUZxv9LuzEkGd2OIN9xwTpzmQ/D+EO6pzNBdkJ6N8XqGswT35FPzjnEQKspNx15LZ2Fzl3aewemkpTTZxiHuOCf2NCCVU6WgGHL6Fjn6rR/TR0/86jMGh+K2FNKckE4NDdmg4gAGDC6dlor1nCKbeIVw6KxfFBWnIStPLoo8Mei1+9+ohD8VXPrcAogiKPgoRcqL6h2Tkm3h0NKtWKQDKf5CWzkH87MX9Pq+boOPw0O1zkZWm94joiDTObmvBhhM6E92CCUekH7RvSD7+IRn5Jh6VgurMR/5QMi+55wY4HXROO+3GrbVjKlftDy0LOBuvDVsdu5jNVXUoK8oEgIDMAWQ6IAgiEFS7UzBbrOAZFpwowGjQoaVzEDWN3dj26Rl0muMrnHJyjgEtXRb4KujKALhwWia0GhbpKTr0mK0YtvGYOTkNJYVpmJKXKpl5eEEAx7JI1mvQZBpAeqoOGal6GPQa6bzf/v0ABoYFpCdr8OiaKyQl4uxS19U3DJtdQFlRJgaHbDjW2I0LijIxY3K6atpx0irYPyQj38TjTkGVSsGZfKXRsLDbBUyflIaaxvHdCMZbR7VAuXdZGSDCb5e6wmwDOnqHVZE1SxOef0hGvolHpaA685Fr9zCnL2C8KwRgbAoBANa/UwMNC7/1npo7LbLPTjOX+45BLbsJgkg0VKcUlDI7icBgGHfvin+cWbOuEz/V4CGI+EV1yWtZafqoRguNJ0KxNLpnzbr3eXaWz6CyGAQRH6hOKQAYuy0lARnr3ujeZWVYXVHmt0tdYbbBZ9Ys1eAhiPhGleYjnZbDkPV8gpqWYyCIIhgGsMdv3ppPWAa4dm4BegessuijCToOh091QsOxGLHJd0gMgGvmFiAvY0LA0UfOLnWhRh9RDR6CiG9UpxSUJiWbm/dUqaSw0kSnVI44ViRpOVx9UYGstpNzfLwA8ILnGPU6DovmFHjUgzIadHj6B9cqRkYYDTpcWJzlca8Zk9Nl53hzHjtzO9wT6dTubCbHOxEvhKwU9u3bB47jcOmll4ZzPBHHdVLSaFhYbTwYhoHNZWJ3d456c4zGk9NaabXtb3yxWqFTIp0ccrwT8UTAeQorV67ED3/4Q8yfPx/r16/H5s2bwXEc7rjjDtx3332RHqdPxpK8NjQ4gl9tPiBTConI5FwDso0ToNWyKM43IiNVj9auQbyzu9HDhcIxjtDSNIMG0yelY8BiQ0ffEBbMysG0wnSYB614b28jugdtSDNwWHPThZJZqaHVjOJ8o4fJyIm3HdV4VADhiDGPVfObaEF5Cr5J6DyF+vp6XHLJJQCA119/HS+99BIMBgNuv/32mCuFUDAadMjJScWWj+thT3CFAABn2y042+7IEfi0tsPnuU5rWZ/Fjs9OdErHtx1sBg7KGxD1WXj84bXDXu/1zu5GlBVl4KHb5iqueCGCVsE+8OV4Hw9KgUg8AlYKgiCAYRg0NTVBFEWUlJQAAPr6+iI2uEjTNzCCTVW1QUbeE+7UNPbgcH2HFGrqZNPWWsDNNOctmU2tkOOdiDcCVgrz58/HY489ho6ODtxwww0AgKamJmRkZERscJHG1G0BG0JCFuFJdX2nx4qXZRiPWFhaBcshxzsRbwSsFP77v/8bmzZtQmZmJtasWQMAOH36NL71rW9FbHCRJi/T4NFjmAiNeTOysb/GJDvmkK1cK9Aq2BNyvBPxhCoL4jnJyUnFlk/qsWGL7yJvhG8kn4JCzwYAQfdxSBTIieofkpFvEtrRbLVa8dxzz6GyshK9vb347LPPsGvXLjQ2NmLlypVhG2y0ca7Smkz92H24BfvrfDtp45XphanQ6zQw6DVS9FFP/zDOtQ+AF0WMjPDQaFiUFWWC5wXsPNwCq11AYXZy2KKPvK14aRVMEIlDwDuFRx99FCaTCffeey/uueceHDx4ECaTCatXr8bWrVsjPU6fhLPzWrB8WmvCC28fG9M9gkHDAoIAgHEkrFntAiCK0Gm5gFbiwYSH0irPNyQf/5CMfJPQO4UPP/wQ27Ztg8FgAMs6Sibl5eXBZDL5uXJ84T6pGvTRTQoXRECncyiA6+YV4oMDZ2ETIJXt8BXdkwhJUuM1p4EgEoWAZzStVguelxcG6u7uRnp6erjHFHVaOgdRfaIdu75oRXtvfBdmE8TzLTmr9jV5fG/nBby45RgGh60wD9oxvdAIlmHQ3DmApnZ5r4P179Rg+2fnkGVMwtn2AWQa9bjygokYHLbjvX2N6B6wITc9CT/51qWyCdohrw4AIubNzAUA1DR2w5isw+ypGTIzU0F2csDvtu9YGzZV1YJlHLWoVi0tjTullQiQYiXGQsBK4cYbb8SPf/xjPPLIIwCA9vZ2/OY3v8HSpUsjNrho8NK249hR3ez/xARBEIGjDeebBnWZfftITjabcXL09Vu6hmTXAkB77wh+8PQu3LusDFeUTfSQ1792Nvi8f/m8Qqz8yiy/4zZbrC5d3RymwA1baiinIUgSYTdIxDcBl87+4Q9/iMLCQixbtgxmsxmLFy9Gbm4uvvvd70ZyfBHlrKl/XCmESLL+nRrUn+0NWl7bq5vR0jno97wmU79HBBgvOo4TgUG9KohwEPBOQafT4ac//Sl++tOforu7GxkZGaOduBKX42fGfxvOcHKssTuk6xpazUGZkYjQoJIZRDjwqRTOnj3r9bvBwfOrv8mTJ4dvRFFk1tTEzcaOBRcUZeKd3Y1BX+demluJKXmpksnDCccymJKXGvTz1AqVzCDCgU+lcMMNN4BhGJ9tGBmGQW1tbdgHFg0m56WifF4htpMJyS/3LivDjMnpQctr4UUTA9olGA06rKkoxaaqOrCMwzeyiso9BAWVzCDCgeozmjs6+qXoo52HW9DZNxKBESYOLANcUJyJI6fPm4ruXDwT182dJH1Wij769+EW7DjULCuIl6Tl8PCKuQHtFJwkUuRMvMbgx5MM41VG8UJC5ymMZ1IMWlxQnIWifCP+9OZRWatOd5Rq3def7cV/v1wdjaGGFS3HwsbLy4YLIlDr5jt47aOTmD8rV3rnguxkj9X/V6+ciu2H5DsIUQzedOGraxsRGCRDYiwErBTsdjteeeUVHDhwAD09PTKT0ssvvxyRwUUD1xA+uyBC4H33VlBy3PUMJMbugmMd5j4tx4IXRCy9cioq955x6zoHcCwLu+C9E50SNQ3dMtlxLEOmC4JIQIKqkrpv3z5885vfxFNPPYUf/OAHePXVVwPKU1i3bh3ef/99NDc3Y8uWLZg5cyYAoKGhAWvXrkVvby/S09Oxbt06FBUVhfwywdI3MOLRA8AfQ1Yej//1YARHFTl4AdCyIoasPBgAb/7bM8dAqZ+z1cZjcNiGvUdbUdfUi9lT0mG2WHGwrh1WO4+peanYc8wEmQVPFFFWlBnZFwqRaHWHiyczDkEESsA+hUWLFuG1115DQUEBFixYgIMHD+LUqVP45S9/ib///e8+rz148CAKCwtxxx134IUXXpCUwre+9S18/etfx/Lly/H222/jjTfewN/+9regXyJUn0LPkB3/9fROhHApEQDF+an4+bfjq4d3OLvD+bIHUxKZA/Ip+CYefQoBJ68NDw8jPz8fAKDX6zE0NISSkhLU1NT4vXbBggXStU66urpQU1ODiooKAEBFRQVqamrQ3R1aLHwo2HmBFEIEaWjtR/3Z3lgPQ0IpuWtTVR02VdWGNeGLksiIRCZg81FJSQmOHDmCOXPm4MILL8QzzzyDlJQU5OXlhfTg1tZW5OXlgeM4AADHccjNzUVraysyM4MzO3jTeP443OBZO4gIL6dNA7hqXnzksfQ09UCjYWXmQo5lIIIBXOp6aTQseIZFTo7/HAmlc5SeE8w9xxtqfOdgiDf5BKwUfvKTn0CjcZy+du1aPProo7BYLHj88ccjNrhACdV8RMlrkWdaXkrcmA84UYDdzX/ECyLgZkG12wVwouB33N62/krPCfSe4w0yH/kmoc1HFosFqakOjWYwGJCbm4uSkhJMnTo1pEHl5+fDZDJJlVd5nkd7e7uHmSmSOJPXiMhQVpQhNeCJB5zJXToNiwk6DjoNi1VLZmPV0lLZsbFGTSk9hyKxiEQh4J3Cr371K2zYsAGAI5oIADQaDX7+85/jhRdeCPrBWVlZKC0tRWVlJZYvX47KykqUlpYGbToaKyu/Mgvl8yah+kQ7Pq014VyHxf9F4wiOBfQ6FkMjAnLS9ZgxKQ1N7QNoMp0vY/LIHfOg0bBo6xpUjD6aMSkdEzOTUVaUqdiRLZ6IVnc46rtMJCoBRx/NmzcP1dXVsNvtuPrqq7F9+3ZotVosWrQI+/fv93ntE088gW3btqGzsxMZGRlIT0/H1q1bcerUKaxduxZmsxlGoxHr1q3DtGnTgn6JcHVe8xZC2NBqxpOvHpIltU3QcXjodke2buWeRvxr5+mgnx9rXN9BCdr6+4bk4x+SkW/i0XwU8E4hJSUFnZ2dqK+vR0lJCZKTk2G1WmG32/1e+7Of/Qw/+9nPPI6XlJTg9ddfD3QIEcUZQsiyDOy8gNuvnyGVdvBXaGzE6l8G0YBjGTAQwbEsRgLIvaBiaQRBuBOwUli5ciVuueUW2Gw2/OQnPwEAVFdXh7SyjwfMFit6mnowNDiCI6c68dqOU7LvX3r/BF56/4TX6612AT94elekhxkUoiiCFyHLRvaF1S5g7Z92YUKSFpYRO2YUpmHYJsDUY0G/5byi+/aNszBjUjq6+4fR1TeMhpY+nGoxY8TGY/qkNOQY9Th0shMpBi0KspLR0TuMvMwJuPaSSUgxaMmEQhAJRFAF8RoaGsBxHKZMmSJ9tlqtmDXLf2etSBKs+ci5KxAhwmanRIVIwgDQj/aUHm8JXGQa8Q/JyDcJbT4CgOLiYp+fEwHXxCIi8oiA5IvZXFVH7TUJIs4JOCR1vKDUnYqIDs7CegRBxC+qUwpKTmMiOpBjmyDiH9UpBdfEIq2GdgyRhgEogYsgEghVNtlxJhbxDIuhwRHUn+vF/po2NLaZMWwdH7uI9GQtegdtYbmXhmNwd0UZDHoNRR8RxDhHlUoBcOwYnJ7/guxkXHOJZ7kLX+WPlRLa4gW9lsVt18/Ei5U1sPPeldyyq4vwzu7GgO45KSdF6ramJKuvXTPd67WkDAgicVCd+ShQ/JU/jmffxIhdwIattfBnHKvc06h4nGUALcdAp3X882AAPLb5APbVtPl9ttliRUOrOeQy0WO9PtzEcjzxJgtCHQSVpxCvhFLmYu/RVnx+qhuXlGTCkKTBO3sa0NIxgJH4SE6OKQwcoaRKXFySifbeIXT1DsHKAzoNkKRlMWwVMSnHgLMdFgiiCFEArp1bgDsXzw74ud52ZrHqYOavUU4kY8z3HWvDpqpasAwDQRSxamlpQuZ4UJ6CbxI+T2G88KNn/42eAYe9/UCtKcajiT98qdfDp+RNkKx2SDkfDW2Dsu92HGpB9Yl2/PGBL/l9plL+yOaqOgyN2PHaRyej3sHM23iikWdhtlixobIGDsuf46+xYUsN5XgQUUF15qO9R1slhUBEnr5BO/YebfV7nlL+CMsAr35YH5UOZu6mGqXxRCvPosnUD3dXEC86jpNJiYg0qtspHKhrj/UQVMcnn7fggmlZPle5Sj4auyBCw7Gwu3RFc07M4VwxK5mJyooyfRZBDIRwm71qz/Tg2TeOqL7vMxFZVLdTuHR2bqyHoDoa2vrx8PN7fDqqlRrT3H79DA9fka+JOZRVtLeAAgBjapSz71gbHn5+D5589ZDfd3dnSl6q4i7lwwNnqe8zEXFUt1O48sJ8/PPjk2RCiiK2Ubu8P5u8UmOaCUkabK6Sr+KVrg/VMevLTBRqo5yx+iOMBh3WVJRiU1UdWAYQRKDiyql4b38TbBHeNRGE6pQCAHzjuhn4y5YagHG05/UVbaMmdBpAr9NgcNgO3qVeoHNicsLA0bHNLrhGHwmw+UnZCGQSMxp0su8DmZjH4pj11yvDfTyB4EvRBHov9/cGgK17z3gdJ0GEC9UpBecqzmX+kCkEnYbF7+6/SvrxNrSase7lasWqquNNmYgig2GrgDUVZbiibCJyclJx6kwXHn5+j+z9tW4ycrLj0DmfPShCncT8Tcy+HLMXFmf5vfddS2YHtBsJFH+KJlDc3/uuJbOxaev53RCVDSEigeqUgr8qqe4rOscPXLnMtoZjYPORMZxoON5FlEwdOQhu0iyaaIRey2LYJpdXkpaFKCJuJ7Fw91OOhKIB4FiBMIxjNeI3NZEgQkN1SsFfJvKQlcfjfz0Y0L3Gk0Jwxb2r3I2XT8asKelI0rLgBRE7DzfjjR0nMTBsQ1pKEuw2Ad0DVui1wLCbq0bLMVi1pBQGvQZT8lIjMt4peanS6tkJyzBBPS8UM5EvwqFoXKOXAGDzu3WSfwbw7qeIVLJfrJIIieiiOqVgNOggitRgJxje23/W63ftPefj9t0VAuAwm2zcWhvxMEr3xHzn51hOZGNRNO5hskuunOphqhQBDz+F63U2O4+r5xTghgWTpbpVoeIvu5sYP6hOKWz/7KxfhygRPgQREc8KbjL1K06Y7+9vwkefnUu4iUwpeqlyT6NHcUObXUCSlvN53Seft+CTz1tQPq8QK78SWtvcWGZ3E9FHdXkKn9ZS8losiWb3tW0HmhIyrt9b9BLn5kbQcgxGXFY4vvxl26ub0dI5qPhdIONR2olRF73xieqUwpySzFgPQdVEIozSW7KXlmM9jiXCRKbk9xJERxEzVxiGkcnSn7+sodUc0niStJyH/8zGi7JdCjF+UJ1SKC3yHaJIhBcth6CygkPJSnYme2k1LJK0LLQaFitumAH3+TFR4vqVsrtXLZmNVUtLfcrSeZ3Gy6+6ON8Y0nhGbDy0bjfValjZLoUYP6jOp5CVpgfHwCOunfBP2dR0pBi0MFus6OgeQu+gVZbk5g7HAGtXLkBWmj4gZ+9YnJljyYaOR7xFL/mLaLqibCKm5Kbi5y/ul/lZGAZIMWhDGktWmt4jAJYZPU6MP1SnFAA4fiEhtJHQalj8z/1XAYD0w/ysrh0vbfOesJWouGcxA0B9sxn/M5q0ZrZY8fDze7zmcAAAx7HS5OVvMo6EMzPc+QfRJtTopXMdA56O9wCT+byNIyJ5F0Rcojql0NU3DJ2GDamNJscyOHKqCxeVZCErTY+6Mz3jUiEADr3pPrOwzPkQSCWnplbDQhRFaDk26IljrKUhvO0ywp1/EEsC2UntO9aGv1TWKF4/Fn9KoitYInBUpxSy0vSKJSsCYdjKY8PW2jCPKD5RMguN2ASfiX02uwAN60gA5ADUnO7ClNxUjNh4JGk5jNh4rxPKWEpDqCFksqVzEBuramVhqe7vaLZYsWFrrddNsC3Ef/dOxpOCJbyjOqUAIG57K48HnPMOD2DXURN2HTVJK1stx4BhGMUVrtGgw8I5+dhe3SwdWzgnP6BJqKtvGHY3LWbnhXFTQXTfsTYPhQB47qSaTP0+/21PjVBGOTG+UF300Z/fOhLrIagO50Rl40Wv+QJmixW7vpB3aNv1RWtAUUh2u+Dh/xBEx/F4IdSOac5dkLtCADx3UpZh3w3GewZGgno2oU5Ut1M41RJarDYRPpR8BWPxKbT3Dnk9PmNy+pjHO1bGElWllDgGABzHBO3sPWvqx2WleQGfT6gT1e0ULi6hPIVYo+Qr8OVTaOkcxO4jrV4zcr3F3xfnG/1eG2m8dXYLdMeglDgGADwv4uS5Ptmxnn7fjuR+CzWWIvyjup3Cd742Bwd+uz3Ww1Alrj4F1xWus2jdrV+ejn98dFIKh71ryWy8vasBO1z8DEo1fAqyk1E+r1DmjyifV4iPqs/5vTbSjDWqypk4puQk3l7djPJ5k6Rid739vhWNr/DhQGjpHERDqxnF+cYxF9gj4hfVKQUA2Li2HH968wscaejB7MlpuOLCfGz/7CxOnCPTki80HGD3E8mrYQC728I2ScNi1dJS5KRP8Ig+cjWtWO0CREEEq2EBUURP/7BsUgc8J0InK78yC+XzJkmTFgD87MX9AV0bScbacEcpccyVhlaz9D7zZubg/QPeK9pmp08I6JlKbKiqwe4vzveZjoWCJaKDKpUC4Ngx5OSkYsvH9fjLlhrph8swwMqvzERO+gT84bXDMR5l7Jg7PQtHG3tkK1SWYfHE3Zf6DC11JrW5hocKAAx6jcc1SqGkADAy+vlfn5xWHJvrROhKikGLguxkpBi0OHKqK6hrI8VYE7+c12/c6hl9BMhNZ3lZBp/3StaHltH87r5GmUIAYqNgieigWqUAAH0DI9j0bp1sJSeKwEvvn8CX5xfGcGSx59DJLnBuZTkFQcCjmz6VJae5Okyd5oXli4rx9r8bpNU/zwt47l9HIIjAKpdrAumCpxRimZs+waNPgrszd/miYsV7evM/RLLvwlgTv5ylK9x3PoC8dEVX3zAm6DiviZllRcEXgzRbrPjXzuCUM5HYxIVSKC8vh06nQ1JSEgDgoYcewqJFiyL6zLV/2o32Pu8heh991uz1O7XAu61MHQt4EXbeMemsf6cG699Rzp4FgJQJHHir4x4joy06179TI0to8xVXL4iefbBZBqhr6sGT/zgkdVu77foZeO2jk7Idx9v/bsDCiyZi1xG5yUNpEotGAxln4pczNNWfcnBXUuc6BhTPqzvTg9lTMxy5GnYBNi/FqDJSHLuoYJVfV98wNF6Uc6gF9oj4Ji6UAgA8/fTTmDlzZlSetZoczVFhYEh5xfrzF/dDr3MohOmT0lDT2KN43rQCI+rPyiNsBBF4e1fDaF6CY6L6u0KpETsv4Lp5k3Dj5VN9OkejmQ0dqPJROm/AS+TQ4ZOd2Li1FqIo+mwP2zNgw1s7T+PdT5skR/6qAJRfVpoeSukeC+dMpF3COEV1Ialr/7Q71kNQPSIghWd6UwgAcOJsn0dhN8CzUJ8oetY3dCavFWQn4+qL8r1OYL6ig8JJoKGp3s7zlo18oNYEq10IqF/4lj2NsNkFjNgE2OwCNlTWBhYaq5Anccu10/1fRyQkcbNTeOihhyCKIubPn48HH3wQRmPgW9OsrJSAz/VlMiLGFxa7iJwcz8m0b2AEpm4L8jINmDUt26OMOi8Cs6ZlIy0lye8zlO6vRE9TDzQaVrYj0WhY8Awru4e387KzUzAlLwVNpvNmpImZE2C22GD3k8nsxH1q5wURfcM8SqZ6f4eeph4kJWk8sqUPnOjEbTcEFn0UqIzUSrzJJy6Uwssvv4z8/HxYrVb8+te/xmOPPYYnn3wy4Ou7ugYgBFjPKDctiRRDguPugPbmkM5J1aGjo192TMk0c9dXZ8mjg746C9YhKzqGfK+ic3JSPe7vdcyi4FF2w24XwIkCOjr6JVt/kpZTPK/pXI9MIQBAW/eQ14Y6gdLXZ/H5DpwowKbQTOf/PjyBS2dm+zWxBSMjNRIr+bAs43UxHRfmo/z8fACATqfDihUrUF1dHbFnff8bl0Ts3kTgJGlZaDgGCy+a6DMOn2Xk35bPK/TosramohTl8wo9znM3GXkzzZQVZeJ391+Fh26fi9/df1XYncyAcjc1Z2jqvmNt+K/n9+B3r1TjV5sPYOGcfNl5V8/JxzNvKtfsurQsDzoNC617A+cA4BhHK1N/4156VZHHcUGgHs3jlZjvFCwWC3ieR2pqKkRRRFVVFUpLSyP2vFD71BKhwbKMxy7usrJcVB/vgIZj8WltO1YunolkvRb/t70e3W5ZuUlaFt/+6mzY7ILMWewe4nlF2URZ8pqSD8GX/6A43xjxiqpKoanOcteuO51PPm/Br1Zfhu7+YViG7dhQWQNvycgXl2Tj1vIZaDL146n/O+zhb3ElI1WHHhf5XjO3MKB3Lp2SgbfQIDvGC2JcFRwkwkfMlUJXVxceeOAB8DwPQRBQUlKCX/7ylxF7Xoo+5q+sKtwVwuVluTh0ohN2/nxo62sfncR3ll/goRAAwGrnMXtqhsfkpVTbvyA72WdEzFizi504TT26CcErEfdxK5W75gURu4+24qOD58Aw8OlEnpSTAqNBh2S9FhqWgdVXBJKbfD851IxlC4sTvuAgEV5iPkNOnjwZb731VtSe10Fb3phyoLYdOjdDOMsA1fWdiudfPN2/3TpQnCacTVtrpRyHYCuNynwSInDXV2dFxNz0wYGzihnM7nT3DyPFoMXgsC2gCCRX+ABbdPoqOEiMP2KuFKKNdqyeOWJMCCIwbJObHYZtAjp6LYrnL7ooX7EQm1ISlrfELLPFiiaTw5k3NGwHwMAxguDs8MHmNASSKJaZqrxL4VhW2kn54vOTnXjmjSNgICqG7/rDMmz3m0xXkJ2MiZkT0NZ9fsdQmG2IWp5CJLPNCU9UpxQmJKnulROCuqY+xeMffHZOlstQPq8Q0wvTPCKIIEIxMWzfsTYPm70rGyprA05UC6biaaCJakpVUDUcA6u/yoOj7PisOSRl4OTFyhqvZUucvLu/UaYQAKC504KWzsGIK4ZoZJsTclS3bH7h7WOxHgIRBO7Jbdurm7Fxa40sgmjT1lpsUogqaukc9Kht5Q4viNIuwh+B+iSC6aGQlaaH4FaaghdEr32W3RlrY1k7L56Xo5dkutd3KNc++vjzc2N8um+U5Lhpa4AJd0TIqE4pEIkP4xamyjIM3OvqcSyDhlazx/Gx4BFWquUUfRK+dhRKbTlFdzNWjFqI2+wCPj4kr/nV1Tfs1cjWY45svo+SHG28iE8OUV2ySEK2FCLhcG9PKYiiR8imdTSE1V9Oo79YfXd7tmtY6axp2bAqJLh521E0tpmx7uVqmSkkZYIWgtv7xEgnAAC27mnEtS6hqllpejCMYqULlE7NCOrewfoGstL0sCsU+Ht7V0PA4bRE8Khup/DU9xfGegjEGGAA3H7DTGg5BkmjSVu3XT/Dc9YSRaQYtFi1ZLZstcnAEe3kvHbNTWVeJ5d9x9rw8PN78OSrh/Dw83uwr8ZRcdVo0KE43+i1DIZSotqtX54uVXJ1NSk1e6l+Gis0HCtLSjMadJg6UVlpnuscDNiU402WvjAadIrlvoXRqCkiMqhup+Ct2iQRn3AMZLWJ9DrO8TdkmNHgIQYDFht0WnkfAZ2WQ1ffMMqKMvH/vjEHlmE7DHqNtCvwt2Ida/VU90Q1byalL7w0A4oW7qXJeUFEkpaTIpIAoKFVeQL+9+FW7DnS5tf5G6oszRYrjjV2B/U+xNhRnVKo3Nvg/yQibnAPvbfzAir3npFF61TuPeOxU/BmrnFOQr6UQVffMAaHbWPqrex8huu57iYlOy/gxFnlqKto4W4VWjgnH49tPiDJbOGcfK/X8oIIXhD9TvCh9qnu6huGhvMMzWXgvzwHETqqMx+dNQ36PSeEMjJEhEhP1snMMEuvKlIsGHfx9GzZsctKcxXNNWaLVdHZC8hNHM+8cQTDbh3MrHYh6OxnJ06TkpZz1GvSco53ca/tFGt2VDfLZObeI1sJf6XGQ80kV7oOiK3PZax4+7cXT6hup3BRSSaaO30rhiATQ4kIcsn0TNx8zXTJDNPQ7NljQQRQfbxDdmxfjQkaTr7m4VgGHx9qxpZdDXD26Llj8UxcN3eS137R8gedf7LZYkVPUw84UfC763CaqerP9ck6o7V2DnrtlBYrQvmn72+CNxp0uPXL0/Hqh/XQcCyEAPtUGw063DB/Eqr2N3l8F0gmdryx71gbNr1bJ2tydNM18bfjUZ1SmDUpHe/tPxvrYRABYuPlZpi6pl7F89ynVjsvQhTlR612AW/9W24+fOl9R9e2oolGn/2iAceE2dU3jJqGbmx+tw4aDQu7XVC0qSv1jHZfde+vbQerMPZ4wpeSYOCoEOBvgt93rA2vfXQSGpaBnRdw+/UzAk5Am12UoagUEg2lwocbKmvxpflTYjgqZVRnPvrfN5RLEBPxyf5j8igVWwClH5womSyUeOWDer/9ogHH6s48MCLtKCzDdsXENKWkq399opwAFs8KwR8Tkhi/pcb7XOQ1bBNg50W89tHJgM0nU/JSFf0RieZT8Fb48FRzbH1KSqhOKRCJhd1tnu4dCNwW65nQpnweA0e5CdcwUm+7hrqmXr/tO5Ucq+6mLCdT8uKvz3GgPjXLiOjXBGTqtniVV0vnIHYfaUWLD3Ou0aBT7J8xbnIUAk1djyKqMx8RiYWOk3+eMSkN1SeUK6p6oOR8UIJxODWL841SGGmTqR9/fe+4x6kTswx+naZKDlJRFLHwoonYdeT8zqd8XiF6B0bQFEDwQzQJ5zyVl2mA1a1zm9XGY3v1Oex2k8XKryi391TqQxEorsUQp+SlxkyZTMlL9Qiv5higZFK6YgJkLFHdToGS1xKLrPQJsoiNi6ZlK56Xky5PJCvMNuDuZWWyyKU1N5WhrEiehcswwOqljqZORxu60GTqR1aaHnNn5niUd2AAzJ2ZI+0oDHqNrIOaE29d1lYvLcMTd1+ONUtL8cTdl2PZwuLAFVwUuWRmVkBtPgOePNwirERAphAAR00rfzuGYBsh7TvWhh89swt/eO0w/vDaYfzo2d0BJc1FIkLIaNBhzU1lsqTLNTeV+ewDHqtIJdXtFCgTMrGw2wU8/PweyWF7/YJJiufduXg2eLuA6vpOzJuRjYtn5ABQ7tD21s7T2LK3EZpRs8bJc334yzs10kaCY4A1N5XhnmVl2FhZA4ZhIIoiVleUSVm23/v6RUhLMyBNzylOVN5Wt66NgI42xDZxzTsMvlk+A//4qN5nmRABjonL10Rt6rZAp2FliYUsy0BQCPGr2teIuysuGMvAJcwWKzZV1cpW5rwgYpOfnIpIVmW9omwipuSm+uwOGI1x+EN1SqGWMiQTio4+edG1d71EojR3DGDxZVMlZQDITQdO847ZYsV7nzZBFM93NNvuFhXEi8CmrbX4n+9ejUdXXy77EbuGFYoicNeS2V5/6Erd4dzHHI9Un+jE0VOdfutGAcDDz+/xOWHlZRo8wny9OfT315jwzfIZHjILpZ9CV9/waA6I/Fks4/huwGJT7NExlix2fyhN9EohqZEehz9UpxROU4/mhMbzZ+6g3a3ev3sfBefqPy/D0yeghCiK+PhQM7buPSPFld/25el45YN62fXr36mRXedqG/c3mZl6lNtcxgPWAIO8nNFXPicsNyeFt7+hVsN5ZDk7J1KGcWSA/8c10/DVy4v8jisrTe9RaBBw/B29+TNCzbwOBG8TvVJIaiTHEQiqUwq5aXocR/yFgRGB4W0+T0vR4XB9B/bVmpCfaUDl3jOyyZsXgY2VNbioJDsgpWAX4JHT8PdtJ/w6YbdXN6N83iQ0mfr9bv/zMib4HUci4Bp95a4ETd0Wj7pUSVoO180t9Mg/EBTqLrlPpK/vOI3WLgtWLynzOSajQYdVS0vlZkGWwc2LivH6jlOyc51/syQt55FMOGLl8cGnTbi8LE/ahYbivO7qG/ao+GrnBZi6LciYIJ+Gw9VLPFRUpxTcW0ES44Pt1edgtth9nmMXgENeekEHQqBROVv3NeKzug6f2/99x9rwr53jow6XtzpTV5RN9Bp9lJWul3ZggGPCvtqt7tLSK6e6+6gBALu+aMONl031sMkrZZC7/smuvCDP6wT+wcGz2Hu0bbRXhwgtx8DGixAA7Kttx77adhRmG7D0yiK8WFlzftyjO1B/9n67XfBY0AgiPBSF8x0uK8vFri/O72YWzsmPWuSU6pTCgboO/ycRCYc/hRBN9h8zQafxLLHRZOpHsl6LJC2HTe/WyYr6JTKudaacOJVg9gSdx2TIi8Ar2064HRex64tWj0KH3mS092gbbrhssqQEnFnm5zPIizwyyHcdaYMhiVO8364vWuU7S4XdZHOnBX/ZUiOvKjvqf/Jn7z9w3KR4fPfhZtx8dTGA86YylvFcvO6obsayhcVkPiKIRMXuNqlYbTyeeeMINCyDERsfkBM3UfBWZ6qrbxh9Q3ZF/4F78BEvAIJbWRJBEJGXOQGmbk/fy9GGTnxw8Cy40dIZgiifyL1lkHsrVe7euEnBR+04T+FaBvBr71d6BwBSGK7ZYvW5UBAB1J3pwWWleV6fES5Ul6dAEJFGEIElV0yR8hS0HAMwDGyjZS/Gk0IAINU0csXZl+Ho6cDDbt3Nc7wggrcre7zPmAalMiI2XvRY2XsrY5WWojxxK/RoChheVO7T7ZpjMK3AqHjtzMnpAICPDzX73TmebY9OOL3qdgoP3nox/vDa4VgPgxjn5KQb8Iu7LkVNYzeGrTyq9jYG5OBORAQRuKIsT5atPX1SGh7bfGDM79xpDi1xy+YlesrspcmW+yi9DdtpnnJlxQ0zFSOmWMaxY7z9+hlI95KklpnmSM7cuqdR+YEudPQM+c0LCQeqUwrxWICKGH909Fqwqap23O0K3NFwDJYvKsbbbpFaNY09MRqRA72WVQwqae0aWxhwSYERDa39YBjHTuY/rpmG6+ZOQkvnIBpazchNn4BNVbVSDgzgqMSbkaxVvN/Hn51FxqJp0HCs32KPB+o68PnJrognsqlOKez8vCWo8zmOAU8NFoggeWf3mVgPISpwDPDmztMeJUFCwRnxEw4iFWV44lyfw+bOADoNi7f/3YBjDd1+lWDPoPIO5WRzr9dmQu6ICDAvZIyozqcQTJVNABDH+1KPIMbAiF2EnRfDMpmHSyFEGgEO89KwTYDVLoxpV8SIotSESMMx0Gv9T8n+Ot2NFdUphSRdcGsa0gkEQUSKEZuIfcfa8I+PToJlGdgFEXo/9hsbLyBJqxxaGw5UpxRy0sZHFilBEImPIAIbttbCZhdgHW1CNOwn5YZhGDy2+UBAFV9DQXVK4WyHJdZDIAiCkAg2QstmFxQ7/oUL1SkFgiCI8UCkfAukFAiCIBIQq12ISJE81SmFi4oz/J9EEAQR50QqGVJ1SqHmTGyTagiCIMLFoRPhL/CpOqXAj4/ClARBEPjre8fDHoWkOqVAEAQxngh3FFJcKIWGhgbceuutWLx4MW699VY0NjbGekgEQRAJQbijkOJCKfzyl7/EihUr8P7772PFihX4xS9+EeshEQQRIOGoe0SETrhbdcZcKXR1daGmpgYVFRUAgIqKCtTU1KC7uzvGIyMIwh8P3nox/vj9hSiamOz/ZCLsMADuWjI7rMXxYl4ltbW1FXl5eeA4Ry0PjuOQm5uL1tZWZGZmBnSPrKyUgJ+XpAVGlAsWhoRSfXWCUAvXXVYEAHjmv67Hyl9Uoc9LNVAiMjz3cDkm56WG9Z4xVwrhoKtrAEKAE/NNVxXjn58E3jA9PVmLsqIM7DnWrvj9ihtm4GWPfrMEoQ46Os53A/vVmsvxg6d3RX0MXjpnKqIBED/dvAEOgO8uCm7nM4DIAIwIrLmpDHpW/jcIFJZlvC6mY24+ys/Ph8lkAj/aYILnebS3tyM/Pz8iz1tyZTECLTBoSGLxhwcW4e6bLlT8/qnvL8R1cyfh7pvKwHnr/0eMCcZNrBouOnLeuLZc8bj7nzkSo0mdEPhaLdzFMguzDbhubgEAx7tqfcjbXUZGgw73LisDyzjkwjLAQ3fMV7x2WoF8dVtWlIHyeYWyY+6fXbnlmmJMyU3GLdcUY8PachiSApvK1nv5uwZKRoq8WU62Mcnjb6DlHO/jivtnJ2/9frkkM+f/7l1WpnjuxrXl+P0DC/HTOxfg9w8sjFijHUZ071gdA+68807ccsstWL58Od5++23885//xEsvvRTw9cHsFJxU7W3AwRNdWDAzCzMmZeBYYzeKJ6ai0dSPU+f6cOWFE3HlhXLF9OKWozh8sgsXT8/yUBRmixVNpn5Yhu3o6R/GuY4BcAwLQERjmxmtXRavLQLjibx0PcqKMrHDpRlRVqoOA8NWyew2OceAGy+fivrmPnx8yHvTopw0HXgB6O4/Hy6n44CUCTqYh6xI0rAozE3BnGlZ+Ox4O9p7h2Gz8bDygNHA4eEVC5Bi0KLJ5FgJZabqMWLjkZWmxy/+shfmIR56DYMf3joXGg2Lx/96UPb8jWvL8d3fb8eQDdCwwKOrL0eKQeuxmt24thyrf7td9tmJ0vE3PzmJT+vace38yVi8YDIA4B8fHsfB4x1YMCsHt10/C2aLFQ89swv20X+Wt1xTjCVXFmP7Z2ex52gbZk5Ox41XTIXRoMM/PjyOPcfakJs+AbeWz8SMyelo6RzEX945itZuCy4uycL1C6bgb+/VobnLUdAxM1mLJx9YBMDxb/nDg+fAcQy+evlUHD3dhc9PyX1yG9eW43B9B17bcRL9FiuuvnAibrt+Fh7ftB8NpkEY9CzuWXoBLp6RA8Dx77mrbxhZaXoYDTqZHNxl5I7rtSVTs9DR0a/426k/24tjjd24oCgTM0Z7FTs7mBXnG1GQ7fBTOP+GADAlx4BH11yh+Nztn53FnmOjsr3cIds/vfkFjpzuxkXTMvGdr82Rzl37p91o7xtBbloSfvudqwEAf/hHNeqaejF7SjoevG2edKy2qRdTcpPx/745F0aDDofrO1Bd34l5M7IleVXtbcCnte24rDQXS64s9vp+9/x2O3g4dgh/WVuOnJxUdHT0e8jb9b0naIHnfjQ2ZeaOr51CXCiFU6dOYe3atTCbzTAajVi3bh2mTZsW8PWhKAUA0h+EUIbk4xuSj39IRr6JlXx8KYW48CmUlJTg9ddfj/UwCIIgVE/MfQoEQRBE/EBKgSAIgpAgpUAQBEFIxIVPYaywYwgHHcu1aoDk4xuSj39IRr6JhXx8PTMuoo8IgiCI+IDMRwRBEIQEKQWCIAhCgpQCQRAEIUFKgSAIgpAgpUAQBEFIkFIgCIIgJEgpEARBEBKkFAiCIAgJUgoEQRCEhCqVQkNDA2699VYsXrwYt956KxobG2M9pKizbt06lJeXY9asWThx4oR03Jds1CS3np4e3HPPPVi8eDFuuukmfO9730N3t6NxDcnIwf33349ly5bh5ptvxooVK1BbWwuA5OPOs88+K/udxb18RBVy5513im+99ZYoiqL41ltviXfeeWeMRxR9Dhw4ILa0tIjXXXedePz4cem4L9moSW49PT3ivn37pM+//e1vxUceeUQURZKRE7PZLP33Bx98IN58882iKJJ8XDl69Ki4Zs0a8dprr5V+Z/EuH9Uphc7OTnH+/Pmi3W4XRVEU7Xa7OH/+fLGrqyvGI4sNrkrBl2zULrf33ntP/Pa3v00y8sKbb74pfu1rXyP5uDAyMiJ+85vfFJuamqTfWSLIZ1xUSQ2G1tZW5OXlgeMc3bY5jkNubi5aW1uRmZkZ49HFFl+yEUVRtXITBAGvvvoqysvLSUZu/PSnP8Xu3bshiiJefPFFko8L//u//4tly5Zh8uTJ0rFEkI8qfQoEEQyPP/44DAYDVq5cGeuhxB2//vWv8fHHH+OHP/whfve738V6OHHDoUOHcOTIEaxYsSLWQwka1SmF/Px8mEwm8DwPAOB5Hu3t7cjPz4/xyGKPL9moVW7r1q3DmTNn8NRTT4FlWZKRF26++Wbs378fEydOJPkAOHDgAE6fPo0vf/nLKC8vR1tbG9asWYOmpqa4l4/qlEJWVhZKS0tRWVkJAKisrERpaem43L4Giy/ZqFFuf/zjH3H06FE899xz0Ol0AEhGTgYHB9Ha2ip93r59O9LS0kg+o9x7773YtWsXtm/fju3bt2PixInYsGEDlixZEvfyUWWTnVOnTmHt2rUwm80wGo1Yt24dpk2bFuthRZUnnngC27ZtQ2dnJzIyMpCeno6tW7f6lI2a5FZfX4+KigoUFRVBr9cDACZNmoTnnnuOZASgs7MT999/P4aGhsCyLNLS0vDjH/8YF1xwAclHgfLycrzwwguYOXNm3MtHlUqBIAiCUEZ15iOCIAjCO6QUCIIgCAlSCgRBEIQEKQWCIAhCgpQCQRAEIUFKgSDCyMGDB7F48eJYD4MgQoZCUgmCIAgJ2ikQRJiw2+2xHgJBjBlSCgThh/Lycvz5z3/GkiVLcOmll+KRRx7ByMgI9u/fjy996UtYv349rr76ajzyyCPSMSetra343ve+hyuuuAKXX345HnvsMem7f/7zn/jqV7+KSy+9FGvWrEFzc3MsXo8gZJBSIIgA2LJlCzZs2IAPPvgADQ0NeP755wE4yj309fVhx44dePzxx2XX8DyP//zP/0RBQQG2b9+OnTt3YsmSJQCADz/8EH/+85/x7LPPYu/evZg/fz5+9KMfRf29CMIdUgoEEQB33HEH8vPzkZ6eju985zvYunUrAIBlWXz/+9+HTqeTaiQ5+eKLL9De3o6HH34YBoMBSUlJWLBgAQDgH//4B+69916UlJRAo9HgvvvuQ21tLe0WiJijuiY7BBEKruWLCwoK0N7eDgDIyMhAUlKS4jWtra0oKCiARuP5M2tpacFvfvMbrFu3TjomiiJMJhMKCwvDPHqCCBxSCgQRAK5loltaWpCbmwsAYBjG6zX5+flobW2F3W73UAz5+fm47777sGzZssgMmCBChMxHBBEAr7zyCtra2tDb2ys5nf0xZ84c5OTk4Pe//z0sFgtGRkbw2WefAQBuu+02rF+/HvX19QCA/v5+vPvuuxF9B4IIBFIKBBEAFRUVWL16Na6//npMnjwZ3/nOd/xew3EcXnjhBZw5cwbXXXcdvvSlL0kT/w033IC7774bDz74IObNm4eKigrs3Lkz0q9BEH6h5DWC8EN5eTmeeOIJXHXVVbEeCkFEHNopEARBEBKkFAiCIAgJMh8RBEEQErRTIAiCICRIKRAEQRASpBQIgiAICVIKBEEQhAQpBYIgCEKClAJBEAQh8f8BKY63QcxGIR4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = rw_sls['price'].quantile(0.99)\n",
    "j = rw_sls['sales'].quantile(0.99)\n",
    "cl_sls = rw_sls[rw_sls['price']<q]\n",
    "cl_sls = cl_sls[cl_sls['sales']<j]\n",
    "\n",
    "cl_sls.plot.scatter(x=\"price\", y=\"sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>geoCluster</th>\n",
       "      <th>SKU</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RR27956459</td>\n",
       "      <td>92</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>38.290000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RR27956474</td>\n",
       "      <td>92</td>\n",
       "      <td>32549</td>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>89.390000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RR27956489</td>\n",
       "      <td>112</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-05-27</td>\n",
       "      <td>45.390000</td>\n",
       "      <td>3.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RR27956490</td>\n",
       "      <td>112</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>87.422699</td>\n",
       "      <td>1.770718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RR27956491</td>\n",
       "      <td>112</td>\n",
       "      <td>32485</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>87.422699</td>\n",
       "      <td>1.770718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4605980</th>\n",
       "      <td>RR55912525</td>\n",
       "      <td>3230</td>\n",
       "      <td>711838</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>28.490000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4605981</th>\n",
       "      <td>RR55912526</td>\n",
       "      <td>3230</td>\n",
       "      <td>711838</td>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>28.490000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4605982</th>\n",
       "      <td>RR55912527</td>\n",
       "      <td>3230</td>\n",
       "      <td>711838</td>\n",
       "      <td>2021-07-03</td>\n",
       "      <td>28.490000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4605983</th>\n",
       "      <td>RR55912528</td>\n",
       "      <td>3230</td>\n",
       "      <td>711838</td>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>28.490000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4605984</th>\n",
       "      <td>RR55912529</td>\n",
       "      <td>3230</td>\n",
       "      <td>711838</td>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>28.490000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4605985 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  geoCluster     SKU        date      price      sales\n",
       "0        RR27956459          92   32485  2021-07-05  38.290000   4.000000\n",
       "1        RR27956474          92   32549  2021-07-05  89.390000   0.500000\n",
       "2        RR27956489         112   32485  2021-05-27  45.390000   3.700000\n",
       "3        RR27956490         112   32485  2021-05-28  87.422699   1.770718\n",
       "4        RR27956491         112   32485  2021-05-29  87.422699   1.770718\n",
       "...             ...         ...     ...         ...        ...        ...\n",
       "4605980  RR55912525        3230  711838  2021-07-01  28.490000   5.000000\n",
       "4605981  RR55912526        3230  711838  2021-07-02  28.490000  11.000000\n",
       "4605982  RR55912527        3230  711838  2021-07-03  28.490000   2.000000\n",
       "4605983  RR55912528        3230  711838  2021-07-04  28.490000   6.000000\n",
       "4605984  RR55912529        3230  711838  2021-07-05  28.490000  13.000000\n",
       "\n",
       "[4605985 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filled = rw_sls.copy()\n",
    "filled[\"price\"] = filled[\"price\"].fillna(67.490666 + np.random.uniform(-1,1) * 85.631611) # mean + random(0,1) * std\n",
    "filled[\"sales\"] = filled[\"sales\"].fillna(3.593748 + np.random.uniform(-1,1) * 4.627158) # mean + random(0,1) * std\n",
    "filled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "filled = rw_sls.copy()\n",
    "filled.update(toup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "filled.to_csv('sales_filled.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID            0\n",
       "geoCluster    0\n",
       "SKU           0\n",
       "date          0\n",
       "price         0\n",
       "sales         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped = cl_sls.dropna()\n",
    "dropped.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "poly = PolynomialFeatures(degree=9)\n",
    "X_F1_poly = poly.fit_transform(dropped[\"price\"].to_numpy().reshape(-1,1))\n",
    "\n",
    "X_F1 = dropped[\"price\"].to_numpy().reshape(1,-1)[0]\n",
    "y_F1 = dropped[\"sales\"].to_numpy().reshape(1,-1)[0]\n",
    "\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X_F1, y_F1,\n",
    "#                                                   random_state = 0)\n",
    "\n",
    "model = np.poly1d(np.polyfit(X_F1,y_F1,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f20cfb243d0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_F1, y_F1, color = \"darkorange\", label = \"data\")\n",
    "plt.scatter(X_F1, model(X_F1), color = \"navy\", label = \"prediction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = rw_sls[\"price\"].median()\n",
    "c2 = rw_sls[\"price\"].std()\n",
    "\n",
    "toup = rw_sls.copy()\n",
    "toup[\"price\"] = toup[\"price\"].apply(lambda x: abs(c1 + np.random.uniform(-1,1) * c2)) # mean + random(0,1) * std\n",
    "toup[\"sales\"] = toup[\"price\"].apply(lambda x: model(x)) # mean + random(0,1) * std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>geoCluster</th>\n",
       "      <th>SKU</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4605985</td>\n",
       "      <td>4.605985e+06</td>\n",
       "      <td>4.605985e+06</td>\n",
       "      <td>4605985</td>\n",
       "      <td>4.605985e+06</td>\n",
       "      <td>4.605985e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4605985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>RR27956459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14361</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.220031e+03</td>\n",
       "      <td>3.671353e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.146647e+01</td>\n",
       "      <td>3.351387e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.459249e+02</td>\n",
       "      <td>3.005135e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.573023e+01</td>\n",
       "      <td>1.059250e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.200000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.845491e-05</td>\n",
       "      <td>1.527037e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.049000e+03</td>\n",
       "      <td>3.946500e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.141102e+01</td>\n",
       "      <td>2.381149e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.158000e+03</td>\n",
       "      <td>3.637130e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.280405e+01</td>\n",
       "      <td>3.479215e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.269000e+03</td>\n",
       "      <td>6.427000e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.131546e+01</td>\n",
       "      <td>4.243614e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.230000e+03</td>\n",
       "      <td>8.381370e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.241216e+02</td>\n",
       "      <td>5.130147e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID    geoCluster           SKU        date         price  \\\n",
       "count      4605985  4.605985e+06  4.605985e+06     4605985  4.605985e+06   \n",
       "unique     4605985           NaN           NaN         552           NaN   \n",
       "top     RR27956459           NaN           NaN  2021-07-05           NaN   \n",
       "freq             1           NaN           NaN       14361           NaN   \n",
       "mean           NaN  2.220031e+03  3.671353e+05         NaN  5.146647e+01   \n",
       "std            NaN  2.459249e+02  3.005135e+05         NaN  3.573023e+01   \n",
       "min            NaN  9.200000e+01  2.400000e+01         NaN  1.845491e-05   \n",
       "25%            NaN  2.049000e+03  3.946500e+04         NaN  2.141102e+01   \n",
       "50%            NaN  2.158000e+03  3.637130e+05         NaN  4.280405e+01   \n",
       "75%            NaN  2.269000e+03  6.427000e+05         NaN  8.131546e+01   \n",
       "max            NaN  3.230000e+03  8.381370e+05         NaN  1.241216e+02   \n",
       "\n",
       "               sales  \n",
       "count   4.605985e+06  \n",
       "unique           NaN  \n",
       "top              NaN  \n",
       "freq             NaN  \n",
       "mean    3.351387e+00  \n",
       "std     1.059250e+00  \n",
       "min     1.527037e+00  \n",
       "25%     2.381149e+00  \n",
       "50%     3.479215e+00  \n",
       "75%     4.243614e+00  \n",
       "max     5.130147e+00  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toup.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>geoCluster</th>\n",
       "      <th>SKU</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>757936</td>\n",
       "      <td>757936.000000</td>\n",
       "      <td>757936.000000</td>\n",
       "      <td>757936</td>\n",
       "      <td>757936.000000</td>\n",
       "      <td>757936.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>757936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>RR27956459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2230.227985</td>\n",
       "      <td>320378.784725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.476835</td>\n",
       "      <td>3.350950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>268.249510</td>\n",
       "      <td>295883.155727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.716859</td>\n",
       "      <td>1.058781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>1.527041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2041.000000</td>\n",
       "      <td>32549.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.430741</td>\n",
       "      <td>2.381773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2131.000000</td>\n",
       "      <td>221793.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.810162</td>\n",
       "      <td>3.479013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2279.000000</td>\n",
       "      <td>591899.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.289689</td>\n",
       "      <td>4.242855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3230.000000</td>\n",
       "      <td>838137.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124.121337</td>\n",
       "      <td>5.130145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID     geoCluster            SKU        date          price  \\\n",
       "count       757936  757936.000000  757936.000000      757936  757936.000000   \n",
       "unique      757936            NaN            NaN         464            NaN   \n",
       "top     RR27956459            NaN            NaN  2021-04-15            NaN   \n",
       "freq             1            NaN            NaN        3649            NaN   \n",
       "mean           NaN    2230.227985  320378.784725         NaN      51.476835   \n",
       "std            NaN     268.249510  295883.155727         NaN      35.716859   \n",
       "min            NaN      92.000000      24.000000         NaN       0.000053   \n",
       "25%            NaN    2041.000000   32549.000000         NaN      21.430741   \n",
       "50%            NaN    2131.000000  221793.000000         NaN      42.810162   \n",
       "75%            NaN    2279.000000  591899.000000         NaN      81.289689   \n",
       "max            NaN    3230.000000  838137.000000         NaN     124.121337   \n",
       "\n",
       "                sales  \n",
       "count   757936.000000  \n",
       "unique            NaN  \n",
       "top               NaN  \n",
       "freq              NaN  \n",
       "mean         3.350950  \n",
       "std          1.058781  \n",
       "min          1.527041  \n",
       "25%          2.381773  \n",
       "50%          3.479013  \n",
       "75%          4.242855  \n",
       "max          5.130145  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl_sls.update(toup)\n",
    "cl_sls.describe(include=\"all\")"
   ]
  }
 ],
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