ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d6177e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dcb78af6",
   "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>article_id</th>\n",
       "      <th>product_code</th>\n",
       "      <th>prod_name</th>\n",
       "      <th>product_type_no</th>\n",
       "      <th>product_type_name</th>\n",
       "      <th>product_group_name</th>\n",
       "      <th>graphical_appearance_no</th>\n",
       "      <th>graphical_appearance_name</th>\n",
       "      <th>colour_group_code</th>\n",
       "      <th>colour_group_name</th>\n",
       "      <th>...</th>\n",
       "      <th>department_name</th>\n",
       "      <th>index_code</th>\n",
       "      <th>index_name</th>\n",
       "      <th>index_group_no</th>\n",
       "      <th>index_group_name</th>\n",
       "      <th>section_no</th>\n",
       "      <th>section_name</th>\n",
       "      <th>garment_group_no</th>\n",
       "      <th>garment_group_name</th>\n",
       "      <th>detail_desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0108775015</td>\n",
       "      <td>108775</td>\n",
       "      <td>Strap top</td>\n",
       "      <td>253</td>\n",
       "      <td>Vest top</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>9</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>A</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>16</td>\n",
       "      <td>Womens Everyday Basics</td>\n",
       "      <td>1002</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>Jersey top with narrow shoulder straps.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0108775044</td>\n",
       "      <td>108775</td>\n",
       "      <td>Strap top</td>\n",
       "      <td>253</td>\n",
       "      <td>Vest top</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>10</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>A</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>16</td>\n",
       "      <td>Womens Everyday Basics</td>\n",
       "      <td>1002</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>Jersey top with narrow shoulder straps.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0108775051</td>\n",
       "      <td>108775</td>\n",
       "      <td>Strap top (1)</td>\n",
       "      <td>253</td>\n",
       "      <td>Vest top</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010017</td>\n",
       "      <td>Stripe</td>\n",
       "      <td>11</td>\n",
       "      <td>Off White</td>\n",
       "      <td>...</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>A</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>16</td>\n",
       "      <td>Womens Everyday Basics</td>\n",
       "      <td>1002</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>Jersey top with narrow shoulder straps.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0110065001</td>\n",
       "      <td>110065</td>\n",
       "      <td>OP T-shirt (Idro)</td>\n",
       "      <td>306</td>\n",
       "      <td>Bra</td>\n",
       "      <td>Underwear</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>9</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>Clean Lingerie</td>\n",
       "      <td>B</td>\n",
       "      <td>Lingeries/Tights</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>61</td>\n",
       "      <td>Womens Lingerie</td>\n",
       "      <td>1017</td>\n",
       "      <td>Under-, Nightwear</td>\n",
       "      <td>Microfibre T-shirt bra with underwired, moulde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0110065002</td>\n",
       "      <td>110065</td>\n",
       "      <td>OP T-shirt (Idro)</td>\n",
       "      <td>306</td>\n",
       "      <td>Bra</td>\n",
       "      <td>Underwear</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>10</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>Clean Lingerie</td>\n",
       "      <td>B</td>\n",
       "      <td>Lingeries/Tights</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>61</td>\n",
       "      <td>Womens Lingerie</td>\n",
       "      <td>1017</td>\n",
       "      <td>Under-, Nightwear</td>\n",
       "      <td>Microfibre T-shirt bra with underwired, moulde...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   article_id  product_code          prod_name  product_type_no  \\\n",
       "0  0108775015        108775          Strap top              253   \n",
       "1  0108775044        108775          Strap top              253   \n",
       "2  0108775051        108775      Strap top (1)              253   \n",
       "3  0110065001        110065  OP T-shirt (Idro)              306   \n",
       "4  0110065002        110065  OP T-shirt (Idro)              306   \n",
       "\n",
       "  product_type_name  product_group_name  graphical_appearance_no  \\\n",
       "0          Vest top  Garment Upper body                  1010016   \n",
       "1          Vest top  Garment Upper body                  1010016   \n",
       "2          Vest top  Garment Upper body                  1010017   \n",
       "3               Bra           Underwear                  1010016   \n",
       "4               Bra           Underwear                  1010016   \n",
       "\n",
       "  graphical_appearance_name  colour_group_code colour_group_name  ...  \\\n",
       "0                     Solid                  9             Black  ...   \n",
       "1                     Solid                 10             White  ...   \n",
       "2                    Stripe                 11         Off White  ...   \n",
       "3                     Solid                  9             Black  ...   \n",
       "4                     Solid                 10             White  ...   \n",
       "\n",
       "   department_name index_code        index_name index_group_no  \\\n",
       "0     Jersey Basic          A        Ladieswear              1   \n",
       "1     Jersey Basic          A        Ladieswear              1   \n",
       "2     Jersey Basic          A        Ladieswear              1   \n",
       "3   Clean Lingerie          B  Lingeries/Tights              1   \n",
       "4   Clean Lingerie          B  Lingeries/Tights              1   \n",
       "\n",
       "   index_group_name section_no            section_name garment_group_no  \\\n",
       "0        Ladieswear         16  Womens Everyday Basics             1002   \n",
       "1        Ladieswear         16  Womens Everyday Basics             1002   \n",
       "2        Ladieswear         16  Womens Everyday Basics             1002   \n",
       "3        Ladieswear         61         Womens Lingerie             1017   \n",
       "4        Ladieswear         61         Womens Lingerie             1017   \n",
       "\n",
       "   garment_group_name                                        detail_desc  \n",
       "0        Jersey Basic            Jersey top with narrow shoulder straps.  \n",
       "1        Jersey Basic            Jersey top with narrow shoulder straps.  \n",
       "2        Jersey Basic            Jersey top with narrow shoulder straps.  \n",
       "3   Under-, Nightwear  Microfibre T-shirt bra with underwired, moulde...  \n",
       "4   Under-, Nightwear  Microfibre T-shirt bra with underwired, moulde...  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articles = pd.read_csv(\"articles.csv\",dtype={'article_id':str}) # The article_id should be imported as a string to preserve the leading zero.\n",
    "articles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3d2f573",
   "metadata": {},
   "outputs": [
    {
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       "      <th>perceived_colour_value_id</th>\n",
       "      <th>perceived_colour_value_name</th>\n",
       "      <th>perceived_colour_master_id</th>\n",
       "      <th>perceived_colour_master_name</th>\n",
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       "      <td>0108775015</td>\n",
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       "      <td>Strap top</td>\n",
       "      <td>253</td>\n",
       "      <td>Vest top</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
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       "      <td>Ladieswear</td>\n",
       "      <td>16</td>\n",
       "      <td>Womens Everyday Basics</td>\n",
       "      <td>1002</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>Jersey top with narrow shoulder straps.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0108775044</td>\n",
       "      <td>108775</td>\n",
       "      <td>Strap top</td>\n",
       "      <td>253</td>\n",
       "      <td>Vest top</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>10</td>\n",
       "      <td>White</td>\n",
       "      <td>3</td>\n",
       "      <td>Light</td>\n",
       "      <td>9</td>\n",
       "      <td>White</td>\n",
       "      <td>1676</td>\n",
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       "      <td>Ladieswear</td>\n",
       "      <td>16</td>\n",
       "      <td>Womens Everyday Basics</td>\n",
       "      <td>1002</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>Jersey top with narrow shoulder straps.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0108775051</td>\n",
       "      <td>108775</td>\n",
       "      <td>Strap top (1)</td>\n",
       "      <td>253</td>\n",
       "      <td>Vest top</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010017</td>\n",
       "      <td>Stripe</td>\n",
       "      <td>11</td>\n",
       "      <td>Off White</td>\n",
       "      <td>1</td>\n",
       "      <td>Dusty Light</td>\n",
       "      <td>9</td>\n",
       "      <td>White</td>\n",
       "      <td>1676</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>A</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>16</td>\n",
       "      <td>Womens Everyday Basics</td>\n",
       "      <td>1002</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>Jersey top with narrow shoulder straps.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0110065001</td>\n",
       "      <td>110065</td>\n",
       "      <td>OP T-shirt (Idro)</td>\n",
       "      <td>306</td>\n",
       "      <td>Bra</td>\n",
       "      <td>Underwear</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>9</td>\n",
       "      <td>Black</td>\n",
       "      <td>4</td>\n",
       "      <td>Dark</td>\n",
       "      <td>5</td>\n",
       "      <td>Black</td>\n",
       "      <td>1339</td>\n",
       "      <td>Clean Lingerie</td>\n",
       "      <td>B</td>\n",
       "      <td>Lingeries/Tights</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>61</td>\n",
       "      <td>Womens Lingerie</td>\n",
       "      <td>1017</td>\n",
       "      <td>Under-, Nightwear</td>\n",
       "      <td>Microfibre T-shirt bra with underwired, moulde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0110065002</td>\n",
       "      <td>110065</td>\n",
       "      <td>OP T-shirt (Idro)</td>\n",
       "      <td>306</td>\n",
       "      <td>Bra</td>\n",
       "      <td>Underwear</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>10</td>\n",
       "      <td>White</td>\n",
       "      <td>3</td>\n",
       "      <td>Light</td>\n",
       "      <td>9</td>\n",
       "      <td>White</td>\n",
       "      <td>1339</td>\n",
       "      <td>Clean Lingerie</td>\n",
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       "      <td>Lingeries/Tights</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>61</td>\n",
       "      <td>Womens Lingerie</td>\n",
       "      <td>1017</td>\n",
       "      <td>Under-, Nightwear</td>\n",
       "      <td>Microfibre T-shirt bra with underwired, moulde...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   article_id  product_code          prod_name  product_type_no  \\\n",
       "0  0108775015        108775          Strap top              253   \n",
       "1  0108775044        108775          Strap top              253   \n",
       "2  0108775051        108775      Strap top (1)              253   \n",
       "3  0110065001        110065  OP T-shirt (Idro)              306   \n",
       "4  0110065002        110065  OP T-shirt (Idro)              306   \n",
       "\n",
       "  product_type_name  product_group_name  graphical_appearance_no  \\\n",
       "0          Vest top  Garment Upper body                  1010016   \n",
       "1          Vest top  Garment Upper body                  1010016   \n",
       "2          Vest top  Garment Upper body                  1010017   \n",
       "3               Bra           Underwear                  1010016   \n",
       "4               Bra           Underwear                  1010016   \n",
       "\n",
       "  graphical_appearance_name  colour_group_code colour_group_name  \\\n",
       "0                     Solid                  9             Black   \n",
       "1                     Solid                 10             White   \n",
       "2                    Stripe                 11         Off White   \n",
       "3                     Solid                  9             Black   \n",
       "4                     Solid                 10             White   \n",
       "\n",
       "   perceived_colour_value_id perceived_colour_value_name  \\\n",
       "0                          4                        Dark   \n",
       "1                          3                       Light   \n",
       "2                          1                 Dusty Light   \n",
       "3                          4                        Dark   \n",
       "4                          3                       Light   \n",
       "\n",
       "   perceived_colour_master_id perceived_colour_master_name  department_no  \\\n",
       "0                           5                        Black           1676   \n",
       "1                           9                        White           1676   \n",
       "2                           9                        White           1676   \n",
       "3                           5                        Black           1339   \n",
       "4                           9                        White           1339   \n",
       "\n",
       "  department_name index_code        index_name  index_group_no  \\\n",
       "0    Jersey Basic          A        Ladieswear               1   \n",
       "1    Jersey Basic          A        Ladieswear               1   \n",
       "2    Jersey Basic          A        Ladieswear               1   \n",
       "3  Clean Lingerie          B  Lingeries/Tights               1   \n",
       "4  Clean Lingerie          B  Lingeries/Tights               1   \n",
       "\n",
       "  index_group_name  section_no            section_name  garment_group_no  \\\n",
       "0       Ladieswear          16  Womens Everyday Basics              1002   \n",
       "1       Ladieswear          16  Womens Everyday Basics              1002   \n",
       "2       Ladieswear          16  Womens Everyday Basics              1002   \n",
       "3       Ladieswear          61         Womens Lingerie              1017   \n",
       "4       Ladieswear          61         Womens Lingerie              1017   \n",
       "\n",
       "  garment_group_name                                        detail_desc  \n",
       "0       Jersey Basic            Jersey top with narrow shoulder straps.  \n",
       "1       Jersey Basic            Jersey top with narrow shoulder straps.  \n",
       "2       Jersey Basic            Jersey top with narrow shoulder straps.  \n",
       "3  Under-, Nightwear  Microfibre T-shirt bra with underwired, moulde...  \n",
       "4  Under-, Nightwear  Microfibre T-shirt bra with underwired, moulde...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "articles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f85f860d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>49.0</td>\n",
       "      <td>52043ee2162cf5aa7ee79974281641c6f11a68d276429a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000423b00ade91418cceaf3b26c6af3dd342b51fd051e...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2973abc54daa8a5f8ccfe9362140c63247c5eee03f1d93...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>64f17e6a330a85798e4998f62d0930d14db8db1c054af6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>54.0</td>\n",
       "      <td>5d36574f52495e81f019b680c843c443bd343d5ca5b1c2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00006413d8573cd20ed7128e53b7b13819fe5cfc2d801f...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>Regularly</td>\n",
       "      <td>52.0</td>\n",
       "      <td>25fa5ddee9aac01b35208d01736e57942317d756b32ddd...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         customer_id   FN  Active  \\\n",
       "0  00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...  NaN     NaN   \n",
       "1  0000423b00ade91418cceaf3b26c6af3dd342b51fd051e...  NaN     NaN   \n",
       "2  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...  NaN     NaN   \n",
       "3  00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...  NaN     NaN   \n",
       "4  00006413d8573cd20ed7128e53b7b13819fe5cfc2d801f...  1.0     1.0   \n",
       "\n",
       "  club_member_status fashion_news_frequency   age  \\\n",
       "0             ACTIVE                   NONE  49.0   \n",
       "1             ACTIVE                   NONE  25.0   \n",
       "2             ACTIVE                   NONE  24.0   \n",
       "3             ACTIVE                   NONE  54.0   \n",
       "4             ACTIVE              Regularly  52.0   \n",
       "\n",
       "                                         postal_code  \n",
       "0  52043ee2162cf5aa7ee79974281641c6f11a68d276429a...  \n",
       "1  2973abc54daa8a5f8ccfe9362140c63247c5eee03f1d93...  \n",
       "2  64f17e6a330a85798e4998f62d0930d14db8db1c054af6...  \n",
       "3  5d36574f52495e81f019b680c843c443bd343d5ca5b1c2...  \n",
       "4  25fa5ddee9aac01b35208d01736e57942317d756b32ddd...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers = pd.read_csv(\"customers.csv\",dtype={'customer_id':str}) # The customer_id should be imported as a string to preserve the leading zero.\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01e42cec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14.9 s, sys: 1.61 s, total: 16.5 s\n",
      "Wall time: 16.7 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_dat</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>article_id</th>\n",
       "      <th>price</th>\n",
       "      <th>sales_channel_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-09-20</td>\n",
       "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
       "      <td>0663713001</td>\n",
       "      <td>0.050831</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-09-20</td>\n",
       "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
       "      <td>0541518023</td>\n",
       "      <td>0.030492</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-09-20</td>\n",
       "      <td>00007d2de826758b65a93dd24ce629ed66842531df6699...</td>\n",
       "      <td>0505221004</td>\n",
       "      <td>0.015237</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-09-20</td>\n",
       "      <td>00007d2de826758b65a93dd24ce629ed66842531df6699...</td>\n",
       "      <td>0685687003</td>\n",
       "      <td>0.016932</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-20</td>\n",
       "      <td>00007d2de826758b65a93dd24ce629ed66842531df6699...</td>\n",
       "      <td>0685687004</td>\n",
       "      <td>0.016932</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        t_dat                                        customer_id  article_id  \\\n",
       "0  2018-09-20  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...  0663713001   \n",
       "1  2018-09-20  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...  0541518023   \n",
       "2  2018-09-20  00007d2de826758b65a93dd24ce629ed66842531df6699...  0505221004   \n",
       "3  2018-09-20  00007d2de826758b65a93dd24ce629ed66842531df6699...  0685687003   \n",
       "4  2018-09-20  00007d2de826758b65a93dd24ce629ed66842531df6699...  0685687004   \n",
       "\n",
       "      price  sales_channel_id  \n",
       "0  0.050831                 2  \n",
       "1  0.030492                 2  \n",
       "2  0.015237                 2  \n",
       "3  0.016932                 2  \n",
       "4  0.016932                 2  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time transactions = pd.read_csv('transactions_train.csv',dtype={\"article_id\": str, \"customer_id\": str})\n",
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cc62bf8",
   "metadata": {},
   "source": [
    "# Data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "426ae21f",
   "metadata": {},
   "outputs": [],
   "source": [
    "articles['article_id'] = articles['article_id'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd490bc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105542, 25)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articles.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59b4db55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 105542 entries, 0 to 105541\n",
      "Data columns (total 25 columns):\n",
      " #   Column                        Non-Null Count   Dtype \n",
      "---  ------                        --------------   ----- \n",
      " 0   article_id                    105542 non-null  int32 \n",
      " 1   product_code                  105542 non-null  int64 \n",
      " 2   prod_name                     105542 non-null  object\n",
      " 3   product_type_no               105542 non-null  int64 \n",
      " 4   product_type_name             105542 non-null  object\n",
      " 5   product_group_name            105542 non-null  object\n",
      " 6   graphical_appearance_no       105542 non-null  int64 \n",
      " 7   graphical_appearance_name     105542 non-null  object\n",
      " 8   colour_group_code             105542 non-null  int64 \n",
      " 9   colour_group_name             105542 non-null  object\n",
      " 10  perceived_colour_value_id     105542 non-null  int64 \n",
      " 11  perceived_colour_value_name   105542 non-null  object\n",
      " 12  perceived_colour_master_id    105542 non-null  int64 \n",
      " 13  perceived_colour_master_name  105542 non-null  object\n",
      " 14  department_no                 105542 non-null  int64 \n",
      " 15  department_name               105542 non-null  object\n",
      " 16  index_code                    105542 non-null  object\n",
      " 17  index_name                    105542 non-null  object\n",
      " 18  index_group_no                105542 non-null  int64 \n",
      " 19  index_group_name              105542 non-null  object\n",
      " 20  section_no                    105542 non-null  int64 \n",
      " 21  section_name                  105542 non-null  object\n",
      " 22  garment_group_no              105542 non-null  int64 \n",
      " 23  garment_group_name            105542 non-null  object\n",
      " 24  detail_desc                   105126 non-null  object\n",
      "dtypes: int32(1), int64(10), object(14)\n",
      "memory usage: 19.7+ MB\n"
     ]
    }
   ],
   "source": [
    "articles.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b5ce376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "article_id                        0\n",
       "product_code                      0\n",
       "prod_name                         0\n",
       "product_type_no                   0\n",
       "product_type_name                 0\n",
       "product_group_name                0\n",
       "graphical_appearance_no           0\n",
       "graphical_appearance_name         0\n",
       "colour_group_code                 0\n",
       "colour_group_name                 0\n",
       "perceived_colour_value_id         0\n",
       "perceived_colour_value_name       0\n",
       "perceived_colour_master_id        0\n",
       "perceived_colour_master_name      0\n",
       "department_no                     0\n",
       "department_name                   0\n",
       "index_code                        0\n",
       "index_name                        0\n",
       "index_group_no                    0\n",
       "index_group_name                  0\n",
       "section_no                        0\n",
       "section_name                      0\n",
       "garment_group_no                  0\n",
       "garment_group_name                0\n",
       "detail_desc                     416\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of missing values\n",
    "articles.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8160ff5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "article_id                      105542\n",
       "product_code                     47224\n",
       "prod_name                        45875\n",
       "product_type_no                    132\n",
       "product_type_name                  131\n",
       "product_group_name                  19\n",
       "graphical_appearance_no             30\n",
       "graphical_appearance_name           30\n",
       "colour_group_code                   50\n",
       "colour_group_name                   50\n",
       "perceived_colour_value_id            8\n",
       "perceived_colour_value_name          8\n",
       "perceived_colour_master_id          20\n",
       "perceived_colour_master_name        20\n",
       "department_no                      299\n",
       "department_name                    250\n",
       "index_code                          10\n",
       "index_name                          10\n",
       "index_group_no                       5\n",
       "index_group_name                     5\n",
       "section_no                          57\n",
       "section_name                        56\n",
       "garment_group_no                    21\n",
       "garment_group_name                  21\n",
       "detail_desc                      43404\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of unique values in each feature\n",
    "articles.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "638624d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1371980, 7)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7542826f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of                                                customer_id   FN  Active  \\\n",
       "0        00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...  NaN     NaN   \n",
       "1        0000423b00ade91418cceaf3b26c6af3dd342b51fd051e...  NaN     NaN   \n",
       "2        000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...  NaN     NaN   \n",
       "3        00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...  NaN     NaN   \n",
       "4        00006413d8573cd20ed7128e53b7b13819fe5cfc2d801f...  1.0     1.0   \n",
       "...                                                    ...  ...     ...   \n",
       "1371975  ffffbbf78b6eaac697a8a5dfbfd2bfa8113ee5b403e474...  NaN     NaN   \n",
       "1371976  ffffcd5046a6143d29a04fb8c424ce494a76e5cdf4fab5...  NaN     NaN   \n",
       "1371977  ffffcf35913a0bee60e8741cb2b4e78b8a98ee5ff2e6a1...  1.0     1.0   \n",
       "1371978  ffffd7744cebcf3aca44ae7049d2a94b87074c3d4ffe38...  1.0     1.0   \n",
       "1371979  ffffd9ac14e89946416d80e791d064701994755c3ab686...  NaN     NaN   \n",
       "\n",
       "        club_member_status fashion_news_frequency   age  \\\n",
       "0                   ACTIVE                   NONE  49.0   \n",
       "1                   ACTIVE                   NONE  25.0   \n",
       "2                   ACTIVE                   NONE  24.0   \n",
       "3                   ACTIVE                   NONE  54.0   \n",
       "4                   ACTIVE              Regularly  52.0   \n",
       "...                    ...                    ...   ...   \n",
       "1371975             ACTIVE                   NONE  24.0   \n",
       "1371976             ACTIVE                   NONE  21.0   \n",
       "1371977             ACTIVE              Regularly  21.0   \n",
       "1371978             ACTIVE              Regularly  18.0   \n",
       "1371979         PRE-CREATE                   NONE  65.0   \n",
       "\n",
       "                                               postal_code  \n",
       "0        52043ee2162cf5aa7ee79974281641c6f11a68d276429a...  \n",
       "1        2973abc54daa8a5f8ccfe9362140c63247c5eee03f1d93...  \n",
       "2        64f17e6a330a85798e4998f62d0930d14db8db1c054af6...  \n",
       "3        5d36574f52495e81f019b680c843c443bd343d5ca5b1c2...  \n",
       "4        25fa5ddee9aac01b35208d01736e57942317d756b32ddd...  \n",
       "...                                                    ...  \n",
       "1371975  7aa399f7e669990daba2d92c577b52237380662f36480b...  \n",
       "1371976  3f47f1279beb72215f4de557d950e0bfa73789d24acb5e...  \n",
       "1371977  4563fc79215672cd6a863f2b4bf56b8f898f2d96ed590e...  \n",
       "1371978  8892c18e9bc3dca6aa4000cb8094fc4b51ee8db2ed14d7...  \n",
       "1371979  0a1a03306fb2f62164c2a439b38c0caa64b40deaae8687...  \n",
       "\n",
       "[1371980 rows x 7 columns]>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "657d6602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                    0\n",
       "FN                        895050\n",
       "Active                    907576\n",
       "club_member_status          6062\n",
       "fashion_news_frequency     16009\n",
       "age                        15861\n",
       "postal_code                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cb5b85a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id               1371980\n",
       "FN                              1\n",
       "Active                          1\n",
       "club_member_status              3\n",
       "fashion_news_frequency          4\n",
       "age                            84\n",
       "postal_code                352899\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5128cf81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ACTIVE', nan, 'PRE-CREATE', 'LEFT CLUB'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# missing/unusual values\n",
    "customers['club_member_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "304049d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers['club_member_status']=customers['club_member_status'].fillna('INACTIVE') # fill nan with 'INACTIVE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3546f203",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers[\"club_member_status\"].replace([\"PRE-CREATE\"], \"INACTIVE\",inplace=True)\n",
    "customers[\"club_member_status\"].replace([\"LEFT CLUB\"], \"ACTIVE\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6e52e214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ACTIVE', 'INACTIVE'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['club_member_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "919c517f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NONE', 'Regularly', nan, 'Monthly', 'None'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['fashion_news_frequency'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1a9b312b",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers['fashion_news_frequency'] = customers['fashion_news_frequency'].fillna('NONE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a80b30eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers['fashion_news_frequency'].replace(\"Regularly\",\"REGULARLY\",inplace=True)\n",
    "customers['fashion_news_frequency'].replace(\"None\",\"NONE\",inplace=True)\n",
    "customers['fashion_news_frequency'].replace(np.nan,\"NONE\",inplace=True)\n",
    "customers['fashion_news_frequency'].replace(\"Monthly\",\"MONTHLY\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f762686b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NONE', 'REGULARLY', 'MONTHLY'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['fashion_news_frequency'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "61b8a42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  1.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['FN'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4c93afe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers['FN'] = customers['FN'].fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "57c2294f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  1.])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['Active'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "699bf65b",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers['Active'] = customers['Active'].fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8a1a100a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([49., 25., 24., 54., 52., nan, 20., 32., 29., 31., 56., 75., 41.,\n",
       "       27., 30., 48., 35., 22., 40., 38., 45., 68., 55., 19., 60., 44.,\n",
       "       21., 26., 28., 53., 33., 17., 23., 51., 18., 34., 57., 47., 70.,\n",
       "       50., 63., 58., 43., 67., 72., 42., 39., 79., 71., 59., 36., 62.,\n",
       "       37., 46., 73., 64., 74., 61., 85., 69., 76., 66., 65., 82., 16.,\n",
       "       90., 80., 78., 81., 84., 77., 97., 89., 83., 98., 88., 86., 87.,\n",
       "       93., 91., 99., 96., 94., 92., 95.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d4d98ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the best way to proceed will be to fill these missing values with the media age of all customers. \n",
    "# This will be a neutral value.\n",
    "customers['age'].fillna(customers['age'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a599f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id               0\n",
       "FN                        0\n",
       "Active                    0\n",
       "club_member_status        0\n",
       "fashion_news_frequency    0\n",
       "age                       0\n",
       "postal_code               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "056402d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id               1371980\n",
       "FN                              2\n",
       "Active                          2\n",
       "club_member_status              2\n",
       "fashion_news_frequency          3\n",
       "age                            84\n",
       "postal_code                352899\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "44590326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t_dat               0\n",
       "customer_id         0\n",
       "article_id          0\n",
       "price               0\n",
       "sales_channel_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4b180c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t_dat                   734\n",
       "customer_id         1362281\n",
       "article_id           104547\n",
       "price                  9857\n",
       "sales_channel_id          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dbc32137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31788324 entries, 0 to 31788323\n",
      "Data columns (total 5 columns):\n",
      " #   Column            Dtype  \n",
      "---  ------            -----  \n",
      " 0   t_dat             object \n",
      " 1   customer_id       object \n",
      " 2   article_id        object \n",
      " 3   price             float64\n",
      " 4   sales_channel_id  int64  \n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 1.2+ GB\n"
     ]
    }
   ],
   "source": [
    "transactions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1a3c3735",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions['t_dat'] = pd.to_datetime(transactions['t_dat'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1628dd17",
   "metadata": {},
   "source": [
    "# Descriptive Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a770bca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "articles['product_type_name'].value_counts()[:30].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b253a0d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "articles['product_group_name'].value_counts()[:50].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b292df7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "articles['colour_group_name'].value_counts()[:35].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "54262909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "articles['perceived_colour_value_name'].value_counts()[:35].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c67fa99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "articles['perceived_colour_master_name'].value_counts()[:35].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "04ec7541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "articles['department_name'].value_counts()[:35].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "713dde22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "articles['index_group_name'].value_counts()[:35].plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "07cc6de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers['club_member_status'].value_counts()[:10].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bf2cc5af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers['fashion_news_frequency'].value_counts()[:100].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c21e7c5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot: title={'center': 'age'}>]], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers.hist(column='age',by=None,bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "77963e68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1362281"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions[\"customer_id\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736e065e",
   "metadata": {},
   "source": [
    "There are 1,362,281 customers included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dec94dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MGIE9e/Zg4MCBGDhwIPbv3y+WmTFjBj744APMnz8f27ZtQ7Vq1ZCamorCwkJPm00QBEEECWQQIniMnn6gf//+6N+/v9MyFosFCQkJisf++usvrF69Gjt27EDHjh0BAB9++CHuuecevP/++0hMTMTSpUthtVqxcOFCmM1mtGrVChkZGZg1a5YomObOnYt+/frhlVdeAQC8/fbbSEtLw0cffYT58+eDMYY5c+Zg0qRJeOCBBwAAX375JeLj47FixQo8+uijnjadIIgQgPIIBT+kgwgen8QIbdy4EXFxcWjWrBmef/55XL16VTyWnp6OmJgYUQQBQJ8+faDX67Ft2zaxTPfu3WE2m8UyqampOHz4MK5fvy6W6dOnj+R3U1NTkZ6eDgA4efIksrKyJGWio6PRuXNnsYycoqIi5OTkSP4RBBFa0CAZ/FCMEMHjdSHUr18/fPnll1i/fj3+9a9/YdOmTejfvz9KS0sBAFlZWYiLi5N8xmg0IjY2FllZWWKZ+Ph4SRnhtasy/HH+c0pl5EyfPh3R0dHiv6SkJI/bTxAEQWgPkj6EGh67xlzBu5zatGmDtm3bonHjxti4cSN69+7t7Z/zKhMnTsSECRPE1zk5OSSGCIIgggwSRQSPz5fPN2rUCLVq1cKxY8cAAAkJCbh06ZKkTElJCa5duybGFSUkJODixYuSMsJrV2X44/znlMrIsVgsiIqKkvwjCILQAjoKXvIa5BkjeHwuhM6dO4erV6+iTp06AIAuXbogOzsbu3btEsv8+uuvsNls6Ny5s1hm8+bNKC4uFsukpaWhWbNmqFGjhlhm/fr1kt9KS0tDly5dAADJyclISEiQlMnJycG2bdvEMgRBEIEC6aDKwZ8/RjYhgsNjIZSbm4uMjAxkZGQAsAclZ2Rk4MyZM8jNzcUrr7yCrVu34tSpU1i/fj0eeOABNGnSBKmpqQCAFi1aoF+/fhg5ciS2b9+OP/74A2PHjsWjjz6KxMREAMDjjz8Os9mMESNG4MCBA/j2228xd+5cidvqpZdewurVqzFz5kwcOnQIU6dOxc6dOzF27FgAgE6nw7hx4/DOO+/gp59+wp9//omnnnoKiYmJGDhwYCVPG0EQRNWiI5NQpSDpQ6jhcYzQzp070atXL/G1IE6GDRuGTz75BPv27cPixYuRnZ2NxMRE9O3bF2+//TYsFov4maVLl2Ls2LHo3bs39Ho9Bg8ejA8++EA8Hh0djbVr12LMmDHo0KEDatWqhSlTpkhyDd1xxx34+uuvMWnSJLzxxhto2rQpVqxYgdatW4tlXn31VeTl5WHUqFHIzs5G165dsXr1aoSFhXnabIIgCCJIINcYwaNjtI5QlZycHERHR+PGjRsUL0QQQU7D11cBAG6tH4Mf/nann2tTjlAvo16HY/+8x8+1CVz+OHYFQz+3p2g59HY/hJkMfq4R4Us8Gb9przGCIIgAgDxj3oOm/wQPCSGCIIgAQEfh0gThE0gIEQRBBAKkg7wGrRojeEgIEQRBECEFucYIHhJCBEEQAQAZhLwH6SCCh4SQhsjOt+LHjPMoLC71az0+23wCfWZtwuWbRX6tB0EQ5VCwdOUgKxChBgkhDfHUwu14aVkGpq086Nd6/OOXv3DsUi4+WH/Ur/UgCKIcCpb2HpQ1huAhIaQh9p27AQD4OeOCn2tix1pi83cVCIIogyxClYM/fySDCB4SQgRBEERIQQYhgoeEkBbRyMyPlpgSBBEskPgh1CAhRBAEEQBoZH4UHJAoIjhICBGq0AyKILQD7T7vPcjaTfCQENIgWunuqKsgCO2glX4hGKBJHsFDQoggCCIQICVEED6BhJAGIRM4QRCE7yCDEMFDQkiDkA4iCEIOdQvegxIqEjwkhAhVqK8gCO1AluLKwQdIU9dG8JAQIgiCCABIBxGEbyAhpEG00t/RElOC0A5a6RcCFd7CTdZugoeEEEEQBAcNksEJk/xNF5koh4QQQRBEAEAxQpVDEiBNOojgICGkQTTT4VFnQYQgWnn85Gi0WgEDdWeEGiSECIIgAgCtCrSAgQxChAokhAhVqLMgCC1BSshbUBwYwUNCSINopbujpGNEKKLV254sQpVDmkdIoxeZ8AskhAiCIIigR6sCl/A/JIQIgiA4tGp50Wi1AgbKI0SoQUKIIAiCQ6uDpFYFWqDAVP4mCBJCGkQrHR51FgShHXRkE/IaFP9I8HgshDZv3oz77rsPiYmJ0Ol0WLFihXisuLgYr732Gtq0aYNq1aohMTERTz31FC5cuCD5joYNG0Kn00n+vfvuu5Iy+/btQ7du3RAWFoakpCTMmDHDoS7Lly9H8+bNERYWhjZt2uCXX36RHGeMYcqUKahTpw7Cw8PRp08fHD161NMmhyzUVxCEdtDKBClQ4cUP9W0Ej8dCKC8vDykpKZg3b57Dsfz8fOzevRuTJ0/G7t278f333+Pw4cO4//77HcpOmzYNmZmZ4r8XXnhBPJaTk4O+ffuiQYMG2LVrF9577z1MnToVCxYsEMts2bIFjz32GEaMGIE9e/Zg4MCBGDhwIPbv3y+WmTFjBj744APMnz8f27ZtQ7Vq1ZCamorCwkJPm00QBEEEMKR9CDWMnn6gf//+6N+/v+Kx6OhopKWlSd776KOP0KlTJ5w5cwb169cX34+MjERCQoLi9yxduhRWqxULFy6E2WxGq1atkJGRgVmzZmHUqFEAgLlz56Jfv3545ZVXAABvv/020tLS8NFHH2H+/PlgjGHOnDmYNGkSHnjgAQDAl19+ifj4eKxYsQKPPvqop02vQrQx9aOOgwhFtGp50Wi1AgayAhFq+DxG6MaNG9DpdIiJiZG8/+6776JmzZq49dZb8d5776GkpEQ8lp6eju7du8NsNovvpaam4vDhw7h+/bpYpk+fPpLvTE1NRXp6OgDg5MmTyMrKkpSJjo5G586dxTJyioqKkJOTI/lHEIR3OHbpJgqspf6uhku0OmBqZuudgIVcY4QyHluEPKGwsBCvvfYaHnvsMURFRYnvv/jii2jfvj1iY2OxZcsWTJw4EZmZmZg1axYAICsrC8nJyZLvio+PF4/VqFEDWVlZ4nt8maysLLEc/zmlMnKmT5+Ot956qxItJghCifTjV/HYZ1uRXKsaNvy9p7+rQ4Qg0j1XSQkR5fhMCBUXF+ORRx4BYwyffPKJ5NiECRPEv9u2bQuz2YznnnsO06dPh8Vi8VWVXDJx4kRJ3XJycpCUlFTl9dDKxI9WVhDe4qe99gUTJ6/k+bkmgYtW+gWCCDZ84hoTRNDp06eRlpYmsQYp0blzZ5SUlODUqVMAgISEBFy8eFFSRngtxBWpleGP859TKiPHYrEgKipK8s8fUH9HEAThXSR5hGiOR3B4XQgJIujo0aNYt24datas6fIzGRkZ0Ov1iIuLAwB06dIFmzdvRnFxsVgmLS0NzZo1Q40aNcQy69evl3xPWloaunTpAgBITk5GQkKCpExOTg62bdsmliGcQ30FQWgHsghVDqlrjCDK8dg1lpubi2PHjomvT548iYyMDMTGxqJOnTp46KGHsHv3bqxcuRKlpaViPE5sbCzMZjPS09Oxbds29OrVC5GRkUhPT8f48ePxxBNPiCLn8ccfx1tvvYURI0bgtddew/79+zF37lzMnj1b/N2XXnoJPXr0wMyZMzFgwAAsW7YMO3fuFJfY63Q6jBs3Du+88w6aNm2K5ORkTJ48GYmJiRg4cGBlzpnPoQ6PIAg5lFCxckg2XSWTEMHhsRDauXMnevXqJb4WYmqGDRuGqVOn4qeffgIAtGvXTvK5DRs2oGfPnrBYLFi2bBmmTp2KoqIiJCcnY/z48ZLYnOjoaKxduxZjxoxBhw4dUKtWLUyZMkVcOg8Ad9xxB77++mtMmjQJb7zxBpo2bYoVK1agdevWYplXX30VeXl5GDVqFLKzs9G1a1esXr0aYWFhnjY7NKG+giD8Cj9g0wSpcpD2IdTwWAj17NnTqZp2pbTbt2+PrVu3uvydtm3b4rfffnNa5uGHH8bDDz+selyn02HatGmYNm2ay98jCILQGnx3SjrIe5AmInhorzENQibw4CE730pmeKLC2Oje8RoULE2oQUKIUIVybVSOHaeuod20NLy4LMPfVSECFP4JpISKlUM6IaG+jSiHhBBB+Ij5G48DAH7ee8FFSYJQhrcIkQwiCN9AQkiDaGXiR+ZjgvAv9Ax6D8nyeTqvBAcJIYIgQh6txnHZKFraa0iWz/uxHoT2ICFEEETIo1EdBBvpIJ+g1etN+AcSQhpEKx0edRYE4V+0aqkKROhUEmqQECIIIuTR6hgpsQhpJXgwQKHd5wk1SAgRqlBnQRD+hdGqMa9BeYQINUgIaRCa+RFE1aJVF5TUIuS/egQD/DXW6OUm/AQJIUIV6iyIUEGrtzplliYI30NCiFCFumCC8C+U+8Z7SPNK08kkyiEhRBBEyKNVkSFx5/ixHkEBiUpCBRJCBEH4HIpvqRg2yeBNo3dlICsQoQYJIQ1CgwYRbGh9DNfqIGkji5DX0Po9KHDySh62nbjq72qEFEZ/V4BwxJ9CiFZWEKGIVu91mzT5DVEJAmX5fK/3NwIA0sZ3R9P4SP9WJkQgixAhQdpBaLi3IAIKsnJWDNJBvkGrFkCeg5k5/q5CyEBCiJCg/e6BCES0PAPXMoxihLxGoK3AC4Q6BgskhDSIzo85ZMk1RhDagWKEvEcgWIF4KIdU1UFCiJBgo2ePCEG0OubYaGLiNQLNzUh9cdVBQoiQEGizJoLwBlq97yXL5zVax0BBGiyt/XNpIyVUZZAQIiQE2qyJCAwoWLpikKvaNwTCqSTXWNVBQkiD0KBBBBta79O1Wr9AWfIdEATYCSSDUNVBQoiQQKtUiFBEq3c6WQW8R6CJSrr2VQcJIUICxSEQvoCsnBXDZiv/myYmlSPQcqTR9a46SAhpEH+OGfTsEVdzi7DnzHV/V6NK0eqgw09MtFnDwCHQ4q3INVZ1kBAiJJA5luj6rw148OMt2H7ymr+rEvIEWhJALRNop4/64qqDhBAhgan8TYQOBcWlAID1hy765Pu1aH3R6n0vXcWppZoFNoFwJkvJJFRlkBDSIDo/BlTQDJQQKC7xzQ2gxftKi3UCZK4xjdYxUAi0vi0Q6hgseCyENm/ejPvuuw+JiYnQ6XRYsWKF5DhjDFOmTEGdOnUQHh6OPn364OjRo5Iy165dw9ChQxEVFYWYmBiMGDECubm5kjL79u1Dt27dEBYWhqSkJMyYMcOhLsuXL0fz5s0RFhaGNm3a4JdffvG4LoQMeviIMqylpT75Xq3fYlqK66a8Xt4jEBIq8vUi11jV4bEQysvLQ0pKCubNm6d4fMaMGfjggw8wf/58bNu2DdWqVUNqaioKCwvFMkOHDsWBAweQlpaGlStXYvPmzRg1apR4PCcnB3379kWDBg2wa9cuvPfee5g6dSoWLFggltmyZQsee+wxjBgxAnv27MHAgQMxcOBA7N+/36O6EFIoOJMQ8JVFSJMdvAarBNAWG95Eq+KHh68iecaqDqOnH+jfvz/69++veIwxhjlz5mDSpEl44IEHAABffvkl4uPjsWLFCjz66KP466+/sHr1auzYsQMdO3YEAHz44Ye455578P777yMxMRFLly6F1WrFwoULYTab0apVK2RkZGDWrFmiYJo7dy769euHV155BQDw9ttvIy0tDR999BHmz5/vVl20Cq0aI7RAcanNdaEKoEkhpFGYk1dExdHqmeTrRc9J1eHVGKGTJ08iKysLffr0Ed+Ljo5G586dkZ6eDgBIT09HTEyMKIIAoE+fPtDr9di2bZtYpnv37jCbzWKZ1NRUHD58GNevXxfL8L8jlBF+x526yCkqKkJOTo7kX6hBDx8hUOQjIaTFW0yrltBAi2sJFLR6Lvn+l/Yaqzq8KoSysrIAAPHx8ZL34+PjxWNZWVmIi4uTHDcajYiNjZWUUfoO/jfUyvDHXdVFzvTp0xEdHS3+S0pKcqPVPsCPJqFA8KMTVYO1JISEkAbrBMhy3/ixHsFAIKzAI9eYf6BVYxwTJ07EjRs3xH9nz571Sz204hrT6uBAVA2h6hrTVLA0/7fGz5vW0ar44ZHEhJXVd93Bi7hr5kbsPZvtp1oFP14VQgkJCQCAixel+UcuXrwoHktISMClS5ckx0tKSnDt2jVJGaXv4H9DrQx/3FVd5FgsFkRFRUn+hRpSF4H2Ow7Cd/hKCGnxrtJqHiHePaKlegUiTKsXmUNiESq79s9+uRMnLufhmUU7/FSr4MerQig5ORkJCQlYv369+F5OTg62bduGLl26AAC6dOmC7Oxs7Nq1Syzz66+/wmazoXPnzmKZzZs3o7i4WCyTlpaGZs2aoUaNGmIZ/neEMsLvuFMXQgHJg+i/ahDuceDCDXz061EUFnt/qbuvXGNatwhpCUnwLPlKKkUA6CDJ5FN+ubMLikH4Bo9XjeXm5uLYsWPi65MnTyIjIwOxsbGoX78+xo0bh3feeQdNmzZFcnIyJk+ejMTERAwcOBAA0KJFC/Tr1w8jR47E/PnzUVxcjLFjx+LRRx9FYmIiAODxxx/HW2+9hREjRuC1117D/v37MXfuXMyePVv83Zdeegk9evTAzJkzMWDAACxbtgw7d+4Ul9jrdDqXddEqfk2oKPlbq90FITDgg98B2DvNF3s39ep3W0v5pdvMa/elFnWQVt1OlEfIewSC258XP/IJA2Wa9h0eC6GdO3eiV69e4usJEyYAAIYNG4ZFixbh1VdfRV5eHkaNGoXs7Gx07doVq1evRlhYmPiZpUuXYuzYsejduzf0ej0GDx6MDz74QDweHR2NtWvXYsyYMejQoQNq1aqFKVOmSHIN3XHHHfj6668xadIkvPHGG2jatClWrFiB1q1bi2XcqQshhYL1ApMDF254/TsFi9CGw5fw0jd7MOOhFPRrrexW9gQtig7t1cgOIyUUUkhWjdH1rjI8FkI9e/Z02pHpdDpMmzYN06ZNUy0TGxuLr7/+2unvtG3bFr/99pvTMg8//DAefvjhStWFkCJN4EZPYqCg80GIrxAjNPwLe2zC6CW7cOrdAZX+Xurg3ScQ3DmBQiDEP0qtVtqsYzBCq8Y0iF9XjfF/03MY0lhLbJi59rBXvkvrHTzT6I2v9fMWSASCa4y/xuQKqzpICGkErXRytNdNYOKLGW5xqQ0f/nrMdUG3IJN/RZAupya8hVbPJYUm+AcSQhpBK5qDQhIIAfny+QizocLfpXXLhmYzS/N/a6liAYgW7zs5NpqI+gUSQhqBv+X9uGhMAs1IAgdfxAgVyZbPV7N4HFIoonnLhkbdJtLM0hqqWICjVVHkbNUY4TtICGkErTyYWp+5E1WHPI9Q9UoIIUYdfIUIhLiWQCEQrN282C2hmWiVQUJII2jlnpe4CDRSJ8I1vrAWyC1ClXGNSWe6Ff4an6HV/FladdkFIkz1hXbg+9zSUo1WMgghIaQR+A7PF24OdyHTbGjjzApoMVa8u5C4eOi+cptA2BYiUAiE204ihAKhwkECCSGNoJV7Xjpg+bEihEd4SzzLl+yGm8qtQMWVmKFqPehXqy4o/nJoyVIViARCHiF+8ik8i3qNxIwGMySENIJWOl++GmQRCj3kbqsCbg+zyuw9ZtO4wNaqS5gmJt5Dq2KXh39OhBgho4GGaV9DZ1gjSFxjfpwBaLWDIJzjrRmuM/FrrcRu9BQsXTHIM+YbtHoLSlxjZbtemziTELmVfQMJIY2gnfub8liEMk6FkJcsQlq8r7S6oohiq7xHIJw9qRBytAjJFzAQ3oGEkEbQykNKmU0DE2/FCDm75pXphLUqNASkMUzaqaHWz1tAofVcVpBadkUhxFmECqylDp8hKg8JIY2glVmyJDhTI3Uiqg7nFqGKd8LSGBy6r9xF+jz6rx7BgFbFLg9/vYUYIb6mhZV4Bgl1SAhpBP651PkxSEirQaOEc7wVI8ScGH0qEyNk4z6qRUujVgdGra5uCkQCwbqmtGqshHvuCovJNeYLSAhpBY08mYHQWRC+w1nuksrECJHArhjyc6VVwUZ4B6UYIT7DdGExWYR8AQkhjaAV1xit7gltnF1zG5POTj37Xvd+w19odWm1/FxpqW6BRiCIcV7ologWofL3CkgI+QQSQhqBfy79mT+L0aoxnxAoM3lX17yi7jG+/Vq/r7TsjtJuzbSP9LbT5pnka1VuEeJdYySEfAEJIY2glYFSqzPjQCdQzqWrelbUPRbK99WmI5dx57u/4o9jVzz+LLnGvIc0WNpv1XCKJKFiqQ02G5NYU4soRsgnkBDSCNJU+tpAq51FIBIop1K+xYaAsIK3okJI85mlfSjUhi3cjvPZBRj6+TaPP+vgGvNWpUIQLd53cvhFBQXFpQ470JNFyDeQENIIWlleLB2wAqDnCBAC5Vyqua3MZRuuVjSXUCDtmaWl2jlahPxTj2BAuteYNuHrmFdUKnGLARQj5CtICGkFjTyZlFDRNwTKuRSuf7jJgCZx1cX3zWXZbSscI8T9rcVzoVVxJq+VVusZaGhVUPL1yi0qUbAIkWvMF5AQ0ghaSZwmHbA02lsEIIEygAnX3KDXwcSl9reU7UJfUdO81oOlpa4x7dSPVo15kQCwSvLXO7eoRLJiDCDXmK8gIaQRpGZb/z2kkr2N/FaL4MOdAay4EgkLvYUQI6TTARZjefdQ3WIEAORXMMV/KAdLVwo6V14jEE6lPI9QXlGJ5DhllvYNJIQ0glYGCunKikDoOoKDzBsFSHlrLf7vhz/9Wg/BMqnX6cS4IACoZrFbhHILS5Q+5sb3ajv2jKn87W/kkyINnjrNcjGnEOOW7cHuM9cByDew9VetnCO3AGbnF0tek2vMN5AQ0gg2jVhiKEbIN7hyB/37t5PIt5Zi6bYzVVQjZYTBQq8DptzbEga9Di/2bipahHKLKi+ENH9faah+8nOlVZeOFnn1P/uwIuMCBn28BUBgZM2X1yu7wCp5Ta4x30BCSCNoJUZBktlUA66aYEGrM1A5wsBr0OvQum40Dk5LxYS7b6m0ENLK/a2GFusE0KqxynD8cq7qMe1eb1cWIRJCvoCEkAbxq0WI+1stpwzhOYFyJstjhOyJgyxGu0tMEELymAV30bqlMWBcY36qRyDiICL9Uw2PkD8b2QUkhKoCEkIawaYRuy1fjWItjlgBilZnoABw7NJNPL9kFw5eyBHvQ71sn5dqZULoZgVjhLSSJ8sdtFQ/B9eYhuqmdQJxxZ28jjfy5a4xstL7AhJCGkEjOohcYz5Cy5py2MId+N/+LDz48R/ifWjQSZVQ9bDKWYS0mDmdR7ODJGWWrjCOWbkDP1iaEir6Bq8LoYYNG0Kn0zn8GzNmDACgZ8+eDsdGjx4t+Y4zZ85gwIABiIiIQFxcHF555RWUlEg74I0bN6J9+/awWCxo0qQJFi1a5FCXefPmoWHDhggLC0Pnzp2xfft2bzfXa2hltZY88Z1NyyN4IKHh03g+uwCAPWt0KZO6xgSqm70ZLK3hkwFtXSpHi5B/6hGIODt3Wg06l1/fa3lWp68J7+B1IbRjxw5kZmaK/9LS0gAADz/8sFhm5MiRkjIzZswQj5WWlmLAgAGwWq3YsmULFi9ejEWLFmHKlClimZMnT2LAgAHo1asXMjIyMG7cODz77LNYs2aNWObbb7/FhAkT8Oabb2L37t1ISUlBamoqLl265O0mewWt5O+RP4jyzKbBQlWLTVcdr0x3+A3RNSbrGQSLkDeCpbV5S2myUg73KbnG3MfZudLqaZTXec/ZbABAVNnzd/pqflVXKSTwuhCqXbs2EhISxH8rV65E48aN0aNHD7FMRESEpExUVJR4bO3atTh48CCWLFmCdu3aoX///nj77bcxb948WK12NTx//nwkJydj5syZaNGiBcaOHYuHHnoIs2fPFr9n1qxZGDlyJIYPH46WLVti/vz5iIiIwMKFC73dZK+gnczS0h+X73UTDORbS9Dz/Y14ZfneKvtNrXa8csqXz0uVWbVKrxrTdoyQVvJ4yZFXRUt10zraFNzOkdf55JU8AMCg9vUAAFdyiyrsnibU8WmMkNVqxZIlS/DMM89ITO1Lly5FrVq10Lp1a0ycOBH5+eUqNz09HW3atEF8fLz4XmpqKnJycnDgwAGxTJ8+fSS/lZqaivT0dPF3d+3aJSmj1+vRp08fsYwSRUVFyMnJkfyrOrTx1Mo72uJSbdTLm6zcm4nTV/OxfNe5KvtNT9xB/t101/6/PEYoUhBCFQ6W5v7W+C2lJbeJYx4hwl2cWdO0eg+q3XvdmtZCTIQJAFmFfIHRl1++YsUKZGdn4+mnnxbfe/zxx9GgQQMkJiZi3759eO2113D48GF8//33AICsrCyJCAIgvs7KynJaJicnBwUFBbh+/TpKS0sVyxw6dEi1vtOnT8dbb71V4fZWBq34r+UdRDAuoS/yQxC4J2exuJTBbPSPr4zfYoMn3GxfRl/RLTakCUO1d09pr0Z2yDVWcZyJSK2eRbXuNjrchPqxEcjOv4Fz1/PRMjFKuSBRIXwqhP7973+jf//+SExMFN8bNWqU+HebNm1Qp04d9O7dG8ePH0fjxo19WR2XTJw4ERMmTBBf5+TkICkpqUp+W7OusSBcOVZcUjVt8sQKwltMS2w2mP20oNOm4hoLK9t0taiCex3xHlate1u1rDU0XDXN4Wz5vFYFpVq9TAY9osLsFqE8K7nGvI3PhNDp06exbt060dKjRufOnQEAx44dQ+PGjZGQkOCwuuvixYsAgISEBPF/4T2+TFRUFMLDw2EwGGAwGBTLCN+hhMVigcVica+BXkYrSzsdXGNBaBGqqs1NK2oFKS5hgBn49dBFXMsrxkMd6imW84WpX/geuRASNmAtqqCIlK5G1N49pdUYoUDMhaMVHBMqav/kqV1fo0GHMJP9GaRcQt7HZ9POL774AnFxcRgwYIDTchkZGQCAOnXqAAC6dOmCP//8U7K6Ky0tDVFRUWjZsqVYZv369ZLvSUtLQ5cuXQAAZrMZHTp0kJSx2WxYv369WEZraKWDk1ejNAhjhKpKCHkyuPJpCqxl9Xtm0U78ffle1a0CfHHPlK8aU7YInbtegNFf7cKOU9c8+l6trIpUQ6uDZCAO5lrBmeDW6llUq7PJoIel7Bmk7NLexydCyGaz4YsvvsCwYcNgNJYbnY4fP463334bu3btwqlTp/DTTz/hqaeeQvfu3dG2bVsAQN++fdGyZUs8+eST2Lt3L9asWYNJkyZhzJgxorVm9OjROHHiBF599VUcOnQIH3/8Mb777juMHz9e/K0JEybgs88+w+LFi/HXX3/h+eefR15eHoYPH+6LJlcarezOLf/tYq37MSqAtYrEnSeuMT5NQXGpTSLWzlxTDo7k7xlvLb8XYoTkmaUt3E70qw9k4eH56osOlNDK/R1oOJwpOnVu43SfNo2eR1WLkF6HMKMghIKvT/Y3PhFC69atw5kzZ/DMM89I3jebzVi3bh369u2L5s2b4+WXX8bgwYPx888/i2UMBgNWrlwJg8GALl264IknnsBTTz2FadOmiWWSk5OxatUqpKWlISUlBTNnzsTnn3+O1NRUscyQIUPw/vvvY8qUKWjXrh0yMjKwevVqhwBqraCdzNLS1yVkEaowzAPXmJWrU0kpk2SQVVup5QuvpaprrGw2yjNjtfrCA7XvBbS5rFmr2swxOzLhLs7OnVYta84sQuWusaqxCF3MKcQbP/yJvzKrcvW0f/BJjFDfvn0VZ31JSUnYtGmTy883aNAAv/zyi9MyPXv2xJ49e5yWGTt2LMaOHevy97SGloKlq0o0VCVVFiztgWuMD0q3ltpQyK3Okm+8KCC1sjj//ryiEjEXkDNUXWNGxznTxxuP49V+zV1+J/+9gDZFh1YDaWn3+YoTiOdOrYomgx7hgmusggsWPGX8txnYcvwqvt52BqfedR7iEujQXmMaQSvLi0Nh+XyVWYS46+gqQJjP11RcapMsU79ys6hS9Vix5zxavbkGi/44qXicN/4Il9vBNaZgEfIErQdL82i5dlq1ZGgRx/tM22IcUBfh9mDpMiFUwRQWnnLgQvBbggRICGkEraxacdxiI/gsQlUWI+TBNeXFWXGpTeIau6QihNyNERr3bQYAYOrPBxWP8x8tjxFSXjVWUaSuMe2NQloVGPK9/jR46jSL0+XzVVwXdxEudzWzdOJh0utp1ZgPISGkEbQSxyf/7WDMLO2PVWOukAsh3iJ0+Wah4mfcNdYZ5OYdGXwOo/ItNqRlTAa9w/eYDe53H1oUP2poqaryqhy9pLyCkHDE2WXU0jXmEeoVIXNjm4ycRaiKXGOhBAkhjaCVGAq5aTbYXWO+jAfxZMd1qWuMSQIi1fb3cueembfhmMtryMubcteYo3iSW4WspTYHi4UamrcISawF2qmf/FwNW7hdpSQhx18xQoezbmLDoYpt7i1cb7lFyKiv+uXzWoqV8zUkhDSC9J7zY4yQ7HVQBktzbfKl0PNk+bwzi5BVJbjbnX7qvTWHXZaRxggpu8aA8lxCPFY37w+tCP1Ag86V95AkrfVhH5s6ZzOGL9qBP8/d8PizwnMSYZZZhAy68mBpco15HRJCmkEbA4V8FhCMy+etJbylxoc/5EFMgrMYIbVszt6asek4m1D5qjHHckpxQu5mmtb68nkeLYkPDVUl4KnqOMyMs9cr/NkImUVIpyvPLF1QVRahKvkVbUBCSCPwE2u/xgiFRLB0eZt86aaRbpvi/Hd4wWktYSjg9hNSswjxgqJSCRXdtAgpCyH3OmXmgZvQH2iwSgAqJnZX7cvE2ysPuu22DBWqOg4zRyX/lzOEZ0PJ+iokVCyizNJex6ebrhLuwwsOv2aWlr0uCcLOlM8j5MtB2eaBFYS3CJXYbCiwurYIecvdJIkRKvspd11jRW6a6W1VPQpVAi1VryLXdczXuwEA7ZJicF9KoovShK+4WQEhJCY0VVjgEFbVrjEtPQg+hixCGoG3CFT0/ruaW1TpmJ5QyCzNi05f6jypoPUkWNqGfIlrTHkG6C13E695SlVWjQGVdI15kFPJH2hl02M5SrEsJW4+41k3lFcbhirSx9H3FzmnUDkRqjOE51jJwCsun6dVY16HhJBG4IN21Z5RZ5ai01fz0OGddXjgoz8qVY9QyCzN5xHSZLB0CZMkTVMPlnbf9SagNIjyMULMqWtMwSLkZqfsiXXMH2hJ/PAI5yolKUZ8L8/NhHrBaM2tDNJgaR/9BncjVcQiZHMyEQmjTVd9BgkhjVBicz6ovbPyIHq8txE38pVnGb/8mQUAOFjJfWFCIbM0Lyx86Yb0JIEbf/2tslVj6q4x979fQGkQVcwsrdATW0zqFqHDWTex4bD6kmH+XFg1P6Nl+G7HWew/7/mqH6/XpOy8dWxQAyaD/ZrkqaRTkOOu5ShkqIJgad6ym6OyNY5TnKSvqHLXWAhBQkgjuOq0Pv/9JM5cy8eiLacUj3uQ284pcrdFcRAKIb6D8qlFyIMAYV6cWUukq8bUg6U9dzcpDaLSPELOXGMKy+fL6pY6ZzOGf7EDBy4oiwfecuSuRaMq4c/elVwrXv3vPtz74e9+q4+AYMXQ6yDuFeeuEArGZ7cyVMXZ4N3uNyvkGrPXUmnxQ1WvGgslSAhpBIlFyEm56/lWxfeVZhDeIBhnlbwQ8mmMEP+3B66xguJSWfA0UxRs/DvutkMpOSOfWVpYaaR0P8VEmMS/jWVKSW6tOnDe0SJpLbFJ3ARqCSIJR4T7RqfToVpZbhm3XWNB+OxWhoq4kj2FtwjlW0vx8cZjGDjvD1VLvhzh0zonFiFrifuJTCtDKMloEkIaodRNP4ea35nf/qAyD3mwB0sXl9pwkxuIfbp83gNTPC+E84pKHKxASlYhmwt3qhLZLjpkZ5ml60SHiX9HhdtFUVFxqeS3lWKGruZJ90rLrUDshK/RahZdoV46ANUs9oHQXYtQMLq1vYWvzoxcfM5YfRgZZ7Mxb+Mxtz7vTowQ4P4iBcI9SAhpBMm2D07KqZlbeSHkbrZfJeTB0hUJuGSMYd6GY1hzIKvC9fAVciFQZXmEXK0a4zq2fGupwx5vSgKjIgniruY6buCqmFlaoSdO4IRQZJixrF42SafM/y0ItSs3pVZMdwfyqkSrkkFiESpzjblrUQvGfQIrQ1UsGuP7S16I/nfXOew9m+3y8+WrxhQsQtyqzWELt6u6zL2FVicHvoCEkEYodXN2r7Ykk5/BF1orIYQcLEKef9fuM9fx3prDeO6rXRWuh6+4USAdlH05aeZzUbp0jXGFC6ylDmJWaQZYkSXpV/IcXavKe405fjYhirMIhZnEeinlPPphzzmkvLUWfxy7gisyi9BNDQohrSIOjDqguocxQsGYDLUyVMW4zk9o+Wf4ap4VD8z7w/WSeieZ3Y0GvRgwv/3UNazIOF/5ChMASAhphmI3Y4TUXGP8ZyoTTCfvLCoScJlTUF5HrcUpXJdbhDSzfJ6LLSgudc81JlmS7vgDSm1TsghJ62H/HaNCTxxbzSz+HRUuWIRKJTmPhPir8d/uxc2iEjzx7224cjMQXGNq7/t3ViyIXR3K47LctdKSRUgKU/nbm/ChBNdyHScdrpbU8xYhRfcYt2Bhz5nsCtXRXULp7iEhpBFKJTuiq5cTHqS/MnPw7OKd+KtsuTw/UFZKCMnrVYFZJb9PTnZFlpD6kOt5cotQ1awac+Yas8mCoQusJQ75m5RcY9JVY47fW6xw7a4qdM58YKaQoyRMYal83Rrh4t+iRajYJtkORB7Mz5h9FRZQLqTyrNoTQmpUxs3sDcRMwzqd6P52V7xX5NkNZtwNll685RQmfJtRoRir89kF4t9Klk9X106MCdMpT0Z4N/bhrMqlSiHKISGkEaSrxtQfFiFG6PHPtmLdXxfx+GdbAciEUCWWJ8uFgTy+wx34DuTpL7Zras8juTCrqoBSZ3pLLljyrY4WIUXXmIuOXckiIA9cBqSdqyCiwxW204iLDMPMh1Mw99F2YryKPOeRYHHjY9ayy9yR9cqElBYtQmrzX38HpfIDoyBYnd2y/H0QbAsdqoo3fzqA7/ecx0YnebGUuJZnxdDPtzkt46q/4RcrKLnH+I9fdmHdJdyHhJBGkCZUVC8nbOQnDDjC/7wFoVIp2GW/fexyrsdfwc+i95/PqXSSR28ij6/waYyQm3l+5ILFHiztjhBS/vvz307gkfnpDi4pQFnY8hZ4Ye8wpX3FAGBwh3p4oF1dRJetGrtys0givLPLLEK1q1vE94Qs2cJ7Wlw+r3Z53N1LzVfwy6kNZUKo1Mm9xA+0lEdIiqdnw9Os+n+qJOBsWSdKXFzg7NoB0jxCBoWVm/z1vZRT5HfXbbBAQkgjlDqJEeJvdrUZBW9BKKyERUiwRtWNsc/ej13K9fhhk1szDErObj8hr5sWMkvL46gKrKUOwseTGKF3Vv2F7aeu4e2VBx0+o5SHyl3XGE9yrWoAgJNX8iQxQoLrtgYXTyS4C2ppWAip4e4WIr5CHBhR/hw5u2elq5ZC1zWmaIV2Y5Ul/yxaVCYDqr+p8qUxESYxvstd6zjvCuXhhVRRiQ03fBh6EEoai4SQRpCYsWU3oPzZUdprxipLyFdRl49w8zeqXQ06HXCjoFiM8XAXeVyFlkz08lmeqxlaZXA3WFp+vvIUY4SUhJDzGKH1hxxN+0oxW3x3WyAKIeeDQKPadiF04kqexCIkfJ7fRiOzbPPPmtXt4kjJ9edv1C6Pv+tZvny+3IXp7Nm2Bbhr7FJOIab/8hfOXM2v1PcoPdfupLPgk1UquYedonK6YyJMoqhxFeiuJHwlx2Wfz6SNdb0CCSGNwC91lT+k8o5PKSke32FP/fkAbp22Fmeved6ZCL9kMRrEgFhPZx0OK55KtZMSXl43X06a3d19Xj5gKS6fVxC//KAn/JYrAXwjv9jBosBb4POK3BRCtaoDsFuElm47Lak7/z8AXMyxu+gSosMQVeYiOHbJc5erP/B7jFDZ/7yFwNk15o+tPXgRz321M6DcJy98swefbj6BRz5Nr9T3KGZid8MixLvOPbVkq12X6HCzW9eOr5dOp0PrutEOx+VCKivHd0LIVe6zYIKEkEZwFiMkf3iU3Bv8wHn2WgFyCkvwyabjFa6HQV++XNfTlVWOS7+180BZZaKjqjJLq/V/S7edxt2zNkney7eWorjsnAm5YwpdxQiV/e9qfyNrqU1hVWF5hy8MBK6EUHxUeQzQH8euin8LAoh3l10pC+oMMxnQMjEKgH1z4LUHsvD97nNOf6eqUI0R8rcQ4i0EOsE1pl5eLuzXHLjosUXXn+w4dQ1A5Qd4JcuLO496vpVP/eFZ36C2WjcmwiReO1f9DZ/Ha+bDKXisUxJWvdhVtXwWWYS8AgkhjVDiJLO0PDGaohBS6LBNFYjNEb7HYjSI2YU97RDk1gxPgw59iYNFyM+usf/7Yb/D3lEFxaVibIqQzVkp8FkqtOwv5HmSlJBbFHmLkDAQuHIL6HQ6TB/UxuH9/LItN/IV4tTCTAa0rGOf5e4/fwOjvtqFCd/trZDl0tuoWU2ULHFVidQ15kawtMIxf6cA8ASlJeMVodhFLKDaGcwtKr/envYNaqt1Y8JN5X2pu64xHRAXFYbpg9qiVaKjZUjgmkKC1IqQeaMAQz/fitX7M8X3AsiQWGlICGkEqUXIuWvsep7jYKckNjwN9gPKg0MtRr1oEfI03sidZID+Qu6m8+mmqy6Wt8vPK59/SQg6blgzAoDyDFkSI1R2itU25eWRl+Hlcq5oEXLdNfBWIYFSG0NhsU3xmluMetSPtQfhL9pySnz/7HX/CyE1/C0ieFeJoeySuOsaE6hMOo2qxlsLK+T9IWOyGCGVU5jPucY87ffyVfJjVTRY2h2U8oJVhAWbT+CPY1cxeslur3xfoEFCSCM4WzUmn0Uo5YJRG3g8RbQImfTlfm0PpwZyd4I7FqEruUXYdfqazzNRF5c4F5nexFWEkNwSwlthhGteP9YelHyBS9QmoLQ839ku14K4Wfj7KclMsiIxQoA0yy2P0v0pfGf1MJPD+5nZ6ub9Q1k5VTKQq90F/l4+z1sI9KJrzL1gaYFAEkJGbwkh2XNtY0zmSlY+h/yKRk/7vXwV62F0uFliEXLmvhaEkps6CNdUnjVP4YWX4B53tw7BAAkhjcDnknEVI/TtjrMOn1d0jRk8v7yCiDEbDJxFyLPBQC58rKU2LN12GmO/3q0qip5fsguDP0nHmK99OyORz/B9GUjqKo+QPGBYSXwIFhSl1SFKNXdmEapZzW7B+e/uc3jhm/LzzG/wKGR9dkcIqVkcp/50QPH9MKNejHniUbMIbT5yGf3m/IZRX+10WZfKotkYobL/ddCJg6mzuYKiRcjP7j1PMBq8JIRk1+3DX49h7cGLLj/Hu3Q9TQSr6hrjLEI/ZpxHm6lr8dXW04ply/eWUz4PZlmffs0NV7g71Igon6BsP2mP0yLXGFElWEtsmLvuKPacue5UbMgtQgcuOCYoVDLhV8QlxVuEKhwjpOAa+78f9mPlvkz8d5dycOyOU9cB2IM7fYn8PPnUIuTCJHRJFvdjUXBHNSjL16MUFMkUhJawQkuJ2pHSAGeho+dnx3keuMbULI7r/lLOyGsxGcTEcjxnrzlauwDgp70XAAC/Hb3itzgzf+cRKt9iA24F3Crdz2ouGy1i8FaMkOx+mbv+qOS12inMrZRrTF0ICRaXb7bbJ7GTV+xXLCs8i2qGsaUjO6NpXHU82zUZgPcsQrxY/uy3E175zkDC60Jo6tSp0Ol0kn/NmzcXjxcWFmLMmDGoWbMmqlevjsGDB+PiRengd+bMGQwYMAARERGIi4vDK6+8gpIS6cO8ceNGtG/fHhaLBU2aNMGiRYsc6jJv3jw0bNgQYWFh6Ny5M7Zv3+7t5laKb7afwex1R/Dgx1scxI4kiWKZEIkwGxxmBAJKokcp35ArhI7fbPBejBBv7VIaqPklq5EKFgNv4hgs7d7nruVZ0ev9jZiVdsTt33KVUFGelsBs0DvESNQrS2yplE5fmlDR/v+eM9dV69OvdYLktZDxW+l73HKNeRiDFmZStgipbRVQPzZC/Hv3afV2eQM1V4m/49uYxDVmf8+ZECLXmJ2KxnbxfZGnwdKqQohbPu8KftNVJW5rGIu0CT1wb0oiAOWNXQG7EDx26aZTi/eZq/kY+eVO7Dp9TVL3vWezAdCmq5WmVatWyMzMFP/9/vvv4rHx48fj559/xvLly7Fp0yZcuHABgwYNEo+XlpZiwIABsFqt2LJlCxYvXoxFixZhypQpYpmTJ09iwIAB6NWrFzIyMjBu3Dg8++yzWLNmjVjm22+/xYQJE/Dmm29i9+7dSElJQWpqKi5d8mz/GF9y6mqe+Lfc6sLfv8KqMYNeB5OK6Vjpwa+ISVyIibDHCNlvD0995fK68Mn18osdZ6dXuIFQ76WOUA35TNHdzu6LP07i5JU8fCCbWbqL0s8Ie3AJmBSEkLCVhbXE5mCq51/bYyAYdpYJhuF3NnT4vSEdkzDo1rria8E1p+QCcCeZHG814k3rquWNBlRXsAipCXZ+UDqvECNVFWjTNebaIhQZZsTtjWIBAM8v3R0wy6y95hpzYcVWEwj8Ck5XK7zkFKhY3qLDTYoCT7EOzLlFSKBmWeb2q3lWxe8Z+/Vu9Jm1GT9mXFD9jueX7kLawYsY/Em65BksKFv56bKuQYRPhJDRaERCQoL4r1atWgCAGzdu4N///jdmzZqFu+66Cx06dMAXX3yBLVu2YOtW++aha9euxcGDB7FkyRK0a9cO/fv3x9tvv4158+bBarUPHPPnz0dycjJmzpyJFi1aYOzYsXjooYcwe/ZssQ6zZs3CyJEjMXz4cLRs2RLz589HREQEFi5c6IsmVwh+duxO52bU62BScUcouQ4KKxDoKYgYu3XC/p6nHYIzi5DS7PQy5yKqiBXLEyq6fN5Vx6qEqxgheWCzyaCTdJg6HcTNTQHHPeSY7MWNgmIxCPq1fs0hx2LSY9aQdhjSMQlAuRBXqpunFiElgSOvg8WkV7T4qS1R53fvzvHhVgIAVKe/vr4fXSG1CLnedFW4lga9TkyICgCL00/5rI7exFsWoYouuuCvt7dcY2FcmAGP0tJ3VzFCArFlQqioxKb4u0KIwXwnueT4EAtJbBQrGwcULMXBik+E0NGjR5GYmIhGjRph6NChOHPmDABg165dKC4uRp8+fcSyzZs3R/369ZGebs8kmp6ejjZt2iA+Pl4sk5qaipycHBw4cEAsw3+HUEb4DqvVil27dknK6PV69OnTRyyjRFFREXJyciT/fAk/yDks9+T+LhU7N71qALQwwL91fyvxvQq5xkSLkKHcIlTZGCGubUoPLR8rU1Ri8+nsw5VrrKTUhk1HLju4rdxZQZFbVIItx66IHainrjGjzCJkMugllhm5sJULLWHJvcWoR5jJIGZxFhC+u37ZknxhGwOlTs7TGCGl+/KeNlJXnLpFSHnQusntUp/j4x3r1e44eY6nqoYfGIXr50y8iwlRdTqEc+kYKrKC1B94a/m8K9eY2ikskAgCDy1CKv2tTqdTFHhKCyD4VYLOiDAbxGt6Lc+KueuOKi5ScHcMkE9Q5a99GUupBbz+dHTu3BmLFi3C6tWr8cknn+DkyZPo1q0bbt68iaysLJjNZsTExEg+Ex8fj6ysLABAVlaWRAQJx4Vjzsrk5OSgoKAAV65cQWlpqWIZ4TuUmD59OqKjo8V/SUlJFToH7sILIblA4MWA4DYz6nWKMUK2stwtANC6bhRmDG4LoGKuMaEDsfAxQp4un3dwjUn3QZNzWRY07Et3hINrTPaAL9pyCsMWbsdT/94med9Zv3QltwhTfzqAhz7Zgsc/34ZlO+zCX5q3xPEcyhMb8nFZgP0a6PU6mMs6PPm5k2euFlZ8CZbG3i2k97+pTNg2rGkPwD5dtnxfyTVmUVkaz8NbhJTuyzrR4Q7leWHXpVFNAI6WLoFcbpmxzy1CKuT5eYPYctcYZxFyw3psd6OXXxOl2CwtwidUrMyEyKVrTOX9AolFyLPfdCbWlfICKd3TvPXfGTqdTnSPXcwpxOx1R7Boyymc5sItAPe9AvK+paC4VNJ/kRDykP79++Phhx9G27ZtkZqail9++QXZ2dn47rvvvP1TXmfixIm4ceOG+O/sWcdl6t4g31qCxVtO4Usuqdyf529IykgsQpLOzfEBsZbaxNwUkWEmcfVRZYKlLSa9uEqlosHSglWBFx9KrjG5IHAV3FkZd4VcZMlnfSsyzgMA9p6TXg9nCc5e/GYPFm05hUNZNwEAC38/CcC1RUi+AarRoJOsmhHcoGFG5espzywtDNqCwH7rgVZILMtMDZTHXzUoswidVnGN6XXuzcz5gdZk0KNZfKTkuFlmhbAY9RKTv5BAUu16Si1CvhVCqquIfGyJcgXjYkaES+JsYiIsPjXodZLBLcIcIEKI698q4o4WP+tiMqVqEeLOmafL5687yfKs9Dw9/vk2nLoiFS7ChDjcjesVW7aJ8VEuDYe8f1ObZPBEWowOfa58Yu7Lzam1gM/tpTExMbjllltw7NgxJCQkwGq1Ijs7W1Lm4sWLSEiwm9ETEhIcVpEJr12ViYqKQnh4OGrVqgWDwaBYRvgOJSwWC6KioiT/fMHOU9fx5k8HJDew3E0iDZYumyUYdIouiMLiUnE2EhVmEmfdFbIIlfAxQu6lhZcjCB9hFloscY3Z65lTWIwf9pxDblGJQ7ZnZw/v1hNX0XLKaszbcMyjOsnrJiAXeWozZ2em6i3Hr0peC9/Bf7XS7FY+IzRxcVn21/YfFVwc8s6KFzDnrhfg8k2r5PejwkyY8VCKw+8KrrEruVbkFpU4dHJyAeMORoMOXz3byeF93gIkj5MIF4WQDZdvFqHnexswZ135qrxcSYyQfwRJbiUtQpV180ozS7thERKFk05yv8i36dEqvCXEnUFcjYqmWyjkzpmng7+nQggA3l55UPJasOryWebVqBFhF0JHLt4U35Pfr2qTDP781KhmdrQIkWvMu+Tm5uL48eOoU6cOOnToAJPJhPXr14vHDx8+jDNnzqBLly4AgC5duuDPP/+UrO5KS0tDVFQUWrZsKZbhv0MoI3yH2WxGhw4dJGVsNhvWr18vlvEn3ZrWQvOESKdllMySBp2yELqeXyyWiQo3ii6LigRLF3F5hITZmaczI0FMCZYJiWus7AF7dfk+jP92L6b+dMAhbqew2B4npLTS5dEFW2FjwHtrDntUJ7FuZR1A+VJk6XFVIcT9LdkXTmm5stip8K4xaRnGmEPyQ3uwNL8Sy97RCddTntNGbsl58Zs9Dm1QivWJCjOJwZanr+Y5nIOKJOI0GfSIiwwTv1fg1voxAMpXuQAQLUeDO9QDYO+s/7PrHE5dzcecdUfFTpq3CMknCt7GnUzDFaGy4wefV8adYGneesznD/J3hmx34S2GlbH8yjNLy1G73rwg8GQCaC2xicH9nRrGOhxXE0JysSf0j+4IIeGZOnqxfEJ9U2bBVAsz2M95ICLMBscYIdm593QMCDS8LoT+/ve/Y9OmTTh16hS2bNmCBx98EAaDAY899hiio6MxYsQITJgwARs2bMCuXbswfPhwdOnSBbfffjsAoG/fvmjZsiWefPJJ7N27F2vWrMGkSZMwZswYWCz2hHCjR4/GiRMn8Oqrr+LQoUP4+OOP8d1332H8+PFiPSZMmIDPPvsMixcvxl9//YXnn38eeXl5GD58uLeb7DE6nQ49mtV2WkZ1+bzCbF2IsTHqdQg3GbiZdsWDpc0GQ4UtQoKwEQZkfsd3weS6+oA9Vus/u845mMALrKWYt+EYbp++Hp9zyb28EUQtbLEhiAv5d/IiQnKM66D5zkUpL9KZa/lg8pT+sqpfybU6mJ/ly+fr1bDH2IgWPqvj/kk8gsirZinvRDs0qIH7UhLx4l1NJGUbcAHT8nNQkcBawXol7+5nPdIObw9sja9H3i6+9+PYO/HH63ehTd1oAI6d9Y6yzLb8VgR+c41VWghV7p4VDTk6nSiEnLrGGC+Eyu8vf++Z5i685aHQWvE6V7VrLLtsUqPXActG3e5wXC3mJ0q25YzoGnNj1WZsWab4w7xFSCaE+HaevZaPtIMXsf/8DWw4VG5oKC61OQiffGupRHAHu2vM647jc+fO4bHHHsPVq1dRu3ZtdO3aFVu3bkXt2vaBf/bs2dDr9Rg8eDCKioqQmpqKjz/+WPy8wWDAypUr8fzzz6NLly6oVq0ahg0bhmnTpollkpOTsWrVKowfPx5z585FvXr18PnnnyM1NVUsM2TIEFy+fBlTpkxBVlYW2rVrh9WrVzsEUPsLkwcZVCUxQgoP1KWbdstJVLgJOp1O3AOqIkJIDJaWxAh51iEVySxC/PJopVVjSn7t99faXSTvrPoLz3ZrBEDqHmkSV92jOgkI7QszGZBvLXV4wOUB7NWUlnuX2FDWB0kGyuF3NsQXf5xCYbENl24WSeac8gFRvr0GYA8U5TvMxLJkihaT8vVUm9XyddbpdPjwsVsdyjSIjcCeM9k4cy3fwexdEYuQYMmSuxATosPw5O0NJO+FmQyoGxMuOXfnuG02vtt5Fl0a15S6xnwshNSobIxQZYWQcI11gOg2dTYhEK6lXgc8172xuGWNPEWBzcbw/NJdKLUxfPpkR6+t1qosvMvGl64x1WBp3jXmgRC6ViaEakSYJS5g4XlWizEUMq1fvlmEV/+zF5uOXAbgXkxXzbIYIX6xido+ZttOXMWQBVvF13c1jxP/tnJCKDrchBsFxSiwlkraryYKf/kzE9tPXsP/DWhRoX5DK3hdCC1btszp8bCwMMybNw/z5s1TLdOgQQP88ssvTr+nZ8+e2LNnj9MyY8eOxdixY52W8ReuEgd6EiMkuJCE5dIRZRaBinTiQofJxwh5OpkUgnZjy1w7/GxDaUBzdI0pd4BXuHTyJaX2BIP6MhfAij0X0K91goNrRu23hADk73efx8LfT2Le0PaoEx0uESLX8qyoZjEi/fhV/HkuW3yfd1EJdU2ICsOb97XC+r8u4cy1fJy6kicZBOXdyOEsx9QMZqNOMiAJQijcpLxqTE2fKm1jISeqLFFjnmzmB1TUNVZmEfJgp8YwzvJ0htuA9pc/s/DWAyWSep29VoBFf5zE03cme1w3d1Ab8iofI1Spj3NbbOjEc+tsgLZxk6YBbevghz1xWPfXJYeVnAczc8RcM4ezbqJlom/iIT2Ftz5XZsVehWOEeIuQGxdv9f4s/Hb0Mvq2ssee1pD1P0J4gZrQFATPxO/3YcPhy9z7ri1CSs+52v36xR+nJK95t3xRcXkuoprVzLhRUOxw7tUsQn9bahfajWpXw1NdGrqss1YJXAkX4BhcDBiSGKFSoXPTK7rGhKy7wuAm7Cl1s6hEcQVWcakNT3y+DdP/95fDMd4iJDzEnlqEBF+50CnwncvNwhKHTkq+X47aPjxXuXTyp67mo83UNVj/10WM/zYDb/zwJ976WXmzTx7eIgQAvx66hN1nsvHhr8fKjpef92t5VlzMKcRjn22VdFJ8vIUgToSOS3A5DVmwVbIaTuhHpvy4H6/+Z6/4ezxGvV5RCIWpWITUOupqbswmBbGjtJdWRYKlhe979DZ7yolOyY5xEnKMXLoAXghZS23IvOGYSXrqzwfxV6Zvc3vJEQaWvKIS0fLqCZV2jYnB0nBrYsIHSwNAs7JYRHmM0K+ca2SXk21Zqho+/m7E4p24lFOxjNhWVyvOFK6LzcZwgYtLdMciNHrJLizddgafbLQ/z/IM64LlX00ICffHtjJ3sIA7QkjJaqS0hD+nsFhcYSZw/nr581VgLRUniIKVSS6olM4Fb5nceuKqw/FAgoSQn3CVSl7RIqTXwazwucxswSJkfwgjLUbxQcpS6Eg2Hr6M349dwaebHDfXExMqGg1iZ+ppjJAwmxA6Bbkl43DWTclrXmQAwPHL0iWlAldle1LlWUsxYvFOcWYrbNKpBmOM21RW2tHkl9WZt05dy7cqbu1QVGKDtcSGl5btwSOf2hN0CmKF3x9L9uu4dLMQX6afxnc7z+Fq2QqTZzgLh8Wol9wXQjCkEC/ww57zeGbRDlwoq5PaZVFy58kRhZBCEK0nFqEet9hd3sPuaAgAeOGupvjsqY747KmObn1eOG+nr0p3oBfaGCMbWOSpFryFmrspt6gEjDF0n7EBnf6x3iHnlSsqH2PKucbKnkd3XGPCwGs22M+vPEbo+OVy12zGmezKVtJrFMsmIpNUJkWuv8dz19jkH/dLnn9P4mIyyvbnipTF/AgTVzUhVFRsF/3yIGd3XGPVLY5i6fejlx2yap+5mu9w3/JJbPkM7nGR9nQb7gghvszJK/kOxwMJEkJ+wlleGsBZHiHHSybMnoV9qXQ6HRKi7De00sorZ7tRCyZ0s7Fim66W2phoZhXcVAWywZZf7ukJV5wsTwWUV2vw8IJOvpqqmsJSf7XlsEUlpfjstxP4MeOCKFiFAHUhWaGc1/77Jzr9Q7rSsVvTWkiKLU86yO/vBpQHLQtiYcvxq/j10CX88mdmWQnl6+KOa0wQ1HxsjnjMA4vQv4d1xJbX78LtZckRzUY97m4ZL96LrlDLYH2+TNzL2+KOq6q41OYgml2hdoeX2hhOXskThesuDzd/rXSMkMQ1VlYnN4OlgfJrKXc/88+CfPWiM3KLSjDlx/0Ys3S3TzaklS/zP6oQS+fW91Qgs/TSbWckr10FS/MWWmGFrjzI2aTgGhMWCQD2OCilFbDhFbQI7T6TjXSZdeb01XxFC6ucMJNefN7kwkxpDODFlafPm9YgIeQn5Hrm6TsaIn3iXeJrye7zrNwipCSEBHNuVHj5gxFfJoQuKliE5Bu88r8pzSNUtsWGB0IojxNZwvJv+WaEvFnWE1w9bPLZmMD+8zdw74e/Yf1f5e4AeYclLM+WWITyrIquxaISm4MpWPi+tvWiHcrz389TN0Yak2QxGsAb/IRrLd/3S/gutcvS2I1AcuG71/3luAmxktVRDaNBL7rwKoI8g7VgyRTukeoWE6bc21I87s4y+qGfbUOHd9bh5BVly6KnjP9ur/i3pysXWSW1giikJK4xZxYh+//CREsQ0w6JRLnv8GR3+q/ST+PL9NNY9WcmDvrATSnvmyoawu3SNeZOXRTO85bjV9Dh7TQ8PH+L4iTTIhP2wiICPhSiT4t4vD2wNQC7mNp8RGoRB9xzjVVTsAgBwIZD0u87dTVPrKuz+XdkmEkUYPIYISVBf4ULVbieL938deW+Cxj08R+KEy0tQkLIT/AWoUc61sPU+1tJAn2lFiF+93n15fP8UsyEsozCSq4xteRqgrnUoNchOtxUoU1XhQfIqNeJfmm5O+OCG7MTJfgYISWU4l0AYPiiHdh/Pgejl+wS35MLoZX7MnE9zyq1COVbFQM2i4ptDrEqglhJSYpxWkeeujHhMBqkFiA+j1C5EJJe8w9/PYYhn6YrBsM3iauOnrc4T80AQHXzXkC6zYGvkbdNEPCCaywyzIin72gorhLcdfoaTlx2biXYfsoeb/HD7nPuV0ThFn++Z2MAwN4ytwfgOj+NHH4A8SCO3KFa/BYbzrQY31cAvEVIPUGeJ4lXecuCL7Juy11aFTlnSt8jR23FJY+SRWj9X5dwNc+KHaeuOyRSBRwnLcL5513ezRKqiwL1r8ybEkEh4J4QUrb8yicLh7PKf6NlHfWg+Mgwo9gvyhe1KJ1O3iJUXMok1tqxX+/B7jPZ+NfqiuV7q2pICPkJ3hIgdFo6bv4jiRHi9xozqvcMUZw7It6Ja4wXNnyHKMyg69UIh9lYMYuQ0DlWDzOKMS6XZZYcIQOyfH8qYeBRQyleh4ePd+FnJ3L/eLjJoLhqb/KP+yWxFNfyiiUWLoHfjl526LyEmVSYyYDe3NJUZ8RHh0lM5hajNFha6ESVcopsO3kNvx274vB+3Zhwt1ZuOYsDqujgUxHkA0d8lD3QX9hyJtJihF6vw+2N7G7Pb7afxeBPtqiKXh613cCVUMqz0zk5Fh0b1JC8p7Y8WQ3JysEKGCl415jeA4uQwZVFiKuMJ2k2+MmI0rNRWeSTLr1Oh1/+zMSBCzdUPqFMRfMI8Si5IHk3YpbChE7+rCotnw8zGcT7nl8kIPked4SQzDX2dFmc3plrUkuo4CqrHWmRuO7luY0iw0yiALsky4+m7BqTji3XFEIJ8opKMCvtCDYedrQ8awkSQn6CH/CEh0QyAPHJrMQYIfXd5wFIdhtPKBtQFm05hWXbpb5v/qbmZ07CvjfCw1KRGCFhVlDNbBRdY/L+JLfIPpjIA2E7J8diYLtE1e+WB1nLEfKOTP/fX7jtH+sURSBg72SUVu2t3JcpGTB+O3pZcdb76WbHIPMIrgP86PH2eOv+Vsq/bTKgmtkAvQ5oXz9G6hozGSQzR0EoCqsA5fysEBzubmyOM/dXZeNaPEHemdeqbm+rIMqFmAW+Xdfzi13eCwCQ7+YA//PeC3juq10O75sMejSQxXx5muFa/ugIAn33mesY8/Vul+Ke341cuGedXZ9SJzFC1/Ks4rNcUYsQP6mRxxoyxjD1pwOYv+m4299nLbFh9Fe7sGCz/TNy19jRS7n429LdGPDB725/J6BsEeJdrPIzqBRTpGQF4a3bFxT6F8HCObqHfVI3uew3+f4+zGRwSFraqFb5fWZQ2VxbDm81CjcZxNijHaekcWzCRLBZfCSiuT5X3v9GhZXvSiD3JLhyjQFA/7m/OVjRdpy6hg/WH8XTX+yQ3HNnr+VXKnO4tyEh5Cf0ihahcnjTLb9qzKkQCnd0jQHA69//KSnHdzb8DOxU2cqdhmVLwN2JSZCTV2S/uSPDjOJSTDlCIF5kmFEi/sxGPWIi1D5TLA4aQiC4HMEi9OmmE7iSa5VkpeaxW4SU63+Ic3mdu16ADxSWuQsMurVu+XfynZLZgDub1HQoH1vNjC2v34Vdk+/Gxr/3QpO4SIlrzCzLLG0qs/41rKUcgK2EvHNTw9l9VJVJZLs2rSX+HW4yOMQMVVcQQoDjprgCpRWIfXnhG+V8ZAa9DnVjpPeap0JIHlMkvBz08Ras2peJ8d9muPU9Oh2/LYx7eYSA8hisPWeycds/1uGlZfa28nrDkxghPk5PeNYFjl7KxaItp/Du/w65ZbED7Jscrz6QhX/+cggAUOylPdHkLkyzUY9nuiZLVmny8G2pVvYsK51n3iKkFP8iJLN9vX9z/Dm1L7qXuanlll+5EErmnvEIk8Etqy7vGqtmMUj6fCWaJURKniN5TCVj5QHY8kmk0hhwVZb2JN9aigs3CvCpihB+7b/7kHmjAPvP30C3GRswbOF2p/WtSkgI+QmjokWIC6ibtUmMkxAtQioJFQX4GKF4mVh4mQv45GOE+JmQ4P+vV0MqhFzFCN0sLBbVvWDtqWYxIsJsVFwVJFiNTAa9ZEsLi1EvWpHkHCnbTychKkyy0oqnsKRU0qmr7bMTZtJLTNVLn+0sij95Hg5ny6U7cqvU5C4euSUBALo3rYUa1cwIMxnEjU+lFiG9xFIlzAqTVVaiCfzfPS3Ev9X2SpOjFSF0f0q5BTDcbMBZmatA6Kzl9+BBFVcJb6X48/yNSm3LYjLoHALB5RvlusLBIiQ77iqg23PXWFkeIVEIlSfjLLUxrNyXad9ol+sDPLEIXeXcH3KLEB9PdyHbvfw/x7lVYd/uOOO1e0+eEiCy7LkQHi/57wgxMRajHk+WJQZUOs+8RejsNXt/aeKsq/yEiBcaBp3cIiTtL/jJjjtuMaGuArWqW0S3shrxURZJ/yBfkZlTWIxws3LyViU3oVJsU05BCab/75D4mo8b+s+uc7jz3V+x7i97upNtJ69pxipEQshP8AOxKIS441dyrVhQ5oJxlUdIgF81Jp8d/Hf3OXFFFL8ySsjbcTGnUBLfI/we4HwGWmAtRZupa9HpH+vAGENu2cxKmK3EKggb4XeMBp1EvJkMesRWc7Ro2GxMnH01rBWhmj06r6gUd83cKL5WC4gMNxsk579ejXDEqViZnNGM2zhXLvhMBr3D1hY1qzt2VNKZokFSYyGgWdhzTIkIswFPcFtYyDtYNZwFS1ela4y3Gup1QD2ZyBU6brlFSB7DIMDHBR27lIuf92U6lNl64io+3XTcpUiKMBsdhJDnrjHm9LUnn3dr01XBNVZ2WyntG/f70SsOljP73njO61ZcapMIAblFiD/W6/2NioPc0Ys3MWzhduwuS+LI57N57b/llutunKWQ/313OJSV47Cirbps0Jf3DbyV2llIAG8REizUTeLK+wF5fjIBg0EqhOT9hcQi5KYQ4ifOCdFhLnMPhZkMEiEk3+fsRkExwk3K36F0Lq4orOKVp2KQ31I2Vr4QAgB2e5iOwleQEPITBolrzP6/3BoqrExSWzVWV9ZJ8zd2bYVBV5ix8bvSf7/7HBq+vgqd/7ke68uyzQoPi2gRcrIU9dRV+4w2p7AERSU2cfNBIV5JntEUKO90DHq9ZFZiNurFGBGedtPWipsEJsaEqwqhK7lFyORMuiWlyp17uMkgEYOx1cyIkQ20/xrcxmWHdEt8+TJ1JevTfSmJGH5nQ/G1POssILMIGfWSDkewCBkNenRp5OhqA4CYcJNksJMv31XDmaCuQoOQJEbIWmLDa/2aS44L99Hg9vXwzJ3JGNbFLvrUckrJA6T/u8tx5dijC7Zi+v8OKaYO4IkwG5AkS5BZlUKo1MbEmblOpxP7CU9cY0o5oS5kF0i2ZymxMTz31S70n/ub0xm6fNWm3CIkHwR3nJJmSwbsbshNRy5j0MdbAACnrypbxD57qqPDBMDd4Hd+N3YBoZ8R73qHuEVBCJlULW+lNqZ4/Zty6SrCVCYYUouQ3mHCklIvRvy7IlvcxEeGOVil5ViMeqcWocgwo6o1SilWUrgf+HhIteBvHt5aKCyK8DckhPyEJFhaJevotpPXsOZAlmi1Mcm22Li/XaJk8zw+Rsho0CNFltMmVxRC5R3Koi2nHH5XLoScbbHBi7ecwmLRdC4IGiVXl7BCx6CTunLMBr2iZSansAQrMuyBwfViwhEdLv1OtaR8V3Ktimb/MJNB0qFVtxgd6tk8IQotnCw1DTcZJKbvbJWkdHyZaIVzIV8+zw9yfIf41YhO2PtmXzTnrFCA/ZrrZWLKHZx1tlVpEeLrbi21IT4qDIPal8deCTP5MJMBU+5rifvb2Y8J27IUl9owbOF2/PMX+3Yx8sH52KVc/Lz3giiI+et03MUy/HCzQXSZCvD3jTy1QoG1FFtPXJUEjMpPpbunljGGez/8HXvKXDx6HW8Rch0sLZRVEkJX84ocXB1rD17EoaybWHvwoup3y0VLblEpCotL8cj8dLz7v0O4LkuTIcTsMcZEK8AFWXB4psqCBqNe57CxsruxTErPYnWZa0yOsBqwusUoipZDWTm4Z+5vWHMgC4DdLap06nkh5I5bK8xokExYGtWuhqbcpMpYASHUtWkthxVrI7slS8SOxWiQxBVFhhnxWKf6AOwhBzMGp6hO/oTtZQ5eyMHzS3bhyMWbokWoxy21cXdL+2bmE2XxqEpkF7j/DFYVJIT8hMQixMUI3Vo/Bkmx4WK+h3kbjolxKmEm6aqxCJMBL/VuKr6Wmzq/fa6L5LVgieHFgVJHVE0uhGRP/+r9mejwdhpunbYW67iOM6egBFfK6iqsdHI2MBv1eklwr9moR5zKCimBxJhwyQP/XPdG+Oix9oplr+QWKS7pDJcJIZ1OhxiZS85s1Cta1QR4NyQA0SXoUI7riJRWdMlXjfGDHH+PGA16RIebsOrFbhjTqzzNgDw42m3XmFMh5NZXeB1B8PNWokiLtH1CSgZhNvr7sSvYdOQyFmw+ARuX1VzgfHYBXvhmD5bvtFuGeJfJlZtFTrMjVzMbodPpJJONU1fykVNYjM82n0CbqWuwoWxZMGMMwxZux6MLtmIqt+edXLS4K4QKZbmqdNCJ4sbdTVcBZSHErx6Tc9xJJudTMiGUby3BTxkXsP3UNczfdNwhE3t22TM2b8Mx3PHur/gq/ZRkYmCzMdUYPINeh8a1pULIWUZ8HkGQCfveAeUTEsGdJG99uUXIKFredpy6joOZOeKKwqsqVkhexISpPH98jJvFJA2W7tUsTrJKTL6s3Rk/jrkTbw9sjXvb1nHoa9+4p4VkgudoETJh+qA2OP7Pe7D1jd5omRilmKoDsMdKlpTacM8Hv+F/+7MwY/Xh8o1aqzta1J3Bu1BPqGynVNWQEPITvKmUH/C+f/4OrJvQA492sj/E+87dEK02YWaDJDDPZNSjZWIUGteuhnZJMQ6WEbmpNFfBNaaEkLFUzVc+esluXM2z4np+Md5fe0R8//LNInGWUKvMJaa2x45wjA/qNhv0qkvFBerWCJe087V+zXGbygafV3KLFDtauUUIcLRcmY3O6yKImhfuaoIIswFjezVRLMfPyJQ6C0neIIPe5Qo9g14nmdXFyKxjnRu53uwUcBUs7R8lJLQ9gsuYK4/tEGKK8q32wHg+ePlKbpHYOcstOa/+dx+e/Pc2SUzCiSt5TreXEAaFT55oj5fvvgWA/Rnq8s/1+Mcvf8HGgP8rmwH/fuyKmMjxy/TTorVIfjndtbbJV13puczSzhZWyYOllQbVq7lW1XrIxQ6PsJ+UMGj/mHEBr/53n3j8muxcCpYZoY+Y/OMByUCcXVCsuBDDZNBBp3O0CLnjGjt5JQ+z0uy/x69AjZQtIuDv8S3HruClZRkA7BYhNQu90gawKfWiJX2YmkWIj28KM0qDpXs1i1NcRewOKUkxePL2BtBxwfQCOp1Ocr7DTAbJ8yT0TfzvyesvnLfLN4skkwjBkmMuE1eu9s7kJ4R830sWoRCHv2n5wF2dTgeL0aAYBxNmNEhmDmaD3UK0dnwP/Pf5O1wuucxTcI0pITw8Qh35GCFne9bwu7QLrjFnWYqNBpkQMupd+rnrx0ZIHla9XufENaYshOQWIcAxfkc4twLrJnTH4Pb1xNeC9e3lvs2w982+ksBpHt5Kp7S0nRe2FpPeLWsMP2sTBNn2/+uNX17s5jCLVsNZYs6qdI0pIbEIyYRQdYtRtHJczSuSbCFz4UahuJ1LreoWh2v629ErEuF+6mqeqmsG4FdeGfBomQsBsG/2KyDci/ItV4TVYBWNEZLHnOncdo3Z/xcmWgaF5++qE4vQjxkXRDejHCHPWMtEZZfxRdm5VIqn4QfiSzeVz73QBzhahFwLoRe5VAj89a8uixHiT+Hjn28T/44MMznkGBP64osK9R3RrZFMbCj3RXwfqtfrEBNhQq3qFkSYDbgtWZq00xOLkCt4V5fdIqS8qk1AbhFqVNsexH05t0gSeyVsgVO7ugU6nU518YLA7CHt8MTt9meI38fsen6xJlaOkRDyE0qZpXlqVnO0RoSbpbkihAHBoNepziIe6Vg+eB+/nIsP1h/Fqj+lK2lqVTdLAq8Fi4OSRWinLFmXGsIKKWczBb1OJ1k15CyTMmDvZOrViHA4N/LkY+sm9ABgt3zJdzUH7OdRvnmnPH+R2aiXzOKaxEWiY8PyDot3czmzrvCdjdx6A0gHKotR73KjR0DauQniKi4yTHWAUoIXqLc3isUvL3YTX/tZB0naJ3f36nQ6iXvsHLdv3YXsAnElU4TFqJqTSuBanhUnr7g3I1VLVClc392nsyXvHytzMcmta+66HYscrLY6MY+Q001XZa4xpUHVmWsMABZsPqEYxHzkkj2Jpdp+ekKgrGCNy84vdlhZxIsGpWcTKD/XjhYhR9eYcH43H7mMX/7MlATf1ogwi/dK/9Z17G+60Bh215i0kHA/XlQY7C1GvUTcqbmm5dsamQx6rJvQHVvf6O3wGU8sQq7gJ40Wk94hRkiOPEZIWCxwKacIxzjrjRDnKfTfrZz0Pe2SYtDjltqq/bp8CyZ/4F7SEcLrSC1CjseVkhGGGaWZbk1ubI45fVBbHMzMwf7zOXhnlfJMLz7KvtWDsBy0PFi6bIsNruM96+YmekquMZ1OOsga9TqJqBEERc9mtfG//fYAxagwo5jbp3Ht6jDodUhtFY++LePRrn5M2feW/8Yt8dXRJK46IswG5FtLMW+jY0JEJauT3DVmMugxomsy1hzIwrCy1PV8JxHtZuJCPihS6TP87NNiNLhlMQjnLCauXIlq8OKtd/N4iYjyV4yQAB/XopQXKT4qDJk3CpF5o0Cyge+F7AJx+XKEyeByZn2joBjHL9kH/Dub1JTsyeSsTjyCG3l/WV6jW+vHYM+ZbBy7lOuw3ByA20vy5K6xUhuDqexecnZ9BFeTYD1Smoi4sxntmgNZGNW9PBatsLhUtAjd0bgmvkw/7fCZ04IQqlUNp67mI7ugGA989IekDH9N0sriC+vVCEdSjQhxKwhB/Mqt4vJg6Yyz2Ri2cDtSkmKw+chlh0Do6AgT1k3ogTPX8sU9AIVtjBjsIkpuRVcSQoJlSylTfZhJGoCsV7HKW0scL5qaUHflZvIEqUXI4JC3TY68b6xfJoSuyCxCAoLQfK5HY+j1OsxZd9ShzBdP3wajQa8uhAqsLpNB+hqyCPkJfgBU8knXVHCNhZsNEstNToHr4EGDXoemccpuG4HYamZJpyM8IEKnVWJjWHfwIv61+pBLE6hAXGSY5DsAx0HNnrm3vD1C2XcHt0W3prXw4l1NsGLMneJxwf1kNOix4KmO+FtPx7gcIR+HIGyUZhvhJoM4q72tzMojd6NYjHo0rFUN297ojTFl8T+8y8ZZIDUP3355nAIgzWfirmuM386jabzza6sG7xoLk80CW3tgWfIGnctivIRrwVvF5DFCQHlepXPXCyQWocwbhWIiveqyAW3mwykO38MYxFxd3ZvWxo7/6yNmFXaXwmIbcotKRHN/35YJAOxbaNz7we94aH66pHxFXWOXbxaWu8acBUuLW2yg7P+KDaoHL+QgO9+KLcevYNEfJ3Hk4k3YmP056dmsPHictyoIgefCM5idb3XYQoRv13/KUht0So7FrCHl14e3vt3bto74d55MCE34LgM3CorF3dsdctbYGGpUMytuhFxUUor+c3/D/R9Jt+5QEkI3C0tQamOKrjyLUe+wTYwS7uZAApTdmRWFzy0UZpLWVUm0WYx6ycRcCF24WViiGM8jhEBUsxgxrs8tkmOtEqNwR+OaotU6XOU8Xc8ji1DIInGNKdyQSrOFMJNBMjN1dxd3V9mGo8JMEsuFMEsS82mUMjz75U4A6m4rOeVuu/LvjbQYJf5ho0GH1nWj8FSXBqhZzSL+bnS4CV+N6AzA3oG0rhuFklKGF+9qClck17Kb0+WuL54wkx6fPtkBy3eew+Od7X5r/nybDXpxJsXPGPkgXrXtQ+Sk1ItB7+ZxSIqNUBS8vItCvnxeDb6jblZBIcRbhIRr+suL3bAi4zzGKAhMXzJvaHss33kOgzvYl8bzaauU3I6Cuf7stXzJQPvDnvPiKsH7UxJxKKs8uLN3C+WNcAUTf7OESOh0Ore2NuDJzreKloLqFiPubhmHf60+pLgzOVDxYOmsnCIkl8XMOHNrCbEownPnLEYPKHsmuWelUe1qOHE5D0cv5aLv7M1iwkNheXSTuOoSq0HTuOrIt5biELf3W7kQchzglOJBakdapIlVuT7ug0dvxY2CYvx29Ar+vnwvsvOteLZbIwBK7sNyYquZcUdjx6SMwuVdc+CiYvxgdYtJ8RrdLCwWrYU1q5nFFWTCRsmD29dDVk6BQ3oLAblrzBnejBEKl1mE1GJTBXQ6HcJNBlF0Ctmq7YtjHK2lzty0K1/oKn4nAISrxE85W7BQVZBFyE+4WiWg9J7QAaW2sndKj3EBnM5QmlXzRHI7xfMIDyQ/m/EkHT//HfbfkVpdDHo9dDodpj3QGi/1URY5JoMeK1/ohtXjujvdc+uu5nHQ64AnyxLu8YGaBr109USdmHDUiQ7Hi72bijMaPpBZWLUih59dKcVwKaHX6/Dvp2/DVJVNWOUJFD0NVHaVVl8NJSHUMjEKb9zTwm23n7eoVd2C53s2Fq2IruKkBIvQ/gs5EsEriKC7mseh+y210a2pfZ+nMJPeIdaoAbeqLMJsEMt6OgRdulmE7Sftq8USosPQuHZ1p+5Kd92O8pWdD3Wo59amq8L+T0KGdnk/EiXrC/iEpylJMXh3UFsAwIELOZKszzvLVsQJ1pp/PNga1cwGvHlfK4dYE8ECLd+4EwB2KmQSjosMk7hw+Brr9TrJ+eTd+2qCsH39GKRPvEvxPha+W23ZvpJFCLD3J0KMUg2urxT65JmPpGDps7errjgrdpKUVo43Y4R467Fg6U9tFY/E6DD0bFZb+TMW3vVe7rKyMfv9M/fRduJ7zrackU8s1FbU/W3pbnyy8bjb+9P5AhJCfkJp93k5G/7e02H5IwB8MrQD9ky+G7e4aQ1QCorjiQo34fHOdgHBp7YX6ihknObpxO2zJYfv1Hh/N29RAcq3AfAGC57sgIw3+4quNn6gSxvfHf1bJ4ivlQL7eGGg1hHxbhN3LUKu4GfXOp1OccdrOd2a1sKgW+vinYGtPbZgCPDtVVvp4i96Nbd30EpZxgEgqWwvvF0q6flfuMtu0XrxrqZ4rV9zrHyhm2SAMhuke9r1bRnv1uCjFCR8s7AEb/xgX0KfEBUGnU7nsHSfR761g9qvCoNC67pR2DulL5rEVXe66eq1PCue+HybGLsjuDTk1gX5ZISfAIWb9KijEqshiA7BvTG0cwMcmNYPKUkx0qSoRn35SiMn+/TxxEVaJPex/JZWcz2pWSMSY8JVg5ZdCdHqYUZFC31OQQnyywLx+TQY7iYwVdrdXg2l368ovAdBiJ+b/0QHbH61lyS2iYcfL2pVN0tcZU3iquOBdnUxtlcTmAw6/E2WNqR1XXvfemtZ/CaPsxXBS7ae9usiDXKN+QmDC4sQYDcx920Vj+93nwdQPnPX63WSWYkr6sc6dsx1Y8JFt0KkxYjkWtWwa1IfSXZqZw/kWw+0wjfbzygGTc4Z0k78m++IjXq72VWwKnnTF2406BHFDe7zHm+PD389ildSm6FR7eoSi4+rTUzVMrvyMyW1QdpTWiXaXYOCgHNn1ZjRoMcs7hxXBEkaBjc786qiSVwkNv69p6rYdBZY+cTt9XFrfXusUbjZgOd7NnYoExVuksTEudv+L5/phJ2nrotuYjmC+JBveNyiTpSYHNEh0zTsQbuXbxZJsqpvPWG3wISbDKJlQ23rh18PXcQzi6R1EhKTyvsWuZs8lrNshpsMiFOxMAoLFpRiqHgRY9TrUKu6BUa9zuVmzQKuAv7VkqyqPSvy/eF49p3LdvpbkRYjrurLXTVhJj0Ki224nm9FXplFiI9hcpXuQ6yrB4O8sxVYniLf9R6wXy9nAdm8WA432zPoCxZ24d7+e2oz/K1XY4f9zT57qiOWbj2Dobc7eiuc7YX2ar9mbp9LX6CtHjCEcBUsLcAH5VZ05s4HS8dEmDC6R2M0rFUujgTxU7O6RWIpcPaw1KpuURQDk+9tib6tyq0vvNgx6HVSa5EXTcByWteNxqdPdhQ3RHyqS0NUMxswqH1dp+dbqKcSEQr5eyqL4Bp8rod9wK6qHD4mo2sh7k8a1qqmmOcEcBShfVrEi387GwQFYiJMDhnNBf4xqA0A4MXejq7amAgz+rSMx9T7WqJ/6wSH1Z4J0fZ68ULo/pRE/O+lbqLwLCq24XUuCSEAfP7bSXT653p8s/0MAGDD4UtiEDdv2RAsx/wtcvpqnoMIAspdGvJnrJrMKstbhCLMRpeZyZUGM16o5FtLYVAQQSvG3InHOiXJP+rwecDRShavZqVSeVaU8nUJyCeQ8kUSRoM0WLhBrH3SdDWvSLQIRVXAIjT53haoWc2M/7unhWqZFWPuxAt3NcHI7o3c+k534EWqu3W1yNzmfBZ9PkZU6V6oEx2Ov6c2Q51ox+dQHl/Kx1Pdn5LoVt18BQkhP6G0xYYS/KzY3UBlObzoeeGupni9f3NJEKWa60xtgNTr7MGIvBioHWlBfJQFg7l9ogDHfEm8n9jgTd+YC5JiI7Br8t147yHH1UNy1AQaf57UZs6VxVnwoTeRCF4vWuaqAnmGbn7FUayT3EEjuyUDAKbe10ryLPGD//0pidgz+W5MuPsWh88LPH1nMj55ooOYK0sgoUwAJXBCSBRtZbfUdzvPYtmOs+JxHYB/lCUwFPZp+mTjca5u/ETC/j9/j6hl5lW1CMldY9Ud412mPdAK96ck4j+jpVv0AI5CCgCG35ks/t2xQQ2H44A9l4xaXJ0gHAUBw29pAkjPJw9vGevSqCbuS0mETmdfAajG67JNfdvJVpTFRpgl267UL3NzXrlZbhGS5uZxr09uEheJnZP6OBU57ZJi8HJf71pGDBKXo3v9LT8BDjMZJNvcyFeYekK4WdrPDL+zIV5JbYYtr99VYRe/tyDXmJ+Qxgipl6slsQhV7CbkO/r2Zb5bPgeRPJBUQG2AjK1mhkGvQ/syF0TbetFY8Tf7Mne5tUXqApTmkvCmL9wdXJ0/wYXxQLu6iseNBj12T74bgPt7enlKVfnJJS7LKhSk3kB+j0UqxNEp8cY9LfBcj8aoVd0i7hEGOLrG3HU7yy20woDOi2Qh5kao8kcbHPNayeshpAAApHmolDJLn892DEgGyt1N9l3rdaJokKdwiJVYhOzn7qkuDfFUl4YAgKXPdsZQLvOyUsBrs4RInPjnPfhp7wV0KBNCtzWsgR2y5Kvy/fkEhFiVX17shp2nr2NAmzqS4/K4JSH/D+8aiwwzYu6Qdph2fyun1y8pNgKPdUrCN9vtYrRpfKSYDX/GQ21Rv2YEfj1UHrQrBOafzy4Q3Vv8fmJqu80r4Y/BvgL7t0pCA0wGvapFyFPkz2at6hYMuc29BT++hoSQn3Bn93lA2lFVZqbw26u9cOpqnhg/wYucKBU3T/sGMYrvC+KsTb1orH+5B+rVCFdtgzRNgDSQWmsumS+f6YTNRy5jQNs6qmWUtj7xJq4C270F3yk3ruXethxaRKeztyWlXjT2X8hB91vUrQE6nU68d3kRI89M7i5yMSzELvEZkR+41W7yV1sQweveqDAjFm85JS7pl1OeR6j8PT6hJA/fV/BCSB4jxE+0lEROA1ngt1rgsl6vw8BbyycQcx+9FXe8+6vkN/kJ18t334LF6afx6ZPlGyYnxoTjfgXXptw1VlhsQ7jZILGMVSvbI8wdEcv3Oyn1YsS/H+lod90VcCv2BEHJZ9rmxWlFdor3JaN7NMb8TccxoqvdSucqDEAJk5MA+8osrJCHE2gpNpGEkJ/gO0ZnlpGocO/chEmxEWL+FUBqBVAbfOMiw/B8z8b4bPMJ3JeSiB/22IO2+eBGV3tbGSWrsfSSztaXMUIVoXakBYM71HNd0IfMeiQFL36ToRij4m12TeqDwhJblS+X9yamMkG/fPQdKCgudTt2S+Iaq+Bz9fe+t2D0kt3ia8GF0yoxGp891RFN46qLKQHU9sniV1YVldjw5k8HJMf5zwkDeKmNwWZjOHe9ABeyHYWQfHWbUa+DEP4rT6XRok4U2iXFIONstmKQbr0aERjQpo64LY98CwY1EmPCsXvy3Xh/7WFxF3h+ldKTXRrgBTfvcbkVK7eoBOFmgyQA2ZO+pHvT2liy9QyMeh3uaZOAF3s3RZu65eeMz3VUq8ydd6Isq7a77fcXr6Q2w30pddA8wX4t1az9zpBbiPnvqIxFSB4UX9EJiC8gIeQn3Fk1BkiDGb3pO+Znos5Wbbya2gzj+9yC/+4+JwohT1ZMSWOEpA9SVcYIBQpN4iLxy0vdXBf0AvIYl0CidqQFl28WoWtZugezUe/RDJN/liraIae2SsArqc3w3prDAKTnU0hA6Al8slEBflsJ3jU2beVBLNpySjzWp0UcDmXdRHWLER8+dqvkO/j+Rb5kOsJswA9/uwPX84tVrZ3tkmI4IeT+kBFbzYx/PthGfB2mEpflCp1Oh+1v9Eanf64HYN9zzGYzS/KbeeLevbtlPOY/0QGtEqOg0+kc4sEkQijSfk6EfdH0Mpec1jDodWiVWC7qHupQD2sOZDmNm5Ijt3LxE+XKjEEWowE1Iky4XpZokyxChEQgODNfNqhZDaN7NEZ1i8Hp5p6ecpnbKkMpmaKATqeD2aiTdKC1PMihw3fCRr0eRi5grqpjhIjg4ZuRt+Ob7Wcwuofj8nh3sEgG5Yo9VzqdDk90boAP1h9Fs4RIpxOans1qY+/ZbNSPjcDeczecfq+wZBuQDsqCN9vGmEQEAcCYXk1Et7cc6TY30oFMr7cnvXPm8uXTFchzgXkCf549HQTjosJE8bv95DVsPnpFEk/niZtdp9OhH5dXTM7tjWvi080noNeVbxUkkFtU4jSzt9YIMxnELP3uIneN8V4JtaSI7hIXGSYKIV/FWVYEr0uy6dOn47bbbkNkZCTi4uIwcOBAHD58WFKmZ8+eYtZJ4d/o0aMlZc6cOYMBAwYgIiICcXFxeOWVV1BSIp0xbdy4Ee3bt4fFYkGTJk2waNEih/rMmzcPDRs2RFhYGDp37ozt27d7u8kVQpJZ2oUgeL1/c4x1Y3sJT8jMKTepuxPEx+cPUVoaqQY/U9PLVo1pzTVGBA5N4qpj8r0tK7zpLB/kWpkOOTrChB2T+uC/z9/htNzCYbdhx//1wY9ju2JIR+ky8oHtpEuHxZ3SIc3kXp5Z2vH769ZQfyb5FBbVLbLl4m48g4kx5WLAnX211JDkKKvAsy/0Qa/8Zx8mr9gvOebNlY89b6mNpc92xtaJvcXknTwBpIMqRCNZuAOfjbwyrjFAmtpAS4lcvV6TTZs2YcyYMdi6dSvS0tJQXFyMvn37Ii9PuuPxyJEjkZmZKf6bMWOGeKy0tBQDBgyA1WrFli1bsHjxYixatAhTpkwRy5w8eRIDBgxAr169kJGRgXHjxuHZZ5/FmjVrxDLffvstJkyYgDfffBO7d+9GSkoKUlNTcemSY6bkqoYXP/5wlSrtpOyMelyHMKi98qoqJaQWIZ3UNRZgy7aJ4EHiGqukiT4qzOTSWqvX60SXg3xW3YYL2AUg2YiYF0LChEVukTAb9GIsixISi5AsRkgtiJsngZv4VGYgTKkXjUHt7VmJK4Izt5w3F17odDrc2aQW4qLCFOPnqirXl78Y1b0Rhnauj8XPdALgvRghADh5pVwHNHCR2LYq8bprbPXq1ZLXixYtQlxcHHbt2oXu3buL70dERCAhQdk8uXbtWhw8eBDr1q1DfHw82rVrh7fffhuvvfYapk6dCrPZjPnz5yM5ORkzZ84EALRo0QK///47Zs+ejdTUVADArFmzMHLkSAwfPhwAMH/+fKxatQoLFy7E66+/7u2mewT/4PpjWeUt8ZE4cCFHNUeHnGYJkfjymU5oHFddcUNYNSQuQJ1OdfsNgqhKvCmEPEUecJtcKwKx1cziXmn1aoSjff0Y7D6TjQe5VA5Cn3FDtr9TYkyYU/c639fIBzJ3BASf8LAyea50Oh1mPdKuwp9XymEk0EElf5G3qRFhwr1t62DRllPi5rLBRpjJgH9wsV2SGKFKusZe6N0Uk1fsx/g+t2hq1bDPe4AbN+z+8NhY6d5US5cuRa1atdC6dWtMnDgR+fn54rH09HS0adMG8fHlAYepqanIycnBgQMHxDJ9+vSRfGdqairS09MBAFarFbt27ZKU0ev16NOnj1hGTlFREXJyciT/fIU7e435ko8eb49Hb0vCt8/d7vZnut9SWzJbdQejJLO0dDaspQeBCC14s3xFY4QqinxlYtO4SDTj9g1smRiFL4Z3wqdPdpC4xNVc6K6yafMTDqNBJwkOdydOz2TQ44nb6+OOxjUlq6uqGvnSf8C+x+DMh1Mkewl6G34yt3z0HejYMBbrJnTHqhe7+uw3tYQkRqiSFqHHO9XH+pd74MXeFbMK+gqfBkvbbDaMGzcOd955J1q3bi2+//jjj6NBgwZITEzEvn378Nprr+Hw4cP4/vvvAQBZWVkSEQRAfJ2VleW0TE5ODgoKCnD9+nWUlpYqljl06JBifadPn4633nqrco12E38HCifXqoZ3B7f1+e/wnbDFaPBrQkWCEPCnRahx7erYOakPfj10CYwxJMVGSJLWNU+IgtmoR2or6eCu9rg0jXOewoKfcJj0epgMOgiL0dz1Tr8zsI3rQj5Gnu+s+y21Jdv5+Ipvn+uCD389ikkDWog5oprEubfhdTAQ6cUYIYNe5zLlij/wqRAaM2YM9u/fj99//13y/qhRo8S/27Rpgzp16qB37944fvw4Gjeu2CoQbzBx4kRMmDBBfJ2Tk4OkJOX9cSoLv3ScBbHPme+Ew0x6TSdUJEIHPjuwP/KZ1KpuERP4AUC3prWx5sBF6HXqwkzteenYMFbxfQF5FnGTUQ9BCQXSM8jHqtzZpCY+faJDlfxuhwY1sGh4pyr5LS0SFea9VWNaxWdCaOzYsVi5ciU2b96MevWcJ6nr3Nm+vO/YsWNo3LgxEhISHFZ3Xbx4EQDEuKKEhATxPb5MVFQUwsPDYTAYYDAYFMuoxSZZLBZYLFWTWyVUrCFGWXwCP6ujGCHCX0hcY37c9Vrg8U72rQZub6QuatSCdG8t2zZHDX5RglGvkwR2+8MtX1F4q1lKvZigHZS1Bt9nezOFi5bweqsYYxg7dix++OEH/Prrr0hOTnb5mYyMDABAnTr2ZaNdunTBn3/+KVndlZaWhqioKLRs2VIss379esn3pKWloUsX+0aBZrMZHTp0kJSx2WxYv369WMafhMqCKb4TDjMbKr2EliC8gTcSKnoTvV6HJ25v4NTlUqu6RSLg2iXFYO6j7SQrOpUwSWKE9NIYoQB6BnnLRCAnAw00eNeYKUgnr163CI0ZMwZff/01fvzxR0RGRooxPdHR0QgPD8fx48fx9ddf45577kHNmjWxb98+jB8/Ht27d0fbtvaYlb59+6Jly5Z48sknMWPGDGRlZWHSpEkYM2aMaLEZPXo0PvroI7z66qt45pln8Ouvv+K7777DqlWrxLpMmDABw4YNQ8eOHdGpUyfMmTMHeXl54ioyf8JbhILXMSa1+oSbDJItEELFKkZoD35bDbMxMO7DMJMB6a/3xturDmLX6etYNPw2t1ZwylNY8INZID2D/CTKk6SuROUwGfSY2L85cgqLXYruQMXrQuiTTz4BYE+ayPPFF1/g6aefhtlsxrp160RRkpSUhMGDB2PSpEliWYPBgJUrV+L5559Hly5dUK1aNQwbNgzTpk0TyyQnJ2PVqlUYP3485s6di3r16uHzzz8Xl84DwJAhQ3D58mVMmTIFWVlZaNeuHVavXu0QQO0P+M4piEOEJK6xMLkQCqDZKBFcSLcKCJz7sEY1M2Y90k7cgd0d+GfQZNBLtlCoyKac/oK3CHmyzQ9ReZ6rYAb3QMHrQshV4G9SUhI2bdrk8nsaNGiAX375xWmZnj17Ys+ePU7LjB07FmPHjnX5e1WNP3IH+QN5DhNeCIXKOSC0R5gkm3TgzUQ8eXYM8mBpDbgCKwIfI1STLEKEF6G9xjRB4HXE7sLnEZJbhPKtjptMEkRVYDLo0Dk5FjcKipFcS3vLeb0J/wyaDXqYAzTOg88sTRYhwpuQECJ8inz5PD8bzS0iIUT4B51Oh2WjbgdjgeUeqgiMm2hVtxgD1iLE9yU1PMhuTxCuICFE+BT58nme3EISQoT/sG/47O9a+J4Ca/l+ZdXDAlcItakbjbuax6F+bATFFxJehYQQ4VMkq8ZkeT9o93mC8D35nBCyB0sH5nNn0Ouw8Onb/F0NIggJzKlBkOHJJqaBhjxGCADeGdgatzWsgRFdG/mrWgQRMhRyO9gD2sibRBBagixCfmTOkHY4eukmOic7T5EfyCjtfP3E7Q3wxO0N/FUlgggpeIsQELzZgQmiopAQ8iMDb63r7yr4HIMsjxBBEFVLgVwIVfEmswShdeiJIHxKqc0m/k17AxFE1ZNfLLcIBWaMEEH4ChJChE8pLi1fuhtGM1GCqHJKbdI8ZRQjRBBS6IkgfEr92PK9aYzUAROE3wnUVWME4SsoRojwKdUsRuyefDeZ4wlCI1CwNEFIoSeC8Dmx1cyI5DZMJAii6vjgsVthNugx/4n2AMg1RhByyCJEEAQRxNyfkoj+rRNES1CHBjX8XCOC0BYkhAiCIIIc3h12d8t4zH20HVolRvmxRgShHUgIEQRBhBA6nQ4PtAv+HGYE4S7kLCYIgiAIImQhIUQQBEEQRMhCQoggCIIgiJCFhBBBEARBECELCSGCIAiCIEIWEkIEQRAEQYQsJIQIgiAIgghZSAgRBEEQBBGykBAiCIIgCCJkISFEEARBEETIQkKIIAiCIIiQhfYacwJjDACQk5Pj55oQBEEQBOEuwrgtjOPOICHkhJs3bwIAkpKS/FwTgiAIgiA85ebNm4iOjnZaRsfckUshis1mw4ULFxAZGQmdTldlv5uTk4OkpCScPXsWUVFRVfa7VQW1L7Ch9gU+wd5Gal9g4432McZw8+ZNJCYmQq93HgVEFiEn6PV61KtXz2+/HxUVFZQ3uQC1L7Ch9gU+wd5Gal9gU9n2ubIECVCwNEEQBEEQIQsJIYIgCIIgQhYSQhrEYrHgzTffhMVi8XdVfAK1L7Ch9gU+wd5Gal9gU9Xto2BpgiAIgiBCFrIIEQRBEAQRspAQIgiCIAgiZCEhRBAEQRBEyEJCiCAIgiCIkIWEEEEQEs6ePYvS0lJ/V4MgQprc3Fx/V8FnaG2NFgkhgnCTc+fOYc6cOThx4gQA7T3MleXkyZO477778Nhjj+HGjRtB175QwGazAUBQC9mzZ89i9erV/q6Gzzh9+jRSU1Px2muvASi/psHClStXcPnyZfEe1UI/Q0KoCsnMzMS+fftw5coVf1fFZxQVFfm7Cj7h6tWruPfee/Haa69h3bp1KC0trdL953wJYwyjR49G06ZNcfz4cezcuRMAgqZ9PMH8DE6YMAFPPPEEAMBgMPi5Nr7h6NGjaNCgAQYNGoSjR4/6uzpehTGG5557Dk2aNMHWrVuxadMm2Gw2l/tkBRJjxoxBmzZt0LdvX6SmpuLYsWOa6GeC5wxrnHHjxqFZs2YYOnQoWrdujf/+97/i7vbBwvjx43HXXXfh4sWL/q6K14mIiEBMTAxatGiB5cuX488///R3lbzCe++9h5iYGGRkZGD79u1YtmwZGjZsiD/++MPfVfM6wfoM7tmzB3fffTeWLFmCb7/9FmvWrAEQnFah4uJipKamombNmnjnnXf8XR2vMWvWLPE53L17N/75z3/CZDIFVV/697//Henp6Vi2bBlefvllWK1WDBo0CL/99pu/q0ZCqCpYuHAhNmzYgJ9//hnLli3DAw88gMmTJ2Pu3Ln+rppXOH78OAYOHIjVq1cjPT0dixYt8neVvM6hQ4dQvXp1/PDDDzh06BB++uknZGdnA9CGabei/PHHH5g9eza2bt2K9u3bo3r16rhw4YJojg8Ws3wwP4M7duxA3bp1sWjRIjz++OP4+9//DsBuFQrke1OJvXv3wmw2Y/ny5ViyZAk2btzo7ypVmqNHj+LHH3/E3LlzsW3bNrRp0wZt2rTB3r17NeU+qiiMMeTn52Pz5s144IEH0KNHDzz55JNYs2YNjEYj5s+fj+PHj/u9koSPGThwIHvggQck773yyiusbdu2bP369f6plBfZuHEje/7559nvv//O3n//fRYVFcWOHj3q72pViOLiYslrm83GGGPsxIkTrGfPnowx+7VLSUlhBw4cYDdv3qzyOlYGtfYxxlhJSQljjLH27duzl156qSqr5XOC+RnMyspi+/btY4wxtmHDBlanTh02a9Ysxlj5NQ1kSktLxb+XL1/OXnjhBcYYY3fffTfr3r07Y4yx3Nxcv9TNGxQVFUmeQ5vNxvbu3csaN27MvvzySz/WzHucO3eOJSQksJ9++okxZm8zY4x99913rE2bNmzu3Ln+rB4ji5CPYGUKvqCgAHq9Ho0bN5Ycf+KJJ1C/fn188MEH/qhepSgpKZG8bteuHV5++WXceeedmDBhAhITEwPSbD1lyhQ88sgjeOGFF/DXX39J4oC2bdsmWkdmzJgBq9WKp556ClFRUQETuKnWPmHWaTAYkJ+fj3r16uH69esBH+8VjM/g9OnTMX78eHz66aewWq0AgPj4eLRp0waA/VkcNmwY/vWvf+HmzZswGAwBZ9WTt5GPkdm3bx9ycnIAAEuXLkV6ejr69++PHj16ICMjw0819gx5+8xmM3Q6nXiddDodateujaKiIvEZZAFkEfr+++/FawTY6163bl00bNgQy5YtAwDxmj788MNo1KgRNmzYgMuXL/ulvgC5xrzKwoULkZaWBsB+MzPGEB4ejpo1a2Ljxo2SAM22bdvi7rvvRmZmJjZs2OCvKnuMfDAtKSlBdHS0OMjodDrMmDEDS5YswebNm/1cW/e4fPkyunbtihUrViAlJQVr167FY489JhkgS0tLcccddwAAVqxYgfPnz2P//v14+eWX0a9fP39V3S1ctU/olBhjiIiIQEJCAo4cOQKLxRJQHTAQvM/g4cOH0apVK3zzzTfIzMzExIkTkZqaim3btgEoHyhjYmIwZMgQ1K5dW3SRBQqu2gjYV24+8MADAIC0tDRYLBakpaXhpZdeQrt27fxUc/dw1T7hObTZbKhTpw4aNmyI33//3Z9V9oiNGzeiefPmeOihh0TBwzNy5EgsX74cR48ehdFoRGFhIQB7APXq1atFYe8X/GaLCiJ+//131r59e6bT6dgzzzzDLly4wBgrd0OcPHmSGQwGBzPnkSNHWFJSEvvhhx+qusoec+nSJXbnnXeyNm3asKlTp7JbbrmFpaSkiCZ43rTLGGP9+/dnXbt2ZQUFBf6orkf89NNPrEWLFuzMmTOMMcYKCwvZuHHjWHJyMvvtt98YY4xNnjyZtWnThnXr1o3VqFGDzZ49m3Xv3p0NGTKEHTlyxJ/Vd4mz9v3xxx+MMbsLRbiGS5YsYQkJCezcuXN+q7OnBPszOHPmTNalSxexPZmZmSwlJYU98sgj7NixY4yx8rYWFhayjz76iEVGRrIDBw4wxuzu62vXrvmn8m7irI2HDh1ijDE2cuRI9vjjj4vP4dSpU1lcXBybOnWqP6vuFu5cQ8ENWFRUxJ555hl2zz33BIT7/eDBg2zIkCFszJgxbNSoUax+/friMyhw4sQJ1rVrV9anTx/J+0eOHGHR0dF+dVGTRaiSZGdn49tvv0XHjh3xj3/8Axs3bhQD+IxGI2w2Gxo2bIiRI0fizTffxJEjR8TPNm3aFHl5ecjMzPRT7d1n69atuHbtGlatWoU333wT+/btQ69evfDhhx9iy5Yt0Ol0EpfZe++9h23btmH58uUoLi7GypUrNbsS6dKlS8jNzUV8fDwAwGKxYPTo0WjdujVeeeUVAECzZs1w9epVNGvWDDt37sS4cePw1ltvYfny5di4caOm3Q/O2scH1goYjUZERETg0qVLfqmvpwT7M1hSUoIDBw4gLi5OvE4JCQn4v//7P5w5cwb//ve/AdjbyhiDxWLBPffcg65du2Lo0KHo2rUr7rnnHk1fT1dt/OqrrwAA+fn5WLVqFZo1a4Y9e/bgzTffxJtvvom33noLhw4d8mcTnOLuNdTr9bDZbDCbzahVqxYyMzNRvXp1zVtmY2Njcffdd2PMmDF4//33UVpaipkzZ0rKNGzYEG+88QZ+++03vPfee6IrbOPGjWjatCluu+02f1Tdjt8kWJBQVFTE1q9fz3bv3s0YY6xv377svvvuE2cwwiy7sLCQ1atXjz366KOilWHVqlWsTZs27ODBg/6pvAd8/vnnLCkpSQxyY4yxQ4cOsfvuu4916dJF8TPjx49ntWvXZikpKSwsLIytXbu2qqrrER9//DHr2LEj2759u+T977//ntWrV4/99NNPrKioiB0+fNjB8vXVV1+xwsLCqqyuxzhrX/369dm3337LGCu3KFy5coXpdDr266+/VnldK0IoPINDhw5lffv2ZSUlJZIA6DFjxrC77rpLbLtgUThy5Ajr3Lkz0+l0bMSIESwnJ8cv9fYEZ23s3r07O3nyJDt8+DDLyMiQPIelpaVsxowZkr5Ji7h7DYXncM2aNUyv14vWIq3DB7UvXLiQWSwWlpGR4VDus88+Y/Hx8axFixbsoYceYhaLhb3zzjvMZrM59K9VBQkhL7NlyxaWmJjI5syZIz6YVquVMWY3T/fv359FRESw1NRUZjab2fjx4yU3kFZxdzAV2nLs2DE2aNAgptPp2KhRo/zaEas9XML7p0+fZrGxsWzOnDnitRLev++++9hzzz3n8B1aumaVad/999/PRo0aJfmOU6dOsZdeeomdOXPGbx1TZQimZ1AYMDds2MD0ej3bs2cPY6x8sNy4cSNr0qQJ++6778TP7Nixg91yyy2sXbt2omtMy7jTxkaNGrHly5f7q4qVoiLXkDHG/vOf/7ARI0awK1euBMxzyNezc+fO7P7773dYqcoYY3/88Qf74IMP2Lhx4xTFUlVDQsiLCJ3ps88+yzp37szS09Mdyly9epX99NNPbM6cOezPP/+s6iqq4s3B9MKFC+zuu+9mzZo1Y/v37/d95Z2Qk5PjsDRVgH9Ax4wZwxo0aCB2UgKDBg1ijz76qMNntYI326dVMcCY++c+EJ/B/Px81WPCNSwoKGA9evQQ4yv489G4cWM2bdo08fWVK1fY77//7qPaVgxvtlGLz6E32ycIJy210532CQj13rx5M9Pr9eKS+ZKSEnbp0iXfVbISkBByQWZmJjt//rx4I/AmTfkNILy+cOECS05OZq+//jq7ceMGY4xpOq+ONwdTxuwuCH+312q1sueee4517dqVDRo0iC1evFg8xrepoKCA7d69m5WUlLB69eqxESNGsFOnTonHBw0axEaPHl2ldXeHYG8fj7v3J/86EJ5Bq9XKRo8ezfr168eefPJJlp6eLraNd/OUlJSwrKwstnHjRmYymdgnn3wiCr5r166xtm3bso8++ogxpq3BkzHftFFLUPvsFBcXs6ysLIfPDx06lHXo0IGtW7eOpaamskmTJkkm0lqBhJAKVquVjRo1ijVs2JC1b9+e9ejRQ4wF4S+k1WqVzL4EofTuu++yVq1asZkzZ7I+ffqwbt26aS7pV7AOpsePH2cpKSmsR48e7KeffmLDhw9nLVq0YKNGjZKUmzt3LouMjGR///vfGWN2U3SnTp1Y69at2eeff85eeuklVqtWLbZu3Tp/NEOVYG+fgLv3ZyA+g5mZmezWW29ld9xxB5s3bx5LSUlhKSkp7N1335WUmzt3LjObzWzRokWMMcbeeecdFhcXx5599lm2efNmNn78eJacnMz++usvfzTDKcHeRmqfnblz5zKLxcK++OILByG+ZcsWptPpmE6nY6mpqZpduUhCSIFz586x22+/nfXq1Ytt2bKFLV68mDVq1EjMaCogDDQTJ04ULUbCjXDmzBlmNBqZTqdjAwcOZJcvX67ydjgjmAfTjz76iPXs2ZPl5eUxxuzX5JNPPmE6nY7997//ZaWlpez1119nNWrUYEuWLJG4hPbu3cuGDh3KUlNTWZcuXRRdK/4m2NvHmOf3Z6A9g//5z39Yq1atxBQF2dnZbOrUqSwsLEx0Jw8ZMoQlJiayxYsXSwaYDz74gHXr1o21adOGpaSksG3btvmlDa4I9jZS+8rb9+WXXzpkqV+8eDEzmUysc+fOYiC4ViEhpMA333zDUlJSWGZmpvjeU089xSZNmiS+fvnll1lsbCxbsmSJgwpevnw50+l07LbbbtPsDRDMg+m4ceNY165dGWPlg+LHH3/MdDodu/XWW9nVq1fZpUuXRJcJX06AP6Y1gr19jHl+fwbKMyg8R5988glLTEyUHMvMzGS9e/cWt43YunWr5Drxz2BpaSk7ceJEFdTYc4K9jdQ+9fYJ5OXlsTlz5rBPP/3U9xX2AiSEFPjkk09YRESE+PrChQusXbt2bNasWWzz5s2MMXuCQbWVUDt27ND8DRAsg6kwk+I7mMmTJ7M+ffqwVatWie8NHTqUTZs2jVksFtFEHQj7MAV7+9SoyP3Jo6VncPny5SwtLU2SYG7BggWsffv2Yn8isG7dOmYymdiaNWsYY9oOYOcJ9jZS+8oJxPa5IuSFkNJAk5GRwRITE1mnTp3Y4MGDmdFoZD179mS9e/dmkZGRbOrUqZoM+FIjGAfTH374gSUmJrLY2Fh28uRJxlh58N7BgwfZgw8+yKKjo9mQIUNY9erVWadOndj58+fZo48+yu69914/1tw9gr19PMF4fzLG2Jdffsni4uJYp06dWO3atdmdd97J/vOf/zDGGNu9ezdr2bIle/fddyVBp1lZWez+++9nTz75pL+q7RHB3kZqX2C3z11CVggpDTR8AObJkyfZ6tWrWcuWLSVp+b/++msWERHBzp49W9VV9phgHUyXLFnCbrvtNvboo4+yrl27sueee048xseHLFy4kI0ZM4atWLFCPD5w4EA2duzYKq+zJwR7+wSC9f4sLi5mc+bMYS1atGCff/45KyoqYn/88Qd76qmnWP/+/cVYplGjRrFOnTqxDRs2SD4/ePBg9vTTT/uh5u4T7G2k9gV2+zwlJLfYWLp0Kf75z3+ie/fuaNmyJd59910A9hT1Ag0bNsS1a9dgMBjw5JNPilsodOnSBcXFxdi3b59f6u4uam00m81gjKFFixaYO3cuZs+ejVq1amHJkiXYtm0bEhMTUVhYiIYNG/q3AQoIu6Q3adIEvXv3xr/+9S/cf//9ki0VhDJJSUkYPnw4PvroI3GTxqysLJw9e9ZhF3KtEOzt4wnG+1MgLy8Ply9fxrBhwzB8+HCYzWbccccdaNmyJXJycsTNJd966y0UFxdjwYIFOH/+vPj5goIC1KhRw1/Vd4tgbyO1L7Db5zH+VmJViWBG37p1K3v99dfZ6dOn2YwZM1izZs1Excub2pcuXcpSUlIk+RHmzZvHOnfurKkVKDzutFEp06dAZmYm69ChA5s9e3YV1NY9jhw54hCfJLRh//797P7772f33HOPeExe9tSpU+zcuXNs6NCh7NZbb2WnT5/2faU9INjbxxOM9ydjjtdwz549YlsFl9/SpUtZu3btJG6G5cuXs27durEGDRqwmTNnsieffJLFxcWJW4BoiWBvI7UvsNtXGUJCCHk60Ag3RVpaGuvRowdr3bo1mz9/Phs+fDiLjY3VXCfMWHAOpt9++y1r2LAha9asGevUqRP797//LR7j679w4ULWsmVLtnDhQsaYNNYkPz+fTZo0icXGxrJu3bppat+eYG8fTzDen4w5XsPPP/9ccpy/Vo8//rjoTuAHmnPnzrFRo0axgQMHsnvuuUfcI00rBHsbqX2B3T5vENRCqKIDDT8j/eOPP9h9993HUlNT2QMPPKC5GyBYB9O1a9eyhg0bsnnz5rHVq1ezCRMmMJPJxBYsWCD6r4XrdO7cOTZixAh22223sZs3bzLGpEkvMzIy2KZNm6q+EU4I9vYJBOv9yZjza1hQUMAYY+JGkgUFBaxt27bsq6++Uv0+4TNaItjbSO0L7PZ5i6AVQpUdaPgdxUtLS1l2dnbVN8IFwTiYCoPjW2+9xTp06CCp49/+9jfWsWNH9v333zt8buXKlaxjx47szTffZHv37mX33nsvO3PmTJXV212CvX08wXh/Mlaxa3j+/HnWsGFDduTIEcaY3UI2fvz4qqu0hwR7G6l9gd0+bxN0wdKMMQBAeno6atasiZEjRyI1NRUzZ87EyJEjsWDBAqxevRpAeXB03bp18eCDD4Ixhvfffx/79u3DQw89hLNnzwIA9Ho9oqOj/dMgBbzVxkGDBoltTElJQffu3f3TIA6dTgcAOHjwIBo3bgyTyYTi4mIAwDvvvIOwsDD8+OOPyMrKAlAePNyrVy906tQJ06ZNQ4cOHVBcXIy4uDj/NMIJwd4+ILjvT8DzawgA69atQ1JSEurUqYOXXnoJLVu2xOnTp1FcXCyeLy0R7G2k9gV2+7yOvxSYrxkyZAh75JFHGGPlM8tr166xrl27smHDholZo4Vgsby8PPa3v/2N6XQ6ZjQaWWpqqsQqpEWCoY1r165lL7zwAps9e7YkzfyCBQtYZGSkWHehfQsWLGC33HIL27hxo1g2NzeXzZ49mxkMBtazZ0+2b9++qm2EE4K9fc4IhvuTsYpfQyH422azsYcffpjVqFGD1axZk7Vq1Yrt2LGjytvhjGBvI7UvsNvnawJeCIXCQBOMbbxw4QK79957WVxcHBs6dChr06YNi46OFtt3+PBhVrduXTZ58mTGmDRwLyEhQRKwfuDAAda5c2dJvid/E+zt4wnG+5Mx713DvLw8du+997J69eqxZcuWVXk7nBHsbaT2BXb7qoqAFUKhMNAEaxvz8vLYsGHD2JAhQyR77XTq1ElcsZCTk8PeeecdFh4eLsbCCH7vHj16sGeffbbqK+4mwd4+gWC9Pxnz/jXcuXNnFdbePYK9jdS+wG5fVRKQQigUBppgb+OoUaPY//73P8ZYecDs1KlTWefOncU2nDhxgt15553s9ttvZ6dOnWKMMXb69GnWokULtnLlSv9U3E2CvX3Bfn8yFvzXkLHgbyO1L7DbV1UEpBBiLDRugGBuI7+KQVgq/fjjj7ORI0dKyp07d441adKENWzYkD300EMsMTGR3XXXXZIkl1ok2NvHWHDfn4yFxjUM9jZS++wEavuqCh1jgRkOXlxcDJPJBACw2WzQ6/UYOnQoqlWrhgULFojlzp8/j549e6KkpAQdO3bEli1b0Lx5c3z99deIj4/3V/XdIhTayNO1a1eMHDkSw4YNE7c00ev1OHbsGHbt2oVt27YhJSUFw4YN83NNK0awtS/U7k8g+K6hEsHeRmpfYLfPJ/hbiXmTO++8U9yVurS0VFTIR48eZcuWLWPjx48XjwcqwdrG48ePs/j4eImfmo8pCXSCvX0CwXp/MhYa1zDY20jtI5QIGiEUCjdAMLZRcKEsXryYNW7cWHx/6tSpbPTo0ezixYv+qppXCPb28QTj/clYaFzDYG8jtS+w2+drAj6hIivz7P3++++oXr06OnToAMC+a+5LL72ES5cu+bN6XiGY2ygk/tq+fTsGDx6MtLQ0JCcn4+OPP8aDDz6o2aSB7hLs7QOC+/4EQuMaBnsbqX2B3T6f42ch5jXGjBnDXn31VTGtf1xcHFuzZo2/q+VVgrWNBQUFrEmTJkyn0zGLxcLeffddf1fJqwR7+wSC9f5kLDSuYbC3kdpHqBEUQigUboBgb2OfPn3Y888/H7Sb+gV7+4L9/mQs+K8hY8HfRmofoUTArhqTc/fdd6Np06aYNWsWwsLC/F0dnxDMbSwtLYXBYPB3NXxGsLcPCO77EwiNaxjsbaT2EUoEjRAKhRsgFNpIBC50fxIEEYgEjRAiCIIgCILwlIBfNUYQBEEQBFFRSAgRBEEQBBGykBAiCIIgCCJkISFEEARBEETIQkKIIAiCIIiQhYQQQRAEQRAhCwkhgiAIgiBCFhJCBEEQBEGELCSECIIgCIIIWUgIEQRBEAQRsvw/3Y5zAzl/LU4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "transactions['t_dat'].value_counts().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "26b3b092",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3f/0c48rx816935875jnxcqj5sr0000gn/T/ipykernel_27795/1992590881.py:1: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  transactions['sales_channel_id'].value_counts()[:60].plot(kind='barh')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transactions['sales_channel_id'].value_counts()[:60].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "557e4870",
   "metadata": {},
   "source": [
    "# Initial Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c8cb696f",
   "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>article_id</th>\n",
       "      <th>product_code</th>\n",
       "      <th>prod_name</th>\n",
       "      <th>product_type_no</th>\n",
       "      <th>product_type_name</th>\n",
       "      <th>product_group_name</th>\n",
       "      <th>graphical_appearance_no</th>\n",
       "      <th>graphical_appearance_name</th>\n",
       "      <th>colour_group_code</th>\n",
       "      <th>colour_group_name</th>\n",
       "      <th>perceived_colour_value_id</th>\n",
       "      <th>perceived_colour_value_name</th>\n",
       "      <th>perceived_colour_master_id</th>\n",
       "      <th>perceived_colour_master_name</th>\n",
       "      <th>department_no</th>\n",
       "      <th>department_name</th>\n",
       "      <th>index_code</th>\n",
       "      <th>index_name</th>\n",
       "      <th>index_group_no</th>\n",
       "      <th>index_group_name</th>\n",
       "      <th>section_no</th>\n",
       "      <th>section_name</th>\n",
       "      <th>garment_group_no</th>\n",
       "      <th>garment_group_name</th>\n",
       "      <th>detail_desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108775015</td>\n",
       "      <td>108775</td>\n",
       "      <td>Strap top</td>\n",
       "      <td>253</td>\n",
       "      <td>Vest top</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>9</td>\n",
       "      <td>Black</td>\n",
       "      <td>4</td>\n",
       "      <td>Dark</td>\n",
       "      <td>5</td>\n",
       "      <td>Black</td>\n",
       "      <td>1676</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>A</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>16</td>\n",
       "      <td>Womens Everyday Basics</td>\n",
       "      <td>1002</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>Jersey top with narrow shoulder straps.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108775044</td>\n",
       "      <td>108775</td>\n",
       "      <td>Strap top</td>\n",
       "      <td>253</td>\n",
       "      <td>Vest top</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>10</td>\n",
       "      <td>White</td>\n",
       "      <td>3</td>\n",
       "      <td>Light</td>\n",
       "      <td>9</td>\n",
       "      <td>White</td>\n",
       "      <td>1676</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>A</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>16</td>\n",
       "      <td>Womens Everyday Basics</td>\n",
       "      <td>1002</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>Jersey top with narrow shoulder straps.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108775051</td>\n",
       "      <td>108775</td>\n",
       "      <td>Strap top (1)</td>\n",
       "      <td>253</td>\n",
       "      <td>Vest top</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010017</td>\n",
       "      <td>Stripe</td>\n",
       "      <td>11</td>\n",
       "      <td>Off White</td>\n",
       "      <td>1</td>\n",
       "      <td>Dusty Light</td>\n",
       "      <td>9</td>\n",
       "      <td>White</td>\n",
       "      <td>1676</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>A</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>16</td>\n",
       "      <td>Womens Everyday Basics</td>\n",
       "      <td>1002</td>\n",
       "      <td>Jersey Basic</td>\n",
       "      <td>Jersey top with narrow shoulder straps.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110065001</td>\n",
       "      <td>110065</td>\n",
       "      <td>OP T-shirt (Idro)</td>\n",
       "      <td>306</td>\n",
       "      <td>Bra</td>\n",
       "      <td>Underwear</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>9</td>\n",
       "      <td>Black</td>\n",
       "      <td>4</td>\n",
       "      <td>Dark</td>\n",
       "      <td>5</td>\n",
       "      <td>Black</td>\n",
       "      <td>1339</td>\n",
       "      <td>Clean Lingerie</td>\n",
       "      <td>B</td>\n",
       "      <td>Lingeries/Tights</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>61</td>\n",
       "      <td>Womens Lingerie</td>\n",
       "      <td>1017</td>\n",
       "      <td>Under-, Nightwear</td>\n",
       "      <td>Microfibre T-shirt bra with underwired, moulde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110065002</td>\n",
       "      <td>110065</td>\n",
       "      <td>OP T-shirt (Idro)</td>\n",
       "      <td>306</td>\n",
       "      <td>Bra</td>\n",
       "      <td>Underwear</td>\n",
       "      <td>1010016</td>\n",
       "      <td>Solid</td>\n",
       "      <td>10</td>\n",
       "      <td>White</td>\n",
       "      <td>3</td>\n",
       "      <td>Light</td>\n",
       "      <td>9</td>\n",
       "      <td>White</td>\n",
       "      <td>1339</td>\n",
       "      <td>Clean Lingerie</td>\n",
       "      <td>B</td>\n",
       "      <td>Lingeries/Tights</td>\n",
       "      <td>1</td>\n",
       "      <td>Ladieswear</td>\n",
       "      <td>61</td>\n",
       "      <td>Womens Lingerie</td>\n",
       "      <td>1017</td>\n",
       "      <td>Under-, Nightwear</td>\n",
       "      <td>Microfibre T-shirt bra with underwired, moulde...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   article_id  product_code          prod_name  product_type_no  \\\n",
       "0   108775015        108775          Strap top              253   \n",
       "1   108775044        108775          Strap top              253   \n",
       "2   108775051        108775      Strap top (1)              253   \n",
       "3   110065001        110065  OP T-shirt (Idro)              306   \n",
       "4   110065002        110065  OP T-shirt (Idro)              306   \n",
       "\n",
       "  product_type_name  product_group_name  graphical_appearance_no  \\\n",
       "0          Vest top  Garment Upper body                  1010016   \n",
       "1          Vest top  Garment Upper body                  1010016   \n",
       "2          Vest top  Garment Upper body                  1010017   \n",
       "3               Bra           Underwear                  1010016   \n",
       "4               Bra           Underwear                  1010016   \n",
       "\n",
       "  graphical_appearance_name  colour_group_code colour_group_name  \\\n",
       "0                     Solid                  9             Black   \n",
       "1                     Solid                 10             White   \n",
       "2                    Stripe                 11         Off White   \n",
       "3                     Solid                  9             Black   \n",
       "4                     Solid                 10             White   \n",
       "\n",
       "   perceived_colour_value_id perceived_colour_value_name  \\\n",
       "0                          4                        Dark   \n",
       "1                          3                       Light   \n",
       "2                          1                 Dusty Light   \n",
       "3                          4                        Dark   \n",
       "4                          3                       Light   \n",
       "\n",
       "   perceived_colour_master_id perceived_colour_master_name  department_no  \\\n",
       "0                           5                        Black           1676   \n",
       "1                           9                        White           1676   \n",
       "2                           9                        White           1676   \n",
       "3                           5                        Black           1339   \n",
       "4                           9                        White           1339   \n",
       "\n",
       "  department_name index_code        index_name  index_group_no  \\\n",
       "0    Jersey Basic          A        Ladieswear               1   \n",
       "1    Jersey Basic          A        Ladieswear               1   \n",
       "2    Jersey Basic          A        Ladieswear               1   \n",
       "3  Clean Lingerie          B  Lingeries/Tights               1   \n",
       "4  Clean Lingerie          B  Lingeries/Tights               1   \n",
       "\n",
       "  index_group_name  section_no            section_name  garment_group_no  \\\n",
       "0       Ladieswear          16  Womens Everyday Basics              1002   \n",
       "1       Ladieswear          16  Womens Everyday Basics              1002   \n",
       "2       Ladieswear          16  Womens Everyday Basics              1002   \n",
       "3       Ladieswear          61         Womens Lingerie              1017   \n",
       "4       Ladieswear          61         Womens Lingerie              1017   \n",
       "\n",
       "  garment_group_name                                        detail_desc  \n",
       "0       Jersey Basic            Jersey top with narrow shoulder straps.  \n",
       "1       Jersey Basic            Jersey top with narrow shoulder straps.  \n",
       "2       Jersey Basic            Jersey top with narrow shoulder straps.  \n",
       "3  Under-, Nightwear  Microfibre T-shirt bra with underwired, moulde...  \n",
       "4  Under-, Nightwear  Microfibre T-shirt bra with underwired, moulde...  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1df159b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>product_code</th>\n",
       "      <th>product_type_no</th>\n",
       "      <th>product_group_name</th>\n",
       "      <th>graphical_appearance_no</th>\n",
       "      <th>colour_group_code</th>\n",
       "      <th>perceived_colour_value_id</th>\n",
       "      <th>perceived_colour_master_id</th>\n",
       "      <th>department_no</th>\n",
       "      <th>index_code</th>\n",
       "      <th>index_group_no</th>\n",
       "      <th>section_no</th>\n",
       "      <th>garment_group_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108775015</td>\n",
       "      <td>108775</td>\n",
       "      <td>253</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010016</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1676</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108775044</td>\n",
       "      <td>108775</td>\n",
       "      <td>253</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010016</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1676</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108775051</td>\n",
       "      <td>108775</td>\n",
       "      <td>253</td>\n",
       "      <td>Garment Upper body</td>\n",
       "      <td>1010017</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1676</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110065001</td>\n",
       "      <td>110065</td>\n",
       "      <td>306</td>\n",
       "      <td>Underwear</td>\n",
       "      <td>1010016</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1339</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>1017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110065002</td>\n",
       "      <td>110065</td>\n",
       "      <td>306</td>\n",
       "      <td>Underwear</td>\n",
       "      <td>1010016</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1339</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>1017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   article_id  product_code  product_type_no  product_group_name  \\\n",
       "0   108775015        108775              253  Garment Upper body   \n",
       "1   108775044        108775              253  Garment Upper body   \n",
       "2   108775051        108775              253  Garment Upper body   \n",
       "3   110065001        110065              306           Underwear   \n",
       "4   110065002        110065              306           Underwear   \n",
       "\n",
       "   graphical_appearance_no  colour_group_code  perceived_colour_value_id  \\\n",
       "0                  1010016                  9                          4   \n",
       "1                  1010016                 10                          3   \n",
       "2                  1010017                 11                          1   \n",
       "3                  1010016                  9                          4   \n",
       "4                  1010016                 10                          3   \n",
       "\n",
       "   perceived_colour_master_id  department_no index_code  index_group_no  \\\n",
       "0                           5           1676          A               1   \n",
       "1                           9           1676          A               1   \n",
       "2                           9           1676          A               1   \n",
       "3                           5           1339          B               1   \n",
       "4                           9           1339          B               1   \n",
       "\n",
       "   section_no  garment_group_no  \n",
       "0          16              1002  \n",
       "1          16              1002  \n",
       "2          16              1002  \n",
       "3          61              1017  \n",
       "4          61              1017  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articles = articles[[\"article_id\", \"product_code\", \"product_type_no\", \"product_group_name\", \"graphical_appearance_no\", \"colour_group_code\", \"perceived_colour_value_id\", \"perceived_colour_master_id\", \"department_no\", \"index_code\", \"index_group_no\", \"section_no\", \"garment_group_no\"]]\n",
    "articles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e0871ed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>FN</th>\n",
       "      <th>Active</th>\n",
       "      <th>club_member_status</th>\n",
       "      <th>fashion_news_frequency</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ACTIVE</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         customer_id  FN  Active  \\\n",
       "0  00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...   0       0   \n",
       "1  0000423b00ade91418cceaf3b26c6af3dd342b51fd051e...   0       0   \n",
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       "3  00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...   0       0   \n",
       "4  00006413d8573cd20ed7128e53b7b13819fe5cfc2d801f...   1       1   \n",
       "\n",
       "  club_member_status fashion_news_frequency   age  \\\n",
       "0             ACTIVE                   NONE  49.0   \n",
       "1             ACTIVE                   NONE  25.0   \n",
       "2             ACTIVE                   NONE  24.0   \n",
       "3             ACTIVE                   NONE  54.0   \n",
       "4             ACTIVE              REGULARLY  52.0   \n",
       "\n",
       "                                         postal_code  \n",
       "0  52043ee2162cf5aa7ee79974281641c6f11a68d276429a...  \n",
       "1  2973abc54daa8a5f8ccfe9362140c63247c5eee03f1d93...  \n",
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       "4  25fa5ddee9aac01b35208d01736e57942317d756b32ddd...  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ca87a975",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>FN</th>\n",
       "      <th>club_member_status</th>\n",
       "      <th>fashion_news_frequency</th>\n",
       "      <th>age</th>\n",
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       "      <td>0</td>\n",
       "      <td>ACTIVE</td>\n",
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       "      <td>1</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>REGULARLY</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         customer_id  FN club_member_status  \\\n",
       "0  00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...   0             ACTIVE   \n",
       "1  0000423b00ade91418cceaf3b26c6af3dd342b51fd051e...   0             ACTIVE   \n",
       "2  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...   0             ACTIVE   \n",
       "3  00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...   0             ACTIVE   \n",
       "4  00006413d8573cd20ed7128e53b7b13819fe5cfc2d801f...   1             ACTIVE   \n",
       "\n",
       "  fashion_news_frequency   age  \n",
       "0                   NONE  49.0  \n",
       "1                   NONE  25.0  \n",
       "2                   NONE  24.0  \n",
       "3                   NONE  54.0  \n",
       "4              REGULARLY  52.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers = customers[[\"customer_id\", \"FN\", \"club_member_status\", \"fashion_news_frequency\", \"age\"]]\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "44fd78f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions['article_id'] = transactions['article_id'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "df964439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31788324, 5)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1c75c1c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-09-20 00:00:00')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions[\"t_dat\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0890758b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-09-22 00:00:00')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions[\"t_dat\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "48f56eb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31788324 entries, 0 to 31788323\n",
      "Data columns (total 5 columns):\n",
      " #   Column            Dtype         \n",
      "---  ------            -----         \n",
      " 0   t_dat             datetime64[ns]\n",
      " 1   customer_id       object        \n",
      " 2   article_id        int32         \n",
      " 3   price             float64       \n",
      " 4   sales_channel_id  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int32(1), int64(1), object(1)\n",
      "memory usage: 1.1+ GB\n"
     ]
    }
   ],
   "source": [
    "transactions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "72e998ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t_dat               0\n",
       "customer_id         0\n",
       "article_id          0\n",
       "price               0\n",
       "sales_channel_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3430bfe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t_dat                   734\n",
       "customer_id         1362281\n",
       "article_id           104547\n",
       "price                  9857\n",
       "sales_channel_id          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d82552",
   "metadata": {},
   "source": [
    "# Splitting the Data and Label Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e59c8dfd",
   "metadata": {},
   "source": [
    "To split the transactions data into transactions which took place prior to September 2020, called transactions_prior (target labels),and transactions which took place during September 2020, called transactions_september (for prediction). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9432311",
   "metadata": {},
   "source": [
    "cutoff_date = '2020-08-31'\n",
    "transaction_prior = transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "57336763",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff = '2020-08-31'\n",
    "transactions_prior = transactions.loc[transactions.t_dat <= pd.to_datetime(cutoff)]\n",
    "transactions_september = transactions.loc[transactions.t_dat > pd.to_datetime(cutoff)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "48b28ca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions_september_id = transactions_september[[\"customer_id\", \"article_id\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e87f4ef",
   "metadata": {},
   "source": [
    "Now, we group the same customers and same articles together and count the quantity purchased:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "15f94e79",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3f/0c48rx816935875jnxcqj5sr0000gn/T/ipykernel_27795/3265603974.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  transactions_september_id[\"article_id_2\"] = transactions_september_id[\"article_id\"]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>customer_id</th>\n",
       "      <th>article_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         customer_id  article_id  quantity\n",
       "0  00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...   568601043         1\n",
       "1  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...   794321007         1\n",
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       "3  0000757967448a6cb83efb3ea7a3fb9d418ac7adf2379d...   719530003         1\n",
       "4  000172a9c322560c849754ffbdfdb2180d408aa7176b94...   685814001         3"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions_september_id[\"article_id_2\"] = transactions_september_id[\"article_id\"]\n",
    "f = {'customer_id': 'first', 'article_id': 'first', 'article_id_2': 'count'} # used for agg's name\n",
    "transactions_september_id = transactions_september_id.groupby(['customer_id', 'article_id'], as_index=False).agg(f)\n",
    "transactions_september_id_new = transactions_september_id.rename({'article_id_2': 'quantity'}, axis='columns')\n",
    "transactions_september_id_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "acd45c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = transactions_september_id_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e7a8c186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "705504"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d95cd04d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id    189510\n",
       "article_id      26252\n",
       "quantity           29\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e57f4978",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 105542 entries, 0 to 105541\n",
      "Data columns (total 13 columns):\n",
      " #   Column                      Non-Null Count   Dtype \n",
      "---  ------                      --------------   ----- \n",
      " 0   article_id                  105542 non-null  int32 \n",
      " 1   product_code                105542 non-null  int64 \n",
      " 2   product_type_no             105542 non-null  int64 \n",
      " 3   product_group_name          105542 non-null  object\n",
      " 4   graphical_appearance_no     105542 non-null  int64 \n",
      " 5   colour_group_code           105542 non-null  int64 \n",
      " 6   perceived_colour_value_id   105542 non-null  int64 \n",
      " 7   perceived_colour_master_id  105542 non-null  int64 \n",
      " 8   department_no               105542 non-null  int64 \n",
      " 9   index_code                  105542 non-null  object\n",
      " 10  index_group_no              105542 non-null  int64 \n",
      " 11  section_no                  105542 non-null  int64 \n",
      " 12  garment_group_no            105542 non-null  int64 \n",
      "dtypes: int32(1), int64(10), object(2)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "articles.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a94fa53e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3f/0c48rx816935875jnxcqj5sr0000gn/T/ipykernel_27795/3645130408.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  transactions_prior.article_id = transactions_prior.article_id.astype(int)\n"
     ]
    }
   ],
   "source": [
    "transactions_prior.article_id = transactions_prior.article_id.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d009474d",
   "metadata": {},
   "source": [
    "# Merge DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "422ee258",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_df = transactions_prior.merge(articles, on='article_id')\n",
    "merged_df = merge_df.merge(customers, on='customer_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "40706f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30990055, 21)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9b37ad2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t_dat                             712\n",
       "customer_id                   1344743\n",
       "article_id                     101852\n",
       "price                            9801\n",
       "sales_channel_id                    2\n",
       "product_code                    45794\n",
       "product_type_no                   129\n",
       "product_group_name                 18\n",
       "graphical_appearance_no            30\n",
       "colour_group_code                  50\n",
       "perceived_colour_value_id           8\n",
       "perceived_colour_master_id         20\n",
       "department_no                     299\n",
       "index_code                         10\n",
       "index_group_no                      5\n",
       "section_no                         57\n",
       "garment_group_no                   21\n",
       "FN                                  2\n",
       "club_member_status                  2\n",
       "fashion_news_frequency              3\n",
       "age                                84\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca443464",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "52dfc795",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_categories = [\n",
    "        \"product_code\", \"product_type_no\", \"product_group_name\", \"graphical_appearance_no\", \n",
    "        \"colour_group_code\", \"perceived_colour_value_id\", \"perceived_colour_master_id\", \n",
    "        \"department_no\", \"index_code\", \"index_group_no\", \"section_no\", \"garment_group_no\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7f30a51e",
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
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      ],
      "text/plain": [
       "                                         customer_id  department_no\n",
       "0  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           1338\n",
       "1  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           1338\n",
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       "4  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           4242"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_df = merged_df[[\"customer_id\", pivot_column]]\n",
    "matrix_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "674244b1",
   "metadata": {},
   "source": [
    "Next, we group all the customers according to this category and the quantity purchased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4cbdc096",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4242</td>\n",
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       "      <td>5831</td>\n",
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       "      <td>1543</td>\n",
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       "      <th>13</th>\n",
       "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
       "      <td>1710</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
       "      <td>5833</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
       "      <td>1522</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
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       "      <th>17</th>\n",
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       "      <td>3608</td>\n",
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      ],
      "text/plain": [
       "                                          customer_id  department_no\n",
       "0   000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           1338\n",
       "1   000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           1338\n",
       "2   000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           1334\n",
       "3   000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           1510\n",
       "4   000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           4242\n",
       "5   000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           4242\n",
       "6   000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           4242\n",
       "7   000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           5831\n",
       "8   000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...           5831\n",
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      ]
     },
     "execution_count": 96,
     "metadata": {},
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    }
   ],
   "source": [
    "matrix_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "23d001f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3f/0c48rx816935875jnxcqj5sr0000gn/T/ipykernel_27795/2807460319.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  matrix_df[\"quantity\"] = matrix_df['department_no']\n"
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      "text/plain": [
       "                                                                                                        customer_id  \\\n",
       "customer_id                                        department_no                                                      \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d... 1212           00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...   \n",
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       "\n",
       "                                                                  department_no  \\\n",
       "customer_id                                        department_no                  \n",
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       "                                                   1322                    1322   \n",
       "                                                   1515                    1515   \n",
       "                                                   1616                    1616   \n",
       "\n",
       "                                                                  quantity  \n",
       "customer_id                                        department_no            \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d... 1212                  4  \n",
       "                                                   1244                  1  \n",
       "                                                   1322                  1  \n",
       "                                                   1515                  1  \n",
       "                                                   1616                  1  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_df[\"quantity\"] = matrix_df['department_no']\n",
    "# Groupby multiple columns & multiple aggregations\n",
    "matrix_df = matrix_df.groupby(['customer_id', 'department_no']).agg({'customer_id':'first','department_no':'first','quantity':'count'})\n",
    "matrix_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c098651f",
   "metadata": {},
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "department_no                                       1201  1202  1212  1222  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   4.0   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   1.0   NaN   NaN   2.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   2.0   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1241  1244  1310  1313  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   1.0   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   1.0   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1322  1334  1336  1338  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   1.0   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   2.0   2.0   NaN   1.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   1.0   NaN   2.0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   1.0   \n",
       "\n",
       "department_no                                       1339  1343  1344  1352  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   2.0   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1410  1414  1422  1444  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   2.0   1.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   1.0   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1447  1510  1515  1522  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   1.0   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   2.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   1.0   NaN   2.0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   1.0   NaN   \n",
       "\n",
       "department_no                                       1543  1545  1547  1610  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   1.0   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1612  1616  1620  1626  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   1.0   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   1.0   NaN   2.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   1.0   NaN   1.0   \n",
       "\n",
       "department_no                                       1636  1640  1641  1642  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   3.0   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   2.0   1.0   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   1.0   NaN   \n",
       "\n",
       "department_no                                       1643  1644  1645  1646  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   1.0   3.0   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1647  1648  1649  1652  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   4.0   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1660  1666  1670  1676  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   1.0   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   2.0   2.0   NaN   1.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1686  1710  1717  1722  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   1.0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   2.0   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1723  1727  1743  1744  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1745  1747  1752  1772  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   2.0   NaN   4.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1773  1774  1778  1779  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   1.0   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1909  1912  1919  1920  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1926  1929  1939  1941  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       1948  1949  2031  2032  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       2033  2034  2035  2080  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       2930  2932  2950  3000  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       3030  3040  3080  3090  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       3209  3409  3419  3420  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   1.0   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       3439  3509  3510  3519  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       3527  3528  3529  3608  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   2.0   NaN   1.0   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       3610  3611  3629  3630  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       3705  3708  3709  3710  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   2.0   \n",
       "\n",
       "department_no                                       3828  3929  3931  3932  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       3936  3937  3938  3939  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   1.0   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       3941  3943  3944  3945  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       3946  3948  4020  4030  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       4090  4091  4092  4210  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       4211  4212  4213  4214  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       4215  4216  4220  4221  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       4222  4224  4225  4242  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   1.0   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN  33.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   3.0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   2.0   \n",
       "\n",
       "department_no                                       4310  4311  4312  4313  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       4314  4317  4320  4321  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       4322  4323  4327  4342  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       4343  4344  4345  4920  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   2.0   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5131  5231  5252  5262  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5283  5454  5555  5626  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   1.0   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5627  5631  5641  5656  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5658  5672  5673  5679  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5683  5686  5687  5690  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5731  5741  5767  5777  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5783  5787  5828  5831  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   2.0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5832  5833  5848  5858  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   2.0   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5868  5878  5882  5883  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5884  5888  5950  5952  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5956  5957  5958  5959  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5960  5961  5962  5963  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       5999  6280  6281  6283  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       6512  6515  6525  6526  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   1.0   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       6541  6545  6546  6550  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       6555  6557  6561  6563  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   2.0   1.0   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       6564  6565  7020  7050  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       7188  7388  7389  7510  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       7520  7530  7613  7616  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   1.0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       7617  7618  7648  7655  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       7656  7657  7658  7659  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       7668  7757  7812  7814  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   1.0   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       7819  7848  7852  7854  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       7857  7912  7917  7920  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       7921  7922  7930  7931  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       7932  7952  7956  7987  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       7988  7989  8030  8090  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       8310  8316  8394  8396  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   2.0   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   1.0   1.0   NaN   NaN   \n",
       "\n",
       "department_no                                       8397  8398  8558  8559  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       8560  8563  8564  8615  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   1.0   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       8616  8617  8713  8716  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   1.0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       8717  8718  8748  8755  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       8756  8757  8758  8768  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       8812  8815  8852  8888  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   1.0   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       8917  8956  9020  9984  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN   NaN   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN   NaN   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN   NaN   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN   NaN   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "department_no                                       9985  9986  9989  \n",
       "customer_id                                                           \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   NaN   NaN   NaN  \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   NaN   NaN   NaN  \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   NaN   NaN   NaN  \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   NaN   NaN   NaN  \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   NaN   NaN   NaN  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df = matrix_df.pivot(index='customer_id', columns=pivot_column, values='quantity')\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "d6898a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1344743, 299)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b35af52",
   "metadata": {},
   "source": [
    "Now, we need to flatten the dataframe(changing data format to a narrow format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "12cf27ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>department_no</th>\n",
       "      <th>1201</th>\n",
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       "      <th>1660</th>\n",
       "      <th>1666</th>\n",
       "      <th>1670</th>\n",
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       "      <th>1723</th>\n",
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       "      <th>1743</th>\n",
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       "      <th>1747</th>\n",
       "      <th>1752</th>\n",
       "      <th>1772</th>\n",
       "      <th>1773</th>\n",
       "      <th>1774</th>\n",
       "      <th>1778</th>\n",
       "      <th>1779</th>\n",
       "      <th>1909</th>\n",
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       "      <th>1919</th>\n",
       "      <th>1920</th>\n",
       "      <th>1926</th>\n",
       "      <th>1929</th>\n",
       "      <th>1939</th>\n",
       "      <th>1941</th>\n",
       "      <th>1948</th>\n",
       "      <th>1949</th>\n",
       "      <th>2031</th>\n",
       "      <th>2032</th>\n",
       "      <th>2033</th>\n",
       "      <th>2034</th>\n",
       "      <th>2035</th>\n",
       "      <th>2080</th>\n",
       "      <th>2930</th>\n",
       "      <th>2932</th>\n",
       "      <th>2950</th>\n",
       "      <th>3000</th>\n",
       "      <th>3030</th>\n",
       "      <th>3040</th>\n",
       "      <th>3080</th>\n",
       "      <th>3090</th>\n",
       "      <th>3209</th>\n",
       "      <th>3409</th>\n",
       "      <th>3419</th>\n",
       "      <th>3420</th>\n",
       "      <th>3439</th>\n",
       "      <th>3509</th>\n",
       "      <th>3510</th>\n",
       "      <th>3519</th>\n",
       "      <th>3527</th>\n",
       "      <th>3528</th>\n",
       "      <th>3529</th>\n",
       "      <th>3608</th>\n",
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       "      <th>3611</th>\n",
       "      <th>3629</th>\n",
       "      <th>3630</th>\n",
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       "      <th>3708</th>\n",
       "      <th>3709</th>\n",
       "      <th>3710</th>\n",
       "      <th>3828</th>\n",
       "      <th>3929</th>\n",
       "      <th>3931</th>\n",
       "      <th>3932</th>\n",
       "      <th>3936</th>\n",
       "      <th>3937</th>\n",
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       "      <th>3946</th>\n",
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       "      <th>4211</th>\n",
       "      <th>4212</th>\n",
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       "      <th>5627</th>\n",
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       "      <th>5656</th>\n",
       "      <th>5658</th>\n",
       "      <th>5672</th>\n",
       "      <th>5673</th>\n",
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       "      <th>5683</th>\n",
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       "      <th>5767</th>\n",
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       "      <th>5783</th>\n",
       "      <th>5787</th>\n",
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       "      <th>5868</th>\n",
       "      <th>5878</th>\n",
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       "      <th>5883</th>\n",
       "      <th>5884</th>\n",
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       "      <th>5952</th>\n",
       "      <th>5956</th>\n",
       "      <th>5957</th>\n",
       "      <th>5958</th>\n",
       "      <th>5959</th>\n",
       "      <th>5960</th>\n",
       "      <th>5961</th>\n",
       "      <th>5962</th>\n",
       "      <th>5963</th>\n",
       "      <th>5999</th>\n",
       "      <th>6280</th>\n",
       "      <th>6281</th>\n",
       "      <th>6283</th>\n",
       "      <th>6512</th>\n",
       "      <th>6515</th>\n",
       "      <th>6525</th>\n",
       "      <th>6526</th>\n",
       "      <th>6541</th>\n",
       "      <th>6545</th>\n",
       "      <th>6546</th>\n",
       "      <th>6550</th>\n",
       "      <th>6555</th>\n",
       "      <th>6557</th>\n",
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       "      <th>6563</th>\n",
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       "      <th>7655</th>\n",
       "      <th>7656</th>\n",
       "      <th>7657</th>\n",
       "      <th>7658</th>\n",
       "      <th>7659</th>\n",
       "      <th>7668</th>\n",
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       "      <th>7814</th>\n",
       "      <th>7819</th>\n",
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       "      <th>7854</th>\n",
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       "      <th>7912</th>\n",
       "      <th>7917</th>\n",
       "      <th>7920</th>\n",
       "      <th>7921</th>\n",
       "      <th>7922</th>\n",
       "      <th>7930</th>\n",
       "      <th>7931</th>\n",
       "      <th>7932</th>\n",
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       "      <th>7956</th>\n",
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       "      <th>7988</th>\n",
       "      <th>7989</th>\n",
       "      <th>8030</th>\n",
       "      <th>8090</th>\n",
       "      <th>8310</th>\n",
       "      <th>8316</th>\n",
       "      <th>8394</th>\n",
       "      <th>8396</th>\n",
       "      <th>8397</th>\n",
       "      <th>8398</th>\n",
       "      <th>8558</th>\n",
       "      <th>8559</th>\n",
       "      <th>8560</th>\n",
       "      <th>8563</th>\n",
       "      <th>8564</th>\n",
       "      <th>8615</th>\n",
       "      <th>8616</th>\n",
       "      <th>8617</th>\n",
       "      <th>8713</th>\n",
       "      <th>8716</th>\n",
       "      <th>8717</th>\n",
       "      <th>8718</th>\n",
       "      <th>8748</th>\n",
       "      <th>8755</th>\n",
       "      <th>8756</th>\n",
       "      <th>8757</th>\n",
       "      <th>8758</th>\n",
       "      <th>8768</th>\n",
       "      <th>8812</th>\n",
       "      <th>8815</th>\n",
       "      <th>8852</th>\n",
       "      <th>8888</th>\n",
       "      <th>8917</th>\n",
       "      <th>8956</th>\n",
       "      <th>9020</th>\n",
       "      <th>9984</th>\n",
       "      <th>9985</th>\n",
       "      <th>9986</th>\n",
       "      <th>9989</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d25f88aa139fdfc657</th>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "department_no                                       1201  1202  1212  1222  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   0.0   0.0   4.0   0.0   \n",
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       "\n",
       "department_no                                       1241  1244  1310  1313  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       1322  1334  1336  1338  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       1339  1343  1344  1352  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       1410  1414  1422  1444  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       1447  1510  1515  1522  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       1543  1545  1547  1610  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       1612  1616  1620  1626  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       1636  1640  1641  1642  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       1643  1644  1645  1646  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       1647  1648  1649  1652  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       1660  1666  1670  1676  \\\n",
       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "\n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       8397  8398  8558  8559  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   0.0   0.0   0.0   0.0   \n",
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       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   0.0   0.0   0.0   0.0   \n",
       "\n",
       "department_no                                       8560  8563  8564  8615  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   0.0   0.0   0.0   0.0   \n",
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       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   0.0   0.0   0.0   0.0   \n",
       "\n",
       "department_no                                       8616  8617  8713  8716  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   0.0   0.0   0.0   1.0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   0.0   0.0   0.0   0.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   0.0   0.0   0.0   0.0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   0.0   0.0   0.0   0.0   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   0.0   0.0   0.0   0.0   \n",
       "\n",
       "department_no                                       8717  8718  8748  8755  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   0.0   0.0   0.0   0.0   \n",
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       "\n",
       "department_no                                       8756  8757  8758  8768  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   0.0   0.0   0.0   0.0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   0.0   0.0   0.0   0.0   \n",
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       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   0.0   0.0   0.0   0.0   \n",
       "\n",
       "department_no                                       8812  8815  8852  8888  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   0.0   0.0   1.0   0.0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   0.0   0.0   0.0   0.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   0.0   0.0   0.0   0.0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   0.0   0.0   0.0   0.0   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   0.0   0.0   0.0   0.0   \n",
       "\n",
       "department_no                                       8917  8956  9020  9984  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   0.0   0.0   0.0   0.0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   0.0   0.0   0.0   0.0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   0.0   0.0   0.0   0.0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   0.0   0.0   0.0   0.0   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   0.0   0.0   0.0   0.0   \n",
       "\n",
       "department_no                                       9985  9986  9989  \n",
       "customer_id                                                           \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...   0.0   0.0   0.0  \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...   0.0   0.0   0.0  \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...   0.0   0.0   0.0  \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...   0.0   0.0   0.0  \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...   0.0   0.0   0.0  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df = pivot_df.fillna(0)\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "12c58a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>department_no</th>\n",
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       "      <th>1926</th>\n",
       "      <th>1929</th>\n",
       "      <th>1939</th>\n",
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       "      <th>1948</th>\n",
       "      <th>1949</th>\n",
       "      <th>2031</th>\n",
       "      <th>2032</th>\n",
       "      <th>2033</th>\n",
       "      <th>2034</th>\n",
       "      <th>2035</th>\n",
       "      <th>2080</th>\n",
       "      <th>2930</th>\n",
       "      <th>2932</th>\n",
       "      <th>2950</th>\n",
       "      <th>3000</th>\n",
       "      <th>3030</th>\n",
       "      <th>3040</th>\n",
       "      <th>3080</th>\n",
       "      <th>3090</th>\n",
       "      <th>3209</th>\n",
       "      <th>3409</th>\n",
       "      <th>3419</th>\n",
       "      <th>3420</th>\n",
       "      <th>3439</th>\n",
       "      <th>3509</th>\n",
       "      <th>3510</th>\n",
       "      <th>3519</th>\n",
       "      <th>3527</th>\n",
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       "      <th>5658</th>\n",
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       "      <th>8090</th>\n",
       "      <th>8310</th>\n",
       "      <th>8316</th>\n",
       "      <th>8394</th>\n",
       "      <th>8396</th>\n",
       "      <th>8397</th>\n",
       "      <th>8398</th>\n",
       "      <th>8558</th>\n",
       "      <th>8559</th>\n",
       "      <th>8560</th>\n",
       "      <th>8563</th>\n",
       "      <th>8564</th>\n",
       "      <th>8615</th>\n",
       "      <th>8616</th>\n",
       "      <th>8617</th>\n",
       "      <th>8713</th>\n",
       "      <th>8716</th>\n",
       "      <th>8717</th>\n",
       "      <th>8718</th>\n",
       "      <th>8748</th>\n",
       "      <th>8755</th>\n",
       "      <th>8756</th>\n",
       "      <th>8757</th>\n",
       "      <th>8758</th>\n",
       "      <th>8768</th>\n",
       "      <th>8812</th>\n",
       "      <th>8815</th>\n",
       "      <th>8852</th>\n",
       "      <th>8888</th>\n",
       "      <th>8917</th>\n",
       "      <th>8956</th>\n",
       "      <th>9020</th>\n",
       "      <th>9984</th>\n",
       "      <th>9985</th>\n",
       "      <th>9986</th>\n",
       "      <th>9989</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d25f88aa139fdfc657</th>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "department_no                                       1201  1202  1212  1222  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...     0     0     4     0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...     1     0     0     2   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...     0     0     0     0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...     0     0     0     0   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...     2     0     0     0   \n",
       "\n",
       "department_no                                       1241  1244  1310  1313  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...     0     1     0     0   \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "\n",
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       "customer_id                                                                  \n",
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       "\n",
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       "customer_id                                                                  \n",
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       "\n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       7921  7922  7930  7931  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       7932  7952  7956  7987  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       7988  7989  8030  8090  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       8310  8316  8394  8396  \\\n",
       "customer_id                                                                  \n",
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       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...     1     1     0     0   \n",
       "\n",
       "department_no                                       8397  8398  8558  8559  \\\n",
       "customer_id                                                                  \n",
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       "\n",
       "department_no                                       8560  8563  8564  8615  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...     0     0     0     0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...     1     0     0     0   \n",
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       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...     0     0     0     0   \n",
       "\n",
       "department_no                                       8616  8617  8713  8716  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...     0     0     0     1   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...     0     0     0     0   \n",
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       "\n",
       "department_no                                       8717  8718  8748  8755  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...     0     0     0     0   \n",
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       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...     0     0     0     0   \n",
       "\n",
       "department_no                                       8756  8757  8758  8768  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...     0     0     0     0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...     0     0     0     0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...     0     0     0     0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...     0     0     0     0   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...     0     0     0     0   \n",
       "\n",
       "department_no                                       8812  8815  8852  8888  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...     0     0     1     0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...     0     0     0     0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...     0     0     0     0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...     0     0     0     0   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...     0     0     0     0   \n",
       "\n",
       "department_no                                       8917  8956  9020  9984  \\\n",
       "customer_id                                                                  \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...     0     0     0     0   \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...     0     0     0     0   \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...     0     0     0     0   \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...     0     0     0     0   \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...     0     0     0     0   \n",
       "\n",
       "department_no                                       9985  9986  9989  \n",
       "customer_id                                                           \n",
       "00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d...     0     0     0  \n",
       "0000423b00ade91418cceaf3b26c6af3dd342b51fd051ee...     0     0     0  \n",
       "000058a12d5b43e67d225668fa1f8d618c13dc232df0cad...     0     0     0  \n",
       "00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2c...     0     0     0  \n",
       "00006413d8573cd20ed7128e53b7b13819fe5cfc2d801fe...     0     0     0  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df = pivot_df.astype('int64', copy=False)\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "2e3688bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3420</th>\n",
       "      <th>3439</th>\n",
       "      <th>3509</th>\n",
       "      <th>3510</th>\n",
       "      <th>3519</th>\n",
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       "      <th>5658</th>\n",
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       "      <th>7812</th>\n",
       "      <th>7814</th>\n",
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       "      <th>7857</th>\n",
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       "      <th>7917</th>\n",
       "      <th>7920</th>\n",
       "      <th>7921</th>\n",
       "      <th>7922</th>\n",
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       "      <th>7932</th>\n",
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       "      <th>7956</th>\n",
       "      <th>7987</th>\n",
       "      <th>7988</th>\n",
       "      <th>7989</th>\n",
       "      <th>8030</th>\n",
       "      <th>8090</th>\n",
       "      <th>8310</th>\n",
       "      <th>8316</th>\n",
       "      <th>8394</th>\n",
       "      <th>8396</th>\n",
       "      <th>8397</th>\n",
       "      <th>8398</th>\n",
       "      <th>8558</th>\n",
       "      <th>8559</th>\n",
       "      <th>8560</th>\n",
       "      <th>8563</th>\n",
       "      <th>8564</th>\n",
       "      <th>8615</th>\n",
       "      <th>8616</th>\n",
       "      <th>8617</th>\n",
       "      <th>8713</th>\n",
       "      <th>8716</th>\n",
       "      <th>8717</th>\n",
       "      <th>8718</th>\n",
       "      <th>8748</th>\n",
       "      <th>8755</th>\n",
       "      <th>8756</th>\n",
       "      <th>8757</th>\n",
       "      <th>8758</th>\n",
       "      <th>8768</th>\n",
       "      <th>8812</th>\n",
       "      <th>8815</th>\n",
       "      <th>8852</th>\n",
       "      <th>8888</th>\n",
       "      <th>8917</th>\n",
       "      <th>8956</th>\n",
       "      <th>9020</th>\n",
       "      <th>9984</th>\n",
       "      <th>9985</th>\n",
       "      <th>9986</th>\n",
       "      <th>9989</th>\n",
       "      <th>FN</th>\n",
       "      <th>club_member_status</th>\n",
       "      <th>fashion_news_frequency</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000423b00ade91418cceaf3b26c6af3dd342b51fd051e...</td>\n",
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       "      <td>33</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00006413d8573cd20ed7128e53b7b13819fe5cfc2d801f...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ACTIVE</td>\n",
       "      <td>REGULARLY</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         customer_id  1201  1202  1212  1222  \\\n",
       "0  00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...     0     0     4     0   \n",
       "1  0000423b00ade91418cceaf3b26c6af3dd342b51fd051e...     1     0     0     2   \n",
       "2  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...     0     0     0     0   \n",
       "3  00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...     0     0     0     0   \n",
       "4  00006413d8573cd20ed7128e53b7b13819fe5cfc2d801f...     2     0     0     0   \n",
       "\n",
       "   1241  1244  1310  1313  1322  1334  1336  1338  1339  1343  1344  1352  \\\n",
       "0     0     1     0     0     1     0     0     0     0     0     0     0   \n",
       "1     0     0     1     0     2     2     0     1     0     0     2     0   \n",
       "2     0     0     0     0     0     1     0     2     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     1     0     0     0     0   \n",
       "\n",
       "   1410  1414  1422  1444  1447  1510  1515  1522  1543  1545  1547  1610  \\\n",
       "0     0     0     0     0     0     0     1     0     0     0     0     0   \n",
       "1     0     0     2     1     0     0     0     2     0     0     0     0   \n",
       "2     0     1     0     0     0     1     0     2     1     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     1     0     0     0     0     0   \n",
       "\n",
       "   1612  1616  1620  1626  1636  1640  1641  1642  1643  1644  1645  1646  \\\n",
       "0     0     1     0     0     3     0     0     0     0     0     0     0   \n",
       "1     0     1     0     2     2     1     0     0     1     3     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     1     0     1     0     0     1     0     0     0     0     0   \n",
       "\n",
       "   1647  1648  1649  1652  1660  1666  1670  1676  1686  1710  1717  1722  \\\n",
       "0     0     0     0     0     0     1     0     0     0     0     0     1   \n",
       "1     4     0     0     0     2     2     0     1     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     2     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   1723  1727  1743  1744  1745  1747  1752  1772  1773  1774  1778  1779  \\\n",
       "0     0     0     0     0     0     0     0     0     1     0     0     0   \n",
       "1     0     0     0     0     0     2     0     4     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   1909  1912  1919  1920  1926  1929  1939  1941  1948  1949  2031  2032  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   2033  2034  2035  2080  2930  2932  2950  3000  3030  3040  3080  3090  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   3209  3409  3419  3420  3439  3509  3510  3519  3527  3528  3529  3608  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     1     0     0     0     0     0     0     0     2     0     1     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   3610  3611  3629  3630  3705  3708  3709  3710  3828  3929  3931  3932  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     2     0     0     0     0   \n",
       "\n",
       "   3936  3937  3938  3939  3941  3943  3944  3945  3946  3948  4020  4030  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     1     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   4090  4091  4092  4210  4211  4212  4213  4214  4215  4216  4220  4221  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   4222  4224  4225  4242  4310  4311  4312  4313  4314  4317  4320  4321  \\\n",
       "0     1     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0    33     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     3     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     2     0     0     0     0     0     0     0     0   \n",
       "\n",
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       "\n",
       "   8397  8398  8558  8559  8560  8563  8564  8615  8616  8617  8713  8716  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     1   \n",
       "1     0     0     0     0     1     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
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       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   8717  8718  8748  8755  8756  8757  8758  8768  8812  8815  8852  8888  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     1     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
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       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   8917  8956  9020  9984  9985  9986  9989  FN club_member_status  \\\n",
       "0     0     0     0     0     0     0     0   0             ACTIVE   \n",
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       "3     0     0     0     0     0     0     0   0             ACTIVE   \n",
       "4     0     0     0     0     0     0     0   1             ACTIVE   \n",
       "\n",
       "  fashion_news_frequency   age  \n",
       "0                   NONE  49.0  \n",
       "1                   NONE  25.0  \n",
       "2                   NONE  24.0  \n",
       "3                   NONE  54.0  \n",
       "4              REGULARLY  52.0  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pivot_df.merge(customers, on='customer_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a9ef7c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1344743, 304)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
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    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "cedc7ade",
   "metadata": {},
   "outputs": [
    {
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       "                                         customer_id  colour_group_code\n",
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       "1  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...                  9\n",
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      ]
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   ],
   "source": [
    "# Aggregate Descriptive Features\n",
    "# First, we can generate a feature which captures he customer's favorite color\n",
    "\n",
    "colour_df = merged_df[[\"customer_id\", \"colour_group_code\"]]\n",
    "colour_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7461d0fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3f/0c48rx816935875jnxcqj5sr0000gn/T/ipykernel_27795/310321360.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  colour_df[\"colour_group_code_2\"] = colour_df[\"colour_group_code\"]\n"
     ]
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       "                                         customer_id  colour_group_code  count\n",
       "0  00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...                  9     14\n",
       "1  00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...                 11      1\n",
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       "4  00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...                 51      1"
      ]
     },
     "execution_count": 148,
     "metadata": {},
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   ],
   "source": [
    "colour_df[\"colour_group_code_2\"] = colour_df[\"colour_group_code\"]\n",
    "f = {'customer_id': 'first', 'colour_group_code': 'first', 'colour_group_code_2': 'count'}\n",
    "colour_df = colour_df.groupby(['customer_id', 'colour_group_code'], as_index=False).agg(f)\n",
    "colour_df = colour_df.rename({'colour_group_code_2': 'count'}, axis='columns')\n",
    "colour_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9861090",
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   "source": [
    "Next, we take those rows with the maximum count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "7478536a",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx=colour_df.groupby('customer_id')['count'].idxmax()\n",
    "colour_df_max=colour_df.loc[idx,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "9900ddf7",
   "metadata": {},
   "outputs": [
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       "\n",
       "    count  \n",
       "0      14  \n",
       "9      26  \n",
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       "32      9  "
      ]
     },
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    "colour_df_max.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "b9cf5451",
   "metadata": {},
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     "execution_count": 151,
     "metadata": {},
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    "colour_df_max['customer_id'].nunique()"
   ]
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   "id": "fc37aa2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "colour_df_max.drop([\"count\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "9456a0aa",
   "metadata": {},
   "outputs": [
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       "      <th>25</th>\n",
       "      <td>000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>00006413d8573cd20ed7128e53b7b13819fe5cfc2d801f...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          customer_id  fave_colour\n",
       "0   00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...            9\n",
       "9   0000423b00ade91418cceaf3b26c6af3dd342b51fd051e...            9\n",
       "25  000058a12d5b43e67d225668fa1f8d618c13dc232df0ca...            9\n",
       "31  00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...            9\n",
       "32  00006413d8573cd20ed7128e53b7b13819fe5cfc2d801f...            9"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colour_df_max = colour_df_max.rename(columns={\"colour_group_code\": \"fave_colour\"})\n",
    "colour_df_max.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837defc5",
   "metadata": {},
   "source": [
    "We can get a feel for the distribution of colours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ef29a257",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3f/0c48rx816935875jnxcqj5sr0000gn/T/ipykernel_27795/34577986.py:1: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  colour_df_max['colour_group_code'].value_counts()[:30].plot(kind='barh')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colour_df_max['colour_group_code'].value_counts()[:30].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "227b4707",
   "metadata": {},
   "source": [
    "Second Favourite Colour:\n",
    "\n",
    "Perhaps even more important than favourite colour is a customer's second favourite colour. We can repeat the same process as above, but extract the colour_group_code with second largest count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a14413d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>customer_id</th>\n",
       "      <th>colour_group_code</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5344453</th>\n",
       "      <td>8df45859ccd71ef1e48e2ee9d1c65d5728c31c46ae957d...</td>\n",
       "      <td>10</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6267342</th>\n",
       "      <td>a65f77281a528bf5c1e9f270141d601d116e1df33bf9df...</td>\n",
       "      <td>10</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5924619</th>\n",
       "      <td>9d50687e366b49577c136952613632b9f157cb9051cb71...</td>\n",
       "      <td>19</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3901302</th>\n",
       "      <td>67931690bdf18d2e328854ae772cd5ce2505fdc1116469...</td>\n",
       "      <td>10</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6417755</th>\n",
       "      <td>aa5d58330818c507141333ef2f4874481921a07fde38f3...</td>\n",
       "      <td>10</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               customer_id  colour_group_code  \\\n",
       "5344453  8df45859ccd71ef1e48e2ee9d1c65d5728c31c46ae957d...                 10   \n",
       "6267342  a65f77281a528bf5c1e9f270141d601d116e1df33bf9df...                 10   \n",
       "5924619  9d50687e366b49577c136952613632b9f157cb9051cb71...                 19   \n",
       "3901302  67931690bdf18d2e328854ae772cd5ce2505fdc1116469...                 10   \n",
       "6417755  aa5d58330818c507141333ef2f4874481921a07fde38f3...                 10   \n",
       "\n",
       "         count  \n",
       "5344453    208  \n",
       "6267342    183  \n",
       "5924619    182  \n",
       "3901302    160  \n",
       "6417755    156  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colour_df = colour_df.sort_values('count',ascending=False).groupby('customer_id', as_index=False).nth(1)\n",
    "colour_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "775e481d",
   "metadata": {},
   "outputs": [],
   "source": [
    "colour_df.drop(['count'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "b1cfacff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>customer_id</th>\n",
       "      <th>2nd_fave_colour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5344453</th>\n",
       "      <td>8df45859ccd71ef1e48e2ee9d1c65d5728c31c46ae957d...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6267342</th>\n",
       "      <td>a65f77281a528bf5c1e9f270141d601d116e1df33bf9df...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5924619</th>\n",
       "      <td>9d50687e366b49577c136952613632b9f157cb9051cb71...</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3901302</th>\n",
       "      <td>67931690bdf18d2e328854ae772cd5ce2505fdc1116469...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6417755</th>\n",
       "      <td>aa5d58330818c507141333ef2f4874481921a07fde38f3...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               customer_id  2nd_fave_colour\n",
       "5344453  8df45859ccd71ef1e48e2ee9d1c65d5728c31c46ae957d...               10\n",
       "6267342  a65f77281a528bf5c1e9f270141d601d116e1df33bf9df...               10\n",
       "5924619  9d50687e366b49577c136952613632b9f157cb9051cb71...               19\n",
       "3901302  67931690bdf18d2e328854ae772cd5ce2505fdc1116469...               10\n",
       "6417755  aa5d58330818c507141333ef2f4874481921a07fde38f3...               10"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colour_df = colour_df.rename(columns={'colour_group_code':'2nd_fave_colour'})\n",
    "colour_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "51cceb3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3f/0c48rx816935875jnxcqj5sr0000gn/T/ipykernel_27795/2419245633.py:1: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  colour_df['2nd_fave_colour'].value_counts()[:30].plot(kind='barh')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colour_df['2nd_fave_colour'].value_counts()[:30].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b341162a",
   "metadata": {},
   "source": [
    "Favorite Pattern\n",
    "\n",
    "Now, we are going to get the Favorite Pattern by customer_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5364607",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}