LTV_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8a7d5d9a",
   "metadata": {},
   "source": [
    "## Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295e9ed7",
   "metadata": {},
   "source": [
    "Evaluate the LTV of clients groups who were attracted in different years, and choose a model for attracting and working with clients, which gives both more Profit and LTV (all 4 years used different models to attract and retain clients)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b06efc",
   "metadata": {},
   "source": [
    "## Metrics and type of analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "016c69c6",
   "metadata": {},
   "source": [
    "To solve this task, I will use cohort analysis and the LTV (Lifetime Value) metric."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e71f9e8",
   "metadata": {},
   "source": [
    "## What is LTV?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "045579ac",
   "metadata": {},
   "source": [
    "LTV (Lifetime Value) is the gross profit that the average user will make over the lifetime of the product.\n",
    "\n",
    "LTV must be calculated:\n",
    "- Based on Gross Profit, not Revenue\n",
    "- Using cohort analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cdd87bc",
   "metadata": {},
   "source": [
    "### Definition for analysis in this case"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a17808",
   "metadata": {},
   "source": [
    "<b> Customer action </b>: purchase by Order Date\n",
    "\n",
    "<b> Cohort size  </b>: year\n",
    "\n",
    "<b> Reporting period  </b>: 4 years, from 2014 to 2017\n",
    "\n",
    "<b> Key metrics  </b>: LTV (which is calculated on the basis of Profit, not Revenue, in the entire existence of the cohort)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e693d66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add libraries\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "pd.set_option('mode.chained_assignment', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c3885b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add data\n",
    "data = pd.read_excel(\"Sample - Superstore.xls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9634d251",
   "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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2016-138688</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2016-138688 2016-06-12 2016-06-16    Second Class    DV-13045   \n",
       "3       4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City  ...  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "\n",
       "  Postal Code  Region       Product ID         Category Sub-Category  \\\n",
       "0       42420   South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1       42420   South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2       90036    West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3       33311   South  FUR-TA-10000577        Furniture       Tables   \n",
       "4       33311   South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "569ab0d9",
   "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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>9985</td>\n",
       "      <td>CA-2015-100251</td>\n",
       "      <td>2015-05-17</td>\n",
       "      <td>2015-05-23</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DV-13465</td>\n",
       "      <td>Dianna Vittorini</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Long Beach</td>\n",
       "      <td>...</td>\n",
       "      <td>11561</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-LA-10003766</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Removable Labels</td>\n",
       "      <td>31.500</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>9986</td>\n",
       "      <td>CA-2015-100251</td>\n",
       "      <td>2015-05-17</td>\n",
       "      <td>2015-05-23</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DV-13465</td>\n",
       "      <td>Dianna Vittorini</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Long Beach</td>\n",
       "      <td>...</td>\n",
       "      <td>11561</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-SU-10000898</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Supplies</td>\n",
       "      <td>Acme Hot Forged Carbon Steel Scissors with Nic...</td>\n",
       "      <td>55.600</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.1240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>9987</td>\n",
       "      <td>CA-2016-125794</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ML-17410</td>\n",
       "      <td>Maris LaWare</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90008</td>\n",
       "      <td>West</td>\n",
       "      <td>TEC-AC-10003399</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Memorex Mini Travel Drive 64 GB USB 2.0 Flash ...</td>\n",
       "      <td>36.240</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.2208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>9988</td>\n",
       "      <td>CA-2017-163629</td>\n",
       "      <td>2017-11-17</td>\n",
       "      <td>2017-11-21</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RA-19885</td>\n",
       "      <td>Ruben Ausman</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Athens</td>\n",
       "      <td>...</td>\n",
       "      <td>30605</td>\n",
       "      <td>South</td>\n",
       "      <td>TEC-AC-10001539</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Logitech G430 Surround Sound Gaming Headset wi...</td>\n",
       "      <td>79.990</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.7964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>9989</td>\n",
       "      <td>CA-2017-163629</td>\n",
       "      <td>2017-11-17</td>\n",
       "      <td>2017-11-21</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RA-19885</td>\n",
       "      <td>Ruben Ausman</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Athens</td>\n",
       "      <td>...</td>\n",
       "      <td>30605</td>\n",
       "      <td>South</td>\n",
       "      <td>TEC-PH-10004006</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Panasonic KX - TS880B Telephone</td>\n",
       "      <td>206.100</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.6470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>9990</td>\n",
       "      <td>CA-2014-110422</td>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>TB-21400</td>\n",
       "      <td>Tom Boeckenhauer</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "      <td>...</td>\n",
       "      <td>33180</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-FU-10001889</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Ultra Door Pull Handle</td>\n",
       "      <td>25.248</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>9991</td>\n",
       "      <td>CA-2017-121258</td>\n",
       "      <td>2017-02-26</td>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DB-13060</td>\n",
       "      <td>Dave Brooks</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>...</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-FU-10000747</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Tenex B1-RE Series Chair Mats for Low Pile Car...</td>\n",
       "      <td>91.960</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.6332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>9992</td>\n",
       "      <td>CA-2017-121258</td>\n",
       "      <td>2017-02-26</td>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DB-13060</td>\n",
       "      <td>Dave Brooks</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>...</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>TEC-PH-10003645</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Aastra 57i VoIP phone</td>\n",
       "      <td>258.576</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>19.3932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>9993</td>\n",
       "      <td>CA-2017-121258</td>\n",
       "      <td>2017-02-26</td>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DB-13060</td>\n",
       "      <td>Dave Brooks</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>...</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10004041</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>It's Hot Message Books with Stickers, 2 3/4\" x 5\"</td>\n",
       "      <td>29.600</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>9994</td>\n",
       "      <td>CA-2017-119914</td>\n",
       "      <td>2017-05-04</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CC-12220</td>\n",
       "      <td>Chris Cortes</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>...</td>\n",
       "      <td>92683</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-AP-10002684</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Acco 7-Outlet Masterpiece Power Center, Wihtou...</td>\n",
       "      <td>243.160</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.9480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Row ID        Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "9984    9985  CA-2015-100251 2015-05-17 2015-05-23  Standard Class   \n",
       "9985    9986  CA-2015-100251 2015-05-17 2015-05-23  Standard Class   \n",
       "9986    9987  CA-2016-125794 2016-09-29 2016-10-03  Standard Class   \n",
       "9987    9988  CA-2017-163629 2017-11-17 2017-11-21  Standard Class   \n",
       "9988    9989  CA-2017-163629 2017-11-17 2017-11-21  Standard Class   \n",
       "9989    9990  CA-2014-110422 2014-01-21 2014-01-23    Second Class   \n",
       "9990    9991  CA-2017-121258 2017-02-26 2017-03-03  Standard Class   \n",
       "9991    9992  CA-2017-121258 2017-02-26 2017-03-03  Standard Class   \n",
       "9992    9993  CA-2017-121258 2017-02-26 2017-03-03  Standard Class   \n",
       "9993    9994  CA-2017-119914 2017-05-04 2017-05-09    Second Class   \n",
       "\n",
       "     Customer ID     Customer Name    Segment        Country         City  \\\n",
       "9984    DV-13465  Dianna Vittorini   Consumer  United States   Long Beach   \n",
       "9985    DV-13465  Dianna Vittorini   Consumer  United States   Long Beach   \n",
       "9986    ML-17410      Maris LaWare   Consumer  United States  Los Angeles   \n",
       "9987    RA-19885      Ruben Ausman  Corporate  United States       Athens   \n",
       "9988    RA-19885      Ruben Ausman  Corporate  United States       Athens   \n",
       "9989    TB-21400  Tom Boeckenhauer   Consumer  United States        Miami   \n",
       "9990    DB-13060       Dave Brooks   Consumer  United States   Costa Mesa   \n",
       "9991    DB-13060       Dave Brooks   Consumer  United States   Costa Mesa   \n",
       "9992    DB-13060       Dave Brooks   Consumer  United States   Costa Mesa   \n",
       "9993    CC-12220      Chris Cortes   Consumer  United States  Westminster   \n",
       "\n",
       "      ... Postal Code  Region       Product ID         Category Sub-Category  \\\n",
       "9984  ...       11561    East  OFF-LA-10003766  Office Supplies       Labels   \n",
       "9985  ...       11561    East  OFF-SU-10000898  Office Supplies     Supplies   \n",
       "9986  ...       90008    West  TEC-AC-10003399       Technology  Accessories   \n",
       "9987  ...       30605   South  TEC-AC-10001539       Technology  Accessories   \n",
       "9988  ...       30605   South  TEC-PH-10004006       Technology       Phones   \n",
       "9989  ...       33180   South  FUR-FU-10001889        Furniture  Furnishings   \n",
       "9990  ...       92627    West  FUR-FU-10000747        Furniture  Furnishings   \n",
       "9991  ...       92627    West  TEC-PH-10003645       Technology       Phones   \n",
       "9992  ...       92627    West  OFF-PA-10004041  Office Supplies        Paper   \n",
       "9993  ...       92683    West  OFF-AP-10002684  Office Supplies   Appliances   \n",
       "\n",
       "                                           Product Name    Sales  Quantity  \\\n",
       "9984                     Self-Adhesive Removable Labels   31.500        10   \n",
       "9985  Acme Hot Forged Carbon Steel Scissors with Nic...   55.600         4   \n",
       "9986  Memorex Mini Travel Drive 64 GB USB 2.0 Flash ...   36.240         1   \n",
       "9987  Logitech G430 Surround Sound Gaming Headset wi...   79.990         1   \n",
       "9988                    Panasonic KX - TS880B Telephone  206.100         5   \n",
       "9989                             Ultra Door Pull Handle   25.248         3   \n",
       "9990  Tenex B1-RE Series Chair Mats for Low Pile Car...   91.960         2   \n",
       "9991                              Aastra 57i VoIP phone  258.576         2   \n",
       "9992  It's Hot Message Books with Stickers, 2 3/4\" x 5\"   29.600         4   \n",
       "9993  Acco 7-Outlet Masterpiece Power Center, Wihtou...  243.160         2   \n",
       "\n",
       "      Discount   Profit  \n",
       "9984       0.0  15.1200  \n",
       "9985       0.0  16.1240  \n",
       "9986       0.0  15.2208  \n",
       "9987       0.0  28.7964  \n",
       "9988       0.0  55.6470  \n",
       "9989       0.2   4.1028  \n",
       "9990       0.0  15.6332  \n",
       "9991       0.2  19.3932  \n",
       "9992       0.0  13.3200  \n",
       "9993       0.0  72.9480  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b97e88ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9994 entries, 0 to 9993\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Row ID         9994 non-null   int64         \n",
      " 1   Order ID       9994 non-null   object        \n",
      " 2   Order Date     9994 non-null   datetime64[ns]\n",
      " 3   Ship Date      9994 non-null   datetime64[ns]\n",
      " 4   Ship Mode      9994 non-null   object        \n",
      " 5   Customer ID    9994 non-null   object        \n",
      " 6   Customer Name  9994 non-null   object        \n",
      " 7   Segment        9994 non-null   object        \n",
      " 8   Country        9994 non-null   object        \n",
      " 9   City           9994 non-null   object        \n",
      " 10  State          9994 non-null   object        \n",
      " 11  Postal Code    9994 non-null   int64         \n",
      " 12  Region         9994 non-null   object        \n",
      " 13  Product ID     9994 non-null   object        \n",
      " 14  Category       9994 non-null   object        \n",
      " 15  Sub-Category   9994 non-null   object        \n",
      " 16  Product Name   9994 non-null   object        \n",
      " 17  Sales          9994 non-null   float64       \n",
      " 18  Quantity       9994 non-null   int64         \n",
      " 19  Discount       9994 non-null   float64       \n",
      " 20  Profit         9994 non-null   float64       \n",
      "dtypes: datetime64[ns](2), float64(3), int64(3), object(13)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "40c9b2a2",
   "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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>...</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>793</td>\n",
       "      <td>793</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>531</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1862</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>1850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CA-2017-100111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>WB-21850</td>\n",
       "      <td>William Brown</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Staple envelope</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5968</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>5191</td>\n",
       "      <td>9994</td>\n",
       "      <td>915</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3203</td>\n",
       "      <td>19</td>\n",
       "      <td>6026</td>\n",
       "      <td>1523</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4997.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-30 00:07:12.259355648</td>\n",
       "      <td>2016-05-03 23:06:58.571142912</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>55190.379428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>229.858001</td>\n",
       "      <td>3.789574</td>\n",
       "      <td>0.156203</td>\n",
       "      <td>28.656896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-01-03 00:00:00</td>\n",
       "      <td>2014-01-07 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1040.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.444000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6599.978000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2499.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-05-23 00:00:00</td>\n",
       "      <td>2015-05-27 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>23223.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.280000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.728750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4997.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-26 00:00:00</td>\n",
       "      <td>2016-06-29 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56430.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.490000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>8.666500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7495.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-05-14 00:00:00</td>\n",
       "      <td>2017-05-18 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>90008.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209.940000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>29.364000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9994.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-30 00:00:00</td>\n",
       "      <td>2018-01-05 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>99301.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22638.480000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>8399.976000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2885.163629</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>32063.693350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623.245101</td>\n",
       "      <td>2.225110</td>\n",
       "      <td>0.206452</td>\n",
       "      <td>234.260108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Row ID        Order ID                     Order Date  \\\n",
       "count   9994.000000            9994                           9994   \n",
       "unique          NaN            5009                            NaN   \n",
       "top             NaN  CA-2017-100111                            NaN   \n",
       "freq            NaN              14                            NaN   \n",
       "mean    4997.500000             NaN  2016-04-30 00:07:12.259355648   \n",
       "min        1.000000             NaN            2014-01-03 00:00:00   \n",
       "25%     2499.250000             NaN            2015-05-23 00:00:00   \n",
       "50%     4997.500000             NaN            2016-06-26 00:00:00   \n",
       "75%     7495.750000             NaN            2017-05-14 00:00:00   \n",
       "max     9994.000000             NaN            2017-12-30 00:00:00   \n",
       "std     2885.163629             NaN                            NaN   \n",
       "\n",
       "                            Ship Date       Ship Mode Customer ID  \\\n",
       "count                            9994            9994        9994   \n",
       "unique                            NaN               4         793   \n",
       "top                               NaN  Standard Class    WB-21850   \n",
       "freq                              NaN            5968          37   \n",
       "mean    2016-05-03 23:06:58.571142912             NaN         NaN   \n",
       "min               2014-01-07 00:00:00             NaN         NaN   \n",
       "25%               2015-05-27 00:00:00             NaN         NaN   \n",
       "50%               2016-06-29 00:00:00             NaN         NaN   \n",
       "75%               2017-05-18 00:00:00             NaN         NaN   \n",
       "max               2018-01-05 00:00:00             NaN         NaN   \n",
       "std                               NaN             NaN         NaN   \n",
       "\n",
       "        Customer Name   Segment        Country           City  ...  \\\n",
       "count            9994      9994           9994           9994  ...   \n",
       "unique            793         3              1            531  ...   \n",
       "top     William Brown  Consumer  United States  New York City  ...   \n",
       "freq               37      5191           9994            915  ...   \n",
       "mean              NaN       NaN            NaN            NaN  ...   \n",
       "min               NaN       NaN            NaN            NaN  ...   \n",
       "25%               NaN       NaN            NaN            NaN  ...   \n",
       "50%               NaN       NaN            NaN            NaN  ...   \n",
       "75%               NaN       NaN            NaN            NaN  ...   \n",
       "max               NaN       NaN            NaN            NaN  ...   \n",
       "std               NaN       NaN            NaN            NaN  ...   \n",
       "\n",
       "         Postal Code  Region       Product ID         Category Sub-Category  \\\n",
       "count    9994.000000    9994             9994             9994         9994   \n",
       "unique           NaN       4             1862                3           17   \n",
       "top              NaN    West  OFF-PA-10001970  Office Supplies      Binders   \n",
       "freq             NaN    3203               19             6026         1523   \n",
       "mean    55190.379428     NaN              NaN              NaN          NaN   \n",
       "min      1040.000000     NaN              NaN              NaN          NaN   \n",
       "25%     23223.000000     NaN              NaN              NaN          NaN   \n",
       "50%     56430.500000     NaN              NaN              NaN          NaN   \n",
       "75%     90008.000000     NaN              NaN              NaN          NaN   \n",
       "max     99301.000000     NaN              NaN              NaN          NaN   \n",
       "std     32063.693350     NaN              NaN              NaN          NaN   \n",
       "\n",
       "           Product Name         Sales     Quantity     Discount       Profit  \n",
       "count              9994   9994.000000  9994.000000  9994.000000  9994.000000  \n",
       "unique             1850           NaN          NaN          NaN          NaN  \n",
       "top     Staple envelope           NaN          NaN          NaN          NaN  \n",
       "freq                 48           NaN          NaN          NaN          NaN  \n",
       "mean                NaN    229.858001     3.789574     0.156203    28.656896  \n",
       "min                 NaN      0.444000     1.000000     0.000000 -6599.978000  \n",
       "25%                 NaN     17.280000     2.000000     0.000000     1.728750  \n",
       "50%                 NaN     54.490000     3.000000     0.200000     8.666500  \n",
       "75%                 NaN    209.940000     5.000000     0.200000    29.364000  \n",
       "max                 NaN  22638.480000    14.000000     0.800000  8399.976000  \n",
       "std                 NaN    623.245101     2.225110     0.206452   234.260108  \n",
       "\n",
       "[11 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all', datetime_is_numeric=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694cde14",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82456739",
   "metadata": {},
   "source": [
    "1. Check & Clean Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35dc16a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1738a24",
   "metadata": {},
   "source": [
    "## Performace of Cohort Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f27748c8",
   "metadata": {},
   "source": [
    "1. Leave only the columns necessary for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "973b3fe1",
   "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>Order Date</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Date Customer ID    Profit\n",
       "0 2016-11-08    CG-12520   41.9136\n",
       "1 2016-11-08    CG-12520  219.5820\n",
       "2 2016-06-12    DV-13045    6.8714\n",
       "3 2015-10-11    SO-20335 -383.0310\n",
       "4 2015-10-11    SO-20335    2.5164"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit = data[[\"Order Date\", \"Customer ID\", \"Profit\"]]\n",
    "profit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dff81f7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9994 entries, 0 to 9993\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   Order Date   9994 non-null   datetime64[ns]\n",
      " 1   Customer ID  9994 non-null   object        \n",
      " 2   Profit       9994 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 234.4+ KB\n"
     ]
    }
   ],
   "source": [
    "profit.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6691916b",
   "metadata": {},
   "source": [
    "2. Sorting by order date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d09036b6",
   "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>Order Date</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>DP-13000</td>\n",
       "      <td>5.5512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>4.2717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>-64.7748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>-5.4870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>MB-18085</td>\n",
       "      <td>4.8840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Date Customer ID   Profit\n",
       "0 2014-01-03    DP-13000   5.5512\n",
       "1 2014-01-04    PO-19195   4.2717\n",
       "2 2014-01-04    PO-19195 -64.7748\n",
       "3 2014-01-04    PO-19195  -5.4870\n",
       "4 2014-01-05    MB-18085   4.8840"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit.sort_values(by=['Order Date'], inplace = True, ignore_index = True)\n",
    "profit.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26be351f",
   "metadata": {},
   "source": [
    "3. Add year of order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f95e045e",
   "metadata": {},
   "outputs": [],
   "source": [
    "profit['Order_Year'] = profit['Order Date'].dt.to_period(\"Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ccdc0be4",
   "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>Order Date</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Order_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>DP-13000</td>\n",
       "      <td>5.5512</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>4.2717</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>-64.7748</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>-5.4870</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>MB-18085</td>\n",
       "      <td>4.8840</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Date Customer ID   Profit Order_Year\n",
       "0 2014-01-03    DP-13000   5.5512       2014\n",
       "1 2014-01-04    PO-19195   4.2717       2014\n",
       "2 2014-01-04    PO-19195 -64.7748       2014\n",
       "3 2014-01-04    PO-19195  -5.4870       2014\n",
       "4 2014-01-05    MB-18085   4.8840       2014"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89339c8a",
   "metadata": {},
   "source": [
    "3. Add month of purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11cdf69f",
   "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>Order Date</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Order_Year</th>\n",
       "      <th>Cohort_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>DP-13000</td>\n",
       "      <td>5.5512</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>4.2717</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>-64.7748</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>-5.4870</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>MB-18085</td>\n",
       "      <td>4.8840</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Date Customer ID   Profit Order_Year Cohort_Year\n",
       "0 2014-01-03    DP-13000   5.5512       2014        2014\n",
       "1 2014-01-04    PO-19195   4.2717       2014        2014\n",
       "2 2014-01-04    PO-19195 -64.7748       2014        2014\n",
       "3 2014-01-04    PO-19195  -5.4870       2014        2014\n",
       "4 2014-01-05    MB-18085   4.8840       2014        2014"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts_year = profit.groupby('Customer ID')['Order_Year']\n",
    "profit['Cohort_Year'] = cohorts_year.transform('min')\n",
    "profit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a980ba24",
   "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>Order Date</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Order_Year</th>\n",
       "      <th>Cohort_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>JM-15580</td>\n",
       "      <td>-0.6048</td>\n",
       "      <td>2017</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>PO-18865</td>\n",
       "      <td>19.7910</td>\n",
       "      <td>2017</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>PO-18865</td>\n",
       "      <td>2.7279</td>\n",
       "      <td>2017</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>EB-13975</td>\n",
       "      <td>4.5188</td>\n",
       "      <td>2017</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>PO-18865</td>\n",
       "      <td>12.1176</td>\n",
       "      <td>2017</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Order Date Customer ID   Profit Order_Year Cohort_Year\n",
       "9989 2017-12-30    JM-15580  -0.6048       2017        2016\n",
       "9990 2017-12-30    PO-18865  19.7910       2017        2014\n",
       "9991 2017-12-30    PO-18865   2.7279       2017        2014\n",
       "9992 2017-12-30    EB-13975   4.5188       2017        2016\n",
       "9993 2017-12-30    PO-18865  12.1176       2017        2014"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0529cf4c",
   "metadata": {},
   "source": [
    "4. Number of customers in every cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e9881ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cohort_Year\n",
       "2014    595\n",
       "2015    136\n",
       "2016     51\n",
       "2017     11\n",
       "Freq: A-DEC, Name: Customer ID, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts_customers = profit.groupby('Cohort_Year')['Customer ID'].nunique()\n",
    "cohorts_customers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "792407eb",
   "metadata": {},
   "source": [
    "5. Profit in every cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "610668be",
   "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>Cohort_Year</th>\n",
       "      <th>Order_Year</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>49543.9741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>2015</td>\n",
       "      <td>47388.9067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>2016</td>\n",
       "      <td>62249.5166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>2017</td>\n",
       "      <td>60616.1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>14229.6970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>2016</td>\n",
       "      <td>11605.6579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>2017</td>\n",
       "      <td>20557.6347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>7939.9998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>2017</td>\n",
       "      <td>10459.2809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>1806.1675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cohort_Year Order_Year      Profit\n",
       "0        2014       2014  49543.9741\n",
       "1        2014       2015  47388.9067\n",
       "2        2014       2016  62249.5166\n",
       "3        2014       2017  60616.1865\n",
       "4        2015       2015  14229.6970\n",
       "5        2015       2016  11605.6579\n",
       "6        2015       2017  20557.6347\n",
       "7        2016       2016   7939.9998\n",
       "8        2016       2017  10459.2809\n",
       "9        2017       2017   1806.1675"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts_profit = profit.groupby(['Cohort_Year', 'Order_Year'])['Profit'].sum().reset_index()\n",
    "cohorts_profit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34057c9d",
   "metadata": {},
   "source": [
    "6. calculate cumulative profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d38907bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "cohorts_profit['Sum_profit'] = 0\n",
    "lst_cohort = cohorts_profit.Cohort_Year.unique()\n",
    "\n",
    "for cohort in lst_cohort:\n",
    "    for row in range(len(cohorts_profit)):\n",
    "        if cohorts_profit.Cohort_Year[row] == cohort:\n",
    "            if row == 0 or row == 4 or row == 7 or row == 9:\n",
    "                cohorts_profit['Sum_profit'][row] = cohorts_profit['Profit'][row]\n",
    "            else:\n",
    "                cohorts_profit['Sum_profit'][row] = cohorts_profit['Sum_profit'][row-1]+cohorts_profit['Profit'][row]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39b93434",
   "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>Cohort_Year</th>\n",
       "      <th>Order_Year</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Sum_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>49543.9741</td>\n",
       "      <td>49543.9741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>2015</td>\n",
       "      <td>47388.9067</td>\n",
       "      <td>96932.8808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>2016</td>\n",
       "      <td>62249.5166</td>\n",
       "      <td>159182.3974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>2017</td>\n",
       "      <td>60616.1865</td>\n",
       "      <td>219798.5839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>14229.6970</td>\n",
       "      <td>14229.6970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>2016</td>\n",
       "      <td>11605.6579</td>\n",
       "      <td>25835.3549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>2017</td>\n",
       "      <td>20557.6347</td>\n",
       "      <td>46392.9896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>7939.9998</td>\n",
       "      <td>7939.9998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>2017</td>\n",
       "      <td>10459.2809</td>\n",
       "      <td>18399.2807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>1806.1675</td>\n",
       "      <td>1806.1675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cohort_Year Order_Year      Profit   Sum_profit\n",
       "0        2014       2014  49543.9741   49543.9741\n",
       "1        2014       2015  47388.9067   96932.8808\n",
       "2        2014       2016  62249.5166  159182.3974\n",
       "3        2014       2017  60616.1865  219798.5839\n",
       "4        2015       2015  14229.6970   14229.6970\n",
       "5        2015       2016  11605.6579   25835.3549\n",
       "6        2015       2017  20557.6347   46392.9896\n",
       "7        2016       2016   7939.9998    7939.9998\n",
       "8        2016       2017  10459.2809   18399.2807\n",
       "9        2017       2017   1806.1675    1806.1675"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts_profit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee37f41",
   "metadata": {},
   "source": [
    "7. Add index of year for cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87111dd8",
   "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>Cohort_Year</th>\n",
       "      <th>Order_Year</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Sum_profit</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>49543.9741</td>\n",
       "      <td>49543.9741</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>2015</td>\n",
       "      <td>47388.9067</td>\n",
       "      <td>96932.8808</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>2016</td>\n",
       "      <td>62249.5166</td>\n",
       "      <td>159182.3974</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>2017</td>\n",
       "      <td>60616.1865</td>\n",
       "      <td>219798.5839</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>14229.6970</td>\n",
       "      <td>14229.6970</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>2016</td>\n",
       "      <td>11605.6579</td>\n",
       "      <td>25835.3549</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>2017</td>\n",
       "      <td>20557.6347</td>\n",
       "      <td>46392.9896</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>7939.9998</td>\n",
       "      <td>7939.9998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>2017</td>\n",
       "      <td>10459.2809</td>\n",
       "      <td>18399.2807</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>1806.1675</td>\n",
       "      <td>1806.1675</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cohort_Year Order_Year      Profit   Sum_profit  Index\n",
       "0        2014       2014  49543.9741   49543.9741      0\n",
       "1        2014       2015  47388.9067   96932.8808      1\n",
       "2        2014       2016  62249.5166  159182.3974      2\n",
       "3        2014       2017  60616.1865  219798.5839      3\n",
       "4        2015       2015  14229.6970   14229.6970      0\n",
       "5        2015       2016  11605.6579   25835.3549      1\n",
       "6        2015       2017  20557.6347   46392.9896      2\n",
       "7        2016       2016   7939.9998    7939.9998      0\n",
       "8        2016       2017  10459.2809   18399.2807      1\n",
       "9        2017       2017   1806.1675    1806.1675      0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts_profit['Index'] = [0, 1, 2, 3, 0, 1, 2, 0, 1, 0]\n",
    "cohorts_profit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29e5fc3",
   "metadata": {},
   "source": [
    "8. Create table for cohort analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "33e6efbb",
   "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>Index</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cohort_Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>49543.9741</td>\n",
       "      <td>96932.8808</td>\n",
       "      <td>159182.3974</td>\n",
       "      <td>219798.5839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>14229.6970</td>\n",
       "      <td>25835.3549</td>\n",
       "      <td>46392.9896</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>7939.9998</td>\n",
       "      <td>18399.2807</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1806.1675</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Index                 0           1            2            3\n",
       "Cohort_Year                                                  \n",
       "2014         49543.9741  96932.8808  159182.3974  219798.5839\n",
       "2015         14229.6970  25835.3549   46392.9896       0.0000\n",
       "2016          7939.9998  18399.2807       0.0000       0.0000\n",
       "2017          1806.1675      0.0000       0.0000       0.0000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_by_year = cohorts_profit.pivot(index='Cohort_Year',columns='Index',values='Sum_profit').fillna(0)\n",
    "cohort_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0073f38a",
   "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>Index</th>\n",
       "      <th>N_customers</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cohort_Year</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>2014</th>\n",
       "      <td>595</td>\n",
       "      <td>49543.9741</td>\n",
       "      <td>96932.8808</td>\n",
       "      <td>159182.3974</td>\n",
       "      <td>219798.5839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>136</td>\n",
       "      <td>14229.6970</td>\n",
       "      <td>25835.3549</td>\n",
       "      <td>46392.9896</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>51</td>\n",
       "      <td>7939.9998</td>\n",
       "      <td>18399.2807</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>11</td>\n",
       "      <td>1806.1675</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Index        N_customers           0           1            2            3\n",
       "Cohort_Year                                                               \n",
       "2014                 595  49543.9741  96932.8808  159182.3974  219798.5839\n",
       "2015                 136  14229.6970  25835.3549   46392.9896       0.0000\n",
       "2016                  51   7939.9998  18399.2807       0.0000       0.0000\n",
       "2017                  11   1806.1675      0.0000       0.0000       0.0000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a column with the number of people in each cohort\n",
    "cohort_by_year.insert(0, 'N_customers', cohorts_customers)\n",
    "cohort_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe35b5b9",
   "metadata": {},
   "source": [
    "## LTV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "743875cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create LTV table\n",
    "LTV = pd.DataFrame()\n",
    "\n",
    "for col in cohort_by_year:\n",
    "    if col != 'N_customers':\n",
    "        LTV[col] = round((cohort_by_year[col]/cohort_by_year['N_customers']), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "94314fc2",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cohort_Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>83.27</td>\n",
       "      <td>162.91</td>\n",
       "      <td>267.53</td>\n",
       "      <td>369.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>104.63</td>\n",
       "      <td>189.97</td>\n",
       "      <td>341.12</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>155.69</td>\n",
       "      <td>360.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>164.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0       1       2       3\n",
       "Cohort_Year                                \n",
       "2014          83.27  162.91  267.53  369.41\n",
       "2015         104.63  189.97  341.12    0.00\n",
       "2016         155.69  360.77    0.00    0.00\n",
       "2017         164.20    0.00    0.00    0.00"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LTV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e92dc32",
   "metadata": {},
   "source": [
    "### LTV Heatmap "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c398b71f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAAHDCAYAAABvd1Z3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABo7ElEQVR4nO3deVyU1f4H8M8My7CDgGyyqKgsAi6kSO6K4pK5YLmlaKap6E0tNcq1bqJlpqZSvxaXEjVLNPfcTcUNxQUQ3FHZQRbZh5nfH9xGHgEDHHlYPm9f87rMOWee5zvc0fhyvucciVKpVIKIiIiIiOh/pGIHQEREREREtQuTBCIiIiIiEmCSQEREREREAkwSiIiIiIhIgEkCEREREREJMEkgIiIiIiIBJglERERERCTAJIGIiIiIiASYJBARERERkQCTBCIiIiIiEmCSQEQkgo0bN0IikeDSpUuqNolEUqnHypUrIZFIcOTIkQqv/8MPP0AikeDPP/+sibdDRET1jKbYARARUYlffvlF8Hzz5s04fPhwmfauXbtizpw5CAkJgY+PT7nXCgkJgZmZGfr37//K4iUiovqLSQIRUS3xzjvvCJ6fO3cOhw8fLtMOAD179sTOnTsRHBwMmUwm6Hv8+DFOnTqFyZMnQ0tL65XGTERE9RPLjYiI6qB33nkHmZmZ2LdvX5m+bdu2QaFQYMyYMSJERkRE9QGTBCKiOmjYsGHQ0dFBSEhImb6QkBA4ODigc+fOIkRGRET1AZMEIqI6yMjICIMGDcK+ffuQlZWlao+JicHly5cxevRoSCQSESMkIqK6jEkCEVEd9c477yA/Px87d+5Utf0zs8BSIyIiehlMEoiI6qj+/fvD1NRUUHK0detWtGnTBq1btxYxMiIiquuYJBAR1VFaWlp4++23cezYMSQlJeHixYu4desWZxGIiOilMUkgIqrDxowZg+LiYmzfvh0hISGQSCQYNWqU2GEREVEdx3MSiIjqsM6dO6Np06b49ddf8ejRI3Tv3h22trZih0VERHUckwQiIhH9/PPPOHjwYJn2Dz74oFKvl0gkGD16NJYuXQoA+Oyzz9QaHxERNUxMEoiIRBQcHFxu+/jx4yt9jTFjxmDp0qWQyWQYPny4miIjIqKGTKJUKpViB0FERERERLUHFy4TEREREZEAkwQiIiIiIhJgkkBERERERAJMEoiIiIiISIBJAhERERERCTBJICIiIiIiASYJREREREQkUC8PU7ucdk7sEKiBOBp3UuwQqIHY8PcpsUOgBiJ6z1WxQ6AGQnn4kdghVEjSx1Zt16rN7/NFOJNAREREREQC9XImgYiIiIio2iQSsSMQHZMEIiIiIqLSWGvDbwEREREREQlxJoGIiIiIqDSWGzFJICIiIiISYI7AciMiIiIiIhLiTAIRERERUWksN2KSQEREREQkwFobfguIiIiIiEiIMwlERERERKWx3IhJAhERERGRAHMElhsREREREZEQZxKIiIiIiEqTciqBSQIRERERUWnMEVhuRERERERUGwQHB8PDwwNGRkYwMjKCt7c3Dhw4oOrv0aMHJBKJ4DFlyhTBNeLi4jBw4EDo6enBwsICc+bMgVwur3IsnEkgIiIiIipNpN2NbG1tsWzZMrRs2RJKpRKbNm3C4MGDceXKFbRu3RoAMGnSJHz22Weq1+jp6am+Li4uxsCBA2FlZYWzZ88iISEB48aNg5aWFpYuXVqlWJgkEBERERGVJlK50aBBgwTPv/jiCwQHB+PcuXOqJEFPTw9WVlblvv6vv/5CVFQUjhw5AktLS7Rt2xaff/455s2bh8WLF0NbW7vSsbDciIiIiIiolikuLsa2bduQk5MDb29vVfuWLVtgbm4ONzc3BAYGIjc3V9UXFhYGd3d3WFpaqtp8fX2RlZWFyMjIKt2fMwlERERERKWpcXejgoICFBQUCNpkMhlkMlm5469fvw5vb2/k5+fDwMAAoaGhcHV1BQCMHj0aDg4OsLGxwbVr1zBv3jzExMRg586dAIDExERBggBA9TwxMbFKcTNJICIiIiIqTY3lRkFBQViyZImgbdGiRVi8eHG5452cnBAREYHMzEz8/vvv8Pf3x8mTJ+Hq6orJkyerxrm7u8Pa2hq9e/fGnTt34OjoqL6gwSSBiIiIiOiVCQwMxOzZswVtFc0iAIC2tjZatGgBAPD09MTFixexevVqfP/992XGenl5AQBu374NR0dHWFlZ4cKFC4IxSUlJAFDhOoaKcE0CEREREVFpEonaHjKZTLWl6T+PFyUJz1MoFGXKlf4REREBALC2tgYAeHt74/r160hOTlaNOXz4MIyMjFQlS5XFmQQiIiIiotJEOnE5MDAQ/fv3h729PbKzsxESEoITJ07g0KFDuHPnDkJCQjBgwACYmZnh2rVrmDVrFrp16wYPDw8AQN++feHq6oqxY8fiyy+/RGJiIubPn4+AgIAqJSYAkwQiIiIiolohOTkZ48aNQ0JCAoyNjeHh4YFDhw6hT58+ePjwIY4cOYJVq1YhJycHdnZ28PPzw/z581Wv19DQwN69ezF16lR4e3tDX18f/v7+gnMVKotJAhERERFRaSKdk/DTTz9V2GdnZ4eTJ0/+6zUcHBywf//+l46FSQIRERERUWkinbhcm3DhMhERERERCXAmgYiIiIioNE4kMEkgIiIiIhIQaXej2oTlRkREREREJMCZBCIiIiKi0jiRUDtnEjZu3IjMzEyxwyAiIiKihkiNJy7XVbUySZg8eTLi4+PFDoOIiIiIqEEStdzI1NS03Ha5XA5vb29IpSU5THp6ek2GRUREREQNWa38NXrNEjVJKCoqQvfu3fHWW2+p2pRKJd577z3MnTsXTZo0ETE6IiIiImqQ6nCZkLqImiRcuXIFo0ePxrFjx7Bu3ToYGBgAACZNmoQhQ4bA1dVVzPCIiIiIiBokUSdTWrRogbNnz8LKygpt27bFmTNnxAyHiIiIiKhkdyN1Peoo0bdA1dTUxPLly+Hr64vRo0djzJgxkHCKh4iIiIjEwp9Fa8+yjF69euHy5cu4efMm9PX1oaGhIXZIREREREQNkugzCaWZmZlh586dYodBRERERA1Zrfk1unhqVZLwPLlcjvj4eNjb24sdSq2lKFbg959CcfrQWWSkZaKRuQm6D+yKoePfVJVt/f5jKMKOnEdacho0tTTRzKkpRrw/HC1aO1Z43V2b9+DiiXDExyVAW1sLrdxbYtS0t2HjYA0ASElIwX/8Pir3tR/8NwCdenVU/5ulGpUQnYhre64j7V4qcp/kwefD3mjawUEw5snjDFwMuYiEqEQoFUqYNDGBz+xeMDA3QP7TAlzecRmPrz3G09Qc6BjpwKGDA157uz209bQrvG9uRh4uhlzE4+uPUZBTCGsXK3iP7wRja2PVmJtHbuL2mbtIu5+GorwijP1pDGT6slf2vaBXa9Jrb8HH8XU0b2SLfHkhIhKi8fWZDbif8Vgwro2VMz7wHgcPKycolArcTLmLSbsWoKC4EB2auGOT37Jyr//2tpm4kXyr3L6Nw4LQ0dZD0Lb9+n4sOb4OAGCsY4gvfefAyawpTHSNkJabgWN3z2FV2CbkFOap4d1TTZryxlhMHTQOTS1tAQCRD2Lx2a+rcPDicdWYTi7t8cWEefBybodiRTEi7kTCN/Ad5BfmAwDatXDD8vc+QQenNihWKPDH3/sx+7slyMnPrVQMwR8EYcobYzFz/SKsDv1J1f7J6BkY2LE32jq2RqG8EI2GtlbjO6cqY7lR7U4SIiMj0b59exQXF4sdSq3156/7cDj0GKbOnwS75k1wN/o+vlv6I/T0ddHv7b4AAGt7K4z/cCwsbBqjsKAQB7YfwtKZX2HVb1/CqJFRudeNvhKDvn690dylGRTFCmz77ncEzfwKX4UEQUdXBjMLMwTvWS14zdHdJ7A35ADadvIo95pUt8jzi2DmYAqnHi1xZOWxMv1ZiVnYu2gfWvVshfbD20NbVwtPHmVAQ6ukVDD3SS5yn+Si4zsd0aiJCZ6mPsXpH88iNz0XPrN7lXtPpVKJI18fgVRDij4f+UBLVxs39t3AgS8Owm/FMGjpaJXEVlgMu7ZNYNe2CS5uDX913wSqEa81ccfWa/twIykWGlINzPT2x49D/otBv05BnrwAQEmC8H+DP8MPl3Zg6cnvIFcUw7lxMyigAABEJESj24/vCK47o9M76GTXtsIE4R+/3TiIted+VT3Pk+ervlYqlTh29xzWhG3Gk7xM2BvbYH6PqTDWMcTcQ1+p61tANeRRagI+/ikItx7fgwSAf9+3sHvJT2g3tR+iHsSik0t7HAz6FUFb12HGugWQF8vRprkrFMqSz5m1mSWOLN+G7Sf/xPS182GkZ4hV0xZj45xv8Nbn7//r/Yd07odOLu3xODWxTJ+2pjZ2nNqLsOhwTOw3Ut1vnajKanWSQP8u9votvNa1Pdp3bgsAaGzdGGePnMPtqLuqMZ37egte885/RuP4nlOIu/MQbq+V/5uKwG+EswRT57+H9wfOwL2b9+DSzhlSDSlMzEwEYy6eDEenXh2ho6fz8m+MRGfXzg527ewq7L+0PRx2bW3hNaaDqs3I6lnSaWrXCD6zewv6XhvpiRNrT0JRrIBUo+xcblZCFpJvpcDvq6FoZNcIANB54uvYMmUr7py9C+deTgAAtwEln9v4yISXe5NUK7y/e6Hg+SdHVuLMpK1wtWiB8PhIAMDH3Sbh16t/4sfwHapxpWcaihRypOY+UT3XlGqgV/NO2HJ1z7/eP78oX/Da0rIKnmL79f2q5/HZKdh2fR8mtPer3JujWmXvuSOC5/M3fImpb4xDJ5f2iHoQi2+mLsaa0J+xfPs61ZjYR8/+e/qGlw+KiosQ8O2nUCqVAIApqwJx/YcjcLRpijvx9yu8t42ZFb4N+By+gWOw77+byvQv3vw1gJLEhWoBTiSImyS0b9/+hf15eZzK/Tet3Fvi6O4TSIhLhLW9FR7cisPNq7EY+59R5Y6XF8lxbPdx6Bnowb5F5cu4cnNK/r8wMDIot//uzXt4cCsO7340rupvguocpUKJh1cewmOQBw4sPYS0+2kwbGyANkPalClJKq0wtxDautrlJggAUCwvmTX8ZzYCACRSCTQ0NZB0M0mVJFD9ZqitDwDIzH8KADDVNUYbK2fsvXkCW95aATtjK9x78girz27G5YSocq/Rs5kXTHQMERp9+F/v94ZzTwxy7onU3Cc4ce8Cgi9sQ/7/ZjCe11jfFD6Or+PS4xvVfHdUW0ilUrzV7Q3o6+giLCocjU3M0MmlPbYcDcWZVbvgaOOAmw/v4NOfl+NM5EUAgExLG4VFRaoEAQDy/leG1MWtQ4VJgkQiwS/zVuOrHd8h6kHsK39vpAZSZgmiJglRUVEYOXIkmjVrVm5/QkICYmP5l+lF3hw7EHk5efhw1MeQSqVQKBR4+30/dPF9XTDu8pkIrFm4HoX5hTAxM8Ynq+bAyMSwUvdQKBTYvGoLnDxaws7Rttwxx/ecQpOmNmjl3vKl3xPVfnlZeSjKl+Pqn9fg+XZ7dBz9Gh5dfYQjK49i4IL+sHa1LvOa/Kx8ROyMgFPvVhVe18TGBAbm+ri47RK6vNcZmjqauLEvEjnpOcjN4C8NGgIJJPi422SEx0fidvoDAICtsRUAIMBrNL46/RNupt7Fm8698fOwpRj86zQ8yIwvcx2/1n1xJu4ykp6mvfB++2JOIj47Gck5aXAyb4bZnSegqYktPtj/hWDcV75z0au5F3S1dHDs7nksOLq6gitSbefW1Blha3ZDR1uGp3k5GLpkEqLjbsHLpeQXl4vHzcZH//c5Im5HYlyf4Tj65Ta4TfbB7cf3cCziDFZOWYiP3pqC1aE/QV9HD8smBgIArE0tKrznvBHTIFfIsabUGgSi2k7UJMHNzQ1eXl6YOnVquf0RERH44YcfXniNgoICFBQIf+NTWFAIbVnFCyPrk3NHL+D0X2GYvngKbJs3wYPYOGxevQWNzBuh+4AuqnGu7V2wbNPnyM7IxrE/T2L1gnX4/IdFMDYtf01CaRu+3oyHdx9j8XeflttfWFCIs4fPYej4N9X2vqh2+195Lhw87eE+0A0AYNbUDEmxyYg+crNMklCYW4hDy/+CSRMTeA6veAZRqimFz+zeOPX9afzy3hZIpBI0cbeBbVtboNRv7qj+WtBjKlqaOeCd3+eo2qT/22bktxsHEBpdUi4SnXIXnezaYFjrPvjmrLB0w9LADJ3t22P2gfIXMpe2I/Kg6utbaQ+QkpOODcOCYGdshYeZz+rGl//9A9ZfCEFTkyaY9bo/5nWdhM9PrH+p90riiHl0B22n+MJY3xDDuw7EpjnfoPuHwyH930LV7/f9io2HfgMARNyJRO92XfCu7wh88vMyRD2Ihf+Xs7ByykIETfwYxcXFWLNrAxLTk6Go4N+o9i3d8cHQiWg/rX+NvUdSAy5cFjdJ6Ny5M2JiYirsNzQ0RLdu3V54jaCgICxZskTQNnnORLw/7z21xFjbbVm3HYPHDsTrfToBAOwd7ZCSmIo/N+8VJAk6ujJY2VrCytYSLd1aYNbbc3F870kMGTfohdff8PVmXD5zFYvWfwIzC9Nyx5w/dhEF+QXo1r+z+t4Y1Wo6RjJINCQwsTURtJvYmCAxJknQVphXhINBf0FLVws+H/aGVPPF+8qZNzfHsOVDUJhbiGJ5MXSNdLH70z9h7miu7rdBtcyn3aege7OOGPfHPMEMQEpuOgDgTvpDwfi76Q9hbdC4zHWGuvRBRn42jt87X+UYriWW/DfJ3thGkCSk5j5Bau4T3HvyCJn52fj1ra8QfGFrhWsZqPYqkhepyoIu37qODk5t8MHQiVj2v3UIUQ+EC92j427B3qKJ6vnW47uw9fguWJiYIyc/F0ooMdtvEu4mPCj3fl3dOsLCxBxxW559HjU1NPH1+wsxc9h7aDbWu9zXkciYI4ibJKxe/eLpWkdHRxw/fvyFYwIDAzF79mxBW9TTiJcNrc4ozC8oc0K1VEOq2omhIgqFAvJCeYX9SqUSG1f+gosnw7FgXSAsbMr+h/gfx/eegmeXdhXulET1j4amBho3b4zM+ExBe2ZiJgzNn61bKcwtxMGgQ5BqaqDvnD7Q1K78Pzn/bJOamZCJ1Ltp8HzbUz3BU630afcp8HH0xvg/AvE4S5hoPs5KQtLTVDRt1ETQ3rRRE/x9/1KZaw117YM/bx6DXFH1nfGcGzcHAKTkpFc45p9/c7U1tKp8fap9pBIpZNrauJ/4EI9TE+Fk21zQ38q2OQ5cLPuzSHJGKgBggu8I5BcW4HD43+Ve/5cjf+DIldOCtkNBW/DLkT+w4dB2Nb0LIvWr87sbyWQyyGTC/dG1ixpGqREAtO/SDrs27YGZpRnsmjfB/dgH2L/tEHoM7AoAyM8rwK5Nf8KzSzuYmJkgOzMbf/1xFE9SM+DV69muNP+dsRwdureH7/A+AICfV2zG2cPn8OHyD6Crp4OMtAwAgJ6BnqCUK/FREm5GxGDu18JEjeq+ovwiZCVmqZ5nJ2cj7X4aZAYyGJgbwGOQG46tPgErFytYt7bGo4hHiAt/iIELS6bUC3MLcWDpIcgL5egT0B2FeYUozCsEAOgY6UAqLZlR2DH7D3QY6YmmHZsCAO6euwcdQx0YmOvjycMnCNt4Hg4d7GHb5tkPiLkZucjLyENWUkl8T+KeQEtXC/rmBtAx4HkJdc2CHtMw0Kk7pu/9HDlFeTDXK9nZKrsgBwXFJZ+Zny/vxHSvMYhJvYebKXcx2KU3mjWyxcz9SwXX6mTbBnbGVvg98lCZ+1jom+HnoV8g8PBKXE+KhZ2xFQa26oFT9y8hIz8LTubNMK/bJFx8fB2xafcBAN0cXoOZngmuJ91CblEeWpg5YE6XdxEeH4n47ORX+40htVv67sc4cPE44pIfw1DXAKN7DUGPNt7wDRwDAPjqt2As8f8QV+9GI+JOJPz7DIezXQsM/+zZ9qYBg8fjbOQlPM3LQR/Pbvhq0nx8/FMQMnOe/XsZ/dMJBP68DLvOHER6dgbSszMEcRTJi5CYnizYOcmusQ1MjUxgb9EEGlINtHF0BQDcfny/0mcwkPo8/wvYhqhWJAkXLlxAWFgYEhNLpnatrKzg7e2Njh15INe/GT/rHfz2w05sWLEZmU+y0MjcBL0H94Dfu0MAAFKpBPEPEnBq/2lkZz6FgbEBHJ2bYdH6T2DX/Nki5KTHycjOeKp6fiS0ZF/8zwOCBPeb8ul76P6/BAQATuw9BVOLRvDo6PYK3yWJIeVOKvZ/fkD1/PwvFwAALbu1QPdp3dC0Y1N0fu91XN19DWEbz8HYxhg+s3vByrlkkWnqvTSk3E4BAPw283fBtUeseQuGFiUL5zPjM1GYW6Tqy32Si/ObLyAvMw96jXTRomsLtPNrK3h99OGbuPJHhOr53iUlW1R2m9IVrXpw8XxdM8pjIABgs99yQfsnh7/Brv+tQfglYjdkGtqY13USjHUMEZN6D++FzheUBAHAsNZ9cTk+CveePCpzH02pBpqb2kFHsySRLCqWw9u+Lca1HQxdLR0kPk3B4dtn8N3FbarX5BcXYrhbP8zrNgnaGlpIzE7F4Ttn8eOlHWWuT7WfhYk5Ns9dBWtTC2TmZOPavWj4Bo7BkcslswCrQ3+CjrYOvpmyCKaGJrh6Nwp95o0SlBJ1dGqLJeM+hIGOHm4+vIP3V3+MX4/8IbiPs30LGOtXbnOQf3w2/iOM7/u26nnEd38BAHp8+BZOXgur7lumamKSAEiUSvFWAyYnJ8PPzw9nzpyBvb09LC0tAQBJSUmIi4tD586d8ccff8DCouIdA8pzOe3cqwiXqIyjcSfFDoEaiA1/nxI7BGogovdcFTsEaiCUh8sm87WFxsw2artW8aq6+XfqxSsIX7Fp06ahuLgY0dHRuH//Ps6fP4/z58/j/v37iI6OhkKhQEBAgJghEhEREVEDI5Go71FXiVpudOjQIZw6dQpOTmUPSHJycsKaNWvQo0ePmg+MiIiIiBosaV3+6V5NRJ1JkMlkyMrKqrA/Ozu7zKJkIiIiIiJ6tURNEkaMGAF/f3+EhoYKkoWsrCyEhoZiwoQJGDVqlIgREhEREVFDI5FI1Paoq0QtN1q5ciUUCgVGjhwJuVwObe2SrTULCwuhqamJiRMnYsWKFWKGSEREREQNTF3+4V5dRE0SZDIZgoODsXz5coSHhwu2QPX09ISREQ/nIiIiIiKqabXinAQjIyP07NlT7DCIiIiIiDiTAJHXJABAXl4eTp8+jaioqDJ9+fn52Lx5swhREREREVFDxS1QRU4SYmNj4eLigm7dusHd3R3du3dHfHy8qj8zMxMTJkwQMUIiIiIiooZH1CRh3rx5cHNzQ3JyMmJiYmBoaIguXbogLi5OzLCIiIiIqAHj7kYir0k4e/Ysjhw5AnNzc5ibm2PPnj2YNm0aunbtiuPHj0NfX1/M8IiIiIioAarLP9yri6gzCXl5edDUfJanSCQSBAcHY9CgQejevTtiY2NFjI6IiIiIqGESdSbB2dkZly5dgouLi6B97dq1AIA333xTjLCIiIiIqAGTgDMJos4kDB06FFu3bi23b+3atRg1ahSUSmUNR0VEREREDRnXJIicJAQGBmL//v0V9q9fvx4KhaIGIyIiIiIiItHPSSAiIiIiqk3EOichODgYHh4eMDIygpGREby9vXHgwAFVf35+PgICAmBmZgYDAwP4+fkhKSlJcI24uDgMHDgQenp6sLCwwJw5cyCXy6v8PWCSQERERERUilQiUdujKmxtbbFs2TKEh4fj0qVL6NWrFwYPHozIyEgAwKxZs7Bnzx7s2LEDJ0+eRHx8PIYNG6Z6fXFxMQYOHIjCwkKcPXsWmzZtwsaNG7Fw4cIqfw8kynpY9H857ZzYIVADcTTupNghUAOx4e9TYodADUT0nqtih0ANhPLwI7FDqFCjTzup7VpPvni5n0tNTU3x1VdfYfjw4WjcuDFCQkIwfPhwAMDNmzfh4uKCsLAwdOrUCQcOHMAbb7yB+Ph4WFpaAgC+++47zJs3DykpKdDW1q70fTmTQERERERUSm1YuFxcXIxt27YhJycH3t7eCA8PR1FREXx8fFRjnJ2dYW9vj7CwMABAWFgY3N3dVQkCAPj6+iIrK0s1G1FZom6BSkRERERU26hzV6KCggIUFBQI2mQyGWQyWbnjr1+/Dm9vb+Tn58PAwAChoaFwdXVFREQEtLW1YWJiIhhvaWmJxMREAEBiYqIgQfin/5++quBMAhERERHRKxIUFARjY2PBIygoqMLxTk5OiIiIwPnz5zF16lT4+/sjKiqqBiMuwZkEIiIiIqJS1Hm8QWBgIGbPni1oq2gWAQC0tbXRokULAICnpycuXryI1atXY8SIESgsLERGRoZgNiEpKQlWVlYAACsrK1y4cEFwvX92P/pnTGVxJoGIiIiIqBR1rkmQyWSqLU3/ebwoSXieQqFAQUEBPD09oaWlhaNHj6r6YmJiEBcXB29vbwCAt7c3rl+/juTkZNWYw4cPw8jICK6urlX6HnAmgYiIiIioFggMDET//v1hb2+P7OxshISE4MSJEzh06BCMjY0xceJEzJ49G6ampjAyMsKMGTPg7e2NTp1KdmPq27cvXF1dMXbsWHz55ZdITEzE/PnzERAQUKXEBGCSQEREREQkoM6Fy1WRnJyMcePGISEhAcbGxvDw8MChQ4fQp08fAMA333wDqVQKPz8/FBQUwNfXF+vXr1e9XkNDA3v37sXUqVPh7e0NfX19+Pv747PPPqtyLDwngegl8JwEqik8J4FqCs9JoJpSm89JsFzSVW3XSlr0t9quVZO4JoGIiIiIiARYbkREREREVIpY5Ua1CZMEIiIiIqJSmCOw3IiIiIiIiJ7DmQQiIiIiolJYbsQkgYiIiIhIgEkCy42IiIiIiOg5nEkgIiIiIipFypkEziQQEREREZEQZxKIiIiIiErhRAKTBCIiIiIiAS5cZrkRERERERE9hzMJRERERESlSMCZBCYJRERERESlsNyI5UZERERERPQcziQQEREREZXCmQQmCUREREREAswRWG5ERERERETPqZczCRkFT8QOgRqIR9kpYodADUT0xVtih0ANRN7BWLFDIBIdy43qaZJARERERFRdTBJYbkRERERERM/hTAIRERERUSmcSWCSQEREREQkwByB5UZERERERPQcziQQEREREZXCciMmCUREREREAkwSWG5ERERERETP4UwCEREREVEpnElgkkBEREREJMAcgeVGRERERET0HM4kEBERERGVwnIjJglERERERAJMElhuREREREREz+FMAhERERFRKZxJYJJARERERCTAHIHlRkRERERE9BzOJBARERERlcJyIyYJRERERERCTBJYbkREREREREKcSSAiIiIiKoXlRkwSiIiIiIgEmCOw3IiIiIiIqFYICgpChw4dYGhoCAsLCwwZMgQxMTGCMT169IBEIhE8pkyZIhgTFxeHgQMHQk9PDxYWFpgzZw7kcnmVYuFMAhERERFRKWKVG508eRIBAQHo0KED5HI5PvnkE/Tt2xdRUVHQ19dXjZs0aRI+++wz1XM9PT3V18XFxRg4cCCsrKxw9uxZJCQkYNy4cdDS0sLSpUsrHQuTBCIiIiKiUsRKEg4ePCh4vnHjRlhYWCA8PBzdunVTtevp6cHKyqrca/z111+IiorCkSNHYGlpibZt2+Lzzz/HvHnzsHjxYmhra1cqFpYbERERERHVQpmZmQAAU1NTQfuWLVtgbm4ONzc3BAYGIjc3V9UXFhYGd3d3WFpaqtp8fX2RlZWFyMjISt+bMwlERERERKWocyahoKAABQUFgjaZTAaZTPbC1ykUCsycOROdO3eGm5ubqn306NFwcHCAjY0Nrl27hnnz5iEmJgY7d+4EACQmJgoSBACq54mJiZWOm0kCEREREVEp6qw2CgoKwpIlSwRtixYtwuLFi1/4uoCAANy4cQOnT58WtE+ePFn1tbu7O6ytrdG7d2/cuXMHjo6Oaoub5UZERERERK9IYGAgMjMzBY/AwMAXvmb69OnYu3cvjh8/Dltb2xeO9fLyAgDcvn0bAGBlZYWkpCTBmH+eV7SOoTxMEoiIiIiISnl+i9GXechkMhgZGQkeFZUaKZVKTJ8+HaGhoTh27BiaNWv2r7FGREQAAKytrQEA3t7euH79OpKTk1VjDh8+DCMjI7i6ulb6e8ByIyIiIiKiUsTa3SggIAAhISHYvXs3DA0NVWsIjI2Noaurizt37iAkJAQDBgyAmZkZrl27hlmzZqFbt27w8PAAAPTt2xeurq4YO3YsvvzySyQmJmL+/PkICAj413UQpXEmgYiIiIioFggODkZmZiZ69OgBa2tr1WP79u0AAG1tbRw5cgR9+/aFs7MzPvzwQ/j5+WHPnj2qa2hoaGDv3r3Q0NCAt7c33nnnHYwbN05wrkJliDqTcOHCBXh6ekJDQwMAsHfvXnz11Ve4ffs2rK2t8Z///Afjxo0TM0QiIiIiamDEmklQKpUv7Lezs8PJkyf/9ToODg7Yv3//S8Ui6kyCt7c30tLSAAB79uzB4MGD0bRpU3z66ado164dJk6ciNDQUDFDJCIiIqIGRp1rEuoqUWcSSmdLX375JebOnYugoCBVW7NmzfDll19i6NChYoRHRERERNQg1Zo1CbGxsRg+fLigzc/PDzdv3hQpIiIiIiJqiCQS9T3qKtF3N4qKikJiYiJ0dXWhUCjK9MvlchGiIiIiIqKGqi6XCamL6ElC7969VWVHZ86cQYcOHVR9V65cgb29vVihERERERE1SKImCffu3RM8NzAwEDwvLCzEvHnzajIkIiIiImrgOJMgcpLg4ODwwn5uf0pERERENY1JQi1auFweuVyOuLg4scMgIiIiImpQRF+T8CKRkZFo3749iouLxQ6l1rh19Q4Obz+GuNiHyEzLwvufv4u2XTxU/UqlEns3HMDpfeeQ9zQPzd2aYfSst2Bh27jMtYoK5fhy2ko8uhOPT374CHYtbF9477uR97D7p/24H/0AUqkEti2aYMaXU6At0wYArP/0Bzy6/RjZT55Cz1APzp6tMHTyIJiYG6v3m0A1Ij02DfcO3UHWgwwUZBag3bTXYNnOWtUvz5cjdmc0kq4koiinELrmenDo1Qz2PZqqxuQm5+Dmjig8uZ0OhVyBxq0bw2W0O2RGFR8LL8+X49aum0i6kojC7AIY2RvDZYQbjJuZqMYcnLSn3Nc6DXdBM98WL/3eqWZN6T0KU3uNQtPGJf8GRT66hc92rcPBa6fKjN3/0Y/o36Ybhqyaht3hR1Ttq8fOR+eW7eFm2wrR8XfQbv7gf73vpJ4jMNr7DbRv2hpGugYwed8TmbnZqn4H8yZYMGQaerl2gpVxY8Q/ScavZ//EF7uDUVRcpIZ3TvXBtpDt2PTzJqSmpqGVUyt8/Ok8uHu4iR0WvSROJNTyJIHKKsgvQBNHG7ze3wvfL/y5TP9f247i+M5T8P94DMyszbDn5/1YM/c7LNr4MbS0tQRjQ7//E8bmxnh0J/5f73s38h6+nfc9+o32wYgZwyDVkOLxnXhIJM8mo5zatkS/MX1gbGqEjNRM7PxuN35YvAFz1s586fdNNa+4QA5DWyPYdrbDleBLZfpv/haJ9Jup8HivHXTN9JAWlYKoLdehY6IDi7ZWkBfIcXHVORjZGqHjh94AgFu7b+LytxfQKbALJNLy/wW+sekqnj7OgsfEdpCZ6CD+3CNc/CYMXZb0gE4jXQBAzxV9BK9JuZGMG5uuwrK9dTlXpNruUXoiPv7ta9xKvA+JRAL/LkOxe9Z6tJs/BFGPb6vGzew3HkpUfBrpz6f+gJdjG3jYOVXqvnraOjh47W8cvPY3lo34qEy/s3VzSCVSvP/zQtxOioObbUv8MPG/0JfpYs7W5VV/o1TvHDxwCCuWf435iz6Fu4cbtvwSgqmTp2H3vl0wMzMVOzx6CSw3EjlJaN++/Qv78/LyaiiSusPNyxVuXq7l9imVShz7/RT6j+2LNl3cAQDjA8dg7rAFiDh9HR16Pft+3zgfhehLNzF5ybuIPB/9r/fdsW4Xeg7rBt/RPqo2K3tLwZjeb/VQfW1mZYq+o3zw/YKfUCwvhoamRlXeJtUCjd0t0djdssL+jDtPYPO6HcyczAEAet0c8PDkA2Tcy4BFWytk3E5HXmouOi/oBk3dkgTVfUI7HJ15EGk3U2HuWnZ2q7iwGEmXE9AuoANMW5kBAFq+6YSUq0mIO/EArYY6AwBkxjqC1yVHJMLUyRx6jfXV8t6pZu29clzwfP7v32Bq71Ho1KKtKkloY++CD/u/i9cWDkPi2rNlrvHBL/8FADQ2NK10krD60CYAQHfnjuX2H7r+Nw5d/1v1/F7KQ6zY/xOm9h7NJIEAAL9s/BXD3hqGIcNKZq7mL/oUp07+jV07d2HipHdFjo7o5YiaJERFRWHkyJFo1qxZuf0JCQmIjY2t4ajqrtSENGSlZ8HZs5WqTddAF81cHHAv8r4qSchKz8aWFdsx5b8Toa2jVdHlVLKeZON+9AN09PHEV9NXISU+FVZ2lnjzvYFo4d683NfkZOXg4pFLaN66KROEesrEsRGSIxJh29kOMhMdpMekISfpKZxHtAYAKOQKSCQSSDWfzTZpaEkhkUjw5HZ6uUmCUqGEUqGEhpZwuZRUW4ont9PLjaMgqwAp15PhPqGt+t4ciUYqkeItr/7Ql+kh7NYVAICutg5Cpn2NgE1LkJSZKmp8xnqGSH+aIWoMVDsUFRYhOipakAxIpVJ08vbCtYhrIkZGasGZBHGTBDc3N3h5eWHq1Knl9kdEROCHH36o4ajqrqz0klpao0aGgnbDRobISs8CUDLbsGn5FnR9szMcnOyRlpj2r9dNTSgZs2/TQQybMhh2LZrg3F8XsfrDdVjw88eC9Q6h3/+JE7tOozC/EM1cHTBt6WR1vT2qZVxHueHGL9dwYu4RSDRKjpV0G+uhmgEwad4IGjINxPwRjVZDnaEEEPtHNJQKJQoy88u9pqaOJkwcG+H23lvQtzaEzEiGhAuPkXHnCfQsyp8leHz2ITRlmiw1quPcbFshbNF26GjJ8DQ/F0NXByA6/g4A4Jsxn+DsrSv48/JRUWN0tLDHjD5j8RFnEQjAk4wnKC4uhpm5sKzIzMwM9+7eFycoUhuWG4mcJHTu3BkxMTEV9hsaGqJbt24vvEZBQQEKCgoEbYUFRdCW/ftvyBui4ztPoSC3AP1KlQ39G6WipAa4yxuv4/X+XgAAu5a2iLkci7MHzmHIpEGqsX1G9sLrAzohPekJ9m06iE1BWzAtaBL/stVDD47dR8bdJ2g/vQN0zfSQHpuGqJDrkJnowNy1MbQNZWj7vicit1zHg2P3IJFIYN3RBkb2xi/8PHi82w7XN0XgxJzDkEglMLI3hnXHJsh6kFnu+Mdn4mDt1QQaWpyxqstiEu6h7aeDYaxniOEd+2HT5OXo/sUYtLB0QC/XTmg3f4io8dk0ssTBuT9hx4WD+PHEb6LGQkRUE0RNElavXv3CfkdHRxw/fvyFY4KCgrBkyRJB27jZo+H/4TsvHV9dY2RaMoOQ9SQbxmbPdhTKfpIN2xZNAACxV27hbtR9zOgrXKS37P2V6ODjifGBY8pc19jMCABg3dRK0G5lb4n0pAxBm4GxAQyMDWBpZwErB0t88vZi3Iu6j+atyy8po7qpuLAYsaHRaDetAyw8StYtGNoaIfthFu7/dUdVSmTe2gLdl/ZGYXYBJBpSaOlp4diHf8G6sV6F19az0IfXnM6QF8ghz5NDx0QHEd+HQ6+c16THpiEnMQdtJnu+mjdKNaaouAh3kku2vL58PxIdmrnjA19/5BXmw9HCHhnfCxfP//Gfb/F3zCX0XDr2lcdmbWKB44GbcfbWFUz+ef4rvx/VDY1MGkFDQwNpqcJSyLS0NJibm4kUFalLBXtrNCh1fnejwMBAzJ49W9B2Nu2EOMGIzNzaDEamRoi5fEu1nWleTj7uRT9A18GdAQBvz/DDoIkDVa/JTM3Et3O/w8SF/mjmWv7hdmZWpjA2N0bSw2RBe9KjFLTu6FJhPP/MQMiL5C/1vqj2URYroCxWli3ZlJaUtD1P27Bky9O06FQUZhegcRurMmOepynThKZME0U5hUiNTIbT8LIL9h+djoORgzGM7LjNbn0jlUog09LGop1r8OPJHYK+G0H7MGvLUuy58uJfIqmDTSNLHA/cjPD7kZjwfx+X+/mmhklLWwsuri44f+48evn0BAAoFAqcP3cBI0ePEDk6elmsgKglScKFCxcQFhaGxMREAICVlRW8vb3RsWP5O06UJpPJIJMJ91zXflp/S43y8wqQ8jhF9TwtIR0Pbz+CvqE+TC0bodfwbtj/y19o3KQxzK1Nsefn/TA2N0bb/+12ZGrZSHA9Hd2SMw4aNzFDo8YmAICMlAys+nA9xgeOQVMXB0gkEvQZ0RN7Nx6EraMNbFs0wblDF5EUl4zJiycAAO5F3ceDmIdwdG8GPQM9pMSnYs/PB9DYxhzNXDmLUBfJ8+XITc5RPc9LzUVWXCa09LWga6aHRq3MEPN7NKTaGtA1LSk3ig97BOe3W6te8+hMHAysDKFtqI2Mu08Qve0Gmvo0h4GVgWrMha/DYNnOCg69Sj4nKTdKklF9SwPkpuQgZkcU9K0M0OR1O2F8eUVICk+A01vl7/ZFdcfStz/EgasnEZeWAEMdfYx+fRB6OHvB96t3kZSZWu5i5bi0BNxPeaR67mhhDwMdfVgZm0NXW4Y29iW/wIh6fBtFxUWwaWSJox9vwrjv5+Li3ZJFpZbG5rAybowWliW/IHG3dUJ2fg7i0uLxJCcTNo0sceKTX/AgNR4fbV2OxkbPas/FXkBNtcPY8e9gQeBCtHZzhZu7G37dHIK8vDwMGfrv53QQ1XaiJgnJycnw8/PDmTNnYG9vD0vLkrKFpKQkzJo1C507d8Yff/wBCwsLMcOsVeJi4vDNrHWq57+v3wUA6OTbAf4fj0Hfkb1RmFeIkK+3I/dpHhzdm2PG8vfLnJHwIsXFxUh6mIzCgkJVW+/hPSAvlOP3dbuQk50LW0cb/GfFVDRuUrL9pbaONq78fQ17Nx5AQV4hjM2M4NrRGQPe8YeWdq3IRamKMh9k4OKKMNXzm79FAQBsvG3h8W47tJ3cHrE7b+Laj1dKDlMz00XLIc6w6/5sRionMQexO2/+r18PzQe0RNM+wh2xclNyUPj02WdNnidHbGg08p/kQ1tfC5btrdFyiLNglyQASLgYDyWUsO7Y5FW8fapBFkam2Pz+l7A2sUBmXjauxcXA96t3ceRG2a1OK/Lje1+gh4uX6nnEF7sBAE1n9cSD1MfQ0tCEs01z6Gk/2z53Sq9RWDxshur53wtCAADj/28eNv0dij5ur6OlVVO0tGqKx2uebYUKAJKxrUDUr78vnqQ/wfpvg5GamgYnZyes/34dzFhuVOdJOZMAiVLEudPhw4cjPj4eGzZsgJOTcF/rmJgYvPvuu7CxscGOHTsquEL5jsUfUGeYRBXafVvc3Vao4Vjzw59ih0ANRN7GCLFDoAZCR6Pi9Wli8w2doLZrHRq6QW3Xqkmi/or30KFDOHXqVJkEAQCcnJywZs0a9OjRo+YDIyIiIiJqwERNEmQyGbKysirsz87OLrPegIiIiIjoVZL++5B6r8rfA6VSibi4OOTnl38YUlWMGDEC/v7+CA0NFSQLWVlZCA0NxYQJEzBq1KiXvg8RERERUWVJJRK1PeqqKs8kKJVKtGjRApGRkWjZsuVL3XzlypVQKBQYOXIk5HI5tLVLdtopLCyEpqYmJk6ciBUrVrzUPYiIiIiIqGqqnCRIpVK0bNkSaWlpL50kyGQyBAcHY/ny5QgPDxdsgerp6QkjI6OXuj4RERERUVXxnIRqrklYtmwZ5syZg+DgYLi5ub10EEZGRujZs+dLX4eIiIiI6GXV5TIhdanWuoxx48bhwoULaNOmDXR1dWFqaip4VEVeXh5Onz6NqKioMn35+fnYvHlzdUIkIiIiIqJqqtZMwqpVq9Ry89jYWPTt2xdxcXGQSCTo0qULtm7dChsbGwBAZmYmJkyYgHHjxqnlfkRERERE/4blRtVMEvz9/dVy83nz5sHNzQ2XLl1CRkYGZs6ciS5duuDEiROwt7dXyz2IiIiIiKqCW6Cq4ZyE/Px8FBYWCtoqu+D47NmzOHLkCMzNzWFubo49e/Zg2rRp6Nq1K44fPw59ff2XDY+IiIiIiKqoWolSTk4Opk+fDgsLC+jr66NRo0aCR2Xl5eVBU/NZniKRSBAcHIxBgwahe/fuiI2NrU54RERERETVxnMSqpkkzJ07F8eOHUNwcDBkMhl+/PFHLFmyBDY2NlVaaOzs7IxLly6VaV+7di0GDx6MN998szrhERERERFVm0QiUdujrqpWkrBnzx6sX78efn5+0NTURNeuXTF//nwsXboUW7ZsqfR1hg4diq1bt5bbt3btWowaNQpKpbI6IRIRERERUTVVK0lIT09H8+bNAZSsP0hPTwcAdOnSBadOnar0dQIDA7F///4K+9evXw+FQlGdEImIiIiIqoXlRtVMEpo3b4579+4BKCkZ+u233wCUzDCYmJioLTgiIiIiopomUeOjrqpWkjBhwgRcvXoVAPDxxx9j3bp10NHRwaxZszBnzhy1BkhERERERDWrWlugzpo1S/W1j48Pbt68ifDwcLRo0QIeHh5qC46IiIiIqKbV5TIhdVHLOQkODg5wcHBQRzxERERERKJiklDNcqPi4mJ8/vnnaNKkCQwMDHD37l0AwIIFC/DTTz+pNUAiIiIiIqpZ1UoSvvjiC2zcuBFffvkltLW1Ve1ubm748ccf1RYcEREREVFN4zkJ1UwSNm/ejP/7v//DmDFjoKGhoWpv06YNbt68qbbgiIiIiIhqGrdArWaS8PjxY7Ro0aJMu0KhQFFR0UsHRURERERE4qlWkuDq6oq///67TPvvv/+Odu3avXRQRERERERiEeuchKCgIHTo0AGGhoawsLDAkCFDEBMTIxiTn5+PgIAAmJmZwcDAAH5+fkhKShKMiYuLw8CBA6GnpwcLCwvMmTMHcrm8SrFUaXcjpVIJiUSChQsXwt/fH48fP4ZCocDOnTsRExODzZs3Y+/evVUKgIiIiIioNhGrTOjkyZMICAhAhw4dIJfL8cknn6Bv376IioqCvr4+gJKjCPbt24cdO3bA2NgY06dPx7Bhw3DmzBkAJRsMDRw4EFZWVjh79iwSEhIwbtw4aGlpYenSpZWORaJUKpWVHfz6669j8+bNaNGiBf7++2989tlnuHr1Kp4+fYr27dtj4cKF6Nu3bxW/Hep3LP6A2CFQA7H79lGxQ6AGYs0Pf4odAjUQeRsjxA6BGggdDT2xQ6jQhMMz1HatDX2+rfZrU1JSYGFhgZMnT6Jbt27IzMxE48aNERISguHDhwMAbt68CRcXF4SFhaFTp044cOAA3njjDcTHx8PS0hIA8N1332HevHlISUkRbDr0IlUqN7K1tUXbtm2xbt06dO3aFYcPH0ZycjJyc3Nx+vTpWpEgEBERERG9jNqycDkzMxMAYGpqCgAIDw9HUVERfHx8VGOcnZ1hb2+PsLAwAEBYWBjc3d1VCQIA+Pr6IisrC5GRkZX/HlQl0N9++w0bNmzAZ599hj59+uDRo0dVeTkRERERUa2nzi1QCwoKkJWVJXgUFBT8awwKhQIzZ85E586d4ebmBgBITEyEtrY2TExMBGMtLS2RmJioGlM6Qfin/5++yqryictvvfUWevTogYCAALi7u2Ps2LHQ1BReZuXKlVW9LBERERFRvRMUFIQlS5YI2hYtWoTFixe/8HUBAQG4ceMGTp8+/Qqjq1iVkwSgZMrDxcUFoaGhuHLliiBJqMuHRhARERERqXPhcmBgIGbPni1ok8lkL3zN9OnTsXfvXpw6dQq2traqdisrKxQWFiIjI0Mwm5CUlAQrKyvVmAsXLgiu98/uR/+MqYwqJwmRkZEYN24c0tPT8ddff6Fnz55VvQQRERERUa2lzl95y2Syf00K/qFUKjFjxgyEhobixIkTaNasmaDf09MTWlpaOHr0KPz8/AAAMTExiIuLg7e3NwDA29sbX3zxBZKTk2FhYQEAOHz4MIyMjODq6lrpuKu0JmHZsmXw9PREmzZtcO3aNSYIRERERERqEhAQgF9//RUhISEwNDREYmIiEhMTkZeXBwAwNjbGxIkTMXv2bBw/fhzh4eGYMGECvL290alTJwBA37594erqirFjx+Lq1as4dOgQ5s+fj4CAgEonK0AVZxJWr16NHTt2YNCgQVV5GRERERFRnSHWOQnBwcEAgB49egjaN2zYgPHjxwMAvvnmG0ilUvj5+aGgoAC+vr5Yv369aqyGhgb27t2LqVOnwtvbG/r6+vD398dnn31WpViqlCTcuHEDZmZmlR7v7u6O/fv3w87OrkpBERERERGJRawkoTLHl+no6GDdunVYt25dhWMcHBywf//+l4qlSuVGVUkQAOD+/fsoKiqq0muIiIiIiEhc1drdiIiIiIiovuJunUwSiIiIiIgEqlRqU0/xe0BERERERAKcSSAiIiIiKoXlRkwSiIiIiIgExNrdqDapVrnR5s2bUVBQUKa9sLAQmzdvVj3//vvvYWlpWf3oiIiIiIioxlUrSZgwYQIyMzPLtGdnZ2PChAmq56NHj4a+vn71oyMiIiIiqmFSiURtj7qqWuVGSqWy3FqtR48ewdjY+KWDIiIiIiISC9ckVDFJaNeuHSQSCSQSCXr37g1NzWcvLy4uxr1799CvXz+1B0lERERERDWnSknCkCFDAAARERHw9fWFgYGBqk9bWxtNmzaFn5+fWgOsjhtpN8UOgRqINct3iB0CNRB5f0aLHQIRUYMhBWcSqpQkLFq0CMXFxWjatCn69u0La2vrVxUXEREREZEoWG5UjYXLGhoaeP/995Gfn/8q4iEiIiIiIpFVa3cjNzc33L17V92xEBERERGJjrsbVTNJ+O9//4uPPvoIe/fuRUJCArKysgQPIiIiIqK6SqLGP3VVtbZAHTBgAADgzTffFNRs/bM1anFxsXqiIyIiIiKiGletJOH48ePqjoOIiIiIqFbgwuVqJgndu3dXdxxERERERLVCXV5LoC7VShIAICMjAz/99BOio0v27m7dujXeffddnrhMRERERFTHVWvh8qVLl+Do6IhvvvkG6enpSE9Px8qVK+Ho6IjLly+rO0YiIiIiohojgVRtj7qqWjMJs2bNwptvvokffvgBmpoll5DL5Xjvvfcwc+ZMnDp1Sq1BEhERERHVFJYbVTNJuHTpkiBBAABNTU3MnTsXr732mtqCIyIiIiKimletORAjIyPExcWVaX/48CEMDQ1fOigiIiIiIrFIJBK1PeqqaiUJI0aMwMSJE7F9+3Y8fPgQDx8+xLZt2/Dee+9h1KhR6o6RiIiIiKjG8DC1apYbrVixAhKJBOPGjYNcLgcAaGlpYerUqVi2bJlaAyQiIiIioppVrSRBW1sbq1evRlBQEO7cuQMAcHR0hJ6enlqDIyIiIiKqaVy4/BLnJACAnp4e3N3d1RULEREREZHo6vJaAnWpVpKQk5ODZcuW4ejRo0hOToZCoRD03717Vy3BERERERFRzatWkvDee+/h5MmTGDt2LKytrZltEREREVG9Ia3Dh6CpS7WShAMHDmDfvn3o3LmzuuMhIiIiIhIVfwFezS1QGzVqBFNTU3XHQkREREREtUC1koTPP/8cCxcuRG5urrrjISIiIiISFQ9Tq0K5Ubt27QRv9Pbt27C0tETTpk2hpaUlGHv58mX1RUhEREREVIOkdfgQNHWpdJIwZMiQVxgGERERERHVFpVOEhYtWvQq4yAiIiIiqhXqcpmQurzUYWrh4eGIjo4GALRu3Rrt2rVTS1BERERERGLhicvVTBKSk5MxcuRInDhxAiYmJgCAjIwM9OzZE9u2bUPjxo3VGSMREREREdWgau1uNGPGDGRnZyMyMhLp6elIT0/HjRs3kJWVhf/85z/qjpGIiIiIqMZI1PinrqrWTMLBgwdx5MgRuLi4qNpcXV2xbt069O3bV23BERERERHVNKmEJy5X6zugUCjKbHsKAFpaWlAoFC8dFBERERERiadaSUKvXr3wwQcfID4+XtX2+PFjzJo1C71791ZbcERERERENY2HqVUzSVi7di2ysrLQtGlTODo6wtHREc2aNUNWVha+/fZbdcdIRERERFRjuCahmkmCnZ0dLl++jH379mHmzJmYOXMm9u/fj8uXL8PW1lbdMRIRERERNQinTp3CoEGDYGNjA4lEgl27dgn6x48fX2a2ol+/foIx6enpGDNmDIyMjGBiYoKJEyfi6dOnVYqjSknCsWPH4OrqiqysLEgkEvTp0wczZszAjBkz0KFDB7Ru3Rp///13lQJ43q1bt3D06FHcvn37pa5DRERERFQdUolEbY+qysnJQZs2bbBu3boKx/Tr1w8JCQmqx9atWwX9Y8aMQWRkJA4fPoy9e/fi1KlTmDx5cpXiqNLuRqtWrcKkSZNgZGRUps/Y2Bjvv/8+Vq5cia5du1bqekFBQejYsSN69+6NJ0+e4K233sKxY8cAlNSC9e3bF1u3blWdxUBERERE9KqJWSbUv39/9O/f/4VjZDIZrKysyu2Ljo7GwYMHcfHiRbz22msAgG+//RYDBgzAihUrYGNjU6k4qjSTcPXq1TLTGaX17dsX4eHhlb7e+vXrYWpqCgCYO3cu0tPTER4ejtzcXFy+fBkZGRn46KOPqhIiEREREVGtUVBQgKysLMGjoKDgpa554sQJWFhYwMnJCVOnTkVaWpqqLywsDCYmJqoEAQB8fHwglUpx/vz5St+jSklCUlJSuVuf/kNTUxMpKSmVvl5KSooqSThy5AhWrVqFdu3aQUdHB23atMHatWuxf//+qoRIRERERPRS1FluFBQUBGNjY8EjKCio2rH169cPmzdvxtGjR7F8+XKcPHkS/fv3R3FxMQAgMTERFhYWgtdoamrC1NQUiYmJlb5PlcqNmjRpghs3bqBFixbl9l+7dg3W1taVvp6DgwNu3LgBBwcHSCQSaGoKw9HQ0EBOTk5VQiQiIiIieikSNR6mFhgYiNmzZwvaZDJZta83cuRI1dfu7u7w8PCAo6MjTpw4odajCKr0HRgwYAAWLFiA/Pz8Mn15eXlYtGgR3njjjUpfb9KkSZgzZw5u376N6dOn46OPPsKdO3cAAPfu3cOsWbN4gjMRERER1VkymQxGRkaCx8skCc9r3rw5zM3NVZv+WFlZITk5WTBGLpcjPT29wnUM5anSTML8+fOxc+dOtGrVCtOnT4eTkxMA4ObNm1i3bh2Ki4vx6aefVvp6H330EeLi4uDq6gpHR0fcv38frVq1gqamJuRyOdq3b19mtTYRERER0atUl843ePToEdLS0lTVPN7e3sjIyEB4eDg8PT0BlOxQqlAo4OXlVenrVilJsLS0xNmzZzF16lQEBgZCqVQCKNmJyNfXF+vWrYOlpWVVLok1a9Zg6tSp2Lt3L+7evQuFQgFra2t07twZPj4+dfqkOiIiIiKqe6qzdam6PH36VHAUwL179xAREQFTU1OYmppiyZIl8PPzg5WVFe7cuYO5c+eiRYsW8PX1BQC4uLigX79+mDRpEr777jsUFRVh+vTpGDlyZKV3NgKqmCQAJesI9u/fjydPnuD27dtQKpVo2bIlGjVqVNVLqbi4uMDFxaXaryciIiIiqg8uXbqEnj17qp7/s57B398fwcHBuHbtGjZt2oSMjAzY2Nigb9+++PzzzwUlTFu2bMH06dPRu3dvSKVS+Pn5Yc2aNVWKo8pJwj8aNWqEDh06VPfllVJUVISEhATY29u/0vvUJfFR8biy+yqS76Yg90ku+s/1RfOOzVT9R9cew80TsYLX2Le1w6D5A1XPN0/9FdkpwlP3Oo3xgufQdi+8d2JMIs5tvYCkW8mQSCUwb2qON+cPhKas5GOUcjcFZ389h+TbKZBIJXDs1Byd/V+Htm7FO2JR7TVlwDuYOuAdNLUsOUU98sEtfLZ1NQ6Gn1CN6eTcHl+MmwMvp7YoVhQj4m4UfBeMRX5hydZujQyM8e2UzzDIqzcUCgX+OHsQH3y/GDn5ueXe08HCFvc3nCm3762gqfj99H74+wzHxllflzvGYnR7pGSmldtHDcu2kO3Y9PMmpKamoZVTK3z86Ty4e7iJHRbVQ/ys1U9iVrL06NFDVa1TnkOHDv3rNUxNTRESEvJScVQ7SagJUVFRaN++vWpLJwKK8uUwa2oGl17OOPBV+R8S+7Z26BXwLAPV0NIoM6bjiA5w9Xk2e/NvP8gnxiRizxf70X5oO3Sd2AVSqRSpD1IhkZb8JcpJz8Huz/aixeuO6DaxKwrzCnF6wxkcW3cc/T7i4vO66FFqAj7euBy34u9BAgn8fYZj94If0O4/AxAVdwudnNvj4GebELRjPWZ8txDy4mK0aeYCheLZP2xb5qyBtWlj9Jn/DrQ0NLFh5gr834xlGPPVf8q958PUeFi985qgbXK/UZgz7H0cuHQCALD91B4cDD8pGLNx1groaMmYIBAA4OCBQ1ix/GvMX/Qp3D3csOWXEEydPA279+2CmZmp2OFRPcLPWv0lrUNrEl6VWp0kUFkO7e3h0P7FMysaWhrQb6T3wjHaulr/Oqa00xvPwqO/m2C2oVETE9XX98MfQKohRff3uqoShx6Tu2HbhzuQkZAJE2vjSt+Laoe9F44Kns/f/BWmDngHnZzbIyruFr6ZtABr/tyI5TuCVWNiH99Vfe1s1wL9X+uB1z54A+G3rwMAZny/CPsXb8RHP/0XCenCnRcAQKFQIOmJ8KyVod798NvpfarZh/zCAuQXPhtjbmSKXh6vY+LquS//pqle+GXjrxj21jAMGTYYADB/0ac4dfJv7Nq5CxMnvStydFSf8LNG9ZmoSUL79u1f2J+Xl1dDkdQvjyPj8fO7GyEzkKGJWxN0GtUROoY6gjHhu67g4u/hMDQ3QMuuLdH2DQ9INcrfETc3Mw9Jt5LRqmtL/PFJKDKTstCoiQm8RnWEjUvJSvriomJoaEpVCQIAaGiXfLwSbiYwSajjpFIp3uoyEPo6ugiLvozGxmbo5NweW07sxpkVO+FoZY+bj+7g081f4UzUJQCAt3N7PHmaqUoQAODIldNQKBXwcmqHXWH/Pl3avoUb2jm2RkDwggrHjOvth9yCPPx+hgcvElBUWIToqGjBD2hSqRSdvL1wLeKaiJFRfcPPWv3GjXNEThKioqIwcuRINGvWrNz+hIQExMbGlttH5bNva4/mXs1hZGGIzKQsnAu5gD1f7IPfF0NVSYDHAHc0bmYOmYFOyTqDkPPIfZKLLuNfL/eaWUlZAIALv11C53HeMG9qjpiTMdi9ZA9GffM2TKxN0MS9Cc5sCsPl3RFoM8AdRQVynNtScvR37pPy68+p9nNzcELY16HQ0ZbhaV4Ohv73fUQ/vAUvp5IZpcWjZ+Kjn75AxN0ojOs9DEeXhsBtWl/cjr8Pq0aNkZyRKrhesaIY6dkZsGrUuFL3n9h3JKLibiEsOvwFY0Yg5OSfqnUQ1LA9yXiC4uJimJkLSz3MzMxw7+59cYKieomftfpNnYep1VWiJglubm7w8vLC1KlTy+2PiIjADz/88MJrFBQUoKBA+MOBvFAOTe2GWUnVssuz07DNHMxg5mCGXwNC8DgyHnYeJQtQ2w5qoxpj3tQMUk0NnPy/U/Ae41Xu+gXl/2rMW/dxhUsvZwBA4+bmeHT9MaKPxcB7jBfM7EzRe3pPnN50Fue2nIdEKoHHAHfomugyG6/DYh7fRdsZ/WGsb4jhnQdg0+yv0X3eCEilJf94fn9gCzYe2QEAiLgbid5tOuPdPm/jk01fvvS9dbRlGN39TXy+7dsKx3Rybg9X+5YY+/XMl74fERERPSPqT9KdO3dGTExMhf2Ghobo1q3bC68RFBSEJUuWCNr6TemL/tP6qSXGus7Y0gg6RjrITMyCnUf5YyxbWUBRrEBWcrZgncE//lm7YGon3Oa2kW0jZKdkq5636toSrbq2RG5GLjRlWpBIgKt7r8HI0kht74dqVpG8CHcSHgAALt++gQ6t2uCDwROw7H/rEKIe3haMj354G/aNmwAAEp+kwMLEXNCvIdWAqaEJEp9bd1Ce4Z0HQE+mi81H/6hwzHu+I3HlTiQu375RpfdF9Vcjk0bQ0NBAWmq6oD0tLQ3m5mYiRUX1ET9r9RsXLgOizqWsXr0aq1atqrDf0dERx48ff+E1AgMDkZmZKXj0ec9HzZHWXU/TniI/O/+Fi5RT75XsUqRrrFtuv6GFIfRN9ZDxOEPQnhGfAcPGhmXG65noQVtXC7fO3IGGlgbs2ti+1Hug2kMqkUKmpY37SQ/xODURTk2aC/pbNWmOB8mPAABhNy+jkYEx2rd4thVgrzavQyqR4nzMlX+918S+I/Dn+SNIzUovt19fRw9vdxmIn/7a/hLviOobLW0tuLi64Py586o2hUKB8+cuwKNtBb8pIaoGftbqN4lEorZHXVXna3JkMpng8AgA9brUqDCvCJmJmarnWUlZSLmXCh0DGWQGOri44xIcOzWHnokuMhOzEPbrORhbGcO+rR2Akq1Mk24lo4mbDbR0tZEYk4gzG8+iVdeW0DEo+T4+TXuK3Uv2wmdGT1i2tIREIkG7N9viwm+XYNbUrGRNwokYPInPEGxveu3ADVg7WUJLRwsPrz7C2V/OodMYL8j0hf//UN2w1H8uDlw6gbiUeBjq6mN0j8Ho4d4JvgvGAgC+2vk9loyZhav3ohFxNxL+vYfD2dYRw5dOAQDcfHgbBy6dwA8zlmPKuk+gpaGFtVM/w7ZTe1Q7G9mYWeLoFyEYt3I2LsZeVd3b0doB3dy8MGDx+ArjG9FtEDQ1NPHr8dBX902gOmns+HewIHAhWru5ws3dDb9uDkFeXh6GDB0sdmhUz/CzRvVZrfhp+sKFCwgLC0NiYiIAwMrKCt7e3ujYsaPIkdU+KXeSsWvxHtXzM5vCAADOPVqh+6RuSHuQhpgTMSjILYR+Iz3YtbGD18gOqrUGGloauHXmNi78dgnF8mIYWRihzRsegnUKimIFMuIzIC+Qq9ravOEBeVExzmw8i/ynBTB3MMObC96AsdWzXYuSbyXjwvaLKMovQqMmjdDj/W5w6t7qVX9L6BWxMDHH5g9XwtrUApk52bh2/yZ8F4zFkYjTAIDVu3+GjrYM30xaAFNDE1y9F40+88fgbmKc6hpjvvoP1k79HEe/CIFCqcAfZw7iP98vUvVraWjB2a4F9GTCWax3+7yNR6kJ+OvyqQrjm9h3BHaePYjMnCw1v3Oq6/r198WT9CdY/20wUlPT4OTshPXfr4MZS0BIzfhZq78kLDeCRPmiI91eseTkZPj5+eHMmTOwt7eHpaUlACApKQlxcXHo3Lkz/vjjD1hYWFTpumuuf/MqwiUq44OPV4kdAjUQeX9Gix0CEZFa6WhU/rymmhZye6ParjW6xXi1XasmibomYdq0aSguLkZ0dDTu37+P8+fP4/z587h//z6io6OhUCgQEBAgZohERERERA2OqOVGhw4dwqlTp+Dk5FSmz8nJCWvWrEGPHj1qPjAiIiIiarC4u5HISYJMJkNWVsX1xNnZ2WUWJRMRERERvUo8TE3kcqMRI0bA398foaGhgmQhKysLoaGhmDBhAkaNGiVihEREREREDY+oMwkrV66EQqHAyJEjIZfLoa2tDQAoLCyEpqYmJk6ciBUrVogZIhERERE1MNzdqBaUGwUHB2P58uUIDw8XbIHq6ekJIyOe1EtERERENasuH4KmLrXinAQjIyP07NlT7DCIiIiIiAgir0kAgLy8PJw+fRpRUVFl+vLz87F582YRoiIiIiKihkqixj91lahJQmxsLFxcXNCtWze4u7uje/fuiI+PV/VnZmZiwoQJIkZIRERERA2NRCJR26OuEjVJmDdvHtzc3JCcnIyYmBgYGhqiS5cuiIuLEzMsIiIiIqIGTdQ1CWfPnsWRI0dgbm4Oc3Nz7NmzB9OmTUPXrl1x/Phx6OvrixkeERERETVAPExN5JmEvLw8aGo+y1MkEgmCg4MxaNAgdO/eHbGxsSJGR0REREQNEcuNRJ5JcHZ2xqVLl+Di4iJoX7t2LQDgzTffFCMsIiIiIqIGTdSZhKFDh2Lr1q3l9q1duxajRo2CUqms4aiIiIiIqCGTQKq2R10lauSBgYHYv39/hf3r16+HQqGowYiIiIiIqKFjuVEtOCeBiIiIiIhql1px4jIRERERUW1Rlw9BUxcmCUREREREpUjrcJmQurDciIiIiIiIBDiTQERERERUCsuNmCQQEREREQnU5V2J1IXlRkREREREJMCZBCIiIiKiUuryIWjqwiSBiIiIiKgUlhux3IiIiIiIiJ7DmQQiIiIiolKk3N2ISQIRERERUWksN2K5ERERERERPYczCUREREREpfAwNSYJREREREQCLDdiuRERERERET2HSQIRERERUSkSSNX2qKpTp05h0KBBsLGxgUQiwa5duwT9SqUSCxcuhLW1NXR1deHj44Nbt24JxqSnp2PMmDEwMjKCiYkJJk6ciKdPn1YpDiYJRERERES1RE5ODtq0aYN169aV2//ll19izZo1+O6773D+/Hno6+vD19cX+fn5qjFjxoxBZGQkDh8+jL179+LUqVOYPHlyleLgmgQiIiIiolKkIq5J6N+/P/r3719un1KpxKpVqzB//nwMHjwYALB582ZYWlpi165dGDlyJKKjo3Hw4EFcvHgRr732GgDg22+/xYABA7BixQrY2NhUKg7OJBARERERlSJR45+CggJkZWUJHgUFBdWK6969e0hMTISPj4+qzdjYGF5eXggLCwMAhIWFwcTERJUgAICPjw+kUinOnz9f6XsxSSAiIiIiekWCgoJgbGwseAQFBVXrWomJiQAAS0tLQbulpaWqLzExERYWFoJ+TU1NmJqaqsZUBsuNiIiIiIhKUecWqIGBgZg9e7agTSaTqe36rwqTBCIiIiKiUtR5mJpMJlNbUmBlZQUASEpKgrW1tao9KSkJbdu2VY1JTk4WvE4ulyM9PV31+spguRERERERUR3QrFkzWFlZ4ejRo6q2rKwsnD9/Ht7e3gAAb29vZGRkIDw8XDXm2LFjUCgU8PLyqvS9OJNARERERFSKmCcuP336FLdv31Y9v3fvHiIiImBqagp7e3vMnDkT//3vf9GyZUs0a9YMCxYsgI2NDYYMGQIAcHFxQb9+/TBp0iR89913KCoqwvTp0zFy5MhK72wEMEkgIiIiIhKQilhsc+nSJfTs2VP1/J/1DP7+/ti4cSPmzp2LnJwcTJ48GRkZGejSpQsOHjwIHR0d1Wu2bNmC6dOno3fv3pBKpfDz88OaNWuqFIdEqVQq1fOWao81178ROwRqID74eJXYIVADkfdntNghEBGplY6GntghVCgs6YTaruVt2UNt16pJnEkgIiIiIipFzHKj2qJeJgl58uodUEFUVfztLhERUf2jzt2N6irubkRERERERAL1ciaBiIiIiKi6WG7EJIGIiIiISIDlRiw3IiIiIiKi53AmgYiIiIioFM4kMEkgIiIiIhLimgSWGxERERERkRBnEoiIiIiISmG5EZMEIiIiIiIBboHKciMiIiIiInoOZxKIiIiIiEphuRGTBCIiIiIiASYJLDciIiIiIqLncCaBiIiIiKgULlxmkkBEREREJMByI5YbERERERHRcziTQERERERUCmcSmCQQEREREQlwTQLLjYiIiIiI6DmcSSAiIiIiKoXlRkwSiIiIiIgEWG7EciMiIiIiInoOZxKIiIiIiEphuRGTBCIiIiIiASYJLDciIiIiIqLncCaBiIiIiKgULlxmkkBEREREJMByI5YbERERERHRcziTQERERERUCmcSmCQQEREREQlwTQLLjYiIiIiI6DmcSSAiIiIiEuBMApMEIiIiIqJSWG4kcrmRoaEhJk6ciLNnz4oZBhERERERlSJqkpCTk4Pz58+jS5cucHFxwddff42UlBQxQyIiIiKiBk6ixj91legLl48dO4YrV67Ax8cHS5cuha2tLfz8/HDgwAEolUqxwyMiIiKiBoZJQi1IEgCgTZs2+PbbbxEfH4+NGzciMzMTb7zxBuzt7bFw4UKxwyMiIiIialBETRKeXxQik8kwatQoHDlyBHfu3MH48eOxceNGcYIjIiIiogZJIpGo7VFXiZokvKicqGnTpvj888/x4MGDGoyIiIiIiBo6lhuJnCQsWrQIBgYGLxxTlzMwIiIiIqLKWrx4cZmZCGdnZ1V/fn4+AgICYGZmBgMDA/j5+SEpKemVxCJ6kqCnpydmCEREREREAmLOJLRu3RoJCQmqx+nTp1V9s2bNwp49e7Bjxw6cPHkS8fHxGDZsmDrfukqtPkxNLpcjPj4e9vb2YodSqyVGJ+L6nhtIvZeKvCd56P1hLzh0cBCMyXicgYshl5AYlQilQgmTJiboNbsnDMyFMzlKpRJ/LTuMx1cfl3udfyjkCoRvD8ejiEfITn4KLT0t2LjZoMOo16BnysSPyrctZDs2/bwJqalpaOXUCh9/Og/uHm5ih0X1ED9rVFP4WaufxKxk0dTUhJWVVZn2zMxM/PTTTwgJCUGvXr0AABs2bICLiwvOnTuHTp06qTWOWrG7UUUiIyPRrFkzscOo9Yry5TB1aATvCd7l9mclZmHfov0wsTHGgIX9MWT5YLQd1gYaWhplxkbuj0Jl/l7IC+VIu5+ONsPaYnDQm+g9uxcy4zNxeMWRl307VE8dPHAIK5Z/jfenvY9tv4fAybkVpk6ehrS0dLFDo3qGnzWqKfysUWUUFBQgKytL8CgoKKhw/K1bt2BjY4PmzZtjzJgxiIuLAwCEh4ejqKgIPj4+qrHOzs6wt7dHWFiY2uOu1UkCVY5dO1t4jvBE047l/9Y/fPtl2La1RYcxHWDWzAxGVkawf80eusa6gnFp99NwY98NdJnS5V/vqa2njX6f+qK5dzMY2xjDoqUFvN/thLS7aXia+lQt74vql182/ophbw3DkGGD4djCEfMXfQodHR3s2rlL7NConuFnjWoKP2v1lzrLjYKCgmBsbCx4BAUFlXtfLy8vbNy4EQcPHkRwcDDu3buHrl27Ijs7G4mJidDW1oaJiYngNZaWlkhMTFT790DUcqP27du/sD8vL6+GIqm/lAolHl55CI9B7ji09BDS7qfDoLEB2gzxEJQSyQvkOPntSXi/2wl6JtUrFyrMLQIkJQkEUWlFhUWIjorGxEnvqtqkUik6eXvhWsQ1ESOj+oafNaop/KzVb+osNwoMDMTs2bMFbTKZrNyx/fv3V33t4eEBLy8vODg44LfffoOurm65r3lVRE0SoqKiMHLkyApLihISEhAbG1vDUdUveVl5kOfLce3P62j/dnu8Nvo1PLr6GEdXHkP/Bf1h7VpS83Z+83lYtLKAw2vlz0b8G3mhHJdCLqH5682ZJFAZTzKeoLi4GGbmpoJ2MzMz3Lt7X5ygqF7iZ41qCj9rVFkymazCpODfmJiYoFWrVrh9+zb69OmDwsJCZGRkCGYTkpKSyl3D8LJETRLc3Nzg5eWFqVOnltsfERGBH3744YXXKCgoKFPXJS+UQ1O7Vq/JrjmKkv+x97SH28DWAACzpmZIjk3GzSM3Ye1qhbhLcUiITMDgZYOrdwu5AsdXnwCUSrw+sfx1EURERER1RW053+Dp06e4c+cOxo4dC09PT2hpaeHo0aPw8/MDAMTExCAuLg7e3ur/+UvUn6Q7d+6MmJiYCvsNDQ3RrVu3F14jKCgIS5YsEbT1ntwbfab4VPCKhkVmJINEQwITW2NBu4mNMZJikgEA8ZEJyErKxq/vbhGMObbyOCydLTFgUX9URCFX4Njq43ia8hT9F/TjLAKVq5FJI2hoaCAtVbiYLy0tDebmZiJFRfURP2tUU/hZq+/ESRI++ugjDBo0CA4ODoiPj8eiRYugoaGBUaNGwdjYGBMnTsTs2bNhamoKIyMjzJgxA97e3mrf2QgQOUlYvXr1C/sdHR1x/PjxF44pr87r2+g1Lx1bfaGhqYHGzc2RGZ8laM9MzFJtf+ox2B1OvVoJ+kPn7ELHcR1h72lX4bX/SRCyErLQf2F/6BjqqP8NUL2gpa0FF1cXnD93Hr18egIAFAoFzp+7gJGjR4gcHdUn/KxRTeFnjV6FR48eYdSoUUhLS0Pjxo3RpUsXnDt3Do0bNwYAfPPNN5BKpfDz80NBQQF8fX2xfv36VxJLna/JKa/Oq6GVGhXlFyEr8VkSkJ38FGn30yAzkMHA3ABug9xxYvUJWLlYwrq1NR5FPMLD8Ifov7BkhkDPRK/cxcoG5vowtDBUPf9j9k54jizZRUkhV+DYN8eQdi8NPvP6QKlQIDcjFwAgM5BBQ7Ps9qrUsI0d/w4WBC5EazdXuLm74dfNIcjLy8OQodUrcyOqCD9rVFP4Wau/xCo22rZt2wv7dXR0sG7dOqxbt+6Vx1Irfpq+cOECwsLCVNs3WVlZwdvbGx07dhQ5sroh9U4qDnx+UPX8wi8XAAAturVAt2ld0bSjA15/zxvXdl/DuY3nYWxjjF6ze8LK2bJK98mMz0RRbiEAICc9B3HhDwEAu+ftFozrv6AfrFtbv8xbonqoX39fPEl/gvXfBiM1NQ1Ozk5Y//06mHFantSMnzWqKfys1V9iHqZWW0iUSqVSrJsnJyfDz88PZ86cgb29PSwtS35oTUpKQlxcHDp37ow//vgDFhYWVbru8ivLXkW4RGV84PEfsUMgIiKqk3Q0qrflek1IzHuotmtZ6VZcul2biXqY2rRp01BcXIzo6Gjcv38f58+fx/nz53H//n1ER0dDoVAgICBAzBCJiIiIqMGRqPFRN4labnTo0CGcOnUKTk5OZfqcnJywZs0a9OjRo+YDIyIiIqIGq+7+aK8+os4kyGQyZGVlVdifnZ1d7cMniIiIiIioekRNEkaMGAF/f3+EhoYKkoWsrCyEhoZiwoQJGDVqlIgREhEREVHDw3IjUcuNVq5cCYVCgZEjR0Iul0Nbu+QgrsLCQmhqamLixIlYsWKFmCESERERUQPD3Y1EThJkMhmCg4OxfPlyhIeHC7ZA9fT0hJGRkZjhERERERE1SLXinAQjIyP07NlT7DCIiIiIiAgir0kAgLy8PJw+fRpRUVFl+vLz87F582YRoiIiIiKihkqixj91lahJQmxsLFxcXNCtWze4u7uje/fuiI+PV/VnZmZiwoQJIkZIRERERNTwiJokzJs3D25ubkhOTkZMTAwMDQ3RpUsXxMXFiRkWERERETVgnEkQOUk4e/YsgoKCYG5ujhYtWmDPnj3w9fVF165dcffuXTFDIyIiIiJqsERNEvLy8qCp+WzttEQiQXBwMAYNGoTu3bsjNjZWxOiIiIiIiBomUXc3cnZ2xqVLl+Di4iJoX7t2LQDgzTffFCMsIiIiImrAeE6CyDMJQ4cOxdatW8vtW7t2LUaNGgWlUlnDURERERERNWwSZT38KXz5lWVih0ANxAce/xE7BCIiojpJR0NP7BAqlFaQpLZrmcks1XatmlQrDlMjIiIiIqot6vKuROrCJIGIiIiISIBJgugnLhMRERERUe3CmQQiIiIiolI4j8AkgYiIiIhIgFugstyIiIiIiIiew5kEIiIiIiIBziQwSSAiIiIiKoUpAsuNiIiIiIjoOZxJICIiIiIS4FwCkwQiIiIiolK4uxHLjYiIiIiI6DlMEoiIiIiISIDlRkREREREpUi4JoEzCUREREREJMSZBCIiIiIiAc4kMEkgIiIiIiqFKQLLjYiIiIiI6DmcSSAiIiIiKoXnJDBJICIiIiJ6DpMElhsREREREZEAZxKIiIiIiErhPAKTBCIiIiKi5zBNYLkREREREREJMEkgIiIiIipFIpGo7VEd69atQ9OmTaGjowMvLy9cuHBBze/w3zFJICIiIiKqJbZv347Zs2dj0aJFuHz5Mtq0aQNfX18kJyfXaBxMEoiIiIiIaomVK1di0qRJmDBhAlxdXfHdd99BT08PP//8c43GwYXLRERERESlSNS4cLmgoAAFBQWCNplMBplMVmZsYWEhwsPDERgYqGqTSqXw8fFBWFiY2mKqjHqZJMxr97HYIdQ5BQUFCAoKQmBgYLkfWiJ14WeNago/a1RT+Fmrf3Q09NR2rcWfL8aSJUsEbYsWLcLixYvLjE1NTUVxcTEsLS0F7ZaWlrh586baYqoMiVKpVNboHalWysrKgrGxMTIzM2FkZCR2OFSP8bNGNYWfNaop/KzRi1RlJiE+Ph5NmjTB2bNn4e3trWqfO3cuTp48ifPnz7/yeP9RL2cSiIiIiIhqg4oSgvKYm5tDQ0MDSUlJgvakpCRYWVm9ivAqxIXLRERERES1gLa2Njw9PXH06FFVm0KhwNGjRwUzCzWBMwlERERERLXE7Nmz4e/vj9deew0dO3bEqlWrkJOTgwkTJtRoHEwSCEDJVNiiRYu44IpeOX7WqKbws0Y1hZ81UqcRI0YgJSUFCxcuRGJiItq2bYuDBw+WWcz8qnHhMhERERERCXBNAhERERERCTBJICIiIiIiASYJREREREQkwCSBiIiIiIgEmCQQ1q1bh6ZNm0JHRwdeXl64cOGC2CFRPXTq1CkMGjQINjY2kEgk2LVrl9ghUT0VFBSEDh06wNDQEBYWFhgyZAhiYmLEDovqoeDgYHh4eMDIyAhGRkbw9vbGgQMHxA6LSC2YJDRw27dvx+zZs7Fo0SJcvnwZbdq0ga+vL5KTk8UOjeqZnJwctGnTBuvWrRM7FKrnTp48iYCAAJw7dw6HDx9GUVER+vbti5ycHLFDo3rG1tYWy5YtQ3h4OC5duoRevXph8ODBiIyMFDs0opfGLVAbOC8vL3To0AFr164FUHKqn52dHWbMmIGPP/5Y5OiovpJIJAgNDcWQIUPEDoUagJSUFFhYWODkyZPo1q2b2OFQPWdqaoqvvvoKEydOFDsUopfCmYQGrLCwEOHh4fDx8VG1SaVS+Pj4ICwsTMTIiIjUJzMzE0DJD29Er0pxcTG2bduGnJwceHt7ix0O0UvjicsNWGpqKoqLi8uc4GdpaYmbN2+KFBURkfooFArMnDkTnTt3hpubm9jhUD10/fp1eHt7Iz8/HwYGBggNDYWrq6vYYRG9NCYJRERUbwUEBODGjRs4ffq02KFQPeXk5ISIiAhkZmbi999/h7+/P06ePMlEgeo8JgkNmLm5OTQ0NJCUlCRoT0pKgpWVlUhRERGpx/Tp07F3716cOnUKtra2YodD9ZS2tjZatGgBAPD09MTFixexevVqfP/99yJHRvRyuCahAdPW1oanpyeOHj2qalMoFDh69CjrKYmozlIqlZg+fTpCQ0Nx7NgxNGvWTOyQqAFRKBQoKCgQOwyil8aZhAZu9uzZ8Pf3x2uvvYaOHTti1apVyMnJwYQJE8QOjeqZp0+f4vbt26rn9+7dQ0REBExNTWFvby9iZFTfBAQEICQkBLt374ahoSESExMBAMbGxtDV1RU5OqpPAgMD0b9/f9jb2yM7OxshISE4ceIEDh06JHZoRC+NW6AS1q5di6+++gqJiYlo27Yt1qxZAy8vL7HDonrmxIkT6NmzZ5l2f39/bNy4seYDonpLIpGU275hwwaMHz++ZoOhem3ixIk4evQoEhISYGxsDA8PD8ybNw99+vQROzSil8YkgYiIiIiIBLgmgYiIiIiIBJgkEBERERGRAJMEIiIiIiISYJJAREREREQCTBKIiIiIiEiASQIREREREQkwSSAiIiIiIgEmCUREREREJMAkgYiIiIiIBJgkEBERERGRAJMEIiIiIiISYJJAREREREQC/w+7Bi6v1uoMyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.title('LTV')\n",
    "sns.heatmap(data=LTV,annot = True,fmt = 'g', cmap = 'Greens')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac0ef47",
   "metadata": {},
   "source": [
    "- In 2015 the store clearly changed its policy for attracting new customers compared to 2014 -> a decrease in the number of new customers by almost 80%. But as we can see from the LTV indicator, the quality of each new customer attracted became better (growth LTV from 83.27 to 104.63)\n",
    "\n",
    "\n",
    "- in 2016 new customers in the first year of purchases LTV is even better = 155.69, and in 2017 it is the highest = 164.2\n",
    "\n",
    "\n",
    "- The 2016 cohort for the second year of purchases shows a better LTV than the 2014 cohort for the fourth year of purchases -> that is, the store managed to find channels to attract new customers that yield high returns per customer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc673d7f",
   "metadata": {},
   "source": [
    "### BUT!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf53245",
   "metadata": {},
   "source": [
    "- At the same time, we see a decrease in total Profit with an increase in LTV in each new cohort because the store has greatly reduced the flow of new customers, and this has reduced the total Profit (although it has improved LTV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a368b1ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [10.00, 3.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "fig, ax =plt.subplots(1,2)\n",
    "sns.heatmap(data=LTV,annot = True,fmt = 'g', cmap = 'Greens', ax=ax[0])\n",
    "sns.heatmap(data=cohort_by_year,annot = True,fmt = 'g', cmap = 'Greens', ax=ax[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c680e3",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd31d77",
   "metadata": {},
   "source": [
    "The store should not only focus on the quality of the flow of new customers (i.e. high LTV), but also the number of new customers -> it is worth trying new channels to attract more customers"
   ]
  }
 ],
 "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}