Quantium - Module 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantium - Module 2\n",
    "\n",
    "We will be examining the performance in trial vs control stores to provide a recommendation for each location based on our insight.\n",
    "\n",
    "- Select control stores – explore the data and define metrics for control store selection – \"What would make them a control store?\" Visualize the drivers to see suitability.\n",
    "\n",
    "- Assessment of the trial – get insights of each of the stores. Compare each trial store with ontrol store to get its overall performance. We want to know if the trial stores were successful or not. \n",
    "\n",
    "- Collate findings – summarise findings for each store and provide recommendations to share with client outlining the impact on sales during trial period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "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>LYLTY_CARD_NBR</th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175</td>\n",
       "      <td>NATURAL</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>2018-09-16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>Red Rock Deli Chikn&amp;Garlic Aioli 150g</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>150</td>\n",
       "      <td>RRD</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>Grain Waves Sour    Cream&amp;Chives 210G</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>210</td>\n",
       "      <td>GRNWVES</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>106</td>\n",
       "      <td>Natural ChipCo      Hony Soy Chckn175g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>175</td>\n",
       "      <td>NATURAL</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>2018-11-02</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>96</td>\n",
       "      <td>WW Original Stacked Chips 160g</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160</td>\n",
       "      <td>WOOLWORTHS</td>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LYLTY_CARD_NBR        DATE  STORE_NBR  TXN_ID  PROD_NBR  \\\n",
       "0            1000  2018-10-17          1       1         5   \n",
       "1            1002  2018-09-16          1       2        58   \n",
       "2            1003  2019-03-07          1       3        52   \n",
       "3            1003  2019-03-08          1       4       106   \n",
       "4            1004  2018-11-02          1       5        96   \n",
       "\n",
       "                                PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "0  Natural Chip        Compny SeaSalt175g         2        6.0        175   \n",
       "1   Red Rock Deli Chikn&Garlic Aioli 150g         1        2.7        150   \n",
       "2   Grain Waves Sour    Cream&Chives 210G         1        3.6        210   \n",
       "3  Natural ChipCo      Hony Soy Chckn175g         1        3.0        175   \n",
       "4          WW Original Stacked Chips 160g         1        1.9        160   \n",
       "\n",
       "        BRAND              LIFESTAGE PREMIUM_CUSTOMER  \n",
       "0     NATURAL  YOUNG SINGLES/COUPLES          Premium  \n",
       "1         RRD  YOUNG SINGLES/COUPLES       Mainstream  \n",
       "2     GRNWVES         YOUNG FAMILIES           Budget  \n",
       "3     NATURAL         YOUNG FAMILIES           Budget  \n",
       "4  WOOLWORTHS  OLDER SINGLES/COUPLES       Mainstream  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qvi = pd.read_csv(\"QVI_data.csv\")\n",
    "qvi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 264834 entries, 0 to 264833\n",
      "Data columns (total 12 columns):\n",
      "LYLTY_CARD_NBR      264834 non-null int64\n",
      "DATE                264834 non-null object\n",
      "STORE_NBR           264834 non-null int64\n",
      "TXN_ID              264834 non-null int64\n",
      "PROD_NBR            264834 non-null int64\n",
      "PROD_NAME           264834 non-null object\n",
      "PROD_QTY            264834 non-null int64\n",
      "TOT_SALES           264834 non-null float64\n",
      "PACK_SIZE           264834 non-null int64\n",
      "BRAND               264834 non-null object\n",
      "LIFESTAGE           264834 non-null object\n",
      "PREMIUM_CUSTOMER    264834 non-null object\n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 24.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# Checking for nulls\n",
    "qvi.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Client has selected store numbers 77, 86 and 88 as trial stores.\n",
    "- Client wants control stores to be established stores that are operational for the entire observation period.\n",
    "- Trial period = 1 Feb 2019 to 30 April 2019.\n",
    "- Compare trial stores to control stores that are similar pre-trial. Similarity measurement:\n",
    "    - Monthly overall sales revenue\n",
    "    - Monthly number of customers\n",
    "    - Monthly number of transactions per customer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "qvi[\"DATE\"] = pd.to_datetime(qvi[\"DATE\"])\n",
    "qvi[\"YEARMONTH\"] = qvi[\"DATE\"].dt.strftime(\"%Y%m\").astype(\"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile each store's monthly:\n",
    "- Total sales\n",
    "- Number of customers,\n",
    "- Average transactions per customer\n",
    "- Average chips per customer\n",
    "- Average price per unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def monthly_store_metrics():\n",
    "    store_yrmo_group = qvi.groupby([\"STORE_NBR\", \"YEARMONTH\"])\n",
    "    total = store_yrmo_group[\"TOT_SALES\"].sum()\n",
    "    num_cust = store_yrmo_group[\"LYLTY_CARD_NBR\"].nunique()\n",
    "    trans_per_cust = store_yrmo_group.size() / num_cust\n",
    "    avg_chips_per_cust = store_yrmo_group[\"PROD_QTY\"].sum() / num_cust\n",
    "    avg_chips_price = total / store_yrmo_group[\"PROD_QTY\"].sum()\n",
    "    aggregates = [total, num_cust, trans_per_cust, avg_chips_per_cust, avg_chips_price]\n",
    "    metrics = pd.concat(aggregates, axis=1)\n",
    "    metrics.columns = [\"TOT_SALES\", \"nCustomers\", \"nTxnPerCust\", \"nChipsPerTxn\", \"avgPricePerUnit\"]\n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3169 entries, 0 to 3168\n",
      "Data columns (total 7 columns):\n",
      "STORE_NBR          3169 non-null int64\n",
      "YEARMONTH          3169 non-null int64\n",
      "TOT_SALES          3169 non-null float64\n",
      "nCustomers         3169 non-null int64\n",
      "nTxnPerCust        3169 non-null float64\n",
      "nChipsPerTxn       3169 non-null float64\n",
      "avgPricePerUnit    3169 non-null float64\n",
      "dtypes: float64(4), int64(3)\n",
      "memory usage: 173.4 KB\n"
     ]
    }
   ],
   "source": [
    "qvi_monthly_metrics = monthly_store_metrics().reset_index()\n",
    "qvi_monthly_metrics.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "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>STORE_NBR</th>\n",
       "      <th>YEARMONTH</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>nCustomers</th>\n",
       "      <th>nTxnPerCust</th>\n",
       "      <th>nChipsPerTxn</th>\n",
       "      <th>avgPricePerUnit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>201807</td>\n",
       "      <td>206.9</td>\n",
       "      <td>49</td>\n",
       "      <td>1.061224</td>\n",
       "      <td>1.265306</td>\n",
       "      <td>3.337097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>201808</td>\n",
       "      <td>176.1</td>\n",
       "      <td>42</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>3.261111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>201809</td>\n",
       "      <td>278.8</td>\n",
       "      <td>59</td>\n",
       "      <td>1.050847</td>\n",
       "      <td>1.271186</td>\n",
       "      <td>3.717333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>201810</td>\n",
       "      <td>188.1</td>\n",
       "      <td>44</td>\n",
       "      <td>1.022727</td>\n",
       "      <td>1.318182</td>\n",
       "      <td>3.243103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>201811</td>\n",
       "      <td>192.6</td>\n",
       "      <td>46</td>\n",
       "      <td>1.021739</td>\n",
       "      <td>1.239130</td>\n",
       "      <td>3.378947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
       "      <td>189.6</td>\n",
       "      <td>42</td>\n",
       "      <td>1.119048</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>3.326316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>201901</td>\n",
       "      <td>154.8</td>\n",
       "      <td>35</td>\n",
       "      <td>1.028571</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>3.685714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>201807</td>\n",
       "      <td>150.8</td>\n",
       "      <td>39</td>\n",
       "      <td>1.051282</td>\n",
       "      <td>1.179487</td>\n",
       "      <td>3.278261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    STORE_NBR  YEARMONTH  TOT_SALES  nCustomers  nTxnPerCust  nChipsPerTxn  \\\n",
       "0           1     201807      206.9          49     1.061224      1.265306   \n",
       "1           1     201808      176.1          42     1.023810      1.285714   \n",
       "2           1     201809      278.8          59     1.050847      1.271186   \n",
       "3           1     201810      188.1          44     1.022727      1.318182   \n",
       "4           1     201811      192.6          46     1.021739      1.239130   \n",
       "5           1     201812      189.6          42     1.119048      1.357143   \n",
       "6           1     201901      154.8          35     1.028571      1.200000   \n",
       "12          2     201807      150.8          39     1.051282      1.179487   \n",
       "\n",
       "    avgPricePerUnit  \n",
       "0          3.337097  \n",
       "1          3.261111  \n",
       "2          3.717333  \n",
       "3          3.243103  \n",
       "4          3.378947  \n",
       "5          3.326316  \n",
       "6          3.685714  \n",
       "12         3.278261  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pre trial observation\n",
    "#filter only stores with full 12 months observation\n",
    "observ_counts = qvi_monthly_metrics[\"STORE_NBR\"].value_counts()\n",
    "full_observ_index = observ_counts[observ_counts == 12].index\n",
    "full_observ = qvi_monthly_metrics[qvi_monthly_metrics[\"STORE_NBR\"].isin(full_observ_index)]\n",
    "pretrial_full_observ = full_observ[full_observ[\"YEARMONTH\"] < 201902]\n",
    "\n",
    "pretrial_full_observ.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcCorrTable(metricCol, storeComparison, inputTable=pretrial_full_observ):\n",
    "    \"\"\"Calculate correlation for a measure, looping through each control store.\n",
    "    Args:\n",
    "        metricCol (str): Name of column containing store's metric to perform correlation test on.\n",
    "        storeComparison (int): Trial store's number.\n",
    "        inputTable (dataframe):  Metric table with potential comparison stores.\n",
    "        \n",
    "    Returns:\n",
    "        DataFrame: Monthly correlation table between Trial and each Control stores.\n",
    "    \"\"\"\n",
    "    control_store_nbrs = inputTable[~inputTable[\"STORE_NBR\"].isin([77, 86, 88])][\"STORE_NBR\"].unique()\n",
    "    corrs = pd.DataFrame(columns = [\"YEARMONTH\", \"Trial_Str\", \"Ctrl_Str\", \"Corr_Score\"])\n",
    "    trial_store = inputTable[inputTable[\"STORE_NBR\"] == storeComparison][metricCol].reset_index()\n",
    "    for control in control_store_nbrs:\n",
    "        concat_df = pd.DataFrame(columns = [\"YEARMONTH\", \"Trial_Str\", \"Ctrl_Str\", \"Corr_Score\"])\n",
    "        control_store = inputTable[inputTable[\"STORE_NBR\"] == control][metricCol].reset_index()\n",
    "        concat_df[\"Corr_Score\"] = trial_store.corrwith(control_store, axis=1)\n",
    "        concat_df[\"Trial_Str\"] = storeComparison\n",
    "        concat_df[\"Ctrl_Str\"] = control\n",
    "        concat_df[\"YEARMONTH\"] = list(inputTable[inputTable[\"STORE_NBR\"] == storeComparison][\"YEARMONTH\"])\n",
    "        corrs = pd.concat([corrs, concat_df])\n",
    "    return corrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "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>YEARMONTH</th>\n",
       "      <th>Trial_Str</th>\n",
       "      <th>Ctrl_Str</th>\n",
       "      <th>Corr_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.070414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201808</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.027276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201809</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201810</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.020045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201811</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.030024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201812</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.063946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201901</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.142957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YEARMONTH Trial_Str Ctrl_Str  Corr_Score\n",
       "0    201807        77        1    0.070414\n",
       "1    201808        77        1    0.027276\n",
       "2    201809        77        1    0.002389\n",
       "3    201810        77        1   -0.020045\n",
       "4    201811        77        1    0.030024\n",
       "5    201812        77        1    0.063946\n",
       "6    201901        77        1    0.001470\n",
       "0    201807        77        2    0.142957"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_table = pd.DataFrame()\n",
    "for trial_num in [77, 86, 88]:\n",
    "    corr_table = pd.concat([corr_table, calcCorrTable([\"TOT_SALES\", \"nCustomers\", \"nTxnPerCust\", \"nChipsPerTxn\", \"avgPricePerUnit\"], trial_num)])\n",
    "    \n",
    "corr_table.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculateMagnitudeDistance(metricCol, storeComparison, inputTable=pretrial_full_observ):\n",
    "    \"\"\"Calculate standardised magnitude distance for a measure, looping through each control store.\n",
    "    Args:\n",
    "        metricCol (str): Name of column containing store's metric to perform distance calculation on.\n",
    "        storeComparison (int): Trial store's number.\n",
    "        inputTable (dataframe):  Metric table with potential comparison stores.\n",
    "        \n",
    "    Returns:\n",
    "        DataFrame: Monthly magnitude-distance table between Trial and each Control stores.\n",
    "    \"\"\"\n",
    "    control_store_nbrs = inputTable[~inputTable[\"STORE_NBR\"].isin([77, 86, 88])][\"STORE_NBR\"].unique()\n",
    "    dists = pd.DataFrame()\n",
    "    trial_store = inputTable[inputTable[\"STORE_NBR\"] == storeComparison][metricCol]\n",
    "    for control in control_store_nbrs:\n",
    "        concat_df  = abs(inputTable[inputTable[\"STORE_NBR\"] == storeComparison].reset_index()[metricCol] - inputTable[inputTable[\"STORE_NBR\"] == control].reset_index()[metricCol])\n",
    "        concat_df[\"YEARMONTH\"] = list(inputTable[inputTable[\"STORE_NBR\"] == storeComparison][\"YEARMONTH\"])\n",
    "        concat_df[\"Trial_Str\"] = storeComparison\n",
    "        concat_df[\"Ctrl_Str\"] = control\n",
    "        dists = pd.concat([dists, concat_df])\n",
    "    for col in metricCol:\n",
    "        dists[col] = 1 - ((dists[col] - dists[col].min()) / (dists[col].max() - dists[col].min()))\n",
    "    dists[\"magnitude\"] = dists[metricCol].mean(axis=1)\n",
    "    return dists    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "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>TOT_SALES</th>\n",
       "      <th>nCustomers</th>\n",
       "      <th>nTxnPerCust</th>\n",
       "      <th>nChipsPerTxn</th>\n",
       "      <th>avgPricePerUnit</th>\n",
       "      <th>YEARMONTH</th>\n",
       "      <th>Trial_Str</th>\n",
       "      <th>Ctrl_Str</th>\n",
       "      <th>magnitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.935431</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.958035</td>\n",
       "      <td>0.739412</td>\n",
       "      <td>0.883569</td>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.899443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.942972</td>\n",
       "      <td>0.951923</td>\n",
       "      <td>0.993823</td>\n",
       "      <td>0.802894</td>\n",
       "      <td>0.886328</td>\n",
       "      <td>201808</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.915588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.961503</td>\n",
       "      <td>0.836538</td>\n",
       "      <td>0.992126</td>\n",
       "      <td>0.730041</td>\n",
       "      <td>0.703027</td>\n",
       "      <td>201809</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.844647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.988221</td>\n",
       "      <td>0.932692</td>\n",
       "      <td>0.989514</td>\n",
       "      <td>0.940460</td>\n",
       "      <td>0.590528</td>\n",
       "      <td>201810</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.888283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.962149</td>\n",
       "      <td>0.951923</td>\n",
       "      <td>0.874566</td>\n",
       "      <td>0.730358</td>\n",
       "      <td>0.832481</td>\n",
       "      <td>201811</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.870296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.944193</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.868716</td>\n",
       "      <td>0.857966</td>\n",
       "      <td>0.770744</td>\n",
       "      <td>201812</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.880632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.964375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.790956</td>\n",
       "      <td>0.551428</td>\n",
       "      <td>0.678728</td>\n",
       "      <td>201901</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.797098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.895138</td>\n",
       "      <td>0.884615</td>\n",
       "      <td>0.933787</td>\n",
       "      <td>0.680831</td>\n",
       "      <td>0.848622</td>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.848599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.955685</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.801752</td>\n",
       "      <td>0.887907</td>\n",
       "      <td>0.957997</td>\n",
       "      <td>201808</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.905284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.949149</td>\n",
       "      <td>0.942308</td>\n",
       "      <td>0.951610</td>\n",
       "      <td>0.639734</td>\n",
       "      <td>0.674207</td>\n",
       "      <td>201809</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.831401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.973641</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.946947</td>\n",
       "      <td>0.789862</td>\n",
       "      <td>0.879075</td>\n",
       "      <td>201810</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.910213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.940817</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.884082</td>\n",
       "      <td>0.654639</td>\n",
       "      <td>0.975730</td>\n",
       "      <td>201811</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.887208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.905696</td>\n",
       "      <td>0.894231</td>\n",
       "      <td>0.950011</td>\n",
       "      <td>0.711693</td>\n",
       "      <td>0.814511</td>\n",
       "      <td>201812</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.855228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.970121</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.834709</td>\n",
       "      <td>0.510153</td>\n",
       "      <td>0.894500</td>\n",
       "      <td>201901</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.826512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.347195</td>\n",
       "      <td>0.413462</td>\n",
       "      <td>0.625121</td>\n",
       "      <td>0.472626</td>\n",
       "      <td>0.456197</td>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>0.462920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.407994</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.572831</td>\n",
       "      <td>0.532780</td>\n",
       "      <td>0.515987</td>\n",
       "      <td>201808</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>0.480919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.428069</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.709661</td>\n",
       "      <td>0.585080</td>\n",
       "      <td>0.295745</td>\n",
       "      <td>201809</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>0.488326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.401422</td>\n",
       "      <td>0.355769</td>\n",
       "      <td>0.714158</td>\n",
       "      <td>0.416908</td>\n",
       "      <td>0.712701</td>\n",
       "      <td>201810</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>0.520192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.452201</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.767953</td>\n",
       "      <td>0.581302</td>\n",
       "      <td>0.537221</td>\n",
       "      <td>201811</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>0.552351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.386411</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.763567</td>\n",
       "      <td>0.500275</td>\n",
       "      <td>0.592701</td>\n",
       "      <td>201812</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>0.523591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.391439</td>\n",
       "      <td>0.355769</td>\n",
       "      <td>0.824430</td>\n",
       "      <td>0.688332</td>\n",
       "      <td>0.220983</td>\n",
       "      <td>201901</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>0.496191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.207714</td>\n",
       "      <td>0.259615</td>\n",
       "      <td>0.581571</td>\n",
       "      <td>0.428439</td>\n",
       "      <td>0.484040</td>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0.392276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.278891</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.496706</td>\n",
       "      <td>0.465342</td>\n",
       "      <td>0.471250</td>\n",
       "      <td>201808</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0.396284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.300869</td>\n",
       "      <td>0.259615</td>\n",
       "      <td>0.726740</td>\n",
       "      <td>0.554487</td>\n",
       "      <td>0.331541</td>\n",
       "      <td>201809</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0.434650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.179846</td>\n",
       "      <td>0.173077</td>\n",
       "      <td>0.431418</td>\n",
       "      <td>0.238944</td>\n",
       "      <td>0.756281</td>\n",
       "      <td>201810</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0.355914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.305681</td>\n",
       "      <td>0.259615</td>\n",
       "      <td>0.744420</td>\n",
       "      <td>0.507302</td>\n",
       "      <td>0.585225</td>\n",
       "      <td>201811</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0.480449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.340444</td>\n",
       "      <td>0.442308</td>\n",
       "      <td>0.478993</td>\n",
       "      <td>0.322527</td>\n",
       "      <td>0.557828</td>\n",
       "      <td>201812</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0.428420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.051498</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>0.659914</td>\n",
       "      <td>0.561179</td>\n",
       "      <td>0.164031</td>\n",
       "      <td>201901</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0.296940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.629965</td>\n",
       "      <td>0.596154</td>\n",
       "      <td>0.483230</td>\n",
       "      <td>0.362724</td>\n",
       "      <td>0.911033</td>\n",
       "      <td>201807</td>\n",
       "      <td>77</td>\n",
       "      <td>5</td>\n",
       "      <td>0.596621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.648352</td>\n",
       "      <td>0.519231</td>\n",
       "      <td>0.674749</td>\n",
       "      <td>0.575820</td>\n",
       "      <td>0.973152</td>\n",
       "      <td>201808</td>\n",
       "      <td>77</td>\n",
       "      <td>5</td>\n",
       "      <td>0.678261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.207837</td>\n",
       "      <td>0.348837</td>\n",
       "      <td>0.457816</td>\n",
       "      <td>0.357745</td>\n",
       "      <td>0.663059</td>\n",
       "      <td>201812</td>\n",
       "      <td>88</td>\n",
       "      <td>268</td>\n",
       "      <td>0.407059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.214357</td>\n",
       "      <td>0.379845</td>\n",
       "      <td>0.245593</td>\n",
       "      <td>0.219077</td>\n",
       "      <td>0.591686</td>\n",
       "      <td>201901</td>\n",
       "      <td>88</td>\n",
       "      <td>268</td>\n",
       "      <td>0.330111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.767574</td>\n",
       "      <td>0.798450</td>\n",
       "      <td>0.441626</td>\n",
       "      <td>0.791883</td>\n",
       "      <td>0.688635</td>\n",
       "      <td>201807</td>\n",
       "      <td>88</td>\n",
       "      <td>269</td>\n",
       "      <td>0.697633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.653699</td>\n",
       "      <td>0.829457</td>\n",
       "      <td>0.964796</td>\n",
       "      <td>0.982089</td>\n",
       "      <td>0.577749</td>\n",
       "      <td>201808</td>\n",
       "      <td>88</td>\n",
       "      <td>269</td>\n",
       "      <td>0.801558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619473</td>\n",
       "      <td>0.813953</td>\n",
       "      <td>0.855814</td>\n",
       "      <td>0.946426</td>\n",
       "      <td>0.624726</td>\n",
       "      <td>201809</td>\n",
       "      <td>88</td>\n",
       "      <td>269</td>\n",
       "      <td>0.772078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.805839</td>\n",
       "      <td>0.906977</td>\n",
       "      <td>0.833526</td>\n",
       "      <td>0.938110</td>\n",
       "      <td>0.735307</td>\n",
       "      <td>201810</td>\n",
       "      <td>88</td>\n",
       "      <td>269</td>\n",
       "      <td>0.843952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.705499</td>\n",
       "      <td>0.813953</td>\n",
       "      <td>0.742815</td>\n",
       "      <td>0.904264</td>\n",
       "      <td>0.647329</td>\n",
       "      <td>201811</td>\n",
       "      <td>88</td>\n",
       "      <td>269</td>\n",
       "      <td>0.762772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.723781</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.793646</td>\n",
       "      <td>0.923230</td>\n",
       "      <td>0.613002</td>\n",
       "      <td>201812</td>\n",
       "      <td>88</td>\n",
       "      <td>269</td>\n",
       "      <td>0.781274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.797336</td>\n",
       "      <td>0.945736</td>\n",
       "      <td>0.790992</td>\n",
       "      <td>0.922240</td>\n",
       "      <td>0.612011</td>\n",
       "      <td>201901</td>\n",
       "      <td>88</td>\n",
       "      <td>269</td>\n",
       "      <td>0.813663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.753968</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.105376</td>\n",
       "      <td>0.666420</td>\n",
       "      <td>0.659897</td>\n",
       "      <td>201807</td>\n",
       "      <td>88</td>\n",
       "      <td>270</td>\n",
       "      <td>0.585969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.773207</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.860382</td>\n",
       "      <td>0.964684</td>\n",
       "      <td>0.627417</td>\n",
       "      <td>201808</td>\n",
       "      <td>88</td>\n",
       "      <td>270</td>\n",
       "      <td>0.831185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.590703</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.881526</td>\n",
       "      <td>0.956020</td>\n",
       "      <td>0.534092</td>\n",
       "      <td>201809</td>\n",
       "      <td>88</td>\n",
       "      <td>270</td>\n",
       "      <td>0.756809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.620181</td>\n",
       "      <td>0.790698</td>\n",
       "      <td>0.846548</td>\n",
       "      <td>0.942969</td>\n",
       "      <td>0.646682</td>\n",
       "      <td>201810</td>\n",
       "      <td>88</td>\n",
       "      <td>270</td>\n",
       "      <td>0.769416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.703940</td>\n",
       "      <td>0.775194</td>\n",
       "      <td>0.627273</td>\n",
       "      <td>0.861152</td>\n",
       "      <td>0.677262</td>\n",
       "      <td>201811</td>\n",
       "      <td>88</td>\n",
       "      <td>270</td>\n",
       "      <td>0.728964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.796485</td>\n",
       "      <td>0.806202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.627102</td>\n",
       "      <td>0.599137</td>\n",
       "      <td>201812</td>\n",
       "      <td>88</td>\n",
       "      <td>270</td>\n",
       "      <td>0.565785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.858418</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.737359</td>\n",
       "      <td>0.902228</td>\n",
       "      <td>0.627190</td>\n",
       "      <td>201901</td>\n",
       "      <td>88</td>\n",
       "      <td>270</td>\n",
       "      <td>0.825039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.749575</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.461324</td>\n",
       "      <td>0.799233</td>\n",
       "      <td>0.761609</td>\n",
       "      <td>201807</td>\n",
       "      <td>88</td>\n",
       "      <td>271</td>\n",
       "      <td>0.703185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546556</td>\n",
       "      <td>0.635659</td>\n",
       "      <td>0.935312</td>\n",
       "      <td>0.982925</td>\n",
       "      <td>0.664365</td>\n",
       "      <td>201808</td>\n",
       "      <td>88</td>\n",
       "      <td>271</td>\n",
       "      <td>0.752963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.557398</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.899372</td>\n",
       "      <td>0.962679</td>\n",
       "      <td>0.595255</td>\n",
       "      <td>201809</td>\n",
       "      <td>88</td>\n",
       "      <td>271</td>\n",
       "      <td>0.751778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.601474</td>\n",
       "      <td>0.767442</td>\n",
       "      <td>0.799471</td>\n",
       "      <td>0.925403</td>\n",
       "      <td>0.661104</td>\n",
       "      <td>201810</td>\n",
       "      <td>88</td>\n",
       "      <td>271</td>\n",
       "      <td>0.750979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.648243</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.873078</td>\n",
       "      <td>0.952868</td>\n",
       "      <td>0.679329</td>\n",
       "      <td>201811</td>\n",
       "      <td>88</td>\n",
       "      <td>271</td>\n",
       "      <td>0.781091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.633078</td>\n",
       "      <td>0.782946</td>\n",
       "      <td>0.961471</td>\n",
       "      <td>0.985849</td>\n",
       "      <td>0.585492</td>\n",
       "      <td>201812</td>\n",
       "      <td>88</td>\n",
       "      <td>271</td>\n",
       "      <td>0.789767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.699546</td>\n",
       "      <td>0.806202</td>\n",
       "      <td>0.807200</td>\n",
       "      <td>0.928287</td>\n",
       "      <td>0.656048</td>\n",
       "      <td>201901</td>\n",
       "      <td>88</td>\n",
       "      <td>271</td>\n",
       "      <td>0.779457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.378614</td>\n",
       "      <td>0.372093</td>\n",
       "      <td>0.649232</td>\n",
       "      <td>0.789658</td>\n",
       "      <td>0.942592</td>\n",
       "      <td>201807</td>\n",
       "      <td>88</td>\n",
       "      <td>272</td>\n",
       "      <td>0.626438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.326141</td>\n",
       "      <td>0.325581</td>\n",
       "      <td>0.554401</td>\n",
       "      <td>0.757316</td>\n",
       "      <td>0.992876</td>\n",
       "      <td>201808</td>\n",
       "      <td>88</td>\n",
       "      <td>272</td>\n",
       "      <td>0.591263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.207554</td>\n",
       "      <td>0.286822</td>\n",
       "      <td>0.462846</td>\n",
       "      <td>0.779879</td>\n",
       "      <td>0.923887</td>\n",
       "      <td>201809</td>\n",
       "      <td>88</td>\n",
       "      <td>272</td>\n",
       "      <td>0.532198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.346797</td>\n",
       "      <td>0.387597</td>\n",
       "      <td>0.571497</td>\n",
       "      <td>0.796875</td>\n",
       "      <td>0.971133</td>\n",
       "      <td>201810</td>\n",
       "      <td>88</td>\n",
       "      <td>272</td>\n",
       "      <td>0.614780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.286706</td>\n",
       "      <td>0.310078</td>\n",
       "      <td>0.623883</td>\n",
       "      <td>0.813241</td>\n",
       "      <td>0.966999</td>\n",
       "      <td>201811</td>\n",
       "      <td>88</td>\n",
       "      <td>272</td>\n",
       "      <td>0.600181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.347151</td>\n",
       "      <td>0.387597</td>\n",
       "      <td>0.376456</td>\n",
       "      <td>0.699748</td>\n",
       "      <td>0.962198</td>\n",
       "      <td>201812</td>\n",
       "      <td>88</td>\n",
       "      <td>272</td>\n",
       "      <td>0.554630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.402353</td>\n",
       "      <td>0.449612</td>\n",
       "      <td>0.450378</td>\n",
       "      <td>0.739714</td>\n",
       "      <td>0.971335</td>\n",
       "      <td>201901</td>\n",
       "      <td>88</td>\n",
       "      <td>272</td>\n",
       "      <td>0.602678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5397 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    TOT_SALES  nCustomers  nTxnPerCust  nChipsPerTxn  avgPricePerUnit  \\\n",
       "0    0.935431    0.980769     0.958035      0.739412         0.883569   \n",
       "1    0.942972    0.951923     0.993823      0.802894         0.886328   \n",
       "2    0.961503    0.836538     0.992126      0.730041         0.703027   \n",
       "3    0.988221    0.932692     0.989514      0.940460         0.590528   \n",
       "4    0.962149    0.951923     0.874566      0.730358         0.832481   \n",
       "5    0.944193    0.961538     0.868716      0.857966         0.770744   \n",
       "6    0.964375    1.000000     0.790956      0.551428         0.678728   \n",
       "0    0.895138    0.884615     0.933787      0.680831         0.848622   \n",
       "1    0.955685    0.923077     0.801752      0.887907         0.957997   \n",
       "2    0.949149    0.942308     0.951610      0.639734         0.674207   \n",
       "3    0.973641    0.961538     0.946947      0.789862         0.879075   \n",
       "4    0.940817    0.980769     0.884082      0.654639         0.975730   \n",
       "5    0.905696    0.894231     0.950011      0.711693         0.814511   \n",
       "6    0.970121    0.923077     0.834709      0.510153         0.894500   \n",
       "0    0.347195    0.413462     0.625121      0.472626         0.456197   \n",
       "1    0.407994    0.375000     0.572831      0.532780         0.515987   \n",
       "2    0.428069    0.423077     0.709661      0.585080         0.295745   \n",
       "3    0.401422    0.355769     0.714158      0.416908         0.712701   \n",
       "4    0.452201    0.423077     0.767953      0.581302         0.537221   \n",
       "5    0.386411    0.375000     0.763567      0.500275         0.592701   \n",
       "6    0.391439    0.355769     0.824430      0.688332         0.220983   \n",
       "0    0.207714    0.259615     0.581571      0.428439         0.484040   \n",
       "1    0.278891    0.269231     0.496706      0.465342         0.471250   \n",
       "2    0.300869    0.259615     0.726740      0.554487         0.331541   \n",
       "3    0.179846    0.173077     0.431418      0.238944         0.756281   \n",
       "4    0.305681    0.259615     0.744420      0.507302         0.585225   \n",
       "5    0.340444    0.442308     0.478993      0.322527         0.557828   \n",
       "6    0.051498    0.048077     0.659914      0.561179         0.164031   \n",
       "0    0.629965    0.596154     0.483230      0.362724         0.911033   \n",
       "1    0.648352    0.519231     0.674749      0.575820         0.973152   \n",
       "..        ...         ...          ...           ...              ...   \n",
       "5    0.207837    0.348837     0.457816      0.357745         0.663059   \n",
       "6    0.214357    0.379845     0.245593      0.219077         0.591686   \n",
       "0    0.767574    0.798450     0.441626      0.791883         0.688635   \n",
       "1    0.653699    0.829457     0.964796      0.982089         0.577749   \n",
       "2    0.619473    0.813953     0.855814      0.946426         0.624726   \n",
       "3    0.805839    0.906977     0.833526      0.938110         0.735307   \n",
       "4    0.705499    0.813953     0.742815      0.904264         0.647329   \n",
       "5    0.723781    0.852713     0.793646      0.923230         0.613002   \n",
       "6    0.797336    0.945736     0.790992      0.922240         0.612011   \n",
       "0    0.753968    0.744186     0.105376      0.666420         0.659897   \n",
       "1    0.773207    0.930233     0.860382      0.964684         0.627417   \n",
       "2    0.590703    0.821705     0.881526      0.956020         0.534092   \n",
       "3    0.620181    0.790698     0.846548      0.942969         0.646682   \n",
       "4    0.703940    0.775194     0.627273      0.861152         0.677262   \n",
       "5    0.796485    0.806202     0.000000      0.627102         0.599137   \n",
       "6    0.858418    1.000000     0.737359      0.902228         0.627190   \n",
       "0    0.749575    0.744186     0.461324      0.799233         0.761609   \n",
       "1    0.546556    0.635659     0.935312      0.982925         0.664365   \n",
       "2    0.557398    0.744186     0.899372      0.962679         0.595255   \n",
       "3    0.601474    0.767442     0.799471      0.925403         0.661104   \n",
       "4    0.648243    0.751938     0.873078      0.952868         0.679329   \n",
       "5    0.633078    0.782946     0.961471      0.985849         0.585492   \n",
       "6    0.699546    0.806202     0.807200      0.928287         0.656048   \n",
       "0    0.378614    0.372093     0.649232      0.789658         0.942592   \n",
       "1    0.326141    0.325581     0.554401      0.757316         0.992876   \n",
       "2    0.207554    0.286822     0.462846      0.779879         0.923887   \n",
       "3    0.346797    0.387597     0.571497      0.796875         0.971133   \n",
       "4    0.286706    0.310078     0.623883      0.813241         0.966999   \n",
       "5    0.347151    0.387597     0.376456      0.699748         0.962198   \n",
       "6    0.402353    0.449612     0.450378      0.739714         0.971335   \n",
       "\n",
       "    YEARMONTH  Trial_Str  Ctrl_Str  magnitude  \n",
       "0      201807         77         1   0.899443  \n",
       "1      201808         77         1   0.915588  \n",
       "2      201809         77         1   0.844647  \n",
       "3      201810         77         1   0.888283  \n",
       "4      201811         77         1   0.870296  \n",
       "5      201812         77         1   0.880632  \n",
       "6      201901         77         1   0.797098  \n",
       "0      201807         77         2   0.848599  \n",
       "1      201808         77         2   0.905284  \n",
       "2      201809         77         2   0.831401  \n",
       "3      201810         77         2   0.910213  \n",
       "4      201811         77         2   0.887208  \n",
       "5      201812         77         2   0.855228  \n",
       "6      201901         77         2   0.826512  \n",
       "0      201807         77         3   0.462920  \n",
       "1      201808         77         3   0.480919  \n",
       "2      201809         77         3   0.488326  \n",
       "3      201810         77         3   0.520192  \n",
       "4      201811         77         3   0.552351  \n",
       "5      201812         77         3   0.523591  \n",
       "6      201901         77         3   0.496191  \n",
       "0      201807         77         4   0.392276  \n",
       "1      201808         77         4   0.396284  \n",
       "2      201809         77         4   0.434650  \n",
       "3      201810         77         4   0.355914  \n",
       "4      201811         77         4   0.480449  \n",
       "5      201812         77         4   0.428420  \n",
       "6      201901         77         4   0.296940  \n",
       "0      201807         77         5   0.596621  \n",
       "1      201808         77         5   0.678261  \n",
       "..        ...        ...       ...        ...  \n",
       "5      201812         88       268   0.407059  \n",
       "6      201901         88       268   0.330111  \n",
       "0      201807         88       269   0.697633  \n",
       "1      201808         88       269   0.801558  \n",
       "2      201809         88       269   0.772078  \n",
       "3      201810         88       269   0.843952  \n",
       "4      201811         88       269   0.762772  \n",
       "5      201812         88       269   0.781274  \n",
       "6      201901         88       269   0.813663  \n",
       "0      201807         88       270   0.585969  \n",
       "1      201808         88       270   0.831185  \n",
       "2      201809         88       270   0.756809  \n",
       "3      201810         88       270   0.769416  \n",
       "4      201811         88       270   0.728964  \n",
       "5      201812         88       270   0.565785  \n",
       "6      201901         88       270   0.825039  \n",
       "0      201807         88       271   0.703185  \n",
       "1      201808         88       271   0.752963  \n",
       "2      201809         88       271   0.751778  \n",
       "3      201810         88       271   0.750979  \n",
       "4      201811         88       271   0.781091  \n",
       "5      201812         88       271   0.789767  \n",
       "6      201901         88       271   0.779457  \n",
       "0      201807         88       272   0.626438  \n",
       "1      201808         88       272   0.591263  \n",
       "2      201809         88       272   0.532198  \n",
       "3      201810         88       272   0.614780  \n",
       "4      201811         88       272   0.600181  \n",
       "5      201812         88       272   0.554630  \n",
       "6      201901         88       272   0.602678  \n",
       "\n",
       "[5397 rows x 9 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_table = pd.DataFrame()\n",
    "for trial_num in [77, 86, 88]:\n",
    "    dist_table = pd.concat([dist_table, calculateMagnitudeDistance([\"TOT_SALES\", \"nCustomers\", \"nTxnPerCust\", \"nChipsPerTxn\", \"avgPricePerUnit\"], trial_num)])\n",
    "    \n",
    "dist_table.head(8)\n",
    "dist_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll select control stores based on how similar monthly total sales in dollar amounts and monthly number of customers are to the trial stores by using correlation and magnitude distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_corr_dist(metricCol, storeComparison, inputTable=pretrial_full_observ):\n",
    "    corrs = calcCorrTable(metricCol, storeComparison, inputTable)\n",
    "    dists = calculateMagnitudeDistance(metricCol, storeComparison, inputTable)\n",
    "    dists = dists.drop(metricCol, axis=1)\n",
    "    combine = pd.merge(corrs, dists, on=[\"YEARMONTH\", \"Trial_Str\", \"Ctrl_Str\"])\n",
    "    return combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_metrics_table1 = pd.DataFrame()\n",
    "for trial_num in [77, 86, 88]:\n",
    "    compare_metrics_table1 = pd.concat([compare_metrics_table1, combine_corr_dist([\"TOT_SALES\"], trial_num)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_weight = 0.5\n",
    "dist_weight = 1 - corr_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "218         77       233         1.0   0.986477   0.993238\n",
      "239         77       255         1.0   0.979479   0.989739\n",
      "177         77       188         1.0   0.977663   0.988831\n",
      "49          77        53         1.0   0.976678   0.988339\n",
      "120         77       131         1.0   0.976267   0.988134 \n",
      "\n",
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "356         86       109         1.0   0.966783   0.983391\n",
      "401         86       155         1.0   0.965876   0.982938\n",
      "464         86       222         1.0   0.962280   0.981140\n",
      "467         86       225         1.0   0.960512   0.980256\n",
      "471         86       229         1.0   0.951704   0.975852 \n",
      "\n",
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "551         88        40         1.0   0.941165   0.970582\n",
      "538         88        26         1.0   0.904377   0.952189\n",
      "582         88        72         1.0   0.903800   0.951900\n",
      "517         88         4         1.0   0.903466   0.951733\n",
      "568         88        58         1.0   0.891678   0.945839 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Top 5 highest Composite Score for each Trial Store based on TOT_SALES\n",
    "grouped_comparison_table1 = compare_metrics_table1.groupby([\"Trial_Str\", \"Ctrl_Str\"]).mean().reset_index()\n",
    "grouped_comparison_table1[\"CompScore\"] = (corr_weight * grouped_comparison_table1[\"Corr_Score\"]) + (dist_weight * grouped_comparison_table1[\"magnitude\"])\n",
    "for trial_num in compare_metrics_table1[\"Trial_Str\"].unique():\n",
    "    print(grouped_comparison_table1[grouped_comparison_table1[\"Trial_Str\"] == trial_num].sort_values(ascending=False, by=\"CompScore\").head(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_metrics_table2 = pd.DataFrame()\n",
    "for trial_num in [77, 86, 88]:\n",
    "    compare_metrics_table2 = pd.concat([compare_metrics_table2, combine_corr_dist([\"nCustomers\"], trial_num)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "218         77       233         1.0   0.993132   0.996566\n",
      "38          77        41         1.0   0.976648   0.988324\n",
      "101         77       111         1.0   0.968407   0.984203\n",
      "105         77       115         1.0   0.967033   0.983516\n",
      "15          77        17         1.0   0.965659   0.982830 \n",
      "\n",
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "401         86       155         1.0   0.986772   0.993386\n",
      "467         86       225         1.0   0.969577   0.984788\n",
      "356         86       109         1.0   0.969577   0.984788\n",
      "471         86       229         1.0   0.964286   0.982143\n",
      "293         86        39         1.0   0.961640   0.980820 \n",
      "\n",
      "     Trial_Str  Ctrl_Str  Corr_Score  magnitude  CompScore\n",
      "736         88       237         1.0   0.987818   0.993909\n",
      "705         88       203         1.0   0.944629   0.972315\n",
      "551         88        40         1.0   0.942414   0.971207\n",
      "668         88       165         1.0   0.935770   0.967885\n",
      "701         88       199         1.0   0.932447   0.966224 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Top 5 highest Composite Score for each Trial Store based on nCustomers\n",
    "grouped_comparison_table2 = compare_metrics_table2.groupby([\"Trial_Str\", \"Ctrl_Str\"]).mean().reset_index()\n",
    "grouped_comparison_table2[\"CompScore\"] = (corr_weight * grouped_comparison_table2[\"Corr_Score\"]) + (dist_weight * grouped_comparison_table2[\"magnitude\"])\n",
    "for trial_num in compare_metrics_table2[\"Trial_Str\"].unique():\n",
    "    print(grouped_comparison_table2[grouped_comparison_table2[\"Trial_Str\"] == trial_num].sort_values(ascending=False, by=\"CompScore\").head(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial_Str  Ctrl_Str\n",
      "77         233         0.994902\n",
      "           41          0.986020\n",
      "           46          0.984762\n",
      "dtype: float64 \n",
      "\n",
      "Trial_Str  Ctrl_Str\n",
      "86         155         0.988162\n",
      "           109         0.984090\n",
      "           225         0.982522\n",
      "dtype: float64 \n",
      "\n",
      "Trial_Str  Ctrl_Str\n",
      "88         40          0.970895\n",
      "           26          0.958929\n",
      "           72          0.954079\n",
      "dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for trial_num in compare_metrics_table2[\"Trial_Str\"].unique():\n",
    "    a = grouped_comparison_table1[grouped_comparison_table1[\"Trial_Str\"] == trial_num].sort_values(ascending=False, by=\"CompScore\").set_index([\"Trial_Str\", \"Ctrl_Str\"])[\"CompScore\"]\n",
    "    b = grouped_comparison_table2[grouped_comparison_table2[\"Trial_Str\"] == trial_num].sort_values(ascending=False, by=\"CompScore\").set_index([\"Trial_Str\", \"Ctrl_Str\"])[\"CompScore\"]\n",
    "    print((pd.concat([a,b], axis=1).sum(axis=1)/2).sort_values(ascending=False).head(3), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3 similarity based on TOT_SALES:\n",
    "- Trial store 77: Store 233, 255, 188\n",
    "- Trial store 86: Store 109, 155, 222\n",
    "- Trial store 88: Store 40, 26, 72\n",
    "\n",
    "Top 3 similartiy based on nCustomers:\n",
    "- Trial store 77: Store 233, 41, 111\n",
    "- Trial store 86: Store 155, 225, 109\n",
    "- Trial store 88: Store 237, 203, 40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on highest average of both features combined:\n",
    "- Trial store 77: Store 233\n",
    "- Trial store 86: Store 155\n",
    "- Trial store 88: Store 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trial_control_dic = {77:233, 86:155, 88:40}\n",
    "for key, val in trial_control_dic.items():\n",
    "    pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"].isin([key, val])].groupby(\n",
    "        [\"YEARMONTH\", \"STORE_NBR\"]).sum()[\"TOT_SALES\"].unstack().plot.bar()\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(key)+\" and Control Store \"+str(val)+\" - TOT_SALES\")\n",
    "    plt.show()\n",
    "    pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"].isin([key, val])].groupby(\n",
    "    [\"YEARMONTH\", \"STORE_NBR\"]).sum()[\"nCustomers\"].unstack().plot.bar()\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(key)+\" and Control Store \"+str(val)+\" - nCustomer\")\n",
    "    plt.show()\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we'll compare the performance of Trial stores to Control stores during the trial period. To ensure their performance is comparable during Trial period, we need to scale (multiply to ratio of trial / control) all of Control stores' performance to Trial store's performance during pre-trial. Starting with TOT_SALES."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ratio of Store 77 and its Control store.\n",
    "sales_ratio_77 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 77][\"TOT_SALES\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 233][\"TOT_SALES\"].sum()\n",
    "\n",
    "#Ratio of Store 86 and its Control store.\n",
    "sales_ratio_86 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 86][\"TOT_SALES\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 155][\"TOT_SALES\"].sum()\n",
    "\n",
    "#Ratio of Store 77 and its Control store.\n",
    "sales_ratio_88 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 88][\"TOT_SALES\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 40][\"TOT_SALES\"].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "trial_full_observ = full_observ[(full_observ[\"YEARMONTH\"] >= 201902) & (full_observ[\"YEARMONTH\"] <= 201904)]\n",
    "scaled_sales_control_stores = full_observ[full_observ[\"STORE_NBR\"].isin([233, 155, 40])][[\"STORE_NBR\", \"YEARMONTH\", \"TOT_SALES\"]]\n",
    "\n",
    "def scaler(row):\n",
    "    if row[\"STORE_NBR\"] == 233:\n",
    "        return row[\"TOT_SALES\"] * sales_ratio_77\n",
    "    elif row[\"STORE_NBR\"] == 155:\n",
    "        return row[\"TOT_SALES\"] * sales_ratio_86\n",
    "    elif row[\"STORE_NBR\"] == 40:\n",
    "        return row[\"TOT_SALES\"] * sales_ratio_88\n",
    "\n",
    "scaled_sales_control_stores[\"ScaledSales\"] = scaled_sales_control_stores.apply(lambda row: scaler(row), axis=1)\n",
    "\n",
    "trial_scaled_sales_control_stores = scaled_sales_control_stores[(scaled_sales_control_stores[\"YEARMONTH\"] >= 201902) & (scaled_sales_control_stores[\"YEARMONTH\"] <= 201904)]\n",
    "pretrial_scaled_sales_control_stores = scaled_sales_control_stores[scaled_sales_control_stores[\"YEARMONTH\"] < 201902]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "percentage_diff = {}\n",
    "\n",
    "for trial, control in trial_control_dic.items():\n",
    "    a = trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == control]\n",
    "    b = trial_full_observ[trial_full_observ[\"STORE_NBR\"] == trial][[\"STORE_NBR\", \"YEARMONTH\", \"TOT_SALES\"]]\n",
    "    percentage_diff[trial] = b[\"TOT_SALES\"].sum() / a[\"ScaledSales\"].sum()\n",
    "    b[[\"YEARMONTH\", \"TOT_SALES\"]].merge(a[[\"YEARMONTH\", \"ScaledSales\"]],on=\"YEARMONTH\").set_index(\"YEARMONTH\").rename(columns={\"ScaledSales\":\"Scaled_Control_Sales\", \"TOT_SALES\":\"Trial_Sales\"}).plot.bar()\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(trial)+\" and Control Store \"+str(control))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{77: 1.2615468650086274, 86: 1.13150143573637, 88: 1.0434583458542188}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>c_STORE_NBR</th>\n",
       "      <th>YEARMONTH</th>\n",
       "      <th>c_ScaledSales</th>\n",
       "      <th>t_STORE_NBR</th>\n",
       "      <th>t_TOT_SALES</th>\n",
       "      <th>Sales_Percentage_Diff</th>\n",
       "      <th>trial_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>233</td>\n",
       "      <td>201902</td>\n",
       "      <td>249.762622</td>\n",
       "      <td>77</td>\n",
       "      <td>235.0</td>\n",
       "      <td>-0.060907</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>233</td>\n",
       "      <td>201903</td>\n",
       "      <td>203.802205</td>\n",
       "      <td>77</td>\n",
       "      <td>278.5</td>\n",
       "      <td>0.309755</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>233</td>\n",
       "      <td>201904</td>\n",
       "      <td>162.345704</td>\n",
       "      <td>77</td>\n",
       "      <td>263.5</td>\n",
       "      <td>0.475075</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>155</td>\n",
       "      <td>201902</td>\n",
       "      <td>864.522060</td>\n",
       "      <td>86</td>\n",
       "      <td>913.2</td>\n",
       "      <td>0.054764</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>155</td>\n",
       "      <td>201903</td>\n",
       "      <td>780.320405</td>\n",
       "      <td>86</td>\n",
       "      <td>1026.8</td>\n",
       "      <td>0.272787</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>155</td>\n",
       "      <td>201904</td>\n",
       "      <td>819.317024</td>\n",
       "      <td>86</td>\n",
       "      <td>848.2</td>\n",
       "      <td>0.034642</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>40</td>\n",
       "      <td>201902</td>\n",
       "      <td>1434.399269</td>\n",
       "      <td>88</td>\n",
       "      <td>1370.2</td>\n",
       "      <td>-0.045781</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40</td>\n",
       "      <td>201903</td>\n",
       "      <td>1352.064709</td>\n",
       "      <td>88</td>\n",
       "      <td>1477.2</td>\n",
       "      <td>0.088458</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>40</td>\n",
       "      <td>201904</td>\n",
       "      <td>1321.797762</td>\n",
       "      <td>88</td>\n",
       "      <td>1439.4</td>\n",
       "      <td>0.085182</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    c_STORE_NBR  YEARMONTH  c_ScaledSales  t_STORE_NBR  t_TOT_SALES  \\\n",
       "7           233     201902     249.762622           77        235.0   \n",
       "8           233     201903     203.802205           77        278.5   \n",
       "9           233     201904     162.345704           77        263.5   \n",
       "19          155     201902     864.522060           86        913.2   \n",
       "20          155     201903     780.320405           86       1026.8   \n",
       "21          155     201904     819.317024           86        848.2   \n",
       "31           40     201902    1434.399269           88       1370.2   \n",
       "32           40     201903    1352.064709           88       1477.2   \n",
       "33           40     201904    1321.797762           88       1439.4   \n",
       "\n",
       "    Sales_Percentage_Diff trial_period  \n",
       "7               -0.060907        trial  \n",
       "8                0.309755        trial  \n",
       "9                0.475075        trial  \n",
       "19               0.054764        trial  \n",
       "20               0.272787        trial  \n",
       "21               0.034642        trial  \n",
       "31              -0.045781        trial  \n",
       "32               0.088458        trial  \n",
       "33               0.085182        trial  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a compiled percentage_difference table\n",
    "temp1 = scaled_sales_control_stores.sort_values(by=[\"STORE_NBR\", \"YEARMONTH\"], ascending=[False, True]).reset_index().drop([\"TOT_SALES\", \"index\"], axis=1)\n",
    "temp2 = full_observ[full_observ[\"STORE_NBR\"].isin([77,86,88])][[\"STORE_NBR\", \"YEARMONTH\", \"TOT_SALES\"]].reset_index().drop([\"index\", \"YEARMONTH\"], axis=1)\n",
    "scaledsales_vs_trial = pd.concat([temp1, temp2], axis=1)\n",
    "scaledsales_vs_trial.columns = [\"c_STORE_NBR\", \"YEARMONTH\", \"c_ScaledSales\", \"t_STORE_NBR\", \"t_TOT_SALES\"]\n",
    "scaledsales_vs_trial[\"Sales_Percentage_Diff\"] = (scaledsales_vs_trial[\"t_TOT_SALES\"] - scaledsales_vs_trial[\"c_ScaledSales\"]) / (((scaledsales_vs_trial[\"t_TOT_SALES\"] + scaledsales_vs_trial[\"c_ScaledSales\"])/2))\n",
    "def label_period(cell):\n",
    "    if cell < 201902:\n",
    "        return \"pre\"\n",
    "    elif cell > 201904:\n",
    "        return \"post\"\n",
    "    else:\n",
    "        return \"trial\"\n",
    "scaledsales_vs_trial[\"trial_period\"] = scaledsales_vs_trial[\"YEARMONTH\"].apply(lambda cell: label_period(cell))\n",
    "scaledsales_vs_trial[scaledsales_vs_trial[\"trial_period\"] == \"trial\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check significance of Trial minus Control stores TOT_SALES Percentage Difference Pre-Trial vs Trial.\n",
    "- Step 1: Check null hypothesis of 0 difference between control store's Pre-Trial and Trial period performance.\n",
    "\n",
    "- Step 2: Proof control and trial stores are similar statistically\n",
    "    - Check p-value of control store's Pre-Trial vs Trial store's Pre-Trial.\n",
    "    - If <5%, it is significantly different. If >5%, it is not significantly different (similar).\n",
    "\n",
    "- Step 3: After checking Null Hypothesis of first 2 step to be true, we can check Null Hypothesis of Percentage Difference between Trial and Control stores during pre-trial is the same as during trial.\n",
    "    - Check T-Value of Percentage Difference of each Trial month (Feb, March, April 2019).\n",
    "    - Mean is mean of Percentage Difference during pre-trial.\n",
    "    - Standard deviation is stdev of Percentage Difference during pre-trial.\n",
    "    - Formula is Trial month's Percentage Difference minus Mean, divided by Standard deviation.\n",
    "    - Compare each T-Value with 95% percentage significance critical t-value of 6 degrees of freedom (7 months of sample - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Store 40\n",
      "Ttest_indResult(statistic=-0.5958372343168585, pvalue=0.5722861621434009) \n",
      "\n",
      "Store 155\n",
      "Ttest_indResult(statistic=1.429195687929098, pvalue=0.19727058651603258) \n",
      "\n",
      "Store 233\n",
      "Ttest_indResult(statistic=1.1911026010974504, pvalue=0.29445006064862156) \n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "[-4.30265273  4.30265273]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind, t\n",
    "\n",
    "# Step 1\n",
    "for num in [40, 155, 233]:\n",
    "    print(\"Store\", num)\n",
    "    print(ttest_ind(pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == num][\"ScaledSales\"],\n",
    "                   trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == num][\"ScaledSales\"],\n",
    "                   equal_var=False), '\\n')\n",
    "    #print(len(pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == num][\"ScaledSales\"]), len(trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == num][\"ScaledSales\"]))\n",
    "    \n",
    "alpha = 0.05\n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "print(t.ppf((alpha/2, 1-alpha/2), df=min([len(pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == num]),\n",
    "                       len(trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == num])])-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a = pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == 40][\"ScaledSales\"]\n",
    "b = trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == 40][\"ScaledSales\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Null hypothesis is true. There isn't any statistically significant difference between control store's scaled Pre-Trial and Trial period sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial store: 77 , Control store: 233\n",
      "Ttest_indResult(statistic=-1.2533353315065926e-15, pvalue=0.999999999999999) \n",
      "\n",
      "Trial store: 86 , Control store: 155\n",
      "Ttest_indResult(statistic=0.0, pvalue=1.0) \n",
      "\n",
      "Trial store: 88 , Control store: 40\n",
      "Ttest_indResult(statistic=0.0, pvalue=1.0) \n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "[-2.44691185  2.44691185]\n"
     ]
    }
   ],
   "source": [
    "# Step 2\n",
    "for trial, cont in trial_control_dic.items():\n",
    "    print(\"Trial store:\", trial, \", Control store:\", cont)\n",
    "    print(ttest_ind(pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == trial][\"TOT_SALES\"],\n",
    "                   pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == cont][\"ScaledSales\"],\n",
    "                   equal_var=True), '\\n')\n",
    "    #print(len(pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == trial][\"TOT_SALES\"]),len(pretrial_scaled_sales_control_stores[pretrial_scaled_sales_control_stores[\"STORE_NBR\"] == cont][\"ScaledSales\"]))\n",
    "\n",
    "alpha = 0.05\n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "print(t.ppf((alpha/2, 1-alpha/2), df=len(pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == trial])-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Null hypothesis is true. There isn't any statistically significant difference between Trial store's sales and Control store's scaled-sales performance during pre-trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial store: 77 , Control store: 233\n",
      "201902 : -0.7171038288055888\n",
      "201903 : 3.035317928855662\n",
      "201904 : 4.708944418758203\n",
      "\n",
      "\n",
      "Trial store: 86 , Control store: 155\n",
      "201902 : 1.4133618775921797\n",
      "201903 : 7.123063846042149\n",
      "201904 : 0.8863824572944162\n",
      "\n",
      "\n",
      "Trial store: 88 , Control store: 40\n",
      "201902 : -0.5481633746817604\n",
      "201903 : 1.0089992743637755\n",
      "201904 : 0.9710006270463645\n",
      "\n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "1.9431802803927816\n"
     ]
    }
   ],
   "source": [
    "# Step 3\n",
    "for trial, cont in trial_control_dic.items():\n",
    "    print(\"Trial store:\", trial, \", Control store:\", cont)\n",
    "    temp_pre = scaledsales_vs_trial[(scaledsales_vs_trial[\"c_STORE_NBR\"] == cont) & (scaledsales_vs_trial[\"trial_period\"]==\"pre\")]\n",
    "    std = temp_pre[\"Sales_Percentage_Diff\"].std()\n",
    "    mean = temp_pre[\"Sales_Percentage_Diff\"].mean()\n",
    "    #print(std, mean)\n",
    "    for t_month in scaledsales_vs_trial[scaledsales_vs_trial[\"trial_period\"] == \"trial\"][\"YEARMONTH\"].unique():\n",
    "        pdif = scaledsales_vs_trial[(scaledsales_vs_trial[\"YEARMONTH\"] == t_month) & (scaledsales_vs_trial[\"t_STORE_NBR\"] == trial)][\"Sales_Percentage_Diff\"]\n",
    "        print(t_month,\":\",(float(pdif)-mean)/std)\n",
    "    print('\\n')\n",
    "    \n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "conf_intv_95 = t.ppf(0.95, df=len(temp_pre)-1)\n",
    "print(conf_intv_95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 3 months' increase in performance that are statistically significant (Above the 95% confidence interval t-score):\n",
    "- March and April trial months for trial store 77\n",
    "- March trial months for trial store 86"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for trial, control in trial_control_dic.items():\n",
    "    a = trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == control].rename(columns={\"TOT_SALES\": \"control_TOT_SALES\"})\n",
    "    b = trial_full_observ[trial_full_observ[\"STORE_NBR\"] == trial][[\"STORE_NBR\", \"YEARMONTH\", \"TOT_SALES\"]].rename(columns={\"TOT_SALES\": \"trial_TOT_SALES\"})\n",
    "    comb = b[[\"YEARMONTH\", \"trial_TOT_SALES\"]].merge(a[[\"YEARMONTH\", \"control_TOT_SALES\"]],on=\"YEARMONTH\").set_index(\"YEARMONTH\")\n",
    "    comb.plot.bar()\n",
    "    cont_sc_sales = trial_scaled_sales_control_stores[trial_scaled_sales_control_stores[\"STORE_NBR\"] == control][\"TOT_SALES\"]\n",
    "    std = scaledsales_vs_trial[(scaledsales_vs_trial[\"c_STORE_NBR\"] == control) & (scaledsales_vs_trial[\"trial_period\"]==\"pre\")][\"Sales_Percentage_Diff\"].std()\n",
    "    thresh95 = cont_sc_sales.mean() + (cont_sc_sales.mean() * std * 2)\n",
    "    thresh5 = cont_sc_sales.mean() - (cont_sc_sales.mean() * std * 2)\n",
    "    plt.axhline(y=thresh95,linewidth=1, color='b', label=\"95% threshold\")\n",
    "    plt.axhline(y=thresh5,linewidth=1, color='r', label=\"5% threshold\")\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(trial)+\" and Control Store \"+str(control)+\" - Total Sales\")\n",
    "    plt.savefig(\"TS {} and CS {} - TOT_SALES.png\".format(trial,control), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Trial store 77 sales for March and April exceeds 95% threshold of control store. Same goes to store 86 sales for March.\n",
    "\n",
    "Next, we'll look into nCustomers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ratio of Store 77 and its Control store.\n",
    "ncust_ratio_77 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 77][\"nCustomers\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 233][\"nCustomers\"].sum()\n",
    "\n",
    "#Ratio of Store 86 and its Control store.\n",
    "ncust_ratio_86 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 86][\"nCustomers\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 155][\"nCustomers\"].sum()\n",
    "\n",
    "#Ratio of Store 77 and its Control store.\n",
    "ncust_ratio_88 = pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 88][\"nCustomers\"].sum() / pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == 40][\"nCustomers\"].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trial_full_observ = full_observ[(full_observ[\"YEARMONTH\"] >= 201902) & (full_observ[\"YEARMONTH\"] <= 201904)]\n",
    "scaled_ncust_control_stores = full_observ[full_observ[\"STORE_NBR\"].isin([233, 155, 40])][[\"STORE_NBR\", \"YEARMONTH\", \"nCustomers\"]]\n",
    "\n",
    "def scaler_c(row):\n",
    "    if row[\"STORE_NBR\"] == 233:\n",
    "        return row[\"nCustomers\"] * ncust_ratio_77\n",
    "    elif row[\"STORE_NBR\"] == 155:\n",
    "        return row[\"nCustomers\"] * ncust_ratio_86\n",
    "    elif row[\"STORE_NBR\"] == 40:\n",
    "        return row[\"nCustomers\"] * ncust_ratio_88\n",
    "\n",
    "scaled_ncust_control_stores[\"ScaledNcust\"] = scaled_ncust_control_stores.apply(lambda row: scaler_c(row), axis=1)\n",
    "\n",
    "trial_scaled_ncust_control_stores = scaled_ncust_control_stores[(scaled_ncust_control_stores[\"YEARMONTH\"] >= 201902) & (scaled_ncust_control_stores[\"YEARMONTH\"] <= 201904)]\n",
    "pretrial_scaled_ncust_control_stores = scaled_ncust_control_stores[scaled_ncust_control_stores[\"YEARMONTH\"] < 201902]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncust_percentage_diff = {}\n",
    "\n",
    "for trial, control in trial_control_dic.items():\n",
    "    a = trial_scaled_ncust_control_stores[trial_scaled_ncust_control_stores[\"STORE_NBR\"] == control]\n",
    "    b = trial_full_observ[trial_full_observ[\"STORE_NBR\"] == trial][[\"STORE_NBR\", \"YEARMONTH\", \"nCustomers\"]]\n",
    "    ncust_percentage_diff[trial] = b[\"nCustomers\"].sum() / a[\"ScaledNcust\"].sum()\n",
    "    b[[\"YEARMONTH\", \"nCustomers\"]].merge(a[[\"YEARMONTH\", \"ScaledNcust\"]],on=\"YEARMONTH\").set_index(\"YEARMONTH\").rename(columns={\"ScaledSales\":\"Scaled_Control_nCust\", \"TOT_SALES\":\"Trial_nCust\"}).plot.bar()\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(trial)+\" and Control Store \"+str(control))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{77: 1.2306529009742622, 86: 1.1354166666666667, 88: 1.0444876946258161}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncust_percentage_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "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>c_STORE_NBR</th>\n",
       "      <th>YEARMONTH</th>\n",
       "      <th>c_ScaledNcust</th>\n",
       "      <th>t_STORE_NBR</th>\n",
       "      <th>t_nCustomers</th>\n",
       "      <th>nCust_Percentage_Diff</th>\n",
       "      <th>trial_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>233</td>\n",
       "      <td>201902</td>\n",
       "      <td>45.151007</td>\n",
       "      <td>77</td>\n",
       "      <td>45</td>\n",
       "      <td>-0.003350</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>233</td>\n",
       "      <td>201903</td>\n",
       "      <td>40.134228</td>\n",
       "      <td>77</td>\n",
       "      <td>50</td>\n",
       "      <td>0.218913</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>233</td>\n",
       "      <td>201904</td>\n",
       "      <td>30.100671</td>\n",
       "      <td>77</td>\n",
       "      <td>47</td>\n",
       "      <td>0.438370</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>155</td>\n",
       "      <td>201902</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>86</td>\n",
       "      <td>107</td>\n",
       "      <td>0.118812</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>155</td>\n",
       "      <td>201903</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>86</td>\n",
       "      <td>115</td>\n",
       "      <td>0.200957</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>155</td>\n",
       "      <td>201904</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>86</td>\n",
       "      <td>105</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>40</td>\n",
       "      <td>201902</td>\n",
       "      <td>127.610209</td>\n",
       "      <td>88</td>\n",
       "      <td>124</td>\n",
       "      <td>-0.028697</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40</td>\n",
       "      <td>201903</td>\n",
       "      <td>120.464037</td>\n",
       "      <td>88</td>\n",
       "      <td>134</td>\n",
       "      <td>0.106388</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>40</td>\n",
       "      <td>201904</td>\n",
       "      <td>121.484919</td>\n",
       "      <td>88</td>\n",
       "      <td>128</td>\n",
       "      <td>0.052228</td>\n",
       "      <td>trial</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    c_STORE_NBR  YEARMONTH  c_ScaledNcust  t_STORE_NBR  t_nCustomers  \\\n",
       "7           233     201902      45.151007           77            45   \n",
       "8           233     201903      40.134228           77            50   \n",
       "9           233     201904      30.100671           77            47   \n",
       "19          155     201902      95.000000           86           107   \n",
       "20          155     201903      94.000000           86           115   \n",
       "21          155     201904      99.000000           86           105   \n",
       "31           40     201902     127.610209           88           124   \n",
       "32           40     201903     120.464037           88           134   \n",
       "33           40     201904     121.484919           88           128   \n",
       "\n",
       "    nCust_Percentage_Diff trial_period  \n",
       "7               -0.003350        trial  \n",
       "8                0.218913        trial  \n",
       "9                0.438370        trial  \n",
       "19               0.118812        trial  \n",
       "20               0.200957        trial  \n",
       "21               0.058824        trial  \n",
       "31              -0.028697        trial  \n",
       "32               0.106388        trial  \n",
       "33               0.052228        trial  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a compiled ncust_percentage_difference table\n",
    "temp1 = scaled_ncust_control_stores.sort_values(by=[\"STORE_NBR\", \"YEARMONTH\"], ascending=[False, True]).reset_index().drop([\"nCustomers\", \"index\"], axis=1)\n",
    "temp2 = full_observ[full_observ[\"STORE_NBR\"].isin([77,86,88])][[\"STORE_NBR\", \"YEARMONTH\", \"nCustomers\"]].reset_index().drop([\"index\", \"YEARMONTH\"], axis=1)\n",
    "scaledncust_vs_trial = pd.concat([temp1, temp2], axis=1)\n",
    "scaledncust_vs_trial.columns = [\"c_STORE_NBR\", \"YEARMONTH\", \"c_ScaledNcust\", \"t_STORE_NBR\", \"t_nCustomers\"]\n",
    "scaledncust_vs_trial[\"nCust_Percentage_Diff\"] = (scaledncust_vs_trial[\"t_nCustomers\"] - scaledncust_vs_trial[\"c_ScaledNcust\"]) / (((scaledncust_vs_trial[\"t_nCustomers\"] + scaledncust_vs_trial[\"c_ScaledNcust\"])/2))\n",
    "\n",
    "scaledncust_vs_trial[\"trial_period\"] = scaledncust_vs_trial[\"YEARMONTH\"].apply(lambda cell: label_period(cell))\n",
    "scaledncust_vs_trial[scaledncust_vs_trial[\"trial_period\"] == \"trial\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check significance of Trial minus Control stores nCustomers Percentage Difference Pre-Trial vs Trial.\n",
    "- Step 1: Check null hypothesis of 0 difference between control store's Pre-Trial and Trial period performance.\n",
    "- Step 2: Proof control and trial stores are similar statistically\n",
    "- Step 3: After checking Null Hypothesis of first 2 step to be true, we can check Null Hypothesis of Percentage Difference between Trial and Control stores during pre-trial is the same as during trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Store 40\n",
      "Ttest_indResult(statistic=0.644732693420032, pvalue=0.5376573016017127) \n",
      "\n",
      "Store 155\n",
      "Ttest_indResult(statistic=1.3888888888888882, pvalue=0.204345986327886) \n",
      "\n",
      "Store 233\n",
      "Ttest_indResult(statistic=0.8442563765225701, pvalue=0.4559280037660254) \n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "[-4.30265273  4.30265273]\n"
     ]
    }
   ],
   "source": [
    "# Step 1\n",
    "for num in [40, 155, 233]:\n",
    "    print(\"Store\", num)\n",
    "    print(ttest_ind(pretrial_scaled_ncust_control_stores[pretrial_scaled_ncust_control_stores[\"STORE_NBR\"] == num][\"ScaledNcust\"],\n",
    "                   trial_scaled_ncust_control_stores[trial_scaled_ncust_control_stores[\"STORE_NBR\"] == num][\"ScaledNcust\"],\n",
    "                   equal_var=False), '\\n')\n",
    "    \n",
    "alpha = 0.05\n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "print(t.ppf((alpha/2, 1-alpha/2), df=min([len(pretrial_scaled_ncust_control_stores[pretrial_scaled_ncust_control_stores[\"STORE_NBR\"] == num]),\n",
    "                       len(trial_scaled_ncust_control_stores[trial_scaled_ncust_control_stores[\"STORE_NBR\"] == num])])-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial store: 77 , Control store: 233\n",
      "Ttest_indResult(statistic=0.0, pvalue=1.0) \n",
      "\n",
      "Trial store: 86 , Control store: 155\n",
      "Ttest_indResult(statistic=0.0, pvalue=1.0) \n",
      "\n",
      "Trial store: 88 , Control store: 40\n",
      "Ttest_indResult(statistic=-7.648483953264653e-15, pvalue=0.999999999999994) \n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "[-2.44691185  2.44691185]\n"
     ]
    }
   ],
   "source": [
    "# Step 2\n",
    "for trial, cont in trial_control_dic.items():\n",
    "    print(\"Trial store:\", trial, \", Control store:\", cont)\n",
    "    print(ttest_ind(pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == trial][\"nCustomers\"],\n",
    "                   pretrial_scaled_ncust_control_stores[pretrial_scaled_ncust_control_stores[\"STORE_NBR\"] == cont][\"ScaledNcust\"],\n",
    "                   equal_var=True), '\\n')\n",
    "\n",
    "alpha = 0.05\n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "print(t.ppf((alpha/2, 1-alpha/2), df=len(pretrial_full_observ[pretrial_full_observ[\"STORE_NBR\"] == trial])-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial store: 77 , Control store: 233\n",
      "201902 : -0.19886295797440687\n",
      "201903 : 8.009609025380932\n",
      "201904 : 16.114474772873923\n",
      "\n",
      "\n",
      "Trial store: 86 , Control store: 155\n",
      "201902 : 6.220524882227514\n",
      "201903 : 10.52599074274189\n",
      "201904 : 3.0763575852842706\n",
      "\n",
      "\n",
      "Trial store: 88 , Control store: 40\n",
      "201902 : -0.3592881735131531\n",
      "201903 : 1.2575196020616801\n",
      "201904 : 0.6092905590514273\n",
      "\n",
      "\n",
      "Critical t-value for 95% confidence interval:\n",
      "1.9431802803927816\n"
     ]
    }
   ],
   "source": [
    "# Step 3\n",
    "for trial, cont in trial_control_dic.items():\n",
    "    print(\"Trial store:\", trial, \", Control store:\", cont)\n",
    "    temp_pre = scaledncust_vs_trial[(scaledncust_vs_trial[\"c_STORE_NBR\"] == cont) & (scaledncust_vs_trial[\"trial_period\"]==\"pre\")]\n",
    "    std = temp_pre[\"nCust_Percentage_Diff\"].std()\n",
    "    mean = temp_pre[\"nCust_Percentage_Diff\"].mean()\n",
    "    #print(std, mean)\n",
    "    for t_month in scaledncust_vs_trial[scaledncust_vs_trial[\"trial_period\"] == \"trial\"][\"YEARMONTH\"].unique():\n",
    "        pdif = scaledncust_vs_trial[(scaledncust_vs_trial[\"YEARMONTH\"] == t_month) & (scaledncust_vs_trial[\"t_STORE_NBR\"] == trial)][\"nCust_Percentage_Diff\"]\n",
    "        print(t_month,\":\",(float(pdif)-mean)/std)\n",
    "    print('\\n')\n",
    "    \n",
    "print(\"Critical t-value for 95% confidence interval:\")\n",
    "conf_intv_95 = t.ppf(0.95, df=len(temp_pre)-1)\n",
    "print(conf_intv_95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 5 months' increase in performance that are statistically significant (Above the 95% confidence interval t-score):\n",
    "- March and April trial months for trial store 77\n",
    "- Feb, March and April trial months for trial store 86"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAEyCAYAAADwcgnpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl4FeXZP/DvnZysJEASwpYAQbKRsCcNIvAqcfmJIEqRxVCBvlIEtVaWVq1W2RS02BfRuqIoUtywVgqKioKiUDEga0iAaIBA2AIJCQTIcv/+mDn0ELOcnKyH+X6u61zJmeeZmXvmmXPumWfmzIiqgoiIiK58Ho0dABERETUMJn0iIiKLYNInIiKyCCZ9IiIii2DSJyIisggmfSIiIotosKQvIn8RkZedrLtMRGbWc0hXNBGJFBG3/T0mtwH3ICITRWR9I87/fhE5LiKFItKiseIgchcuJX3zA2Z/lYlIkcP7sRWNo6pzVHVy7cIFRMRHRBaKyGERKRCRn0VkgUN5tohcV9v51CCez8utj4si8qNZdlW5skIRURH5Q0PFVxURuUtEtojIWRHJEZHVInJNHUy3XhN2E9wGHhaR3WYsP4nINIcyDxFZJyInRCRfRLaJyFCH8htEZJeI5InISRH5UETa1SKWZeZnso/DsFgRKXF9CZsmEfEFsADAIFUNUNX8Cur4iMhsEdlvbudZIrJYRDrWct7fisiE2kyDqDG4lPTND1iAqgYAOAjgVodh/yhfX0RstQ3UwWMAegBIANAcQDKAbXUxYVfiVNWbyq2PzQA+MMt+KlfWC0AZgH/WRby1ISJ/gvGFOQdAKIBOAF4FcFsDzLu220OT2gZMvwHQEsAQAFNF5A5zuAJ4AEA7VW0B4F4A74hIa7N8F4AbVbUlgDAAWQD+7mIMdqcBzK3lNBqcC+u+LQAfVd1dyfQExmdtMIDRAFrA+AzugLHNXBFExLOxYyA3oqq1esH4krqh3LC5AN4D8A6AAgATzGFvmuUeAFYAOAogD8B6AF0dxl8GYGYl81sD4P5Kyt6BkVSLABQCmGYOvx3AbnNeXwGIcRgnG8AfAewEcNEcFg7gIwAnAPwM4D4n10UXAKUAOlRSPgfAF1WM3w/Af8w4cwAsAuBlltlgJJB7AOyH8cW+yGFcTwD/ByAXQCaA+43mrXA+QQDOARheRSy+5vxzABwG8DcA3mbZDWa7/8lcR0cAjDPL7gVQDOCi2QYfVbGe4wF8bS7vTgBD3H0bMMd9EcD/VTBcAFwN4AKAPpWs878C2FGLz+MyGDtzxwH0N4fFAigpt7zXlfu82j+bkeZ2NsGsdwrA7wD0NddPHoDnHMadCOAbc5nzAeyBceRtL28JYIm5HWUDmA3Ao9y4i8z5/KK9K9sOAXQFcNaMtRDA5xWMezOM7TysivVV1brwB7AcxmcqD8YOfSsAT8P4nJ83573QrD8AQKq5HjYD6Osw3W/NZf+PGfe/AISY2+sZAN8D6OhQPw7AWnO9pAMYUa6N/w7jc3AWwHUAhprrvsBcpqmubkN8Xdmv2k+g8qR/EcCtMBK8H36Z9CcACDQ/1C8ASHUYv6ov/JkADgCYAqAbAClXXv5D3NX8YCYD8ALwZwB78d9kmg1gC4wveT8YyXObWc8bxpdgFoDrnVgXswGsraRMzOn8porxfwXjy9UG4CozzvvNMnvS/xjGEUuE+YVwg1l+P4ykFm5+mXyDypP+ULN9PKuI5SkAG2H0ArQ2v5SeMMtuAFAC4AlznQ4zv3yaV9Z+FaxnbxjJ9E/mNG4w2ynSzbcBDxhHkhPLDf8URrJXAKthJj6zrDOMpFJmtkul24gT819mrp9pANabw1xJ+i8A8AFwC4wdqI/MbSEcRhK071BMNLeFB8x1m2IuS0uzfBWMHQJ/GEfmWwDcXW7cKeY696vhdhiJSrZxs3wBgC+rWV9VrYv7YCRn+zaRCCDALPsWwASH8VrBSPZ3wvis/sZcT0EO9ffC+FwHwUjkGQAGmfWXA3jNrBsIYwdnnFmWYE4rxqGNT8M4SPAw2+kEgGvM8mBUsFPJF1+qWq8X8n2rqv9W1TJVLXIsMIe9qaoFqnoexpdUgog0c2K6c2F8mO+C8QWSLSK/qaL+GAArVfUrVS0GMB9Gl3BfhzrPqWq2GefVMJLXU6p6UVX3A3jdnE6lzK7EuwC8WUmV62B8GCvt2lfVH1T1e1UtUdWfYHS3X1uu2jxVzVfVLBg9JL3M4aNgHF1mq2quuZyVCQFwXFVLq6gzFkbSPaGqx2Hs0NzlUH4ewFxVLVbVlTASWnQV0wMuX8/9YSTUv5rTWAsjMVa5nk1NchswzYGRyJY6DlTVwQACYOxwfaGqZQ5lP6vRvR8K4HEYyaC2XgQQJSI3ujj+HFW9oKqfwNgRWWZuC9kwElhvh7o5AJ4323E5gJ8ADBaRMADXwzjqPKeqRwEsxOXr8aCqvqSqpeW/J0zVbYdVCTFjc1UxjGQeacaXqqqFldS9FcBuVX3H/Pwug7EehjjUeV2NU36nAXwGYK+qrlPVEhinBO3rdJhZttSc1hYYOx93OEzrI1XdZH6XXjBjjRORQFU9papba7HcdAWrz6R/qLICEfEUkWfMi57OwOiuBowPWJXMD8HzqnoNjK7DZwC8KSKVJZz2MI4K7eOXwdi7D6sk1k4AOpoXVuWJSB6Mo9G21YR2LYwvmcqS+ngAH6jqucomYF5wtVpEjprrZTZ+uU6OOvx/DkYiAYzldFyOA6hcLoDWIlJV+7crN40DuHydnSy30+AYS2Uc42sP4wvf8RcG5edRoaa6DZgXaI4BMFRVL1YQd7GqrgYwVERuqaA8F8ZR3MqK2kZExjtcEPrvqmIxd6bnwsVz+6p6zOFtEYDy7x3bOruCdmwPYz36ADjmsB7/DqCNQ91KvydM1W2HVck1x3fVmzC62N83LxqdX8V1B5dtY6bysTq7TjsB6F9u+xuNy5el/HobDmNn4aCIrBeRviCqQH0m/ap+LjYORrdhMoyu6khzuNRoBqpFqvocjK7brpXM9wiMD5ExA+PLNBxG91lFsR4CsE9VWzq8AlX11mrCGQ9gRUVJ3ezBGAHgrWqm8QqMC7siVbU5jKM+Z9dJDoAODu+rujr5OxhHo8OqmV4nh/cdcfk6q0plbe84/AiADmYPiSvzMCbYRLYBEZkEYDqMUwBHqgnbBuP6j8rK2qKCHShVfUv/e2FoddsjACyG0XtQvp3Pwuhut6tuh7Y64eXed4Sxzg/B2BkMdliPzVW1h0Pd6n5WWpvtcC2AfiLSvoo6la4Ls5dnpqp2hXG+fjiMnoeK4r5sG3MhVkeHYJyWcNz+AlT1foc6l83f7CEcBuMUyCoA77owX7KAxro5TyCM7uBcGB+4J50dUUSmisj/iIifiNhE5H9hXBdgv3r7GIzzZnbvAxgmIteJiBeMC7YKYJwbrMgmABdFZLqI+Jq9Et1FJKGKmPxhdL29WUmVETAurNpQzeIFwjgveFZEusK4aM9Z7wN4UETCRCQEwEOVVTS7F2cBeElEhpnr0ktEhoiI/bTAOwAeF5FWIhIK4C8wjkKdUb4NKrIRxo7HdHPeyTB2BN+vbuJNbRsQkfEw1ueN5mkXx7I4EbnZnI63WfcaGNdcQERGiEiUGFoDeBbAD6p6prr1UB3zVMYs/HJb2AZgjLnukgD8upazaifG7+VtIjIGxg7NGlU9BONCzQUi0lyMny9Gisj/1GDatdkOPwOwDsBHItLbbMfmInKv2Q5AFetCRJJFpJu5k3gGRhe6vXer/Da2CkC8iIw2p5UC42Dmkxosq91Kc1op5mfDS0SSRCSmosrm5yBFRJqbbV7gECfRZRor6S+BsWd8BMbFZxtrMO55GOcFjwE4CSMx/lpV7V1rTwGYZXaLPajGz3nGA3gJxsUuNwMYZn44fsE8v3YLgCQYF2+dhHEE3ryKmEaY064sqY8HsLRcF2hFppt1C8x5vldNfUcvAfgSxhXWP8D4dUSlVPVpGMlgJoydr0MwLqj6l1llFoDt5vR2wEiQ85yMZTGAniJyWkQqjMM8D3krjJ8InoRxhXaKqu51YvpNbRuYC+PUzhaH7vcXzDIPGKdpjpuvewHcoarbzfIOAD6H0VOxHcb5c8dzt7W1zJyvo0dhXNyXByOJLq/lPDbC+CXGKRjb0whzxxIwLmhrBiANxsVnH6BmPQsub4fm5+3XMNbvChiJeyeM62C+MqtVtS7awzhddwbG99RaGDshgLH93WluY39T1RMwelQegvF5mgrjNM+pGiyrPe58AP8PxrrLgXFKbx6MUyWVGQ/ggHla8G44f90DWYxUn4eIiIjoSsB77xMREVkEkz4REZFFMOkTERFZBJM+ERGRRdTlg3Cq1apVK42IiGjIWRIRub0tW7acVNXQxo6D3F+DJv2IiAikpqY25CyJiNyeiFR1h00ip7F7n4iIyCKY9ImIiCyCSZ+IiMgimPSJiIgsgkmfiIjIIpj0iYiILMKpn+yJSBb++7jGElVNFJFgGE+Bi4DxJLJRDk/WIiIioiamJkf6g1S1l6ommu8fBvClqkbBeKTrw3UeHREREdWZ2nTv3wbgLfP/twDcXvtwiIiIqL44e0c+BfC5iCiAV1T1VQBtVDUHAFQ1R0RaVzSiiEwCMAkAOnbsWAchkxVEPLy6QeeXNX9Ig86PiKgxOJv0+6vqETOxfyEi6c7OwNxBeBUAEhMT1YUYiYiIqA441b2vqkfMv8cBfAQgCcAxEWkHAObf4/UVJBEREdVetUlfRJqJSKD9fwA3AdgFYCWA8Wa18QA+rq8giYiIqPac6d5vA+AjEbHXX66qa0TkBwDvi8jdAA4CGFl/YRIREVFtVZv0VfUnAD0rGJ4L4Pr6CIqIiIjqHu/IR0REZBFM+kRERBbBpE9ERGQRTPpEREQWwaRPRERkEUz6REREFuHsbXiJiJzGZycQNU080iciIrIIJn0iIiKLYNInIiKyCCZ9IiIii2DSJyIisggmfSIiIotg0iciIrKIK/p3+g3+W2HflAadH2bmN+z8iIjIrfFIn4iIyCKY9ImIiCyCSZ+IiMgimPSJiIgsgkmfiIjIIpj0iYiILIJJn4iIyCKY9ImIiCyCSZ+IiMgimPSJiIgsgkmfiIjIIpj0iYiILIJJn4iIyCKY9ImIiCyCSZ+IiMgimPSJiIgsgkmfiIjIIpxO+iLiKSI/isgq831nEfleRPaJyHsi4l1/YRIREVFtiao6V1FkGoBEAM1VdaiIvA/gn6r6roi8DGC7qr5U9TQSFUitddBERNYiW1Q1sbGjIPdnc6aSiIQDGALgSQDTREQAJANIMau8BWAmgCqTfkICkNqAOT/i4dUNNzMAWb4p1VeqSzPzG3Z+DajB227+kAad35WO7Ve3RBo7ArpSOJX0ASwE8CcAgeb7EAB5qlpivs8GEFbRiCIyCcAkAOjYsaPrkRLVp5ktGnh+V+4OGxE1XdWe0xeRoQCOq+oWx8EVVK3wPIGqvqqqiaqaGBoa6mKYREREVFvOHOn3BzBMRG4B4AugOYwj/5YiYjOP9sMBHKm/MImIiKi2qj3SV9VHVDVcVSMAjAHwlaqOBbAOwB1mtfEAPq63KImIiKjWavM7/YdgXNS3H8Y5/tfrJiQiIiKqD85eyAcAUNX1ANab//8EIKnuQyIiIqL6wDvyERERWQSTPhERkUUw6RMREVkEkz4REZFFMOkTERFZBJM+ERGRRTDpExERWQSTPhERkUUw6RMREVkEkz4REZFFiGqFT8StF4kimtpgcyMiujIIsEVVExs7DnJ/Nbr3fq0lJACpDZf2Ix5e3WDzAoAs35QGnR9m5jfs/BoQ2869NXj7zR/SoPNrcCKNHQFdIdi9T0REZBFM+kRERBbBpE9ERGQRTPpEREQWwaRPRERkEUz6REREFsGkT0REZBEN+zt9IiKqE1u2bGlts9kWA+gGHsCRoQzArpKSkokJCQnHK6rApE9E5IZsNtvitm3bdg0NDT3t4eHRcLdWpSarrKxMTpw4EXf06NHFAIZVVId7h0RE7qlbaGjoGSZ8svPw8NDQ0NB8GL0/FddpwHiIiKjueDDhU3nmNlFpbmfSJyIisggmfSIicsmcOXNaR0VFxUdGRsbPnj27tX34tGnT2rdu3bpHbGxsXGxsbNx7773XAgA+//zzZtHR0XHdunXrumvXLh8AOHnypOeAAQOiysrKKpzH7NmzWxcUFFzKVf7+/r3rejkyMjK8o6Ki4msyzogRIyKWLFkSVH74qlWrAgcNGhRZd9HVLSZ9IiKqsR9++MF36dKloVu3bt2zZ8+e3WvWrGm5c+dOH3v55MmTj6Wnp6elp6enjR49Oh8AFixY0PbDDz/MnDNnzuHnnnsuFAAeeeSRdo888kiOh0fF6eiVV15pU1hYWKNcVVxcXIslu7Ix6RMRUY3t3LnTr0+fPoWBgYFlXl5e6N+/f8F7773XsqpxvLy89Ny5cx5nz5718PLy0t27d/vk5OR4DxkypLCi+nPnzm19/Phxr2uvvTa6b9++0fbhv//978NiYmLievbsGXvo0CEbYBx5T5w4Mbxv377R9957b/iZM2c8Ro4cGdGtW7euXbt2jVu2bFlLAEhNTfXt3r1719jY2Ljo6Og4+45KaWkpxowZ0ykyMjK+f//+UYWFhQIAGzdu9OvZs2dsdHR03I033tjlxIkTnuXjXLFiRfPOnTvHJyQkxKxYsaLKddDYmPSJiK4AIkio61dV8+vVq1fR999/H3j06FHPgoICjy+++KLFoUOHvO3lr7/+euvo6Oi4kSNHRtgT5aOPPpozadKkTs8//3zr6dOnH3/ooYfC5s2bd7iyeTz22GPHW7duXfz111/v/f777/cCQFFRkUe/fv0KMzIy0vr161f4/PPPh9rrZ2Zm+n733Xd7X3vttew///nP7QYNGnRm165dezZs2JDx2GOPhZ85c8bj+eefD7333nuPpaenp+3YsWNP586dLwLAwYMHfR944IHj+/fv392iRYvSpUuXBgHAhAkTOj/11FPZe/fuTYuPjy966KGH2jvGeO7cObn//vsjVq5cuf+HH37IOH78uJcr7ddQ+Dt9IqIrgCq2NOT8+vTpc/4Pf/jD0eTk5Gh/f/+yuLi4czabkVKmTp16/JlnnjkiInjwwQfD7r333g4ffPBB1jXXXFO0ffv2dAD49NNPA9q2bXtRVTFkyJCrbDabvvDCC4c6dOhQUtV8vby8dMyYMfkAkJCQcHbt2rXN7WW//vWvT9tjWL9+ffPPPvus5aJFi9oCwIULF2T//v3e/fr1O7tgwYJ22dnZ3mPGjDndvXv3CwAQFhZ24ZprrikCgN69e5/Lysryyc3N9SwoKPC090T87ne/yx05cuRVjvFs27bNNzw8/IJ9OmPHjs1dvHhxKJooHukTEZFLpk6dejItLW1PampqRnBwcGlUVNR5AOjQoUOJzWaDp6cn7r///hPbtm1r5jheWVkZ5s6d227evHk5jz32WPu5c+ceueuuu3KfeeaZNtXN02azqf38v81mQ0lJidjLAgICLl0NqKpYsWLFfvt1BTk5OTv79OlzfvLkyac+/vjj/X5+fmWDBw+OXrlyZSAAeHt7X/r5o6enpzpOtzoiTldtdEz6RETkksOHD9sAYN++fd6rV69ueffdd58CgAMHDlzq4n733XdbxsTEFDmO98ILL4TcfPPN+aGhoaVFRUUenp6e6uHhgXPnzv0iJzVr1qw0Pz+/xrlq0KBBZ5599tk29l8FfPfdd34AkJaW5t21a9cLjz322PGbbropb9u2bX6VTSMkJKS0efPmpWvWrAkAgNdffz2kX79+l11/0KtXr/PZ2dneu3fv9jGXN7imsTYkdu8TEZFLhg0b1iUvL89ms9l04cKFB0NDQ0sB4A9/+EN4WlqaHwCEh4dfXLJkyQH7OAUFBR7/+Mc/Qr755pt9ADB9+vRjd9xxRxcvLy995513fio/j/Hjx58cPHhwVOvWrYvt5/WdMX/+/COTJk3qGBsbG6eqEh4efmHdunX733777eAPPvggxGazaWhoaPG8efOO5OXl/eLiPLslS5b8PGXKlE4PPPCAR8eOHS+88847WY7l/v7++vzzzx8YOnRoZHBwcEnfvn0L9+zZU+mORGMT1apv6CQivgC+AeADYydhhao+ISKdAbwLIBjAVgB3qerFqqaVmJioqampdRK4MyIeXt1g8wKALN+UBp0fZuY37PwaENvOvTV4+80f0qDza2giskVVEx2Hbd++Patnz54nGysmarq2b9/eqmfPnhEVlTnTZXIBQLKq9gTQC8DNInI1gKcB/J+qRgE4DeDuOoqXiIiI6kG1SV8N9nMYXuZLASQDWGEOfwvA7fUSIREREdUJpy6OEBFPEdkG4DiALwBkAshTVftPK7IBhNVPiERERFQXnLqQT1VLAfQSkZYAPgLQtaJqFY0rIpMATAKAjh07uhgmEVEVZrZo4Pld2ddk0JWrRj+DUNU8AOsBXA2gpYjYdxrCARypZJxXVTVRVRNDQ5vs/QqIiIiueNUmfREJNY/wISJ+AG4AsAfAOgB3mNXGA/i4voIkIiKi2nPmSL8dgHUisgPADwC+UNVVAB4CME1E9gMIAfB6/YVJRERNTVhYWPfo6Oi42NjYuG7dul067TtlypSw6OjouOHDh0fYh/39738PnjNnTuuKppORkeH98ssvX7qpzaJFi0LGjRtX5+eDp02b1v7xxx+v9q5/jip7lG9lj9Zt6qo9p6+qOwD8YqFV9ScASfURFBERuYevv/56b7t27S7dLz83N9dz8+bNAXv37k0bNmxY582bN/vFxcWdX7ZsWav169fvq2ga+/bt83nvvfeCJ0+efKom8y4pKYH9XvvkHN6Gl4iI6oyHh4cWFxdLWVkZzp8/7+Hl5aUzZ85se9999x338fGp8ILvRx99NCw1NTUgNjY2btasWa0B4OjRo14DBw6M6tSpU7fJkyeH2+v6+/v3fvDBB9v36NEj9ssvvwzYsGGD/69+9auY+Pj4rgMGDIiy3wJ47ty5rbt06RIfHR0dN3To0EsPydmzZ49fUlJSTHh4ePe5c+de6nmYOXNmm6ioqPioqKj42bNn/6JHoqysDOPGjevYpUuX+Ouuuy7y5MmTbrm34ZZBExFROSJVPgrXJarVPrnv+uuvjxIR/Pa3vz0xY8aMk0FBQWXDhg07HRcXFzdw4MAzwcHBpVu3bm22YMGCnMqm8eSTTx5+9tln26xbt24/YHTvp6Wl+W/fvj3Nz8+vLDIystuMGTOORUZGFhcVFXl069ataOHChUcuXLggV199dczq1av3t2/fvuS1114LmjFjRtgHH3yQtWjRorYHDhzY6efnpydPnrx0m939+/f7bty4MSMvL8+za9eu3f74xz+e2Lx5s9/y5ctDtmzZskdVkZCQ0PX6668v6N+//6VnBrz99tst9+/f75ORkbE7Ozvbq3v37vETJkzIre0qbmhM+kREVwInEnRd++6779IjIiKKDx8+bEtOTo6Oj48/P3jw4MK5c+cemzt37jEAGD16dKc5c+Yc/tvf/tZq7dq1zbt161b0zDPPVLoDYDdgwIAzISEhpQAQGRl5PjMz0ycyMrLY09MTEyZMOA0AO3bs8Nm3b59fcnJyNGAcjYeGhhYDQExMTNHw4cM7Dxs2LG/s2LF59unedNNNeX5+furn51cSHBxcnJ2dbVu/fn3ALbfckte8efMyABgyZMjpdevWBTom/a+//jpw1KhRp2w2GyIiIor79etXUJfrsqGwe5+IiFwSERFRDABhYWElQ4YMydu0adNlj9C1P9mue/fuF5YvXx7yySef/JSWlua3c+dOn+qmXf5Rt8XFxWIOL7Ofx1dViYyMLLI/Pnfv3r1p33333T4AWLdu3b777rvvxJYtW5r17Nkzrri4GADgeIrB09MTJSUl1T6Dxs6dHqFbGSZ9IiKqsTNnznicPn3aw/7/unXrmvfo0eOyR+g+9thjYU8//fSRixcvSllZmQCAiGhhYeFluadFixalhYWFlT7prjI9evQ4f+rUKdvatWubAcCFCxckNTXVt7S0FJmZmd633nprwYsvvphdUFDgmZ+fX+n0k5OTCz/55JOWBQUFHmfOnPH45JNPggYNGnTZkfy1115b8MEHHwSXlJTgwIEDXv/5z38CaxpvU8DufSIiqrHs7Gzb8OHDIwGgtLRURowYkXvHHXecsZe//fbbLfv06XPW3huQmJhYaP68r6hfv36X7RwkJSUV2Ww2jYmJiUtJSTkZFBRU6kwMvr6++u6772Y+8MADHQsKCjxLS0tlypQpx7p3734hJSWlc0FBgaeqyj333HOsVatWlU5zwIAB51JSUnL79OnTFQDuuuuuE45d++awvC+//LJ5TExMfOfOnc8nJSW5Zfe+090adYGP1q1jV/CtQNl27o3tV7f4aF2qido+WpeIiIiuAEz6REREFsGkT0REZBFM+kRERBbBpE9ERGQRTPpEREQWwd/pExFdASIeXl2n997Pmj+kytv6njx50nPx4sXBDz/88ImKynv37h37448/plc1DX9//97nzp37saaxXbhwQaZOndp+9erVQd7e3urr61v2l7/85fCoUaPOVD/2f23cuNHv0KFD3qNHj76yf0PrgEf6RERUY7m5uZ6vv/76L55GV1JiPGW3uoRfG1OnTm1/9OhRr/T09N379u3b/cknn+w7c+ZMje/ol5qa6r969eoW9RFjZey3A24sPNInIqIamz59evihQ4d8YmNj42w2mzZr1qy0devWxWlpaf6ZmZm77Ufx+fn5HjfffHNkfn6+Z0lJiTz++ONHfvOb3+RVN/1Vq1YFzp49u31wcHBxRkaGX/fu3c/961//+vns2bMey5cvD/3pp592+Pn5KQB06NChZOLEiaeBy3sPlixZErRq1aoWH374YdYbb7wRNG/evPYeHh4aGBhY+u233+6dN29e+/Pnz3vExsYGTJ8+PWfYsGFnxo4dG3Hw4EEfPz+/sldfffVA3759i6ZNm9Y+KyvL+9ixY15ZWVm+Tz311KFNmzYFfPXVV83hhe3FAAAXK0lEQVTbtGlTvHbt2v0+Pj66YcMG/2nTpnU4d+6cR1BQUMk//vGPrE6dOhUnJSXFJCUlFX7//fcBt9xyS16nTp0uOsaSmpqaUb+t9V9M+kREVGPPPvts9tChQ/3S09PTVq1aFThy5MjIH3/8cXdsbOxFx3r+/v5lq1ev3h8cHFyWk5Nj69u3b2xKSkqeh0f1Hc179uzx27Zt208RERHFCQkJsV988UVAy5YtS9u1a3cxODi4rCbxzp8/v93nn3++t3PnzsUnT5709PX11UceeeRIampqs6VLlx4EgPHjx3fo2bPnubVr12auXLkycPz48Z3T09PTAODAgQM+Gzdu3Lt161bf5OTk2Lfeeivz5Zdfzr7xxhu7vP/++y1GjRqV/8ADD3Ss6DG/AJCXl+f5ww8/ZABAdHR0nGMsNVmO2mL3PhER1VqPHj3Olk/4AFBWViYPPvhgeHR0dNygQYOijx8/7p2dne3UAWf37t3PdunSpdjT0xPx8fHnMjMzvV2NLzExsXDs2LERzz77bCv7KYjyNm/eHHj33XfnAsCwYcMK8vLybLm5uZ4AcMMNN+T7+PhoUlJSUWlpqdifMxAfH1/0888/ezs+5jc2Njbur3/9a7sjR4542ad95513nqpJLPWFR/pERFRr/v7+FR55v/LKK8G5ubm2nTt37vHx8dGwsLDuRUVFTh1wVvQY3Li4uAs5OTnep0+f9ggKCvrFPB0ff1tUVHTpzfLlyw9+9dVXzVauXNmiV69e8du2bdtdftyKnkUjIuoYi6enJ2w2m9p7Kjw8POyP55XIyMiibdu2VXgtQ2Bg4KVYK4qlbdu2Tj1kqLZ4pE9ERDXWokWL0rNnz1abQ/Lz8z1btWpV7OPjo//+978Djxw54vLROmAkzzFjxpz83e9+1/H8+fMCAAcOHPB68cUXgwEgJCSkeOvWrb6lpaX4+OOPg+zj7d692yc5OfnswoULjwQFBZX89NNP3s2bNy91fMzv1VdfXbBkyZIQwLimICgoqMTZ0wiVPea3oroVxeL6GqkZHukTEV0BqvuJXV1r27ZtaUJCQmFUVFS8j49PWWhoaIWXpU+cOPHU4MGDI7t169Y1Pj7+XOfOnc/Xdt4LFy48/OCDD4ZFR0fH+/j4qJ+fX+kTTzxxBABmzZp1+Lbbbots165dcWxsbJF9x2Tq1KnhWVlZPqoqAwYMOHP11VcXdenS5eKCBQvaxcbGxk2fPj3n6aefPpKSkhIRHR0d5+fnV/bmm2/+7GxMlT3mNzEx8RfLW1EstV0nzuKjdevQlf54z4bEtnNvbL+6xUfrUk3w0bpERETE7n0iImo8mzdv9hs3blxnx2He3t5lO3bsqLeb+1gZkz4RETWapKSkIvtv4an+sXufiIjIIpj0iYiILIJJn4iIyCKY9ImIiCyCF/IREV0JZrZIqNvp5df7zX4yMjK8161bFzB58uRT1de+fLyhQ4dG7du37xe30q1Ofn6+x5QpUzps2LAh0MfHR1u2bFnyzDPPZCcnJ5+tyXRWrVoV6OPjU3bjjTfWaLzGxiN9IiJqFPv27fN57733gisqq6/nzo8dOzYiKCioJCsra9f+/ft3L1269Ofjx4/X+AD4q6++CtywYUNAfcRYmbp4OA+TPhERueSFF14IiY6OjouJiYm7/fbbO+/du9e7X79+0dHR0XH9+vWL3rdvnzcAjBgxImLChAkdevfuHRseHt59yZIlQQDw6KOPhqWmpgbExsbGzZo1q/WiRYtCBg8efFVycnLkwIEDo8vKynDPPfeER0VFxUdHR8e99tprQVVHZFi0aFHITTfd1GXgwIFRnTp16jZ58uRwwLjn/Y8//tjsueeeO+zpaTzRNi4u7uKYMWPyMzIyvKOiouLt03j88cfbTJs2rT0AzJ07t3WXLl3io6Oj44YOHXpVRkaG99KlS0NffvnlNrGxsXFr1qwJqGrZx44d27Fv377R4eHh3VevXh0wcuTIiKuuuip+xIgREfb5/fOf/2zeq1ev2Li4uK6DBw++Kj8/3wMAwsLCus+YMaNdQkJCzBtvvBFUPpaathm794mIqMZSU1N9FyxY0G7Tpk3p7dq1Kzl27JjnnXfe2TklJSX397//fe7ChQtDpkyZ0mHt2rWZAHDs2DGv1NTU9G3btvkOHz488re//e3pJ5988vCzzz7bZt26dfsBI1lv3bo1YMeOHbvbtGlT+uabb7bcuXOn3549e3bn5OTYkpKSut50002FzsSXlpbmv3379jQ/P7+yyMjIbjNmzDi2bds237i4uHM2W81S36JFi9oeOHBgp5+fn548edKzVatWpePGjTsREBBQOnv27GMAkJycHFnZsufn59s2bdq0d/ny5S1Hjx4d9dVXX6UnJCQU9ejRo+vGjRv9OnfuXPzUU0+1++abb/Y2b9687NFHH207Z86cNgsWLMgBAF9f37ItW7ZkAEDr1q17OMZSowWBE0f6ItJBRNaJyB4R2S0ifzCHB4vIFyKyz/zr1B4YERG5v88++6z5rbfeerpdu3YlANCmTZvSH3/8sdmkSZNOAcCUKVNObdmy5VL397Bhw/I8PT2RkJBwPjc316uy6Q4cOPBMmzZtSgFgw4YNgaNGjTpls9nQoUOHkr59+xZ+++23/s7EN2DAgDMhISGl/v7+GhkZeT4zM9PH1WWNiYkpGj58eOcXX3wx2MvLq8IH1lS17EOGDMnz8PBAnz59zoWEhBQnJSUVeXp6Ijo6uigzM9Nn/fr1zTIzM32TkpJiY2Nj4959992QgwcPXnry3rhx407XJJaqONO9XwJguqp2BXA1gPtEJA7AwwC+VNUoAF+a74mIyAJU9dKz5p3h6+t7qW5VD3rz9/cvc6Zedby9vS+N7OnpqcXFxdKrV6/ze/bs8S8t/eWj6202m5aV/fcpuufPn7+UH9etW7fvvvvuO7Fly5ZmPXv2jKvp9Qb2Zff09LwsLg8PD5SUlIiqYsCAAWfS09PT0tPT0zIzM3e///77B+z1AgMDLwVW21iqTfqqmqOqW83/CwDsARAG4DYAb5nV3gJwe43mTEREbuvmm28+s3LlyuCjR496AsCxY8c8e/fufXbx4sVBAPDKK68EJyYmVtkV36JFi9LCwsJKu6ivvfbaghUrVgSXlJTgyJEjts2bNwcMHDjQ5avl4+PjL/To0ePstGnT2tsT/M6dO32WLVvWMjw8vOTUqVO2o0ePehYVFclnn33WAgBKS0uRmZnpfeuttxa8+OKL2QUFBZ75+fmegYGBpQUFBZdir+myO7ruuuvOpqamBuzatcsHAAoKCjx27Njxi56JymKpyTqo0YkNEYkA0BvA9wDaqGoOYOwYiEjrSsaZBGASAHTs2LEmsyMiImc1wE/sHCUmJp6fPn16zsCBA2M9PDy0W7du51566aWD48ePj3juuefahoSElCxdujSrqmkkJSUV2Ww2jYmJiUtJSTkZFBR02SH4XXfdlbdx48aArl27xouIzpo1K7tjx44lGRkZ3pVNszrLli3Luvfeezt06tSpm5+fX1nLli1L//rXvx7y8fHR6dOn5yQlJXUNDw+/EBkZeR4ASkpKJCUlpXNBQYGnqso999xzrFWrVqUjRozIu+OOO7p8+umnLRcuXHiwpsvuqH379iWvvPJK1pgxY666ePGiAMATTzxxuEePHhcc61UWS02WX5ztPhGRAABfA3hSVf8pInmq2tKh/LSqVnlePzExUVNTU2sSX63wmd7ui23n3th+dUtEtqhqouOw7du3Z/Xs2fNkgwZCbmH79u2tevbsGVFRmVM/2RMRLwAfAviHqv7THHxMRNqZ5e0AHK+DWImIiKieVNu9LyIC4HUAe1T1bw5FKwGMBzDf/PtxvURIRERUgQ8//LD5o48+Gu44rEOHDhe++OKLzMaKqalz5px+fwB3AdgpItvMYX+GkezfF5G7ARwEMLJ+QiQiogqUlZWViYeHh+uXuLu5ESNGnBkxYkRaY8fRlJSVlQmAssrKq036qvotAKmk+HoX4yIiotrZdeLEibjQ0NB8Kyd++q+ysjI5ceJECwC7KqvDO/IREbmhkpKSiUePHl189OjRbuAt1clQBmBXSUnJxMoqMOkTEbmhhISE4wCGNXYc5F64d0hERGQRTPpEREQWwaRPRERkEUz6REREFsGkT0REZBFM+kRERBbBpE9ERGQRTPpEREQWwaRPRERkEUz6REREFsGkT0REZBFM+kRERBbBpE9ERGQRTPpEREQWwaRPRERkEUz6REREFsGkT0REZBFM+kRERBbBpE9ERGQRTPpEREQWwaRPRERkEUz6REREFsGkT0REZBFM+kRERBbBpE9ERGQRTPpEREQWwaRPRERkEUz6REREFsGkT0REZBFM+kRERBZRbdIXkTdE5LiI7HIYFiwiX4jIPvNvUP2GSURERLXlzJH+mwBuLjfsYQBfqmoUgC/N90RERNSEVZv0VfUbAKfKDb4NwFvm/28BuL2O4yIiIqI65uo5/TaqmgMA5t/WlVUUkUkikioiqSdOnHBxdkRERFRb9X4hn6q+qqqJqpoYGhpa37MjIiKiSria9I+JSDsAMP8er7uQiIiIqD64mvRXAhhv/j8ewMd1Ew4RERHVF2d+svcOgE0AYkQkW0TuBjAfwI0isg/AjeZ7IiIiasJs1VVQ1TsrKbq+jmMhIiKiesQ78hEREVkEkz4REZFFMOkTERFZBJM+ERGRRTDpExERWQSTPhERkUUw6RMREVkEkz4REZFFMOkTERFZBJM+ERGRRTDpExERWQSTPhERkUUw6RMREVkEkz4REZFFMOkTERFZBJM+ERGRRTDpExERWQSTPhERkUUw6RMREVkEkz4REZFFMOkTERFZBJM+ERGRRTDpExERWQSTPhERkUUw6RMREVkEkz4REZFFMOkTERFZBJM+ERGRRTDpExERWQSTPhERkUUw6RMREVkEkz4REZFFMOkTERFZRK2SvojcLCIZIrJfRB6uq6CIiIio7rmc9EXEE8DfAQwGEAfgThGJq6vAiIiIqG7V5kg/CcB+Vf1JVS8CeBfAbXUTFhEREdU1Wy3GDQNwyOF9NoC+5SuJyCQAk8y3hSKSUYt5NmkCtAJwssFmOEsabFZXOrade7NA+3Vq6BnSlak2Sb+irV5/MUD1VQCv1mI+bkNEUlU1sbHjoJpj27k3th+Rc2rTvZ8NoIPD+3AAR2oXDhEREdWX2iT9HwBEiUhnEfEGMAbAyroJi4iIiOqay937qloiIvcD+AyAJ4A3VHV3nUXmnixxGuMKxbZzb2w/IieI6i9OwxMREdEViHfkIyIisggmfSIiIotg0iciIrIIJn0iIiKLYNInAiAiwY0dA7lORIY1dgxE7qA2d+SzLBHpDuA1GLci/hTAQ6p62izbrKpJjRkfVU1E+gNYDKAMwP8CmAugi4h4ARilqpsaMz6qmoj8uvwgAH8XERsAqOo/Gz4qIvfApO+alwDMBPAfABMBfCsiw1Q1E4BXYwZGTvk/AKMABABYDeB2Vf1WRPoAeB5A/8YMjqr1PoA1AI7jv7cDbwbgVhi3AmfSJ6oEk75rAlR1jfn/AhHZAmCNiNyFCp4/QE2Ol6ruBAAROaGq3wKAqm4VEb/GDY2c0A/AfBh3BX1ZVVVErlPV3zZyXERNHs/pu0ZEpIX9jaquAzACwNvg07DcgeN2/0i5Mu+GDIRqTlV/AHAjjLb6SkSSwJ1tIqcw6bvmaQBdHQeo6g4A14Ndi+7gLyLiDwCq+i/7QBHpAmBpo0VFTlPVMlV9DsBvAMxo7HiI3AVvw0tERGQRPNJ3gYi0EJH5IpIuIrnma485rGVjx0dVY/u5N7YfkeuY9F3zPoDTAK5T1RBVDQEwyBz2QaNGRs5g+7k3th+Ri9i97wIRyVDVmJqWUdPA9nNvbD8i1/FI3zUHRORPItLGPkBE2ojIQwAONWJc5By2n3tj+xG5iEnfNaMBhAD4WkROi8hpAOsBBMO46Qs1bWw/98b2I3IRu/eJiIgsgnfkc5H5m+7hADoAKAGwD8A7qprfqIGRU9h+7o3tR+Qadu+7QEQeAPAyAF8AvwLgB+PLZ5OIXNeIoZET2H7uje1H5Dp277tARHYC6KWqpead3T5R1etEpCOAj1W1dyOHSFVg+7k3th+R63ik7zr7qREfAIEAoKoHwafsuQu2n3tj+xG5gOf0XbMYwA8i8h8A/wPjXvwQkVAApxozMHIK28+9sf2IXMTufReJSDyMh+7sUtX0xo6Haobt597YfkSuYdKvYyISoKqFjR0HuYbt597YfkRV4zn9upfW2AFQrbD93Bvbj6gKPKfvAhGZVlkRgICGjIVqju3n3th+RK7jkb5rngIQBOOqYcdXALhO3QHbz72x/YhcxCN912wF8C9V3VK+QEQmNkI8VDNsP/fG9iNyES/kc4GIxAA4paonKihro6rHGiEschLbz72x/Yhcx6RPRERkETz/5QIRaSEi80UkXURyzdcec1jLxo6Pqsb2c29sPyLXMem75n0ApwFcp6ohqhoCYJA57INGjYycwfZzb2w/Ihexe98FIpKhqjE1LaOmge3n3th+RK7jkb5rDojIn0SkjX2AiLQRkYcAHGrEuMg5bD/3xvYjchGTvmtGAwgB8LWInBaRUwDWAwgGMKoxAyOnsP3cG9uPyEXs3neRiMQCCAfwH8d7fYvIzaq6pvEiI2ew/dwb24/INTzSd4GIPADgYwD3A9glIrc5FD/VOFGRs9h+7o3tR+Q63pHPNb8DkKCqhSISAWCFiESo6nMw7v9NTRvbz72x/YhcxKTvGk97l6KqZonIdTC+eDqBXzrugO3n3th+RC5i975rjopIL/sb8wtoKIBWALo3WlTkLLafe2P7EbmIF/K5QETCAZSo6tEKyvqr6neNEBY5ie3n3th+RK5j0iciIrIIdu8TERFZBJM+ERGRRTDpU5Mghm9FZLDDsFEiskZESkVkm8PrYYc6oSJSLCL3lJtelojsFJEdIvK1eWW3vUxF5G2H9zYROSEiqxyG3W6Om25O53aHsjdF5LCI+JjvW5nz6+4Q4ykR+dn8f62IRIjIrnIxzhSRGXW1DomIqsOkT02CGheXTAbwNxHxFZFmAJ4EcB+AIlXt5fCa7zDqSAD/AXBnBZMdpKo9YNyi9TGH4WcBdBMRP/P9jQAO2wtFpCeABQBuU9VYAMMALBCRHg7TKAXwv+WWYac9RgArAfzRfH9DzdYGEVH9YNKnJkNVdwH4N4CHADwBYKmqZlYz2p0ApgMIF5GwSupsAlC+7FMAQxym8Y5D2QwAT6nqz2ZcPwOYB+CPDnUWApgqIrzXBRG5DSZ9ampmAUgBMBjAM+Ywv3Ld+6MBQEQ6AGirqpthPGN9dCXTvBnAv8oNexfAGBHxBdADwPcOZfEAtpSrn2oOtzsI4FsAd9Vg2bo4LgeMng0iogbDoxRqUlT1rIi8B6BQVS+Yg4vMLvPyxsBI9oCRxF8H8DeH8nXm41eP4/LufajqDvMWrncC+KTcdAVA+d+yVjTsKRjd+KurWSy7TMflEJGZTo5HRFQneKRPTVGZ+arOnQAmiEgWjOTbU0SiHMoHAegEYDeA2RWMvxLGuft3yg3fDSCx3LA+ANIcB6jqfgDbwMe5EpGbYNIntyQiMQCaqWqYqkaoagSM8+5jHOupahGABwGME5HgcpN5A8BsVd1ZbvgCAI+YPQEw//4ZwLMVhPIkjGsAiIiaPHbvkzvwM8+B260BcB7AR+XqfQijm3+O40BVzRGRd2D8EmCOw/BsAM+Vn5mqbhORhwD8W0S8ABQD+JOqbqug7m4R2QqjJ4CIqEnjbXiJiIgsgt37REREFsGkT0REZBFM+kRERBbBpE9ERGQRTPpEREQWwaRPRERkEUz6REREFvH/AWrWhdnUno71AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for trial, control in trial_control_dic.items():\n",
    "    a = trial_scaled_ncust_control_stores[trial_scaled_ncust_control_stores[\"STORE_NBR\"] == control].rename(columns={\"nCustomers\": \"control_nCustomers\"})\n",
    "    b = trial_full_observ[trial_full_observ[\"STORE_NBR\"] == trial][[\"STORE_NBR\", \"YEARMONTH\", \"nCustomers\"]].rename(columns={\"nCustomers\": \"trial_nCustomers\"})\n",
    "    comb = b[[\"YEARMONTH\", \"trial_nCustomers\"]].merge(a[[\"YEARMONTH\", \"control_nCustomers\"]],on=\"YEARMONTH\").set_index(\"YEARMONTH\")\n",
    "    comb.plot.bar()\n",
    "    cont_sc_ncust = trial_scaled_ncust_control_stores[trial_scaled_ncust_control_stores[\"STORE_NBR\"] == control][\"nCustomers\"]\n",
    "    std = scaledncust_vs_trial[(scaledncust_vs_trial[\"c_STORE_NBR\"] == control) & (scaledncust_vs_trial[\"trial_period\"]==\"pre\")][\"nCust_Percentage_Diff\"].std()\n",
    "    thresh95 = cont_sc_ncust.mean() + (cont_sc_ncust.mean() * std * 2)\n",
    "    thresh5 = cont_sc_ncust.mean() - (cont_sc_ncust.mean() * std * 2)\n",
    "    plt.axhline(y=thresh95,linewidth=1, color='b', label=\"95% threshold\")\n",
    "    plt.axhline(y=thresh5,linewidth=1, color='r', label=\"5% threshold\")\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "    plt.title(\"Trial Store \"+str(trial)+\" and Control Store \"+str(control)+\" - Number of Customers\")\n",
    "    plt.savefig(\"TS {} and CS {} - nCustomers.png\".format(trial,control), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Trial store 77 sales for Feb, March, and April exceeds 95% threshold of control store.\n",
    "Same goes to store 86 sales for all 3 trial months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Trial store 77: Control store 233\n",
    "- Trial store 86: Control store 155\n",
    "- Trial store 88: Control store 40\n",
    "- Both trial store 77 and 86 showed significant increase in Total Sales and Number of Customers during trial period. But not for trial store 88. Perhaps the client knows if there's anything about trial 88 that differs it from the other two trial.\n",
    "- Overall the trial showed positive significant result."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}