eda_supermarket.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import pygraphviz as pgv\n",
    "import numpy as np\n",
    "from faker import Faker\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positions.json\n",
      "friday.csv\n",
      "wednesday.csv\n",
      "monday.csv\n",
      "thursday.csv\n",
      "tuesday.csv\n",
      "aggregated\n"
     ]
    }
   ],
   "source": [
    "for file in os.listdir(\"data\"):\n",
    "    print(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concat DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = []\n",
    "for file in os.listdir(\"data\"):\n",
    "    if file.endswith(\".csv\"):\n",
    "        df = pd.read_csv(\n",
    "            f\"data/{file}\", sep=\";\", parse_dates=True, index_col=\"timestamp\"\n",
    "        )\n",
    "        df_list.append(df)\n",
    "df = pd.concat(df_list, sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>customer_no</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>drinks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:01:00</th>\n",
       "      <td>2</td>\n",
       "      <td>checkout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:01:00</th>\n",
       "      <td>4</td>\n",
       "      <td>drinks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03 21:47:00</th>\n",
       "      <td>1421</td>\n",
       "      <td>drinks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03 21:47:00</th>\n",
       "      <td>1422</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03 21:48:00</th>\n",
       "      <td>1421</td>\n",
       "      <td>checkout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03 21:49:00</th>\n",
       "      <td>1412</td>\n",
       "      <td>checkout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03 21:50:00</th>\n",
       "      <td>1411</td>\n",
       "      <td>dairy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24877 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_no  location\n",
       "timestamp                                 \n",
       "2019-09-06 07:00:00            1     dairy\n",
       "2019-09-06 07:00:00            2    drinks\n",
       "2019-09-06 07:00:00            3     fruit\n",
       "2019-09-06 07:01:00            2  checkout\n",
       "2019-09-06 07:01:00            4    drinks\n",
       "...                          ...       ...\n",
       "2019-09-03 21:47:00         1421    drinks\n",
       "2019-09-03 21:47:00         1422     dairy\n",
       "2019-09-03 21:48:00         1421  checkout\n",
       "2019-09-03 21:49:00         1412  checkout\n",
       "2019-09-03 21:50:00         1411     dairy\n",
       "\n",
       "[24877 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set distinct ID for every customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2019-09-02 07:03:00    Monday-1\n",
       "2019-09-02 07:03:00    Monday-2\n",
       "2019-09-02 07:04:00    Monday-4\n",
       "2019-09-02 07:04:00    Monday-8\n",
       "2019-09-02 07:04:00    Monday-7\n",
       "Name: customer_id, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(inplace=True)\n",
    "df[\"time\"] = df.index.time\n",
    "df[\"day\"] = df.index.day_name()\n",
    "df[\"customer_id\"] = df[\"day\"] + \"-\" + df[\"customer_no\"].astype(str)\n",
    "df[\"customer_id\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set section info on order of visit\n",
    "(first, following, ...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id\n",
       "Friday-1        2019-09-06 07:00:00\n",
       "Friday-10       2019-09-06 07:06:00\n",
       "Friday-100      2019-09-06 08:03:00\n",
       "Friday-1000     2019-09-06 17:19:00\n",
       "Friday-1001     2019-09-06 17:19:00\n",
       "                        ...        \n",
       "Wednesday-995   2019-09-04 16:52:00\n",
       "Wednesday-996   2019-09-04 16:52:00\n",
       "Wednesday-997   2019-09-04 16:52:00\n",
       "Wednesday-998   2019-09-04 16:53:00\n",
       "Wednesday-999   2019-09-04 16:53:00\n",
       "Name: timestamp, Length: 7445, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enter_datetimes = df.reset_index().groupby(\"customer_id\")[\"timestamp\"].min()\n",
    "enter_datetimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for customer in df[\"customer_id\"].unique():\n",
    "    df.loc[\n",
    "        (df.customer_id == customer) & (df.index == enter_datetimes[customer]),\n",
    "        \"section_order\",\n",
    "    ] = \"first\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df[\"location\"] == \"checkout\", \"section_order\"] = \"checkout\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"section_order\"].fillna(\"following\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate probabilities for first section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>2141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>1143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>2810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>1351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer_id\n",
       "location             \n",
       "dairy            2141\n",
       "drinks           1143\n",
       "fruit            2810\n",
       "spices           1351"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_in_first_sections = (\n",
    "    df[df.section_order == \"first\"].groupby(\"location\")[[\"customer_id\"]].count()\n",
    ")\n",
    "customers_in_first_sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_entries = sum(customers_in_first_sections[\"customer_id\"].to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>0.287576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>0.153526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>0.377435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>0.181464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer_id\n",
       "location             \n",
       "dairy        0.287576\n",
       "drinks       0.153526\n",
       "fruit        0.377435\n",
       "spices       0.181464"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_probabilities = customers_in_first_sections / total_entries\n",
    "first_probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the total number of customers in each section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>checkout</th>\n",
       "      <td>7417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>5122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>4679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>3905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>3754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer_id\n",
       "location             \n",
       "checkout         7417\n",
       "fruit            5122\n",
       "dairy            4679\n",
       "drinks           3905\n",
       "spices           3754"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# absolute number of customers per section\n",
    "df.groupby(\"location\")[[\"customer_id\"]].count().sort_values(\n",
    "    \"customer_id\", ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>checkout</th>\n",
       "      <td>1483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer_id\n",
       "location             \n",
       "checkout         1483\n",
       "fruit            1024\n",
       "dairy             936\n",
       "drinks            781\n",
       "spices            751"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# daily average of customers per section\n",
    "round(\n",
    "    df.groupby(\"location\")[[\"customer_id\"]]\n",
    "    .count()\n",
    "    .sort_values(\"customer_id\", ascending=False)\n",
    "    / 5,\n",
    "    0,\n",
    ").astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the total number of customers in each section over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">07:00:00</th>\n",
       "      <th>dairy</th>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">07:01:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">21:50:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4383 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   customer_id\n",
       "time     location             \n",
       "07:00:00 dairy             0.6\n",
       "         drinks            0.6\n",
       "         fruit             0.8\n",
       "07:01:00 checkout          0.4\n",
       "         dairy             0.8\n",
       "...                        ...\n",
       "21:50:00 checkout          0.4\n",
       "         dairy             0.6\n",
       "         drinks            0.2\n",
       "         fruit             0.4\n",
       "         spices            0.2\n",
       "\n",
       "[4383 rows x 1 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average number of customers per minute and location\n",
    "avg_customers_section = (\n",
    "    df.groupby([df.time, df.location])[[\"customer_id\"]].count() / 5\n",
    ")\n",
    "avg_customers_section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:03:00</th>\n",
       "      <th>dairy</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2019-09-02 07:04:00</th>\n",
       "      <th>dairy</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:05:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2019-09-06 21:50:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13839 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              customer_id\n",
       "timestamp           location             \n",
       "2019-09-02 07:03:00 dairy               2\n",
       "2019-09-02 07:04:00 dairy               2\n",
       "                    fruit               1\n",
       "                    spices              3\n",
       "2019-09-02 07:05:00 checkout            2\n",
       "...                                   ...\n",
       "2019-09-06 21:50:00 checkout            2\n",
       "                    dairy               1\n",
       "                    drinks              1\n",
       "                    fruit               1\n",
       "                    spices              1\n",
       "\n",
       "[13839 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of customers per timestamp and location\n",
    "customers_section = df.groupby([df.index, df.location])[[\"customer_id\"]].count()\n",
    "customers_section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the number of customers at checkout over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>07:01:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:02:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:03:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:04:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:05:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:46:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:47:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:48:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:49:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:50:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>890 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location\n",
       "time     location          \n",
       "07:01:00 checkout       0.4\n",
       "07:02:00 checkout       1.0\n",
       "07:03:00 checkout       0.2\n",
       "07:04:00 checkout       0.4\n",
       "07:05:00 checkout       1.4\n",
       "...                     ...\n",
       "21:46:00 checkout       1.0\n",
       "21:47:00 checkout       1.0\n",
       "21:48:00 checkout       1.4\n",
       "21:49:00 checkout       1.2\n",
       "21:50:00 checkout       0.4\n",
       "\n",
       "[890 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average number of customers at checkout per minute\n",
    "avg_checkouts = df[df.location == \"checkout\"]\n",
    "avg_checkouts.groupby([avg_checkouts.time, avg_checkouts.location])[[\"location\"]].count() / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:05:00</th>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:06:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:07:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:08:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:09:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:37:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:45:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:47:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:48:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <th>checkout</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3460 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              location\n",
       "timestamp           location          \n",
       "2019-09-02 07:05:00 checkout         2\n",
       "2019-09-02 07:06:00 checkout         2\n",
       "2019-09-02 07:07:00 checkout         1\n",
       "2019-09-02 07:08:00 checkout         3\n",
       "2019-09-02 07:09:00 checkout         1\n",
       "...                                ...\n",
       "2019-09-06 21:37:00 checkout         1\n",
       "2019-09-06 21:45:00 checkout         1\n",
       "2019-09-06 21:47:00 checkout         2\n",
       "2019-09-06 21:48:00 checkout         2\n",
       "2019-09-06 21:50:00 checkout         2\n",
       "\n",
       "[3460 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# absolute number of customers at checkout per timestamp\n",
    "checkouts = df[df.location == \"checkout\"]\n",
    "checkouts.groupby([checkouts.index, checkouts.location])[[\"location\"]].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the time each customer spent in the market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>enter_datetime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday-1</th>\n",
       "      <td>2019-09-02 07:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-2</th>\n",
       "      <td>2019-09-02 07:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-4</th>\n",
       "      <td>2019-09-02 07:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-3</th>\n",
       "      <td>2019-09-02 07:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-5</th>\n",
       "      <td>2019-09-02 07:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1506</th>\n",
       "      <td>2019-09-06 21:48:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1507</th>\n",
       "      <td>2019-09-06 21:48:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1508</th>\n",
       "      <td>2019-09-06 21:48:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1510</th>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1509</th>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7445 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 enter_datetime\n",
       "customer_id                    \n",
       "Monday-1    2019-09-02 07:03:00\n",
       "Monday-2    2019-09-02 07:03:00\n",
       "Monday-4    2019-09-02 07:04:00\n",
       "Monday-3    2019-09-02 07:04:00\n",
       "Monday-5    2019-09-02 07:04:00\n",
       "...                         ...\n",
       "Friday-1506 2019-09-06 21:48:00\n",
       "Friday-1507 2019-09-06 21:48:00\n",
       "Friday-1508 2019-09-06 21:48:00\n",
       "Friday-1510 2019-09-06 21:50:00\n",
       "Friday-1509 2019-09-06 21:50:00\n",
       "\n",
       "[7445 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enter_datetime = (\n",
    "    df.reset_index()[[\"timestamp\", \"customer_id\"]].groupby(\"customer_id\").min()\n",
    ")\n",
    "enter_datetime.rename(columns={\"timestamp\": \"enter_datetime\"}, inplace=True)\n",
    "enter_datetime.sort_values(\"enter_datetime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday-1</th>\n",
       "      <td>2019-09-02 07:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-5</th>\n",
       "      <td>2019-09-02 07:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-3</th>\n",
       "      <td>2019-09-02 07:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-2</th>\n",
       "      <td>2019-09-02 07:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-8</th>\n",
       "      <td>2019-09-02 07:07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1500</th>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1510</th>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1496</th>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1509</th>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1507</th>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7445 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  exit_datetime\n",
       "customer_id                    \n",
       "Monday-1    2019-09-02 07:05:00\n",
       "Monday-5    2019-09-02 07:05:00\n",
       "Monday-3    2019-09-02 07:06:00\n",
       "Monday-2    2019-09-02 07:06:00\n",
       "Monday-8    2019-09-02 07:07:00\n",
       "...                         ...\n",
       "Friday-1500 2019-09-06 21:50:00\n",
       "Friday-1510 2019-09-06 21:50:00\n",
       "Friday-1496 2019-09-06 21:50:00\n",
       "Friday-1509 2019-09-06 21:50:00\n",
       "Friday-1507 2019-09-06 21:50:00\n",
       "\n",
       "[7445 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exit_datetime = (\n",
    "    df.reset_index()[[\"timestamp\", \"customer_id\"]].groupby(\"customer_id\").max()\n",
    ")\n",
    "exit_datetime.rename(columns={\"timestamp\": \"exit_datetime\"}, inplace=True)\n",
    "exit_datetime.sort_values(\"exit_datetime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>enter_datetime</th>\n",
       "      <th>exit_datetime</th>\n",
       "      <th>time_spent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday-1</th>\n",
       "      <td>2019-09-02 07:03:00</td>\n",
       "      <td>2019-09-02 07:05:00</td>\n",
       "      <td>00:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-2</th>\n",
       "      <td>2019-09-02 07:03:00</td>\n",
       "      <td>2019-09-02 07:06:00</td>\n",
       "      <td>00:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-4</th>\n",
       "      <td>2019-09-02 07:04:00</td>\n",
       "      <td>2019-09-02 07:08:00</td>\n",
       "      <td>00:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-3</th>\n",
       "      <td>2019-09-02 07:04:00</td>\n",
       "      <td>2019-09-02 07:06:00</td>\n",
       "      <td>00:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-5</th>\n",
       "      <td>2019-09-02 07:04:00</td>\n",
       "      <td>2019-09-02 07:05:00</td>\n",
       "      <td>00:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1506</th>\n",
       "      <td>2019-09-06 21:48:00</td>\n",
       "      <td>2019-09-06 21:48:00</td>\n",
       "      <td>00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1507</th>\n",
       "      <td>2019-09-06 21:48:00</td>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "      <td>00:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1508</th>\n",
       "      <td>2019-09-06 21:48:00</td>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "      <td>00:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1510</th>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "      <td>00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-1509</th>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "      <td>2019-09-06 21:50:00</td>\n",
       "      <td>00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7445 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 enter_datetime       exit_datetime time_spent\n",
       "customer_id                                                   \n",
       "Monday-1    2019-09-02 07:03:00 2019-09-02 07:05:00   00:02:00\n",
       "Monday-2    2019-09-02 07:03:00 2019-09-02 07:06:00   00:03:00\n",
       "Monday-4    2019-09-02 07:04:00 2019-09-02 07:08:00   00:04:00\n",
       "Monday-3    2019-09-02 07:04:00 2019-09-02 07:06:00   00:02:00\n",
       "Monday-5    2019-09-02 07:04:00 2019-09-02 07:05:00   00:01:00\n",
       "...                         ...                 ...        ...\n",
       "Friday-1506 2019-09-06 21:48:00 2019-09-06 21:48:00   00:00:00\n",
       "Friday-1507 2019-09-06 21:48:00 2019-09-06 21:50:00   00:02:00\n",
       "Friday-1508 2019-09-06 21:48:00 2019-09-06 21:50:00   00:02:00\n",
       "Friday-1510 2019-09-06 21:50:00 2019-09-06 21:50:00   00:00:00\n",
       "Friday-1509 2019-09-06 21:50:00 2019-09-06 21:50:00   00:00:00\n",
       "\n",
       "[7445 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_time_spent = pd.concat([enter_datetime, exit_datetime], axis=1)\n",
    "customer_time_spent[\"time_spent\"] = (\n",
    "    customer_time_spent[\"exit_datetime\"] - customer_time_spent[\"enter_datetime\"]\n",
    ")\n",
    "customer_time_spent.sort_values(\"enter_datetime\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the total number of customers present in the supermarket over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.time(21, 51)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create default timestamp for missing exit_datetime values\n",
    "# (assuming that customers are leaving, when the market closes)\n",
    "default_exit_datetime = pd.to_datetime(\"21:51:00\").time()\n",
    "default_exit_datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "enter_datetime    0\n",
       "exit_datetime     0\n",
       "time_spent        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_time_spent.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing exit_datetime values\n",
    "customer_time_spent[\"exit_datetime\"].fillna(default_exit_datetime, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-09-06 07:01:00', '2019-09-06 07:02:00',\n",
       "               '2019-09-06 07:03:00', '2019-09-06 07:04:00'],\n",
       "              dtype='datetime64[ns]', freq='T')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\n",
    "    customer_time_spent[\"enter_datetime\"][\"Friday-1\"] + pd.to_timedelta(1, unit=\"min\"),\n",
    "    customer_time_spent[\"exit_datetime\"][\"Friday-1\"] - pd.to_timedelta(1, unit=\"min\"),\n",
    "    freq=\"Min\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to extract all minutes, where customer is present\n",
    "def get_customer_presence(customer_id):\n",
    "    \"\"\"returns DataFrame with all minutes where customer is present\"\"\"\n",
    "    customer_presence = pd.date_range(\n",
    "        customer_time_spent[\"enter_datetime\"][customer_id]\n",
    "        + pd.to_timedelta(1, unit=\"min\"),\n",
    "        customer_time_spent[\"exit_datetime\"][customer_id]\n",
    "        - pd.to_timedelta(1, unit=\"min\"),\n",
    "        freq=\"Min\",\n",
    "    )\n",
    "    return pd.DataFrame(zip(customer_presence, [customer_id] * len(customer_presence)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise empty DataFrame\n",
    "init_df = pd.DataFrame()\n",
    "last_df = init_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concat all timestamp values of all customers\n",
    "for customer in df[\"customer_id\"].unique():\n",
    "    current_df = get_customer_presence(customer)\n",
    "    combined = pd.concat([last_df, current_df], axis=0)\n",
    "    last_df = combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_customers = combined.rename(columns={0: \"timestamp\", 1: \"customer_id\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_customers.set_index(\"timestamp\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data = pd.concat([total_customers, df], axis=0, sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data.drop_duplicates([\"timestamp\", \"customer_id\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = (\n",
    "    full_data.sort_values(by=[\"customer_id\", \"timestamp\"]).set_index(\"timestamp\").copy()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare old and new DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>customer_no</th>\n",
       "      <th>location</th>\n",
       "      <th>time</th>\n",
       "      <th>day</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>section_order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:02:00</th>\n",
       "      <td>6</td>\n",
       "      <td>spices</td>\n",
       "      <td>07:02:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Friday-6</td>\n",
       "      <td>first</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:03:00</th>\n",
       "      <td>6</td>\n",
       "      <td>dairy</td>\n",
       "      <td>07:03:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Friday-6</td>\n",
       "      <td>following</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:05:00</th>\n",
       "      <td>6</td>\n",
       "      <td>drinks</td>\n",
       "      <td>07:05:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Friday-6</td>\n",
       "      <td>following</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:08:00</th>\n",
       "      <td>6</td>\n",
       "      <td>spices</td>\n",
       "      <td>07:08:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Friday-6</td>\n",
       "      <td>following</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:09:00</th>\n",
       "      <td>6</td>\n",
       "      <td>fruit</td>\n",
       "      <td>07:09:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Friday-6</td>\n",
       "      <td>following</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:10:00</th>\n",
       "      <td>6</td>\n",
       "      <td>checkout</td>\n",
       "      <td>07:10:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Friday-6</td>\n",
       "      <td>checkout</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_no  location      time     day customer_id  \\\n",
       "timestamp                                                                  \n",
       "2019-09-06 07:02:00            6    spices  07:02:00  Friday    Friday-6   \n",
       "2019-09-06 07:03:00            6     dairy  07:03:00  Friday    Friday-6   \n",
       "2019-09-06 07:05:00            6    drinks  07:05:00  Friday    Friday-6   \n",
       "2019-09-06 07:08:00            6    spices  07:08:00  Friday    Friday-6   \n",
       "2019-09-06 07:09:00            6     fruit  07:09:00  Friday    Friday-6   \n",
       "2019-09-06 07:10:00            6  checkout  07:10:00  Friday    Friday-6   \n",
       "\n",
       "                    section_order  \n",
       "timestamp                          \n",
       "2019-09-06 07:02:00         first  \n",
       "2019-09-06 07:03:00     following  \n",
       "2019-09-06 07:05:00     following  \n",
       "2019-09-06 07:08:00     following  \n",
       "2019-09-06 07:09:00     following  \n",
       "2019-09-06 07:10:00      checkout  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['customer_id'] == 'Friday-6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>customer_id</th>\n",
       "      <th>customer_no</th>\n",
       "      <th>day</th>\n",
       "      <th>location</th>\n",
       "      <th>section_order</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:02:00</th>\n",
       "      <td>Friday-6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>spices</td>\n",
       "      <td>first</td>\n",
       "      <td>07:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:03:00</th>\n",
       "      <td>Friday-6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>following</td>\n",
       "      <td>07:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:04:00</th>\n",
       "      <td>Friday-6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:05:00</th>\n",
       "      <td>Friday-6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>drinks</td>\n",
       "      <td>following</td>\n",
       "      <td>07:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:06:00</th>\n",
       "      <td>Friday-6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:07:00</th>\n",
       "      <td>Friday-6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:08:00</th>\n",
       "      <td>Friday-6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:09:00</th>\n",
       "      <td>Friday-6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:10:00</th>\n",
       "      <td>Friday-6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>checkout</td>\n",
       "      <td>checkout</td>\n",
       "      <td>07:10:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    customer_id  customer_no     day  location section_order  \\\n",
       "timestamp                                                                      \n",
       "2019-09-06 07:02:00    Friday-6          6.0  Friday    spices         first   \n",
       "2019-09-06 07:03:00    Friday-6          6.0  Friday     dairy     following   \n",
       "2019-09-06 07:04:00    Friday-6          NaN     NaN       NaN           NaN   \n",
       "2019-09-06 07:05:00    Friday-6          6.0  Friday    drinks     following   \n",
       "2019-09-06 07:06:00    Friday-6          NaN     NaN       NaN           NaN   \n",
       "2019-09-06 07:07:00    Friday-6          NaN     NaN       NaN           NaN   \n",
       "2019-09-06 07:08:00    Friday-6          NaN     NaN       NaN           NaN   \n",
       "2019-09-06 07:09:00    Friday-6          NaN     NaN       NaN           NaN   \n",
       "2019-09-06 07:10:00    Friday-6          6.0  Friday  checkout      checkout   \n",
       "\n",
       "                         time  \n",
       "timestamp                      \n",
       "2019-09-06 07:02:00  07:02:00  \n",
       "2019-09-06 07:03:00  07:03:00  \n",
       "2019-09-06 07:04:00       NaN  \n",
       "2019-09-06 07:05:00  07:05:00  \n",
       "2019-09-06 07:06:00       NaN  \n",
       "2019-09-06 07:07:00       NaN  \n",
       "2019-09-06 07:08:00       NaN  \n",
       "2019-09-06 07:09:00       NaN  \n",
       "2019-09-06 07:10:00  07:10:00  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data[final_data['customer_id'] == 'Friday-6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>customer_id</th>\n",
       "      <th>customer_no</th>\n",
       "      <th>day</th>\n",
       "      <th>location</th>\n",
       "      <th>section_order</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:00:00</th>\n",
       "      <td>Friday-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>07:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:01:00</th>\n",
       "      <td>Friday-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:02:00</th>\n",
       "      <td>Friday-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:03:00</th>\n",
       "      <td>Friday-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:04:00</th>\n",
       "      <td>Friday-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>spices</td>\n",
       "      <td>following</td>\n",
       "      <td>07:04:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    customer_id  customer_no     day location section_order  \\\n",
       "timestamp                                                                     \n",
       "2019-09-06 07:00:00    Friday-1          1.0  Friday    dairy         first   \n",
       "2019-09-06 07:01:00    Friday-1          1.0     NaN    dairy         first   \n",
       "2019-09-06 07:02:00    Friday-1          1.0     NaN    dairy         first   \n",
       "2019-09-06 07:03:00    Friday-1          1.0     NaN    dairy         first   \n",
       "2019-09-06 07:04:00    Friday-1          1.0  Friday   spices     following   \n",
       "\n",
       "                         time  \n",
       "timestamp                      \n",
       "2019-09-06 07:00:00  07:00:00  \n",
       "2019-09-06 07:01:00       NaN  \n",
       "2019-09-06 07:02:00       NaN  \n",
       "2019-09-06 07:03:00       NaN  \n",
       "2019-09-06 07:04:00  07:04:00  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data[\"location\"].fillna(method=\"ffill\", inplace=True)\n",
    "final_data[\"section_order\"].fillna(method=\"ffill\", inplace=True)\n",
    "final_data[\"customer_no\"].fillna(method=\"ffill\", inplace=True)\n",
    "final_data[\"customer_no\"].astype(int)\n",
    "final_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_no</th>\n",
       "      <th>day</th>\n",
       "      <th>location</th>\n",
       "      <th>section_order</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:00:00</th>\n",
       "      <td>Friday-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>07:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:01:00</th>\n",
       "      <td>Friday-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>07:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:02:00</th>\n",
       "      <td>Friday-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>07:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:03:00</th>\n",
       "      <td>Friday-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>07:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 07:04:00</th>\n",
       "      <td>Friday-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>spices</td>\n",
       "      <td>following</td>\n",
       "      <td>07:04:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    customer_id  customer_no     day location section_order  \\\n",
       "timestamp                                                                     \n",
       "2019-09-06 07:00:00    Friday-1          1.0  Friday    dairy         first   \n",
       "2019-09-06 07:01:00    Friday-1          1.0  Friday    dairy         first   \n",
       "2019-09-06 07:02:00    Friday-1          1.0  Friday    dairy         first   \n",
       "2019-09-06 07:03:00    Friday-1          1.0  Friday    dairy         first   \n",
       "2019-09-06 07:04:00    Friday-1          1.0  Friday   spices     following   \n",
       "\n",
       "                         time  \n",
       "timestamp                      \n",
       "2019-09-06 07:00:00  07:00:00  \n",
       "2019-09-06 07:01:00  07:01:00  \n",
       "2019-09-06 07:02:00  07:02:00  \n",
       "2019-09-06 07:03:00  07:03:00  \n",
       "2019-09-06 07:04:00  07:04:00  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data[\"day\"] = final_data.index.day_name()\n",
    "final_data[\"time\"] = final_data.index.time\n",
    "final_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>07:00:00</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:01:00</th>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:02:00</th>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:03:00</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:04:00</th>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:46:00</th>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:47:00</th>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:48:00</th>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:49:00</th>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:50:00</th>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer_id\n",
       "time                 \n",
       "07:00:00          2.0\n",
       "07:01:00          3.2\n",
       "07:02:00          3.6\n",
       "07:03:00          5.0\n",
       "07:04:00          6.8\n",
       "...               ...\n",
       "21:46:00          6.8\n",
       "21:47:00          6.6\n",
       "21:48:00          6.6\n",
       "21:49:00          4.4\n",
       "21:50:00          1.8\n",
       "\n",
       "[891 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average number of customers present per minute\n",
    "final_data.groupby(\"time\")[[\"customer_id\"]].count() / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:03:00</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:05:00</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:06:00</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:07:00</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:46:00</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:47:00</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:48:00</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:49:00</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4447 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_id\n",
       "timestamp                       \n",
       "2019-09-02 07:03:00            2\n",
       "2019-09-02 07:04:00            8\n",
       "2019-09-02 07:05:00            8\n",
       "2019-09-02 07:06:00           12\n",
       "2019-09-02 07:07:00           12\n",
       "...                          ...\n",
       "2019-09-06 21:46:00            6\n",
       "2019-09-06 21:47:00            8\n",
       "2019-09-06 21:48:00           10\n",
       "2019-09-06 21:49:00            7\n",
       "2019-09-06 21:50:00            6\n",
       "\n",
       "[4447 rows x 1 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of customers present at any given timestamp\n",
    "final_data.groupby(\"timestamp\")[[\"customer_id\"]].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f01458a4580>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution over week\n",
    "final_data.groupby(final_data.index)[[\"customer_id\"]].count().plot(figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f01433049a0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# daily distribution\n",
    "(final_data.groupby(final_data.time)[[\"customer_id\"]].count() / 5).plot(figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the distribution of customers of their first visited section versus following sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our business managers think that the first section customers\n",
    "# visit follows a different pattern than the following ones.\n",
    "# Plot the distribution of customers of their first visited section\n",
    "# versus following sections (treat all sections visited\n",
    "# after the first as “following”)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_in_first = (\n",
    "    final_data[final_data[\"section_order\"] == \"first\"]\n",
    "    .groupby([\"time\"])[[\"customer_id\"]]\n",
    "    .count()\n",
    "    / 5\n",
    ")\n",
    "customers_in_following = (\n",
    "    final_data[final_data[\"section_order\"] == \"following\"]\n",
    "    .groupby([\"time\"])[[\"customer_no\"]]\n",
    "    .count()\n",
    "    / 5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0140ce6220>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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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oHTImbMtNYw89/JKi+gx1ysDhZAqD6+KIcWVNeaeCphHH+cEz6qvuf8P5jtKoS4e03SWetUrjK6WVCozZ9acO9Zv2deCSn7yGe17YVBRn4gvr9mHeQ2/j7uc2+j4TmXDKMHHZT193Xt/x93X46UtbQrf/5cdXSQlRe9I18MxQs2fDyk7MEp4X0ZnIxrho9Jlxr6RnoeoMdU/aQHfKQH0i5qmQx/8d4xm15jJqHir5pXToNfzSR6lXNGzQ9qfs1K0HrNTnjXs7vRp1gSJX9hzpAQDsPpz0fSYSma5e/+Swv6NHul3+CssIEW9oHUPNMhNNm1FLLJeuEV/UR5gzMRUgfbA5r99JH4SQIYSQxwkhGwgh6wkh7yn2geWLZNpAT9pAXUL33Ew+4YVlMAHBepS4tFMoHtjAYg1K4zopOaN2CtZXztjMinZbIhhcFy9K1AdzyssccqKBlWnSogGUQUaI+G0zghXX3C7lQbU+cnUmMi1bNPpssq6kZyFq44B7ATxLKb2aEJIAULGV1btTBpIpA/UJ3cuoNdGZaJdRtJdUolamGHXp4Hcm6iV3JrIB3Z8KczHteFBdzGuoC1QBkHVdOSRp4iCOj3zlFhmj7pUwamecGiHSB/GH54U7E+2oD2FCYb/pV4yaEDIYwFkAfg0AlNIUpbRi2nRTSvG9Z9Y7r5MpA8m0gdq47rmZMcFQZ0wTL2/cj8Vb2wD4pY9CadSUUjzwyjs40NGb/csDFD5DXYY4amZ4KmlwZsMRnlFzz+v3n93gccjlA0opHlm8HQBwyDbYjy3ZgY17OwD4iU1Qw4XD3Sn87OUtzgSYNkzPWHh+3T7fb/hJ2slMtA112qQhzkTNF/JnmBTvHOjEH9/a7vu+Y6iFbbFnoJIm7SjSx2QABwA8RAhZTgj5FSGkQfwSIeQmQsgSQsiSAwcO+LdSJKzf04EHXtnqvO5JW4y6Lq57mgOIfxsUuOGht7Fg9R4Afl2vUHHU6/a043vPbMAXHq38Cl3lQtowoRFvKFap46iZ1NWffMhMF66N6w4LZA2Cl2071Kdt72t3jSlj7l9+fBUuuudVAP6oqB6JoTZME994ci3uem4jFm05CAB4etXurPvmJ2kWgRFzpI+wzERInYnv/8kifPWvq/37CVg1O6GaFTRpRzHUMQAnA/g5pXQWgC4At4pfopQ+SCmdTSmdPXLkyAIfZjDiQrnSZNpi1PUJ3fN+kDOR4aAde33JjDEACseo2UPTIQlxUrCQMkxPun9NTCtp1I1hUifRpT8xascBalLHcD5w/ckA0GfpiG17UG0sUl0cJn1898oZ3Daow7TZ/1Eur8dQ2993pQ+rcbWsPkdMkkJucgk04jFnnKiPIOkj+7GWClEM9U4AOymli+3Xj8My3BUBcdbrtqWPurhgqHlGTYiPOR3stBjEqKZaAAWUPtgf/aHyS5mQypieDvGlDs/j73V/MtRMfrCMF6sYqTvv9QXsmtQnYr60bv5z91isz/kGxb0Z0ykLwI5VHJc8auMs+9Bl52y3cd2N+ggqc6pJwvP4lbF4zIHSh8n2XTnPQlZDTSndC2AHIWSq/db5ANYV9ahygDigk2kDyZSJWpFRa15GLepPTDcbPcgy1IVyJrJ7rcx0MKwB7d6vhF7azMT+WoyLtQ0zuLrOrGJkX0v2smtSn9CRMUzfdQm6ToPq3PgEp6k03PEkVsvjwYx4SjJxOobaLnMqa07NnIme34eUhQiSPiox6iNqHPXNAOYTQlYBmAngu8U7pNzAt5oHgNufWIODnb2+mXtovTvT6xrxhd8xD/foQZbGJ9O08oMdq6ksdSAsRu1eoETMcgqVwmhu2NuOE775nPO6kgZnNrAwvIxJkTGtvqBulbnCMOq6hA6TwuczCNo+36wjlTEcyYJt73N/XB64T/Zbftu+qA/mTJRYrrbuNLa3dWPVziPOe576PQGMOmgSqqTVVaTwPErpCgCzi3wseSHIu800zx9fexJe39KKr1x8nPNZY00MnT3+pIAZ4wfjWLsDzC5JkH8+UIw6OwzT9GSRssFpGZ/gpXIh8NvXWzyvK8nTnw0e7dV2xsZjXsOYLxgDZr4eMaojaBLlfQ2pjOnGP0sKMDGcd9wovLRhv9OMmk0KHzhpHKaPGwTAjaNmrbhkcdSvbrKCGF7Z5AYz8NKouEpjcdTicTEDXUF2uvoyExlYON6Vsybg7mtOQkONOycNa0w4wfw8brt0mrPEKhTYve4PXSTKBYP6pSmgNDKEeF8qiUVlg4dRGxRxjXiiI/oCR1NmXeEFQx2UEMb7GtIG9cQ/H+Ia2f7XuccAAD59zrvw7snDAAC1MbeeBwDceMZk5/5446jl4XkyeKQPYRWQjVFXEqImvPQ7iK23eAyrT0iXbsMaEp4wPtOkntf5wA2qVwiCaXoHnl5SQ+19XUkhWdkgRjPoGnHjjfsofaTs+iv1toTYJYavBmyfOQ+tbZgO8enNmGjjyBEz9I01MUducp2J3vIOgDt5u3HU0UZUJsRQM43a17exAg112Rj14e4U9h6R1wIoBGJhhpqrqie+z3t6OyTySK5gD4fsuWrrSlXk7F1qGCb1ZY4Crve9mBAfk0ocpEFghtpqEmsZRSYh7evocc4lmTKyVrITwRh1sPQRxKhdqWp7W7czcfRmTOxsc+XEbjsGvLEmxvU4Zc5E6zP+mSCEIKaR0DhqGfj7Ka4CMkGMugIn67IZ6pn/90/M/d6LeEGSmVQI6DJvg42xg2ul7w+uiztLPQBSeSRXOMXJBU59pDuNk7/1T/zg2Q193kd/hyEwJF6jLjbEAd+P7LQrfdixxTGdOATlgVe24p4XrG4rZ9/1ssdhGgWMYNTZhlqs5RHE2HmNOpk2sHqX5djrTmXw4KtuYtqxoxsBAMeNaXJWnTUhjBqw5A+rFZc8jpqvP++cRwTpQ9a8oNJQFkPN62crdxYnGz2MUc89erjvvYVfOgcxXcP4IXW45YIpAPqeNABwD7RwOAfsuG1ZCu1AgykwamY8S8FsfMkOFcimguDEUdsJLzFN8/hYnrazbvfnUb7AjaMOdyY+NG8Orp090Xk/EdPw9tcvwF1XnwjAzXBMZUzHEC/6n3Px0blH4dlbzsS7jx7uGNMaQaMWDXVct6oqBtX6+MN/vtv5+7zjRgHwTvb+OGrqOReGSvRTlMVQ87NxosDOO4aYHmyoNY1gqh3dAVjaWPMINyv+uDGWp7kQSS+O9CG8z7YtZlYORBiUls2ZKKKSkhyygW92YEXOkFDfTC5gxpOtMLsFjZo9v8eManRYN2AZ6pFNNZg21hpDXbbkkspYsdizjxqKCUPrQQhxxhnbF9OomX0QY6XjuoaMGdw4oKnWXQ1PsRk7b2tk/Rll71diGYGyOBP5JBV+qVRIhDFqAGjkbqr/gXDTVfuKoL5sbNuFjjLpjzBMr/ShldOZWIHL3iAwA8NiiwtlpAE/oxajPvjqk/x+GfFi45r9LmWw+Gf/MTplbm1G3RskfWjEKXMqzUzk3mN5FPwYDoqjDgrPqySUxVD3Gu5N/9vyXZg1aShOtUN0CoUwjRqwnBgM4sPDHDKF0EjFvmzr97TjiRW78G+76HuxJqr+BJEhRWXUm/d14EgyjdnN+T87/joPeW+q5GA9Prfs78SuQ90YO7jO8/m21m5PJ+1l2w+hLq47bDcMYhz1b99o8XzOMgx1jXhIkdOlxx5DHRyjNk1/7WfAHWdO1IcRbKhf3LA/MI5aZqh/8uJm5z1fHHWAM7ESHcplsRI8o96wtwMfeuBfeW0nbJmajVG/d/rowO/G7dcsRKkvYLM4e4bed+9reOCVrY42rRi1Fd0hi/rIZqgv/PGruPoX+T07DOJT0p+kD8ao1+9pR3tPxmfYDJPi+5yz+qr738D77n0t4ra9tTk22OVNnc+ZdKdpDtmIacQxoCIBSdlOQBnrv3LWBACursxqfYjjsjtt4EBHL1IZE7Jhw3+dyTF7293IMp8zMSMPz+P9FJXS5LrshrovYOP4Cxcci5Y7L/V8lm0Z+JF3H4V5pzVLv8va/hSEUZvhKeTF0uj7E4KiPkqxBOVXU7VxrV85E8WJrJD+jrQQ9RG075hOMLTeirbgtV6foc4YvvvMcMpRQ9Fy56U4emSj/V3WecX73ZvPm+JuX/cfF/99WfEncTy7pW2Doz4K1X+yryi7M7EvELu0eJbPER5aZhBkSyygMBo1S1MNakuknIn+qA/mMyhUTfAw8Fe/Nq73q/A80fAUUqPOCBq1CDaGY5qG4ZKwOL+hNu3EpuBjZOOOSRQio+aPJR7zb8cjfUiOW7Q7TPoQxzlPEAa0oZYxatGrHAVi30MiYWVhYA+CzLsMFCjqI8CZyKA0an/UR2mdiRyjjukVqU/KYJrUN6nEIq7Oosg7aUejlruxGAvlGTUPcaWYchJVgvepOZKjnFHzLFm2EvVIHzJGHSB9hEV99KQGsvRh+Geptq4Udh9O4rPzl0U22myZygwuf1uzadSA+yD4nYmFY3TZVg9vvNOK7/5jPZ5csQv3L9zS5/31R4hMK9fwvI17O/DFR1fkrCf+4pV38MTyXc5rq1JcZRvqf73TijueWit9NqOuzhhLfGL5LnzkV2+ircuf2PWnt63WVdkZNZFm+voMtR2eF0agGGFy4qgFdlPLGd8aCcHhx7HM98OvQNq6Uti4z9tW7M9v78AvX93qmayXbT+Ez/9puefZSmVMfO6Py/Hvg12B51JolMVQy9ostXWlcOczG7Bg9R48vzZaEgjzejsG18Oos58aMwjiTY1phWPULHU36AHt6LEytj7/pxX4wbMb+7y//oi+Mur//N3b+OvyXdhyoDOn/d75zAaPs6nGLq9ayfjwL9/Eb99ocYzLlFGNzmfsmf/eVTNw0oTBgdtotbsZ/e/f1+L1La3YvK/D9x02RvnoKAAYw+q122VVCZEbapH8pELC6tzjFwx1iPQhW4l6Jntu0jpzyggAXtK0YW+78zeb9L7yl1X4zj/We/wUtzy6Ak+u2O0xysu2H8JTK3fjfx5fFXguhUbFSB9tXSmn0hxFtMHCLii7J/wzEEWuYzfWnwFlvS6Els4qhlW6ASgnDBN9YtSMEcqKyeeC/sCoGVjTgGvnTMQXLzwWgLui/PCpk3DJjLGBv2WlEVh3cdlzTilw9SkTPAbxqpPHO+Mqw7HjoNo5DBdMG21p1FENNUt4EaWPrIba/ZsnX9//oJUlKbJiADhn6khQ6n3WZM8d/06DLQd15Fg/pS+oGGciv/yKGhXiVA2zbwr/EEQZbzGHiXvfZze5EKE5rCFBoTrGVCMsZ6L7mhncqIaapTcz45UvamP9x5nI5EFdI1LHeZBkAbjPJINs5ZgxTcQ04jF4CV1zk2zssqqAV5KQoSamIZUxrM4sYdKHw6jtokyCUfdq1JKoD8lkD8DXvMD62xsnnq0dG/8W+7yzt3R9UCuKUbPCL21d0S6AY6iJ3OBmg0wyAbgbW4BRqxh1dhTKmdhXD31MJ/3mPrGxEtOIU1SfP/Iw49nW6TXUspo2TE+OCw0d+LT1qM7LRMxqrWaa8mJK/PbZ8RDil0/4c8omfYgTDGAZ567eDCjXAJc5S7Mzavc9ZtRZF/hSoCSGes+RJJpvXeA4bmTOxF2Hk/innQQSpWrdE8t3Ye73XgTgzp4njHd1uSjDLWh2jxeg+Prflu9E860LnESBoELrIkrZfbtSEOhMzFGGuP2JNWi+dUGk78oiH3SNVEzCS/OtC/Dlx1YGfs5WEbqmSRl1U23c9x7Doe6U59mWrfYyJrUZtdf4tXal8KEH/oW0YUZy2AN2D8xMcMILA9teb8aUyliDuLIPMsdpkPTBJpT1e9ox/ZvP4X//vs6xQYxRT+eqC0oNNfcWM/KyLlHFQkkM9Zb9lpPn8aU7AQBJScjL9tZu5+/WzuyG+skVrreezbwPfmy20wU5yoCLZWHUfZEr/vy2da7MmRjVAIvlJAcCCsWomRc/yu9kDm2NkIpKeHnMHi8yMEMd04hjiHjDec7Ukbjr6hPxh/88FQ/Nm+P5bW/GRDf3nMmeTcOg0IVqfIPssfXWv9ucsqoML3zxLLz8pXM821jwuTPwxq3n2d3B/YlNIti+KNcfIQwAACAASURBVJXnQYwa5JYnzsWZyK7LzkNWPezn1u51jK0s3lrW3o9/pnrt61XKBswlqfXBkj2YtiNbou7rcL3vURg1zxjYszS4Lo45zcPwwvp9kRg1u7FigAjfmj5fiA7RdMR09GTKCGVD1QjDpB4G1dfqeWkje69FsWwnYD0PpWhWUAgkU25x/Ti1rhdvqGvjOq6xy4/yJAiwrk8Pd/4yg5OxDTFvqIdxDaJZWVWGY0Y1QcT0cYPtY7TGvmn6SREPXSPQiJVxHA+I2po0rB7b27qzh+dxv2fnwOxO2nCljwZJnLgsXDEtcUSWEiVh1OzeMEMtY42sbu3Q+rjP2SEDX9KQD8Vj+4rkTNRZ/LXAqAuYmcgQdfatlEyoUkJseabl6EwUIWPLImTX2TIS5WfUQatBXq5wGLXuMuqgujFiFl/GoJ7zlzJqW6PmVzo8+8zYZVWjQCeWth1UnpSHUzckYNssXDCWJfyW/70VRuhObmnDdCJLZE5XmaHm49Z5Q10qqazohppSisVbW+2/rffEkokAcMAubn7MqEas3HE466zFlmGA1zusOYY6+wXUApyQ7OGMIle0HOzCzkPdvvfF3acyplObNwwD0VAbVGDUerChNk2KN+3nKUjvzPbsLN3W5shxPHhnWRSs2XUEhwvQBUgEfwymSfHsmr1Yu/sItrW5z1kXF/XBLkOQcROTT9Km6VlRpCXXK2OaTlSHDB2SIlBB0DQC06RZE174Yw2adFh5YtmKiId4LeKa5ukxKToTeexr78HoQTWe94IYda4tzvJF0Q31H97chp+8ZGXcscevO2VIUzwBYMLQegDA6+8cDN1uPfd7/uaz+NHjIpRyDMrkIoQ4nupsOOfuhTjj+y/73heHe6+dzZQNskms2mGY3mWrE54nmWwffG0rrnvwTby2+UCwoQ65b/s7evDBn/8LH//NW573L54+xtJSIzIkSinef98i/McvF0f6fi7g2dv8xdvwqYeX4tKfLML5P3zFeT/JadTs+0EsU9Rz/YxaqHVhp6eLSWMzJw51/n5pw/5AeUKEbmv/QeVJvcdqjesgQ/0hW86ZOKxO+jlDXNPQkNCd78V04hj3tGG60keN3w7ta+/F6EHedn386pp/vmR6djFQdEO9xu6ZBrgsN5k20FAjl8dvOL0ZADwamgz8gOIH7OUzx2Pzd96HyVzHliCwcB9ZOm5dXEdvX+JyfYzawLLth5zX91w7U/qzUhQiqjRY0QDu6zBn4iY7imZ/e69Hkz16pHu/wxi17LOamIb7P3IyYjbziwLGpNbtac/yzdzBszc+c5JH0gnP07J2C/Ib6nCN2jH8wvamjmnCUcPrndeRpQ/NlT6yRfTVZJE+rj5lAjZ9+30OoQtCTCdYdcdFWPilc63XGnEk14xJkTIM6BpxmhXw2HMkiVFNgqE25Yy6VOGcRTfUfCdvdk49acObDsrdvboQ48mDn+HEmx+1xrOsCwT/WT6Fohh8zkRhH0FscCCG5+XiTGTPhaifjh/iMqywayi71wldg2ZvL+pEeciO9WfF7gsJ/hgHBTiWnfA8nWTtFuSXPsI1aoO7xiL4lXDU8DxNI5YzUZC4ZGCTTdi2oxQyi+ua5xlJxPzSR1wnSEiq8JkUGOWTPvxx1EDpiFXRDXV7j7s0YI6a7lTGc8NZ00udCzXKNlPxERlR6nrIUCfJSuI/S/Yx041HVGfiQMxgtLpKR3MmspWUpnk7iwzmfBZhjFp2rxmDj+WgUbd2WT6VxprCR+jwzzZ/XjySNomIacQ5pyAWKsoNGUPQqH2MWl5mFBBD4HKQPmyNOrv0Ea5RR4Wsfg9/b9MGRULXpBmOADBaZNTcuOytdka9fPthbNrXgWTaRC3HqJkEEeMGXzZmyYe75VvjwUkflcRk1cb1gunFhFg3lNVWCEMhmhVERU/awHcWrENnbwbLtx/Cw29uK9m+GZ5ZvcfnmMrmTAS88cOA16CFRX3I46et/3VNQ2/GxJ3PbEBrp9u5+61/t+FRu5ocw5MrdgPwJmEUCp7mzwHssZsLz0tnYdSy7fOMmjdC/1y3D/+wu5fLGDV/R3JyJtJoDsigzuC5wldjXpjEejMmEjE98PqKzsQg6aNUK+Cix1GLOu9HfrUYR49o8MRBMnYd17XQQcqDXbhZk4Y4HYdzRW2I9FGf0LMmn4RFlvAf1cQ0Xx2Kc6aOxP9dPh2PL92JVTtdHb9QTRWi4M9LduCXr/0buqbhF69YvfU+Oveoku0fAD49fxkAoRVXiDORfy54xsc3Kw7VqCUDi+07rhO0daXwi1fewa7DSdz34VkA4LSKu3bOJOc3m/dbWvmk4eFaaT7gw/D48x1SH8dhe7Jnhjqua/jQ7Al4cf0+fOL0yZG2zzvTAO81+eTvlzh/s+v7/846GhOGWtIS/8xHLasqi8oKAqtSt1kSldMXiJNYe08aDTXBhlrsw8lfLz56q2oYtYzspg3TY6iZzsd3NM6m/aQNihGNNfjbZ073eWijgk0QMuMYRaMOY2405HunTh6Gpto4PvaeZnztkmmez0qpUbMJqhKyIaN2IWfvpTKmN3mCT/ENuYYyI86W4/xkIbZnEsFWW8VopcY/j/w4GFwXd5ym7NlM6BqGN9bg8U+fhjGDo42DjEEFGUB+rkxS/Ool03D9e5oBeAlItlhmdzvu37Iu5KWAKOPsO9KDYQ0Jz/37yLvdifhdI73BCPx94OOsq0ajFjORBtXG7KwmbxYVYDNqLZpGnTHMPrexYjGUMrmhNp5dow4zcDzzEIkhPzDEcyilRu1OiuV3YPKDOYozMZUxPdeYnwxlccHOZ9y1b7Ijj9hmPBXXshghxmiLMVD5+8FvP5lynfA8o85n++za1sa1wKxZmebNO8lzcSY6f/exFG2+EPX0PUd6MKw+4XEmjuUmOkK8PhB+ldPGxc5XDaMWn/dBdXGkDdPzgNWy2EktOqNmKa59gcOoJQO7LoL0ERZ0H3b0PKsTDUIpGbWMuZayehy/YuF3G8aomUO6127txMAnHkRl1Cy7lW2Td0rLni3+3vSkDd97hQI/WRvc9pNpN/+ALb/zaeWW5hh1XVwPPAeZIfYw6jykj77WDM8XIiHa284YtesrE1fm/Pnxq5y2rpSjCJSKWBVf+hDSs4fUxZEyTKfTN+BGfcR0jWNTwQPgA/ctwt+W74occB8Etl/ZkrE+gvQRlkUYljcxvNF1VNQI4V2ljKOWMddSThSPLXGLDsm0Z9FQL9y4H69tthKhbn9ijUfD5juRRI2jZtmtjC15GbXfoPATN7v3xaj7EBT+1ZM2UJfwZubJal5kwyubDuDXi/4NwFo5/nX5Lnz64aW+72Vz/EWXPqJr1MWCeD8Nk1qGmrt+I+xx2WCvWni/ktjGa2ST9d35i7eh+dYFOPU7L3gi3AqNEkgf3tfjh9YhY4fGMDj5+xE16tV2Ek1fGXVtXMd9H56F+TfO9X1Wl8ge9RE1IeZrlxzn/D1j/GBPssuUUU34n4uPwwPXnwKgMM0KosIt0O++F6VORqGw+4hVzWzeac24nnNiagHORFYyloFSirGDa3HjGZPx5Yum4luXTwcQ/uzwbJt1JuniIigYZJIC/zzwdSMKjUzACidtUNTZEztLIc+3OTJLpGEM/Zk1e33fkRli/spGHX8y/0MQ/nSTfywWArInoj4R81y/dx89DA9cfwr+8fkzfd/lGXVP2nRs1tOrrAiZ/R292B+QnFQIFD3qg4+bbEjoMEzY0oc/HjOucYw6wpKir7GWAPCBk8ZJ36+N61k7hkSNjeZDx+65bqandZGuEXz6nHc5NSNKGfXBDAJfiKiUlcEyBkVjTQx3XDbd837QMyAeW2/GxPtOGIvb3n88AODc40YBT64NzS7kJyJ2H5gxjGUz1ByjZs9GMe4XL8WJkw4zrN29hXFm1oQ0GJCG5wVkBIdBFtEThJMnDQ39PF/I9pqIaR5DXZ+I4aLpY6S/98hRpim9bsUkOSWN+qhL6DBM06dRO9lIHKOO0l0lasB9PqiL60gZZijDDTNq/NHzjGJ4QH85di6ldOyxlkc8aytljd2MIa/Axp4BkVFbkR7u665eb1xu0O94pCWM2vm9pIYxD2aoM4bpXKdiMOp0iM+ApTwzRh3Pk1EzhGVWSjVq7u+o0qPHmZjFuPc1QCAIsicirpPI+xMdvDLJqZgkq6RRHzUx3cqzzwiGWnOzkQghdm2A7AMgrLpXX8G862E6dCj7pPLBFpQSXMiGulGxvc2SHvj638Vk1LsOJ71960wqXV4TQpwkIR4pw/QwIKvIj/t5WK/FnYe6YZgUO+xzBoCh9V5DzRum3oyJg1zSC+DqwvwzUYyJra3L3a/IqOMxy7gwRt8XRq0R78qBVbBk0GVRH310JmaL+siWuVhI1MS0yNcvZZjY3toNSi1HrMxQF3PslKTMKUPc7kmXNqhjmEY11XBpo24qb5DOyDPcvmrUYaiNYKjD2BTP6lKGiZkThwAIZhRssiqlM+83r1sOJeagK+b+O3rSOP3Ol3D7E2uc98JCLGXp3KmM6RtYukT/FGtK7zmSxBnffxk/eG4DHl+6w3n/3UdbSQ2ThtXb+3S3/Yc3t2H2t1/wbKdHYqiL4fVn3YEAv1M9JnRdyZWBemt1eA3VnO94z1fGmPnxHNWm8o98VG51fITql7kgSPpgE8OZU0b4Pj/9mOHO3+t2t+Osu17GA69uRcak0p6UxTTURdeo+cGma1YBGSZ9LLv9QtTE3Kw4NtvGNBKoUfMMppgxmeyB7pG0DWMI06T42NRUxsQfPznXWa7KoNl1hctd66NYDxtjoy+s3++8lzGCQyw1IjHUhpX2+63Lp+L2J9cCCMhoFH53sMNaMSzafBCGSXHmlBH47pUzMHFYPV7677OdjjrZJn6nBVORU4jZam7c4Fo/o9bd+F7e0ETFK185B/e9uAV/eHMbNC3c0A+p96/++KO5IWImZK5p50tvu0BaJ7oQuOH0Zjz0egsA1xH71tfO99S3Z/j1x+dgR1s3Lr73NeyzHYUvbdgfzKglvWALhchXgxCiA1gCYBel9P1Rf8czS1aSMWNSxHXN0QjZ/3z9giBGzQ+SYq6SmKEOlT5CBqnYmr4uoUv7s/GI6Vqf2n/lgqD092I5RNh94+9ZyjADdc4gRl0T0zBhmJu2TSSMWvwd+4phUhxJpjFz4hBMtLdx9Ei3/EA2I8LOgf3fWBMriqFmYyZjUh9hiekuo67JQ/YYWp9w6liYNNwhP7zR70/hH5txg8NrQst+E2Vi4cNXC42juGeHxVCPCshsro3rmDK6CUPr4w6Dbk+mbUPtjuWh9XEc6k5XjPTxeQDrc90BzxBjOkGPfTK81sgMNYtDjOmaVGcEvMaxmF1w3AywYBYctaZE1MEc10jJGHWQr7bYjJq/Z6GMWjJZs9KU/PKdtzN6gPTB0J5Mw6R+JyJDtkw7x1Db97OhRi/K9WLPvmFSP6PWiHPN8nEkWp3F3ezfsG2IGr6IbMSDgc9mLKJaGQl8tEbU0MaYpjn3maWP8/kPLKorVW5nIiFkAoBLAfwq1x2YHkZNHJ2PX3I58ay9bnuhKIy6mKjlGPU/Vu/BkpY2p4u67FjEkDDe4EY11BoheNaOZ93R1o3fvdGCHz6/0decVIRpUvzujZacanZEmQgLgWTKwO/eaJHKPhnTDEya0O0axq9uOoBvPLkGrZ29lkYd0zyGmpe/ZHHhPFgvziBDLSuX+3ZLm/N3WpA+GhKxPjt/N+/rwD/X7fO8x4ZMxqT485Idns9iXJmFfByJrHsRYD0DQdvQCKQ6rFhnPQr4ebNctT4Y+BVEZEOtu00HmOOdlz4cQ10BGvU9AL4CwN9q2AYh5CYANwHApElucRNmEG4+7xi8vuWgIyXwF+zY0U0Y0ZjAx+3CL7GQqI9SSR8soL2jJ4PP2BXeAODsY0c6WUksvA2wBlWCewjThokzjhmBRVsO4rKAWG0RPRkDHYctg/bBn7+B/bYX/nB3Gt+64oTA3z2zZi+++dRa7D6cxFeFIk9BCGKdhX7Y7ntpM+5f+A4+fKrVQom/Z2kjmNEx6eNjdsus7W3d0GwjM5FbvnqTKaz/g86NyToyPZLtU8Q1v/iX8ze7NmmHUfdd+rjwx68CAFruvNR5j40ZWZun2rjmkBwxqzUMX79kGh5evA2A10AFrSKC/D9ffd80fGb+MpwqVJcLA3838smkzAXnTB2JMRIp45YLjsUnf7/EceoD0Se6uK5xCU7W2fDSx6BKMNSEkPcD2E8pXUoIOSfoe5TSBwE8CACzZ8927o1hUlx20jj893unYvHWNrT3WDMSb6hHD6rFktsudF7rIRJAqeJ8hzZYF7+109u8NCjmWOb4mjK6EQ/f+O7I+/z02e/CT17aAkqpY6QBhDohASuiAsitf1uQMSu05sqMY8tB/6ogrIGq6Ew81J1GU00MCdu3ce7UkXh544EARi3XqBmCjEVWjdq+Nuyc6hNWuCml2QviZwOfWyC7NxdNH43n1u7DsIYaJ+Y+qO+oDJ8862h88qyjAXgNVNBE+fnzp0jfv2TGWM+kEgnc+WSTU/qK395wqvT9s44diY3ffh96OXIl6+4iQ0wj6Ml4V6v8MzTEPqdUpnjOxChTyukALiOEtAD4E4DzCCEPR92BQd1KebpGnJkpzNscFp5XKuljeIPFmncfTnre55d+/LGIiSppwx9Klg18dxtvyc3w5Sb7NBdbESh9FPj6srjxfR3+9Np0iEYtOhPr4pojfQDuctNTQpPINWqx3kzQfYmsUXPORHYefcXhbn8nJB6skuOwhrhj0KNqxCJ4Rh00UTYVsCECfzYyB2UpwTPhoO4uImIco2bgZaHBdda1KiaJzGpJKKVfpZROoJQ2A7gOwEuU0o9G3YFhULfVEaf1hHmbw5yJpYozrkvoqI1r2CUYai+jDs4gCzNCQWDfz5jeMrDZCjWxgZ0LqyuVM5EN+P3tvb7PxAxVHppgqOsTMStKxP4+YzGyqA/RXyAaviBtMluma0rUqB1D3fdrxtc4lt0b5tupT8QckpMLo+bBs8GgZyZIHsoH/P0oNqPOBVE16oROfNFQ/DVkZKRfZyYaXENLXSNSjVqExajlDz9/werixQ0DH1Qbx9+W7/K852kb74mntd7f39GDy3+6yPKo58io+aQXnvUtWLUH+yWMlIGNg1z8NEH1MHoFo9PRk8ZV97+O//37Wlz200WeFlVRwHRUVobUF/URcNAxjXhCOxO65oTnAe6AF42kLvwOyMFQ5xz1UThD/YH7Fjn3REZS4vYyvSERc44z3zyChMdQy79TSIPKn01QD8hyILoz0f893j/AJrWKqfVBKV2YSww1YC/juYzDKIZa14KbjLKKdWdOGYHvXTUjl0PJGbILz08gPZJU4seW7MRKu7VWrobaYdQSNi5GnPBgMdHiEj8MUZ2Jm/d3Ytn2w3jo9Ras2nnE6RUYFWHhhmnDDGSxYnhefUJHT8ZwwqsunzkO15wyAe8/0euotRqperclPktB0ke2geuXPqxjKcSSN2WY6LAnM/HefHTuJNx19Um45YIpOGH8IDSPsLqPiCnuUcEv+WWRHUePaMBZx47Ma9sy8KcTFHFTSvzsP07GvNOanYzUbJBN4Pwz1JDQURPT0FvETknFZ9Sml1GzmxYm5Idp1MzQ33bp8U70RbEgSwrxljv0l73ktb18Neq06TdgfL1lEYyJ5cKoGesUHWuioT7U5XWm5hq7K7LNzl5Xi7USn+QHrRMC06ROF5aejIGelIF627A0j2jAXdechGNGeftlaprf0EVl1Nk0X9FQNxRQowbcCUU83mtnT8K4IXW45YJjQQjBB0+eACA8GSsM/DWX1Z759hUnRK6MFwXsbOad1lzSWh5BuPTEsbjjsumRz1FGuHgiVRvXUZ/Q874fUVAaQ+1o1HyNgvwYNUucyFef6yt4w5OUGGr+wc+1DgNz7MgkgTBD7ToTo++P2QKRUYmGtVU01DkOYHHC7UmbzrXKGOFx1HzCR3fKsDqcZDGmuiT1XCS8gYY6yzPlaNRM+rDTnMNaf+UCNgGIx1uX8B4vkyWy1UsPAn/+MqfhsAI7/JwVX/ltdF6Q+ZpimuaQnLqEbvdYrRZDnaXeL0NM05wGnL1CyAszjrWJoh+6FDx7OsR56pNpA5RSDxvKlX06pU4Nv76tayQwoYXZJUIsdi1eMxn4Vkw8REa985DXmRqFhWQM0zH4MiPWaleHC3O4MkPNjjOZMtCdMrIaU+aETGXcvoBRpY9s204bJjp60k7dh/oa3Xm/EGDXXlzJ1Ql1L1jkRL4MjjfUMqfhsCI5/HKR5ioJMjIR04iTvVyf0FHb7xk1F57Hn3A2Rp0xTXzh0RWYetuzns96ysyoWfW+lzbsw6ubDjjvJ9MGHl68HV/880rnvVxbhTmlTk1/Vbk//Gsbjrv9Wexo88cju9IHwZcfX+W7ZjI4hlpgqKLeet9Lmz2vozhg3vvjV3HS/z4PQF5X/FCXNcFZcdQhCS+UOj6BrlQGvRlTqqnyYBmNx972DK570EpWKZT00Zsxcf4PX8HPXn4HMY04xZzylT5Eg8yK+hhC1I+4mmKMelaeRfb5ELXJwy29e+Iwt27HkAIb6glDWV2VhizfrEzIZFpdIzhh/GAA1iq6Lq47tqkYKEn1PBY2xXt8Q+OodYKeDMVTKy3HFZ9QUA7p49IZY7FgtdVyhy3FF21u9XwnmcrgOaGdUW2Oca4xLupDHJxLth0CYGXoTRScIKyQk0aAvyyznI69GcMzIEUwGzFIWPqKjDquaTlXLNx6sMv5my9LO3pQDfa19zqMmo+LFqHZSU/Mzu89YrPYHKSPt1usaxaZUUcw1Ps7enHx9DG44fRmJxEpX0bdJQxs5rw2qVVGk0XKiJESiZiGp28+A0cNj+YME8FXxZsxYTD+8unTMGP8YLy+5SBGcmWHC4WLpo/Gn//fezCnuTjdW4qNWsk4iukEd19zElbsOIxZk4aiPlEN0oc9uIc18IY6PDwvKAMwmTaQ0LWidncRcf60Uc7fbFCKBiOZNnx6X32Ok0mci/oIMhqy6+ZWpiOOQ5Gx1iAwZ+JggT3xkS6UUh/DzrVLOa9Rs9ReFjMcZqh1QjyTxkE7QzSbMdVsRu05ZuF10LOTbfLvtg3niRMH491HD3fuRb6GWnTUMmZuUtd3cOKEwdLfnjB+sMPoc8UQwfCfctRQJGIazj1ulMMSCwlCCE6dPKwiHIn5QPbM6ZqG0YNqcdH0MdA1gtp4P5Y+LM3W1TWHNbhRGuHSh+YJ6+JrQvekjdD2QcUAz2jYcYk3L5kyfYY618wxfuAHGUSZpus6oaijObYJRkAEM2bioOWNo6xnZC5GqTdjeL4/WjTURoih1oijtTdxq4us0ofEmRjWQ5FHtnDKLqFPIft+vuF5oqOWv4/MRRzmRM4XpSQ51QDZBC46++viek5F0XJFUe8YGzCuoXaNQljoWkwjWLen3Xl9/8ItWGov/ZMpo2hFxYPAG2pmeMQb1Z3K+BhONqMighnhRZsPOgxShoUb93vKr/LFgthkkdVQ2/dGLA6/ZX8HNuy1rr2MIWTLkuQdmYe60p4Jd3hjDXSNoK0rBdPu9BP0HOgaceLR+TDMrNKH5o+jznEREAix87c7sVo7WLnjMJ5ds8f3u46eNP701nZfstCbW73y2epdR+zjpc4yuhiGWiE3yBm1d/zXJ3Rs2Nsh9SEVAkU11BnBUL+LK9IeD4mj5nv4AcADr27Flx+3nHRW0kP5GDVzjjG2eeWs8QAstiUakVx1dKZR//Cfm/BvW+c9Yby3JdHWA12Y99Db+Mrjq5z3mEFNG9QJGTuczMaorf+njvEWRFy58wguvuc1z3ZPnuRWHAtr9gt4JZfDyZQnQagmpmFIXRyHk2mHhQYxap7d8vUhskd9+J2Huco1V8z0VzucPKLBKcPrMmrrGWYT5eU/ex2fengZjnR7Zadn1uzFrX9djfsXvuN5Xyxv+q2n1wGwJlEmQVw7Z2JOxx4V44fUee6rQjBkhGu0UKGPfefMH7xclGMoCaNmD/SU0a5RCFtmyjICD9rV5DKc5l0q8DeKGapk2kAipuHH185EU20Mh7pTPoOQjf2JEB2sF00fjadvPtNjzBiTXmOzL8Cd2DKclJAtxpYd6/CGBFruvBQtd16KkyZ6B27S3te80ydj5TfeCyB7cfRWrjFrMmV4IiJqYhrqEpZ33DHUEQokNXCsMpuhlkofnOG+7dLwMrAtd16Ke66bhUdvmuv5zbghtehk0od9jdn/vqQeodohe57FAl8dPWmcM3Uk/vG5M4XjtaIxWu68FOdPGx16vPni9VvPw18/c3pRtl1tEMfxcWOaMHmEN4Kl0A5YEcVl1AZj1P7d5Jpe3d6TQdowQSktefFx/iawc+pJuzG9wxoSaOtK+QZsrhq1qB2y13zLJaaT8rIEkzn4Yk7ZHBvMePFRHKLzM2n7BuriOpfeHp1RJ9NejZoV/U+mDSe+OsoDzg+UbJE0mqTWB2+4oz53YnJWXNecSdIx1EyjzpgeHVycJA37GohlaNu6Uhg7uM5zDSilMChFjpGdCkWESA5GSFqF9aUbfBQUWfqwHlBZKF5YeF4QYWastVSMmg09fnCzULhkSmaovQYiZ41amIBYFiA/kFntad4YMOPI187IxqhlhtrnIE2zim26p7JfGERGzWvUCV1zwpiySR/8XvjrGCU8T0yy4Rl19LRh93sxndiG2utMZMeeypjo6HFZtOhUYteMtZoDLHnjUHcawxrinkHenTJgmrSojZsVcoM4LmTZnP2bUQsaNY+g1GFA3todsBiIYZa+nQ+/u9ueWIPuVAbJtKtJD6tPoLUr5fP+5y59yBk1f/2Yztnek3Ecdyx6IG2YOTBq+LYtMgcnCzSueyr7hYF3YoqM6NPYIgAAIABJREFUOh7TnDAmpusGt4LimD53HbNKHxrxhRjyjDpbhTz3exyj1jRpsX3HUBumZ4IS42nZOFizqx3XPvAvtBzsQnuP1SR1WEONR8dv60rBpMpQVxLEZ05WHyXIUHenMvjsI8tw/a8X47Pzlzl+jlxREkPNZ5/95dOnYd5pzaGMWuRs3/+gVSWvqzcDk1KUKrrokRvn4qNzJ2FwXRxfu+Q469go8M7+LvRmDE8R+87etIfJ3XB6c87yjlggif0+qOLYNruXImPZ3SnDTbnOYqjZ93h7IGrxjBnWxDRomhWjna35Lh8bnEwZHgYe122NmjfUAQ/4HZcd7/zNR/lkdSYK8ddpw2uo82HUJx811PO6xpGk7Op5GdPjABevPb//xf9uw+J/tzoT2rCGOI4a3oARtsPUIiO0oEWRFPqGEycMwenHDMec5qE47V3Dcc3sCb7vBI31d/Z3YcGqPdi4twMLVu/B2t3t0u9lQ5E1amvA8A/dKUcNxR2XTQ8Nfuf1vq9dchwm2imobNCVSvqYMWEwvn3FDBBCcNNZ78LvPmG1+UkZhqdORW1CRzJleozSNz8wPef9iUssZrg/cfpk6fczBrVre1jXubUz5RxDVOmDvzc8c+VZLzuOmK450k8Q+NjgHoFRpzKmVWUsZTjHHGSoJwytx9jBlmedN87ZE16EBKmU4W2wHLFQFq9RHzOq0fPaCc+zI5dShok2XpsXGbUwuaUyJmeorZDFBz82G4BlqCnNv9a0QuExZnAt5t84F4996jQ88sm5mC3pFxk0sbJn8QZ7DGcLmw1CUQ0102xz7XTCO4PiXBailVJcemciA1v+9mZMT3eSuriOZCrT57rEohFiBiGIURsm9bC3Q90pNyolm6FmslSARp2UsN6ErmVn1N0px8Am016NurM37UofWTRqwGUpHmdihKgPnlEn04Ynrlrm2JbB5y+QGGremdjGSR+iRi02au7lDbWdGTq8gWPUlOZUslahcsHG4yg7F6AiDTVb8oXp0fLfuX/HdLfrcto0S8qoRfDOo4xBHUmnTtBd84W4rGeGoDGgf13GND0Ov9aulMNUs9UdYJMhv7LhjWBPyvQZ05hOsmrUBztSGDfEKvBjrTLc73f2ZJwMLoeth8hD7L7zhjqbnKRxGY2Abahp7hq1uJ8ERzYS3ApDI8xQe6NdeIgOWIuB24baljyGcobakveUpa4GsHs/apBlqMUckagoatqTk8WXI6Pmq4rFNeIMGlb6tFyMuoY31CbHqBM6TApPtmA+8BkHe39BjUavvP8NJyFmwtA6bNrXiQ17OwAAC1bvwQ/TRiADpRJnIm8Qu1IZn8MvpmmhleJ2tHXjrZY2XDBtFBIxK5yN//7guji6Uwa6el1JJKwU7Pih9XjnQFfgRCVDTCOe1PcvPbbSyWoF8tOordfucfJFehIxDWnDq1GLDiMxrvsHz250/maMuqnG6oXY1p2CaeZWW1yhcuHWBoqhsSaGu57biLdb2gK7pQehpAkvuf4OsFgLH8Nr0gpg1IZpa9QuowaA9mQGwxsSeOxT7ynI/phxmD5uML57pdt2bN5pzc7fa3ZZzomPzj0K1889yvP7w93BhZnYNebtFp+BeSSZdgw1M6YxjYTWzdhyoBMAcMG00VYGYncaacPEeceNwr3XzcRnzzsGQ+rjSKYNJx48jCH/32XT8Y33H48zjoneFkoXGDVvpNk5RIEY085PKHzVuoSuoTdjorPXuvca8cdLB4U01sV1R24ihGBofQJtnZb0ocpx9C8EPVVM+ovrBA12/fKFGw8EfDsYJYmjjqoLMng1auKW/7QLyZdrWRjXBUZtHwcbbEeSaRwzqhFzJM6GfMDPR5dzac2XzBjr++6EoXW4/j1eQx0mxThx1Ny15BuatnHhhoxR6yEt0gA34mPu0cOt2PLulN0EgeDymeNRE9OdwlwH7LoXYYazeUQDPnHG5MAVhQxxXQvV5/vKqM84ZoSH7SZiOlKGabUJq9EtYyvokEEp7A1CHQ92zVR4XvWA2cCYpvWpNHNJMhNzbd/EJ5bFNE6jzpgwaOnjqBn4lOF0xo36cBh1T7poge+8fCQzNhohPqcjK0QvgyzhZVCdazjaulI+6YMV5Q8Cr7s6SUBC/0dWmOuA3SUliuHMJesrphH0hExQUf0l4vcSAavCmpjVHZ11nxna4DfUGdOUnoNoi4c3JtDa2auiPvohgkYFK7mQiJGcE+B4lC3hJQxe6YO4UR+mlaqbo5JSMLDBtmLHYU8WIGPUHT2ZnGOno4KPRZex0J606StZGta+nvn4eBmJr9R2yGbUcZ04EyNj1JRST60RhtauFOI6QVNNDMMaEjjUZTNqCWv/y7JdgeciIpeJOaZroSuJvjJqKgzJuE7wdksbNu3rQF3COu833mkFpdZ1Wrv7CNbsao9USGxofQK7D0efwBQqHyzqI6ZpnqiqXGuYl8RQ51r/9tIT3aV9XCfOQHfiqMvMqP/41g5sPdjlHBebKY2Qrtp9BTNWl88cJz3/5hH1vuscZrDcqA/3PdYyCQDaui1GzTNBjVhhfX9ftQfvv28RnhU62hzqSmFofQLEZvetXVa4IH9crBLeLrtAUS738vzjRmX9Tjb2HdWxLTrzHEMtUKe0QbGttRtbD3ahLq5BJwRHkmls3t+Jx5buxKU/WYQVOw57UswZ3nP0cM/rofUJ7LVXGmH+BYXKw8yJ8kqEGS5EmXfWi36MbChJwktUBw7Dl9871fk7pvFx1GZZ9Tsxc5Adlye9uICMWjzLVXe8Fz+85iSfsTlzyggcN8aK/lh9x3vx0A1zAGTRqCWrnWENCaz4xoUY0VjjhNDxUk5M02CYFBvsWuGb93V4ttnalXLkl2ENCRxJptGT8fZ/HCp0lIkqRay64734xfWnZP1eNkOc75PDzkE01HzsfF1cx8dtR29nbwbr9wRnoX3i9Mm465oTPe/xz1e+YVwK5cHco4fj6lMm+MpGpJ16R16NOldGXeTwvPwSXvilriV9uAWBysqoxUgA3V80qa9VtBKx4KU7qzEgTnx8bdym2rhTkzoslI6pS+KkN6Q+gYYaK3uwJuY9Ny2LM7FNMNTsPd4Yi41T9YjPhqy+ggz8vjTibxqQb1JSkPTBoy6hO/JRthrYMycN8fW05K91Z541IRTKhxGNNb6EMIdRa0Qo0ZBbjfSKTHjhkdDdgjhpg5WALI+hFvfLBi/PhPrKqKMYJDGKRnRgumGEwc5Ew0kh939WF3cr3HkZteVMpMI2GA51pZzEDd6xGeYIzXW1lQ3eqnf+k8u3Y3iU+2qa7vllDAoSwt+HSTp989e6PcelsUL5EdcJUnYpZgY3l0TzTMwVoVGbFPjpS5udVNpcGTWPmK45gzljMGdiZTha2ATkMWZ91Kj5ruBBJkU0biKL51Obg/DaJiuWUyYjJWIanl+3D21dKY+BYoyaFYE63J1Gb8bAfS9uxq7DSWw92OWkQvOGKGyVUejVEX/9ZZOAWAI1KsIScxiSacPZfzZGLSsLwF/rXDVMhfKD3b+fvbzFkRYzXC4JTyLY+8u3H8L9C7fgvx5ZFrrtohjq/R09uPv5TXh0yQ7rIPvAqOM6cQZz2jBhUpS1DsJF092OG6y0ZSE16m9feQJiGsGQ+jguO8nfEgrwGzcxI5JNHGFRHy2tVqsvsaUQAKyyexW+sumAhwXotjPxSNLaX3tPGr9/Yxt++M9N+M/fvg3Abbc2iItAEScvPjGn0IyaX7197RJvN5fxQ+pyinE//7hR+Lq9jUSARs3jqpPHu4xaUrzqgmmjURvXcNyYJk/SDAO/Mrvt0uN9nytUNpgGfffzm7Bw034ALjGI65qH0DFGfeX9b+AHz27E06v8vTZ5FEWjZrMJ83RH1SFlqE/EQIg1G7GEl3JJHwDwwPWz8eyaPfjUw8uc8/No1H2Moz7tXSOw5buXhH5HNHwsy4+BT3UPQm/GxDlTR/qSLkTwzhHmTGQJJWmD4qCduMKuxftmjAEAnxOSx7euOAF/eHMbgMIzak950klD8dpXznX62L1+63k5bevX8+Zw22UatRwLPncGpo8bjFU7DwOQa5C/+vjs0P25TXMJzpgyIqdjVSg/hnKrJKfhtOlq1Hz4a0Vp1E49hz4MRjZLxTSt7CnkDMwhxnTEuIdRF//YRMPXITienAzKEB2M71ATBv47mmYt6ZmklcoYvgJE7PuJiNekL/4LGfh7EdOJpzFuQbYrjC/GsFnNbJdR01DHowzFbuekUFwMl8hZGbuZByHEQ4pkK64wFOXJYDGobFbpC2tiQeJW5bbyRn0wMIefjFEXK+GFh3j+nT1ePdPJoAxh1Mm0EamnI++pjmkaDEodqSWVMR12zTRZx1B7dPtSatRe52df0nZ5ZIv6qI1rnu/l2vkcKH47J4XiYqjHULsTNlsB86UQcnVql6h6Xv4PIBtoGiH47RstGNGYKKv0AQBD6i1DzQZnIaM+okDUdcXUVL54VBD45rxh4KUP5kxkE3DKcMusHk6mkOBqh4vRIlHPpa/gV28xTStYFTo2oYga9YjGBA529jorA/a9z2ZxDsmgDHX/Bs+oP/XwUnzmnHfhwVe3OnZieIPbFDdX6aOohpoN6L4MRrZsbh7RgJU7DqMnbZZd+hg3pA7/d/l0nD/NcizyS9ZSLF95FvrNDxzvK9IUJeqjO0T6eO6Ws3DRPa8C8EofzJnoSh+mY7h60qYnYoU3OueFZBQWetKNCdIHAPzioydLnaa5gD1y4vD6zbw5eGH9Poy0C8MHPes//Y9ZWfdRikleoXgQI3lYf1NWn/3iE8bgoumj8dzafVk7JYkojvRh/+8Y6j7otowRfcjuU9bZmym79AEAH3tPM8bbN0BM0Ck2eGNww+mTfUaITW5BhppS6mnOK2LqmCYnuoWXR3TbmchLHzz47/IT1uQRDVnPqVCIS8LzLj5hLGZNGlqQ7VOBUo8bUoePvafZeR30bJ51bPZSrYpR928EjacrZo4HYD0bN583BUDuYaJFfTJSXEGSvoJfVlRyZbFyaNQiCCFIxDT0BkgfvTYTrg3RqFk5Ul5W0W1nItOlezOmR17hm9DyhrqURfBjnkmz9IYv6FmPstIK63ajUPkIes75VSmfZZ0LivNk2MebNkwQUhiHEV8jopKf51JIH1EMX01AFbnejIH97VZIXZhGzZoI1MZ5rdlyJrIOKinD9OyjNu7Vs8sBPjGlkCuvqFsK2meU50Ix6upErRDiClg+olySmoqqUZs0f8Ola8TjOR87uM75W1aJrFLAOlOXAidPklfsso5Dbqg/+PM3nK4wzCkqw2i7xxsf+6lpVuNYxqLTgqGui1DKs9jgk6sKGSrJNOhTjgqXUII06igTF0sSmjWxMDKNQmWAJ0Tsmfzin1fmtI2iGGr+kcyX1bz99Qs8RmDS8HrccHozHnq9Bfs7ekN+WV4UKhwsG1758jkY0VgT+HlC16T1BJiRBoBLZ8gzHwHgw6dO8nWr0Ym3H2BKkD6CuqWXEjL2UggcNbwBz3/hrKx6e1+Su6aMasSfbpqLY0Y15r0NhfJiyW0X4La/rcGza90SwIWQ47L+ihAykRDyMiFkHSFkLSHk89l+w6sv+TrXhjUkMGaw10l20XQr662S6yD0pYtDLjhqeENoVmFYFT7AqikSttSujes4c8pIQaPWnOQa1n2b34dYwrQc8OiBBZZfjh3dlNUH0ZdyCYQQzD16eOgErFDZGNFYg9NDskrzXeVFYdQZAP9NKV1GCGkCsJQQ8k9K6brAX3haaRVusDDGdriCa/WWilFnQyKmhcZRR0l2EaFrbqTH4Lq4z1APK1AWYF9QV2advBIikhTKC7FtG58kle9EntVQU0r3ANhj/91BCFkPYDyAQEPtZdSFW36yVlOVzKj5yIdyIqFreG7tPtz34maMHVKH9mTa0/w2n+Pky6sOroujpbXbqfUByEt3lhp1ifLq5IVm8Qr9D2Er1XwVhpxGKyGkGcAsAIsln90E4CYAGDLuaAxmOygCo2axiJWIchsKhkTMinn+4T83Oe/NmDDY/TyPCZT/ycRh9Whp7UZrVwqEWBl708YO8nz/2NGNgXrrFTPHYcm2QzkfQzbUxcs7UfIs/vKZ4/Dkit2eaoEK1Q8xe5X384jNIqIi8lNNCGkE8BcAt1BKfT2GKKUPAngQAMYeM9051ELyi5iuoeXOSwu4xcKjVBp1Nshm9R6ugFI+sgCfEfrhUyfhtc0HAQDXzZmI71wxw7fN579wduC27rkue6ZePshH0ikW7r1uFu4t0nkqVC5YtNpVJ4/Hjz400/OZbFz+6aa5+PPbO/DjkG1GolWEkDgsIz2fUvrXbN/nZ5RUnh01+isqSfoQwevJ+ax0eOmDj/BI6FrZ668wVIqPQGHgghnqqKUu6hN61tyIKFEfBMCvAaynlP4oyo75NNtcy/n1d1SKoZDN3LyhzotRc5vkY7ArKVGjUq6/wsCF2+Yu2hiri+tZm6FEGWGnA7gewHmEkBX2v9DK9jyHzrf1UX+F2Km8XGCMuoGTAvgokHx8GjyjbkjICzCVG7UV4iNQUIg6LmrjetayGFGiPhYhR6mZlz7ybSba3/DUf52OlzbsrxgJgCVeXHj8aGza14l1e9o9rbny0dL5B28sF+OeLVuvlEjoGuad1oxRg8oXi/zV9x2HmRODs0YVqhsfPHkC1uxqxxcvPFb6+a8/Phu7Dydx+5NrAVj17bPZjeK04uK78A4Q6ePECUNw4oTKGZxshj52TBO+esk0vPu7L3oyFRuztOCSgckK504d6Qm7PGtK9spwpQIhBHdcNr2sx/D/zn5XWfevUF7UxnV876oZgZ+z8siOoa6LFUT6yBkmpY6zKqwZqELxwO57XNOk9akba/Mx1PLHpRxV6hQUqgWEkKzSR5EMtdiWRqHUYDN0TCdux5dMHxl1BYW+KShUE8rCqCmlTvW1ScPqi7ELhSxgM3SMa1P/vWc2OJ/nU/inznYgqkWSgkJhkS08rzgatQlMGzMInzzzaF+2mkJpwG58XCPe6l0awaimGnz03blny9UKXuwXvngWCpvSpKAwcPD8F85yxmafoz7ygUkp6hM6Lrdb0CiUHsw2a4R4ZuuMSXHxCWPzjKO2fsN+ecyopr4epoLCgMWxo93xUzZnYl2FZOgNVDDbbEq8uX0tqK+kDwWFwiIbcSqORg2VIVZusKWUrDVbKRrwKigoREe2bPOixVUFdeRVKA3YjacS/ptvA94pttRxyYyxeR+XgoKCH2UJzwNUeF65QQRGvfIb73U+y3cSnTS8Hhu+dTE+NHtin49PQUHBRVk0agAYrgx1WeHceFujHlTn+gz6IktVShlXBYVqQtkYdSU0Oh3IEDVqPvJDGVsFhcpCn8uc5gtlqMsL11C7GjVLfFEZhgoKlYWySR9NedSSUCgcPnnW0Thp4hBcdtI45z0meaiIHAWFykK2JgNFs6aVVKN4IGL8kDo8+dnTPe/VxXUcSaaVoVZQqDCUJY4ayK95qkJxwSQPJX0oKFQWyhZHrUpfVh6YEzGmqXujoFBJKEvUR8SejgolxlF2JUN1fxQUKgvZnIlF0ag1SUW1dDqNnTt3oqenpxi7VIiAm0+pw3/OmISGVBvS6XrE4/HsP1JQUCg6ylI9T7bPnTt3oqmpCc3NzVljBhWKB0opWltbsXPnTkyePLnch6OgoIAyxVHLdtrT04Phw4crI11mEEIwfPhwtbJRUKgglCWOOmifykhXBtR9UFCoLChnooKCgkKFoyyMOtvs0F/x3e9+t9yHoKCgUIUok0ZdjK2WH6U21JlMpqT7U1BQKA/KE/WRpeHp//59Ldbtbi/oPo8fNwjf/MD00O/8/ve/x9133w1CCE488UTouo73v//9uPrqqwEAjY2N6OzsxJ49e3Dttdeivb0dmUwGP//5z7FgwQIkk0nMnDkT06dPx/z58/GjH/0Iv/nNbwAAN954I2655Ra0tLTg4osvxty5c/HGG29gzpw5uOGGG/DNb34T+/fvx/z583Hqqaeiq6sLN998M9asWYN0Oo077rgDl19+OX7729/ir3/9Kzo7O2EYBl555RXfeSxcuBB33HEHRowYgTVr1uCUU07Bww8/DEIIXnzxRXzpS19CJpPBnDlz8POf/xw1NTUFvdYKCgqFRXmciRXIqNeuXYtvf/vbeOmll7By5Urce++9gd995JFHcNFFF2HFihVYuXIlZs6ciTvvvBN1dXVYsWIF5s+fj6VLl+Khhx7C4sWL8eabb+KXv/wlli9fDgDYsmUL/vu//xsbNmzAhg0b8Mgjj2DRokW4++67HVb+ne98B+eddx7eeustvPzyy/jyl7+Mrq4uAMCyZcvw+OOPS400w/Lly3HPPfdg3bp12Lp1K15//XX09PRg3rx5ePTRR7F69WpnklFQUKhslIVRZ9tpNuZbDLz00ku45pprMGLECADAsGHDAr87Z84cfOITn0A6ncYVV1yBmTNn+r6zaNEiXHnllWhoaAAAXHXVVXjttddw2WWXYfLkyZgxYwYAYPr06Tj//PNBCMGMGTPQ0tICAHj++efx1FNP4e677wZghS9u374dAHDhhReGHh8AnHrqqZgwYQIAYObMmWhpaUFTUxMmT56MY489FgDw8Y9/HD/72c9wyy23RL1MCgoKZUA8VgaNur80T43FYjBNEwBgmiZSqRQA4KyzzsKrr76K8ePHY968efj973+f03Z5qUHTNOe1pmmO7kwpxV/+8hesWLECK1aswPbt2zFt2jQAcIx/1H3ouq70bAWFfowrZ00I/bwohlrPJriUAeeddx4ee+wxtLa2AgDa2trQ3NyMpUuXAgCeeuoppNNpAMC2bdswevRofPKTn8SNN96IZcuWAQDi8bjznTPPPBNPPPEEuru70dXVhb/97W8488wzIx/PRRddhPvuuw/ULuzPZJO+YOrUqWhpacGWLVsAAH/4wx9w9tln93m7CgoK5UVRpI9YBRrq6dOn4+tf/zrOPvts6LqOWbNm4fvf/z4uv/xynHTSSbj44osdJrtw4ULcddddiMfjaGxsdBj1TTfdhBNPPBEnn3wy5s+fj3nz5uHUU08FYDkTZ82a5Ugb2XD77bfjlltuwYknngjTNDF58mQ8/fTTfTrH2tpaPPTQQ7jmmmscZ+KnPvWpPm1TQUGh/CCUa9VUKBxz/Il0y7pVnvfWr1/vLO0Vyg91PxQUKguEkKWU0tmyz4oifdTEVGF6BQUFhUKhKNKH6iBSGKxevRrXX3+9572amhosXry4TEekoKBQDqgOtBWMGTNmYMWKFeU+DAUFhTKjpD2ZiqGHK+QOdR8UFPoXSmaoa2tr0draqoxEmcEaB9TW1pb7UBQUFCKiZNLHhAkTsHPnThw4cKBUu1QIQG1trZPVqKCgUPmIZKgJIRcDuBeADuBXlNI7c91RPB5XrZ8UFBQU8kBW6YMQogP4GYD3ATgewIcJIccX+8AUFBQUFCxE0ahPBbCFUrqVUpoC8CcAlxf3sBQUFBQUGKIY6vEAdnCvd9rveUAIuYkQsoQQskTp0AoKCgqFQ8GciZTSBwE8CACEkAOEkG2F2nZEjABwsMT7rBQM1HMfqOcNqHOvxnM/KuiDKIZ6F4CJ3OsJ9nuBoJSOjHZchQMhZElQnny1Y6Ce+0A9b0Cd+0A79yjSx9sAphBCJhNCEgCuA/BUcQ9LQUFBQYEhK6OmlGYIIf8F4DlY4Xm/oZSuLfqRKSgoKCgAiKhRU0r/AeAfRT6WvuLBch9AGTFQz32gnjegzn1AoSj1qBUUFBQUCoeSFmVSUFBQUMgdylArKCgoVDgq0lATQi4mhGwkhGwhhNxqv3c+IWQZIWQFIWQRIeQYye8uJIQsJYSstv8/j/vsFPv9LYSQnxBCSt7YkRDyG0LIfkLIGu69awghawkhJiEkMORIdk3s9wkh5DuEkE2EkPWEkM+FbGMQIWQnIeSn3HsluS4B5/4tQsgq+54+TwgZF/Bbw/7OCkLIU9z7vyaErLS38TghpFHy24p+Juzj8F0b+/2bCSEb7OfjByG/1wkhywkhT3PvTSaELLbP7VE7YqvkCLjvJxFC/mVf+78TQgZJfjeREPIyIWSdff6fFz7v99cmJ1BKK+ofrMiSdwAcDSABYCWsGiObAEyzv/MZAL+V/HYWgHH23ycA2MV99haAuQAIgGcAvK8M53YWgJMBrOHemwZgKoCFAGbnck3sz24A8HsAmv16VMj+7wXwCICflvq6BJz7IO7vzwH4RcBvOwPe53//IwC39rdnIuTanAvgBQA1Ee7rF+37+jT33p8BXGf//QsAn66gc3sbwNn2358A8C3J78YCONn+u8ke/+yZr4prk8u/SmTUQbVFKAA28w4GsFv8IaV0OaWUvb8WQB0hpIYQMhbWoH6TWnfn9wCuKPaJSI7vVeD/t3euIVZVURz/raG0EMrKDEYr0ZQg8YEKZUUh1IcppoeJkTVZGpEYFVgYhh+EYKIHQo8PZUmhWPnqaVSQFUgRKaaW9pgKmrIEpZSk6TH/Pux1xzN3zh1nfNw5c1k/ONxz19l737323rNmP9dhX5lsp6SvDxO1O38rdwFLJLV7envyEjCzScBZwHsZWdXKpYLu+zNfB5HquDdp7oc0qgBOzotf9DbheexSNqR6bZbU5mEq1etw4CpgWUZmwDRgjYtepFi6jQE+9vv3gek58XZL2uL3B4CdHHJdURNl0xuKaKgr+RaZC2wws1bgFqAZwMwazWxJTjrTgS1emcM8nfI0C4uZ1ZtZaUtkd/5WRgEzLflZecfMRnv8yWa2zO/rgMeBBWU/0+fl4tM2PwGzgMUu68i7c5Lr96mZXVsWfznwK3A+8KTLaqFNjAEu9SH6R2Y2Bbq0C4ClwANAe0Z2BvC7pH/9e9F0+5JDHY0Z+Mn/+/BCAAAEE0lEQVTnHN1w+QjSyKj0stBaLptcimioK3Ef0CBpOLCcNNRF0huSFmcDmtkFwCPAnVXP5TFC0i+SGnoQdCDwl9KR2ueAFzz+55Lmeph5wAZJrRXS6DMkLZJ0NrASmO+ybN4BznX9bgKWmtmoTPzbgHpSj2umy2qhTZwAnE6amrkfeNXMLNsuzOxqYI+kzX2YzyPhdmCemW0mTWv8Dflt3tcd1gL3ZkZgtVw2uRTRUOf5FvkNGC+p9B/1FWBqXmQf7qwHmiS1ZNLMvtLksP5KCkZ3/lZagXV+vx4YlxP/ImC+mf0IPAY0mVkzxSqXleQMgQEk/eyf35Pm8ieWPf+PNB2UG7+ftolWYJ0Sn5F6hUPKwlwMNHq9vgxMM7MVwF5gsJmVDrQVSjdJuyRdKWkSsIq0/tIFMzuRZKRXSlqXeVSzZVOJIhrqSr5FTjWzMR7mClIPqhNmNhh4m7SotKkkl7Qb2G9mF/ocVRPw+nHW41jSnb+V10iLKwCXkRZdOiFplqRzJI0gTX+8JGlhX5dLaZrGuQbYlRPmNDMb6PdDSH+AX1niPJcb0Fghfn9tEx316u1+AGUe4yQ9KGm41+uNwAeSbvY5943ADR70Vgqkm5kN9c864CHSgl55GAOeB3ZKeqLscc2WTUWOxwrl0V5AA8ngtACLXHYdsJ204+FDYKTLG0mLaZAq/U9ga+Ya6s8mAzs8zafwU5lV1msVsBv4h9QrmON6tQJtpJHDux62njRdUbFMXF4yRNuBT0gjj5K+y3LyMJvOuz6qUi4VdF/rv70NeBMYVp530sipVO/bgTkurwM2uWwHqUd+Sn9rE92UzQBghedvCzAtr11k0riczjsbRpJ2tXwHrMZ3SBREt3u8LX9DWmsqnZDu0A24hLQ4vC1Tbw3+rCbKpjdXHCEPgiAoOEWc+giCIAgyhKEOgiAoOGGogyAICk4Y6iAIgoIThjoIgqDghKEO+j1mNtjM5vl9vZmtOVycIOhPxPa8oN/jviDekjS2j7MSBMeFHr0zMQgKTjMwysy2At+S3OGONbPZJM9og4DRpOPzA0hOvdpIByj2ue+Qp4EzgYPAHZK6nHIMgr4ipj6CWmAh0CJpAslJT5axwPXAFOBh4KCkiaRTnE0e5lngbiXfEwuAZ6qS6yDoIdGjDmqdjUr+jA+Y2R+ko+qQjp6Pc+9sU4HVdugFLwOrn80gqEwY6qDWacvct2e+t5Pafx3JP/GEamcsCHpKTH0EtcABkl/jXqPk4/gHM5sBHe+gHH8sMxcER0sY6qDfI2kvsMnSC1QfPYIkZgFzzOwLOr99JAgKQWzPC4IgKDjRow6CICg4YaiDIAgKThjqIAiCghOGOgiCoOCEoQ6CICg4YaiDIAgKThjqIAiCgvM/wHlAK52FNDMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers_in_first.plot.bar()\n",
    "customers_in_following.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate the total revenue per customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>location</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fruit</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>spices</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dairy</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>drinks</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location  revenue\n",
       "0    fruit        4\n",
       "1   spices        3\n",
       "2    dairy        5\n",
       "3   drinks        6"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenues = pd.DataFrame(\n",
    "    {\"location\": [\"fruit\", \"spices\", \"dairy\", \"drinks\"], \"revenue\": [4, 3, 5, 6]}\n",
    ")\n",
    "revenues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data.loc[final_data[\"location\"] == \"fruit\", \"revenue\"] = \"4\"\n",
    "final_data.loc[final_data[\"location\"] == \"spices\", \"revenue\"] = \"3\"\n",
    "final_data.loc[final_data[\"location\"] == \"dairy\", \"revenue\"] = \"5\"\n",
    "final_data.loc[final_data[\"location\"] == \"drinks\", \"revenue\"] = \"6\"\n",
    "final_data.loc[final_data[\"location\"] == \"checkout\", \"revenue\"] = \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data[\"customer_no\"] = final_data[\"customer_no\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data[\"revenue\"] = final_data[\"revenue\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Thursday-1047</th>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-666</th>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday-102</th>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday-710</th>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-109</th>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday-829</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-912</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-1250</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday-916</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday-1312</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7445 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               revenue\n",
       "customer_id           \n",
       "Thursday-1047      281\n",
       "Monday-666         270\n",
       "Friday-102         265\n",
       "Wednesday-710      252\n",
       "Monday-109         227\n",
       "...                ...\n",
       "Wednesday-829        3\n",
       "Monday-912           3\n",
       "Monday-1250          3\n",
       "Monday-916           3\n",
       "Tuesday-1312         3\n",
       "\n",
       "[7445 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.groupby(\"customer_id\")[[\"revenue\"]].sum().sort_values(\n",
    "    \"revenue\", ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which is the most profitable section according to your data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>82385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>54684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>48990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>24519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>checkout</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          revenue\n",
       "location         \n",
       "dairy       82385\n",
       "fruit       54684\n",
       "drinks      48990\n",
       "spices      24519\n",
       "checkout        0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.groupby(\"location\")[[\"revenue\"]].sum().sort_values(\n",
    "    \"revenue\", ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Transition Propabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_no</th>\n",
       "      <th>day</th>\n",
       "      <th>location</th>\n",
       "      <th>section_order</th>\n",
       "      <th>time</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:03:00</th>\n",
       "      <td>Monday-1</td>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>07:03:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:03:00</th>\n",
       "      <td>Monday-2</td>\n",
       "      <td>2</td>\n",
       "      <td>Monday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>07:03:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>Monday-1</td>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>07:04:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>Monday-2</td>\n",
       "      <td>2</td>\n",
       "      <td>Monday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>07:04:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>Monday-3</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>first</td>\n",
       "      <td>07:04:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>Friday-1500</td>\n",
       "      <td>1500</td>\n",
       "      <td>Friday</td>\n",
       "      <td>dairy</td>\n",
       "      <td>following</td>\n",
       "      <td>21:50:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>Friday-1507</td>\n",
       "      <td>1507</td>\n",
       "      <td>Friday</td>\n",
       "      <td>checkout</td>\n",
       "      <td>checkout</td>\n",
       "      <td>21:50:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>Friday-1508</td>\n",
       "      <td>1508</td>\n",
       "      <td>Friday</td>\n",
       "      <td>checkout</td>\n",
       "      <td>checkout</td>\n",
       "      <td>21:50:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>Friday-1509</td>\n",
       "      <td>1509</td>\n",
       "      <td>Friday</td>\n",
       "      <td>drinks</td>\n",
       "      <td>first</td>\n",
       "      <td>21:50:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06 21:50:00</th>\n",
       "      <td>Friday-1510</td>\n",
       "      <td>1510</td>\n",
       "      <td>Friday</td>\n",
       "      <td>spices</td>\n",
       "      <td>first</td>\n",
       "      <td>21:50:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53903 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_id  customer_no     day  location section_order  \\\n",
       "timestamp                                                                       \n",
       "2019-09-02 07:03:00     Monday-1            1  Monday     dairy         first   \n",
       "2019-09-02 07:03:00     Monday-2            2  Monday     dairy         first   \n",
       "2019-09-02 07:04:00     Monday-1            1  Monday     dairy         first   \n",
       "2019-09-02 07:04:00     Monday-2            2  Monday     dairy         first   \n",
       "2019-09-02 07:04:00     Monday-3            3  Monday     dairy         first   \n",
       "...                          ...          ...     ...       ...           ...   \n",
       "2019-09-06 21:50:00  Friday-1500         1500  Friday     dairy     following   \n",
       "2019-09-06 21:50:00  Friday-1507         1507  Friday  checkout      checkout   \n",
       "2019-09-06 21:50:00  Friday-1508         1508  Friday  checkout      checkout   \n",
       "2019-09-06 21:50:00  Friday-1509         1509  Friday    drinks         first   \n",
       "2019-09-06 21:50:00  Friday-1510         1510  Friday    spices         first   \n",
       "\n",
       "                         time  revenue  \n",
       "timestamp                               \n",
       "2019-09-02 07:03:00  07:03:00        5  \n",
       "2019-09-02 07:03:00  07:03:00        5  \n",
       "2019-09-02 07:04:00  07:04:00        5  \n",
       "2019-09-02 07:04:00  07:04:00        5  \n",
       "2019-09-02 07:04:00  07:04:00        5  \n",
       "...                       ...      ...  \n",
       "2019-09-06 21:50:00  21:50:00        5  \n",
       "2019-09-06 21:50:00  21:50:00        0  \n",
       "2019-09-06 21:50:00  21:50:00        0  \n",
       "2019-09-06 21:50:00  21:50:00        6  \n",
       "2019-09-06 21:50:00  21:50:00        3  \n",
       "\n",
       "[53903 rows x 7 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data = final_data.sort_values([\"timestamp\", \"customer_no\"])\n",
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_no</th>\n",
       "      <th>day</th>\n",
       "      <th>location</th>\n",
       "      <th>section_order</th>\n",
       "      <th>time</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:04:00</th>\n",
       "      <td>Monday-7</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>spices</td>\n",
       "      <td>first</td>\n",
       "      <td>07:04:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:05:00</th>\n",
       "      <td>Monday-7</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>drinks</td>\n",
       "      <td>following</td>\n",
       "      <td>07:05:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:06:00</th>\n",
       "      <td>Monday-7</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>drinks</td>\n",
       "      <td>following</td>\n",
       "      <td>07:06:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:07:00</th>\n",
       "      <td>Monday-7</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>drinks</td>\n",
       "      <td>following</td>\n",
       "      <td>07:07:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:08:00</th>\n",
       "      <td>Monday-7</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>drinks</td>\n",
       "      <td>following</td>\n",
       "      <td>07:08:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:09:00</th>\n",
       "      <td>Monday-7</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>spices</td>\n",
       "      <td>following</td>\n",
       "      <td>07:09:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:10:00</th>\n",
       "      <td>Monday-7</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>spices</td>\n",
       "      <td>following</td>\n",
       "      <td>07:10:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:11:00</th>\n",
       "      <td>Monday-7</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>fruit</td>\n",
       "      <td>following</td>\n",
       "      <td>07:11:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:12:00</th>\n",
       "      <td>Monday-7</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>fruit</td>\n",
       "      <td>following</td>\n",
       "      <td>07:12:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02 07:13:00</th>\n",
       "      <td>Monday-7</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>checkout</td>\n",
       "      <td>checkout</td>\n",
       "      <td>07:13:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    customer_id  customer_no     day  location section_order  \\\n",
       "timestamp                                                                      \n",
       "2019-09-02 07:04:00    Monday-7            7  Monday    spices         first   \n",
       "2019-09-02 07:05:00    Monday-7            7  Monday    drinks     following   \n",
       "2019-09-02 07:06:00    Monday-7            7  Monday    drinks     following   \n",
       "2019-09-02 07:07:00    Monday-7            7  Monday    drinks     following   \n",
       "2019-09-02 07:08:00    Monday-7            7  Monday    drinks     following   \n",
       "2019-09-02 07:09:00    Monday-7            7  Monday    spices     following   \n",
       "2019-09-02 07:10:00    Monday-7            7  Monday    spices     following   \n",
       "2019-09-02 07:11:00    Monday-7            7  Monday     fruit     following   \n",
       "2019-09-02 07:12:00    Monday-7            7  Monday     fruit     following   \n",
       "2019-09-02 07:13:00    Monday-7            7  Monday  checkout      checkout   \n",
       "\n",
       "                         time  revenue  \n",
       "timestamp                               \n",
       "2019-09-02 07:04:00  07:04:00        3  \n",
       "2019-09-02 07:05:00  07:05:00        6  \n",
       "2019-09-02 07:06:00  07:06:00        6  \n",
       "2019-09-02 07:07:00  07:07:00        6  \n",
       "2019-09-02 07:08:00  07:08:00        6  \n",
       "2019-09-02 07:09:00  07:09:00        3  \n",
       "2019-09-02 07:10:00  07:10:00        3  \n",
       "2019-09-02 07:11:00  07:11:00        4  \n",
       "2019-09-02 07:12:00  07:12:00        4  \n",
       "2019-09-02 07:13:00  07:13:00        0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data[final_data[\"customer_id\"] == \"Monday-7\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_crosstab = (\n",
    "    final_data.reset_index()\n",
    "    .set_index(\"customer_id\")\n",
    "    .sort_values([\"customer_no\", \"timestamp\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_crosstab[\"next_location\"] = pre_crosstab[\"location\"].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_crosstab_final = pre_crosstab[pre_crosstab[\"location\"] != \"checkout\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "P_w_checkout = pd.crosstab(\n",
    "    pre_crosstab[\"location\"], pre_crosstab[\"next_location\"], normalize=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "P = pd.crosstab(\n",
    "    pre_crosstab_final[\"location\"], pre_crosstab_final[\"next_location\"], normalize=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>next_location</th>\n",
       "      <th>checkout</th>\n",
       "      <th>dairy</th>\n",
       "      <th>drinks</th>\n",
       "      <th>fruit</th>\n",
       "      <th>spices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dairy</th>\n",
       "      <td>0.119925</td>\n",
       "      <td>0.799357</td>\n",
       "      <td>0.029738</td>\n",
       "      <td>0.024580</td>\n",
       "      <td>0.026400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks</th>\n",
       "      <td>0.199020</td>\n",
       "      <td>0.015432</td>\n",
       "      <td>0.704103</td>\n",
       "      <td>0.040661</td>\n",
       "      <td>0.040784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>0.187185</td>\n",
       "      <td>0.040889</td>\n",
       "      <td>0.027503</td>\n",
       "      <td>0.718309</td>\n",
       "      <td>0.026114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spices</th>\n",
       "      <td>0.153799</td>\n",
       "      <td>0.059342</td>\n",
       "      <td>0.051022</td>\n",
       "      <td>0.037930</td>\n",
       "      <td>0.697908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "next_location  checkout     dairy    drinks     fruit    spices\n",
       "location                                                       \n",
       "dairy          0.119925  0.799357  0.029738  0.024580  0.026400\n",
       "drinks         0.199020  0.015432  0.704103  0.040661  0.040784\n",
       "fruit          0.187185  0.040889  0.027503  0.718309  0.026114\n",
       "spices         0.153799  0.059342  0.051022  0.037930  0.697908"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.11992474358196274,\n",
       " 0.7993566790071008,\n",
       " 0.0297384232566608,\n",
       " 0.024579717181525764,\n",
       " 0.026400436972749894]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P.loc[\"dairy\"].to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw transition probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = [\"dairy\", \"spices\", \"drinks\", \"fruit\", \"checkout\"]\n",
    "G = pgv.AGraph(directed=True)\n",
    "for start_location in locations[:-1]:\n",
    "    for end_location in locations:\n",
    "        G.add_edge(\n",
    "            start_location,\n",
    "            end_location,\n",
    "            label=np.round(P.loc[start_location, end_location], 2),\n",
    "        )\n",
    "\n",
    "G.draw(\"transition.png\", prog=\"dot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Customer Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake = Faker()\n",
    "\n",
    "\n",
    "def increment():\n",
    "    \"auto-increments by 1 starting from 1 (for customer_no)\"\n",
    "    i = 1\n",
    "    while True:\n",
    "        yield i\n",
    "        i = i + 1\n",
    "\n",
    "\n",
    "auto_increment = increment()\n",
    "\n",
    "\n",
    "class Customer:\n",
    "    \"\"\"\n",
    "    Customer in a supermarket simulation.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    \n",
    "    current_location: The current location of the customer in the supermarket.\n",
    "    customer_nr: Id of the customer.\n",
    "    customer_name: fake first name (generated by Faker)\n",
    "    \"\"\"\n",
    "\n",
    "    \n",
    "\n",
    "    def __init__(self):\n",
    "        self.current_location = np.random.choice(\n",
    "            [\"dairy\", \"spices\", \"drinks\", \"fruit\"],\n",
    "            p=first_probabilities[\"customer_id\"].values,\n",
    "        )\n",
    "        new_customer_nr = next(auto_increment)\n",
    "        self.customer_nr = new_customer_nr\n",
    "        self.customer_name = fake.first_name()\n",
    "\n",
    "    def __repr__(self):\n",
    "        return f\"\"\"{self.customer_name} is in section '{self.current_location}'.\"\"\"\n",
    "\n",
    "    def transition(self):\n",
    "        \"\"\"\n",
    "        Defines how the customer transitions between states/location.\n",
    "        \"\"\"\n",
    "        if self.current_location == \"checkout\":\n",
    "            self.current_location = \"has left the supermarket\"\n",
    "        elif self.current_location == \"has left the supermarket\":\n",
    "            ...\n",
    "        else:\n",
    "            self.current_location = np.random.choice(\n",
    "                [\"checkout\", \"dairy\", \"drinks\", \"fruit\", \"spices\"],\n",
    "                p=P.loc[self.current_location].to_list(),\n",
    "            )\n",
    "        return self.current_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Courtney is in section 'fruit'.\n",
      "Courtney is in section 'fruit'.\n",
      "Courtney is in section 'fruit'.\n",
      "Tammy is in section 'drinks'.\n",
      "Tammy is in section 'drinks'.\n",
      "Tammy is in section 'drinks'.\n",
      "Elizabeth is in section 'drinks'.\n",
      "Elizabeth is in section 'drinks'.\n",
      "Elizabeth is in section 'dairy'.\n",
      "Ryan is in section 'drinks'.\n",
      "Ryan is in section 'drinks'.\n",
      "Ryan is in section 'drinks'.\n",
      "Becky is in section 'dairy'.\n",
      "Becky is in section 'dairy'.\n",
      "Becky is in section 'dairy'.\n",
      "Cory is in section 'fruit'.\n",
      "Cory is in section 'fruit'.\n",
      "Cory is in section 'checkout'.\n",
      "Jean is in section 'fruit'.\n",
      "Jean is in section 'fruit'.\n",
      "Jean is in section 'fruit'.\n",
      "John is in section 'spices'.\n",
      "John is in section 'spices'.\n",
      "John is in section 'fruit'.\n",
      "Debra is in section 'dairy'.\n",
      "Debra is in section 'spices'.\n",
      "Debra is in section 'dairy'.\n",
      "Diane is in section 'dairy'.\n",
      "Diane is in section 'dairy'.\n",
      "Diane is in section 'dairy'.\n"
     ]
    }
   ],
   "source": [
    "for _ in range(10):\n",
    "    customer = Customer()\n",
    "    print(customer)\n",
    "    customer.transition()\n",
    "    print(customer)\n",
    "    customer.transition()\n",
    "    print(customer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Monte-Carlo-Markov-Chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "next_location\n",
       "checkout    0.119925\n",
       "dairy       0.799357\n",
       "drinks      0.029738\n",
       "fruit       0.024580\n",
       "spices      0.026400\n",
       "Name: dairy, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P.loc[\"dairy\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def customer(prob_matrix, initial_state):\n",
    "    while True:\n",
    "        yield np.random.choice([\"checkout\", \"dairy\", \"drinks\", \"fruit\", \"spices\"], p=prob_matrix.loc[initial_state])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_gen = customer(P, \"drinks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'drinks'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(customer_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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