Project6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project description\n",
    "\n",
    "You've done beautifully in the Practicum course, and you've been offered an internship in the analytical department at Yandex.Afisha. Your first task is to help optimize marketing expenses.\n",
    "\n",
    "You have:\n",
    "\n",
    "- Server logs with data on Yandex.Afisha visits from January 2017 through December 2018\n",
    "- Dump file with all orders for the period\n",
    "- Marketing expenses statistics\n",
    "\n",
    "You are going to study:\n",
    "\n",
    "- How people use the product\n",
    "- When they start to buy\n",
    "- How much money each customer brings\n",
    "- When they pay off"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1. Download the data and prepare it for analysis\n",
    "\n",
    "- Store the data on visits, orders, and expenses in variables. \n",
    "- Optimize the data for analysis. \n",
    "- Make sure each column contains the correct data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plot\n",
    "from math import factorial\n",
    "from scipy import stats as st\n",
    "from functools import reduce\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading, optimizing, and examining visits dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 359400 entries, 0 to 359399\n",
      "Data columns (total 5 columns):\n",
      "Device       359400 non-null category\n",
      "End Ts       359400 non-null datetime64[ns]\n",
      "Source Id    359400 non-null int64\n",
      "Start Ts     359400 non-null datetime64[ns]\n",
      "Uid          359400 non-null uint64\n",
      "dtypes: category(1), datetime64[ns](2), int64(1), uint64(1)\n",
      "memory usage: 11.3 MB\n"
     ]
    }
   ],
   "source": [
    "# Had to upload the file directly to Jupyter and change file name in order for python to recognize the file. \n",
    "#That is why it is named differently then the other two datasets\n",
    "#optimizing data by changing device, uid, and source id to category and start ts and end ts to datetime64\n",
    "# visits = pd.read_csv('visits.csv', dtype={'Device': 'category', 'Uid': 'category', 'Source Id': 'category'}, \n",
    "#                   parse_dates=['Start Ts', 'End Ts'])\n",
    "visits = pd.read_csv('/datasets/visits_log_us.csv',\n",
    "#visits = pd.read_csv('datasets/visits_log_us.csv',\n",
    "                    dtype={'Device': 'category'},\n",
    "                    parse_dates=['Start Ts', 'End Ts'])\n",
    "visits.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Device</th>\n",
       "      <th>End Ts</th>\n",
       "      <th>Source Id</th>\n",
       "      <th>Start Ts</th>\n",
       "      <th>Uid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>touch</td>\n",
       "      <td>2017-12-20 17:38:00</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-12-20 17:20:00</td>\n",
       "      <td>16879256277535980062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>desktop</td>\n",
       "      <td>2018-02-19 17:21:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-02-19 16:53:00</td>\n",
       "      <td>104060357244891740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>touch</td>\n",
       "      <td>2017-07-01 01:54:00</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-07-01 01:54:00</td>\n",
       "      <td>7459035603376831527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>desktop</td>\n",
       "      <td>2018-05-20 11:23:00</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-05-20 10:59:00</td>\n",
       "      <td>16174680259334210214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>desktop</td>\n",
       "      <td>2017-12-27 14:06:00</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-12-27 14:06:00</td>\n",
       "      <td>9969694820036681168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Device              End Ts  Source Id            Start Ts  \\\n",
       "0    touch 2017-12-20 17:38:00          4 2017-12-20 17:20:00   \n",
       "1  desktop 2018-02-19 17:21:00          2 2018-02-19 16:53:00   \n",
       "2    touch 2017-07-01 01:54:00          5 2017-07-01 01:54:00   \n",
       "3  desktop 2018-05-20 11:23:00          9 2018-05-20 10:59:00   \n",
       "4  desktop 2017-12-27 14:06:00          3 2017-12-27 14:06:00   \n",
       "\n",
       "                    Uid  \n",
       "0  16879256277535980062  \n",
       "1    104060357244891740  \n",
       "2   7459035603376831527  \n",
       "3  16174680259334210214  \n",
       "4   9969694820036681168  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I see that there are two different devices. Touch and Desktop. \n",
    "- Two of the users here have the same Start Ts and End Ts. They didn't waste anytime and spent no time actually onsite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "visits['source_id']= visits['Source Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "visits=visits.drop(['Source Id'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created a new column source_id that will be identical to costs dataframe source_id column. Dropped the extra column of Source Id. Now the new column source_id will be able to help me merge the visits data and the costs data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing columns to lowercase\n",
    "visits.columns = visits.columns.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 359400 rows with 5 columns. Memory was 13.7+ MB\n",
    "- Changed Device column from object to category and End Ts and Start Ts columns to datetime. Now memory is 19.3 MB\n",
    "- There are no missing values\n",
    "- Source Id and Uid were integers. I changed the Uid and Source Id to category because really it is just the id number of the person and the ad source they used. I will need to use it later in count and will be better to be used in.\n",
    "- The columns are uppercase. I changed them to lowercase to be able to work better with all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    359400.000000\n",
       "mean          3.750515\n",
       "std           1.917116\n",
       "min           1.000000\n",
       "25%           3.000000\n",
       "50%           4.000000\n",
       "75%           5.000000\n",
       "max          10.000000\n",
       "Name: source_id, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits['source_id'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "desktop    262567\n",
       "touch       96833\n",
       "Name: device, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits['device'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 262567 instances of people visiting the Yandex.Afisha site through their desktop\n",
    "- There are 96833 instances of people visiting the Yandex.Afisha site through a touch method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  359400\n",
       "unique                 224303\n",
       "top       2017-11-24 16:06:00\n",
       "freq                       19\n",
       "first     2017-06-01 00:01:00\n",
       "last      2018-05-31 23:59:00\n",
       "Name: start ts, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits['start ts'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 224303 different unique times that people visited Yandex.Afisha\n",
    "- The top time visited was 2017-11-24 at 16:06:00 and it occured 19 times.\n",
    "- The first time someone visited Yandex.Afisha was June 1, 2017 and the last time someone visited in this dataset was May 31, 2018. So therefore the project description of having logs from January 2017 to December 2018 is not correct and we have found the data like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-11-24 16:06:00    19\n",
       "2017-11-24 17:21:00    15\n",
       "2017-11-24 17:19:00    15\n",
       "2018-05-31 10:01:00    15\n",
       "2017-11-24 15:57:00    14\n",
       "                       ..\n",
       "2017-10-23 22:17:00     1\n",
       "2018-01-24 20:19:00     1\n",
       "2018-04-03 14:19:00     1\n",
       "2017-12-14 10:36:00     1\n",
       "2017-06-28 14:39:00     1\n",
       "Name: start ts, Length: 224303, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits['start ts'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "November 24, 2017 was a popular day to visit the site"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  359400\n",
       "unique                 224760\n",
       "top       2017-11-24 16:51:00\n",
       "freq                       23\n",
       "first     2017-06-01 00:02:00\n",
       "last      2018-06-01 01:26:00\n",
       "Name: end ts, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits['end ts'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 224760 different unique times that people ended their visit to Yandex.Afisha\n",
    "- The top time was 2017-11-24 at 16:51:00 and it occured 23 times.\n",
    "- The first time someone ended their session was June 1, 2017 and the last time someone ended their session in this dataset was June 1, 2018 which was a year later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-11-24 16:51:00    23\n",
       "2017-11-24 16:52:00    21\n",
       "2017-11-24 16:50:00    18\n",
       "2017-11-24 16:46:00    15\n",
       "2017-11-24 16:48:00    14\n",
       "                       ..\n",
       "2018-04-28 00:35:00     1\n",
       "2018-03-29 12:00:00     1\n",
       "2017-11-18 23:15:00     1\n",
       "2017-11-15 23:44:00     1\n",
       "2017-06-28 14:39:00     1\n",
       "Name: end ts, Length: 224760, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits['end ts'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.594000e+05\n",
       "mean     9.202557e+18\n",
       "std      5.298433e+18\n",
       "min      1.186350e+13\n",
       "25%      4.613407e+18\n",
       "50%      9.227413e+18\n",
       "75%      1.372824e+19\n",
       "max      1.844668e+19\n",
       "Name: uid, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits['uid'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12869801667763685675    893\n",
       "3263486045884611639     615\n",
       "11255648391090536411    551\n",
       "17030528792926543083    357\n",
       "8056418121947262981     343\n",
       "                       ... \n",
       "13023238853810096243      1\n",
       "13275209559615110952      1\n",
       "10451189761655280393      1\n",
       "11370115024582961201      1\n",
       "15160524768967727475      1\n",
       "Name: uid, Length: 228169, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits['uid'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
       "        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>device</th>\n",
       "      <th>end ts</th>\n",
       "      <th>start ts</th>\n",
       "      <th>uid</th>\n",
       "      <th>source_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>desktop</td>\n",
       "      <td>2017-12-13 15:12:00</td>\n",
       "      <td>2017-12-13 15:08:00</td>\n",
       "      <td>12869801667763685675</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>446</td>\n",
       "      <td>desktop</td>\n",
       "      <td>2017-11-17 12:43:00</td>\n",
       "      <td>2017-11-17 12:42:00</td>\n",
       "      <td>12869801667763685675</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2209</td>\n",
       "      <td>desktop</td>\n",
       "      <td>2018-03-05 16:14:00</td>\n",
       "      <td>2018-03-05 16:01:00</td>\n",
       "      <td>12869801667763685675</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2834</td>\n",
       "      <td>desktop</td>\n",
       "      <td>2018-02-19 18:35:00</td>\n",
       "      <td>2018-02-19 18:31:00</td>\n",
       "      <td>12869801667763685675</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3060</td>\n",
       "      <td>desktop</td>\n",
       "      <td>2018-01-22 16:42:00</td>\n",
       "      <td>2018-01-22 16:39:00</td>\n",
       "      <td>12869801667763685675</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       device              end ts            start ts                   uid  \\\n",
       "181   desktop 2017-12-13 15:12:00 2017-12-13 15:08:00  12869801667763685675   \n",
       "446   desktop 2017-11-17 12:43:00 2017-11-17 12:42:00  12869801667763685675   \n",
       "2209  desktop 2018-03-05 16:14:00 2018-03-05 16:01:00  12869801667763685675   \n",
       "2834  desktop 2018-02-19 18:35:00 2018-02-19 18:31:00  12869801667763685675   \n",
       "3060  desktop 2018-01-22 16:42:00 2018-01-22 16:39:00  12869801667763685675   \n",
       "\n",
       "      source_id  \n",
       "181           5  \n",
       "446           2  \n",
       "2209          2  \n",
       "2834          5  \n",
       "3060          1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular_visits = visits.query('uid == \"12869801667763685675\" ')\n",
    "popular_visits.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There were 228169 visits by different people from June 1, 2017 until May 31, 2018\n",
    "- The user that used the platform the most was 12869801667763685675 with 893 visits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading, optimizing, and examining orders dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50415 entries, 0 to 50414\n",
      "Data columns (total 3 columns):\n",
      "Buy Ts     50415 non-null datetime64[ns]\n",
      "Revenue    50415 non-null float64\n",
      "Uid        50415 non-null uint64\n",
      "dtypes: datetime64[ns](1), float64(1), uint64(1)\n",
      "memory usage: 1.2 MB\n"
     ]
    }
   ],
   "source": [
    "# orders = pd.read_csv('B:\\Yandex\\projects\\Project6\\orders_log_us.csv', parse_dates =['Buy Ts'], dtype={'Uid': 'category'})\n",
    "orders = pd.read_csv('/datasets/orders_log_us.csv',\n",
    "#orders = pd.read_csv('datasets/orders_log_us.csv',\n",
    "                    parse_dates=['Buy Ts'])\n",
    "orders.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Buy Ts</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Uid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017-06-01 00:10:00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>10329302124590727494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-01 00:25:00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>11627257723692907447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-01 00:27:00</td>\n",
       "      <td>0.37</td>\n",
       "      <td>17903680561304213844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-06-01 00:29:00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>16109239769442553005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-06-01 07:58:00</td>\n",
       "      <td>0.37</td>\n",
       "      <td>14200605875248379450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Buy Ts  Revenue                   Uid\n",
       "0 2017-06-01 00:10:00    17.00  10329302124590727494\n",
       "1 2017-06-01 00:25:00     0.55  11627257723692907447\n",
       "2 2017-06-01 00:27:00     0.37  17903680561304213844\n",
       "3 2017-06-01 00:29:00     0.55  16109239769442553005\n",
       "4 2017-06-01 07:58:00     0.37  14200605875248379450"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing columns to lowercase\n",
    "orders.columns = orders.columns.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In orders there are 50415 rows with 3 columns, Memory was 1.2+ MB\n",
    "- Memory is now 2.5 MB\n",
    "- Buy Ts is now datetime\n",
    "- Revenue is float\n",
    "- Uid was integer but changed it to category because really it is just the id number of the person and will need to use it later in count but not with sum.\n",
    "- Here also the columns are uppercase and I changed them to lowercase to work better with the data\n",
    "\n",
    "- Uid is where I can merge the data. The first dataframe has when people signed up and the second dataframe has the information for those that ordered. So I can probably infer that only 50415 orders were made from the 359400 Uid in the first dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                   50415\n",
       "unique                  45991\n",
       "top       2018-05-31 10:13:00\n",
       "freq                        9\n",
       "first     2017-06-01 00:10:00\n",
       "last      2018-06-01 00:02:00\n",
       "Name: buy ts, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['buy ts'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-05-31 10:13:00    9\n",
       "2017-11-24 17:42:00    8\n",
       "2018-05-31 10:02:00    7\n",
       "2017-11-24 17:36:00    7\n",
       "2018-05-31 10:27:00    7\n",
       "                      ..\n",
       "2017-11-11 09:37:00    1\n",
       "2018-01-14 17:49:00    1\n",
       "2017-06-22 11:34:00    1\n",
       "2018-02-14 19:50:00    1\n",
       "2017-08-29 17:06:00    1\n",
       "Name: buy ts, Length: 45991, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['buy ts'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The first and last dates of June 1, 2017 and June 1, 2018 coincide with the dataframe of visits where we have people visiting the website.\n",
    "- May 31, 2018, the last date that we have purchases from may be the most popular date with it showing up 3 times at 9, 7, and 7 buys at different times during the day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50415.000000\n",
       "mean         4.999647\n",
       "std         21.818359\n",
       "min          0.000000\n",
       "25%          1.220000\n",
       "50%          2.500000\n",
       "75%          4.890000\n",
       "max       2633.280000\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['revenue'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.83       2552\n",
       "2.44       2464\n",
       "3.67       2421\n",
       "1.22       2216\n",
       "3.05       2041\n",
       "           ... \n",
       "7.56          1\n",
       "7.55          1\n",
       "1236.28       1\n",
       "14.27         1\n",
       "107.55        1\n",
       "Name: revenue, Length: 1149, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['revenue'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buy ts     51\n",
       "revenue    51\n",
       "uid        51\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_revenue = orders[orders['revenue'] == 0]\n",
    "zero_revenue.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- minimum revenue was 0. Then there was no revenue. How can people have been able to buy on the site for 0? Was there a special sale? Seems 51 users who visited did not help Yandex.Afisha make a profit.\n",
    "- The maximum a user spent on an order wsa 2633.28\n",
    "- Most people spent less than 4.89 and that is a huge difference from 2633"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>buy ts</th>\n",
       "      <th>revenue</th>\n",
       "      <th>uid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [buy ts, revenue, uid]\n",
       "Index: []"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular_visits_orders = orders.query('uid == \"12869801667763685675\" ')\n",
    "popular_visits_orders.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- For all the 893 visits that 12869801667763685675 made to the site they didn't buy. Is something wrong with what they saw?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading, optimizing, and examining costs dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2542 entries, 0 to 2541\n",
      "Data columns (total 3 columns):\n",
      "source_id    2542 non-null int64\n",
      "dt           2542 non-null datetime64[ns]\n",
      "costs        2542 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), int64(1)\n",
      "memory usage: 59.7 KB\n"
     ]
    }
   ],
   "source": [
    "# costs = pd.read_csv('B:\\Yandex\\projects\\Project6\\costs_us.csv', parse_dates = ['dt'], dtype= {'source_id': 'category'})\n",
    "costs = pd.read_csv('/datasets/costs_us.csv',\n",
    "                   parse_dates=['dt'])\n",
    "costs.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The costs dataframe has 2542 rows (dates) and 3 columns\n",
    "- There are only 365 days in a year so having 2542 dates means that some dates had more ads being used.\n",
    "- The columns are source_id, dt, and costs\n",
    "- source_id can be used to make calculations in the costs dataframe with the visits dataframe\n",
    "- source_id was integer but I can change it to category to use for our calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>source_id</th>\n",
       "      <th>dt</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>75.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-02</td>\n",
       "      <td>62.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>36.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-04</td>\n",
       "      <td>55.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-05</td>\n",
       "      <td>57.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source_id         dt  costs\n",
       "0          1 2017-06-01  75.20\n",
       "1          1 2017-06-02  62.25\n",
       "2          1 2017-06-03  36.53\n",
       "3          1 2017-06-04  55.00\n",
       "4          1 2017-06-05  57.08"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                    2542\n",
       "unique                    364\n",
       "top       2018-03-03 00:00:00\n",
       "freq                        7\n",
       "first     2017-06-01 00:00:00\n",
       "last      2018-05-31 00:00:00\n",
       "Name: dt, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs['dt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2542.000000\n",
       "mean        4.857199\n",
       "std         3.181581\n",
       "min         1.000000\n",
       "25%         2.000000\n",
       "50%         4.000000\n",
       "75%         9.000000\n",
       "max        10.000000\n",
       "Name: source_id, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs['source_id'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Only 364 unique dates? That one day less than a year. \n",
    "- First date June 1, 2017.\n",
    "- Last date is May 31, 2018\n",
    "- So really the information is for one whole year minus one day and not two years.\n",
    "- There are 7 unique types of source ads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2542.000000\n",
       "mean      129.477427\n",
       "std       156.296628\n",
       "min         0.540000\n",
       "25%        21.945000\n",
       "50%        77.295000\n",
       "75%       170.065000\n",
       "max      1788.280000\n",
       "Name: costs, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs['costs'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Just by looking at the description of costs here the ads were more expensive than the revenue. However there were less ads being paid for than orders coming in. Only 2542 ads compared to 50415 orders\n",
    "- The most expensive ad was 1788.28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "- Each dataframe was optimized to some level. I parsed the dates in all three dataframes. I changed object dtypes to category.\n",
    "- I discovered that the data we have is from June 1, 2017 until June 1, 2018.\n",
    "- There are 262567 instances of people visiting the Yandex.Afisha site through their desktop and 96833 via touch\n",
    "- There are 224303 different unique times that people visited Yandex.Afisha and 224760 different unique times that people ended their visit to Yandex.Afisha\n",
    "- Discovered that even if visits were many it did not necessarily mean that the user would buy something.\n",
    "- Ad sources were more costly than revenue with a birds eye view but we will continue to check that as we continue through the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting date columns by month, week, day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting dates from visits dataset\n",
    "visits['visit_year'] = pd.DatetimeIndex(visits['start ts']).year\n",
    "visits['visit_month'] = visits['start ts'].astype('datetime64[M]')\n",
    "visits['visit_week']  = pd.DatetimeIndex(visits['start ts']).week\n",
    "visits['visit_date'] = pd.DatetimeIndex(visits['start ts']).date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders['order_year'] = pd.DatetimeIndex(orders['buy ts']).year\n",
    "orders['order_month'] = orders['buy ts'].astype('datetime64[M]')\n",
    "orders['order_week']  = pd.DatetimeIndex(orders['buy ts']).week\n",
    "orders['order_date'] = pd.DatetimeIndex(orders['buy ts']).date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "costs['cost_year'] = pd.DatetimeIndex(costs['dt']).year\n",
    "costs['cost_month'] = costs['dt'].astype('datetime64[M]')\n",
    "costs['cost_week'] = pd.DatetimeIndex(costs['dt']).week\n",
    "costs['cost_date'] = pd.DatetimeIndex(costs['dt']).date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2. Make reports and calculate metrics:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Product "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. How many people use it every day, week, and month? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "907\n"
     ]
    }
   ],
   "source": [
    "#How many use it every day on average\n",
    "dau_total = visits.groupby('visit_date').agg({'uid': 'nunique'}).mean()\n",
    "print(int(dau_total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5716\n"
     ]
    }
   ],
   "source": [
    "#How many use it every week on average\n",
    "wau_total = visits.groupby(['visit_year', 'visit_week']).agg({'uid': 'nunique'}).mean()\n",
    "print(int(wau_total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23228\n"
     ]
    }
   ],
   "source": [
    "#How many use it every month on average\n",
    "mau_total = visits.groupby(['visit_year', 'visit_month']).agg({'uid': 'nunique'}).mean()\n",
    "print(int(mau_total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Daily average users are 907\n",
    "- Weekly average users are 5716\n",
    "- Monthly average users are 23228"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. How many sessions are there per day? (One user might have more than one session.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>n_sessions</th>\n",
       "      <th>n_users</th>\n",
       "      <th>sessions_per_user</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit_week</th>\n",
       "      <th>visit_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">1</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>1065</td>\n",
       "      <td>992</td>\n",
       "      <td>1.073589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>1355</td>\n",
       "      <td>1265</td>\n",
       "      <td>1.071146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>1317</td>\n",
       "      <td>1219</td>\n",
       "      <td>1.080394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>1370</td>\n",
       "      <td>1260</td>\n",
       "      <td>1.087302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>1092</td>\n",
       "      <td>1006</td>\n",
       "      <td>1.085487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">52</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>1444</td>\n",
       "      <td>1334</td>\n",
       "      <td>1.082459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>1496</td>\n",
       "      <td>1377</td>\n",
       "      <td>1.086420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>1315</td>\n",
       "      <td>1192</td>\n",
       "      <td>1.103188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>1192</td>\n",
       "      <td>1106</td>\n",
       "      <td>1.077758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>784</td>\n",
       "      <td>732</td>\n",
       "      <td>1.071038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       n_sessions  n_users  sessions_per_user\n",
       "visit_week visit_date                                        \n",
       "1          2018-01-01        1065      992           1.073589\n",
       "           2018-01-02        1355     1265           1.071146\n",
       "           2018-01-03        1317     1219           1.080394\n",
       "           2018-01-04        1370     1260           1.087302\n",
       "           2018-01-05        1092     1006           1.085487\n",
       "...                           ...      ...                ...\n",
       "52         2017-12-27        1444     1334           1.082459\n",
       "           2017-12-28        1496     1377           1.086420\n",
       "           2017-12-29        1315     1192           1.103188\n",
       "           2017-12-30        1192     1106           1.077758\n",
       "           2017-12-31         784      732           1.071038\n",
       "\n",
       "[364 rows x 3 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sessions_per_user = visits.groupby(['visit_week', 'visit_date']).agg({'uid': ['count','nunique']})\n",
    "sessions_per_user.columns = ['n_sessions', 'n_users']\n",
    "sessions_per_user['sessions_per_user'] = sessions_per_user['n_sessions'] / sessions_per_user['n_users']\n",
    "sessions_per_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>n_sessions</th>\n",
       "      <th>n_users</th>\n",
       "      <th>sessions_per_user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>987.362637</td>\n",
       "      <td>907.991758</td>\n",
       "      <td>1.082170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>418.994796</td>\n",
       "      <td>370.513838</td>\n",
       "      <td>0.020876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>635.500000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>1.070321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1003.000000</td>\n",
       "      <td>921.000000</td>\n",
       "      <td>1.082488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1280.000000</td>\n",
       "      <td>1173.000000</td>\n",
       "      <td>1.093876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>4042.000000</td>\n",
       "      <td>3319.000000</td>\n",
       "      <td>1.217837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        n_sessions      n_users  sessions_per_user\n",
       "count   364.000000   364.000000         364.000000\n",
       "mean    987.362637   907.991758           1.082170\n",
       "std     418.994796   370.513838           0.020876\n",
       "min       1.000000     1.000000           1.000000\n",
       "25%     635.500000   594.000000           1.070321\n",
       "50%    1003.000000   921.000000           1.082488\n",
       "75%    1280.000000  1173.000000           1.093876\n",
       "max    4042.000000  3319.000000           1.217837"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sessions_per_user.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A user had 1.082170 session per day on average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. What is the length of each session? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "643.506488592098\n"
     ]
    }
   ],
   "source": [
    "visits['session_duration_sec'] = (pd.to_datetime(visits['end ts']) - pd.to_datetime(visits['start ts'])).dt.seconds\n",
    "\n",
    "print(visits['session_duration_sec'].mean()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Average session is 644 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(visits['session_duration_sec'], bins = 50, log = True)\n",
    "plt.title(\"Sessions and their duration in seconds\", fontsize=15)\n",
    "plt.xlabel('Seconds')\n",
    "plt.ylabel(\"Sessions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    60\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(visits['session_duration_sec'].mode())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Or a mode of 60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. How often do users come back?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uid    15.884409\n",
      "dtype: float64\n",
      "uid    3.90897\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "dau_total = visits.groupby('visit_date').agg({'uid': 'nunique'}).mean()\n",
    "wau_total = visits.groupby(['visit_year', 'visit_week']).agg({'uid': 'nunique'}).mean()\n",
    "mau_total = visits.groupby(['visit_year', 'visit_month']).agg({'uid': 'nunique'}).mean()\n",
    "\n",
    "sticky_wau = (dau_total/wau_total)*100\n",
    "sticky_mau = (dau_total/mau_total)*100\n",
    "\n",
    "print(sticky_wau)\n",
    "print(sticky_mau)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 15 percent of users came back weekly\n",
    "- Only about 3 percent of users came back monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f3304df2210>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "desktop_visits = visits.query('device == \"desktop\"')\n",
    "(desktop_visits['source_id'].value_counts()).plot(subplots=True, kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most of the visits came from sources 3 ,4 , and 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     25.761044\n",
       "3     25.051511\n",
       "5     19.069038\n",
       "2     13.581676\n",
       "1      9.864530\n",
       "9      3.853873\n",
       "10     2.805760\n",
       "7      0.010664\n",
       "6      0.001904\n",
       "Name: source_id, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desktop_visits['source_id'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "50% of the visits came from sources 4 and 3 from the desktop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f3305bdabd0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "touch_visits = visits.query('device == \"touch\"')\n",
    "(touch_visits['source_id'].value_counts()).plot(subplots=True, kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most visits for touch also came from sources 3, 4, and 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     35.271034\n",
       "3     20.481654\n",
       "5     17.386635\n",
       "2     12.356325\n",
       "1      8.488842\n",
       "9      3.261285\n",
       "10     2.744932\n",
       "7      0.008262\n",
       "6      0.001033\n",
       "Name: source_id, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "touch_visits['source_id'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 55% of the visits came from sources 3 and 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sales \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. When do people start buying?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_visits=visits.groupby('uid')['start ts'].min().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_visits.name='first_visits_date'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_buy=orders.groupby('uid')['buy ts'].min().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_buy.name='first_orders_date'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "firsts = first_visits.merge(first_buy, on='uid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>uid</th>\n",
       "      <th>start ts</th>\n",
       "      <th>buy ts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>313578113262317</td>\n",
       "      <td>2017-09-18 22:49:00</td>\n",
       "      <td>2018-01-03 21:51:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1575281904278712</td>\n",
       "      <td>2017-06-03 10:13:00</td>\n",
       "      <td>2017-06-03 10:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2429014661409475</td>\n",
       "      <td>2017-10-11 17:14:00</td>\n",
       "      <td>2017-10-11 18:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2464366381792757</td>\n",
       "      <td>2018-01-27 20:10:00</td>\n",
       "      <td>2018-01-28 15:54:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2551852515556206</td>\n",
       "      <td>2017-11-24 10:14:00</td>\n",
       "      <td>2017-11-24 10:14:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                uid            start ts              buy ts\n",
       "0   313578113262317 2017-09-18 22:49:00 2018-01-03 21:51:00\n",
       "1  1575281904278712 2017-06-03 10:13:00 2017-06-03 10:13:00\n",
       "2  2429014661409475 2017-10-11 17:14:00 2017-10-11 18:33:00\n",
       "3  2464366381792757 2018-01-27 20:10:00 2018-01-28 15:54:00\n",
       "4  2551852515556206 2017-11-24 10:14:00 2017-11-24 10:14:00"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firsts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 36523 entries, 0 to 36522\n",
      "Data columns (total 3 columns):\n",
      "uid         36523 non-null uint64\n",
      "start ts    36523 non-null datetime64[ns]\n",
      "buy ts      36523 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](2), uint64(1)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "firsts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "firsts['first_difference']= firsts['buy ts'] - firsts['start ts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating slice with unique users\n",
    "users_conversion = firsts[['uid', 'first_difference']].drop_duplicates(subset = ['uid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating days to convertion\n",
    "users_conversion['days_to_conversion'] = users_conversion['first_difference'] / np.timedelta64(1,'D')\n",
    "users_conversion['days_to_conversion'] = users_conversion['days_to_conversion'].round().astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating minutes to convertion\n",
    "users_conversion['mins_to_conversion'] = users_conversion['first_difference'] / np.timedelta64(1,'m')\n",
    "users_conversion['mins_to_conversion'] = users_conversion['mins_to_conversion'].round().astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,4))\n",
    "plt.hist(users_conversion['days_to_conversion'], bins=100, range=(0, 60), color='teal')\n",
    "plt.title('Conversion by Day', fontsize=15,fontweight=\"bold\")\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Users')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,4))\n",
    "plt.hist(users_conversion['mins_to_conversion'], bins=100, range=(0, 60), color='teal')\n",
    "plt.title('Conversion within the first hour', fontsize=15,fontweight=\"bold\")\n",
    "plt.xlabel('Minutes')\n",
    "plt.ylabel('Users')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_amount = firsts['first_difference'].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "conversion_by_day = users_conversion['days_to_conversion'].value_counts().reset_index() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "conversion_by_day.columns = ['Day','Orders']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_amount.columns = ['Time','Orders']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Time</th>\n",
       "      <th>Orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:01:00</td>\n",
       "      <td>2656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>2455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0 days 00:02:00</td>\n",
       "      <td>2098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0 days 00:03:00</td>\n",
       "      <td>1685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0 days 00:04:00</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10556</td>\n",
       "      <td>62 days 19:37:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10557</td>\n",
       "      <td>8 days 20:13:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10558</td>\n",
       "      <td>1 days 07:41:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10559</td>\n",
       "      <td>15 days 13:28:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10560</td>\n",
       "      <td>164 days 02:06:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10561 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Time  Orders\n",
       "0       0 days 00:01:00    2656\n",
       "1       0 days 00:00:00    2455\n",
       "2       0 days 00:02:00    2098\n",
       "3       0 days 00:03:00    1685\n",
       "4       0 days 00:04:00    1460\n",
       "...                 ...     ...\n",
       "10556  62 days 19:37:00       1\n",
       "10557   8 days 20:13:00       1\n",
       "10558   1 days 07:41:00       1\n",
       "10559  15 days 13:28:00       1\n",
       "10560 164 days 02:06:00       1\n",
       "\n",
       "[10561 rows x 2 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>Day</th>\n",
       "      <th>Orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>343</td>\n",
       "      <td>353</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>344</td>\n",
       "      <td>345</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>345</td>\n",
       "      <td>313</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>346</td>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>347</td>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>348 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Day  Orders\n",
       "0      0   25217\n",
       "1      1    1779\n",
       "2      2     671\n",
       "3      3     504\n",
       "4      4     351\n",
       "..   ...     ...\n",
       "343  353       1\n",
       "344  345       1\n",
       "345  313       1\n",
       "346  262       1\n",
       "347  312       1\n",
       "\n",
       "[348 rows x 2 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversion_by_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- After making these calculations I found that 25217 visitors started buying on the first day they visited\n",
    "- 1779 visitors made an order on the next day and from there we see less visitors buying \n",
    "- I also calculated the days and the minutes and we can see that most of the people bought right away"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. How many orders do they make during a given period of time? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   buy ts  revenue                   uid  order_year  \\\n",
      "13176 2017-10-17 10:16:00     7.94  18446167067214817906        2017   \n",
      "35572 2018-02-18 19:34:00     9.78  18446156210226471712        2018   \n",
      "42213 2018-03-26 22:54:00     4.22  18445601152732270159        2018   \n",
      "9023  2017-09-25 15:56:00     0.12  18445407535914413204        2017   \n",
      "8919  2017-09-24 23:48:00     0.24  18445407535914413204        2017   \n",
      "...                   ...      ...                   ...         ...   \n",
      "31462 2018-01-28 15:54:00     2.44      2464366381792757        2018   \n",
      "12235 2017-10-11 18:33:00    73.33      2429014661409475        2017   \n",
      "213   2017-06-03 10:13:00     1.22      1575281904278712        2017   \n",
      "252   2017-06-03 17:39:00     1.83      1575281904278712        2017   \n",
      "27944 2018-01-03 21:51:00     0.55       313578113262317        2018   \n",
      "\n",
      "      order_month  order_week  order_date  \n",
      "13176  2017-10-01          42  2017-10-17  \n",
      "35572  2018-02-01           7  2018-02-18  \n",
      "42213  2018-03-01          13  2018-03-26  \n",
      "9023   2017-09-01          39  2017-09-25  \n",
      "8919   2017-09-01          38  2017-09-24  \n",
      "...           ...         ...         ...  \n",
      "31462  2018-01-01           4  2018-01-28  \n",
      "12235  2017-10-01          41  2017-10-11  \n",
      "213    2017-06-01          22  2017-06-03  \n",
      "252    2017-06-01          22  2017-06-03  \n",
      "27944  2018-01-01           1  2018-01-03  \n",
      "\n",
      "[50415 rows x 7 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "users_count = orders.groupby('order_date').agg({'uid': 'nunique'}).plot()\n",
    "# print(users_count.sort_values(by = 'uid', ascending=False))\n",
    "print(orders.sort_values(by = 'uid', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most users bought on November 24, 2017. Right in the middle of the our year of data at 701 users\n",
    "- The second highest buying time was the last date of our data on May 31, 2018 with 570 users buying and the lone user on June 1, 2018 who actually signed on May 31, 2017 but bought after midnight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   buy ts  revenue                   uid  order_year  \\\n",
      "13176 2017-10-17 10:16:00     7.94  18446167067214817906        2017   \n",
      "35572 2018-02-18 19:34:00     9.78  18446156210226471712        2018   \n",
      "42213 2018-03-26 22:54:00     4.22  18445601152732270159        2018   \n",
      "9023  2017-09-25 15:56:00     0.12  18445407535914413204        2017   \n",
      "8919  2017-09-24 23:48:00     0.24  18445407535914413204        2017   \n",
      "...                   ...      ...                   ...         ...   \n",
      "31462 2018-01-28 15:54:00     2.44      2464366381792757        2018   \n",
      "12235 2017-10-11 18:33:00    73.33      2429014661409475        2017   \n",
      "213   2017-06-03 10:13:00     1.22      1575281904278712        2017   \n",
      "252   2017-06-03 17:39:00     1.83      1575281904278712        2017   \n",
      "27944 2018-01-03 21:51:00     0.55       313578113262317        2018   \n",
      "\n",
      "      order_month  order_week  order_date  \n",
      "13176  2017-10-01          42  2017-10-17  \n",
      "35572  2018-02-01           7  2018-02-18  \n",
      "42213  2018-03-01          13  2018-03-26  \n",
      "9023   2017-09-01          39  2017-09-25  \n",
      "8919   2017-09-01          38  2017-09-24  \n",
      "...           ...         ...         ...  \n",
      "31462  2018-01-01           4  2018-01-28  \n",
      "12235  2017-10-01          41  2017-10-11  \n",
      "213    2017-06-01          22  2017-06-03  \n",
      "252    2017-06-01          22  2017-06-03  \n",
      "27944  2018-01-01           1  2018-01-03  \n",
      "\n",
      "[50415 rows x 7 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "users_count = orders.groupby('order_week').agg({'uid': 'nunique'}).plot()\n",
    "print(orders.sort_values(by = 'uid', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The strongest buying week was Week 47 with 1586 users buying.\n",
    "- The top five weeks are weeks 47, 40, 52, 22, and 49.\n",
    "- The later weeks had more buys overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   buy ts  revenue                   uid  order_year  \\\n",
      "13176 2017-10-17 10:16:00     7.94  18446167067214817906        2017   \n",
      "35572 2018-02-18 19:34:00     9.78  18446156210226471712        2018   \n",
      "42213 2018-03-26 22:54:00     4.22  18445601152732270159        2018   \n",
      "9023  2017-09-25 15:56:00     0.12  18445407535914413204        2017   \n",
      "8919  2017-09-24 23:48:00     0.24  18445407535914413204        2017   \n",
      "...                   ...      ...                   ...         ...   \n",
      "31462 2018-01-28 15:54:00     2.44      2464366381792757        2018   \n",
      "12235 2017-10-11 18:33:00    73.33      2429014661409475        2017   \n",
      "213   2017-06-03 10:13:00     1.22      1575281904278712        2017   \n",
      "252   2017-06-03 17:39:00     1.83      1575281904278712        2017   \n",
      "27944 2018-01-03 21:51:00     0.55       313578113262317        2018   \n",
      "\n",
      "      order_month  order_week  order_date  \n",
      "13176  2017-10-01          42  2017-10-17  \n",
      "35572  2018-02-01           7  2018-02-18  \n",
      "42213  2018-03-01          13  2018-03-26  \n",
      "9023   2017-09-01          39  2017-09-25  \n",
      "8919   2017-09-01          38  2017-09-24  \n",
      "...           ...         ...         ...  \n",
      "31462  2018-01-01           4  2018-01-28  \n",
      "12235  2017-10-01          41  2017-10-11  \n",
      "213    2017-06-01          22  2017-06-03  \n",
      "252    2017-06-01          22  2017-06-03  \n",
      "27944  2018-01-01           1  2018-01-03  \n",
      "\n",
      "[50415 rows x 7 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "users_count = orders.groupby('order_month').agg({'uid': 'nunique'}).plot()\n",
    "print(orders.sort_values(by = 'uid', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The months of December, October, and November are big buy months.\n",
    "- Holiday season maybe.\n",
    "- We can see the months of June, July, and August at the bottom of the buy list here. Most probably because users were still learning about the product and not ready to buy until later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Cohorts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_order_date_by_customers = orders.groupby('uid')['order_date'].min() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_order_date_by_customers.name = 'first_order_date'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = orders.join(first_order_date_by_customers,on='uid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders['first_order_month'] = orders['first_order_date'].astype('datetime64[M]')\n",
    "orders['order_month'] = orders['order_date'].astype('datetime64[M]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   first_order_month   uid   revenue\n",
      "0         2017-06-01  2023  24031.69\n",
      "1         2017-07-01  1923  16127.92\n",
      "2         2017-08-01  1370  11606.26\n",
      "3         2017-09-01  2581  34676.32\n",
      "4         2017-10-01  4340  27603.45\n",
      "5         2017-11-01  4081  26098.99\n",
      "6         2017-12-01  4383  33485.74\n",
      "7         2018-01-01  3373  16663.13\n",
      "8         2018-02-01  3651  16750.50\n",
      "9         2018-03-01  3533  19273.41\n",
      "10        2018-04-01  2276  11810.61\n",
      "11        2018-05-01  2988  13925.76\n",
      "12        2018-06-01     1      3.42\n"
     ]
    }
   ],
   "source": [
    "cohort_grouped = orders.groupby('first_order_month').agg({'uid':'nunique','revenue':'sum'}).reset_index()\n",
    "print(cohort_grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  first_order_month order_month  revenue   uid\n",
      "0        2017-06-01  2017-06-01  9557.49  2023\n",
      "1        2017-06-01  2017-07-01   981.82    61\n",
      "2        2017-06-01  2017-08-01   885.34    50\n",
      "3        2017-06-01  2017-09-01  1931.30    54\n",
      "4        2017-06-01  2017-10-01  2068.58    88\n"
     ]
    }
   ],
   "source": [
    "orders_grouped_by_cohorts = orders.groupby(['first_order_month','order_month']).agg({'revenue':'sum','uid':'nunique'}).reset_index()\n",
    "print(orders_grouped_by_cohorts.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_grouped_by_cohorts['cohort_lifetime'] = orders_grouped_by_cohorts['order_month'] - orders_grouped_by_cohorts['first_order_month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.000000\n",
      "1    0.985647\n",
      "2    2.004148\n",
      "3    3.022649\n",
      "4    4.008296\n",
      "Name: cohort_lifetime, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "orders_grouped_by_cohorts['cohort_lifetime'] = orders_grouped_by_cohorts['cohort_lifetime'] / np.timedelta64(1,'M')\n",
    "print(orders_grouped_by_cohorts['cohort_lifetime'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  first_order_month order_month  cohort_lifetime\n",
      "0        2017-06-01  2017-06-01                0\n",
      "1        2017-06-01  2017-07-01                1\n",
      "2        2017-06-01  2017-08-01                2\n",
      "3        2017-06-01  2017-09-01                3\n",
      "4        2017-06-01  2017-10-01                4\n",
      "   first_order_month order_month  cohort_lifetime\n",
      "74        2018-03-01  2018-05-01                2\n",
      "75        2018-04-01  2018-04-01                0\n",
      "76        2018-04-01  2018-05-01                1\n",
      "77        2018-05-01  2018-05-01                0\n",
      "78        2018-06-01  2018-06-01                0\n"
     ]
    }
   ],
   "source": [
    "orders_grouped_by_cohorts['cohort_lifetime'] = orders_grouped_by_cohorts['cohort_lifetime'].round().astype('int')\n",
    "print(orders_grouped_by_cohorts[['first_order_month','order_month','cohort_lifetime']].head())\n",
    "print(orders_grouped_by_cohorts[['first_order_month','order_month','cohort_lifetime']].tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>first_order_month</th>\n",
       "      <th>order_month</th>\n",
       "      <th>revenue</th>\n",
       "      <th>uid</th>\n",
       "      <th>cohort_lifetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017-06</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>9557.49</td>\n",
       "      <td>2023</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017-06</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>981.82</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-06</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>885.34</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-06</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>1931.30</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-06</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2068.58</td>\n",
       "      <td>88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_order_month order_month  revenue   uid  cohort_lifetime\n",
       "0           2017-06  2017-06-01  9557.49  2023                0\n",
       "1           2017-06  2017-07-01   981.82    61                1\n",
       "2           2017-06  2017-08-01   885.34    50                2\n",
       "3           2017-06  2017-09-01  1931.30    54                3\n",
       "4           2017-06  2017-10-01  2068.58    88                4"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_grouped_by_cohorts['first_order_month'].dt.strftime('%d.%m.%Y')\n",
    "orders_grouped_by_cohorts['first_order_month'] = orders_grouped_by_cohorts['first_order_month'].dt.strftime('%Y-%m')\n",
    "orders_grouped_by_cohorts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. What is the average purchase size?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_grouped_by_cohorts['revenue_per_user'] = orders_grouped_by_cohorts['revenue'] / orders_grouped_by_cohorts['uid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>cohort_lifetime</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_order_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-06</td>\n",
       "      <td>4.724414</td>\n",
       "      <td>16.095410</td>\n",
       "      <td>17.706800</td>\n",
       "      <td>35.764815</td>\n",
       "      <td>23.506591</td>\n",
       "      <td>22.207761</td>\n",
       "      <td>31.011935</td>\n",
       "      <td>25.033191</td>\n",
       "      <td>19.295690</td>\n",
       "      <td>27.233556</td>\n",
       "      <td>25.681333</td>\n",
       "      <td>9.804151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-07</td>\n",
       "      <td>6.010218</td>\n",
       "      <td>12.396346</td>\n",
       "      <td>21.035965</td>\n",
       "      <td>10.786094</td>\n",
       "      <td>6.938163</td>\n",
       "      <td>7.896842</td>\n",
       "      <td>6.421111</td>\n",
       "      <td>6.992821</td>\n",
       "      <td>7.382143</td>\n",
       "      <td>12.861818</td>\n",
       "      <td>11.513846</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-08</td>\n",
       "      <td>5.276518</td>\n",
       "      <td>11.148793</td>\n",
       "      <td>11.851321</td>\n",
       "      <td>12.182955</td>\n",
       "      <td>16.921250</td>\n",
       "      <td>12.139063</td>\n",
       "      <td>9.620333</td>\n",
       "      <td>12.610455</td>\n",
       "      <td>21.070000</td>\n",
       "      <td>8.307419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-09</td>\n",
       "      <td>5.644529</td>\n",
       "      <td>22.188385</td>\n",
       "      <td>13.445200</td>\n",
       "      <td>138.669189</td>\n",
       "      <td>19.881538</td>\n",
       "      <td>26.095000</td>\n",
       "      <td>27.437121</td>\n",
       "      <td>16.961351</td>\n",
       "      <td>11.044651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-10</td>\n",
       "      <td>5.003733</td>\n",
       "      <td>11.287427</td>\n",
       "      <td>6.753252</td>\n",
       "      <td>7.413152</td>\n",
       "      <td>7.072796</td>\n",
       "      <td>7.255139</td>\n",
       "      <td>6.573214</td>\n",
       "      <td>7.479701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-11</td>\n",
       "      <td>5.154683</td>\n",
       "      <td>7.339054</td>\n",
       "      <td>6.786583</td>\n",
       "      <td>12.510660</td>\n",
       "      <td>7.457284</td>\n",
       "      <td>4.580833</td>\n",
       "      <td>7.564839</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",
       "      <td>2017-12</td>\n",
       "      <td>4.738191</td>\n",
       "      <td>7.816575</td>\n",
       "      <td>39.366019</td>\n",
       "      <td>48.135052</td>\n",
       "      <td>27.431400</td>\n",
       "      <td>23.518413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01</td>\n",
       "      <td>4.135636</td>\n",
       "      <td>8.721228</td>\n",
       "      <td>12.365542</td>\n",
       "      <td>11.199767</td>\n",
       "      <td>4.699556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02</td>\n",
       "      <td>4.156987</td>\n",
       "      <td>8.610000</td>\n",
       "      <td>4.942414</td>\n",
       "      <td>6.941026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03</td>\n",
       "      <td>4.838803</td>\n",
       "      <td>11.811667</td>\n",
       "      <td>19.221897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04</td>\n",
       "      <td>4.657597</td>\n",
       "      <td>17.535072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-05</td>\n",
       "      <td>4.660562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06</td>\n",
       "      <td>3.420000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort_lifetime          0          1          2           3          4   \\\n",
       "first_order_month                                                          \n",
       "2017-06            4.724414  16.095410  17.706800   35.764815  23.506591   \n",
       "2017-07            6.010218  12.396346  21.035965   10.786094   6.938163   \n",
       "2017-08            5.276518  11.148793  11.851321   12.182955  16.921250   \n",
       "2017-09            5.644529  22.188385  13.445200  138.669189  19.881538   \n",
       "2017-10            5.003733  11.287427   6.753252    7.413152   7.072796   \n",
       "2017-11            5.154683   7.339054   6.786583   12.510660   7.457284   \n",
       "2017-12            4.738191   7.816575  39.366019   48.135052  27.431400   \n",
       "2018-01            4.135636   8.721228  12.365542   11.199767   4.699556   \n",
       "2018-02            4.156987   8.610000   4.942414    6.941026        NaN   \n",
       "2018-03            4.838803  11.811667  19.221897         NaN        NaN   \n",
       "2018-04            4.657597  17.535072        NaN         NaN        NaN   \n",
       "2018-05            4.660562        NaN        NaN         NaN        NaN   \n",
       "2018-06            3.420000        NaN        NaN         NaN        NaN   \n",
       "\n",
       "cohort_lifetime           5          6          7          8          9   \\\n",
       "first_order_month                                                          \n",
       "2017-06            22.207761  31.011935  25.033191  19.295690  27.233556   \n",
       "2017-07             7.896842   6.421111   6.992821   7.382143  12.861818   \n",
       "2017-08            12.139063   9.620333  12.610455  21.070000   8.307419   \n",
       "2017-09            26.095000  27.437121  16.961351  11.044651        NaN   \n",
       "2017-10             7.255139   6.573214   7.479701        NaN        NaN   \n",
       "2017-11             4.580833   7.564839        NaN        NaN        NaN   \n",
       "2017-12            23.518413        NaN        NaN        NaN        NaN   \n",
       "2018-01                  NaN        NaN        NaN        NaN        NaN   \n",
       "2018-02                  NaN        NaN        NaN        NaN        NaN   \n",
       "2018-03                  NaN        NaN        NaN        NaN        NaN   \n",
       "2018-04                  NaN        NaN        NaN        NaN        NaN   \n",
       "2018-05                  NaN        NaN        NaN        NaN        NaN   \n",
       "2018-06                  NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "cohort_lifetime           10        11  \n",
       "first_order_month                       \n",
       "2017-06            25.681333  9.804151  \n",
       "2017-07            11.513846       NaN  \n",
       "2017-08                  NaN       NaN  \n",
       "2017-09                  NaN       NaN  \n",
       "2017-10                  NaN       NaN  \n",
       "2017-11                  NaN       NaN  \n",
       "2017-12                  NaN       NaN  \n",
       "2018-01                  NaN       NaN  \n",
       "2018-02                  NaN       NaN  \n",
       "2018-03                  NaN       NaN  \n",
       "2018-04                  NaN       NaN  \n",
       "2018-05                  NaN       NaN  \n",
       "2018-06                  NaN       NaN  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenue_per_user_pivot = orders_grouped_by_cohorts.pivot_table(index='first_order_month',columns ='cohort_lifetime',values='revenue_per_user',aggfunc='mean')\n",
    "revenue_per_user_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3306ffc990>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yWStYMnN5uG3t7OzoNaw7PZv249G9R8zaNI2D27y5buVjsG7pRpbMWc6wyYODyn4ZN5Cx7lM46n2cek1r0eb7Fkwd5RGs3T9//0P7Bl159/YdDg72/LFuJgd2enPqmPXewwD+/v7M+NWDy2cukyBRAqZvmsLR/ccY+v2/ExmdBnXkzcvQ50V4bW/63rRq/NOGzMDXjMFj8/Sgc2jFrJUsjeAcunX1Nu1dOwPG+bTCZwn7txywStzhyZM3Jy1bN6ZqhYb88897lq3yZNuW3Vy7+u8+bdehBRcvXKb5N51JkSIZh45tZcWy9bx//96GkccASXf5KPyA3lrrfEBJoItSKh8wANiptc4J7DRfAzwFugNjLFeitb6otS6otS4IFAHeAqtDbkwpZQ9MBaoD+YCm5vYA2gCZgDxa67yA1aZVMmRIR/XqlZgzd1Gkdb9pXJely9ZaIarwHfE+xvNnL4KVNWvTkBkT5/LPP8Yb/cnjZ1Fuaws+h47z/HnYM4QA1epUZuOqbRGuo2S5Yty6fpu7t+9/7PCi5O+3fwFg72CPvaMDaB2ldlprHOM74uDogGM8B+wd7Hn5+HlMhhrKk4dPuXTGF4C3b95x3fcmqdKm5K3FjGGChJ+ho9gna3vy8AkXT18CzPgv3yBVulQc3nsEf39jBufM0bOkSZcqym1t4a+gc8g4D9Caa2ev8vD2wwjbFSpfmBP7j/P6xWvevHjDif3HKVy+iDVCDmJ5Dr17847rvjdImTZllNrmLZSHO9fvcO/mPfze+7Fz7W6cXUvHZLhhOnroBC9CfA79L1tmjnobX7i99/5J5VouYbZ99/YdAA6ODjg4ONjkvfL04VMun7lsxPPmHTcv3wp1DMrXKsfutbuj1TamPX34FF+LGG743oxWDIWdC3Hnxl0e3In4fRPTcuXOzlGfk7x79xf+/v54HfyTWrWrBqujtebzxIkASPR5Ip49e4Gfn58twhUfQYwO0rXW97TWx8znr4DzQAagLhD4G80fQD2zzkOt9REgoq98lYArWuuwpkSKA5e11le11v9gDMTrmsu+A4Zo8w74WmurvdvGjvmFgQOHERAQ8Yds5swZyJIlE7t3H7RSZFGXNfv/KFaqMCu3/sGidbP4slC+yBvFUkVLFuLJo6fcuHYrwno16lWJdCAfk5SdHUM3jWXqsbmc2X+SKyeMAUujPs0YtmUczQe1xSFe6B/DLh+7xHnvM0w+4snkI56c3neCu5fvWDv8IGkzpiFX/hycPX4egE7927HmyFKqfl2ZWeasekjx4sdjzqYZzFo/lXKuZawZbijpMqYld/6cnD12Llh5naY18Np1KFptrcXOzo7xmycx7/gCThw4waUTl6LULkXaFDy++zjo9ZN7T0iRNkVMhRmpwHPonHkO1W9bj9+3z2LA2D58njT0r5Op0qbk4d1HQa8f3Xtk9QFieK5cvEaFauUAqFq7ImnTpw6znp2dHct2/MGeM5vw3vcnp4/b5hwKlCZjGnJ8kZ0Lxy8ElX1ZIj/PHj/jzvW7H9zW2tJmTEPO/Dk4b8bwdZu6eG73oN+YsM8hSxXrVGBXGF9ErO38OV9KlS5KsuROJEjwGZWrlid9xnTB6nh6LCBXruycvXSAfd7r+an/sFg7GfKfBATE/CMWsNqFo0qpLEAh4DCQRmt9z1x0HyMdJqqaAIvDWZYBsBx53TbLALID3yilfJRSm5VSOcOJs6NZx8fHx+cDwgpbjRqVePjoMcePn460buNGdVm1ehMBseTksOTgYI+TUxIauLZmhNsEJs8eaeuQoq1m/apsXL01wjqOjg5UdC3H1vU7rRRVaDoggJ9r9OaHkh3IVjAHGXNlZumohfSr2A23Ov1I5PQ5tTp/Hapd6v+lJX2OjPxQsgPdS3QgX+kvyVUsrw16YMyWD581hAluU4Nm0WeO9KResW/YtnoHDduGjh+gfokmfFujM25dhtLDvSsZ/pfemmEHSZAwASNn/8q4wZOD5Q237d4SPz9/Nq/a/sFtrSkgIICe1bvTrkQbchXIReZc/7NJHP9FgoSfMXTWL0xym8bb129ZM289TUq3pG3Vjjx5+JSugzvbOsQPMrjnML5pU58lW+eS6POEvP8n7FnOgIAAGlduTZVCdclfKB858mSzcqT/+izhZ7jNHMS0X2YE+zWsYt0K7F67J1ptrSlBws9w93Bjyi/GObR23jqalWlF+6qdePLwCd8PCv8ccnB0oEzVUuzZsNeKEYfN99IVJo2fxYrVc1i2ypMzp84H/bIXqEIlZ86cPs8XuZyp4FyXEaMHBc2si7jHKoN0pdTnwEqgh9Y62G9/2viKF6WveUqpeEAdIPxEsvDFB/7SWhcFZgFzwqqktfbQWhfVWhctWrRoNDYTXOlSxahVsyqXLnqzYP5UKriU4fe5YafCN25ch6VLrXthTVTdv/uQrRuNC8dOHT9LQEAAyVM42TiqD2dvb0/lmi5sXrMjwnplK5Xm3OkLPHn01EqRhe/ty7ec9zrDVy6FePHQSDPy+8ePfct3ka1g6O+aRauV4PLxS/z99i/+fvsXp3YfI2fh3NYOG3sHe36bNYStq3ewd/P+UMu3rtqBS41yYbZ9dN+Yxb178x7HvE+QK3+OGI01LPYO9oyc/StbVm1n9+Z9QeW1GlfDuXIpBnX99YPb2sqbl2847X2Kwi6Fo1T/yf0npEz/78xzinQpeHI/9AW+Mc3ewZ6hs35h++qd7Nts5AM/e/yMgIAAtNasX7iRvAXzhGr36P5jUqf/N8UoVbpUPL7/OFQ9W7h++Qadm/SgiWtbNq/ezq0bEf/K9erla44cPEaZCiWtFGFw9g72/OIxiJ1rdnFgy7+/8trZ2+FcrQx71oU/eA2vrTXZO9jj7vELO1bvZH/QOfQ86BzauGgTeQuG//lYokJxLp325ZmVUwbDs3D+CiqVr0/t6s15/vwlVy5fD7a8WYsGbFhnTB5cu3qTmzdukzNXdhtEGrO0DojxR2wQ44N0pZQjxgB9odY68BLjB0qpdObydEBUU0+qA8e01g/MtpmUUifMR2fgDkbeeaCMZhkYs+qB218NfBXdPn2InweNIFv2YuTKXYoWLbuwe89B2rTtHqpe7tzZcXJKyqFDR60R1gfbtnk3JZ2NLy1ZsmcmXjxHnj6JHR9aH6JUuWJc873Bg3sRn3I1v65q01SXxMmTkDBJQgAc48cjf9kC3L18m6Sp/71gt0jVEty+GPoirCd3HpOnRD7s7O2wd7AnT8kvuHv5ttViD/TT2H7cuHyDJR7/fqfOmDVD0POyrmW4cSV0/ImTfo5jPEcAkiZLwlfF8nPtknUv+AMYNLY/131vsMhjWVBZKZfitPy+Gb3bDOTvd39/UFtrS5I8CYmSGDNo8eLHo0DZQty+ErXz4PjeYxQqW4hESRORKGkiCpUtxPG9x2Iy3DANGNuH65dvstTj37vjpEidPOh5uerOXLt4PVS7CycukDFrBtJlSouDowOV6lbgwDYva4QcqeTmRfdKKTr2bMvyeaEuryJZCqegmwzE/yy+8bl12frvAYA+o3txw/cWK2cFv0NIkbKFuXnlVoRffsJra039xvTh5uUbLJ+1MqgsucU55Fwt7HMoUKW6FdgZC1JdAqVMacSeIWM6atWpysrl64Mtv33rLuVcSgGQKlUKcuTMFmlqp4i9YvruLgrwBM5rrcdZLFoHtAZGmP9G9UrJplikumitbwEFLbbnAORUSmXFGJw3AZqZi9cAFYBrQHkgasmZMcRtcB+OHjvJhg3GN97GjeqyfPk6W4YUZILHb5QoU4RkyZ04cGozE0fOYMXCtYyY9Aub9y/jn/fv6dvVDYDUaVMyfPxg2jXtHm7b5QutfyHsmBm/UrxMEZySO7H7xHqmjJrFykXrqPF1VTauDj74TpUmJUPH/0SnZj0B46fR0uVL4NYn/LtfxDSn1MnoOK4bdnZ22NnZcXjDQU7sOsrAxe4kTp4EpRQ3zl1j7o8zAcj6ZXYqtnDFs/80/tzkTb7SX/LbtgmgNaf2Huf4zv+euvUhviqWn+oNq3L53BX+2DYLgBkjZlO7SQ0yZ8+EDgjg/p0HQXd2yfNVLr5uWYfhfceQJef/6D+iFwFaY6cU86cstvpdOQoU/5Kajarhe+4KC7cbt6+cOnwWfX7tTrz48Zi61Pg4O330HCMGjCVlmhT8PKY/PVr2C7dtZPnrH1uy1MnpMa4ndvZ2KDs7Dm7Yj8/OI9RqW5uvOzcgWapkTNo2maO7fJjSfzI5vspBtebVmdJ/Mq9fvGbppKWMXW8cn6UTl/D6Rei7IcWkL4vlp1rDqlw5d5U524zz3GOEJ5XrVSRHvuyg4d7t+4zpb8SYIk0K+o/uTb9WP+LvH8D4nyczdtFI7Ozs2Lh0M9dt8EVv5HR3ipYujFNyJ7YfW8u00bNJmCgB37RtAMDOTXtYs3gDYHwO/TJuIF2a9yZl6hQMnTQYe3s77OwUW9ftYt92689E5y/2BVUaVubq+avM2DINgDkj5/Ln7iO41CkfKtUlRZrk9BrVk59aD4qwrbV8WSw/rg2rcOX8VWZvNe7CNGvkHCrVrUCOL3Kgteb+rfuMHTDBjD8FfUf3YkAr405gnyX4jCLligQtjw3mLphC8uROvH/vR7/e7rx88Yo23xq3Sv19zhLGjprG5Bkj2Oe9HqUUQ9xG8/Rp2Dd6iNNiYVpwTFAxeUGBUsoZ2A+cBgL36I8YeenLgMzADYxbMD5VSqUFfIAkZv3XQD6t9UulVCLgJpBNax3u7UOUUjWACRi3YJyjtR5mljsBC81tvgY6a61PRhS/u7u7HvbbrGj13dZ++tG4fd28yba9U8x/0aqbcc3vkqkbbRxJ9DTpUhOAy3MiPM1irRzfFgBgi8ce2wbyH1Tr6ALAhpm2u7bgv6jVqRIAxzytN7D5mAq3KwbADg/bp/xEV+WORkrWyulbbBxJ9DT4rhoA+2fHjl8SPlTZ9sZdeXbPCp0yF1dU6FAWgMljF9o4kujp1rs5bm5uytZxWPrb1yvGr4aNn7O0zfscozPpWusDQHidrBRG/fsYKSphresNEOntBbTWm4BNYZQ/B2pG1l4IIYQQQsRisSRnPKZZ7e4uQgghhBBCiKiJ0Zl0IYQQQgghPqoA/8jr/D8gM+lCCCGEEELEMjKTLoQQQggh4g7JSRdCCCGEEELYgsykCyGEEEKIuOMTuU+6zKQLIYQQQggRy8hMuhBCCCGEiDskJ10IIYQQQghhCzKTLoQQQggh4g7JSRdCCCGEEELYgsykCyGEEEKIOEPrT+N/HFVaa1vHEGu5u7vLzhFCCCHEJ83NzU3ZOgZLf53YEOPjs88K1rJ5nyXdRQghhBBCiFhG0l0iMXy4p61DiJaBA9sBMGP8UhtHEn2de34DwNoZ220cSfTU7VwFgCnjFtk4kujp2qsZAKNGzrVxJNHXr39bAMaO+sPGkURP736tARgxYo6NI4meAQO+Bf5/nEOjR/5u20CiqW//NgB4TFhm20CiqWOPxgBMH7/ExpFE33c9mwDg5uZm40iix93d3dYhhCYXjgohhBBCCCFsQWbShRBCCCFE3CH/mZEQQgghhBDCFmQmXQghhBBCxB0Bn8YtGGUmXQghhBBCiFhGZtKFEEIIIUTcITnpQgghhBBCCFuQQboQQgghhIg7AgJi/hEJpdQcpdRDpdQZi7LRSqkLSqlTSqnVSiknszyLUuqdUuqE+ZgRlW7KIF0IIYQQQogP8ztQLUTZdiC/1vor4BIw0GLZFa11QfPROSobkJx0IYQQQggRd8SCnHSt9T6lVJYQZdssXh4CGv6XbchMuhBCCCGEEBaUUh2VUj4Wj44fuIpvgc0Wr7MqpY4rpfYqpcpGZQUyky6EEEIIIeKOKOSM/1daaw/AIzptlVI/AX7AQrPoHpBZa/1EKVUEWKOU+kJr/TKi9chMuhBCCCGEEB+BUqoNUAtorrXWAFrrv7XWT8znR4ErQK7I1iWDdCtImjQJixbN4OTJXZw4sZMSJQqHqjN2rDtnz+7jyJGtFCyY3wZR/mvclKGc9t3Pbq+1QWWDhvRh/58b2HlwNXMWTCJJ0sThtrezs2PbvpXMWzLNGuGGkiZ9angdYvgAACAASURBVGaumMSKvfNZvmc+Tds3AqByrQos3zMfnzv7yFsgd7jtS1cowar9i1jrtYQ2XVtYK+xgJk75jXOXvdjnvT6ozClZUpavmcPhY1tZvmYOSZ2ShNl28JC+7D+0gYN/buK3kT9ZK+QIJU2ahAULp3Hs+E6OHttB8eLB3wNOTklYvGQmhw9vZu++NeTLF+lnl1XlyJmVA94bgh63753k+y5tg9VxckrCwsXT8Tq8id17V5M3lvXB+ByazokTOzl+POzPIYAiRb7i1asrfP11DStHGL6cObPhfWhT0OPe/dN06fJtqHqjx7hx6vQeDh/eTMGCX9gg0vDlzJkNr0Mbgx53758KdQ4FKlzkK56/9KVevepWjjK4sZN/5eSlfez0WhNU9vOQ3uw9vJ7tB1Yxe/5EkiQJ+29Bu04t2Om1hl1ea2nfuaW1Qg5l/JShnPE9wB6vdUFlteu6std7PXefnqVABOfJkVM72H1wLTv2r2Lr7uXWCFdEVSy4u0tYlFLVgH5AHa31W4vyVEope/N5NiAncDWy9cXoIF0plUkptVspdU4pdVYp9YNZnlwptV0p5Wv+m8wsz6OU8lZK/a2U6mOxntwWt605oZR6qZTqEc42qymlLiqlLiulBliU77dof1cptSas9jFh7Nhf2L59DwUKVKRYsWpcuHA52HJX1wrkyJGFL74oR5cuA5g0aZi1QgvTskWradYweOrVvt1euJSqS6UyX3Pl8nW69ewQbvsO37XE9+KVmA4zXP5+/ox3n0LD8i1pXbMjjdvUJ2uuLFy5eJU+7X7k2KGT4ba1s7Oj/2+96Na8Dw3Kt6BavcpkzZXFesGblixaRZMG7YOVde/Zkf17vSlR2JX9e73p3jN0elyx4oUoUaIw5UvXoWzJWhQs/CWlnYtbK+xwjR7txvbteylcqBIlS1Tn4sXg74G+fbtw6tQ5SpSoTof2vRk92s1GkYbtsu81nEvVwrlULcqVqcO7d3+xft3WYHV69/2e06fOU7pEDTp26M3I0YNtFG3YxoxxY9u2vRQsWInixUN/DoFx/g8dOpAdO/bbIMLw+fpepVTJGpQqWYMypWvx7t1frAux/11dXciRIytffelC164/MmGibT9HQ/L1vUrpkjUpXbImzqVrm+fQtlD17Ozs+PXX/uzcaftjsGzxGpo37BSsbN9ubyqWrkcV5/pcvXKDrr1C/y3InTcHzVo3pGalJlQpW5/KruXJkjWztcIOZumiNTQN8ffswnlfvm3ZjUNePpG2b1C7NZXL1se1QqOYClHEUUqpxYA3kFspdVsp1Q6YAiQGtoe41WI54JRS6gSwAuistX4a2TZieibdD+ittc4HlAS6KKXyAQOAnVrrnMBO8zXAU6A7MMZyJVrri4G3rQGKAG+B1SE3Zn5LmQpUB/IBTc3tobUua7EOb2DVR+9tGJIkSYyzc3Hmzl0CwPv373nxIngKUu3aVVm4cCUAf/55HCenJKRNm9oa4YXpkNdRnj17Eaxs724v/P39ATjmc5L06dOG2TZd+jRUqlqeRfNXxnic4Xn88AkXTl8C4O2bd1zzvU7qtCm55nuDG1duRdg2f6G83L5+mzs37+L33o+ta3fg4upsjbCD8fbyCXUMqteoxNJFxnfLpYvWUKNm5VDttNbE/ywe8eI5Ej9+PBwdHXn08LFVYg5PkiSJKeNcnD9+XwqE/R7Ikzcne/d4AXDp0hUy/y8jqVOntHqsUeFSoTTXrt7g1q27wcrz5MnJ3r3eAPheusr/MmcgVSzpg/E5VILffw//cwjg++/bsGbNZh49su05E5EKFcpw9eoNbt26E6y8Zq2qLFpofKwfOXKcpEkTkzZtKluEGCmXcPoA0Pm71qxdu4VHD5/YILLgDnsd5XmIz6F9ln8LjpwkXfo0odrlzJWN4z6n+OvdX/j7+3PooA/Va4f+vLKGQ14+PH/2PFiZ76WrXLl83SbxiI9Da/8Yf0Qeg26qtU6ntXbUWmfUWntqrXNorTOFvNWi1nql1voLs6yw1np9ZOuHGB6ka63vaa2Pmc9fAeeBDEBd4A+z2h9APbPOQ631EeB9BKuthHGvyRthLCsOXNZaX9Va/wMsMbcVRCmVBKgIWGUmPUuWTDx69JRZs8Zy6NAmpk8fScKECYLVSZ8+Lbdv3wt6fefO/XAHwbFBkxb12RXOTNuQ4QMYOngMAVa4qCMq0mVMS+4vc3Hm2Lko1U+VNhX37zwMev3w3iNSx5I/9KlSpeDBg0cAPHjwiFSpUoSq43PkBAf2H+bMxQOcuXiA3Tv343sp0l/UYlSWLJl4/PgJM2eOwct7I1OnjQj1Hjh9+jx16xq3my1StACZM2cgfYbY+R5o0LA2K5aH/nw9ffo8deq6AkbKSKbMGcgQS97HgcfAw2MM3t6bmDYtrM+hNNSp44qHx3wbRRk1DRvVZvnydaHK06dPw+3b/35xunvnPuliyf4PqWGjWmGeQ+nMYzDLY4ENovpwTVrUZ3cYfwsunL9MiVJFSJYsKZ8l+IyKVcrG2vdzRLTWLFntydY9K2jRWmbShfVZLSfdvJdkIeAwkEZrHTgqvQ+E/ioevibA4nCWZQAsp0pvm2WW6mHM4od5Ra3lLXd8fCL/KSwyDg4OFCqUHw+P+ZQsWYM3b97Rt+/3/3m9tvJD7074+/mzclnoPzCVXcvz+NFTTp2M2oA4piVImIAxnsMYO3gib16/jbxBHKPRocqyZstMrlzZKZCvPF/lLYdzuZKULFXEBtH9y97BnoIF8zNr9gJKl6rJ2zfv6N3nu2B1xo6ZTlKnJHgf2sR3nVtz8uRZAvxjxxc9S46OjtSoUYnVqzeHWjZ+7AySJk3CAe8NdPquNadOnsM/IPLZGGtwCDwGsxZQqlQN3r59S58+wT+HRo924+efR2Be5xQrGfu/MqtXbbJ1KNHm6OhIzXD6MGrUYAbF8mMQqHvvjvj5+bFq2YZQyy5fusrUiZ4sWjWLhStmcvbMhVj5fo5MnWrNqVq+Ac0bdqRth2aULF3U1iGJQLE0J/1js8otGJVSnwMrgR5a65dKqaBlWmutlIrSJ5JSKh5Qh+D/g9OHagrMDm+h5S133N3d9fbt4ecvR8WdO/e4c+ceR46cAGD16k30CTFAuXv3Phkzpgt6nSFDWu7evf+fthsTGjerR2XX8jSuG/qCLYDiJQpTtXoFKlUtR/z48UmcOBFTZo6ka6f+Vo7UGJSM8RzKplXb2LVpX5TbPbr/iLQZ/k01Sp0uFQ/vP4qJED/Yo0dPSJMmFQ8ePCJNmlQ8fhQ6na1GrSr4HDnJmzfGl5Kd2/dTtHghDnkftXa4Qe7euc+dO/fxsXgPhBykv3r1ms6d+ga9Pnf+ANeu3bRqnFFRpWp5Tp48G2YK0atXr/m+c7+g16fP7eP6tYjTq6zlzp37oT6HevcOPkgvXPgr5s2bDECKFMlxda2An58f69eHzpu2laquLpw8cYaHYez/u3cfkDFj+qDX6TOk5V4s/Byt6urCiRNnw+xDocJf8nvQMUiGq6sLfv5+bFi/3dphRqhx03pUrlqexvXahVtnyYJVLFlgpB8NGPQD9+4+sFZ4H839e8avqo8fP2Xzhh0UKvxllPLYhRXEgv/MyBpifCZdKeWIMUBfqLUOzAN/oJRKZy5PBzwMr30I1YFjWusHZttMFheDdgbuAJks6mc0ywJjSYmRErPxv/TpQzx48Ijbt++RM2c2wMinPH/eN1idDRu207x5AwCKFy/EixevuH8/qrvEOipUcqZL93a0adqFd+/+CrPOb0PGU+SLihT/qgqd2/XmwL7DNhmgAwweN5BrvjdYOHPpB7U7e+ICmbJmIn2mdDg4OuBatzJ7tx6MoSg/zJbNu/imWT0AvmlWj82bdoaqc+f2XUo7F8Pe3h4HBwdKOxfjkg0v4oXA98DdoPeAS4UyXAjxHkiaNAmOjo4AtGnbhIMHDvPq1WurxxqZRo1qszyMNAWApEkTB/WhdZtv8Dr4Z6zpQ8jPIReXMly4EPwY5M3rTJ48xmP16k306DEoVg3QARo1qhPu/t+4cTvNmtcHoFixQrx8+Yr7seQLtqVG4aTrAOTPV44v8pbli7xlWbN6Mz17DI51A3SXSs581/1b2jTryl/h/C0ASJEyOQDpM6ajeq3KrF5utT+7H0XChAlI9HnCoOflw/jcEiKmxehMujKmzD2B81rrcRaL1gGtgRHmv2vDaB6WplikumitbwEFLbbnAORUSmXFGJw3AZpZtG8IbNBah//JEgN69hzM779PIl48R65du0nHjn1o3964td/s2QvYsmUX1apV4Ny5/bx9+46OHftEssaYNW32aEo7Fyd5CieOnt3FmBFT6NazI/HiObJkjSdgXDDUv5c7adKmYuykX2nRuLNNY7ZUsPhX1GpUDd9zl1m8fS4AU4bPJF78ePQb2oNkKZyYNH80l8760qVpb1KmScHgsQPo3qIv/v7+jPxxHFMXj8PO3o51SzZy9dI1q/dhpudYyjgXJ3mKZJw8t5dRwyczaZwHs/+YQPOWDbl16y7t2xg3OCpQKD9tvm1Cz24/s27NVpzLlWSf93q01uzasZ9tW3ZbPf6Q+vT+hTlzJxDP0ZFr12/RuVMf2rVvDoDn7IXkzp0Dj1lj0Fpz/rwv33/XL5I1Wl/ChAmoUNGZH7r/HFT2bTvj42WO5yJy587BDI9/+9D1e9t8QQ1Pr15uzJ07kXjxHLl+PfBzyDgGs2cvjKS17SVMmICKFZ3p3u3HoDLLc2jrlt24ulbg9Jm9vHv7jk6d+4a3KpsJPIe6d/v31qjt2hvnkOfsRbYKK1xTZ4+mVJliJE/hhM+ZnYwZMZWuPTsQP74jS1YbP0gf8znJgF5DSJM2FaMnDaFVY+NXslnzJpAsmRN+fn781HcoL1++skkfps8eE/T37NjZ3YweMYXnz14wbORPpEiZnAXLZnDm9AWaNuhAmrSpGDdpKM0bdyJlqhTMXWj8quFg78CqFRvYvfOATfogwhBL0lFimorJ3DellDOwHzgNBO7RHzHy0pcBmYEbQGOt9VOlVFrAB0hi1n8N5DNTZBIBN4FsWuvgl5sH32YNYAJgD8zRWg+zWLYHGKG13hKV+N3d3fXw4Z4f0OPYY+BA42fIGeM/bCY5Nunc8xsA1s6IXTNJUVW3cxUApoyLfX98o6JrL2PwMGrkXBtHEn39+hv3oR476o9IasZOvfu1BmDEiDk2jiR6BgwwUuP+P5xDo0f+bttAoqlv/zYAeExYZttAoqljj8YATB+/xMaRRN93PZsA4OYWu24tG1Xu7u64ubmpyGtaz7udHjF+4UaCSh1t3ucYnUnXWh8AwutkpTDq38dIUQlrXW+A0LezCF1vExDmVUVaa5fI2gshhBBCiFhMctKFEEIIIYQQtmCVu7sIIYQQQgjxUXwiOekyky6EEEIIIUQsIzPpQgghhBAi7pCcdCGEEEIIIYQtyEy6EEIIIYSIOyQnXQghhBBCCGELMpMuhBBCCCHiDplJF0IIIYQQQtiCzKQLIYQQQoi4Q+7uIoQQQgghhLAFmUkXQgghhBBxxyeSk6601raOIdZyd3eXnSOEEEKIT5qbm5uydQyW3q0bE+PjswR1+ti8zzKTLoQQQggh4o5PJCddBumR+G34bFuHEC0/DmwPwLRxi20cSfR936spANPHL7FxJNHzXc8mAMwYv9TGkURP557fALBwynobRxJ9zbvWBuL+MZg4ZoGNI4meH/q0AMBjwjIbRxJ9HXs0BmDtjO02jiR66nauAsC40fNsHEn09OrbCgA3NzcbRxJ97u7uQNztQ2D8wvpkkC6EEEIIIeKOTyQnXe7uIoQQQgghRCwjM+lCCCGEECLukJx0IYQQQgghYhlJdxFCCCGEEELYgsykCyGEEEKIuENm0oUQQgghhBC2IDPpQgghhBAi7tCfxn8ILzPpQgghhBBCxDIyky6EEEIIIeIOyUkXQgghhBBC2ILMpAshhBBCiLhDZtLFx3LxohdHfbbz5+EteB3cGGp57VpV8TmyLWh56dLFbBDlvyZMGcbZywfZ670uqKx2PVf2HlrPvWfnKFAo/we1tYXxU4ZyxvcAe7ws+lDXlb3e67n79CwFCn4Rbtv2nVuyx2sde73X0+G7VtYIN5RxU4Zy2nc/u73WBpUNGtKH/X9uYOfB1cxZMIkkSROHapc+Q1pWrJ/L3kPr2eO9jvadW1gz7CC/TRyM97ltbNi3NFh5y/bfsMVrBRv3L6Xv4O5htm3VsQkb9i1l4/6ltO7U1Brhhim6xyC8trbW6bvWHDy8Ea8/N9H5+zZh1injXJy9B9fh9ecm1m9eaN0AQxg7+VdOXtrHTq81QWU/D+nN3sPr2X5gFbPnTyRJkrD3f5IkifH4fTx7D69nz6F1FClWwFphB0mTPjUzV0xixd75LN8zn6btGwFQuVYFlu+Zj8+dfeQtkDvc9m7jBrLj9HqW7Z5nrZAj9X2Xthw6shnvPzfjOXcC8ePHC7b823ZN8Tq8if1e69mybSm58+SwUaRC/P8Qo4N0pVQmpdRupdQ5pdRZpdQPZnlypdR2pZSv+W8yszyPUspbKfW3UqqPxXpyK6VOWDxeKqV6hLPNakqpi0qpy0qpARbllZRSx8z2B5RSVv30qOramOIlqlG6TM1Qy3btPkDRYlUpXqIaHTv1Zsb0UdYMLZQli1bTpEGHYGUXzvnybYvueB/0+eC2trB00RqaNuwYrOzCeV++bdmNQ17h9yFP3py0aNWI6pUaU9G5HlVcXciSNXNMhxvKskWraRYi/n27vXApVZdKZb7myuXrdOsZej/7+fnh/vMoypesTc0qTWjTvhm5cme3VthBVi1ZT7sm3YKVlShThErVylHbpSk1y36D57T5odrlzJOdxi2+pqFrK+q4NKNCFWcyZ81orbCDie4xCK+tLeXNm5NWbRpT2aUBZUvVpmo1F7JmC35eJ0mamDHj3Wn2TSdKF69B25bdwlmbdSxbvIbmDTsFK9u325uKpetRxbk+V6/coGuvsPf/kBED2b3zAOVL1KZK2Qb4XrxqjZCD8ffzZ7z7FBqWb0nrmh1p3KY+WXNl4crFq/Rp9yPHDp2MsP36ZZvo2qy3laKNXLp0aej8XWtcytajVPHq2Nvb0aBh7WB1li9bT+kSNShbujYTJ3jw2/CfbBSt+H9PB8T8IxaI6Zl0P6C31jofUBLoopTKBwwAdmqtcwI7zdcAT4HuwBjLlWitL2qtC2qtCwJFgLfA6pAbU0rZA1OB6kA+oKm5PYDpQHNzHYuAnz9qT/+DN2/eBj1PlCgh2sa3Fjrk5cPzZy+ClfleusqVy9ei1dYWjDieBysz+nA9wnY5c2Xj2NFTvHv3F/7+/ngfPELN2lViMNKwHfI6yrMQ+3Hvbi/8/f0BOOZzkvTp04Zq9/DBY06fPA/Am9dv8b10lbTpUsd8wCH4eB/nxbOXwcqatm2Ix6Q/eP/PewCePn4Wql32XFk4eewMf737G39/f/70OkbVmhWtEnNI0T0G4bW1pVy5s3PU52TQee114Ai16rgGq9OwUW3Wr9vGndv3AHj8+KktQg1y2OtoqM+SfZb7/8hJ0qVPE6pd4iSfU6J0ERbPXwnA+/fvefnyVcwHHMLjh0+4cPoSAG/fvOOa73VSp03JNd8b3LhyK9L2xw6dDPUesjV7BwcSJPgMe3t7EiRIwP17D4Itf/XqddDzhLHgb5kQcV2MDtK11ve01sfM56+A80AGoC7wh1ntD6CeWeeh1voI8D6C1VYCrmitb4SxrDhwWWt9VWv9D7DE3BaABpKYz5MCd6PdsQ+lNRs3LMTbayPt2jULs0qdOtU4dXI3a1b/QcdOfcKsI2LehfO+lChVhGTJnEiQ4DMqVSlH+oxhD8RsqUmL+uzasT/COhkzp+fLL/Ny7OgpK0UVsazZM1O0ZEGWb/mdBWtn8mXBfKHq+J6/QtGSBXFKlpTPEsSnfOUypMsQeiAWG0TlGMQW58/7UrJ0UZIlN87rKq7lyZAh+HmdI0dWnJySsG7TAnbtW803TevZKNqoadKiPrvD2P+ZM2fkyeNnjJ86jK17VzB6ojsJEiawQYT/SpcxLbm/zMWZY+dsGsd/ce/eAyZPms2Z8/u5dMWbly9fsWvXgVD12ndswYlTuxjya3/69R1ig0jFJyEgIOYfsYDVctKVUlmAQsBhII3W+p656D7wIX+FmwCLw1mWAbCcorhtlgG0BzYppW4DLYERH7DN/6RCxQaULFWDOnVb0blTa5ydS4Sqs27dFr4qUIFGjdvzi5sM0m3F99JVpkyczZLVs1m0chZnT1/A3z92vFkD/dC7E/5+/qxctj7cOgkTJcRz3kQG/zic16/eWDG68NnbO5A0WVIaVWvDqF8mMWH28FB1rvheZ9bkecxZPgXPpZM5f+ZS0MxpbBKVYxCbXLp4hUnjPVi5Zi7LV8/h9KnzBIQ4r+0d7ClYKD9NGnag4dff0qdfF7LnyGKbgCPRvXdH/Pz8WLVsQ6hl9g72fFkgL/PmLMG1fEPevn1H1x7tbRClIUHCBIzxHMbYwRN58/pt5A1iKSenJNSsWZmv8ruQO0dpEiZMSONv6oaqN9tjAQW/qojboJH07dfFBpEK8f+HVQbpSqnPgZVAD611sN/vtPF7WJR+E1NKxQPqAMujEUZPoIbWOiMwFxgXzjY6KqV8lFI+Pj4R519H1d279wF49OgJa9dtoVjRguHWPXDgMFmzZiZFimQfZdviwy2evxJXl4Z8XaMlz5+/4GokKTLW1LhZPSq7lqdLh37h1nFwcMBz3gRWLd/ApvU7rBhdxO7fe8C2DbsAOHX8LDpAkyyFU6h6KxaupX7lljSv05GXL15y/cpNa4caoagcg9howbwVVCz3NbWqNeP58xdcDpG+dvfufXbt2M/bt+94+uQZ3l5HyJ8/j42iDV/jpvWoXLU8XTv2D3P5vbsPuHf3AcePngZg47ptfFkgrzVDDOLgYM8Yz6FsWrWNXZv22SSGj8WlQhluXL/Fk8dP8fPzY/26rZQoWTjc+itXbKBmLeunCopPhNYx/4gFYnyQrpRyxBigL9RarzKLHyil0pnL0wEPo7i66sAxrfUDs20mi4tJOwN3gEwW9TMCd5RSqYACWuvDZvlSoHRYG9Bae2iti2qtixYtWvQDehq2hAkT8PnniYKeV65UjrNnLwarkz1blqDnBQvmJ168+Dx5EjpfV1hHypTJAciQMR01aldh1YrQs3W2UKGSM126t6NN0y68e/dXuPXGTfkV30tXmTn1j3Dr2MKOTXsp4Wy8p7Jky4xjPAeePXkeql7ylMYX1HQZ0lC1ZkXWr9xi1TgjEtVjEBtZnte16lRlxfLgvwJs3riTEqWKmPnGn1GkaAEuXbxii1DD5VLJme+6f0ubZl35K5z9/+jhY+7euR/0K4BzuZI268fgcQO55nuDhTOXRl45lrt16y5FixckQYLPACjvUpqLIfZrtuxZgp67VqvA1SvXrRihEP//xOh90pVSCvAEzmutLWeu1wGtMVJOWgNRvU9ZUyxSXbTWt4CgaWmllAOQUymVFWPA3gRoBjwDkiqlcmmtLwFVMPLjY1yaNKlYtnQWYMyqLFm6lm3b99ChvXFrvFmzF1Dv6+q0aN6A9+/9ePfuL1q0/N4aoYVrhudYSjsXI3mKZBw/t4fRwyfz7NkLfhv1MylSJmfhshmcOX2BJvXbkyZtasZN/pXmjTqF23aReQGXNU2fPYbSzsVJnsKJY2d3M3rEFJ4/e8GwkT+RImVyFph9aNqgA2nSpmLcpKE0b2z0Yfa8iSRP7sR7Pz8G9vmVly+sf9HZtNmjg+I/enYXY0ZMoVvPjsSL58iSNZ6AceFc/17upEmbirGTfqVF484UL1mYRk3qcu7sRbbvN74TDx8ygV3brTuLN27mMIqXKUKy5E7sO7mRSaM8WLloLb9NHMyGfUt5//49/bv+AkDqNCkZNmEQHZr+AMCUuaNwSpYUv/d+uPcfyauXryPYUsyJ7jEIr+3i+asi2lyM+2PhFJInT8b79+/p18udly9e0eZb4xaXv89ZzKWLV9i1Yz8HDm0gICCA+X8s5/x5X5vFO3X2aEqVKUbyFE74nNnJmBFT6dqzA/HjO7Jk9WzAuHh3QK8hpEmbitGThtCq8XcADOr3G5M9RuIYz5Gb12/Tq4v17xNQsPhX1GpUDd9zl1m8fS4AU4bPJF78ePQb2oNkKZyYNH80l8760qVpb1KmScHgsQPo3qIvAL9N+4UipQvilNyJzUdXMWOMJ2sXh76Fr7Uc9TnJ2jVb2HdwHX5+/pw6eZbf5yzhx597cPzYaTZv2knHTi1xqVCa9+/9eP78JZ079bVZvOL/uViSMx7TVExefa2Ucgb2A6eBwD36I0Ze+jIgM3ADaKy1fqqUSgv4YFzgGQC8BvJprV8qpRIBN4FsWutwb5uglKoBTADsgTla62Fm+dfAEHO9z4BvtdYR3pfL3d1d/zZ8drT6bms/DjRyMKeNCy99P/b7vpcxgJg+fomNI4me73o2AWDG+Lg5i9a55zcALJwSN/Kuw9K8q3GLuLh+DCaOWWDjSKLnhz7GZITHhGU2jiT6OvZoDMDaGdttHEn01O1spJyMGx177rf+IXr1Nf6vCjc3NxtHEn3u7u5A3O2Du7s7bm5uytZxWHo3t1+M56MkaDvK5n2O0Zl0rfUBILxOVgqj/n2MFJWw1vUGSBGFbW4CNoVRvpowbtsohBBCCCHikE9kJl3+x1EhhBBCCCFimRidSRdCCCGEEOKjiiX/I2hMk0G6EEIIIYSIM3RA7LhFYkyTdBchhBBCCCFiGZlJF0IIIYQQcYdcOCqEEEIIIYSwBZlJF0IIIYQQcccncuGozKQLIYQQQggRy8hMuhBCCCGEiDvk7i5CCCGEEEII3k80FwAAIABJREFUW5CZdCGEEEIIEXfI3V2EEEIIIYQQtiAz6UIIIYQQIu74RGbSldafRvJ9dLi7u8vOEUIIIcQnzc3NTdk6BktvJ3aO8fFZwh9m2LzPMpMuhBBCCCHijk9kglkG6ZEYPtzT1iFEy8CB7QDYMHOnjSP5P/buOzqq4u/j+HtSVEKVFoooIMUfoiLSQi+h9yotdKQJotJbDAqCFEEQ6UXpCKGX0AMkAUKTTkIJoYTeJOhDknn+2E1MyG6yRLbEfF/n7Mnu3Jl7P7ubhdnJ3Lkp16BHDQDmTF1l5yQp0/2LlgAM6+Nl5yQpM/bn3wBY88s2OydJuWa9agOwe84+OydJmWrdKwGwb26AnZOkTKVu5QFYMn2DnZOkXLvPGwIwY/IyOydJmd5ftQHA29vbzklSxsfHB0i9+SH1P4fY/ML2pJMuhBBCCCFSjzQyJ11WdxFCCCGEEMLByEi6EEIIIYRIPeSKo0IIIYQQQgh7kJF0IYQQQgiRemiZky6EEEIIIYSwAxlJF0IIIYQQqUcamZMunXQhhBBCCJFqaFmCUQghhBBCCGEPMpIuhBBCCCFSjzQy3UVG0oUQQgghhHAwMpIuhBBCCCFSjzSyBKN00m3g/PkDPHnylOjoaKKioqlQoUGiOpUrl2PCBG9cXV25d+8+NWu2skNSA/c8Oflm6jCy5sgKWuO7eAPL5/1Ov5G9qFSzPM//L4prYdcZ/eU4/nz8p0VtbW3itG/xrFWZu3fv41mhKQADhn1O7brViYmJ4e7d+3zVZzi3Iu6YbJ8hY3p2B65j26ZdjBg81iaZR4ydjP+BQ2R9MwtrF88EYNrsX9m1PxAn5UTWNzMzZvjX5MyRjSd/PmXI6B+4eesO0VHRdGrbnKb1ayXY39OnkXToPTDu8a07d2lQqxpD+ve0+nPx+XEYlWtW4P7dBzSv2h6AIsUKMeKHQbilT8eN8JsM7f0NT/+MTNS2bbdWNG/fCKVg9eL1LJmz0up5X5Qjdw6GTR3Mm9nfRGvNxqWbWD3PF4CmnZvQtGMjoqNjCNp1kFlj5iRqP2jiADw8y/Lw7kM6e3a3dXzA8BwGTxnIm9mzoDVsWroZ3/lrGTFjGG8VfAuADJnS8+fjp/Ss09vkPpycnJixaRp3I+4xovMoW8YHYOzUUVSrWZF7dx/QoPKnAEyZM5YChd4BIGOmjDx5/ITG1dolalupugfDxwzA2dmJVYvXMvunRTbNDjBl+hhq1qnK3Tv3qOLRCICGTWozYMjnFCn6LnWqt+LEsVMm21arUZHvxg/H2dmJJb/+zrQfE/+eCSH+26w63UUplU8ptVspdUYpdVop9YWxPKtSartSKsT4801j+XtKqUCl1N9KqQHx9lNUKXU83u2xUqq/mWPWUUqdV0qFKqWGxCuvrpQ6qpQ6pZRapJSy6ReU2rU/pWzZuiY76JkzZ2Lq1DG0aNGVkiU9adu2ly2jJRIVFc2U0TP4tGoHOjfoSYtOTSlQ+B0O+gfTulon2np25uqla3Tq297itra2aula2rdM2BmdOW0BNSs1o3aVFuzctpf+A82/zgOH9eVgwBFrx0ygSb2azJz8XYKyzu2a4/vrL6xe9DNVKpTllwVLAVi2egPv5n+bNYtmsGD6eCZMm8Pz588TtE2f3o3Vi36Ou+XJlRPPqhVs8lzWrdhMrzZfJijznjyUqWNm0KKaF7u27KVT78Qdq0LvFaR5+0a0q9uVltU7UrlmBfLlz2uTzPFFR0czY/RMOlXvSu9GfWnSsTHvFH6bEuU/omKt8nSt1YPONbqxYuYqk+23rtrGoPZDbZw6oejoaGZ+O5uuNT6jb+MvaNyxIW8Xfpvveo+lZ53e9KzTm31bDrB/ywGz+2jatQlXQ8NtmDqhNcs30LV13wRl/bsPo3G1djSu1g6/jbvw27g7UTsnJye8xw2me+t+1KvQkgZNa/NukQK2ih1n+VJfWjdP+CXt3JkQurTvR+CBYLPtnJycGDdpFG1bdKdSmQY0bV6fIkXftXZcIVKPGG39mwOw9pz0KOBrrXUxoBzQRylVDBgC7NRaFwZ2Gh8D3Af6ARPj70RrfV5rXUJrXQL4BIgEfF88mFLKGfgZqAsUA9oopYoppZyARUBrrXVxIAzo+MqfbQp9+mlj1q3bQnj4DQDu3Lln1zz3bt/j/MkLAEQ+fcaV0DBy5M7Bwb2HiY6OBuDUkdO4585hcVtbOxh4hIcPHiUo+/PJ07j76dzSobXpD+EHHxUje45s7N0dYNWMLypV4gMyZ8qYoCxD+vRx9589+wulDPeVUjyNfIbWmshnf5E5U0acnZ3N7vvK1Wvce/CQTz4qbpXsLzoadJzHDx8nKHunYD6OBB4HIHDvYWo0qJqoXYHC73Dy6Gn+evY30dHRHAk8Ro36ietZ2/3b9wk5FQrAs6fPCAu5SvZc2Wns1YilPy/n+f8ZvhA9vPfQZPs/Dp7kycMnNstryv3b9wmN9xyuhoaTPVf2BHWqNKjM7nWJO7kA2XNlp2z1MmxetsXqWc0JDjzGowePzW6v29iTjb7bEpV/WPJ9wq6EEx52nefPo9i01g/PulWsGdWkoIDgRP8OhVy4xMXQy0m2K/nJh1y+dJWwK9d4/vw5a9dspk79GtaMKoRwQFbtpGutb2qtjxrvPwHOAnmBxhg6zRh/NjHWua21Pgw8N7G7WDWAi1rrMBPbygChWutLWuv/A5Ybj5UN+D+t9QVjve1A83/15F6C1pqNGxcTELCJrl3bJtpeuHBBsmTJjJ/fCgICNtGunc2iJSv3W7koWrwwp4+eSVDeqE09AnYFpaitPQ0a3o9DJ3fQtGV9Jn4/PdF2pRSjvh3Id6MmmmhtH1NnLaRGUy82+e3m825eALRt3pBLV8Kp1rgdTTv0Ykj/njg5mf84b9mxlzo1KqNie/l2cPH8ZarVqQxArYbVyZUnZ6I6oecuUbLsR2R+MxNvpHudijXKm6xnS7necqdw8UKcPXaOfAXz8kHZ4szYMI0pv0+i6EdF7ZrNUu5vuVPo/Xc5d+xcXNkHZYvz4O4Drl+5YbJN7296MmfsXLSDjCi9qJTHx9y9c5+wS4lH+t1z5yTi+q24xxE3buOe276/Ry8jVx53bly/Gff4xvUIcuV2t2MiIRxMTIz1bw7AZqu7KKXyAx8DBwF3rXXsv0ARwMv869MaWGZmW14g/r/Y14xldwEXpVQpY3kLIJ+ZnJ8ppYKVUsHBweb/HPkyqldvjodHfRo37kCPHh2oWLFMgu0uLs58/PEHNGnSiYYN2zN0aD8KFbL9n2ZflM4tHePnfsvkUdMSzB3u3M+LqKhotqzZ/tJt7e2HMT9R5gNPfFdtonP3xF+YOnZtza7t/ty8cctEa/v4okcndvr+Rv1a1Vi6egMABw4d4b3CBdm9bgmrF/7M2Mkz+PPpU7P72LJzL/U8q9oosWneX47l007NWLZtPm4Z3Hj+f1GJ6lwOCWPB9MXMXD6FGUt/5PzpC0RH2+8fy3Rub+Az25vp38wg8s9InJ2dyZQlE70b9mXmd7P55pcRdstmqTfc3sB71khmfDOTyHifxeqNq7F73R6TbcrWKMvDew8JORlqo5Qvr0HT2mxak3gUXQgh/its0klXSmUAVgP9tdYJ/napDXMOLBqqUUq9BjQCTE8ENcN4jNbAj0qpQ8ATINpM3dla61Ja61KlSpUyVeWl3TB2+O7cucf69dsoVapEgu3Xr0ewY4c/kZHPuHfvAfv3H+TDD4u9kmOnlLOLM+PnfsvWNdvZvcU/rrxBqzpU9PRg5OffvnRbR+K7aiN1G3omKv+k9Ed06t6WwOPbGDl6AM1bN2LoKJOnP9hcg1rV2LHHMH/Yd9N2PKtUQCnF22/lIW/uXFwOu2ay3bmQS0RHx/D+e4VtGTeRK6Fh9Gzdnza1u7DVdzvXwq6brOe7bCNtanehS9PePH74xORIqS04uzjjM/sbdvjuZN+W/QDcibiL/5Z9AJw7fp6YGE3mrJntks8Szi7OfDN7JDvX7mL/1n/mnjs5O1GxTgX2rN9rsl3xUsXwqFmOxQGLGP7zUEpU+IghUwfZKnaynJ2dqVW/GpvWmh4ouHXzNrny/jP2kytPTm7dvG2reP9axI1b5MmbO+5xnry5iLjpOAMHQtidzEl/NZRSrhg66Eu01muMxbeUUrmN23MDlv7rWRc4qrW+ZWybL97JpD2B6yQcIX/LWIbWOlBrXUlrXQbwBy5gA25u6ciQIX3c/Ro1KnH69PkEdTZs8KN8+dI4OzuTLt0blC79MefOhdginlkjJw3mSkgYS2f/s7KGR9UyePVuy9edhvL3s79fqq0jKFDw7bj7tetV52JI4nmhfXsMoeyHNfEoUZtvR01k9fL1fD96ii1jJhAW/k9Hdte+QAq8Y1iVI7d7DoKOGOZ3373/gCtXr/FWnlwm97Flxx7qetp+Pu6LsmZ/EzBMKer+ZSdW/ZrotJIE9XLldadGvapsWeNns4zxDZo4gKuhYayaszqubP/WA3xc3vAl+60CeXF9zYVH9x+Z24XdDZjwFWEh4ayesyZB+SeVSnL1Yjh3I+6abDdv/ALalGlP+/IdGdPne44fOMG4L36wRWSLlK9ShkuhV8x2vE8eO0P+Avl46+08uLq6UL9JLXZudcwBA1OOHT1JwXff4e138uLq6kqTZvXYtnmXvWMJIWzMqiucKMME2HnAWa315Hib1mM4cXOc8ec6C3fZhnhTXbTW4UDcsLRxxZbCSqkCGDrnrYG2xm05tda3lVKvA4OBMSl9Xi/D3T0HK1bMBsDFxYUVK9ayffteunUzrIwyd+5izp8Pxc9vD8HBfsTExLBgwXLOnLHJdwiTPirzAfVb1iHkzEWWbJ8HwM/fz2HAt/147fXX+HmF4a08eeQM44ZMIrt7NkZMHEx/r0Fm2yY3f/1Vmz7nBzwqlCZrtiwcPrWDSeNmUL1mJQoWyo+O0VwLv8HQr0cD8GGJ9/Hq3IqBX3jbNOOLBnqP4/CxP3j48DE1mrSnd1cv9gUe5srVaygnRZ5cORk10LDSRc9ObRk+ZhJNvXqhtebL3l14M4thRLd5xz6sXvRz3H637drHjImjbfpcxv3iQ6nyH5Mlaxb8jq7llwlzSZfejdadmwGwc/Ne1i7bBEAO9+x4Tx7C5+0MCzpNmjuGzFkzE/U8irFDJ/LkhWU+beGD0sWp3aImF89eYu42w3KYc8bPZ/OKrQyeNIAFO+bw/HkU3/c3dFyzuWdj4ISvGNJhOAAjpw+jhMdHZM6amVWHl7Fg0iI2L99q0+dQvPT71GzhyaWzl5i5dQYA88cv4NDuw1RtVCXRVJds7ln56ocvGd5xpE1zJmXyrDGUqfAJb2bNgv+JTfz0w2x+X7KO+k1rsfGFL2853bMzZspIurf5gujoaEYPncC8ldNwdnLm92XrCT1/yeb5Z86bRPmKpcma7U2OndnDhO+n8eDBI8b+MIJs2bOyZOVMTp08R+tm3XDPlZPJ076lXcseREdHM3TAtyxfMw9nZyeWLV7N+XOOO/VICJtLI+ukK3MrXLySnStVEdgHnARiX9FhGOalrwTexrDSSiut9X2lVC4gGMhkrP8nUExr/VgplR64ChTUWpsdulJK1QOmAM7AfK31GGP5BKABhr8e/KK1TnZ41MfHR3///byXf+IOYOjQrgBsnLXTzklSrkEPw2oGc6a+1Owmh9H9i5YADOvjZeckKTP2598AWPNL6p3326xXbQB2z9ln5yQpU617JQD2zbXtSkOvSqVu5QFYMn2DnZOkXLvPGwIwY7K5U6EcW++v2gDg7W3fQYiU8vHxAVJvfkj9z8HHxwdvb2/7rTpgwtORraw+HyX9tyvt/pytOpKutd4PmHuSidaT0lpHYJiiYmpfTzGs0pLcMTcDm02UDwQGJm4hhBBCCCFSDQeYM66Umo9h8Pe2cXlvlFJZgRVAfuAKhkHoB8aZJVOBehiWEe8Uu/phUmy2uosQQgghhBD/EQuBOi+UmbsOUF2gsPH2GfCLJQew6VU3hRBCCCGE+De0A6xjrrX2Ny4vHl9joKrx/iJgD4bzIBsDvxpXGwxSSmVRSuWOtxy5STKSLoQQQgghxL9n7jpA5q7jkyQZSRdCCCGEEKmHDeakK6U+wzA1JdZsrfVsS9trrbVS6l8FlU66EEIIIYQQ8Rg75BZ3yo1uxU5jeeE6QGav45MUme4ihBBCCCFSD8e94mjsdYAg4XWA1gMdlEE54FFy89FBRtKFEEIIIURq4gAXM1JKLcNwkmh2pdQ1wBvDRTpXKqW6YrwOkLH6ZgzLL4ZiWIKxsyXHkE66EEIIIYQQL0Fr3cbMJlPXAdJAn5c9hnTShRBCCCFE6uEAFzOyBZmTLoQQQgghhIORkXQhhBBCCJFqaBlJF0IIIYQQQtiDMsxlF6b4+PjIiyOEEEKINM3b21vZO0N8T/o1sHr/LONPG+3+nGUkXQghhBBCCAcjc9KTMWbsHHtHSJHhw7oDMGPyMjsnSbneXxlWN/ph/AI7J0mZQYMNy6BOGL/QvkFSaODgTkDqff3hv/MejBs3375BUmjIkC5A6n394Z/3wNvb275BUsjHxweQ/PaU2p9DbH6HEmP/ddJtQUbShRBCCCGEcDAyki6EEEIIIVIPWd1FCCGEEEIIYQ8yki6EEEIIIVIPGUkXQgghhBBC2IOMpAshhBBCiFQjrVzjR0bShRBCCCGEcDAyki6EEEIIIVIPmZMuhBBCCCGEsAcZSRdCCCGEEKmHjKQLIYQQQggh7EFG0oUQQgghRKqhZSRdCCGEEEIIYQ/SSbeBC+cDOXpkB4cPbSMwYJPJOpMnj+bMmf0cCd5OiRLFbZwwoSnTx3A69AB7A9fHlTVsUpu9QRu4+eAMH31sOt/rr7/G1l0r2bV/LXuDNjBwaF9bRU5W5syZWLxkBkeP7eTI0R2UKVMywfZMmTKy6ve5BAVt4XCwH15eLe2UNLHChQsSELQp7nYj4g969+lssm7JTz7k4eMQmjSpa+OU5hUuXJDAoM1xt5sRJ+nTp0uCOkWKvMuu3Wu4/+A8X3zR3U5JzbPkPajfoCZBB7cQELQJ//3r8PAoZae0pmXOnImlS3/h+PGdHDu2k7JlSyaqU6lSOYKCNnPkyHb8/FbYIaVplrz+lSqV5frNE3F1hjjQvz9CiFcsRlv/5gCsOt1FKZUP+BVwBzQwW2s9VSmVFVgB5AeuAK201g+UUu8BC4CSwHCt9UTjfooa68cqCIzSWk8xccz5QAPgtta6eLxyk8d8lc83KTVrteTePdOHq1OnOoUKFaBYsYqUKVOS6dO+p2KlhraKlsjypb7Mm7OE6TPHxZWdOxNCl/b9mDDFx2y7v//+P5o17ETk00hcXFzYsG0Ju7b7cyT4hC1iJ2nCBG+2b99L+3a9cXV1xc0tXYLtn/Xw4tzZUFq26Eb27Fk5dnwXy5ev5fnz53ZK/I+QkEuUL1cfACcnJ0IuBrFhvV+iek5OTnz77WB27txn64hJCgm5hEe5eoAhY+jFg6xfvy1BnQcPHjJgwDc0bFjLHhGTZcl7sGf3ATZt3A7A+8Xf47ffplPyY0+bZzVn4kRv/Pz20rZtL5OfgcyZMzF16nc0btyB8PAb5MiRzU5JE7P0MxAQcJiWzbvZOp4QwtZi7B3ANqw9kh4FfK21LgaUA/oopYoBQ4CdWuvCwE7jY4D7QD9gYvydaK3Pa61LaK1LAJ8AkYCvmWMuBOqYKDd3TLtr2LAWSxb/DsChQ0fJkiUTuXLltFueoIBgHj54lKAs5MIlLoZeTrZt5NNIAFxdXXBxdXGIq4JlypSRChXLsGih4Xve8+fPefToccJKGjJkTA9A+vRuPHjwkKioKFtHTVbVahW4dCmM8PDribb17NWRdeu2cuf2PTsks0w1M/nv3LnH0SN/8Py5473mLzL3Hjw1/u4DpHdL5xC/+7EyZcpIxYplWbhwOWD6M/Dpp41Zt24r4eE3AMN74oiS+gwIIcR/iVU76Vrrm1rro8b7T4CzQF6gMbDIWG0R0MRY57bW+jCQ1PBlDeCi1jrMzDH9MXT2X2TymLag0WzetJSgwM107dou0fY8eXIRfu1G3ONr12+SJ08uW8V7pZycnNi5z9cwXWZ3AEeP/GHvSOTPn4+7d+8xa9ZEAgI38fOMcYlGEWfOXETRooW4eOkQhw5vY+BAH4fqZMVq0bIBv6/akKg8dx53GjWqzZzZi+2QynItWjZk1ar1yVd0YObeA4CGjWpx9NgOfl8zn149B9k4mXmxn4HZsycSGLiZGTPGJ/oMFC5cgCxZMrNt23IOHNhI27bN7JQ2aUm9/mXKlCQwaDNr1i7gf/8rbONkQghb0THa6jdHYLM56Uqp/MDHwEHAXWt907gpAsN0GEu1BpalIIJFx1RKfaaUClZKBQcHB6fgMIlVq9aMsuXq0rCRF716dqRixbKvZL+OKCYmhhqVmlKiWFVKlvyQ9xzgP0pnF2dKlCjOnLmLKe9Rn8inz/h6QK8EdTw9K3PyjzO8W7AMHuXqMXnyaDJmzGCnxKa5urpSv54nvms2J9r2ww+jGDlinEN+sYjl6upKPTP5U4uk3gOADev9KPmxJ20+7cHIUV/ZOJ15LrGfgTmL8fCoR2RkJAMG9H6hjgslSxanadPONGrkxdCh/ShUqICdEpuW1Ot//Phpir1XEY9y9Zj5yyKWrZhlh4RCCPHq2KSTrpTKAKwG+mutE/yNVRt6FRb1LJRSrwGNgFX/Jk9Sx9Raz9Zal9JalypV6tWc+HXjRgRg+PPxunVbKV26RKLt+d7KE/f4rby549qkVo8fPWH/voNU86xk7yjcuB7B9esRBB8+DoCv7+ZEJ+d6dWjJunVbAbh0KYywK+EUKfquzbMmpVbtqhw/fprbt+8m2vZxyQ9Y+Os0Tp/dR5OmdflxymgaNKxph5Tm1apdlRPHT5nMn1ok9R7Ed+DAIfIXeJts2d60UbKkXb8ewfXrNzmcxGfg+vWbbN/uT2TkM+7de8D+/Yf48MP/2SOuWUm9/k+e/Bk35chv2x5cXV0d5vUXQrxiaeTEUat30pVSrhg66Eu01muMxbeUUrmN23MDty3cXV3gqNb6lrFtPqXUceOtZzJtU3rMf8XNLR0ZMqSPu+/pWZnTp88nqLNxox/t2rcADH+uffToCRERNon3SmXL9iaZMmcE4I03XqdKtfKEXrhk51Rw69Ydrl27QeHCBQHDnNZzZ0MS1AkPv0HVahUAyJkzO4WLFOTK5as2z5qUlklMFSlerDLv/68S7/+vEmt9t/Bl/1Fs3LDdxgmT1rJlI1aZmaaQWiT1HhQs+E7c/Y9KvM/rr79m9mRxWzN8Bm7+8xmoWoFz5xJ+BjZs2E758qVxdnYmXbo3KF26BOfOhdojrllJvf453bPH3f+k1Ec4OSmHef2FECIlrL26iwLmAWe11pPjbVoPdATGGX+us3CXbYg31UVrHQ6UMF89gZQe819xd8/BqpVzAcOfnJcvX4uf3x66d28PwJw5i9myZRd16lTn7Nn9PIv8i27d7ftn8pnzJlG+YmmyZnuTY2f2MOH7aTx48IixP4wgW/asLFk5k1Mnz9G6WTfcc+Vk8rRvadeyB+65cvDTzHE4Oznj5KRY57uV7dv22PW5xBrw9TfMXzCF11xduXwlnJ49BtC1m+H8gHlzlzBu3E/MnjWRQ4e2opRi5IhxDvUfvJtbOqpVr0i/vsPjyrp2awvAvLlL7RXLYm5u6ahevSL9+g6LK4v/+ru752Df/vVkzJiBmBhNn8+78EnJmjx58qe9IieS3HvQuEkd2rZtxvOoKJ49+4uOXo61BOBXX3mzYMFUXnvNlStXrvLZZwPoZnwP5s5dwvnzoWzfvpfDh7cRExPDwoXLOXPmgp1T/yO5179p03p069aOqKhonv31F5069LNXVCGEtaWR1V2sfcXRCoAXcFIpddxYNgxDR3mlUqorEAa0AlBK5QKCgUxAjFKqP1BMa/1YKZUeqAn0SOqASqllQFUgu1LqGuCttZ5n7pjWdvnyVUqVTrys3Jw5CU/w++KLEbaIY5GeXb82Wb5l445EZbcibtOupeEtOXP6Ap6VHPNksz/+OEOlio0SlM2buyTufsTN2zRq1MHWsSwWGfmMd/IlXNfaXOe8Z4+Btoj0UiIjn/F2vo8TlMV//W/dukORwh62jvVSknsPfpw8ix8nO+486D/+OEPFigmXdp0b7z0A+PHHWfz4o2M+h+Re/1kzf2XWzF9tHUsIIazGqp10rfV+QJnZXMNE/QjgLTP7egoku3Cv1rqNmfJ7po4phBBCCCFSD0dZfcXa5IqjQgghhBBCOBhrT3cRQgghhBDi1Ukjc9JlJF0IIYQQQggHIyPpQgghhBAi1ZA56UIIIYQQQgi7kJF0IYQQQgiResicdCGEEEIIIYQ9yEi6EEIIIYRINbSMpAshhBBCCCHsQUbShRBCCCFE6iEj6UIIIYQQQgh7kJF0IYQQQgiRaqSVOelK67SxIHxK+Pj4yIsjhBBCiDTN29tb2TtDfHdrV7F6/yz7tr12f84y3UUIIYQQQggHI9NdkjH2+7n2jpAiw4Z2A2DC+IX2DfIvDBzcCYAfxi+wb5AUGjS4MwCzp6y0c5KU+ax/K+C/8Ts0Zuwc+wZJoeHDugOp9z2Iff29vb3tG+Rf8PHxAVLvc5D89pfan0NsfkeSVqa7yEi6EEIIIYQQDkZG0oUQQgghRKohI+lCCCGEEEIIu5CRdCGEEEIIkWrISLoQQgghhBDCLmQkXQghhBBCpB7a7kuY24SMpAshhBBCCOFgZCRdCCGEEEKkGjInXQghhBBCCGEXMpIuhBBCCCFSDR0jc9KFEEIIIYQQdiAj6UIIIYQQItWQOenilTl/PoAjwds5dHArAQc2JdreunUTgg/7cSR4O3t2+/LBB/+gOjrGAAAgAElEQVSzQ0rTChcuSEDQprjbjYg/6N2nc4I69RvUJOjgFgKCNuG/fx0eHqXslNa8zJkzsXjJDI4e28mRozsoU6Zkgu2ZMmVk1e9zCQrawuFgP7y8WtopqcGkad9y4oI/OwPWxpWNGP01ew9uYPv+Ncz9bSqZMmU02TbohB87Dvji57+azbtW2CqyWZb8DlWqVJbrN0/E1RkytK+d0prn5OTEoYNb8fVdaHJ7i+YNOHF8F8eP7eTXRdNtGy4ZlrwHYHgfAoI2cTh4G1u3LbdDUiGEELEsGklXSlUAvgHeMbZRgNZaF0ymXT7gV8Ad0MBsrfVUpVRWYAWQH7gCtNJaP1BKvQcsAEoCw7XWE437KWqsH6sgMEprPcXEMecDDYDbWuvi8cpbGp/D/4AyWutgS577q1Krdivu3XtgctuVK+F41mzJw4ePqF2rKjN+Hk+lyo1sGc+skJBLlC9XHzB0UkIuBrFhvV+COnt2H2DTxu0AvF/8PX77bTolP/a0edakTJjgzfbte2nfrjeurq64uaVLsP2zHl6cOxtKyxbdyJ49K8eO72L58rU8f/7cLnlXLlvLgjlLmTrz+7gy/92BfO8zhejoaIZ98xWff9Wdsd9MNtm+ZcPOPLj/0FZxk2TJ7xBAQMBhWjbvZut4FuvbtyvnzoWSMVOGRNsKFSrAoEGfU6VqUx4+fESOHNnskNA8S96DzJkz8uOUb2nSuBPXrt1wuOcghBCxtKyTnsA8YDJQESgNlDL+TE4U8LXWuhhQDuijlCoGDAF2aq0LAzuNjwHuA/2AifF3orU+r7UuobUuAXwCRAK+Zo65EKhjovwU0AzwtyC3TQUFHeHhw0cAHDx0jLx5c9s5kWlVq1Xg0qUwwsOvJyh/+jQy7n56t3RorW0dLUmZMmWkQsUyLFpo+J73/PlzHj16nLCShgwZ0wOQPr0bDx48JCoqytZR4xwMOMLDB48SlPnvDiA6OhqAo4dPkDuPuz2i/SvmfoccXd68ualbtwbzFyw1ub1rl7b8MnNR3Of4zp17toz3Usy9B60+bcz69du4du0G4NjPQQgh0gJLO+mPtNZbtNa3tdb3Ym/JNdJa39RaHzXefwKcBfICjYFFxmqLgCbGOre11oeBpIYvawAXtdZhZo7pj6Gz/2L5Wa31+eQyW4XWbNq4hMCATXTt2jbJqp07tWab324bBXs5LVo24PdVG0xua9ioFkeP7eD3NfPp1XOQjZMlLX/+fNy9e49ZsyYSELiJn2eMSzSSPnPmIooWLcTFS4c4dHgbAwf6ONyXjfhat2/G7h37TG7TWrNszRy27F5Ju472nbbzoqR+h8qUKUlg0GbWrF3A//5X2MbJkjZp4jcMHTqGmBjTvxOFCxegSOGC7Nntyz7/9dSqVdW2AV+CufegUKECZMmSmS1bl7HvwHratG1mh3RCCJE8HWP9myNIspOulCqplCoJ7FZKTVBKecSWGcstppTKD3wMHATctdY3jZsiMEyHsVRrYNnLHNveqlVvTjmPejRq3IGePTpSsWJZk/WqVPGgU6dPGT58rI0TJs/V1ZX69TzxXbPZ5PYN6/0o+bEnbT7twchRX9k4XdKcXZwpUaI4c+YuprxHfSKfPuPrAb0S1PH0rMzJP87wbsEyeJSrx+TJo8mYMfG0BkfQ7+vPiIqKYs3KjSa3N63rRZ2qLWnfsiedurWhbPlPbJzQtKR+h44fP02x9yriUa4eM39ZxLIVs+yQ0LR69Wpw+85djh07abaOs4sLhQoVwLNmS7w69OGXGT+QOXMmG6a0TFLvgYuLCyU+Lk7zZl1o0qgjg4d8TqFCBeyQUgghBCQ/kj7JeCuLYYrL2HhlE5Nol4BSKgOwGuivtU4wz0AbhistGrJUSr0GNAJWWXrsl6WU+kwpFayUCg4OfjXT1m/ciAAMfz5et34rpUuVSFSnePH3mPnLBFq06Mp9B5lLHF+t2lU5fvw0t2/fTbLegQOHyF/gbbJle9NGyZJ343oE169HEHz4OAC+vpspUaJ4gjpeHVqybt1WAC5dCiPsSjhFir5r86zJadWmCZ61qvD5Z4PN1om4eRuAe3fvs2XjDkqU/MBW8ZKU1O/Qkyd/xk2b8tu2B1dXV4f5HSrvUZoG9Wtx4Xwgi3/7mWpVK7BwwU8J6ly/fpMNG/2IioriypVwQkIvOWQHN6n34Pr1m+zc4U9k5DPu3XvAgQOHHOokdiGEiKVjlNVvjiDJTrrWuprWuhrQNfZ+vDKLzvBSSrli6KAv0VqvMRbfUkrlNm7PDdy2MG9d4KjW+paxbT6l1HHjraeF+0iS1nq21rqU1rpUqVL/fpUSN7d0ZMiQPu6+Z43KnD6dcNZNvnx5WLliDp27fEFI6OV/fUxraNmyIatWrTe5rWDBd+Luf1TifV5//TWzJ8naw61bd7h27QaFCxvOc65arQLnzoYkqBMefoOq1SoAkDNndgoXKciVy1dtnjUpVWtUpFe/LnRq+zl/PfvLZJ10bulIn8Et7n6V6uU5fzbUljHNSup3KKd79rj7n5T6CCcn5TC/QyNGjqPgu6UpUtSD9l592L3nAJ0690tQZ/36bVSp7AFAtmxvUrhQQS5fNjkjz66Seg82bdyOh0cpnJ2dSZfuDUqXKsH5847xuyOEEPFpbf2bI7B0nfTfMay4Et8qDCdxmqWUUhhOOj2rtY6/DMV6oCMwzvhznYU52hBvqovWOhxIPCztQNzdc7ByxRwAXFycWb5iHX7b99C9W3sA5sxdzLBh/cmaNQs/TR0DQFRUNOUr1Ldb5he5uaWjWvWK9Os7PK6sazfD3Pp5c5fSuEkd2rZtxvOoKJ49+4uOXo63fN6Ar79h/oIpvObqyuUr4fTsMYCu3doBMG/uEsaN+4nZsyZy6NBWlFKMHDHOrp3En+dOwKNCabJmy0LwqZ1MHPczn3/Znddfd2W571wAjgafYMhXo3HPlYMJP42mQ6te5MiRjXmLDaO8zs7OrF29iT0799vtecRK7neoadN6dOvWjqioaJ799RedOvQztyuH4T1qAEeOnmDjxu34+e3B07MyJ47vIjo6hqFDv3O4v4gl9x6cP3+R7dv9OXhoCzExMSxcuIIzZy7YK64QQqR5SXbSjUsivg9kVkrFP4soE/CGBfuvAHgBJ5VSx41lwzB0zlcqpboCYUAr4/FyAcHG/ccopfoDxbTWj5VS6YGaQI9kMi8DqgLZlVLXAG+t9TylVFNgGpAD2KSUOq61rm3Bc/hXLl++SukyiQ8zZ+7iuPu9eg2iVy/HOtkyvsjIZ7yTL+F3tHlz/1nl4sfJs/hxsuPMITbljz/OUKliwmUt581dEnc/4uZtGjXqYOtYZvXpNjBR2fLFa0zUhFsRd+jQyjDH/mrYNWpWcrwT/pL7HZo181dmzfzV1rFemr9/IP7+gQD4jE4442/QoNEMGjTaHrEsktx7ADB1ymymTplty1hCCPHSHGU6irUlN5JeFMOa41mAhvHKnwDdk9u51no/hjXVTalhon4E8JaZfT0Fkl24V2vdxky5L+aXbRRCCCGEEMJhJNlJ11qvA9YppTy01oE2yiSEEEIIIYRJMpKeUKhSahiGK4TGtdFad7FGKCGEEEIIIdIySzvp64B9wA4g2npxhBBCCCGEMM9RVl+xNks76W5aa/MLMwshhBBCCCFeGUs76RuVUvW01qYvNymEEEIIIYQNpJU56cldcTTWFxg66n8ppZ4Yb4+TbSWEEEIIIYR4aRaNpGutM1o7iBBCCCGEEMnROm2MpFs63QWlVCOgsvHhHq31RutEEkIIIYQQIm2zqJOulBoHlAZiL9H4hVKqgtZ6qNWSCSGEEEII8QIdY+8EtmHpSHo9oITWhpdFKbUIOAZIJ10IIYQQQohXzOLpLkAW4L7xfmYrZBFCCCGEECJJMTInPYHvgWNKqd2AwjA3fYjVUgkhhBBCCJGGKW3hZZuUUrkxzEsHOKS1jrBaKgfh4+OTRq5pJYQQQghhmre3t0MNXZ9/r67V+2dFz22x+3O2dJ10gBzGny5AeaVUMyvkEUIIIYQQIs2zdHWX+cCHwGkg9pxaDayxUi6HMWbsHHtHSJHhw7oDMOmHRXZOknJfD+oIQMSC03ZOkjK5Or8PgNP8C3ZOkjIxXYoAsG9ugJ2TpFylbuUB2Dp7j32DpFCdz6oC4O3tbd8gKeTj4wOk3vyQ+p+D5Le/1P4cYvM7krRyxVFL56SX01oXs2oSIYQQQgghUgGlVFFgRbyigsAoDAutdAfuGMuHaa03p+QYlnbSA5VSxbTWZ1JyECGEEEIIIV4FC0+ntHIGfR4oAaCUcgauA75AZ+BHrfXEf3sMSzvpv2LoqEcAf2NY4UVrrT/8twGEEEIIIYSwlANOd6kBXNRahyn16rJZ2kmfB3gBJ/lnTroQQgghhBD/OUqpz4DP4hXN1lrPNlO9NbAs3uPPlVIdgGDga631g5RksLSTfkdrvT4lBxBCCCGEEOJVscXFjIwdcnOd8jhKqdeARsBQY9EvwLcYFlj5FpgEdElJBks76ceUUkuBDRimuwCgtf7Pr+4ihBBCCCGEGXWBo1rrWwCxPwGUUnOAjSndsaWd9HQYOue14pWliSUYhRBCCCGE49A2GEl/CW2IN9VFKZVba33T+LApcCqlO7aok6617pzUdqXUUK319ykNIYQQQgghRGqilEoP1AR6xCv+QSlVAsNg9pUXtr0US0fSk9MSkE66EEIIIYSwKkdYghFAa/0UyPZCmder2r/TK9qPQ/3dQQghhBBCiNTsVY2kO8h3GiGEEEII8V9mi9VdHIGMpAshhBBCCOFgku2kK6WclVJfJlNt1SvKI4QQQgghhFlaK6vfHEGy01201tFKqTbAj0nUGftKU/0HOTk5ERS4mes3ImjatFOCbRMmeFO1SnkA3NzSkSNHNnK6v2+HlIkVKlyAhb9Oi3ucP38+xn43hRk/L4gry5QpI3PmTeatfHlwcXbmp5/msuS33+0RFwCX1135eoUPLq+74OTszLEtQWz8cRVFPd6n2XAvXFxduHrqMr8N+oWYaNMX0H0jQzpGbZ/MCb/DrPCeb+NnYKCcFN02fsfjiAes6DKR/BXex3NYG5Ry4v8i/2L917N4EHYrQZt0WTLQYuYX5PmwICd+92frqEV2yZ4jdw4GTxnIm9mzoDVsWroZ3/lrGTFjGG8VfAuADJnS8+fjp/Ss09vkPpycnJixaRp3I+4xovMoW8YnZ54cjJo6lKzZ30RrWLdkIyvnreazgZ2pVKsCMVrz4O4DvvtyPHdv3UvUfv/VHVw8dxmAW9dvMajzCJvmF0IIkfpZOif9gFJqOrACeBpbqLU+mlQjpVQ+4FfAHcO89dla66lKqazGfeXHsDxNK631A6XUe8ACoCQwXGs90bifosb6sQoCo7TWU0wccz7QALittS4er3wC0BD4P+Ai0Flr/dDC5/+v9e3blXPnQsmYKUOibQMH+sTd7927MyU+cowOOkBoyGUqejQADJ2m86GBbFi/LUGd7p95ce5cKJ+27E627Fk5emwHK5ev4/nz5/aITNTfz5nS1oe/I//GycWZAb+P5szeE3SY1Iep7b7l9uWbNPiyFeWaVyFg5W6T+2j49aeEHjpr4+QJlelSh7uhN3gtQzoA6n3XmZXdJ3M39AafeHlSqW8T1g+YlaBN1N/P2TNxFTmK5iNn0bfsERuA6OhoZn47m9BToaRLn45fNk/nyL6jfNf7n+/zPUZ+xtPHT83uo2nXJlwNDcctg5stIicQHRXNTz6/cOFUCG7p07Fg6ywO+Qez+JcVzJ5g+ILaskszunzZgR+GJB6/+Puv/6Njre62ji2EEGmCo6zuYm2WzkkvAbwPjMZwedNJwEQL2kUBX2utiwHlgD5KqWLAEGCn1rowsNP4GOA+0O/FfWutz2utS2itSwCfAJGAr5ljLgTqmCjfDhTXWn8IXOCfy7daXd68ualbtwbzFyxNtu6nrRqzYuU6G6R6eVWrlefypTDCw28kKNdoMmZID0CG9G48ePCQqKgoe0SM83ek4cK4zi7OOLs4ExMTQ/TzKG5fNlxf4Nz+P/i4blmTbd8uXoBM2TNzZt8Jm+V9UcZcWSlcvQTHlsf7EqF1XIf9jYxuPLn1IFG758/+Jjz4AlF/2+cLUqz7t+8TeioUgGdPn3E1NJzsubInqFOlQWV2rzP9JSl7ruyUrV6Gzcu2WD2rKfdu3+fCqRAAIp8+40rIVXLkyk7kn5FxddK5vYFOK/9TCCGEsDlLL2ZULSU7N15x6abx/hOl1FkgL9AYqGqstgjYAwzWWt8Gbiul6iex2xrARa11mJlj+iul8pso94v3MAho8TLP5d+YNPEbhg4dQ8aMiUfR43v77bzkz5+P3bsP2CjZy2neoiG/r9qQqHz2zF9ZvnI2Fy4GkSFDejp36Gf3zotyUgzdOJ4c7+Ri72/buHI8FCdnZ97+oCBXT17i43rleDN39sTtlKL5iA4s6D+N9yp+YIfkBrW9vdgxdhmvGzvlABsGz6XNwoFE/fWcv/98xvwm3nbL9zLc33Kn0Pvvcu7YubiyD8oW58HdB1y/csNkm97f9GTO2Lm4pbf9KPqLcr3lTpHihTh9zPCXlR6Du1K3RS3+fPyUz1uaPl3ntddfY/7mmURHR/Pb9KX4b3PMz7QQQqRGsrpLPEopd6XUPKXUFuPjYkqpri9zIGPH+WPgIOAe75KpERimw1iqNfEuv5pCXQCTQ3RKqc+UUsFKqeDg4OB/eRioV68Gt+/c5dixk8nWbdWyMWt8NxMTY3qetD25urpSr14NfH0Tv2w1PCtz8uRZirxbjooeDZgw+Ztkv5BYm47RjK03iGEePcn/0bvkKZKPef2m0HJkRwavHcvffz4z+TpX9qrFqd3HeBhx3w6pDQpX/5in9x4RcepKgvJy3eqyrNMEppbry4lVe6k1sp19Ar6EN9zewHvWSGZ8MzPBKHT1xtXYvW6PyTZla5Tl4b2HhJwMtVFK89K5vcH3c0YzxfvnuPyzxs+jSelP8fPdQYvOTU22a1a2NV3q9cS7z3f09/mcvO/ksWVsIYQQ/wGWTndZCGwDYv+nuQD0t/QgSqkMwGqgv9b6cfxt2jDkatGwq1LqNaAR/2I1GaXUcAzTcJaY2q61nq21LqW1LlWqVKmUHiZOeY/SNKhfiwvnA1n8289Uq1qBhQt+Mlm3VatGrFix9l8f0xpq1qrCiROnuXP7bqJt7b1asH6dYZ76pUthhIWFU6RIQVtHNOnZ40guBJ6mWJUSXD4awqRW3oxvMoyQQ2e5felmovoFSxahaoc6fLd/Os2HeVG2WWWaDG5r08z5ShWhiOcn9N0/hWbTPqdA+WK0XjCAnP97mxvHLwJwekMQb31SxKa5XpazizPfzB7JzrW72L/1n5FkJ2cnKtapwJ71e022K16qGB41y7E4YBHDfx5KiQofMWTqIFvFjuPs4szYOaPZ5ruDvVv2Jdq+bc0OqtarbLLtnQjD5+TG1ZscDTxOkeKFrJpVCCHSkrSyuoulnfTsWuuVQAyA1joKiLakoVLKFUMHfYnWeo2x+JZSKrdxe27gtoU56gJHtda3jG3zKaWOG289LcjSCcNJpe20jeZjjBg5joLvlqZIUQ/ae/Vh954DdOrcL1G9okXfJUuWzAQFHbFFrJfWsmVDVpmY6gIQHn6DqlUNq9PkyJmdwoULcvlKuC3jJZAha0bSZTJMk3B93ZX/VfyQiIvXyZgtEwAur7lQq2dj9i3xS9R2Qf9pDK/QmxEVP2f12N84uMafteOTP5fgVdr1wwqmluvLtIr9WdN3OpcDzrCi22TeyOhG1gK5AChYqTh3Q6/bNNfLGjDhK8JCwlk9Z02C8k8qleTqxXDuRiT+wgcwb/wC2pRpT/vyHRnT53uOHzjBuC9+sEXkBIZPGkRYaBjLZ/8zJvBWgbxx9yvVrkDYxauJ2mXMnAHX11wByPxmJj4sXZzLF0zOzhNCCCHMsnR1l6dKqWwYR7yVUuWAR8k1UkopYB5wVms9Od6m9UBHYJzxp6VnSrYh3lQXrXU4hpNak6WUqgMMAqporSOTq29t3qMGcOToCTZu3A4YprqsWrXezqlMc3NLR7XqFfmi3z/LyHXpahhdnj9vKT+Mm8bM2RMIPLQFpcB75Hju30t8UqOtZM75Jh0n9UE5OeHkpDiyKZBTu47SbGh7itcoiZNywn+JH+cDTwPw9gcFqdyuJouHzEpmz/ajo2PYOGQuLWf2R8fE8OzRUzYMnA1AEc+S5P6wAHsnrwag7/4pvJ4xHc6uLhStVYolXuO4G2LbDn3x0u9Ts4Unl85eYubWGQDMH7+AQ7sPU7VRlURTXbK5Z+WrH75keMeRNs1pzoeli1O3RS1Cz1xkkd8cAGaOm0vD1vV4+9186JgYIq7filvZ5b0Pi9DUqxHfD5xI/sLvMHjcV8RojZNS/DZ9GVdCpJMuhBCvSlqZk25pJ/0rDB3rd5VSB4AcWHbiZQXACziplDpuLBuGoXO+0jivPQxoBaCUygUEA5mAGKVUf6CY1vqxUio9UBPokdQBlVLLMJyUml0pdQ3w1lrPA6YDrwPbDd8dCNJaJzv6/ir5+wfi7x8IgM/ohIvjfPvdZFNNHEJk5DPyv/1JgrL58/4ZXY6IuE2TRh1tHcus6+euMrb+4ETla75fzJrvFycqv3rykskOetDvewn63fSUDFsJCzpLWJDhhMXz24I5vy3xeRIXdhzlwo5/VkOdVtHimWhWc+rwaTzz1Ta5bcJXkxKV3bt132QH/UTQH5wI+uOV50vOH4dP4ZE38fnygbsOmqx/7o8LfD/Q8Jk+GXya9p4vdcqOEEIIkYilq7scVUpVAYoCCjivtU52jTet9X5jfVNqmKgfAZhc3Flr/RTIZsEx25gpl0mhQgghhBCpXFpZ/DbJTrpSqpmZTUWUUsSbYy6EEEIIIYR4RZIbSW9o/JkTKA/sMj6uBgQA0kkXQgghhBA2I3PSAa11ZwCllB+GueE3jY9zY1iWUQghhBBCCJtxlCUSrc3SJRjzxbv4EMAt4G0r5BFCCCGEECLNs3R1l51KqW38s/zhp8AO60QSQgghhBDCNMe7Lrt1WLq6y+dKqaZA7OX1Zmutfa0XSwghhBBCiLQr2U66UsoZ2KG1rgZIx1wIIYQQQtiNNru6939LsnPStdbRGC4slNkGeYQQQgghhEjzLJ2T/ieGq4ZuB57GFmqt+1kllRBCCCGEECbEpJGrGVnaSV+DrIkuhBBCCCGETVh64ugipdRrQBFj0Xmt9XPrxRJCCCGEECKxmDQyJ92iTrpSqiqwCLgCKCCfUqqj1trfetGEEEIIIYRIm5TWyU/sUUodAdpqrc8bHxcBlmmtP7FyPrvy8fFJI7OehBBCCCFM8/b2dqih653un1q9f1bj1gq7P2dLrzjqGttBB9BaXwBcrRNJCCGEEEKItM3SE0eDlVJzgcXGx+2AYOtEcizfjZlt7wgpMmL4ZwBMmfCbnZOkXP+BXgDM/HGFnZOkTM8vPwVgxuRlydR0TL2/agPAmLFz7Jwk5YYP6w6At7e3nZOkjI+PDyD57Sm1PwfJb3+p/TnE5nckcsXRhHoBfYDYJRf3ATOskkgIIYQQQog0ztLVXf4GJhtviSilVmutm7/KYEIIIYQQQrxIrjj6cgq+ov0IIYQQQgiR5lk63SU5sgqKEEIIIYSwurQyJ/1VjaQLIYQQQgghXpFXNZKeNiYHCSGEEEIIu5KR9HiUUl8kUzb4lSUSQgghhBAijbN0uktHE2WdYu9orf1eSRohhBBCCCGSoFFWvzmCJKe7KKXaAG2BAkqp9fE2ZQLuWzOYEEIIIYQQL4pxjD601SU3Jz0AuAlkBybFK38C/GGtUEIIIYQQQqRlSXbStdZhQJhSyhN4prWOUUoVAd4DTtoioBBCCCGEELFiHGQ6irVZOifdH3hDKZUX8AO8gIXWCiWEEEIIIURaZmknXWmtI4FmwAytdUvgfevF+u9xcnLi8KFtrPVdlGhbxYplOXRwK88iw2jWrL4d0iWtV5/OBBzeQsChzcxd8COvv/5agu1jxg3HP2A9/gHrOXxsO1euHbVT0n9Mnv4dJ0P2sTtgXVzZyNED2HdoIzsP+DJ/8U9kypzRbHsnJyf8/Ffz6/IZtoibyJTpYzgdeoC9gf+cCtKwSW32Bm3g5oMzfPRxcZPt8uTNxZoNi/A/uJG9QRvo3tPLVpGT5eTkxKGDW/H1XZho24QJ3hw+tI3Dh7Zx+pQ/t2+dtn1AIYQQqYK2wc0RWNxJV0p5AO2ATcYyZwsa5VNK7VZKnVFKnY5dtlEplVUptV0pFWL8+aax/D2lVKBS6m+l1IAX9vWlcR+nlFLLlFJvmDlmR+N+Q5RSHeOVj1FKhSul/rTwOb9S/fp24+y5EJPbwsOv07XblyxfvtbGqZKXO7c7PXp1oHqlJpQvUw8nZ2eatWiQoM7wIWOoXL4Rlcs3YvasX9mwfpud0v5j5VJf2rb4LEGZ/+4Aqno0pkaFplwMvULfL7ubbd+9lxch5y9aO6ZZy5f60rp5wnznzoTQpX0/Ag8Em20XFRWN94jxVC7bgHqerencvR1Fir5r7bgW6du3K+fOhZrcNnCgD6XL1KZ0mdr8PGMBa9dusXE6IYQQwrFY2kn/AhgK+GqtTyulCgK7LWgXBXyttS4GlAP6KKWKAUOAnVrrwsBO42MwrBjTD5gYfyfGaTb9gFJa6+IYviC0fvFgSqmsgDdQFigDeMd+AQA2GMtsLm/e3NStW4P585eZ3B4Wdo2TJ88SE+OYy/O7uLjwRro3cHZ2xi3dG0TcvG22bosWDVm9aqMN05kWFHCEBw8eJSjbuzuA6OhoAI4GnyBPnlwm2+bO406NWlVY+ttqq+c0JyggmIcv5A+5cImLoZeTbHf71upmrOkAACAASURBVB1OnjgDwNM/nxJy/iK58rhbLael4j4DC5YmW/fTVo1ZsXJdsvWEEEKkTTE2uDmCZDvpSilnoJHWupHWejyA1vqS1v/f3r3HWTmv/x9/XR02Takck5EdITt+hOlMSgdKipBTvtXOaWsj2zGnMRTZ5bBtx9CBEkkRUo102FJT04FKJaco00ko7E2Z6/fHustMs+bYrHWvZd5Pj/vRWp/7c9/39VmVPuua6/7cfl1xx7p7jrsvCl5vA1YAqUA3YGfdxyjgnKDPRndfAGyPcroqQDUzqwKkAN9E6XMGkOnuW9z9OyATODM49zx3zyku5lh46KEMBgwYmLCT8KLk5Gzg3489x9IVs1n52Vy2bt3GjPfej9q3Xr1DOKz+ocyeNTfOUZbeRT278967/4m6794HbmPg3UOT8vcrr3qHpXLc8X9hUfaHYYfCQ0PvYcCAQeTmFv1DxMMOS6V+/XrMmDEnTpGJiIgkpmIn6e7+G3DKnl7IzOoDJwJZQJ08E+b1QJGpPndfRyS7/hWRJSF/KOQBSqnA13nerw3aQtO5c3s2bdzMosXJuRhOrdo16XxWexof15a/HNmSlJQUelzYLWrf7ud3YdLrUxJ+cnv9jVfx247feG3cmwX2tT/jNDZv2sJHQTY6WaVUT+H5Fx/jrgEP8OO2n0KNpXPndmzctJnFJfg70OOCbkyYODnh/wyJiEh4cs1iviWCkpa7LDazSWZ2mZl137mV9CJmVgN4Dejv7lvz7nP3Ymv0g5KVbsDhwCFAdTPrWdLrl4aZXWlm2WaWnZ1deO1vSbVsmUaXLh1Z/ck8xox+krZtWzFq5GPlEGl8tGnbijVfruXbzVvYsWMHb06aStPmJ0Xt2/38Lrz2asGJbyLpcck5tD/jNPpdcUvU/U2bnUTHTm2Z/1EmTz//EKe0bsbjzzwY5yj3TJUqVRj+4mO8Nu5NJr+ZGXY4tGzRhC5ndeSTVXMZ/eITtG3TipEjov8d6NGjK6+8knj3ZoiIiMRbSSfpewPfAqcDZwdblyKPCJhZVSIT9DHuPiFo3mBmdYP9dYHCi5wj2gNfuPsmd98OTABamlkzM1sSbF2BdUC9PMcdGrSVmLsPc/c0d09LS0srzaFR3XnnYA4/Io2jjm7OpT2vYcaMOfTqXWylUMJY+/U3pDVtTLVqkft0T2vTklWrCt78d9TRR1C7dk3mZy2Od4gl1rbdKfS7ri+9L+7Hf//7v6h97r/3EU4+9nSaHt+Bq/veyPuzs/j7VbfGOdI988jjA1m96jOeeWJk2KEAcOddgzmiQROObtiCnpf1Y8bMOfTuU/DvQMOGDahduxbz5i0MIUoREUkWWt0lD3fvE2X7a3HHmZkBzwMr3P3hPLsmATtXXukFFHeX2FdAczNLCc7ZLjhnlrs3DrZJwFSgo5ntG2TfOwZtCSc9/Sa6dOkAQNrJJ/DF59mcd14XnnziQZYseS/k6H63MPtDJr0+hZlz3uCD+ZOpVKkSo4a/woA7r6dT53a7+nU/vwsTxr9dxJni68nnhvDWtLE0OKo+C5e/x8WXdWfQkDupXiOFl19/nsz/TODBh9MBqHPwgYwe93TIEef39PMP8XbmWBocdTiLP57JJZedR6cu7Vn88UzSmjZmzLineXnCcwDUOfggxrz6DABNm59Ej4vP4ZTWzZn+n4lM/89E2nVoHeZQCpV+9+9/ByBS6vLqq5OKOEJERKTiKPKJo2Z2i7v/08z+TZQvFiW4ebQVkQcfLTWzJUHb7cBgYJyZ9QXWAD2C6x0MZAM1gVwz6w80cvcsMxsPLCKyYsxiYFiUeLaY2X3AgqDpXnffEpz7n8AlQIqZrQWec/d7iom/XM2ePZfZsyM3VWZk/L6ATfbCDzn8iD3P2sfK4EH/YvCgf+Vre2Bg/vcP3p9YJTzXXH5zgbaxL06I0hM2rN9Ezx5XF2if+/4C5r6/IMoRsXd13xujtr/z1rsF2jas38ilF1wFwPx5i6hT65iYxrYn8v0duDffIk7cN/DhaIeIiIjkU1HuWipykg7cCvwT+Az4rrQnd/f3odBnt7bbvcHd1xMpUYl2rnQiyysWd83hwPAo7bcA0QuRRUREREQSSHGT9A1mdgjQB2hD4RNuEREREZGYy60gs9HiJulPEXnY0BFA3ru5jEj5yxExiktEREREpMIqcpLu7v8G/m1mT7n73+IUk4iIiIhIVLkVpLCjpKu7aIIuIiIiIhInxZW7iIiIiIgkjERZxzzWSvowIxERERERiRNl0kVEREQkaWh1FxERERGRBFNRHmakchcRERERkQSjTLqIiIiIJA3dOCoiIiIiIqFQJl1EREREkkZFuXFUmXQRERERkQRj7hWlsqf0MjIy9OGIiIhIhZaenp5QuetnD+0Z8/nZFWtHhz5mZdJFRERERBKMatKLMXDQsLBDKJM777gSgIeHvBByJGX3j5v/D4BB9z8bciRlc8ftVwDwzwdHhBxJ2dxyax8A0tPTQ46k7DIyMoDkHYPiD1+yj0Hxhy/Zx7Az/kSiddJFRERERCQUyqSLiIiISNLw0KvF40OZdBERERGRBKNMuoiIiIgkDdWki4iIiIhIKJRJFxEREZGkoUy6iIiIiIiEQpl0EREREUkaFeVx8Mqki4iIiIgkGGXSRURERCRp5GqddBERERERCYMy6SIiIiKSNLS6i5SrSpUqsWD+VF6fOKrAvv7XX8mHH85g0cJMpk55hcMOSw0hwsJd068P8xa8w9z57/D8iEfZa68/FehzbvfOZGVPYd6Cd3hu+CMhRFm0SpUqMT9rChMnjiyw77DDUpky5WUWZmeSOe1VUlPrxj/AYtSqVZPRY55k0eLpLFz0Lk2bnpRvf+3aNRn78jNkZb3DrNmv06jR0SFFKiIiElu5cdgSQUwn6WZWz8xmmNnHZrbczK4P2vczs0wzWx38um/QfoyZzTWzX8zspt3OdUNwjmVmNtbM9i7kmr2C8642s15BW4qZvW1mK4NzDI7luKO57trLWbFyddR9i5cso3nzTpx0cgcmTHibBx64M87RFa5u3Tpc/bdetDn1HFo07UTlypU47/yz8/U5okF9/nHj1ZzRvgfNm3TitlsHhhRt4a69ti8rV34add+Dg+9izOjxnJzWgUH3P8LA+26Lc3TFGzIknczMWZx0YjuaN+vEqlX5x3Lzzf346KOPadasE1dcfiNDhqSHFKmIiIiUh1hn0ncAN7p7I6A50M/MGgG3AdPd/ShgevAeYAtwHTA070nMLDVoT3P344DKwEW7X8zM9gPSgWZAUyB95xcAYKi7HwOcCLQys07lOtIipKbWpVOndgwfPjbq/lmzPuC///0fAFnzF3JogmVyK1epQrVqe1O5cmWqVavG+pwN+fb37n0hzw4bzfffbwVg86ZvwwizULs+/xEvRd3/l78cxYyZcwCYOfMDzj67YzzDK1bNmvvQ6pSmjBr5CgDbt2/nhx+25utzzF+OYtbMDwD45JPPOOzPh3LQQQfEPVYREZFY8zhsJWFmX5rZUjNbYmbZQVvURHRZxHSS7u457r4oeL0NWAGkAt2AnXUfo4Bzgj4b3X0BsD3K6aoA1cysCpACfBOlzxlAprtvcffvgEzgTHf/2d1nBNf4FVgEHFpOwyzWQw9lMGDAQHJzi/8BSp/eFzNl6ow4RFUyOTkb+Pdjz7FsxX/45LO5bN26jffeez9fnwZHHs6RRx7O1MxxvPveeNq1bx1StNE9NPQeBgwYRG5u9L92H320gnPO6QzAOd06UbPmPuy3X+14hlik+vXrsXnztzzzzFA+mPs2Tzw5mJSUavn6LF26gm7dzgTg5LQTOOywVA5JPTiMcEVERCqStu7e2N3TgveFJaJLLW416WZWn0gWOwuo4+45wa71QJ2ijnX3dUSy618BOcAP7j4tStdU4Os879cGbXnjqA2cTeSDi7nOnduzaeNmFi1eWmzfSy7pzsknn8BDDz0Vh8hKpnbtmpx1VnuOP64NDY9sSUpKCj0u7JavT5UqlTniyPqc1ekS+vbpz2OP30+tWvuEFHF+nTu3Y+OmzSwu4vO/9bb7aH1qc+ZnTeHU1s1ZuzaH335LlIo0qFylMo0bH8ezz42mZYuz+Pmn/3LjTX/L1+ehoU9Rq3ZN5s6bzN+u7sWHHy4nN4HGICIiUl5yLfbbHoiaiC6LuEzSzawG8BrQ393z/Zze3Yv9yULwo4JuwOHAIUB1M+tZhjiqAGOBx9z980L6XGlm2WaWnZ2dXdpLFNCyZRpdunRk9SfzGDP6Sdq2bcWokY8V6Hf66ady223XcW733vz66697fN3y0qZtK9Z8+TXfbt7Cjh07eHPSVJo1z3/T4jffrOedt99lx44drFmzls8+/YIGDeqHE/BuWrZoQpezOvLJqrmMfvEJ2rZpxcgR+T//nJwN9LjwCpo2O5O7734QoEA5SZi+WbeedevWk71gCQATJ06mcePj8vXZtu1Hrr7qZlo078zll/+DAw7Yny+++CqMcEVERJJe3vlgsF0ZpZsD08xsYZ79pUpEFyXmk3Qzq0pkgj7G3ScEzRvMrG6wvy6wsZjTtAe+cPdN7r4dmAC0NLNmQR3QEjPrCqwD6uU57tCgbadhwGp3f7SwC7n7MHdPc/e0tLS0wrqV2J13DubwI9I46ujmXNrzGmbMmEOv3tfl69O48bE8+cRgunfvw6YEq+f++utvSGvamGrVIvfpntamJatWfZavz1tvZnLKqc0B2G//fWlw5OF88eXXBc4VhjvvGswRDZpwdMMW9LysHzNmzqF3n/yf//7774tZ5Gvzrbf8nVGjXgkj1EJt2LCJtWu/4aijjgAiX5xWrsh/E3KtWjWpWrUqAL37XMSc97PYtu3HuMcqIiISa/FY3SXvfDDYhkUJ5RR3PwnoROS+y3z1viVJRBcl1qu7GPA8sMLdH86zaxLQK3jdC3ijmFN9BTQPVmkxoF1wzqygDqixu08CpgIdzWzfIPveMWjDzAYCtYD+5TW+PZGefhNdunQAYPADd1GjRnVeHvsM2QumMWHCiJCj+93C7A954/UpzJ4zibnz36FSJWPk8Je5/c7+dOrcDoDp785my5bvyMqewluTx3D3nYP5bsv3IUdetPS7f//8T2vdkmXLZrN82WwOOuhAHhhc8CcdYbvpxnsYPuJRsrLe4fjjGzFkyBP0vfxS+l5+KQANGx7JguypLF4ynY4d23DzzRkhRywiIvLHFpRj4+4bgYlEFi0pbSK6ULF+mFEr4DJgqZktCdpuBwYD48ysL7AG6AFgZgcD2UBNINfM+gON3D3LzMYTueFzB7CYSFY8H3ffYmb3AQuCpnuDtkOBO4CVwKIga/q4uz8Xi0EXZvbsucyePReAjIzfF7A5s1OBhWoSygOD/sUDg/6Vr+3+gfl/GHHHgPu5Y8D98Qyr1PJ9/vf+/vlPmPg2Eya+HVZYJfLRRx9z6ild87U9/9yYXa/nz19E4xNOj3dYIiIicVfm1HQ5MrPqQCV33xa87gjcy++J6MGULBFdqJhO0t39faCw8vt2Ufqvp5BVV9w9ncjyisVdczgwfLe2tUXEISIiIiJSGnWAiUHitwrwkrtPMbMFRElEl0WsM+kiIiIiIuUmNwFy6cECJCdEaf+WKInosojbEowiIiIiIlIyyqSLiIiISNKoKE8BUSZdRERERCTBKJMuIiIiIkkj/Ir0+FAmXUREREQkwSiTLiIiIiJJQzXpIiIiIiISCmXSRURERCRp5FaQx1Mqky4iIiIikmCUSRcRERGRpJEITxyNB2XSRUREREQSjLlXjG8jZZGRkaEPR0RERCq09PT0hKoCv6P+JTGfnw368qXQx6xyFxERERFJGhVlCUZN0osx6P5nww6hTO64/QoAnnrk5ZAjKbu/3XARAK8++U7IkZTNBdd0AiA9PT3kSMomIyMDSN74IfnHoPjDl+xjUPzhS/Yx7Ixf4k+TdBERERFJGrpxVEREREREQqFMuoiIiIgkjYqRR1cmXUREREQk4SiTLiIiIiJJo6Ks7qJMuoiIiIhIglEmXURERESShlZ3ERERERGRUCiTLiIiIiJJo2Lk0ZVJFxERERFJOMqki4iIiEjS0OouIiIiIiISCmXSRURERCRpeAWpSlcmPU4qVarE/KwpTJw4ssC+evUOYdrUcczPmsLC7EzOPPP0+AeYxyOPD2TZ6veZ+cGkXW1ndzuDWXPf5Jstyzmh8bGFHnvlNb2YNfdNZn4wiaeeG8pee/0pHiEXcN+jdzBr+WQmzhqzq61hoyMZ/fazTJg5msdfHEr1GikFjjv4kIMYPuEJ3pg9ltdnvUTPK3rEM2wRERERIMaTdDOrZ2YzzOxjM1tuZtcH7fuZWaaZrQ5+3TdoP8bM5prZL2Z2027nuiE4xzIzG2tmexdyzV7BeVebWa887VPM7MPgHE+bWeVYjn13117bl5UrP426b8CA6xn/2ps0bXYmPXtew2P/GhTP0Ap45aXXufj8K/O1rVyxmr9edi3zPsgu9LiD6x7E5Vf15Iy259OmZVcqV67EOed1jnW4Ub3+8ttcfdEN+doyHr6dRwc+Sfc2PZk+eSZ9+vUscNyOHb8xJP0xurW+mEs6X85Ffc7niKPrxylqERERKU5uHLZEEOtM+g7gRndvBDQH+plZI+A2YLq7HwVMD94DbAGuA4bmPYmZpQbtae5+HFAZuGj3i5nZfkA60AxoCqTv/AIA9HD3E4DjgAOBC8pzoEVJTa1Lp07tGD7ipaj73Z2a++wDQM1a+5CTsyFeoUU174Nsvv/u+3xtqz/5nM8+/bLYYytXrszee+9N5cqVqVatGutzNsYoyqItnLeEH77fmq/tzw0OI3vuYgDmzppPh7PaFjhu88ZvWbF0FQA///Qzn6/+kjoHHxT7gEVERETyiOkk3d1z3H1R8HobsAJIBboBo4Juo4Bzgj4b3X0BsD3K6aoA1cysCpACfBOlzxlAprtvcffvgEzgzODcW/Oc50/EcZnNh4bew4ABg8jNjX7J++57mEsu6c7nny1g0hsv0P+Gu+IVWrlan7ORpx4fwcJl0/lo1Wy2bt3GrBkfhB3WLp+t+pzTO7UGoOPZ7Tg4tejJ9yH16vKX447mo0XL4hGeiIiIlEAuHvMtEcStJt3M6gMnAllAHXfPCXatB+oUday7ryOSXf8KyAF+cPdpUbqmAl/neb82aNsZw1RgI7ANGF9InFeaWbaZZWdnF17aUVKdO7dj46bNLF68tNA+F17YjRdeHMcRDZrQtdv/MXLEvzCzPb52vNWqVZMzO59O0xM6cMIxp5FSvRrn9Tg77LB2uav/IC7qfR6vTBtJ9RopbP91R6F9q6VU45HnH+DBux7lpx9/jmOUIiIiInGapJtZDeA1oH+ejDYA7u4Uk9UOSla6AYcDhwDVzaxgQXEx3P0MoC6wFxD17kx3H+buae6elpaWVtpLFNCyRRO6nNWRT1bNZfSLT9C2TStGjngsX58+vS9i/Pg3AcjKWsRee+/FAQfst8fXjrfWbVrw1Zp1fPvtd+zYsYPJb75Lk6Ynhh3WLl98uoYrL7yeCzv2ZvLEaXy9Zm3UflWqVObR4Q/w9mtTeXfyzPgGKSIiIkXyOGyJIOaTdDOrSmSCPsbdJwTNG8ysbrC/LpHsdlHaA1+4+yZ33w5MAFqaWTMzWxJsXYF1QL08xx0atO3i7v8D3iAy6Y+5O+8azBENmnB0wxb0vKwfM2bOoXef6/L1+errb2jb9hQAjjnmSPbeay82bfo2HuGVq7Vrczg57QSqVYvc03vqac1Z/clnIUf1u/0OiNyeYGZcdUMfxo2aGLXfvY/cweerv+SFZ8bGMzwREREpAZW7lAOL1Gw8D6xw94fz7JoE7Fx5pReRSXNRvgKam1lKcM52wTmz3L1xsE0CpgIdzWzfIPveEZhqZjXyfCmoApwFrCyvcZZF+t030aVLBwBuveVe+v71ErIXTOPFF57g8iv+EWZoPPXcUN6a9jINjqrPouUzuPiy8+jUpT2Lls/g5CaNGT3uaca+9iwAdQ4+kDHjngFg8cKPeGvSVKbNeo2ZH0zCKlXixZHjQhnDP5++lzFvP0v9Bn/m3cWT6H7J2XQ+twNvfTCON+e8wsYNm5k49i0ADqxzAE+OifzxPLHpCXTt0Zlmp6QxfvoLjJ/+Aqe2axHKGERERKTiivXDjFoBlwFLzWxJ0HY7MBgYZ2Z9gTVADwAzOxjIBmoCuWbWH2jk7llmNh5YRGTFmMXAsN0v5u5bzOw+YEHQdG/QVgeYZGZ7EfliMgN4OiYjLsLs2XOZPXsuABn3/r6AzYqVq2nT9tx4h1Oov11+U9T2d956t0DbhvWbuLTHVbveD3ngcYY88HjMYiupW66+O2r76GcLfmnYtGEz11wa+WK0eP6HHFeneUxjExERkbJLlCUSYy2mk3R3fx8o7A7IdlH6rydSohLtXOlEllcs7prDgeG7tW0AmhR3rIiIiIhIIoh1Jl1EREREpNx4gtSMx1rclmAUEREREZGSUSZdRERERJJGRalJVyZdRERERCTBKJMuIiIiIklDNekiIiIiIhIKZdJFREREJGmoJl1EREREREKhTLqIiIiIJI1cV026iIiIiIiEQJl0EREREUkaFSOPrky6iIiIiEjCMa8gdT1lkZGRoQ9HREREKrT09HQLO4a8LvnzuTGfn720ZmLoY1YmXUREREQkwagmvRiD7n827BDK5I7brwBg7BNvhRxJ2V3crwsA6enpIUdSNhkZGYDiD1Oyj0Hxhy/Zx6D4w5fsY9gZfyLRE0dFRERERCQUyqSLiIiISNKoKE8c1SRdRERERJJGrspdREREREQkDMqki4iIiEjS0I2jIiIiIiISCmXSRURERCRpVJQbR5VJFxERERFJMMqki4iIiEjScFdNuoiIiIiIhECZdBERERFJGlonXUREREREQqFMepxUqlSJeXMns+6b9Zx7bu98+4YMSafNaS0BSEmpxoEH7s9BdY4NIcqIQY/eRZsOp/Dt5u/oetpFADw87H4OP/LPANSsWYOtW3/k3NMvLXDs9Ow3+OnHn/ktN5ffduzg/I694hq7iIiI/LFVlNVdYjpJN7N6wAtAHcCBYe7+LzPbD3gFqA98CfRw9+/M7BhgBHAScIe7D81zrhuAy4PzLAX6uPv/olyzF3Bn8Hagu4/abf8k4Ah3P648x1qca6/ty8qVn7JPzRoF9t18c8au19dc04fGJ4Q3QQeY+PJbjHl+HIMf/z2uf1x5+67Xt2b0Z9vWHws9/v+6X833W36IaYwiIiIif2SxLnfZAdzo7o2A5kA/M2sE3AZMd/ejgOnBe4AtwHXA0LwnMbPUoD0tmFxXBi7a/WLB5D8daAY0BdLNbN88+7sDhc8uYyQ1tS6dOrVj+IiXiu17YY9uvDLujThEVbjseYv54futhe4/s2t73p4wNY4RiYiIiER4HP5LBDGdpLt7jrsvCl5vA1YAqUA3YGeGexRwTtBno7svALZHOV0VoJqZVQFSgG+i9DkDyHT3Le7+HZAJnAlgZjWAfwADy2l4JfbQ0HsYMGAQublF/6Yfdlgq9evXY8aMOXGKrPTSmp/It5u+Zc0XX0fd7+48P+5xXst8gR6XnRvn6ERERET+GOJWk25m9YETgSygjrvnBLvWEymHKZS7rzOzocBXwH+Bae4+LUrXVCDv7HFt0AZwH/AQ8HMZh1AmnTu3Y+OmzSxevJTWrVsU2bfHBd2YMHEyubmJW211VveOvD0x2kcfccnZV7Bx/Sb2O2Bfhr/6OJ+v/pLseYvjGKGIiIj8kWl1l3IUZLFfA/q7e746Co+sSF/kpx2UrHQDDgcOAaqbWc9SXL8x0MDdJ5ag75Vmlm1m2dnZ2SW9RKFatmhCl7M68smquYx+8QnatmnFyBGPRe3bo0dXXnnl9T2+ZqxUrlyZDme1ZfLrmYX22bh+EwBbNn/Hu5NncvxJ4dbXi4iIiCSjmE/SzawqkQn6GHefEDRvMLO6wf66wMZiTtMe+MLdN7n7dmAC0NLMmpnZkmDrCqwD6uU57tCgrQWQZmZfAu8DR5vZzGgXcvdh7p7m7mlpaWllGXI+d941mCMaNOHohi3oeVk/ZsycQ+8+1xXo17BhA2rXrsW8eQv3+Jqx0qJ1U75YvYYNOdF/u6ql7E316im7Xrdq05xPVnwWzxBFRETkD87dY74lgphO0s3MgOeBFe7+cJ5dk4Cda/P1Aoq7U/IroLmZpQTnbBecM8vdGwfbJGAq0NHM9g2y7x2Bqe7+lLsf4u71gVOAT9y9TXmNsyzS776JLl067Hrf44JuvPrqpBAj+t1DTw9k7OThHH7kn5m55C3Ou6QrAGed25G3Jua/YfSgOgfwzEuPArD/gfsz5q1neX3GGMZNGcXMzPd5f8bcuMcvIiIikuxiXZPeCrgMWGpmS4K224HBwDgz6wusAXoAmNnBQDZQE8g1s/5AI3fPMrPxwCIiK8YsBobtfjF332Jm9wELgqZ73X1LzEZXSrNnz2X27MikNePefAvYcN/Ah6MdEoobr74zavuA6zIKtG3csJmrLukPwNo16zinbcG100VERETKS+LeuVe+YjpJd/f3AStkd7so/dcTKVGJdq50IssrFnfN4cDwIvZ/CcR1jXQRERERkdLQE0dFREREJGkkyjrmsaZJuoiIiIgkDS3BKCIiIiIioVAmXURERESSRqIskRhryqSLiIiIiCQYZdJFREREJGmoJl1EREREREKhSbqIiIiIJA2Pw3/FMbN6ZjbDzD42s+Vmdn3Qfo+ZrTOzJcHWuazjVLmLiIiIiEjp7ABudPdFZrYPsNDMMoN9j7j70CKOLRFN0kVEREQkaeQmwOou7p4D5ASvt5nZCiC1PK+hchcRERERkTIys/rAiUBW0PR3M/vIzIab2b5lPa8m6SIivt6hsAAAE99JREFUIiKSNDwOm5ldaWbZebYro8ViZjWA14D+7r4VeApoADQmkml/qKzjVLmLiIiIiEge7j4MGFZUHzOrSmSCPsbdJwTHbciz/1ngrbLGYBXlqU1lkZGRoQ9HREREKrT09HQLO4a8WqWeHvP52Zx17xU5ZjMzYBSwxd3752mvG9SrY2Y3AM3c/aKyxKBMuoiIiIhI6bQCLgOWmtmSoO124GIza0ykauZL4KqyXkCT9GIMuv/ZsEMokztuvwKA9PT0kCMpu4yMDCB5x6D4w5fsY1D84Uv2MSj+8CX7GHbGn0gS4Ymj7v4+EC3bPrm8rqEbR0VEREREEowy6SIiIiKSNCrK/ZTKpIuIiIiIJBhl0kVEREQkaSRCTXo8KJMuIiIiIpJglEkXERERkaThyqSLiIiIiEgYlEkXERERkaRRUVZ30SRdRERERJKGbhwVEREREZFQKJMuIiIiIkmjopS7KJMuIiIiIpJglEmPk0qVKjFv7mTWfbOec8/tnW/fkCHptDmtJQApKdU48MD9OajOsSFEKSIiIpLYVJNeDsysnpnNMLOPzWy5mV0ftO9nZplmtjr4dd+g/Rgzm2tmv5jZTbud64bgHMvMbKyZ7V3INXsF511tZr3ytM80s1VmtiTYDorl2Hd37bV9Wbny06j7br45gyZNz6BJ0zN44skRvP76O/EMTUREREQSTKzLXXYAN7p7I6A50M/MGgG3AdPd/ShgevAeYAtwHTA070nMLDVoT3P344DKwEW7X8zM9gPSgWZAUyB95xeAwKXu3jjYNpbjOIuUmlqXTp3aMXzES8X2vbBHN14Z90YcohIRERFJPh6H/xJBTCfp7p7j7ouC19uAFUAq0A0YFXQbBZwT9Nno7guA7VFOVwWoZmZVgBTgmyh9zgAy3X2Lu38HZAJnluOQyuShofcwYMAgcnOL/k0/7LBU6tevx4wZc+IUmYiIiIgkorjdOGpm9YETgSygjrvnBLvWA3WKOtbd1xHJrn8F5AA/uPu0KF1Tga/zvF8btO00Iih1ucvMrJA4rzSzbDPLzs7OLn5gxejcuR0bN21m8eKlxfbtcUE3JkycTG5u7h5fV0REROSPKNc95lsiiMsk3cxqAK8B/d19a959HllHp8hPIyhZ6QYcDhwCVDeznqUM41J3/3/AqcF2WbRO7j7M3dPcPS0tLa2UlyioZYsmdDmrI5+smsvoF5+gbZtWjBzxWNS+PXp05ZVXXt/ja4qIiIhIcov5JN3MqhKZoI9x9wlB8wYzqxvsrwsUVx/eHvjC3Te5+3ZgAtDSzJrluRG0K7AOqJfnuEODtp3Z+J1lNy8RqVmPuTvvGswRDZpwdMMW9LysHzNmzqF3n+sK9GvYsAG1a9di3ryF8QhLREREJCmpJr0cBCUlzwMr3P3hPLsmATtXXukFFHen5FdAczNLCc7ZLjhnVp4bQScBU4GOZrZvkH3vCEw1sypmdkAQU1WgC7CsvMZZFul330SXLh12ve9xQTdefXVSiBGJiIiISKKI9TrprYiUlSw1syVB2+3AYGCcmfUF1gA9AMzsYCAbqAnkmll/oJG7Z5nZeGARkRVjFgPDdr+Yu28xs/uABUHTvUFbdSKT9apEVoZ5F3g2JiMuwuzZc5k9ey4AGffmW8CG+wY+HO0QEREREckjUWrGYy2mk3R3fx+IeoMmkWz47v3XEylRiXaudCLLKxZ3zeHA8N3afgJOLu5YEREREZFEoCeOioiIiEjSSJSa8ViL2xKMIiIiIiJSMsqki4iIiEjSqCg16cqki4iIiIgkGGXSRURERCRpqCZdRERERERCoUy6iIiIiCSNilKTrkm6iIiIiCQNlbuIiIiIiEgolEkXERERkaThnht2CHGhTLqIiIiISIJRJl1EREREkkZuBalJN68gd8iWRUZGhj4cERERqdDS09Mt7Bjy+vP+x8d8frbm249CH7My6SIiIiKSNCpKglmT9GLcN2hY2CGUyV13XAlAenp6yJGUXUZGBpC8Y1D84Uv2MSj+8CX7GBR/+JJ9DDvjl/jTJF1EREREkkZFqUnX6i4iIiIiIglGmXQRERERSRoVpSZdmXQRERERkQSjTLqIiIiIJI1cZdJFRERERCQMyqSLiIiISNJwre4iIiIiIiJhUCZdRERERJKGVncREREREZFQKJMuIiIiIklDTxwVEREREZFQKJMuIiIiIklDNelSLvbaay/mznmLhdmZfLjkPdLvvrHQvuee25kdv67j5JOOj2OEIiIiIskj1z3mWyKI6STdzOqZ2Qwz+9jMlpvZ9UH7fmaWaWarg1/3DdqPMbO5ZvaLmd2027luCM6xzMzGmtnehVyzV3De1WbWK0/7n8xsmJl9YmYrzey8WI59p19++YX2HXtwcloHTk7ryBkd29Cs6UkF+tWoUZ3r/t6XrKxF8QhLRERERBJYrDPpO4Ab3b0R0BzoZ2aNgNuA6e5+FDA9eA+wBbgOGJr3JGaWGrSnuftxQGXgot0vZmb7AelAM6ApkL7zCwBwB7DR3Y8GGgGzynOgRfnpp58BqFq1ClWqVo36Y5qMe25hyNAn+d///hevsERERESSjrvHfEsEMZ2ku3uOuy8KXm8DVgCpQDdgVNBtFHBO0Gejuy8Atkc5XRWgmplVAVKAb6L0OQPIdPct7v4dkAmcGez7K/BAcJ1cd99cDkMskUqVKpG9YBo56z5i+vTZzF+wON/+ExsfR716dZn8zvR4hSQiIiIiCSxuNelmVh84EcgC6rh7TrBrPVCnqGPdfR2R7PpXQA7wg7tPi9I1Ffg6z/u1QKqZ1Q7e32dmi8zsVTOLek0zu9LMss0sOzs7u2SDK0Zubi5pTTry58PTaJJ2Isce2zDv9Rg6JJ2bb7m3XK4lIiIi8keWi8d8SwRxmaSbWQ3gNaC/u2/Nu88jP1Mo8tMISla6AYcDhwDVzaxnKUKoAhwKfODuJwFz2a2kJk88w9w9zd3T0tLSSnGJ4v3ww1ZmzprDGR3b7GrbZ58aHHvsMUzPHM+nn8yjWbOTmDhhhG4eFREREanAYj5JN7OqRCboY9x9QtC8wczqBvvrAhuLOU174At33+Tu24EJQEsza2ZmS4KtK7AOqJfnuEODtm+Bn4PjAF4FCt69GQMHHLAftWrVBGDvvfemfbvWrFr12a79W7du4+BD/h9HHt2cI49uTlbWIs7t3oeFiz6KR3giIiIiSUU16eXAzAx4Hljh7g/n2TUJ2LnySi/gjWJO9RXQ3MxSgnO2C86Z5e6Ng20SMBXoaGb7Btn3jsDUIFv/JtAmOF874ONyGGKx6tatw7uZr7JoYSbz5r7Nu9Nn8/bkd7kn/Sa6dOkQjxBEREREJMnE+mFGrYDLgKVmtiRoux0YDIwzs77AGqAHgJkdDGQDNYFcM+sPNHL3LDMbDywismLMYmDY7hdz9y1mdh+wIGi61923BK9vBV40s0eBTUCfch9tFEuXrqBJ0zMKtN+TEbXahnYdLoh1SCIiIiJJK1HWMY+1mE7S3f19wArZ3S5K//VESlSinSudyPKKxV1zODA8SvsaoHVxx4uIiIiIhC3WmXQRERERkXLjCbL6SqzFbQlGEREREREpGWXSRURERCRpVJSadGXSRUREREQSjDLpIiIiIpI0EmUd81hTJl1EREREJMEoky4iIiIiSUOru4iIiIiISCiUSRcRERGRpKGadBERERERCYUy6SIiIiKSNCpKJl2TdBERERFJGhVjig5WUb6NlEVGRoY+HBEREanQ0tPTLewY8qryp9SYz892/Lou9DFrkh4iM7vS3YeFHUdZJXv8kPxjUPzhS/YxJHv8kPxjUPzhS/YxJHv8Ep1uHA3XlWEHsIeSPX5I/jEo/vAl+xiSPX5I/jEo/vAl+xiSPX6JQpN0EREREZEEo0m6iIiIiEiC0SQ9XMleP5bs8UPyj0Hxhy/Zx5Ds8UPyj0Hxhy/Zx5Ds8UsUunFURERERCTBKJMuIiIiIpJgNEkPiZmdaWarzOxTM7st7HhKw8yGm9lGM1sWdixlYWb1zGyGmX1sZsvN7PqwYyotM9vbzOab2YfBGDLCjqkszKyymS02s7fCjqW0zOxLM1tqZkvMLDvseMrCzGqb2XgzW2lmK8ysRdgxlZSZNQw++53bVjPrH3ZcpWFmNwR/f5eZ2Vgz2zvsmErLzK4P4l+eDJ9/tH+/zGw/M8s0s9XBr/uGGWNxChnDBcHvQa6ZpYUZn5QfTdJDYGaVgSeATkAj4GIzaxRuVKUyEjgz7CD2wA7gRndvBDQH+iXZ5w/wC3C6u58ANAbONLPmIcdUFtcDK8IOYg+0dffG7p6s/yj+C5ji7scAJ5BEvxfuvir47BsDJwM/AxNDDqvEzCwVuA5Ic/fjgMrAReFGVTpmdhxwBdCUyJ+fLmZ2ZLhRFWskBf/9ug2Y7u5HAdOD94lsJAXHsAzoDsyOezQSM5qkh6Mp8Km7f+7uvwIvA91CjqnE3H02sCXsOMrK3XPcfVHwehuRiUlquFGVjkf8GLytGmxJdYOJmR0KnAU8F3YsFZGZ1QJaA88DuPuv7v59uFGVWTvgM3dfE3YgpVQFqGZmVYAU4JuQ4ymtvwBZ7v6zu+8AZhGZKCasQv796gaMCl6PAs6Ja1ClFG0M7r7C3VeFFJLEiCbp4UgFvs7zfi1JNkn8ozCz+sCJQFa4kZReUCqyBNgIZLp7so3hUeAWIDfsQMrIgWlmttDMkvFBIocDm4ARQcnRc2ZWPeygyugiYGzYQZSGu68DhgJfATnAD+4+LdyoSm0ZcKqZ7W9mKUBnoF7IMZVFHXfPCV6vB+qEGYzITpqkS4VlZjWA14D+7r417HhKy91/C37UfyjQNPjRc1Iwsy7ARndfGHYse+AUdz+JSNlaPzNrHXZApVQFOAl4yt1PBH4i8X/MX4CZ/QnoCrwadiylEdQ9dyPyZekQoLqZ9Qw3qtJx9xXAg8A0YAqwBPgt1KD2kEeWvEuqn0rKH5cm6eFYR/5sw6FBm8SJmVUlMkEf4+4Two5nTwQlCjNIrvsEWgFdzexLIuVep5vZ6HBDKp0gE4q7byRSC9003IhKbS2wNs9PYMYTmbQnm07AInffEHYgpdQe+MLdN7n7dmAC0DLkmErN3Z9395PdvTXwHfBJ2DGVwQYzqwsQ/Lox5HhEAE3Sw7IAOMrMDg+yQBcBk0KOqcIwMyNSh7vC3R8OO56yMLMDzax28Loa0AFYGW5UJefuA9z9UHevT+TP/3vunjRZRDOrbmb77HwNdCTyo/+k4e7rga/NrGHQ1A74OMSQyupikqzUJfAV0NzMUoL/J7UjiW7c3cnMDgp+PYxIPfpL4UZUJpOAXsHrXsAbIcYiskuVsAOoiNx9h5n9HZhK5I7+4e6+POSwSszMxgJtgAPMbC2Q7u7PhxtVqbQCLgOWBjXdALe7++QQYyqtusCoYKWgSsA4d0+6ZQyTWB1gYmRuRRXgJXefEm5IZXItMCZIFnwO9Ak5nlIJviB1AK4KO5bScvcsMxsPLCKy4tRikvOpka+Z2f7AdqBfot98HO3fL2AwMM7M+gJrgB7hRVi8QsawBfg3cCDwtpktcfczwotSyoOeOCoiIiIikmBU7iIiIiIikmA0SRcRERERSTCapIuIiIiIJBhN0kVEREREEowm6SIiIiIiCUaTdBERERGRBKNJuohIGZjZSDM7fw+Or21m1xTTp76ZLQtep5nZY8HrvczsXTNbYmYXFnF8bzM7JM/758ysUVljFhGR+NHDjERE4szMqgC1gWuAJ0tyjLtnA9nB2xODtsbFHNabyJNQvwn6X16GcEVEJATKpIuI5GFm/2dmH5nZh2b2YpDNfi9omx48/nyn1mb2gZl9vjOrbhFDzGyZmS3dmek2szZm9h8zmwR8TOQphw2CbPiQEsTVxszeCh7DPhpoEhzbwMxONrNZZrbQzKaaWd0gnjQiTxRdYmbVzGymmaUF5/sxiHN5kJVvGuz/3My6Bn0qB30WBONPuid7iogkK2XSRUQCZnYscCfQ0t03m9l+wChglLuPMrO/Ao8B5wSH1AVOAY4BJgHjge5AY+AE4ABggZnNDvqfBBzn7l+YWf3gdXHZ8HzcfaOZXQ7c5O5dzKwq8CLQzd03BV8KBrn7X83s70G/7GB8eU9VHXjP3W82s4nAQKAD0CgY8ySgL/CDuzcxs72AOWY2zd2/KE3MIiJSepqki4j87nTgVXffDODuW8ysBZGJN0Qmw//M0/91d88FPjazOkHbKcBYd/8N2GBms4AmwFZgfgwmuA2B44DMYBJeGcgpwXG/AlOC10uBX9x9u5ktBeoH7R2B4/PU3tcCjgI0SRcRiTFN0kVEyu6XPK+t0F6/+ykGMRiw3N1blPK47e7uwetcgrG4e25QM7/z3Ne6+9TyCVVEREpKNekiIr97D7jAzPYHCMpdPgAuCvZfCvynmHP8B7gwqOc+EGgNzI/SbxuwTznEvAo4MMj4Y2ZVg7Kd8rjGVOBvQUkNZna0mVXfo2hFRKRElEkXEQm4+3IzGwTMMrPfgMXAtcAIM7sZ2AT0KeY0E4EWwIeAA7e4+3ozO2a3a31rZnOCJRbfcfebyxjzr0E5ymNmVovI/9cfBZYDI4Gnzey/QUyl9RyR0pdFFqml2cTv9fgiIhJD9vtPO0VEREREJBGo3EVEREREJMGo3EVEJGRm9v+IrByT1y/u3iyMeEREJHwqdxERERERSTAqdxERERERSTCapIuIiIiIJBhN0kVEREREEowm6SIiIiIiCUaTdBERERGRBPP/Ac8MjZA9kK0hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(13, 9))\n",
    "plt.title('Average customer purchase size')\n",
    "sns.heatmap(revenue_per_user_pivot, annot=True, fmt='.1f', linewidths=1, linecolor='gray')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The June 2017 cohort started off slow from 4.7% then was in the 30s and 20s until the last month of May 31, 2018\n",
    "- The July 2017 cohort started slow as well and its highest average purchase size was in September 2017 at 21%. Then it lowered and started picking up again the last months of our data at April and May 1t 12% and 11%.\n",
    "- The August 2017 cohort hit 21% in April 2018 but remained below 20% for the rest.\n",
    "- The September 2017 cohort had a strong percentage of 138% in December 2017 but remained lower than that with a low of 5.6 when they first started.\n",
    "- The October 2017 cohort stayed around 7% except for its second month in November 2017 with 11%\n",
    "- The November 2017 cohort stayed below 8% except for February 2018 with 12.5%\n",
    "- The December 2017 cohort started slow with 4.7% but reached a high 48% in March 2018 but then dropped to less than 30%\n",
    "- The January 2018 cohort only reached 12% in March 2018.\n",
    "- The February 2018 cohort stayed below 10% its whole run.\n",
    "- The March 2018 cohort rose to 11% in April and then 19% in May\n",
    "- The April 2018 cohort came in at 4.7% but then had 17.5% in May\n",
    "- The May 2018 cohort came at 4.7 % and the June cohort (that really visited in May but ordered a few hours into June came at 3.4%\n",
    "\n",
    "- From all this data I can see that all the cohorts started slowly between 3-5 percent of average orders but some would eventually pick up with a stand out at 138.7% for the September cohort. Usually there is a steady drop in each cohort with some exceptions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. How much money do they bring? (LTV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_order_month</th>\n",
       "      <th>uid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>1370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>2581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>4340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>4081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>4383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>3651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>3533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>2276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>2988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first_order_month   uid\n",
       "0         2017-06-01  2023\n",
       "1         2017-07-01  1923\n",
       "2         2017-08-01  1370\n",
       "3         2017-09-01  2581\n",
       "4         2017-10-01  4340\n",
       "5         2017-11-01  4081\n",
       "6         2017-12-01  4383\n",
       "7         2018-01-01  3373\n",
       "8         2018-02-01  3651\n",
       "9         2018-03-01  3533\n",
       "10        2018-04-01  2276\n",
       "11        2018-05-01  2988\n",
       "12        2018-06-01     1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_sizes = orders.groupby('first_order_month').agg({'uid': 'nunique'}).reset_index()\n",
    "cohort_sizes.columns = ['first_order_month', 'uid']\n",
    "cohort_sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - The cohorts are the largest in months of October, November, and December and then start dropping significantly.\n",
    " - We stiil have that lone 1 in June 1 which is the visitor that came in May 31 and ordered in June"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "margin_rate = .5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Can't calculate the margin rate with the data we have but the range in e-commerce is 20% to 50% so I chose to go with 50%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_ = pd.merge(orders,first_order_date_by_customers, on='uid')\n",
    "cohorts = orders_.groupby(['first_order_month','order_month']).agg({'revenue': 'sum'}).reset_index()\n",
    "report = pd.merge(cohort_sizes, cohorts, on='first_order_month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I merged the orders with the cohorts to check their revenue per month to get the gross profit per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "report['gp'] = report['revenue'] * margin_rate\n",
    "report['age'] = (report['order_month'] - report['first_order_month']) / np.timedelta64(1, 'M')\n",
    "report['age'] = report['age'].round().astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Here we found the gross profit using the margin_rate\n",
    "- Also created the different ages of the cohorts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "report['ltv'] = report['gp'] / report['uid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_order_month</th>\n",
       "      <th>uid</th>\n",
       "      <th>order_month</th>\n",
       "      <th>revenue</th>\n",
       "      <th>gp</th>\n",
       "      <th>age</th>\n",
       "      <th>ltv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>9557.49</td>\n",
       "      <td>4778.745</td>\n",
       "      <td>0</td>\n",
       "      <td>2.362207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>981.82</td>\n",
       "      <td>490.910</td>\n",
       "      <td>1</td>\n",
       "      <td>0.242664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>885.34</td>\n",
       "      <td>442.670</td>\n",
       "      <td>2</td>\n",
       "      <td>0.218819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>1931.30</td>\n",
       "      <td>965.650</td>\n",
       "      <td>3</td>\n",
       "      <td>0.477336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2068.58</td>\n",
       "      <td>1034.290</td>\n",
       "      <td>4</td>\n",
       "      <td>0.511265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>3533</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>1114.87</td>\n",
       "      <td>557.435</td>\n",
       "      <td>2</td>\n",
       "      <td>0.157780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>2276</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>10600.69</td>\n",
       "      <td>5300.345</td>\n",
       "      <td>0</td>\n",
       "      <td>2.328798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>2276</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>1209.92</td>\n",
       "      <td>604.960</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>2988</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>13925.76</td>\n",
       "      <td>6962.880</td>\n",
       "      <td>0</td>\n",
       "      <td>2.330281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1.710</td>\n",
       "      <td>0</td>\n",
       "      <td>1.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   first_order_month   uid order_month   revenue        gp  age       ltv\n",
       "0         2017-06-01  2023  2017-06-01   9557.49  4778.745    0  2.362207\n",
       "1         2017-06-01  2023  2017-07-01    981.82   490.910    1  0.242664\n",
       "2         2017-06-01  2023  2017-08-01    885.34   442.670    2  0.218819\n",
       "3         2017-06-01  2023  2017-09-01   1931.30   965.650    3  0.477336\n",
       "4         2017-06-01  2023  2017-10-01   2068.58  1034.290    4  0.511265\n",
       "..               ...   ...         ...       ...       ...  ...       ...\n",
       "74        2018-03-01  3533  2018-05-01   1114.87   557.435    2  0.157780\n",
       "75        2018-04-01  2276  2018-04-01  10600.69  5300.345    0  2.328798\n",
       "76        2018-04-01  2276  2018-05-01   1209.92   604.960    1  0.265800\n",
       "77        2018-05-01  2988  2018-05-01  13925.76  6962.880    0  2.330281\n",
       "78        2018-06-01     1  2018-06-01      3.42     1.710    0  1.710000\n",
       "\n",
       "[79 rows x 7 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "0     177184.47\n",
       "1      14536.42\n",
       "2      12184.64\n",
       "3      20848.71\n",
       "4       6964.10\n",
       "5       6070.44\n",
       "6       5090.48\n",
       "7       3132.85\n",
       "8       2304.45\n",
       "9       1766.00\n",
       "10      1455.02\n",
       "11       519.62\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.groupby('age')['revenue'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3307050f50>"
      ]
     },
     "execution_count": 103,
     "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"
    }
   ],
   "source": [
    "report.groupby('age')['revenue'].sum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Of course the first Cohort brought in the most revenue and the 1, 2, and 3 were up there too. \n",
    "- after that it started to drop significantly because it was closer to the end of our data but also started having less visitors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "0     31.210935\n",
       "1      2.505506\n",
       "2      2.027491\n",
       "3      3.722505\n",
       "4      1.384566\n",
       "5      1.167222\n",
       "6      1.091332\n",
       "7      0.743520\n",
       "8      0.595332\n",
       "9      0.470456\n",
       "10     0.363467\n",
       "11     0.128428\n",
       "Name: ltv, dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.groupby('age')['ltv'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3306e3ead0>"
      ]
     },
     "execution_count": 105,
     "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"
    }
   ],
   "source": [
    "report.groupby('age')['ltv'].sum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that the ltv for the first Cohort was very big at 31 and they did very well staying strong \n",
    "- 1, 2, 3 were strong but not as strong as the first group\n",
    "- 4, 5, 6 wer consistent at 1.0 to 1.4 and then again you can see the ltv dropping after that"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Marketing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. How much money was spent? Overall/per source/over time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total money spent in marketing: 329131.62\n"
     ]
    }
   ],
   "source": [
    "total_costs =costs['costs'].sum()\n",
    "print('Total money spent in marketing:', total_costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "source_id\n",
       "9       5517.49\n",
       "10      5822.49\n",
       "1      20833.27\n",
       "2      42806.04\n",
       "5      51757.10\n",
       "4      61073.60\n",
       "3     141321.63\n",
       "Name: costs, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_per_source= costs.groupby('source_id')['costs'].sum().sort_values()\n",
    "cost_per_source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that source 3, 4, 5 had the most costs and if we look back at the visits data we see that most visits came from these sources.\n",
    "- Source 3 had the most money put into it with 141321.63\n",
    "- The next was 4 at 61073.60 and then 5 at 51757.10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cost_per_source_graph = {\"source_id\":  [\"1\", \"2\", \"3\", \"4\",\"5\",\"9\",\"10\"],\n",
    "\n",
    "              \"cost\": [20833.27, 42806.04, 141321.63, 61073.60, 51757.10, 5517.49, 5822.49]\n",
    "                  };\n",
    "\n",
    "cost_per_source_graph = pd.DataFrame(data=cost_per_source_graph);\n",
    "\n",
    "cost_per_source_graph.plot.line(x=\"source_id\", title=\"Cost Per Source Over Time\");\n",
    "\n",
    "plot.show(block=True);                 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that source 3 was the peak of the marketing money put in and then the money started dropping.\n",
    "- Where is source 6, 7, 8?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. How much did customer acquisition from each of the sources cost? This is desktop and touch separately?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20833.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>42806.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>141321.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>61073.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>51757.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5517.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5822.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source_id      costs\n",
       "0          1   20833.27\n",
       "1          2   42806.04\n",
       "2          3  141321.63\n",
       "3          4   61073.60\n",
       "4          5   51757.10\n",
       "5          9    5517.49\n",
       "6         10    5822.49"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marketing_cost_per_source= costs.groupby(['source_id']).agg({'costs': 'sum'}).reset_index()\n",
    "marketing_cost_per_source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks like source 3 cost the most at 141321.63 which was a significantly larger sum than any of the other sources with source 4 coming in at second by costing 61073.60 and then source 5 at 51757.10.\n",
    "- As we saw earlier in the pie charts these 3 sources attracted the most visits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cost_year</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>187961.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>141170.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cost_year      costs\n",
       "0       2017  187961.15\n",
       "1       2018  141170.47"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_costs=costs.groupby('cost_year')['costs'].sum().reset_index()\n",
    "yearly_costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>cost_month</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>18240.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>14790.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>24368.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>36322.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>37907.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>38315.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>33518.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>32723.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>30415.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>22289.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>22224.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cost_month     costs\n",
       "0  2017-06-01  18015.00\n",
       "1  2017-07-01  18240.59\n",
       "2  2017-08-01  14790.54\n",
       "3  2017-09-01  24368.91\n",
       "4  2017-10-01  36322.88\n",
       "5  2017-11-01  37907.88\n",
       "6  2017-12-01  38315.35\n",
       "7  2018-01-01  33518.52\n",
       "8  2018-02-01  32723.03\n",
       "9  2018-03-01  30415.27\n",
       "10 2018-04-01  22289.38\n",
       "11 2018-05-01  22224.27"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_costs=costs.groupby('cost_month')['costs'].sum().reset_index()\n",
    "monthly_costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>source_id</th>\n",
       "      <th>dt</th>\n",
       "      <th>costs</th>\n",
       "      <th>cost_year</th>\n",
       "      <th>cost_month</th>\n",
       "      <th>cost_week</th>\n",
       "      <th>cost_date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>75.20</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>22</td>\n",
       "      <td>2017-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-02</td>\n",
       "      <td>62.25</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>22</td>\n",
       "      <td>2017-06-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>36.53</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>22</td>\n",
       "      <td>2017-06-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-04</td>\n",
       "      <td>55.00</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>22</td>\n",
       "      <td>2017-06-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-05</td>\n",
       "      <td>57.08</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>23</td>\n",
       "      <td>2017-06-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2537</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-05-27</td>\n",
       "      <td>9.92</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>21</td>\n",
       "      <td>2018-05-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2538</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-05-28</td>\n",
       "      <td>21.26</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>22</td>\n",
       "      <td>2018-05-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2539</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-05-29</td>\n",
       "      <td>11.32</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>22</td>\n",
       "      <td>2018-05-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2540</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>33.15</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>22</td>\n",
       "      <td>2018-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2541</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>17.60</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>22</td>\n",
       "      <td>2018-05-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2542 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      source_id         dt  costs  cost_year cost_month  cost_week   cost_date\n",
       "0             1 2017-06-01  75.20       2017 2017-06-01         22  2017-06-01\n",
       "1             1 2017-06-02  62.25       2017 2017-06-01         22  2017-06-02\n",
       "2             1 2017-06-03  36.53       2017 2017-06-01         22  2017-06-03\n",
       "3             1 2017-06-04  55.00       2017 2017-06-01         22  2017-06-04\n",
       "4             1 2017-06-05  57.08       2017 2017-06-01         23  2017-06-05\n",
       "...         ...        ...    ...        ...        ...        ...         ...\n",
       "2537         10 2018-05-27   9.92       2018 2018-05-01         21  2018-05-27\n",
       "2538         10 2018-05-28  21.26       2018 2018-05-01         22  2018-05-28\n",
       "2539         10 2018-05-29  11.32       2018 2018-05-01         22  2018-05-29\n",
       "2540         10 2018-05-30  33.15       2018 2018-05-01         22  2018-05-30\n",
       "2541         10 2018-05-31  17.60       2018 2018-05-01         22  2018-05-31\n",
       "\n",
       "[2542 rows x 7 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There were more costs in 2017 than 2018 for marketing by: 46790.68\n"
     ]
    }
   ],
   "source": [
    "cost_difference_yearly= 187961.15 - 141170.47\n",
    "print('There were more costs in 2017 than 2018 for marketing by: {:.2f}'.format(cost_difference_yearly))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>source_id</th>\n",
       "      <th>uid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>26245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>74756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>83525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>56974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>9264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source_id    uid\n",
       "0          1  18999\n",
       "1          2  26245\n",
       "2          3  74756\n",
       "3          4  83525\n",
       "4          5  56974\n",
       "5          6      6\n",
       "6          7     34\n",
       "7          9   9264\n",
       "8         10   8067"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits_per_source= visits.groupby(['source_id']).agg({'uid': 'nunique'}).reset_index()\n",
    "visits_per_source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- And here we see that sources 3, 4, and 5 collet the most visits. with 4 at 83525, 3 at 74756, and 5 at 56974. The next best is source 2 at 26245 visits from source. the Sources that I have not seen until now 6 and 7 have 34 and 6 visits. No wonder they don't really exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>costs</th>\n",
       "      <th>uid</th>\n",
       "      <th>cac</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>141321.63</td>\n",
       "      <td>74756</td>\n",
       "      <td>1.890439</td>\n",
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       "      <td>1</td>\n",
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       "      <td>26245</td>\n",
       "      <td>1.631017</td>\n",
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       "      <td>0</td>\n",
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       "      <td>20833.27</td>\n",
       "      <td>18999</td>\n",
       "      <td>1.096546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>51757.10</td>\n",
       "      <td>56974</td>\n",
       "      <td>0.908434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>61073.60</td>\n",
       "      <td>83525</td>\n",
       "      <td>0.731201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5822.49</td>\n",
       "      <td>8067</td>\n",
       "      <td>0.721766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5517.49</td>\n",
       "      <td>9264</td>\n",
       "      <td>0.595584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source_id      costs    uid       cac\n",
       "2          3  141321.63  74756  1.890439\n",
       "1          2   42806.04  26245  1.631017\n",
       "0          1   20833.27  18999  1.096546\n",
       "4          5   51757.10  56974  0.908434\n",
       "3          4   61073.60  83525  0.731201\n",
       "6         10    5822.49   8067  0.721766\n",
       "5          9    5517.49   9264  0.595584"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marketing_costs = marketing_cost_per_source.merge(visits_per_source, on='source_id')\n",
    "marketing_costs['cac'] = marketing_costs['costs']/marketing_costs['uid']\n",
    "marketing_costs.sort_values(by = 'cac', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks with the cac being 0.731201 for source 4 that it is doing much better than 3 which cost the most.\n",
    "- Source 9 looks decent with 0.60 but it did not attract nearly as many customers as source 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. How worthwhile were the investments? (ROMI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>costs</th>\n",
       "      <th>uid</th>\n",
       "      <th>cac</th>\n",
       "      <th>romi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20833.27</td>\n",
       "      <td>18999</td>\n",
       "      <td>1.096546</td>\n",
       "      <td>2.154226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5822.49</td>\n",
       "      <td>8067</td>\n",
       "      <td>0.721766</td>\n",
       "      <td>0.658412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>61073.60</td>\n",
       "      <td>83525</td>\n",
       "      <td>0.731201</td>\n",
       "      <td>0.652810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5517.49</td>\n",
       "      <td>9264</td>\n",
       "      <td>0.595584</td>\n",
       "      <td>0.617463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>51757.10</td>\n",
       "      <td>56974</td>\n",
       "      <td>0.908434</td>\n",
       "      <td>0.562799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>42806.04</td>\n",
       "      <td>26245</td>\n",
       "      <td>1.631017</td>\n",
       "      <td>0.148781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>141321.63</td>\n",
       "      <td>74756</td>\n",
       "      <td>1.890439</td>\n",
       "      <td>0.115750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source_id      costs    uid       cac      romi\n",
       "0          1   20833.27  18999  1.096546  2.154226\n",
       "6         10    5822.49   8067  0.721766  0.658412\n",
       "3          4   61073.60  83525  0.731201  0.652810\n",
       "5          9    5517.49   9264  0.595584  0.617463\n",
       "4          5   51757.10  56974  0.908434  0.562799\n",
       "1          2   42806.04  26245  1.631017  0.148781\n",
       "2          3  141321.63  74756  1.890439  0.115750"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marketing_costs['romi'] = report['ltv']/marketing_costs['cac']\n",
    "marketing_costs.sort_values(by= 'romi', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Source 1 has the best Return on marketing investment\n",
    "- Source 9 and 4 whic have very different costs come second and third in romi.\n",
    "- Sources 4 and 5 are very good with romi. But Source 3 which cost the most but had similar numbers of visitors to 4 and 5 is at the bottom of our list with source 2 being the least return on marketing investment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "cohort_costs = cohort_sizes.join(monthly_costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>first_order_month</th>\n",
       "      <th>uid</th>\n",
       "      <th>cost_month</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1923</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>18240.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>1370</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>14790.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>2581</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>24368.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>4340</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>36322.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>4081</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>37907.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>4383</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>38315.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3373</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>33518.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>3651</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>32723.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>3533</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>30415.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>2276</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>22289.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>2988</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>22224.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first_order_month   uid cost_month     costs\n",
       "0         2017-06-01  2023 2017-06-01  18015.00\n",
       "1         2017-07-01  1923 2017-07-01  18240.59\n",
       "2         2017-08-01  1370 2017-08-01  14790.54\n",
       "3         2017-09-01  2581 2017-09-01  24368.91\n",
       "4         2017-10-01  4340 2017-10-01  36322.88\n",
       "5         2017-11-01  4081 2017-11-01  37907.88\n",
       "6         2017-12-01  4383 2017-12-01  38315.35\n",
       "7         2018-01-01  3373 2018-01-01  33518.52\n",
       "8         2018-02-01  3651 2018-02-01  32723.03\n",
       "9         2018-03-01  3533 2018-03-01  30415.27\n",
       "10        2018-04-01  2276 2018-04-01  22289.38\n",
       "11        2018-05-01  2988 2018-05-01  22224.27\n",
       "12        2018-06-01     1        NaT       NaN"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      8.905091\n",
       "1      9.485486\n",
       "2     10.796015\n",
       "3      9.441654\n",
       "4      8.369327\n",
       "5      9.288870\n",
       "6      8.741809\n",
       "7      9.937302\n",
       "8      8.962758\n",
       "9      8.608907\n",
       "10     9.793225\n",
       "11     7.437841\n",
       "12          NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_month_per_cohort = cohort_costs['costs']/ cohort_costs['uid']\n",
    "cost_month_per_cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3304db6f50>"
      ]
     },
     "execution_count": 120,
     "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"
    }
   ],
   "source": [
    "cost_month_per_cohort.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The costs for marketing were high the first three months and were not very worthwhile. The middle months saw the costs go down but then the costs went up again but the users started going down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>source_id</th>\n",
       "      <th>visit_month</th>\n",
       "      <th>uid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>1395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>1088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>1167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     source_id visit_month   uid\n",
       "0            1  2017-06-01   972\n",
       "1            1  2017-07-01  1047\n",
       "2            1  2017-08-01   794\n",
       "3            1  2017-09-01  1395\n",
       "4            1  2017-10-01  2170\n",
       "..         ...         ...   ...\n",
       "97          10  2018-01-01   925\n",
       "98          10  2018-02-01  1088\n",
       "99          10  2018-03-01  1167\n",
       "100         10  2018-04-01   603\n",
       "101         10  2018-05-01   777\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits_per_source_per_month= visits.groupby(['source_id', 'visit_month']).agg({'uid': 'nunique', }).reset_index()\n",
    "visits_per_source_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>age</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_order_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age                 0  1  2  3  4  5  6  7  8  9  10 11\n",
       "first_order_month                                      \n",
       "2017-06-01         2.0  0  0  0  1  0  0  0  0  0  0  0\n",
       "2017-07-01         3.0  0  0  0  0  0  0  0  0  0  0   \n",
       "2017-08-01         3.0  0  0  0  0  0  0  0  0  0      \n",
       "2017-09-01         3.0  1  0  2  0  0  0  0  0         \n",
       "2017-10-01         3.0  0  0  0  0  0  0  0            \n",
       "2017-11-01         3.0  0  0  0  0  0  0               \n",
       "2017-12-01         2.0  0  0  1  0  0                  \n",
       "2018-01-01         2.0  0  0  0  0                     \n",
       "2018-02-01         2.0  0  0  0                        \n",
       "2018-03-01         2.0  0  0                           \n",
       "2018-04-01         2.0  0                              \n",
       "2018-05-01         2.0                                 \n",
       "2018-06-01         2.0                                 "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report['ltv'] = report['gp'] / report['uid']\n",
    "output = report.pivot_table(\n",
    " index='first_order_month',\n",
    " columns='age',\n",
    " values='ltv',\n",
    " aggfunc='mean').round()\n",
    "\n",
    "output.fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_order_month</th>\n",
       "      <th>uid</th>\n",
       "      <th>order_month</th>\n",
       "      <th>revenue</th>\n",
       "      <th>gp</th>\n",
       "      <th>age</th>\n",
       "      <th>ltv</th>\n",
       "      <th>cost_month</th>\n",
       "      <th>costs</th>\n",
       "      <th>cac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>9557.49</td>\n",
       "      <td>4778.745</td>\n",
       "      <td>0</td>\n",
       "      <td>2.362207</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.0</td>\n",
       "      <td>8.905091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>981.82</td>\n",
       "      <td>490.910</td>\n",
       "      <td>1</td>\n",
       "      <td>0.242664</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.0</td>\n",
       "      <td>8.905091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>885.34</td>\n",
       "      <td>442.670</td>\n",
       "      <td>2</td>\n",
       "      <td>0.218819</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.0</td>\n",
       "      <td>8.905091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>1931.30</td>\n",
       "      <td>965.650</td>\n",
       "      <td>3</td>\n",
       "      <td>0.477336</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.0</td>\n",
       "      <td>8.905091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2068.58</td>\n",
       "      <td>1034.290</td>\n",
       "      <td>4</td>\n",
       "      <td>0.511265</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.0</td>\n",
       "      <td>8.905091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_order_month   uid order_month  revenue        gp  age       ltv  \\\n",
       "0        2017-06-01  2023  2017-06-01  9557.49  4778.745    0  2.362207   \n",
       "1        2017-06-01  2023  2017-07-01   981.82   490.910    1  0.242664   \n",
       "2        2017-06-01  2023  2017-08-01   885.34   442.670    2  0.218819   \n",
       "3        2017-06-01  2023  2017-09-01  1931.30   965.650    3  0.477336   \n",
       "4        2017-06-01  2023  2017-10-01  2068.58  1034.290    4  0.511265   \n",
       "\n",
       "  cost_month    costs       cac  \n",
       "0 2017-06-01  18015.0  8.905091  \n",
       "1 2017-06-01  18015.0  8.905091  \n",
       "2 2017-06-01  18015.0  8.905091  \n",
       "3 2017-06-01  18015.0  8.905091  \n",
       "4 2017-06-01  18015.0  8.905091  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report_2 = pd.merge(report, monthly_costs, left_on='first_order_month',\n",
    "right_on='cost_month')\n",
    "report_2['cac'] = report_2['costs'] / report_2['uid']\n",
    "report_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_2['romi'] = report_2['ltv'] / report_2['cac']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_2 = report_2.pivot_table(\n",
    " index='first_order_month',\n",
    " columns='age',\n",
    " values='romi',\n",
    " aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_graph = output_2.cumsum(axis=1).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_order_month</th>\n",
       "      <th>uid</th>\n",
       "      <th>order_month</th>\n",
       "      <th>revenue</th>\n",
       "      <th>gp</th>\n",
       "      <th>age</th>\n",
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       "      <th>cost_month</th>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>2023</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>9557.49</td>\n",
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       "      <td>1</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>981.82</td>\n",
       "      <td>490.910</td>\n",
       "      <td>1</td>\n",
       "      <td>0.242664</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.00</td>\n",
       "      <td>8.905091</td>\n",
       "      <td>0.027250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>885.34</td>\n",
       "      <td>442.670</td>\n",
       "      <td>2</td>\n",
       "      <td>0.218819</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.00</td>\n",
       "      <td>8.905091</td>\n",
       "      <td>0.024572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>1931.30</td>\n",
       "      <td>965.650</td>\n",
       "      <td>3</td>\n",
       "      <td>0.477336</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.00</td>\n",
       "      <td>8.905091</td>\n",
       "      <td>0.053603</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2068.58</td>\n",
       "      <td>1034.290</td>\n",
       "      <td>4</td>\n",
       "      <td>0.511265</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>18015.00</td>\n",
       "      <td>8.905091</td>\n",
       "      <td>0.057413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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",
       "      <td>73</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>3533</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>1063.05</td>\n",
       "      <td>531.525</td>\n",
       "      <td>1</td>\n",
       "      <td>0.150446</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>30415.27</td>\n",
       "      <td>8.608907</td>\n",
       "      <td>0.017476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>3533</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>1114.87</td>\n",
       "      <td>557.435</td>\n",
       "      <td>2</td>\n",
       "      <td>0.157780</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>30415.27</td>\n",
       "      <td>8.608907</td>\n",
       "      <td>0.018327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>2276</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>10600.69</td>\n",
       "      <td>5300.345</td>\n",
       "      <td>0</td>\n",
       "      <td>2.328798</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>22289.38</td>\n",
       "      <td>9.793225</td>\n",
       "      <td>0.237797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>2276</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>1209.92</td>\n",
       "      <td>604.960</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265800</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>22289.38</td>\n",
       "      <td>9.793225</td>\n",
       "      <td>0.027141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>2988</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>13925.76</td>\n",
       "      <td>6962.880</td>\n",
       "      <td>0</td>\n",
       "      <td>2.330281</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>22224.27</td>\n",
       "      <td>7.437841</td>\n",
       "      <td>0.313301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   first_order_month   uid order_month   revenue        gp  age       ltv  \\\n",
       "0         2017-06-01  2023  2017-06-01   9557.49  4778.745    0  2.362207   \n",
       "1         2017-06-01  2023  2017-07-01    981.82   490.910    1  0.242664   \n",
       "2         2017-06-01  2023  2017-08-01    885.34   442.670    2  0.218819   \n",
       "3         2017-06-01  2023  2017-09-01   1931.30   965.650    3  0.477336   \n",
       "4         2017-06-01  2023  2017-10-01   2068.58  1034.290    4  0.511265   \n",
       "..               ...   ...         ...       ...       ...  ...       ...   \n",
       "73        2018-03-01  3533  2018-04-01   1063.05   531.525    1  0.150446   \n",
       "74        2018-03-01  3533  2018-05-01   1114.87   557.435    2  0.157780   \n",
       "75        2018-04-01  2276  2018-04-01  10600.69  5300.345    0  2.328798   \n",
       "76        2018-04-01  2276  2018-05-01   1209.92   604.960    1  0.265800   \n",
       "77        2018-05-01  2988  2018-05-01  13925.76  6962.880    0  2.330281   \n",
       "\n",
       "   cost_month     costs       cac      romi  \n",
       "0  2017-06-01  18015.00  8.905091  0.265265  \n",
       "1  2017-06-01  18015.00  8.905091  0.027250  \n",
       "2  2017-06-01  18015.00  8.905091  0.024572  \n",
       "3  2017-06-01  18015.00  8.905091  0.053603  \n",
       "4  2017-06-01  18015.00  8.905091  0.057413  \n",
       "..        ...       ...       ...       ...  \n",
       "73 2018-03-01  30415.27  8.608907  0.017476  \n",
       "74 2018-03-01  30415.27  8.608907  0.018327  \n",
       "75 2018-04-01  22289.38  9.793225  0.237797  \n",
       "76 2018-04-01  22289.38  9.793225  0.027141  \n",
       "77 2018-05-01  22224.27  7.437841  0.313301  \n",
       "\n",
       "[78 rows x 11 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f33071c8150>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(13, 9))\n",
    "plt.title('ROMI Per Cohort')\n",
    "sns.heatmap(output_graph, annot=True, fmt='.1f', linewidths=1, linecolor='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The Cohorts did not really pay back for the costs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. Write a conclusion \n",
    "- After looking at the different metrics I have concluded that we want to focus on the months of October, November, and December to attract visitors. This is probably because these months are considered big holiday months with Christmas coming and so ordering during those months are big.\n",
    "- I also would continue to use our marketing source 4 since it brought a nice number of visitors at 83525 which was the most out of any of the sources and its cac at .73 and romi at .70 were very close compared to other sources. \n",
    "- Source 1 is the best source to use for marketing as it does have a good return on marketing investment at 2.15 which is more than the other sources.\n",
    "- Even though it may seem that the Source may have been worthwhile to use we see when looking at Cohorts that the return on investment was not actually good for any of the groups and we probably need to think about our costs acquiring users when we use these sources again."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}