case_study_2.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Study 2 "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is 1 dataset(csv) with 3 years worth of customer orders. There are 4 columns in the csv dataset:\n",
    "index, CUSTOMER_EMAIL(unique identifier as hash), Net_Revenue, and Year.\n",
    "\n",
    "For each year we need the following information:\n",
    "- Total revenue for the current year\n",
    "- New Customer Revenue e.g. new customers not present in previous year only\n",
    "- Existing Customer Growth. To calculate this, use the Revenue of existing customers for current\n",
    "year –(minus) Revenue of existing customers from the previous year\n",
    "- Revenue lost from attrition\n",
    "- Existing Customer Revenue Current Year\n",
    "- Existing Customer Revenue Prior Year\n",
    "- Total Customers Current Year\n",
    "- Total Customers Previous Year\n",
    "- New Customers\n",
    "- Lost Customers\n",
    "\n",
    "Additionally, generate a few unique plots highlighting some information from the dataset. Are there any\n",
    "interesting observations?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "import matplotlib.colors as mcolors"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=2)  # reduced display precision on numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = pd.read_csv('casestudy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(685927, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 685,927 orders in total and 4 columns: `customer_email, net_revenue, year`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>customer_email</th>\n",
       "      <th>net_revenue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>nhknapwsbx@gmail.com</td>\n",
       "      <td>249.92</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>joiuzbvcpn@gmail.com</td>\n",
       "      <td>87.61</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>ukkjctepxt@gmail.com</td>\n",
       "      <td>168.38</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>gykatilzrt@gmail.com</td>\n",
       "      <td>62.40</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>mmsgsrtxah@gmail.com</td>\n",
       "      <td>43.08</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         customer_email  net_revenue  year\n",
       "0           0   nhknapwsbx@gmail.com       249.92  2015\n",
       "1           1   joiuzbvcpn@gmail.com        87.61  2015\n",
       "2           2   ukkjctepxt@gmail.com       168.38  2015\n",
       "3           3   gykatilzrt@gmail.com        62.40  2015\n",
       "4           4   mmsgsrtxah@gmail.com        43.08  2015"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't need to have the index two times, so we drop the first column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_email</th>\n",
       "      <th>net_revenue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nhknapwsbx@gmail.com</td>\n",
       "      <td>249.92</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>joiuzbvcpn@gmail.com</td>\n",
       "      <td>87.61</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ukkjctepxt@gmail.com</td>\n",
       "      <td>168.38</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gykatilzrt@gmail.com</td>\n",
       "      <td>62.40</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mmsgsrtxah@gmail.com</td>\n",
       "      <td>43.08</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          customer_email  net_revenue  year\n",
       "0   nhknapwsbx@gmail.com       249.92  2015\n",
       "1   joiuzbvcpn@gmail.com        87.61  2015\n",
       "2   ukkjctepxt@gmail.com       168.38  2015\n",
       "3   gykatilzrt@gmail.com        62.40  2015\n",
       "4   mmsgsrtxah@gmail.com        43.08  2015"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have information for 3 years: 2015, 2016, 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    685927.00\n",
       "mean        125.65\n",
       "std          71.85\n",
       "min           1.00\n",
       "25%          63.51\n",
       "50%         125.73\n",
       "75%         187.82\n",
       "max         250.00\n",
       "Name: net_revenue, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.net_revenue.describe().round(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_email    0\n",
       "net_revenue       0\n",
       "year              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For each year:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### - Total revenue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\evi_9\\AppData\\Local\\Temp\\ipykernel_20224\\3133762715.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  total_revenue = orders.groupby(\"year\", as_index=False).sum()\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>net_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>29036749.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>25730943.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>31417495.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   year  net_revenue\n",
       "0  2015  29036749.19\n",
       "1  2016  25730943.59\n",
       "2  2017  31417495.03"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "total_revenue = orders.groupby(\"year\", as_index=False).sum()\n",
    "total_revenue"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - New Customers \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_customer(prev, next):\n",
    "    oldnew = orders[orders.year.isin({prev, next})] # customers with orders in the years prev and/or next \n",
    "    once = oldnew.drop_duplicates('customer_email',keep=False) # customers that made orders only in one of the prev/next year\n",
    "    new = once[once.year == next]\n",
    "    return new\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_email</th>\n",
       "      <th>net_revenue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nhknapwsbx@gmail.com</td>\n",
       "      <td>249.92</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>joiuzbvcpn@gmail.com</td>\n",
       "      <td>87.61</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ukkjctepxt@gmail.com</td>\n",
       "      <td>168.38</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gykatilzrt@gmail.com</td>\n",
       "      <td>62.40</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mmsgsrtxah@gmail.com</td>\n",
       "      <td>43.08</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer_email  net_revenue  year\n",
       "0   nhknapwsbx@gmail.com       249.92  2015\n",
       "1   joiuzbvcpn@gmail.com        87.61  2015\n",
       "2   ukkjctepxt@gmail.com       168.38  2015\n",
       "3   gykatilzrt@gmail.com        62.40  2015\n",
       "4   mmsgsrtxah@gmail.com        43.08  2015"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_16 = new_customer(2015,2015)\n",
    "new_16.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_email</th>\n",
       "      <th>net_revenue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>435940</th>\n",
       "      <td>mwrossuukz@gmail.com</td>\n",
       "      <td>96.61</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435941</th>\n",
       "      <td>qvjfterwnk@gmail.com</td>\n",
       "      <td>176.00</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435942</th>\n",
       "      <td>vlyigtgfzs@gmail.com</td>\n",
       "      <td>73.47</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435943</th>\n",
       "      <td>yfirychuhk@gmail.com</td>\n",
       "      <td>138.17</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435944</th>\n",
       "      <td>fdkiioqtli@gmail.com</td>\n",
       "      <td>156.87</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               customer_email  net_revenue  year\n",
       "435940   mwrossuukz@gmail.com        96.61  2017\n",
       "435941  qvjfterwnk@gmail.com        176.00  2017\n",
       "435942   vlyigtgfzs@gmail.com        73.47  2017\n",
       "435943   yfirychuhk@gmail.com       138.17  2017\n",
       "435944   fdkiioqtli@gmail.com       156.87  2017"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_17 = new_customer(2016,2017)\n",
    "new_17.head()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the `new_16` dataframe, we have all the customers that ordered only in 2016 and not in 2015. There are `145062` new customers.\n",
    "\n",
    "In the `new_17` dataframe, we have all the customers that ordered only in 2015 and not in 2016. There are `229028` new customers.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - Lost Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lost_customer(prev, next):\n",
    "    oldnew = orders[orders.year.isin({prev, next})] # customers with orders in the years prev and/or next \n",
    "    once = oldnew.drop_duplicates('customer_email',keep=False) # customers that made orders only in one of the prev/next year\n",
    "    lost = once[once.year == prev]\n",
    "    return lost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_email</th>\n",
       "      <th>net_revenue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nhknapwsbx@gmail.com</td>\n",
       "      <td>249.92</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>joiuzbvcpn@gmail.com</td>\n",
       "      <td>87.61</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ukkjctepxt@gmail.com</td>\n",
       "      <td>168.38</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gykatilzrt@gmail.com</td>\n",
       "      <td>62.40</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mmsgsrtxah@gmail.com</td>\n",
       "      <td>43.08</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231289</th>\n",
       "      <td>xtrpmgjbwp@gmail.com</td>\n",
       "      <td>216.89</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231290</th>\n",
       "      <td>peeorxpsbr@gmail.com</td>\n",
       "      <td>39.16</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231291</th>\n",
       "      <td>vanasezjpw@gmail.com</td>\n",
       "      <td>233.46</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231292</th>\n",
       "      <td>dnpremlztb@gmail.com</td>\n",
       "      <td>136.27</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231293</th>\n",
       "      <td>qsgswrpycl@gmail.com</td>\n",
       "      <td>60.24</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171710 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               customer_email  net_revenue  year\n",
       "0        nhknapwsbx@gmail.com       249.92  2015\n",
       "1        joiuzbvcpn@gmail.com        87.61  2015\n",
       "2        ukkjctepxt@gmail.com       168.38  2015\n",
       "3        gykatilzrt@gmail.com        62.40  2015\n",
       "4        mmsgsrtxah@gmail.com        43.08  2015\n",
       "...                       ...          ...   ...\n",
       "231289   xtrpmgjbwp@gmail.com       216.89  2015\n",
       "231290   peeorxpsbr@gmail.com        39.16  2015\n",
       "231291   vanasezjpw@gmail.com       233.46  2015\n",
       "231292   dnpremlztb@gmail.com       136.27  2015\n",
       "231293   qsgswrpycl@gmail.com        60.24  2015\n",
       "\n",
       "[171710 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lost_15 = lost_customer(2015,2016)\n",
    "lost_15\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_email</th>\n",
       "      <th>net_revenue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>231294</th>\n",
       "      <td>mwrossuukz@gmail.com</td>\n",
       "      <td>197.26</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231295</th>\n",
       "      <td>gkwsoupawk@gmail.com</td>\n",
       "      <td>38.28</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231296</th>\n",
       "      <td>vlyigtgfzs@gmail.com</td>\n",
       "      <td>154.57</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231297</th>\n",
       "      <td>yfirychuhk@gmail.com</td>\n",
       "      <td>102.47</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231298</th>\n",
       "      <td>trnzgqinuu@gmail.com</td>\n",
       "      <td>32.03</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435933</th>\n",
       "      <td>sqodpufunf@gmail.com</td>\n",
       "      <td>29.62</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435935</th>\n",
       "      <td>mezrjfkced@gmail.com</td>\n",
       "      <td>114.17</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435936</th>\n",
       "      <td>rdotspqdxi@gmail.com</td>\n",
       "      <td>169.79</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435938</th>\n",
       "      <td>pidugzoeej@gmail.com</td>\n",
       "      <td>116.80</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435939</th>\n",
       "      <td>kxqglfdktu@gmail.com</td>\n",
       "      <td>37.31</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183687 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               customer_email  net_revenue  year\n",
       "231294   mwrossuukz@gmail.com       197.26  2016\n",
       "231295   gkwsoupawk@gmail.com        38.28  2016\n",
       "231296   vlyigtgfzs@gmail.com       154.57  2016\n",
       "231297   yfirychuhk@gmail.com       102.47  2016\n",
       "231298   trnzgqinuu@gmail.com        32.03  2016\n",
       "...                       ...          ...   ...\n",
       "435933   sqodpufunf@gmail.com        29.62  2016\n",
       "435935   mezrjfkced@gmail.com       114.17  2016\n",
       "435936   rdotspqdxi@gmail.com       169.79  2016\n",
       "435938   pidugzoeej@gmail.com       116.80  2016\n",
       "435939   kxqglfdktu@gmail.com        37.31  2016\n",
       "\n",
       "[183687 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lost_16 = lost_customer(2016,2017)\n",
    "lost_16\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the `lost_15` dataframe, we have all the customers that ordered in 2015 but did not in 2016. There are `171710 ` lost customers.\n",
    "\n",
    "In the `lost_16` dataframe, we have all the customers that ordered in 2016 but did not in 2017. There are `183687` new customers."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - New Customer Revenue e.g. new customers not present in previous year only\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_revenue_16 = new_16.net_revenue.sum().round(2)\n",
    "new_revenue_17 = new_17.net_revenue.sum().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29036749.19, 28776235.04)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_revenue_16, new_revenue_17"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the `new_revenue_16` variable, we have the total revenue of the customers that ordered only in 2016 and not in 2015. There is `29036749.19` revenue due to new customers.\n",
    "\n",
    "In the `new_revenue_17` variable, we have the total revenue of the customers that ordered only in 2017 and not in 2016. There is `28776235.04` revenue due to new customers."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - Existing customer growth. \n",
    "\n",
    "revenue of existing customers for current year - revenue of existing customers for previous year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def growth(prev, next):\n",
    "    oldnew = orders[orders.year.isin({prev, next})] # customers with orders in the prev or next years \n",
    "    # oldnew.shape\n",
    "    duplicate =  oldnew[oldnew.duplicated('customer_email',keep=False)] # customers that have made orders in both prev and next years\n",
    "    # once = oldnew.drop_duplicates('customer_email',keep=False)\n",
    "    duplicate_prev = duplicate[duplicate.year == prev].set_index(duplicate[duplicate.year == prev].customer_email).sort_index(axis=1)\n",
    "    duplicate_next = duplicate[duplicate.year == next].set_index(duplicate[duplicate.year == next].customer_email).sort_index(axis=1)\n",
    "    growth = duplicate_next.net_revenue.subtract(duplicate_prev.net_revenue)\n",
    "    return growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "growth_16 = growth(2015,2016)\n",
    "growth_16.name = 'growth_for_16'\n",
    "growth_17 = growth(2016,2017)\n",
    "growth_17.name = 'growth_for_17'\n",
    "growths = pd.concat([growth_16, growth_17], axis=1, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = orders.merge(growths, how='outer',on='customer_email')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "growth_for_16    40336.44\n",
       "growth_for_17    33082.61\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_growth = merged[['growth_for_16', 'growth_for_17']].sum()\n",
    "total_growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "growth_for_16    20335.46\n",
       "growth_for_17    20611.34\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "growths.sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the `growth_16` we have the difference in revenue for customers from 2015 to 2016.\n",
    "\n",
    "In the `growth_17` we have the difference in revenue for customers from 2016 to 2017.\n",
    "\n",
    "`merged` is the new dataframe containing two new columns `growth 16, growth 17` with the growth information for period `2015-2016` and `2016-2017` respectively"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - Revenue lost from attrition"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We focus on the customers that were lost from one year to the other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def attrition_loss(prev, next):\n",
    "    prev_rev = orders[orders.year == prev].net_revenue.sum()\n",
    "    oldnew = orders[orders.year.isin({prev, next})] # customers with orders in the prev or next years \n",
    "    duplicate =  oldnew[oldnew.duplicated('customer_email',keep=False)] # customers that have made orders in both prev and next years\n",
    "    stable_rev = duplicate[duplicate.year == next].net_revenue.sum()\n",
    "    loss = (prev_rev - stable_rev).round(2)\n",
    "    return loss \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21551296.61"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_16 = attrition_loss(2015,2016)\n",
    "loss_16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23089683.6"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_17 = attrition_loss(2016,2017)\n",
    "loss_17"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`loss_16` is the calculated revenue loss due to attrition  (customers lost) from 2015 to 2016\n",
    "\n",
    "`loss_17` is the calculated revenue loss due to attrition  (customers lost) from 2016 to 2017\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - Existing customer revenue current year "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rev_current_year(year):\n",
    "    revenue = orders[orders.year == year].net_revenue.sum()\n",
    "    return revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         249.92\n",
       "1          87.61\n",
       "2         168.38\n",
       "3          62.40\n",
       "4          43.08\n",
       "           ...  \n",
       "231289    216.89\n",
       "231290     39.16\n",
       "231291    233.46\n",
       "231292    136.27\n",
       "231293     60.24\n",
       "Name: net_revenue, Length: 231294, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders.year == 2015].net_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29036749.189999994"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_15 = rev_current_year(2015)\n",
    "rev_15"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - Total Customers for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2015    231294\n",
       "2016    204646\n",
       "2017    249987\n",
       "Name: customer_email, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_customers = orders.groupby(\"year\").customer_email.count()\n",
    "total_customers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing some information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>year</th>\n",
       "      <th>net_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>29036749.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>25730943.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>31417495.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  net_revenue\n",
       "0  2015  29036749.19\n",
       "1  2016  25730943.59\n",
       "2  2017  31417495.03"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y = np.linspace(0,35000000,)\n",
    "plt.figure(figsize = (10,8)) \n",
    "plt.bar([2015, 2016, 2017], total_revenue.net_revenue, width=0.5)\n",
    "# plt.xticks(['15', '16', '17'] ,['15', '16', '17'])\n",
    "plt.yticks([5000000, 10000000, 15000000, 20000000, 25000000, 30000000, 35000000], ['5','10', '15', '20', '25', '30', '35'])\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Total Revenue x 1,000,000')\n",
    "plt.title('Total Revenue for all years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "growths_info = growths.describe()\n",
    "growths_info['mean_info'] = growths_info.apply(lambda row: row.mean(), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = growths_info.mean_info[1].round(2)\n",
    "sigma = growths_info.mean_info[2].round(2)\n",
    "min = growths_info.mean_info[3].round(2)\n",
    "max = growths_info.mean_info[7].round(2)\n",
    "x_axis = np.arange(min, max, 1)\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.hist([growths.growth_for_16.round(),growths.growth_for_17.round()], bins=50, density=True, label=['2016','2017'], alpha=0.75, color=['mediumspringgreen', 'royalblue'])\n",
    "plt.plot(x_axis, norm.pdf(x_axis, mu, sigma), c='r', label='$N(0.66, 101.39)$')\n",
    "plt.xlabel('Growth')\n",
    "plt.ylabel('Distribution')\n",
    "plt.title('Histogram of Growth')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of growth for both year periods is normal, with `mean = 0`, and `std = 101`"
   ]
  }
 ],
 "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": "3.9.13 (tags/v3.9.13:6de2ca5, May 17 2022, 16:36:42) [MSC v.1929 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
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