Employee_Retention_PeopleAnalytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Employee Retention - People Analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal:\n",
    "### 1. Predict Employee Retention\n",
    "####  ----create a table with 3 columns, day, employee_headcount, company_id\n",
    "### 2. What are the main factors drive employee churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import datetime\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\lshen\\Downloads\\employee_retention_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 813,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  company_id              dept  seniority    salary   join_date  \\\n",
       "0      13021.0           7  customer_service         28   89000.0  2014-03-24   \n",
       "1     825355.0           7         marketing         20  183000.0  2013-04-29   \n",
       "2     927315.0           4         marketing         14  101000.0  2014-10-13   \n",
       "3     662910.0           7  customer_service         20  115000.0  2012-05-14   \n",
       "4     256971.0           2      data_science         23  276000.0  2011-10-17   \n",
       "\n",
       "    quit_date  \n",
       "0  2015-10-30  \n",
       "1  2014-04-04  \n",
       "2         NaN  \n",
       "3  2013-06-07  \n",
       "4  2014-08-22  "
      ]
     },
     "execution_count": 813,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 814,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee_id    float64\n",
       "company_id       int64\n",
       "dept            object\n",
       "seniority        int64\n",
       "salary         float64\n",
       "join_date       object\n",
       "quit_date       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 814,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 815,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24702, 7)"
      ]
     },
     "execution_count": 815,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 816,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee_id        0\n",
       "company_id         0\n",
       "dept               0\n",
       "seniority          0\n",
       "salary             0\n",
       "join_date          0\n",
       "quit_date      11192\n",
       "dtype: int64"
      ]
     },
     "execution_count": 816,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### change into proper data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 817,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change join and quit date's type to date time\n",
    "# one way -----data['join_date'] = data.join_date.astype(datetime.datetime)\n",
    "data['join_date'] = pd.to_datetime(data.join_date)\n",
    "data['quit_date'] = pd.to_datetime(data.quit_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 818,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee_id           float64\n",
       "company_id              int64\n",
       "dept                   object\n",
       "seniority               int64\n",
       "salary                float64\n",
       "join_date      datetime64[ns]\n",
       "quit_date      datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 818,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 819,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24702.000000</td>\n",
       "      <td>24702.000000</td>\n",
       "      <td>24702</td>\n",
       "      <td>24702.000000</td>\n",
       "      <td>24702.000000</td>\n",
       "      <td>24702</td>\n",
       "      <td>13510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>995</td>\n",
       "      <td>664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-01-03 00:00:00</td>\n",
       "      <td>2015-05-08 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-01-24 00:00:00</td>\n",
       "      <td>2011-10-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-10 00:00:00</td>\n",
       "      <td>2015-12-09 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>501604.403530</td>\n",
       "      <td>3.426969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.127803</td>\n",
       "      <td>138183.345478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>288909.026101</td>\n",
       "      <td>2.700011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.089520</td>\n",
       "      <td>76058.184573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>250133.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>79000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>500793.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>123000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>753137.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>187000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>999969.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>408000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          employee_id    company_id              dept     seniority  \\\n",
       "count    24702.000000  24702.000000             24702  24702.000000   \n",
       "unique            NaN           NaN                 6           NaN   \n",
       "top               NaN           NaN  customer_service           NaN   \n",
       "freq              NaN           NaN              9180           NaN   \n",
       "first             NaN           NaN               NaN           NaN   \n",
       "last              NaN           NaN               NaN           NaN   \n",
       "mean    501604.403530      3.426969               NaN     14.127803   \n",
       "std     288909.026101      2.700011               NaN      8.089520   \n",
       "min         36.000000      1.000000               NaN      1.000000   \n",
       "25%     250133.750000      1.000000               NaN      7.000000   \n",
       "50%     500793.000000      2.000000               NaN     14.000000   \n",
       "75%     753137.250000      5.000000               NaN     21.000000   \n",
       "max     999969.000000     12.000000               NaN     99.000000   \n",
       "\n",
       "               salary            join_date            quit_date  \n",
       "count    24702.000000                24702                13510  \n",
       "unique            NaN                  995                  664  \n",
       "top               NaN  2012-01-03 00:00:00  2015-05-08 00:00:00  \n",
       "freq              NaN                  105                  111  \n",
       "first             NaN  2011-01-24 00:00:00  2011-10-13 00:00:00  \n",
       "last              NaN  2015-12-10 00:00:00  2015-12-09 00:00:00  \n",
       "mean    138183.345478                  NaN                  NaN  \n",
       "std      76058.184573                  NaN                  NaN  \n",
       "min      17000.000000                  NaN                  NaN  \n",
       "25%      79000.000000                  NaN                  NaN  \n",
       "50%     123000.000000                  NaN                  NaN  \n",
       "75%     187000.000000                  NaN                  NaN  \n",
       "max     408000.000000                  NaN                  NaN  "
      ]
     },
     "execution_count": 819,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get new hire number for each company by each day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 820,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_hire_by_date = data.groupby(['company_id','join_date'], as_index = False).employee_id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 821,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_hire_by_date.columns = ['company_id','day','new_hire_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 822,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_id</th>\n",
       "      <th>day</th>\n",
       "      <th>new_hire_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-01</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   company_id        day  new_hire_count\n",
       "0           1 2011-01-24              25\n",
       "1           1 2011-01-25               2\n",
       "2           1 2011-01-26               2\n",
       "3           1 2011-01-31              30\n",
       "4           1 2011-02-01               7"
      ]
     },
     "execution_count": 822,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_hire_by_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 823,
   "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>company_id</th>\n",
       "      <th>day</th>\n",
       "      <th>new_hire_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5125</th>\n",
       "      <td>12</td>\n",
       "      <td>2014-05-19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5126</th>\n",
       "      <td>12</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5127</th>\n",
       "      <td>12</td>\n",
       "      <td>2015-03-23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5128</th>\n",
       "      <td>12</td>\n",
       "      <td>2015-07-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5129</th>\n",
       "      <td>12</td>\n",
       "      <td>2015-07-27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      company_id        day  new_hire_count\n",
       "5125          12 2014-05-19               2\n",
       "5126          12 2014-10-13               1\n",
       "5127          12 2015-03-23               1\n",
       "5128          12 2015-07-06               1\n",
       "5129          12 2015-07-27               1"
      ]
     },
     "execution_count": 823,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_hire_by_date.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get quitted number for each company each day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "metadata": {},
   "outputs": [],
   "source": [
    "quit_by_date = data.groupby(['company_id','quit_date'],as_index=False).employee_id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "metadata": {},
   "outputs": [],
   "source": [
    "quit_by_date.columns = ['company_id','day','quit_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 826,
   "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>company_id</th>\n",
       "      <th>day</th>\n",
       "      <th>quit_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   company_id        day  quit_count\n",
       "0           1 2011-10-21           1\n",
       "1           1 2011-11-11           1\n",
       "2           1 2011-11-22           1\n",
       "3           1 2011-11-25           1\n",
       "4           1 2011-12-09           1"
      ]
     },
     "execution_count": 826,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quit_by_date.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a dataframe storing the date from start to end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = '2011-01-23'\n",
    "end_date = '2015-12-13'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 828,
   "metadata": {},
   "outputs": [],
   "source": [
    "# continuous day dataframe\n",
    "d = pd.DataFrame(pd.date_range(start_date, end_date),columns = ['day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day\n",
       "0 2011-01-23\n",
       "1 2011-01-24\n",
       "2 2011-01-25\n",
       "3 2011-01-26\n",
       "4 2011-01-27"
      ]
     },
     "execution_count": 829,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the company list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 830,
   "metadata": {},
   "outputs": [],
   "source": [
    "company_list = data.company_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 831,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  4,  2,  9,  1,  6, 10,  5,  3,  8, 11, 12], dtype=int64)"
      ]
     },
     "execution_count": 831,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 832,
   "metadata": {},
   "outputs": [],
   "source": [
    "company_list.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 833,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int64)"
      ]
     },
     "execution_count": 833,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 834,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = pd.DataFrame(company_list,columns=['company_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Join date and company list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 835,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge on a dummy column and drop it\n",
    "headcount = d.assign(foo = 1).merge(c.assign(foo=1)).drop('foo',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 836,
   "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>company_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21427</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21428</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21429</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21430</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21431</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             day  company_id\n",
       "21427 2015-12-13           8\n",
       "21428 2015-12-13           9\n",
       "21429 2015-12-13          10\n",
       "21430 2015-12-13          11\n",
       "21431 2015-12-13          12"
      ]
     },
     "execution_count": 836,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headcount.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### merge with new_hire and quit data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 837,
   "metadata": {},
   "outputs": [],
   "source": [
    "headcount = (headcount.merge(new_hire_by_date, how='left',\\\n",
    "                             on=['day','company_id']).fillna(0)).merge(quit_by_date, how='left',\\\n",
    "                                                                                on =['day','company_id']).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 838,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>day</th>\n",
       "      <th>company_id</th>\n",
       "      <th>new_hire_count</th>\n",
       "      <th>quit_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-23</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day  company_id  new_hire_count  quit_count\n",
       "0  2011-01-23           1             0.0         0.0\n",
       "1  2011-01-23           2             0.0         0.0\n",
       "2  2011-01-23           3             0.0         0.0\n",
       "3  2011-01-23           4             0.0         0.0\n",
       "4  2011-01-23           5             0.0         0.0\n",
       "5  2011-01-23           6             0.0         0.0\n",
       "6  2011-01-23           7             0.0         0.0\n",
       "7  2011-01-23           8             0.0         0.0\n",
       "8  2011-01-23           9             0.0         0.0\n",
       "9  2011-01-23          10             0.0         0.0\n",
       "10 2011-01-23          11             0.0         0.0\n",
       "11 2011-01-23          12             0.0         0.0\n",
       "12 2011-01-24           1            25.0         0.0\n",
       "13 2011-01-24           2            17.0         0.0\n",
       "14 2011-01-24           3             9.0         0.0\n",
       "15 2011-01-24           4            12.0         0.0\n",
       "16 2011-01-24           5             5.0         0.0\n",
       "17 2011-01-24           6             3.0         0.0\n",
       "18 2011-01-24           7             1.0         0.0\n",
       "19 2011-01-24           8             6.0         0.0\n",
       "20 2011-01-24           9             3.0         0.0\n",
       "21 2011-01-24          10             0.0         0.0\n",
       "22 2011-01-24          11             0.0         0.0\n",
       "23 2011-01-24          12             0.0         0.0"
      ]
     },
     "execution_count": 838,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headcount.head(24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate net headcount change per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 839,
   "metadata": {},
   "outputs": [],
   "source": [
    "headcount['head_count_net_change']=headcount.new_hire_count - headcount.quit_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer#1:Get the headcount per day per company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the cumulative sum of headcount per day per company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 840,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumsums = headcount[['company_id','head_count_net_change']].groupby(['company_id']).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumsums.columns = ['head_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21432"
      ]
     },
     "execution_count": 842,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cumsums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 843,
   "metadata": {},
   "outputs": [],
   "source": [
    "headcount = pd.concat([headcount,cumsums], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>day</th>\n",
       "      <th>company_id</th>\n",
       "      <th>new_hire_count</th>\n",
       "      <th>quit_count</th>\n",
       "      <th>head_count_net_change</th>\n",
       "      <th>head_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21427</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21428</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>432.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21429</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21430</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21431</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             day  company_id  new_hire_count  quit_count  \\\n",
       "21427 2015-12-13           8             0.0         0.0   \n",
       "21428 2015-12-13           9             0.0         0.0   \n",
       "21429 2015-12-13          10             0.0         0.0   \n",
       "21430 2015-12-13          11             0.0         0.0   \n",
       "21431 2015-12-13          12             0.0         0.0   \n",
       "\n",
       "       head_count_net_change  head_count  \n",
       "21427                    0.0       468.0  \n",
       "21428                    0.0       432.0  \n",
       "21429                    0.0       385.0  \n",
       "21430                    0.0         4.0  \n",
       "21431                    0.0        12.0  "
      ]
     },
     "execution_count": 844,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headcount.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the factors drive employee churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check employment length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the timedelta between join and quit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 845,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['emp_length'] = data.quit_date-data.join_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "      <th>emp_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "      <td>585 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "      <td>340 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>389 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "      <td>1040 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  company_id              dept  seniority    salary  join_date  \\\n",
       "0      13021.0           7  customer_service         28   89000.0 2014-03-24   \n",
       "1     825355.0           7         marketing         20  183000.0 2013-04-29   \n",
       "2     927315.0           4         marketing         14  101000.0 2014-10-13   \n",
       "3     662910.0           7  customer_service         20  115000.0 2012-05-14   \n",
       "4     256971.0           2      data_science         23  276000.0 2011-10-17   \n",
       "\n",
       "   quit_date emp_length  \n",
       "0 2015-10-30   585 days  \n",
       "1 2014-04-04   340 days  \n",
       "2        NaT        NaT  \n",
       "3 2013-06-07   389 days  \n",
       "4 2014-08-22  1040 days  "
      ]
     },
     "execution_count": 846,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 847,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee_id            float64\n",
       "company_id               int64\n",
       "dept                    object\n",
       "seniority                int64\n",
       "salary                 float64\n",
       "join_date       datetime64[ns]\n",
       "quit_date       datetime64[ns]\n",
       "emp_length     timedelta64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 847,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                       13510\n",
       "mean     613 days 11:41:01.643227\n",
       "std      328 days 14:56:33.800149\n",
       "min             102 days 00:00:00\n",
       "25%             361 days 00:00:00\n",
       "50%             417 days 00:00:00\n",
       "75%             781 days 00:00:00\n",
       "max            1726 days 00:00:00\n",
       "Name: emp_length, dtype: object"
      ]
     },
     "execution_count": 848,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.emp_length.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 862,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "375 days    370\n",
       "361 days    368\n",
       "354 days    367\n",
       "368 days    333\n",
       "382 days    325\n",
       "Name: emp_length, dtype: int64"
      ]
     },
     "execution_count": 862,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.emp_length.value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 860,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1407fdf4710>"
      ]
     },
     "execution_count": 860,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VWSAkSVUWCElSlQVCklTlSWod0TyRLQ2PexCSpCoLhCSpygIhSaqyQEiSqiwQkqQqr2LSEWFty/+NT9LCLBDqFAuB1B0eYpIkVVkgJElVHmLSWPgJaKn73IOQJFVZICRJVX0fYoqINcANwKuBvwO2ZeYnI2IF8HlgLbAHeGdmPh8RAXwSuAD4MbApM+8bLL7m89CNpLYMcg5iH7AlM++LiFcAuyLiLmATcHdmXhMRW4GtwBXA+cC6cjsD+HT5KbVi/iWyFkhpMH0XiMx8BnimTP/viHgYWAVcBEyVbtcD0zQF4iLghsxM4N6IWBYRK8t6pNbtLxhb1u878ISUtHitnIOIiLXAG4GdwMT+F/3y8+TSbRXwZM/dZkubJKmDonlDP8AKIpYCfwF8JDNvjYgfZuaynuXPZ+byiPgi8DuZ+dXSfjfw4czcNW99m4HNABMTE6dt3759oHxtmJubY+nSpQOvZ/dTe1tI84KJ4+F7/+fgy9evOrHV7S3GYseq7bE4lInj4eQVox+LhbT1vGpTFzNBN3N1MRM0uTZs2LArMycHXddAn4OIiJ8CvgDcmJm3lubv7T90FBErgWdL+yywpufuq4Gn568zM7cB2wAmJydzampqkIitmJ6epo0cm1r+Gokt6/fx73cf/Fe457KpVre3GIsdq7bH4lC2rN/HOzvwPJqvredVm7qYCbqZq4uZoMnVlkGuYgrgWuDhzPz9nkU7gI3ANeXnbT3t742I7TQnp/d6/uHoMs7vWfLqLunwDbIH8WbgV4DdEfHN0vZvaArDzRFxOfAEcHFZdgfNJa4zNJe5vnuAbUuShmyQq5i+CsRBFp9d6Z/Ae/rd3pHKbyeVdKTyk9SSpCoLhCSpygIhSaqyQEiSqvx/EGpd10/Me8mrtDgWiJcwXwglDcJDTJKkKvcgdFQ72OEw97gkC4R0RPBwocbBAiG9RFhE1DYLhFrR+895fFodnlG/sFtItFj+JUsdNcjlwl2/1FhHBgvEEPjHqX4d7nPH55qGyQIhDdEwDueMoih4GEpggZD69lJ4EX0pPAYNjwVCffPwxgv6OTS0Zf2+kf7r1YV0MZPGywIhjchLoaAO8sHCtvZW3OsZHQtES14Kf/yqO9p/t8M+cb6Y/p8774S+12kR6Z8FQqo42ovCsB3u+O5+am/rh77mZ7CQvJgF4igxyDsqXyy1kHE9R3xuDtfIC0REnAd8EjgG+GxmXjPqDJKOHot9c+RhqRcbaYGIiGOAPwB+CZgFvh4ROzLzW6PMoYX5zkwvRYt9Xvstv41R70GcDsxk5uMAEbEduAg4IgvEkfoieqTmlsat929nMZcEH+kFZdQFYhXwZM/8LHDGiDP8hMW8WHptuKR+HO6bsa4VlMjM0W0s4mLg3Mz8tTL/K8Dpmfm+nj6bgc1l9rXAoyMLeHAnAT8Yd4iKLuYy0+J1MVcXM0E3c3UxEzS5TsjMnx50RaPeg5gF1vTMrwae7u2QmduAbaMMtZCI+EZmTo47x3xdzGWmxetiri5mgm7m6mImOJBrbRvrGvX/pP46sC4iTomIY4FLgB0jziBJWoSR7kFk5r6IeC9wJ81lrtdl5kOjzCBJWpyRfw4iM+8A7hj1dgfUqUNePbqYy0yL18VcXcwE3czVxUzQYq6RnqSWJB05Rn0OQpJ0hDjqC0RErImIeyLi4Yh4KCLeX9pXRMRdEfFY+bm8tEdEfCoiZiLigYg4dcj5jomIv4yI28v8KRGxs+T6fDnZT0QcV+ZnyvK1Q8qzLCJuiYhHypi9qQtjFREfKL+/ByPipoh42TjGKiKui4hnI+LBnrbDHp+I2Fj6PxYRG4eQ6WPld/hARPxJRCzrWXZlyfRoRJzb035eaZuJiK1tZ+pZ9qGIyIg4qcyPZJwOlSsi3lce+0MR8bs97WMZq4h4Q0TcGxHfjIhvRMTppb3dscrMo/oGrAROLdOvAP4KeB3wu8DW0r4V+GiZvgD4EhDAmcDOIef7IPBHwO1l/mbgkjL9GeDXy/RvAJ8p05cAnx9SnuuBXyvTxwLLxj1WNB/A/A5wfM8YbRrHWAG/CJwKPNjTdljjA6wAHi8/l5fp5S1nOgdYUqY/2pPpdcD9wHHAKcC3aS4oOaZMv6b83u8HXtdmptK+huYilv8BnDTKcTrEWP1z4L8Bx5X5k8c9VsCfA+f3jM/0MMaq9T/WI/0G3EbzXVGPAitL20rg0TL9h8ClPf0P9BtCltXA3cBZwO3ll/6Dnj/sNwF3luk7gTeV6SWlX7Sc55U0L8Qxr32sY8ULn9BfUR777cC54xorYO28P+bDGh/gUuAPe9p/ol8bmeYt+xfAjWX6SuDKnmV3lrE7MH61fm1lAm4Bfh7YwwsFYmTjdJDf383AWyr9xjZWZVu/XKYvBf5oGGN11B9i6lUONbwR2AlMZOYzAOXnyaVb7etCVg0p0ieADwN/V+ZfBfwwM/dVtn0gV1m+t/Rv02uA7wP/OZrDXp+NiBMY81hl5lPA7wFPAM/QPPZdjHeseh3u+IzyOQbwqzTvOseaKSIuBJ7KzPvnLRr3OP0c8E/L4ci/iIh/3IFcvwl8LCKepHnuXzmMTBaIIiKWAl8AfjMz/9ehulbaWr8ULCLeBjybmbsWue1R5FpCs6v76cx8I/AjmkMmBzOqsVpO86WPpwB/HzgBOP8Q2x5JrkU4WI6R5YuIq4B9wI3jzBQRLweuAv5tbfE4MvVYQnNY5kzgXwM3R0SMOdevAx/IzDXAB4BrS3urmSwQQET8FE1xuDEzby3N34uIlWX5SuDZ0r7g14W05M3AhRGxB9hOc5jpE8CyiNj/+ZXebR/IVZafCDzXcqZZYDYzd5b5W2gKxrjH6i3AdzLz+5n5t8CtwC8w3rHqdbjjM5JxKycq3wZcluW4wxgz/QxNgb+/POdXA/dFxKvHmGm/WeDWbHyNZo/+pDHn2kjzPAf4Y5pvyt6ftbVMR32BKO8ErgUezszf71m0g+aXQPl5W0/7u8rVAmcCe/cfPmhTZl6Zmauz+U6VS4AvZ+ZlwD3AOw6Sa3/ed5T+rb5ryczvAk9GxGtL09k0X9U+1rGiObR0ZkS8vPw+9+ca21jNc7jjcydwTkQsL3tH55S21kTzj7uuAC7MzB/Py3pJNFd6nQKsA77GkL8mJzN3Z+bJmbm2POdnaS4e+S5jHKfiT2neoBERP0dz4vkHjGmsiqeBf1amzwIeK9PtjtWgJ3SO9BvwT2h2tR4AvlluF9Ack767DPzdwIrSP2j+6dG3gd3A5AgyTvHCVUyvoXkSztC8c9h/ZcXLyvxMWf6aIWV5A/CNMl5/SrPrPfaxAv4d8AjwIPBfaK4sGflYATfRnAf5W5oXucv7GR+a8wIz5fbuIWSaoTkmvf85/5me/leVTI9SrpQp7RfQXOX3beCqtjPNW76HF05Sj2ScDjFWxwL/tTy37gPOGvdY0bxu7aK5QmoncNowxspPUkuSqo76Q0ySpDoLhCSpygIhSaqyQEiSqiwQkqQqC4QkqcoCIUmqskBIkqr+P4Zg0cF+j07kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1408183ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# need to convert timedelta datatype to day or hour or min or second before plot\n",
    "((data.emp_length.dropna() / np.timedelta64(1, 'D'))).hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation:<br>\n",
    "- Very high churn rate at the beginning of the second year of employment<br>\n",
    "- relatively high churn rate between 1.5 to 2 years of employment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dig deeper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since it has such a clear pattern, let's dig into deeper.<br>\n",
    "Break into two groups: quitted early and not(if they haven’t been in the current\n",
    "company for at least 13 months, we remove them)<br>\n",
    "Let's define the early quitters are the ones quitted before 13 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 930,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get data quitted before 13 months\n",
    "early_quitter = data[data.emp_length/np.timedelta64(1,'D') < 365+30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 929,
   "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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "      <th>emp_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "      <td>340 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>389 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>939058.0</td>\n",
       "      <td>1</td>\n",
       "      <td>marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>2012-12-10</td>\n",
       "      <td>2013-11-15</td>\n",
       "      <td>340 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>461248.0</td>\n",
       "      <td>2</td>\n",
       "      <td>sales</td>\n",
       "      <td>20</td>\n",
       "      <td>201000.0</td>\n",
       "      <td>2013-09-16</td>\n",
       "      <td>2014-08-22</td>\n",
       "      <td>340 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>219944.0</td>\n",
       "      <td>6</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>15</td>\n",
       "      <td>98000.0</td>\n",
       "      <td>2012-06-25</td>\n",
       "      <td>2013-05-31</td>\n",
       "      <td>340 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    employee_id  company_id              dept  seniority    salary  join_date  \\\n",
       "1      825355.0           7         marketing         20  183000.0 2013-04-29   \n",
       "3      662910.0           7  customer_service         20  115000.0 2012-05-14   \n",
       "12     939058.0           1         marketing          1   48000.0 2012-12-10   \n",
       "14     461248.0           2             sales         20  201000.0 2013-09-16   \n",
       "21     219944.0           6  customer_service         15   98000.0 2012-06-25   \n",
       "\n",
       "    quit_date emp_length  \n",
       "1  2014-04-04   340 days  \n",
       "3  2013-06-07   389 days  \n",
       "12 2013-11-15   340 days  \n",
       "14 2014-08-22   340 days  \n",
       "21 2013-05-31   340 days  "
      ]
     },
     "execution_count": 929,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "early_quitter.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 931,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_day = pd.to_datetime(\"2015-12-13\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 932,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-12-13 00:00:00')"
      ]
     },
     "execution_count": 932,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 944,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the data not early quitter and exclude the ones employed less than 13 months\n",
    "longer_emp = data[((last_day - data.join_date)/np.timedelta64(1,'D') >365+30)\\\n",
    "    &(data.emp_length/np.timedelta64(1,'D') > 365+30)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 945,
   "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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "      <th>emp_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "      <td>585 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "      <td>1040 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>509529.0</td>\n",
       "      <td>4</td>\n",
       "      <td>data_science</td>\n",
       "      <td>14</td>\n",
       "      <td>165000.0</td>\n",
       "      <td>2012-01-30</td>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>578 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>172999.0</td>\n",
       "      <td>9</td>\n",
       "      <td>engineer</td>\n",
       "      <td>7</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>2012-12-10</td>\n",
       "      <td>2015-10-23</td>\n",
       "      <td>1047 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>892155.0</td>\n",
       "      <td>6</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>13</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>2012-11-12</td>\n",
       "      <td>2015-02-27</td>\n",
       "      <td>837 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    employee_id  company_id              dept  seniority    salary  join_date  \\\n",
       "0       13021.0           7  customer_service         28   89000.0 2014-03-24   \n",
       "4      256971.0           2      data_science         23  276000.0 2011-10-17   \n",
       "5      509529.0           4      data_science         14  165000.0 2012-01-30   \n",
       "8      172999.0           9          engineer          7  160000.0 2012-12-10   \n",
       "10     892155.0           6  customer_service         13   72000.0 2012-11-12   \n",
       "\n",
       "    quit_date emp_length  \n",
       "0  2015-10-30   585 days  \n",
       "4  2014-08-22  1040 days  \n",
       "5  2013-08-30   578 days  \n",
       "8  2015-10-23  1047 days  \n",
       "10 2015-02-27   837 days  "
      ]
     },
     "execution_count": 945,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_emp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "might use decision tree here to model it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 949,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      5654.000000\n",
       "mean     131393.880439\n",
       "std       65464.211853\n",
       "min       17000.000000\n",
       "25%       81000.000000\n",
       "50%      122000.000000\n",
       "75%      173000.000000\n",
       "max      372000.000000\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 949,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "early_quitter.salary.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 950,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      7795.000000\n",
       "mean     138768.313021\n",
       "std       75379.904785\n",
       "min       19000.000000\n",
       "25%       80000.000000\n",
       "50%      123000.000000\n",
       "75%      187000.000000\n",
       "max      379000.000000\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 950,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_emp.salary.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check week of year-quit time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 850,
   "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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "      <th>emp_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "      <td>585 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "      <td>340 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>389 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "      <td>1040 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  company_id              dept  seniority    salary  join_date  \\\n",
       "0      13021.0           7  customer_service         28   89000.0 2014-03-24   \n",
       "1     825355.0           7         marketing         20  183000.0 2013-04-29   \n",
       "2     927315.0           4         marketing         14  101000.0 2014-10-13   \n",
       "3     662910.0           7  customer_service         20  115000.0 2012-05-14   \n",
       "4     256971.0           2      data_science         23  276000.0 2011-10-17   \n",
       "\n",
       "   quit_date emp_length  \n",
       "0 2015-10-30   585 days  \n",
       "1 2014-04-04   340 days  \n",
       "2        NaT        NaT  \n",
       "3 2013-06-07   389 days  \n",
       "4 2014-08-22  1040 days  "
      ]
     },
     "execution_count": 850,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 868,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get week of the year\n",
    "week = data.quit_date.dropna().dt.week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 869,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14082c3b208>"
      ]
     },
     "execution_count": 869,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x140af507048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "week.hist(bins = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation:<br>\n",
    "No significant pattern"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check if different dept matters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 876,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dept quitted\n",
    "dept_q = data[data['quit_date'].notnull()].dept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 879,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_service    0.554902\n",
       "data_science        0.527273\n",
       "design              0.563768\n",
       "engineer            0.512031\n",
       "marketing           0.562993\n",
       "sales               0.570933\n",
       "Name: dept, dtype: float64"
      ]
     },
     "execution_count": 879,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of churned in each dept\n",
    "dept.value_counts()/data.dept.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation:\n",
    "No significant diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seniority"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 888,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = data[data['quit_date'].notnull()].seniority.value_counts()/data.seniority.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 889,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     0.499419\n",
       "2     0.530786\n",
       "3     0.507378\n",
       "4     0.471508\n",
       "5     0.569444\n",
       "6     0.601053\n",
       "7     0.550647\n",
       "8     0.581349\n",
       "9     0.552966\n",
       "10    0.564186\n",
       "11    0.554113\n",
       "12    0.590081\n",
       "13    0.559284\n",
       "14    0.552174\n",
       "15    0.554336\n",
       "16    0.570513\n",
       "17    0.535274\n",
       "18    0.524083\n",
       "19    0.546154\n",
       "20    0.555687\n",
       "21    0.581841\n",
       "22    0.530105\n",
       "23    0.547771\n",
       "24    0.535666\n",
       "25    0.563636\n",
       "26    0.517291\n",
       "27    0.532710\n",
       "28    0.547009\n",
       "29    0.492013\n",
       "98    1.000000\n",
       "99    1.000000\n",
       "Name: seniority, dtype: float64"
      ]
     },
     "execution_count": 889,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 890,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_df = pd.DataFrame(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 892,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1408a007be0>"
      ]
     },
     "execution_count": 892,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1408a4d04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_df.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation:\n",
    "No significant diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Empolyee quit at their working anniversaries and has a extremly high churn rate at the first and second year.<br>\n",
    "2. Salary is an import factor.(need to dig deeper).Employees with low and high salaries are less likely to quit. Probably because employees with high\n",
    "salaries are happy there and employees with low salaries are not that marketable, so they have a\n",
    "hard time finding a new job."
   ]
  }
 ],
 "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}