Starbucks_FINAL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import DataFrame as df\n",
    "from sklearn import preprocessing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrameGroupBy' object has no attribute 'value_counts'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-fc0efd4f1de0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mstate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'PRTNR_ID'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, attr)\u001b[0m\n\u001b[0;32m    701\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mattr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    702\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 703\u001b[1;33m         raise AttributeError(\n\u001b[0m\u001b[0;32m    704\u001b[0m             \u001b[1;34mf\"'{type(self).__name__}' object has no attribute '{attr}'\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    705\u001b[0m         )\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrameGroupBy' object has no attribute 'value_counts'"
     ]
    }
   ],
   "source": [
    "#import Data File to CSV\n",
    "#import Data File to CSV\n",
    "export30 = pd.read_csv('export30_drop_duplicated.csv')\n",
    "export31 = pd.read_csv('export31_drop_duplicated.csv')\n",
    "export32 = pd.read_csv('export32_drop_duplicated.csv')\n",
    "export33 = pd.read_csv('export33_drop_duplicated.csv')\n",
    "frames = [export30, export31, export32,export33]\n",
    "state = pd.concat(frames).drop_duplicates()\n",
    "\n",
    "state.groupby('PRTNR_ID').value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Delete the employee whose active status is always 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2456,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Delete the employee whose active status is always 0\n",
    "# Find employees whose active status is alwyas 0\n",
    "state1 = pd.DataFrame(state.groupby(['PRTNR_ID'])['PRTNR_ACTIVE_FLG'].sum())\n",
    "state1 = state1['PRTNR_ACTIVE_FLG'].reset_index()\n",
    "state1 = state1[state1['PRTNR_ACTIVE_FLG'] == 0]\n",
    "state1 = state1.drop(['PRTNR_ACTIVE_FLG'], axis=1).reset_index().drop(['index'], axis=1)\n",
    "state1 =list(state1['PRTNR_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2457,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the partners row whose active stats is always 0\n",
    "to_drop = state1\n",
    "state = state[~state['PRTNR_ID'].isin(to_drop)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *change time format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2458,
   "metadata": {},
   "outputs": [],
   "source": [
    "state['FiscalWeekBeginDate']=pd.to_datetime(state['FiscalWeekBeginDate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### count employee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2459,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13491"
      ]
     },
     "execution_count": 2459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total # of Employees\n",
    "len(state['PRTNR_ID'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * get a dataframe contains Unique Partner's ID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2460,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_prtnr_ID = pd.DataFrame(state['PRTNR_ID'].unique())\n",
    "unique_prtnr_ID = unique_prtnr_ID.rename(columns = {0:'PRTNR_ID'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * Each Employee's the earliest start date (=FiscalWeekBeginDate min_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2461,
   "metadata": {},
   "outputs": [],
   "source": [
    "earliest_FiscalWeekBeginDate = state.groupby(['PRTNR_ID'])['FiscalWeekBeginDate'].min()\n",
    "earliest_FiscalWeekBeginDate = earliest_FiscalWeekBeginDate.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * The number of positions that employees covered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2462,
   "metadata": {},
   "outputs": [],
   "source": [
    "# each employees' job count\n",
    "employee_jobs = state[['PRTNR_ID','JOB_NM']].drop_duplicates()\n",
    "the_number_of_jobs = employee_jobs.groupby(['PRTNR_ID'])['JOB_NM'].count().reset_index()\n",
    "the_number_of_jobs = the_number_of_jobs.rename(columns={'JOB_NM': 'count_position'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * The last position name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2463,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the last job position name\n",
    "last_position = state[['PRTNR_ID','FiscalWeekBeginDate', 'JOB_NM']]\n",
    "last_position = last_position.groupby('PRTNR_ID').max().reset_index().drop(['FiscalWeekBeginDate'], axis=1)\n",
    "last_position = last_position.rename(columns = {'JOB_NM':'last_position'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * Obtaining the last Com_Tenure,   last FiscalWeekBeginDate and the last status of PRTNR_ACTIVE_FLG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2464,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collect the cols 'PRTNR_ID','FiscalWeekBeginDate', 'FSCL_WK_END_DT_JOB_TNUR' from the original dataset\n",
    "FSCL_WK_END_DT_COM_TNUR = state[['PRTNR_ID','FiscalWeekBeginDate', 'FSCL_WK_END_DT_COM_TNUR','PRTNR_ACTIVE_FLG']]\n",
    "\n",
    "#Employee whose last active flg = 0\n",
    "FSCL_WK_END_DT_COM_TNUR_0 = FSCL_WK_END_DT_COM_TNUR[FSCL_WK_END_DT_COM_TNUR['PRTNR_ACTIVE_FLG'] == 0]\n",
    "FSCL_WK_END_DT_COM_TNUR_0 =  FSCL_WK_END_DT_COM_TNUR_0.groupby(['PRTNR_ID','FSCL_WK_END_DT_COM_TNUR','PRTNR_ACTIVE_FLG'])['FiscalWeekBeginDate'].agg('min')\n",
    "FSCL_WK_END_DT_COM_TNUR_0 = pd.DataFrame(FSCL_WK_END_DT_COM_TNUR_0).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2465,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2465-6a6bac8a7aaf>:4: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  FSCL_WK_END_DT_COM_TNUR_1 =  FSCL_WK_END_DT_COM_TNUR_1.groupby(['PRTNR_ID'])['FSCL_WK_END_DT_COM_TNUR','FiscalWeekBeginDate','PRTNR_ACTIVE_FLG'].agg('max')\n"
     ]
    }
   ],
   "source": [
    "#Employee whose last active flg = 1\n",
    "FSCL_WK_END_DT_COM_TNUR = state[['PRTNR_ID','FiscalWeekBeginDate', 'FSCL_WK_END_DT_COM_TNUR','PRTNR_ACTIVE_FLG']]\n",
    "FSCL_WK_END_DT_COM_TNUR_1 = FSCL_WK_END_DT_COM_TNUR[FSCL_WK_END_DT_COM_TNUR['PRTNR_ACTIVE_FLG'] == 1]\n",
    "FSCL_WK_END_DT_COM_TNUR_1 =  FSCL_WK_END_DT_COM_TNUR_1.groupby(['PRTNR_ID'])['FSCL_WK_END_DT_COM_TNUR','FiscalWeekBeginDate','PRTNR_ACTIVE_FLG'].agg('max')\n",
    "FSCL_WK_END_DT_COM_TNUR_1 = pd.DataFrame(FSCL_WK_END_DT_COM_TNUR_1).reset_index()\n",
    "# #drop the partners row whose active stats is always 0\n",
    "to_drop = FSCL_WK_END_DT_COM_TNUR_0['PRTNR_ID']\n",
    "FSCL_WK_END_DT_COM_TNUR_1['PRTNR_ID'] = FSCL_WK_END_DT_COM_TNUR_1[~FSCL_WK_END_DT_COM_TNUR_1['PRTNR_ID'].isin(to_drop)]\n",
    "FSCL_WK_END_DT_COM_TNUR_1=FSCL_WK_END_DT_COM_TNUR_1.dropna()\n",
    "FSCL_WK_END_DT_COM_TNUR_1\n",
    "\n",
    "#concat FSCL_WK_END_DT_COM_TNUR_0 and FSCL_WK_END_DT_COM_TNUR_1\n",
    "FSCL_WK_END_DT_COM_TNUR =pd.concat([FSCL_WK_END_DT_COM_TNUR_0,FSCL_WK_END_DT_COM_TNUR_1], axis=0)\n",
    "FSCL_WK_END_DT_COM_TNUR['Last_Month_Year'] = pd.to_datetime(FSCL_WK_END_DT_COM_TNUR['FiscalWeekBeginDate']).dt.to_period('M')\n",
    "FSCL_WK_END_DT_COM_TNUR = FSCL_WK_END_DT_COM_TNUR.rename(columns = {'FiscalWeekBeginDate' : 'Last_FiscalWeekBeginDate',\n",
    "                                                                   'FSCL_WK_END_DT_COM_TNUR': 'Last_FSCL_WK_END_DT_COM_TNUR'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *the longest position holding period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2466,
   "metadata": {},
   "outputs": [],
   "source": [
    "the_longest_position_holding_period = state[['PRTNR_ID','FiscalWeekBeginDate', 'FSCL_WK_END_DT_JOB_TNUR']]\n",
    "the_longest_position_holding_period = pd.merge(FSCL_WK_END_DT_COM_TNUR,the_longest_position_holding_period, how ='inner',\n",
    "                                              left_on = ['PRTNR_ID','Last_FiscalWeekBeginDate'],\n",
    "                                                         right_on = ['PRTNR_ID','FiscalWeekBeginDate']).drop_duplicates()\n",
    "\n",
    "the_longest_position_holding_period = the_longest_position_holding_period.drop(['Last_FSCL_WK_END_DT_COM_TNUR', 'PRTNR_ACTIVE_FLG',\n",
    "                                                                               'Last_FiscalWeekBeginDate',\n",
    "                                                                               'Last_Month_Year','FiscalWeekBeginDate'], axis=1) \n",
    "the_longest_position_holding_period = the_longest_position_holding_period.rename(columns = {'FSCL_WK_END_DT_JOB_TNUR' : 'longest_position_holding_period'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *generation group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2467,
   "metadata": {},
   "outputs": [],
   "source": [
    "generation = state[['PRTNR_ID','GEN_GRP_CD']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Average count_of_prtnrs by Quarter and Branch to calculate labor density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2468,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collect the cols 'PRTNR_ID','FiscalWeekBeginDate','PRTNR_ACTIVE_FLG','count_of_prtnrs' from the original dataset\n",
    "count_of_prtnrs = state\n",
    "\n",
    "#convert y-m-d to y-m\n",
    "count_of_prtnrs['Month_Year']= pd.to_datetime(count_of_prtnrs['FiscalWeekBeginDate']).dt.to_period('M')\n",
    "count_of_prtnrs['Qtr']= pd.to_datetime(count_of_prtnrs['FiscalWeekBeginDate']).dt.to_period('Q')\n",
    "\n",
    "#Calculate Average Qtr by branch\n",
    "Qtr_avg= round( pd.DataFrame(count_of_prtnrs.groupby(['Qtr','hashedstorenumber'])['count_of_prtnrs'].mean()).reset_index(),0)\n",
    "Qtr_avg = Qtr_avg.rename(columns={'count_of_prtnrs' : 'Qtr_avg_count_of_prtnrs'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If 'Count of partners on employee's active week' < 'Qtr_avg_count_of_prtnrs', 'Labor Density = 1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2469,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge to count_of_prtnrs data-frame\n",
    "count_of_prtnrs = pd.merge(count_of_prtnrs, Qtr_avg, how = 'left', left_on = ['Qtr','hashedstorenumber'],\n",
    "                           right_on = ['Qtr','hashedstorenumber'])\n",
    "\n",
    "count_of_prtnrs[\"labor density\"] = [ 1 if x < y and z == 1 else 0 for x, y, z in zip(count_of_prtnrs[\"count_of_prtnrs\"],\n",
    "                                                                    count_of_prtnrs[\"Qtr_avg_count_of_prtnrs\"],\n",
    "                                                                                count_of_prtnrs[\"PRTNR_ACTIVE_FLG\"])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * labor density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2470,
   "metadata": {},
   "outputs": [],
   "source": [
    "labor_density = pd.DataFrame(count_of_prtnrs.groupby(['PRTNR_ID'])['labor density'].sum()).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * 'STATE_CODE','CITY_NAME', 'primary_venue', 'urbanity','store_type', 'hashedstorenumber' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2471,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collect the cols 'PRTNR_ID',STATE_CODE','CITY_NAME', 'primary_venue', 'urbanity','store_type', 'hashedstorenumber' from the original dataset\n",
    "general = state[['PRTNR_ID','FiscalWeekBeginDate','STATE_CODE','CITY_NAME','urbanity','store_type', 'hashedstorenumber']].drop_duplicates()\n",
    "general1 = general.groupby(['PRTNR_ID'])['FiscalWeekBeginDate'].max()\n",
    "general2 = general[['PRTNR_ID','FiscalWeekBeginDate','STATE_CODE','CITY_NAME', 'urbanity','store_type', 'hashedstorenumber']]\n",
    "general1 = pd.merge(general1,general2, how = 'left', left_on = ['PRTNR_ID','FiscalWeekBeginDate'] ,\n",
    "                        right_on = ['PRTNR_ID','FiscalWeekBeginDate']).drop(['FiscalWeekBeginDate'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * benefitselig\t& benefitsused"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2472,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2472-97c3655ebd65>:7: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  benefit_first_0 = pd.DataFrame(benefit_0.groupby(['PRTNR_ID'])['FiscalWeekBeginDate','PRTNR_ACTIVE_FLG','benefitselig','benefitsused'].agg('first')).reset_index()\n"
     ]
    }
   ],
   "source": [
    "# Collect the cols 'PRTNR_ID',STATE_CODE','CITY_NAME', 'primary_venue', 'urbanity','store_type',\n",
    "benefit =state[['PRTNR_ID', 'FiscalWeekBeginDate', 'PRTNR_ACTIVE_FLG','benefitselig', 'benefitsused']] \n",
    "benefit = benefit.sort_values(by=\"FiscalWeekBeginDate\")\n",
    "\n",
    "#Find partners whose active flg is 0 and remain only the first 0\n",
    "benefit_0 = benefit[benefit['PRTNR_ACTIVE_FLG']==0]\n",
    "benefit_first_0 = pd.DataFrame(benefit_0.groupby(['PRTNR_ID'])['FiscalWeekBeginDate','PRTNR_ACTIVE_FLG','benefitselig','benefitsused'].agg('first')).reset_index()\n",
    "\n",
    "benefit_second_0 = pd.merge(benefit_0, benefit_first_0, on=['PRTNR_ID','FiscalWeekBeginDate'],how='left', indicator=True).query(\"_merge=='left_only'\").drop('_merge', 1)\n",
    "benefit_second_0 = benefit_second_0.drop(['PRTNR_ACTIVE_FLG_y','benefitselig_y','benefitsused_y'], axis=1)\n",
    "benefit_second_0 = benefit_second_0.rename(columns={'PRTNR_ACTIVE_FLG_x':'PRTNR_ACTIVE_FLG',\n",
    "                                                   'benefitselig_x':'benefitselig',\n",
    "                                                   'benefitsused_x':'benefitsused'})\n",
    "\n",
    "benefit = pd.merge(benefit, benefit_second_0, on=['PRTNR_ID','FiscalWeekBeginDate'],how='left', indicator=True).query(\"_merge=='left_only'\").drop('_merge', 1)\n",
    "benefit = benefit.drop(['PRTNR_ACTIVE_FLG_y','benefitselig_y','benefitsused_y'], axis=1)\n",
    "benefit = benefit.rename(columns={'PRTNR_ACTIVE_FLG_x':'PRTNR_ACTIVE_FLG',\n",
    "                                                   'benefitselig_x':'benefitselig',\n",
    "                                                   'benefitsused_x':'benefitsused'})\n",
    "\n",
    "# Convert True and False to 1 and 0\n",
    "benefit['benefitselig'].replace({False: 0, True: 1}, inplace=True)\n",
    "benefit['benefitsused'].replace({False: 0, True: 1}, inplace=True)\n",
    "\n",
    "# aggregate in a line for each 'PRTNR_ID'\n",
    "benefitselig_sum= benefit.groupby(['PRTNR_ID'])['benefitselig'].sum().reset_index().round()\n",
    "benefitsused_sum = benefit.groupby(['PRTNR_ID'])['benefitsused'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Obtaining pctpay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2473,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2473-79a4f3ab1488>:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  pctpay_min = state[['PRTNR_ID','FiscalWeekBeginDate','FSCL_WK_END_DT_JOB_TNUR' ,'pctpay','pulse']].groupby(['PRTNR_ID'])['FiscalWeekBeginDate','FSCL_WK_END_DT_JOB_TNUR','pctpay','pulse'].min().reset_index().drop(['FiscalWeekBeginDate','pulse'], axis=1)\n",
      "<ipython-input-2473-79a4f3ab1488>:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  pctpay_max = state[['PRTNR_ID','FiscalWeekBeginDate', 'FSCL_WK_END_DT_JOB_TNUR','pctpay','pulse']].groupby(['PRTNR_ID'])['FiscalWeekBeginDate','FSCL_WK_END_DT_JOB_TNUR' ,'pctpay','pulse'].max().reset_index().drop(['FiscalWeekBeginDate','pulse'], axis=1)\n"
     ]
    }
   ],
   "source": [
    "state['FSCL_WK_END_DT_JOB_TNUR'] = round(state['FSCL_WK_END_DT_JOB_TNUR']/365,6)\n",
    "pctpay_min = state[['PRTNR_ID','FiscalWeekBeginDate','FSCL_WK_END_DT_JOB_TNUR' ,'pctpay','pulse']].groupby(['PRTNR_ID'])['FiscalWeekBeginDate','FSCL_WK_END_DT_JOB_TNUR','pctpay','pulse'].min().reset_index().drop(['FiscalWeekBeginDate','pulse'], axis=1)\n",
    "pctpay_max = state[['PRTNR_ID','FiscalWeekBeginDate', 'FSCL_WK_END_DT_JOB_TNUR','pctpay','pulse']].groupby(['PRTNR_ID'])['FiscalWeekBeginDate','FSCL_WK_END_DT_JOB_TNUR' ,'pctpay','pulse'].max().reset_index().drop(['FiscalWeekBeginDate','pulse'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2474,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pctpay_max = pctpay_max.rename(columns={'FSCL_WK_END_DT_JOB_TNUR':'MAX_FSCL_WK_END_DT_JOB_TNUR',\n",
    "                                        'pctpay':'max_pctpay'})\n",
    "pctpay_min = pctpay_min.rename(columns={'FSCL_WK_END_DT_JOB_TNUR':'MIN_FSCL_WK_END_DT_JOB_TNUR',\n",
    "                                        'pctpay':'min_pctpay'})\n",
    "\n",
    "pctpay_min_max = pctpay_min.merge(pctpay_max, how='left', on='PRTNR_ID')\n",
    "\n",
    "pctpay_min_max['max-min_tnur'] = round((pctpay_min_max['MAX_FSCL_WK_END_DT_JOB_TNUR'] - pctpay_min_max['MIN_FSCL_WK_END_DT_JOB_TNUR'])/365,6)\n",
    "pctpay_min_max['max-min_pctpay'] = pctpay_min_max['max_pctpay'] - pctpay_min_max['min_pctpay']\n",
    "\n",
    "pctpay_min_max['anual_average_Wage_increase'] = round(pctpay_min_max['max-min_tnur']/pctpay_min_max['max-min_pctpay'],4)*100\n",
    "pctpay_min_max = pctpay_min_max.drop(['MAX_FSCL_WK_END_DT_JOB_TNUR','max-min_tnur','max-min_pctpay'],axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Create a new data-frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2475,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # Merge the data-frame\n",
    "new_df = earliest_FiscalWeekBeginDate.merge(the_number_of_jobs, how='inner', on='PRTNR_ID')\n",
    "new_df = new_df.merge(last_position, how='inner', on='PRTNR_ID')\n",
    "new_df = new_df.merge(FSCL_WK_END_DT_COM_TNUR, how='inner', on='PRTNR_ID')\n",
    "new_df = new_df.merge(the_longest_position_holding_period, how='inner', on='PRTNR_ID')\n",
    "new_df = new_df.merge(generation, how='inner', on='PRTNR_ID')\n",
    "new_df = new_df.merge(labor_density, how='inner', on='PRTNR_ID')\n",
    "new_df = new_df.merge(general1, how='inner', on='PRTNR_ID')\n",
    "new_df = new_df.merge(benefitselig_sum, how='inner', on='PRTNR_ID')\n",
    "new_df = new_df.merge(benefitsused_sum, how='inner', on='PRTNR_ID')\n",
    "new_df = new_df.merge(pctpay_min_max, how='inner', on='PRTNR_ID').replace([np.inf, -np.inf], 0).dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Job Position Changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2476,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df[\"Job Position Changed\"] = [ 1 if x > y else 0 for x, y in zip(new_df[\"Last_FSCL_WK_END_DT_COM_TNUR\"],\n",
    "                                                                                   new_df[\"longest_position_holding_period\"])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Fromating Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2477,
   "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>PRTNR_ID</th>\n",
       "      <th>last_position</th>\n",
       "      <th>Last_FSCL_WK_END_DT_COM_TNUR</th>\n",
       "      <th>PRTNR_ACTIVE_FLG</th>\n",
       "      <th>Last_Month_Year</th>\n",
       "      <th>longest_position_holding_period</th>\n",
       "      <th>GEN_GRP_CD</th>\n",
       "      <th>labor density</th>\n",
       "      <th>STATE_CODE</th>\n",
       "      <th>CITY_NAME</th>\n",
       "      <th>urbanity</th>\n",
       "      <th>store_type</th>\n",
       "      <th>hashedstorenumber</th>\n",
       "      <th>benefitselig</th>\n",
       "      <th>benefitsused</th>\n",
       "      <th>MIN_FSCL_WK_END_DT_JOB_TNUR</th>\n",
       "      <th>min_pctpay</th>\n",
       "      <th>max_pctpay</th>\n",
       "      <th>anual_average_Wage_increase</th>\n",
       "      <th>Job Position Changed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005F00557E5CD0BCAC6F8A1F343DC35</td>\n",
       "      <td>barista</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>1.34</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>27</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>U4</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>24</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000FF458D677F2E37B0EC7B3FB093968</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>4.64</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>3.21</td>\n",
       "      <td>Millennials</td>\n",
       "      <td>28</td>\n",
       "      <td>WA</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>U5</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>4</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.78</td>\n",
       "      <td>4.98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0014B120A048068D92EE2A06B99E48A6</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>9.46</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Millennials</td>\n",
       "      <td>20</td>\n",
       "      <td>NV</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>U3</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>264</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>7.12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>001802D72E5685A8A81DAE17BE44ADD7</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>1.85</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>25</td>\n",
       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>U2</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>37</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.73</td>\n",
       "      <td>4.73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0018A858F59D9F3BFA5CF6AB93DA74B8</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>0.38</td>\n",
       "      <td>Millennials</td>\n",
       "      <td>13</td>\n",
       "      <td>WA</td>\n",
       "      <td>Tacoma</td>\n",
       "      <td>U4</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>359</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13719</th>\n",
       "      <td>FFE432EE30DAE9BF78A45D3970ECF0A0</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>0.28</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>8</td>\n",
       "      <td>CA</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>U3</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>108</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13720</th>\n",
       "      <td>FFF3F91FF8FFD54F8962C0C5DA8238BD</td>\n",
       "      <td>barista</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-08</td>\n",
       "      <td>0.16</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>0</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>U3</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>321</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13721</th>\n",
       "      <td>FFF417C1B02B4DD879AD8B7FC4E8856A</td>\n",
       "      <td>barista</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>6</td>\n",
       "      <td>OR</td>\n",
       "      <td>Portland</td>\n",
       "      <td>U4</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>34</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.74</td>\n",
       "      <td>5.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13722</th>\n",
       "      <td>FFF549C28AF0218448441980B3F4A52F</td>\n",
       "      <td>barista</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>0.60</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>11</td>\n",
       "      <td>FL</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>U5</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>287</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13723</th>\n",
       "      <td>FFFEB61EB08DAFF74035D738B2A760BE</td>\n",
       "      <td>barista</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>0.58</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>5</td>\n",
       "      <td>CA</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>U3</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12513 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               PRTNR_ID     last_position  \\\n",
       "0      0005F00557E5CD0BCAC6F8A1F343DC35           barista   \n",
       "1      000FF458D677F2E37B0EC7B3FB093968  shift supervisor   \n",
       "2      0014B120A048068D92EE2A06B99E48A6  shift supervisor   \n",
       "4      001802D72E5685A8A81DAE17BE44ADD7  shift supervisor   \n",
       "5      0018A858F59D9F3BFA5CF6AB93DA74B8  shift supervisor   \n",
       "...                                 ...               ...   \n",
       "13719  FFE432EE30DAE9BF78A45D3970ECF0A0  shift supervisor   \n",
       "13720  FFF3F91FF8FFD54F8962C0C5DA8238BD           barista   \n",
       "13721  FFF417C1B02B4DD879AD8B7FC4E8856A           barista   \n",
       "13722  FFF549C28AF0218448441980B3F4A52F           barista   \n",
       "13723  FFFEB61EB08DAFF74035D738B2A760BE           barista   \n",
       "\n",
       "       Last_FSCL_WK_END_DT_COM_TNUR  PRTNR_ACTIVE_FLG Last_Month_Year  \\\n",
       "0                              1.34                 1         2019-09   \n",
       "1                              4.64                 0         2019-04   \n",
       "2                              9.46                 0         2018-11   \n",
       "4                              3.24                 0         2019-06   \n",
       "5                              0.78                 1         2019-09   \n",
       "...                             ...               ...             ...   \n",
       "13719                          0.87                 0         2019-02   \n",
       "13720                          0.16                 0         2019-08   \n",
       "13721                          1.30                 0         2018-07   \n",
       "13722                          0.60                 0         2018-09   \n",
       "13723                          0.58                 0         2019-09   \n",
       "\n",
       "       longest_position_holding_period    GEN_GRP_CD  labor density  \\\n",
       "0                                 1.34  Generation Z             27   \n",
       "1                                 3.21   Millennials             28   \n",
       "2                                 0.72   Millennials             20   \n",
       "4                                 1.85  Generation Z             25   \n",
       "5                                 0.38   Millennials             13   \n",
       "...                                ...           ...            ...   \n",
       "13719                             0.28  Generation Z              8   \n",
       "13720                             0.16  Generation Z              0   \n",
       "13721                             1.30  Generation Z              6   \n",
       "13722                             0.60  Generation Z             11   \n",
       "13723                             0.58  Generation Z              5   \n",
       "\n",
       "      STATE_CODE    CITY_NAME urbanity  store_type  hashedstorenumber  \\\n",
       "0             AZ         Mesa       U4  Drive Thru                 24   \n",
       "1             WA    Vancouver       U5  Drive Thru                  4   \n",
       "2             NV    Las Vegas       U3  Drive Thru                264   \n",
       "4             IL      Chicago       U2        Cafe                 37   \n",
       "5             WA       Tacoma       U4  Drive Thru                359   \n",
       "...          ...          ...      ...         ...                ...   \n",
       "13719         CA  Santa Clara       U3        Cafe                108   \n",
       "13720         CA    San Diego       U3  Drive Thru                321   \n",
       "13721         OR     Portland       U4        Cafe                 34   \n",
       "13722         FL      Orlando       U5  Drive Thru                287   \n",
       "13723         CA  Los Angeles       U3  Drive Thru                 20   \n",
       "\n",
       "       benefitselig  benefitsused  MIN_FSCL_WK_END_DT_JOB_TNUR  min_pctpay  \\\n",
       "0              71.0             1                         0.02        0.41   \n",
       "1              82.0             0                         1.67        0.70   \n",
       "2              61.0             0                         0.02        0.46   \n",
       "4              90.0             0                         0.15        0.63   \n",
       "5              41.0             0                         0.02        0.63   \n",
       "...             ...           ...                          ...         ...   \n",
       "13719          21.0             0                         0.02        0.83   \n",
       "13720           0.0             0                         0.02        0.61   \n",
       "13721          40.0             0                         0.56        0.70   \n",
       "13722          12.0             0                         0.01        0.15   \n",
       "13723           7.0             0                         0.01        0.83   \n",
       "\n",
       "       max_pctpay  anual_average_Wage_increase  Job Position Changed  \n",
       "0            0.44                        10.33                     0  \n",
       "1            0.78                         4.98                     1  \n",
       "2            0.55                         7.12                     1  \n",
       "4            0.73                         4.73                     1  \n",
       "5            0.76                         0.86                     1  \n",
       "...           ...                          ...                   ...  \n",
       "13719        0.95                         1.39                     1  \n",
       "13720        0.61                         0.00                     0  \n",
       "13721        0.74                         5.55                     0  \n",
       "13722        0.29                         1.19                     0  \n",
       "13723        0.91                         1.92                     0  \n",
       "\n",
       "[12513 rows x 20 columns]"
      ]
     },
     "execution_count": 2477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['FiscalWeekBeginDate'] = pd.to_datetime(count_of_prtnrs['FiscalWeekBeginDate']).dt.to_period('M')\n",
    "new_df['Last_FSCL_WK_END_DT_COM_TNUR'] = round(new_df['Last_FSCL_WK_END_DT_COM_TNUR']/365,2)\n",
    "new_df['longest_position_holding_period'] = round(new_df['longest_position_holding_period']/365,2)\n",
    "new_df[['MIN_FSCL_WK_END_DT_JOB_TNUR', 'min_pctpay','max_pctpay']] = round(new_df[['MIN_FSCL_WK_END_DT_JOB_TNUR', 'min_pctpay','max_pctpay']],2)\n",
    "new_df = new_df.drop(['FiscalWeekBeginDate','count_position','Last_FiscalWeekBeginDate'], axis=1)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PRTNR_ID',\n",
       " 'last_position',\n",
       " 'Last_FSCL_WK_END_DT_COM_TNUR',\n",
       " 'PRTNR_ACTIVE_FLG',\n",
       " 'Last_Month_Year',\n",
       " 'longest_position_holding_period',\n",
       " 'GEN_GRP_CD',\n",
       " 'labor density',\n",
       " 'STATE_CODE',\n",
       " 'CITY_NAME',\n",
       " 'urbanity',\n",
       " 'store_type',\n",
       " 'hashedstorenumber',\n",
       " 'benefitselig',\n",
       " 'benefitsused',\n",
       " 'MIN_FSCL_WK_END_DT_JOB_TNUR',\n",
       " 'min_pctpay',\n",
       " 'max_pctpay',\n",
       " 'anual_average_Wage_increase',\n",
       " 'Job Position Changed']"
      ]
     },
     "execution_count": 2478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(new_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Re-ordering Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2479,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df[['PRTNR_ID','Last_FSCL_WK_END_DT_COM_TNUR','MIN_FSCL_WK_END_DT_JOB_TNUR','longest_position_holding_period',\n",
    "                 'last_position','Job Position Changed','labor density','PRTNR_ACTIVE_FLG', 'Last_Month_Year', 'GEN_GRP_CD',\n",
    "                 'STATE_CODE','CITY_NAME','urbanity','store_type', 'hashedstorenumber', 'benefitselig','benefitsused',\n",
    "                 'min_pctpay','max_pctpay','anual_average_Wage_increase'\n",
    "                ]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change cols' name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2480,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.rename(columns = {'PRTNR_ID':'Prtnr_ID',\n",
    "                                  'Last_FSCL_WK_END_DT_COM_TNUR': 'Tenure_Com',\n",
    "                                  'MIN_FSCL_WK_END_DT_JOB_TNUR' :'Min_Tenure_Position',\n",
    "                                  'longest_position_holding_period': 'Longest_Position',\n",
    "                                  'last_position':'Last_Position',\n",
    "                                  'Job Position Changed':'Job_changed',\n",
    "                                  'labor density':'Labor_Density',\n",
    "                                  'PRTNR_ACTIVE_FLG' :'Last_Active_Flg',\n",
    "                                  'Last_Month_Year':'Last_Status_Month_Year',  \n",
    "                                  'GEN_GRP_CD':'Gen',\n",
    "                                  'STATE_CODE': 'State',\n",
    "                                  'CITY_NAME' : 'City',\n",
    "                                  'urbanity':'Urbanity',\n",
    "                                  'store_type':'Store_Type',\n",
    "                                  'hashedstorenumber':'Store_Num',\n",
    "                                  'benefitselig':'Benefit_Selling',\n",
    "                                  'benefitsused': 'Benefit_Used',\n",
    "                                  'min_pctpay':'Min_Pctpay',\n",
    "                                  'max_pctpay':'Max_Pctpay',\n",
    "                                  'anual_average_Wage_increase':'Yearly_Wage_Increase(%)'\n",
    "                                 })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Get Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2481,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prtnr_ID</th>\n",
       "      <th>Tenure_Com</th>\n",
       "      <th>Min_Tenure_Position</th>\n",
       "      <th>Longest_Position</th>\n",
       "      <th>Last_Position</th>\n",
       "      <th>Job_changed</th>\n",
       "      <th>Labor_Density</th>\n",
       "      <th>Last_Active_Flg</th>\n",
       "      <th>Last_Status_Month_Year</th>\n",
       "      <th>Gen</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Urbanity</th>\n",
       "      <th>Store_Type</th>\n",
       "      <th>Store_Num</th>\n",
       "      <th>Benefit_Selling</th>\n",
       "      <th>Benefit_Used</th>\n",
       "      <th>Min_Pctpay</th>\n",
       "      <th>Max_Pctpay</th>\n",
       "      <th>Yearly_Wage_Increase(%)</th>\n",
       "      <th>assistant store manager</th>\n",
       "      <th>barista</th>\n",
       "      <th>cafe attendant</th>\n",
       "      <th>shift manager</th>\n",
       "      <th>shift supervisor</th>\n",
       "      <th>siren retail barista</th>\n",
       "      <th>siren retail operations lead</th>\n",
       "      <th>siren retail store manager</th>\n",
       "      <th>store manager</th>\n",
       "      <th>Baby Boomer</th>\n",
       "      <th>Generation X</th>\n",
       "      <th>Generation Z</th>\n",
       "      <th>Millennials</th>\n",
       "      <th>Pre-Baby Boomer</th>\n",
       "      <th>AL</th>\n",
       "      <th>AZ</th>\n",
       "      <th>CA</th>\n",
       "      <th>CO</th>\n",
       "      <th>DC</th>\n",
       "      <th>FL</th>\n",
       "      <th>GA</th>\n",
       "      <th>IL</th>\n",
       "      <th>IN</th>\n",
       "      <th>KS</th>\n",
       "      <th>KY</th>\n",
       "      <th>MA</th>\n",
       "      <th>NC</th>\n",
       "      <th>NM</th>\n",
       "      <th>NV</th>\n",
       "      <th>NY</th>\n",
       "      <th>...</th>\n",
       "      <th>Denver</th>\n",
       "      <th>El Paso</th>\n",
       "      <th>Everett</th>\n",
       "      <th>Fort Wayne</th>\n",
       "      <th>Fort Worth</th>\n",
       "      <th>Fredericksburg</th>\n",
       "      <th>Greenville</th>\n",
       "      <th>Houston</th>\n",
       "      <th>Indianapolis</th>\n",
       "      <th>Katy</th>\n",
       "      <th>Kent</th>\n",
       "      <th>La Mesa</th>\n",
       "      <th>Las Vegas</th>\n",
       "      <th>Long Beach</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>Louisville</th>\n",
       "      <th>Marietta</th>\n",
       "      <th>Mesa</th>\n",
       "      <th>Miami</th>\n",
       "      <th>Mobile</th>\n",
       "      <th>Modesto</th>\n",
       "      <th>Naples</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ontario</th>\n",
       "      <th>Orlando</th>\n",
       "      <th>Overland Park</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Portland</th>\n",
       "      <th>Richmond</th>\n",
       "      <th>Sacramento</th>\n",
       "      <th>San Diego</th>\n",
       "      <th>San Francisco</th>\n",
       "      <th>Santa Clara</th>\n",
       "      <th>Sarasota</th>\n",
       "      <th>Seattle</th>\n",
       "      <th>Spring</th>\n",
       "      <th>Tacoma</th>\n",
       "      <th>Tampa</th>\n",
       "      <th>Tempe</th>\n",
       "      <th>Tucson</th>\n",
       "      <th>Vancouver</th>\n",
       "      <th>Washington</th>\n",
       "      <th>U1</th>\n",
       "      <th>U2</th>\n",
       "      <th>U3</th>\n",
       "      <th>U4</th>\n",
       "      <th>U5</th>\n",
       "      <th>U6</th>\n",
       "      <th>Cafe</th>\n",
       "      <th>Drive Thru</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005F00557E5CD0BCAC6F8A1F343DC35</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.34</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09</td>\n",
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       "      <td>AZ</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>U4</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>24</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000FF458D677F2E37B0EC7B3FB093968</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1.67</td>\n",
       "      <td>3.21</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>Millennials</td>\n",
       "      <td>WA</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>U5</td>\n",
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       "      <td>4</td>\n",
       "      <td>82.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0014B120A048068D92EE2A06B99E48A6</td>\n",
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       "      <td>shift supervisor</td>\n",
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       "      <td>20</td>\n",
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       "      <td>2018-11</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>001802D72E5685A8A81DAE17BE44ADD7</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.15</td>\n",
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       "      <td>25</td>\n",
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       "      <td>2019-06</td>\n",
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       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>U2</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>37</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.73</td>\n",
       "      <td>4.73</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0018A858F59D9F3BFA5CF6AB93DA74B8</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.38</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>Millennials</td>\n",
       "      <td>WA</td>\n",
       "      <td>Tacoma</td>\n",
       "      <td>U4</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>359</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13719</th>\n",
       "      <td>FFE432EE30DAE9BF78A45D3970ECF0A0</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.28</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>CA</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>U3</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>108</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13720</th>\n",
       "      <td>FFF3F91FF8FFD54F8962C0C5DA8238BD</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.16</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-08</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>U3</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>321</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13721</th>\n",
       "      <td>FFF417C1B02B4DD879AD8B7FC4E8856A</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.30</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>OR</td>\n",
       "      <td>Portland</td>\n",
       "      <td>U4</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>34</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.74</td>\n",
       "      <td>5.55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13722</th>\n",
       "      <td>FFF549C28AF0218448441980B3F4A52F</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.60</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>FL</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>U5</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>287</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13723</th>\n",
       "      <td>FFFEB61EB08DAFF74035D738B2A760BE</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.58</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>CA</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>U3</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12513 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Prtnr_ID  Tenure_Com  Min_Tenure_Position  \\\n",
       "0      0005F00557E5CD0BCAC6F8A1F343DC35        1.34                 0.02   \n",
       "1      000FF458D677F2E37B0EC7B3FB093968        4.64                 1.67   \n",
       "2      0014B120A048068D92EE2A06B99E48A6        9.46                 0.02   \n",
       "4      001802D72E5685A8A81DAE17BE44ADD7        3.24                 0.15   \n",
       "5      0018A858F59D9F3BFA5CF6AB93DA74B8        0.78                 0.02   \n",
       "...                                 ...         ...                  ...   \n",
       "13719  FFE432EE30DAE9BF78A45D3970ECF0A0        0.87                 0.02   \n",
       "13720  FFF3F91FF8FFD54F8962C0C5DA8238BD        0.16                 0.02   \n",
       "13721  FFF417C1B02B4DD879AD8B7FC4E8856A        1.30                 0.56   \n",
       "13722  FFF549C28AF0218448441980B3F4A52F        0.60                 0.01   \n",
       "13723  FFFEB61EB08DAFF74035D738B2A760BE        0.58                 0.01   \n",
       "\n",
       "       Longest_Position     Last_Position  Job_changed  Labor_Density  \\\n",
       "0                  1.34           barista            0             27   \n",
       "1                  3.21  shift supervisor            1             28   \n",
       "2                  0.72  shift supervisor            1             20   \n",
       "4                  1.85  shift supervisor            1             25   \n",
       "5                  0.38  shift supervisor            1             13   \n",
       "...                 ...               ...          ...            ...   \n",
       "13719              0.28  shift supervisor            1              8   \n",
       "13720              0.16           barista            0              0   \n",
       "13721              1.30           barista            0              6   \n",
       "13722              0.60           barista            0             11   \n",
       "13723              0.58           barista            0              5   \n",
       "\n",
       "       Last_Active_Flg Last_Status_Month_Year           Gen State  \\\n",
       "0                    1                2019-09  Generation Z    AZ   \n",
       "1                    0                2019-04   Millennials    WA   \n",
       "2                    0                2018-11   Millennials    NV   \n",
       "4                    0                2019-06  Generation Z    IL   \n",
       "5                    1                2019-09   Millennials    WA   \n",
       "...                ...                    ...           ...   ...   \n",
       "13719                0                2019-02  Generation Z    CA   \n",
       "13720                0                2019-08  Generation Z    CA   \n",
       "13721                0                2018-07  Generation Z    OR   \n",
       "13722                0                2018-09  Generation Z    FL   \n",
       "13723                0                2019-09  Generation Z    CA   \n",
       "\n",
       "              City Urbanity  Store_Type  Store_Num  Benefit_Selling  \\\n",
       "0             Mesa       U4  Drive Thru         24             71.0   \n",
       "1        Vancouver       U5  Drive Thru          4             82.0   \n",
       "2        Las Vegas       U3  Drive Thru        264             61.0   \n",
       "4          Chicago       U2        Cafe         37             90.0   \n",
       "5           Tacoma       U4  Drive Thru        359             41.0   \n",
       "...            ...      ...         ...        ...              ...   \n",
       "13719  Santa Clara       U3        Cafe        108             21.0   \n",
       "13720    San Diego       U3  Drive Thru        321              0.0   \n",
       "13721     Portland       U4        Cafe         34             40.0   \n",
       "13722      Orlando       U5  Drive Thru        287             12.0   \n",
       "13723  Los Angeles       U3  Drive Thru         20              7.0   \n",
       "\n",
       "       Benefit_Used  Min_Pctpay  Max_Pctpay  Yearly_Wage_Increase(%)  \\\n",
       "0                 1        0.41        0.44                    10.33   \n",
       "1                 0        0.70        0.78                     4.98   \n",
       "2                 0        0.46        0.55                     7.12   \n",
       "4                 0        0.63        0.73                     4.73   \n",
       "5                 0        0.63        0.76                     0.86   \n",
       "...             ...         ...         ...                      ...   \n",
       "13719             0        0.83        0.95                     1.39   \n",
       "13720             0        0.61        0.61                     0.00   \n",
       "13721             0        0.70        0.74                     5.55   \n",
       "13722             0        0.15        0.29                     1.19   \n",
       "13723             0        0.83        0.91                     1.92   \n",
       "\n",
       "       assistant store manager  barista  cafe attendant  shift manager  \\\n",
       "0                            0        1               0              0   \n",
       "1                            0        0               0              0   \n",
       "2                            0        0               0              0   \n",
       "4                            0        0               0              0   \n",
       "5                            0        0               0              0   \n",
       "...                        ...      ...             ...            ...   \n",
       "13719                        0        0               0              0   \n",
       "13720                        0        1               0              0   \n",
       "13721                        0        1               0              0   \n",
       "13722                        0        1               0              0   \n",
       "13723                        0        1               0              0   \n",
       "\n",
       "       shift supervisor  siren retail barista  siren retail operations lead  \\\n",
       "0                     0                     0                             0   \n",
       "1                     1                     0                             0   \n",
       "2                     1                     0                             0   \n",
       "4                     1                     0                             0   \n",
       "5                     1                     0                             0   \n",
       "...                 ...                   ...                           ...   \n",
       "13719                 1                     0                             0   \n",
       "13720                 0                     0                             0   \n",
       "13721                 0                     0                             0   \n",
       "13722                 0                     0                             0   \n",
       "13723                 0                     0                             0   \n",
       "\n",
       "       siren retail store manager  store manager  Baby Boomer  Generation X  \\\n",
       "0                               0              0            0             0   \n",
       "1                               0              0            0             0   \n",
       "2                               0              0            0             0   \n",
       "4                               0              0            0             0   \n",
       "5                               0              0            0             0   \n",
       "...                           ...            ...          ...           ...   \n",
       "13719                           0              0            0             0   \n",
       "13720                           0              0            0             0   \n",
       "13721                           0              0            0             0   \n",
       "13722                           0              0            0             0   \n",
       "13723                           0              0            0             0   \n",
       "\n",
       "       Generation Z  Millennials  Pre-Baby Boomer  AL  AZ  CA  CO  DC  FL  GA  \\\n",
       "0                 1            0                0   0   1   0   0   0   0   0   \n",
       "1                 0            1                0   0   0   0   0   0   0   0   \n",
       "2                 0            1                0   0   0   0   0   0   0   0   \n",
       "4                 1            0                0   0   0   0   0   0   0   0   \n",
       "5                 0            1                0   0   0   0   0   0   0   0   \n",
       "...             ...          ...              ...  ..  ..  ..  ..  ..  ..  ..   \n",
       "13719             1            0                0   0   0   1   0   0   0   0   \n",
       "13720             1            0                0   0   0   1   0   0   0   0   \n",
       "13721             1            0                0   0   0   0   0   0   0   0   \n",
       "13722             1            0                0   0   0   0   0   0   1   0   \n",
       "13723             1            0                0   0   0   1   0   0   0   0   \n",
       "\n",
       "       IL  IN  KS  KY  MA  NC  NM  NV  NY  ...  Denver  El Paso  Everett  \\\n",
       "0       0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "1       0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "2       0   0   0   0   0   0   0   1   0  ...       0        0        0   \n",
       "4       1   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "5       0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "...    ..  ..  ..  ..  ..  ..  ..  ..  ..  ...     ...      ...      ...   \n",
       "13719   0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "13720   0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "13721   0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "13722   0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "13723   0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "\n",
       "       Fort Wayne  Fort Worth  Fredericksburg  Greenville  Houston  \\\n",
       "0               0           0               0           0        0   \n",
       "1               0           0               0           0        0   \n",
       "2               0           0               0           0        0   \n",
       "4               0           0               0           0        0   \n",
       "5               0           0               0           0        0   \n",
       "...           ...         ...             ...         ...      ...   \n",
       "13719           0           0               0           0        0   \n",
       "13720           0           0               0           0        0   \n",
       "13721           0           0               0           0        0   \n",
       "13722           0           0               0           0        0   \n",
       "13723           0           0               0           0        0   \n",
       "\n",
       "       Indianapolis  Katy  Kent  La Mesa  Las Vegas  Long Beach  Los Angeles  \\\n",
       "0                 0     0     0        0          0           0            0   \n",
       "1                 0     0     0        0          0           0            0   \n",
       "2                 0     0     0        0          1           0            0   \n",
       "4                 0     0     0        0          0           0            0   \n",
       "5                 0     0     0        0          0           0            0   \n",
       "...             ...   ...   ...      ...        ...         ...          ...   \n",
       "13719             0     0     0        0          0           0            0   \n",
       "13720             0     0     0        0          0           0            0   \n",
       "13721             0     0     0        0          0           0            0   \n",
       "13722             0     0     0        0          0           0            0   \n",
       "13723             0     0     0        0          0           0            1   \n",
       "\n",
       "       Louisville  Marietta  Mesa  Miami  Mobile  Modesto  Naples  New York  \\\n",
       "0               0         0     1      0       0        0       0         0   \n",
       "1               0         0     0      0       0        0       0         0   \n",
       "2               0         0     0      0       0        0       0         0   \n",
       "4               0         0     0      0       0        0       0         0   \n",
       "5               0         0     0      0       0        0       0         0   \n",
       "...           ...       ...   ...    ...     ...      ...     ...       ...   \n",
       "13719           0         0     0      0       0        0       0         0   \n",
       "13720           0         0     0      0       0        0       0         0   \n",
       "13721           0         0     0      0       0        0       0         0   \n",
       "13722           0         0     0      0       0        0       0         0   \n",
       "13723           0         0     0      0       0        0       0         0   \n",
       "\n",
       "       Ontario  Orlando  Overland Park  Philadelphia  Portland  Richmond  \\\n",
       "0            0        0              0             0         0         0   \n",
       "1            0        0              0             0         0         0   \n",
       "2            0        0              0             0         0         0   \n",
       "4            0        0              0             0         0         0   \n",
       "5            0        0              0             0         0         0   \n",
       "...        ...      ...            ...           ...       ...       ...   \n",
       "13719        0        0              0             0         0         0   \n",
       "13720        0        0              0             0         0         0   \n",
       "13721        0        0              0             0         1         0   \n",
       "13722        0        1              0             0         0         0   \n",
       "13723        0        0              0             0         0         0   \n",
       "\n",
       "       Sacramento  San Diego  San Francisco  Santa Clara  Sarasota  Seattle  \\\n",
       "0               0          0              0            0         0        0   \n",
       "1               0          0              0            0         0        0   \n",
       "2               0          0              0            0         0        0   \n",
       "4               0          0              0            0         0        0   \n",
       "5               0          0              0            0         0        0   \n",
       "...           ...        ...            ...          ...       ...      ...   \n",
       "13719           0          0              0            1         0        0   \n",
       "13720           0          1              0            0         0        0   \n",
       "13721           0          0              0            0         0        0   \n",
       "13722           0          0              0            0         0        0   \n",
       "13723           0          0              0            0         0        0   \n",
       "\n",
       "       Spring  Tacoma  Tampa  Tempe  Tucson  Vancouver  Washington  U1  U2  \\\n",
       "0           0       0      0      0       0          0           0   0   0   \n",
       "1           0       0      0      0       0          1           0   0   0   \n",
       "2           0       0      0      0       0          0           0   0   0   \n",
       "4           0       0      0      0       0          0           0   0   1   \n",
       "5           0       1      0      0       0          0           0   0   0   \n",
       "...       ...     ...    ...    ...     ...        ...         ...  ..  ..   \n",
       "13719       0       0      0      0       0          0           0   0   0   \n",
       "13720       0       0      0      0       0          0           0   0   0   \n",
       "13721       0       0      0      0       0          0           0   0   0   \n",
       "13722       0       0      0      0       0          0           0   0   0   \n",
       "13723       0       0      0      0       0          0           0   0   0   \n",
       "\n",
       "       U3  U4  U5  U6  Cafe  Drive Thru  \n",
       "0       0   1   0   0     0           1  \n",
       "1       0   0   1   0     0           1  \n",
       "2       1   0   0   0     0           1  \n",
       "4       0   0   0   0     1           0  \n",
       "5       0   1   0   0     0           1  \n",
       "...    ..  ..  ..  ..   ...         ...  \n",
       "13719   1   0   0   0     1           0  \n",
       "13720   1   0   0   0     0           1  \n",
       "13721   0   1   0   0     1           0  \n",
       "13722   0   0   1   0     0           1  \n",
       "13723   1   0   0   0     0           1  \n",
       "\n",
       "[12513 rows x 123 columns]"
      ]
     },
     "execution_count": 2481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Position dummies\n",
    "position_dummy = pd.get_dummies(new_df['Last_Position'])\n",
    "new_df= pd.concat([new_df,position_dummy], axis=1)\n",
    "\n",
    "#GEN_GRP_CD dummies\n",
    "gen_dummy = pd.get_dummies(new_df['Gen'])\n",
    "new_df= pd.concat([new_df,gen_dummy], axis=1)\n",
    "\n",
    "#state_code dummies\n",
    "state_code_dummy = pd.get_dummies(new_df['State'])\n",
    "new_df= pd.concat([new_df,state_code_dummy], axis=1)\n",
    "\n",
    "#City dummies\n",
    "city_dummy = pd.get_dummies(new_df['City'])\n",
    "new_df= pd.concat([new_df,city_dummy], axis=1)\n",
    "\n",
    "#Urbanity\n",
    "u_dummy = pd.get_dummies(new_df['Urbanity'])\n",
    "new_df = pd.concat([new_df,u_dummy], axis=1)\n",
    "\n",
    "#Store_Type\n",
    "store_type_dummy = pd.get_dummies(new_df['Store_Type'])\n",
    "new_df= pd.concat([new_df,store_type_dummy], axis=1)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *drop employees who quit the job several times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2482,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df =new_df.drop_duplicates()\n",
    "duplicated = new_df[new_df['Prtnr_ID'].duplicated()]\n",
    "duplicated = list(duplicated['Prtnr_ID'].drop_duplicates())\n",
    "# pd.merge(a,b, indicator=True, how='outer').query('_merge==\"left_only\"').drop('_merge', axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'duplicated' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-d9943f053291>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mto_drop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mduplicated\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mnew_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mnew_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Prtnr_ID'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_drop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'duplicated' is not defined"
     ]
    }
   ],
   "source": [
    "to_drop = duplicated\n",
    "new_df = new_df[~new_df['Prtnr_ID'].isin(to_drop)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Export Data File to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2489,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Export Data File to CSV\n",
    "# new_df.to_csv(\"starbucks(12208)_FINAL.csv\", index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12205 employees statistic summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2485,
   "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",
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       "    .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>Prtnr_ID</th>\n",
       "      <th>Tenure_Com</th>\n",
       "      <th>Min_Tenure_Position</th>\n",
       "      <th>Longest_Position</th>\n",
       "      <th>Last_Position</th>\n",
       "      <th>Job_changed</th>\n",
       "      <th>Labor_Density</th>\n",
       "      <th>Last_Active_Flg</th>\n",
       "      <th>Last_Status_Month_Year</th>\n",
       "      <th>Gen</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Urbanity</th>\n",
       "      <th>Store_Type</th>\n",
       "      <th>Store_Num</th>\n",
       "      <th>Benefit_Selling</th>\n",
       "      <th>Benefit_Used</th>\n",
       "      <th>Min_Pctpay</th>\n",
       "      <th>Max_Pctpay</th>\n",
       "      <th>Yearly_Wage_Increase(%)</th>\n",
       "      <th>assistant store manager</th>\n",
       "      <th>barista</th>\n",
       "      <th>cafe attendant</th>\n",
       "      <th>shift manager</th>\n",
       "      <th>shift supervisor</th>\n",
       "      <th>siren retail barista</th>\n",
       "      <th>siren retail operations lead</th>\n",
       "      <th>siren retail store manager</th>\n",
       "      <th>store manager</th>\n",
       "      <th>Baby Boomer</th>\n",
       "      <th>Generation X</th>\n",
       "      <th>Generation Z</th>\n",
       "      <th>Millennials</th>\n",
       "      <th>Pre-Baby Boomer</th>\n",
       "      <th>AL</th>\n",
       "      <th>AZ</th>\n",
       "      <th>CA</th>\n",
       "      <th>CO</th>\n",
       "      <th>DC</th>\n",
       "      <th>FL</th>\n",
       "      <th>GA</th>\n",
       "      <th>IL</th>\n",
       "      <th>IN</th>\n",
       "      <th>KS</th>\n",
       "      <th>KY</th>\n",
       "      <th>MA</th>\n",
       "      <th>NC</th>\n",
       "      <th>NM</th>\n",
       "      <th>NV</th>\n",
       "      <th>NY</th>\n",
       "      <th>...</th>\n",
       "      <th>Denver</th>\n",
       "      <th>El Paso</th>\n",
       "      <th>Everett</th>\n",
       "      <th>Fort Wayne</th>\n",
       "      <th>Fort Worth</th>\n",
       "      <th>Fredericksburg</th>\n",
       "      <th>Greenville</th>\n",
       "      <th>Houston</th>\n",
       "      <th>Indianapolis</th>\n",
       "      <th>Katy</th>\n",
       "      <th>Kent</th>\n",
       "      <th>La Mesa</th>\n",
       "      <th>Las Vegas</th>\n",
       "      <th>Long Beach</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>Louisville</th>\n",
       "      <th>Marietta</th>\n",
       "      <th>Mesa</th>\n",
       "      <th>Miami</th>\n",
       "      <th>Mobile</th>\n",
       "      <th>Modesto</th>\n",
       "      <th>Naples</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ontario</th>\n",
       "      <th>Orlando</th>\n",
       "      <th>Overland Park</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Portland</th>\n",
       "      <th>Richmond</th>\n",
       "      <th>Sacramento</th>\n",
       "      <th>San Diego</th>\n",
       "      <th>San Francisco</th>\n",
       "      <th>Santa Clara</th>\n",
       "      <th>Sarasota</th>\n",
       "      <th>Seattle</th>\n",
       "      <th>Spring</th>\n",
       "      <th>Tacoma</th>\n",
       "      <th>Tampa</th>\n",
       "      <th>Tempe</th>\n",
       "      <th>Tucson</th>\n",
       "      <th>Vancouver</th>\n",
       "      <th>Washington</th>\n",
       "      <th>U1</th>\n",
       "      <th>U2</th>\n",
       "      <th>U3</th>\n",
       "      <th>U4</th>\n",
       "      <th>U5</th>\n",
       "      <th>U6</th>\n",
       "      <th>Cafe</th>\n",
       "      <th>Drive Thru</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12013</th>\n",
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       "      <td>9.08</td>\n",
       "      <td>3.53</td>\n",
       "      <td>4.95</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>Millennials</td>\n",
       "      <td>CA</td>\n",
       "      <td>Modesto</td>\n",
       "      <td>U4</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>254</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>7976</th>\n",
       "      <td>94AA83CE9EEEA2C85358F6E1DF0A1516</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
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       "      <td>2019-09</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>CA</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>U5</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>428</td>\n",
       "      <td>142.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12700</th>\n",
       "      <td>EC6B20C13F5A87F9DB00AAB25D5BF9B6</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2.22</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>U2</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>296</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.68</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>25A29EA3D401DBF9DD209790B1155A2E</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.44</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>Millennials</td>\n",
       "      <td>TX</td>\n",
       "      <td>Spring</td>\n",
       "      <td>U5</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>225</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8164</th>\n",
       "      <td>985CEA2F375C165FB8B7F64E6859E4A8</td>\n",
       "      <td>4.75</td>\n",
       "      <td>1.76</td>\n",
       "      <td>3.39</td>\n",
       "      <td>shift supervisor</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>NY</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>U2</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>337</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.97</td>\n",
       "      <td>10.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5333</th>\n",
       "      <td>62AFC770AA2956C051E4A98112F73BB4</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1.45</td>\n",
       "      <td>3.42</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>Millennials</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>U5</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>439</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>15.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9776</th>\n",
       "      <td>B701EC90F5508BEC98C07EA22914D713</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.43</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>Baby Boomer</td>\n",
       "      <td>WA</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>U4</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>395</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10759</th>\n",
       "      <td>C8C01A45BCF287A62C9AB3105E114E8B</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.17</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>SC</td>\n",
       "      <td>Greenville</td>\n",
       "      <td>U5</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7017</th>\n",
       "      <td>82BBF6471D0462728A49B1C8C202E006</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.33</td>\n",
       "      <td>2.30</td>\n",
       "      <td>barista</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>Millennials</td>\n",
       "      <td>FL</td>\n",
       "      <td>Sarasota</td>\n",
       "      <td>U5</td>\n",
       "      <td>Drive Thru</td>\n",
       "      <td>300</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.35</td>\n",
       "      <td>7.86</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6104 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Prtnr_ID  Tenure_Com  Min_Tenure_Position  \\\n",
       "12013  DFE0F3371C615110E29604F8303C04A4        9.08                 3.53   \n",
       "4733   578A9DEDEABC80F9C4BA7C55ABC1223F        0.42                 0.02   \n",
       "7976   94AA83CE9EEEA2C85358F6E1DF0A1516        4.10                 2.13   \n",
       "12700  EC6B20C13F5A87F9DB00AAB25D5BF9B6        3.09                 0.92   \n",
       "2035   25A29EA3D401DBF9DD209790B1155A2E        0.44                 0.02   \n",
       "...                                 ...         ...                  ...   \n",
       "8164   985CEA2F375C165FB8B7F64E6859E4A8        4.75                 1.76   \n",
       "5333   62AFC770AA2956C051E4A98112F73BB4        3.42                 1.45   \n",
       "9776   B701EC90F5508BEC98C07EA22914D713        0.43                 0.02   \n",
       "10759  C8C01A45BCF287A62C9AB3105E114E8B        0.17                 0.02   \n",
       "7017   82BBF6471D0462728A49B1C8C202E006        2.30                 0.33   \n",
       "\n",
       "       Longest_Position     Last_Position  Job_changed  Labor_Density  \\\n",
       "12013              4.95  shift supervisor            1             20   \n",
       "4733               0.42           barista            0             10   \n",
       "7976               4.10           barista            0             61   \n",
       "12700              2.22  shift supervisor            1             18   \n",
       "2035               0.44           barista            0              5   \n",
       "...                 ...               ...          ...            ...   \n",
       "8164               3.39  shift supervisor            1             35   \n",
       "5333               3.42           barista            0             41   \n",
       "9776               0.43           barista            0              6   \n",
       "10759              0.17           barista            0             10   \n",
       "7017               2.30           barista            0             43   \n",
       "\n",
       "       Last_Active_Flg Last_Status_Month_Year           Gen State  \\\n",
       "12013                1                2019-09   Millennials    CA   \n",
       "4733                 1                2019-09  Generation Z    NC   \n",
       "7976                 1                2019-09  Generation Z    CA   \n",
       "12700                1                2019-09  Generation Z    PA   \n",
       "2035                 0                2018-11   Millennials    TX   \n",
       "...                ...                    ...           ...   ...   \n",
       "8164                 0                2019-05  Generation Z    NY   \n",
       "5333                 1                2019-09   Millennials    AZ   \n",
       "9776                 0                2019-09   Baby Boomer    WA   \n",
       "10759                1                2019-09  Generation Z    SC   \n",
       "7017                 1                2019-09   Millennials    FL   \n",
       "\n",
       "               City Urbanity  Store_Type  Store_Num  Benefit_Selling  \\\n",
       "12013       Modesto       U4  Drive Thru        254             76.0   \n",
       "4733      Charlotte       U5        Cafe        420              0.0   \n",
       "7976    Bakersfield       U5  Drive Thru        428            142.0   \n",
       "12700  Philadelphia       U2        Cafe        296             69.0   \n",
       "2035         Spring       U5  Drive Thru        225             23.0   \n",
       "...             ...      ...         ...        ...              ...   \n",
       "8164       Brooklyn       U2        Cafe        337             86.0   \n",
       "5333         Tucson       U5  Drive Thru        439             39.0   \n",
       "9776        Seattle       U4        Cafe        395              3.0   \n",
       "10759    Greenville       U5  Drive Thru        275              0.0   \n",
       "7017       Sarasota       U5  Drive Thru        300             99.0   \n",
       "\n",
       "       Benefit_Used  Min_Pctpay  Max_Pctpay  Yearly_Wage_Increase(%)  \\\n",
       "12013             1        0.50        0.65                     2.64   \n",
       "4733              0        0.02        0.02                     0.00   \n",
       "7976             38        0.46        0.72                     2.09   \n",
       "12700             0        0.57        0.68                     3.20   \n",
       "2035              0        0.15        0.15                     0.00   \n",
       "...             ...         ...         ...                      ...   \n",
       "8164              0        0.93        0.97                    10.76   \n",
       "5333              1        0.41        0.44                    15.43   \n",
       "9776              0        0.99        0.99                     0.00   \n",
       "10759             8        0.06        0.06                     0.00   \n",
       "7017              0        0.28        0.35                     7.86   \n",
       "\n",
       "       assistant store manager  barista  cafe attendant  shift manager  \\\n",
       "12013                        0        0               0              0   \n",
       "4733                         0        1               0              0   \n",
       "7976                         0        1               0              0   \n",
       "12700                        0        0               0              0   \n",
       "2035                         0        1               0              0   \n",
       "...                        ...      ...             ...            ...   \n",
       "8164                         0        0               0              0   \n",
       "5333                         0        1               0              0   \n",
       "9776                         0        1               0              0   \n",
       "10759                        0        1               0              0   \n",
       "7017                         0        1               0              0   \n",
       "\n",
       "       shift supervisor  siren retail barista  siren retail operations lead  \\\n",
       "12013                 1                     0                             0   \n",
       "4733                  0                     0                             0   \n",
       "7976                  0                     0                             0   \n",
       "12700                 1                     0                             0   \n",
       "2035                  0                     0                             0   \n",
       "...                 ...                   ...                           ...   \n",
       "8164                  1                     0                             0   \n",
       "5333                  0                     0                             0   \n",
       "9776                  0                     0                             0   \n",
       "10759                 0                     0                             0   \n",
       "7017                  0                     0                             0   \n",
       "\n",
       "       siren retail store manager  store manager  Baby Boomer  Generation X  \\\n",
       "12013                           0              0            0             0   \n",
       "4733                            0              0            0             0   \n",
       "7976                            0              0            0             0   \n",
       "12700                           0              0            0             0   \n",
       "2035                            0              0            0             0   \n",
       "...                           ...            ...          ...           ...   \n",
       "8164                            0              0            0             0   \n",
       "5333                            0              0            0             0   \n",
       "9776                            0              0            1             0   \n",
       "10759                           0              0            0             0   \n",
       "7017                            0              0            0             0   \n",
       "\n",
       "       Generation Z  Millennials  Pre-Baby Boomer  AL  AZ  CA  CO  DC  FL  GA  \\\n",
       "12013             0            1                0   0   0   1   0   0   0   0   \n",
       "4733              1            0                0   0   0   0   0   0   0   0   \n",
       "7976              1            0                0   0   0   1   0   0   0   0   \n",
       "12700             1            0                0   0   0   0   0   0   0   0   \n",
       "2035              0            1                0   0   0   0   0   0   0   0   \n",
       "...             ...          ...              ...  ..  ..  ..  ..  ..  ..  ..   \n",
       "8164              1            0                0   0   0   0   0   0   0   0   \n",
       "5333              0            1                0   0   1   0   0   0   0   0   \n",
       "9776              0            0                0   0   0   0   0   0   0   0   \n",
       "10759             1            0                0   0   0   0   0   0   0   0   \n",
       "7017              0            1                0   0   0   0   0   0   1   0   \n",
       "\n",
       "       IL  IN  KS  KY  MA  NC  NM  NV  NY  ...  Denver  El Paso  Everett  \\\n",
       "12013   0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "4733    0   0   0   0   0   1   0   0   0  ...       0        0        0   \n",
       "7976    0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "12700   0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "2035    0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "...    ..  ..  ..  ..  ..  ..  ..  ..  ..  ...     ...      ...      ...   \n",
       "8164    0   0   0   0   0   0   0   0   1  ...       0        0        0   \n",
       "5333    0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "9776    0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "10759   0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "7017    0   0   0   0   0   0   0   0   0  ...       0        0        0   \n",
       "\n",
       "       Fort Wayne  Fort Worth  Fredericksburg  Greenville  Houston  \\\n",
       "12013           0           0               0           0        0   \n",
       "4733            0           0               0           0        0   \n",
       "7976            0           0               0           0        0   \n",
       "12700           0           0               0           0        0   \n",
       "2035            0           0               0           0        0   \n",
       "...           ...         ...             ...         ...      ...   \n",
       "8164            0           0               0           0        0   \n",
       "5333            0           0               0           0        0   \n",
       "9776            0           0               0           0        0   \n",
       "10759           0           0               0           1        0   \n",
       "7017            0           0               0           0        0   \n",
       "\n",
       "       Indianapolis  Katy  Kent  La Mesa  Las Vegas  Long Beach  Los Angeles  \\\n",
       "12013             0     0     0        0          0           0            0   \n",
       "4733              0     0     0        0          0           0            0   \n",
       "7976              0     0     0        0          0           0            0   \n",
       "12700             0     0     0        0          0           0            0   \n",
       "2035              0     0     0        0          0           0            0   \n",
       "...             ...   ...   ...      ...        ...         ...          ...   \n",
       "8164              0     0     0        0          0           0            0   \n",
       "5333              0     0     0        0          0           0            0   \n",
       "9776              0     0     0        0          0           0            0   \n",
       "10759             0     0     0        0          0           0            0   \n",
       "7017              0     0     0        0          0           0            0   \n",
       "\n",
       "       Louisville  Marietta  Mesa  Miami  Mobile  Modesto  Naples  New York  \\\n",
       "12013           0         0     0      0       0        1       0         0   \n",
       "4733            0         0     0      0       0        0       0         0   \n",
       "7976            0         0     0      0       0        0       0         0   \n",
       "12700           0         0     0      0       0        0       0         0   \n",
       "2035            0         0     0      0       0        0       0         0   \n",
       "...           ...       ...   ...    ...     ...      ...     ...       ...   \n",
       "8164            0         0     0      0       0        0       0         0   \n",
       "5333            0         0     0      0       0        0       0         0   \n",
       "9776            0         0     0      0       0        0       0         0   \n",
       "10759           0         0     0      0       0        0       0         0   \n",
       "7017            0         0     0      0       0        0       0         0   \n",
       "\n",
       "       Ontario  Orlando  Overland Park  Philadelphia  Portland  Richmond  \\\n",
       "12013        0        0              0             0         0         0   \n",
       "4733         0        0              0             0         0         0   \n",
       "7976         0        0              0             0         0         0   \n",
       "12700        0        0              0             1         0         0   \n",
       "2035         0        0              0             0         0         0   \n",
       "...        ...      ...            ...           ...       ...       ...   \n",
       "8164         0        0              0             0         0         0   \n",
       "5333         0        0              0             0         0         0   \n",
       "9776         0        0              0             0         0         0   \n",
       "10759        0        0              0             0         0         0   \n",
       "7017         0        0              0             0         0         0   \n",
       "\n",
       "       Sacramento  San Diego  San Francisco  Santa Clara  Sarasota  Seattle  \\\n",
       "12013           0          0              0            0         0        0   \n",
       "4733            0          0              0            0         0        0   \n",
       "7976            0          0              0            0         0        0   \n",
       "12700           0          0              0            0         0        0   \n",
       "2035            0          0              0            0         0        0   \n",
       "...           ...        ...            ...          ...       ...      ...   \n",
       "8164            0          0              0            0         0        0   \n",
       "5333            0          0              0            0         0        0   \n",
       "9776            0          0              0            0         0        1   \n",
       "10759           0          0              0            0         0        0   \n",
       "7017            0          0              0            0         1        0   \n",
       "\n",
       "       Spring  Tacoma  Tampa  Tempe  Tucson  Vancouver  Washington  U1  U2  \\\n",
       "12013       0       0      0      0       0          0           0   0   0   \n",
       "4733        0       0      0      0       0          0           0   0   0   \n",
       "7976        0       0      0      0       0          0           0   0   0   \n",
       "12700       0       0      0      0       0          0           0   0   1   \n",
       "2035        1       0      0      0       0          0           0   0   0   \n",
       "...       ...     ...    ...    ...     ...        ...         ...  ..  ..   \n",
       "8164        0       0      0      0       0          0           0   0   1   \n",
       "5333        0       0      0      0       1          0           0   0   0   \n",
       "9776        0       0      0      0       0          0           0   0   0   \n",
       "10759       0       0      0      0       0          0           0   0   0   \n",
       "7017        0       0      0      0       0          0           0   0   0   \n",
       "\n",
       "       U3  U4  U5  U6  Cafe  Drive Thru  \n",
       "12013   0   1   0   0     0           1  \n",
       "4733    0   0   1   0     1           0  \n",
       "7976    0   0   1   0     0           1  \n",
       "12700   0   0   0   0     1           0  \n",
       "2035    0   0   1   0     0           1  \n",
       "...    ..  ..  ..  ..   ...         ...  \n",
       "8164    0   0   0   0     1           0  \n",
       "5333    0   0   1   0     0           1  \n",
       "9776    0   1   0   0     1           0  \n",
       "10759   0   0   1   0     0           1  \n",
       "7017    0   0   1   0     0           1  \n",
       "\n",
       "[6104 rows x 123 columns]"
      ]
     },
     "execution_count": 2485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "new_df_describe = pd.DataFrame(new_df.describe())\n",
    "new_df_describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "new_df_median = pd.DataFrame(new_df.median()).transpose().rename(index={0: 'median'})\n",
    "new_df_median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obtain talented employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df_talented = new_df[(new_df['Tenure_Com']> 1.09) ]\n",
    "new_df_talented = new_df[(new_df['Longest_Position']> 0.83) ]\n",
    "new_df_talented.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_df_talented.to_csv('talented_employee.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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