All-Fiscal-Years-PM_to_CM-KPI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('9.14.2017 impact analysis.xlsx',index_col='date_requested',\\\n",
    "                  parse_dates=['date_requested','date_completed'])\n",
    "df = df[['wo_id','date_completed','prob_type']]\n",
    "df['duration'] = df.date_completed - df.index\n",
    "df['fiscal_year'] = np.where(df.index.month >= 7,df.index.year+1, df.index.year)\n",
    "df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annual Work Order Volume:\n",
      "2014 12209\n",
      "2015 14521\n",
      "2016 16668\n",
      "2017 17193\n",
      "2018 3338\n"
     ]
    }
   ],
   "source": [
    "print('Annual Work Order Volume:')\n",
    "work_order_volume = []\n",
    "for year in df.fiscal_year.unique():\n",
    "    print(year, df.loc[(df.fiscal_year==year) & (df.prob_type != 'TEST(DO NOT USE)')].wo_id.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# filter to get only work orders requested in that fiscal year and exclude all 'test' work orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fy_list = []\n",
    "fy_dfs = []\n",
    "for year in df.fiscal_year.unique():\n",
    "    fy_dfs.append(df.loc[(df.fiscal_year==year) &\\\n",
    "                    (df.prob_type != 'TEST(DO NOT USE)')])\n",
    "    fy_list.append(year)\n",
    "fy_dfs = dict(zip(fy_list,fy_dfs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# set the hvac filters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm_prob_types = ['BOILER','CHILLERS','COOLING TOWERS',\\\n",
    "                 'HVAC','HVAC INFRASTRUCTURE','HVAC|REPAIR']\n",
    "pm_prob_types = ['PREVENTIVE MAINT','HVAC|PM']\n",
    "hvac_prob_types = cm_prob_types+pm_prob_types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# add column for count of each problem type for each year to later see volumes in all pm or all cm or in the work order categories that constitute these groupings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http:https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for year in df.fiscal_year.unique():\n",
    "    fy_dfs[year]['count'] = fy_dfs[year].groupby(['prob_type'])['prob_type'].transform('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get and store the pm:cm kpi for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2014: 0.0,\n",
       " 2015: 4.7688564476885649,\n",
       " 2016: 46.938775510204081,\n",
       " 2017: 58.189845474613691,\n",
       " 2018: 78.733031674208149}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm_cm_kpi = []\n",
    "for key,value in fy_dfs.items():\n",
    "    pm_cm_kpi.append(fy_dfs[key][fy_dfs[key].prob_type.isin(pm_prob_types)].\\\n",
    "          prob_type.value_counts().sum() /\\\n",
    "          fy_dfs[key][fy_dfs[key].prob_type.isin(cm_prob_types)].\\\n",
    "                     prob_type.value_counts().sum()*100)\n",
    "\n",
    "pm_cm_kpi = dict(zip(fy_list,pm_cm_kpi))\n",
    "pm_cm_kpi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# briefly visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lIiKmpZASERHTUkiJiIhpKaRERMS0FFIiImJaCikRETEtp0Pq4MGDbNy4EYALFy6QnZ1t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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116868438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(pm_cm_kpi)),list(pm_cm_kpi.values()),color='#e2d391')\n",
    "plt.xticks(range(len(pm_cm_kpi)),list(pm_cm_kpi.keys()))\n",
    "plt.title('pm:cm kpi')\n",
    "plt.xlabel('fiscal year',fontsize=10)\n",
    "plt.ylabel('percentage', fontsize=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}