10_1_Chicago_Payroll_Data_Exercise_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href='http://www.scienceacademy.ca'> <img style=\"float: left;height:70px\" src=\"Logo_SA.png\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chicago Payroll Data Exercise Overview\n",
    "\n",
    "Hi guys, <br>\n",
    "So far, you have made great progress, Excellent job!<br>\n",
    "Let's explore real [payroll data of Chicago city](https://www.kaggle.com/chicago/chicago-citywide-payroll-data). This data is available on [kaggle](https://www.kaggle.com) website and can be downloaded using the provided link. However, a copy of dataset is provided in the course material as well. <br>\n",
    "\n",
    "\n",
    "Please follow the instructions and answer the questions accordingly. <br>\n",
    "Good luck!<br>\n",
    "\n",
    "**Links to few similar datasets are given below to explore ang practice your skills.**<br>\n",
    "&#9758; [City payroll information for all Los Angeles employees since 2013](https://www.kaggle.com/cityofLA/city-payroll-data) <br>\n",
    "&#9758; [San Francisco city employee salary data](https://www.kaggle.com/kaggle/sf-salaries)<br>\n",
    "&#9758; [New York City - Citywide Payroll Data](https://www.kaggle.com/new-york-city/nyc-citywide-payroll-data) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Import pandas as pd.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Read City_of_Chicago_Payroll_Data.csv as a dataframe in pay.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Show first five records in your data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Job Titles</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>ALLISON,  PAUL W</td>\n",
       "      <td>LIEUTENANT</td>\n",
       "      <td>FIRE</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$107790.00</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRUNO,  KEVIN D</td>\n",
       "      <td>SERGEANT</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COOPER,  JOHN E</td>\n",
       "      <td>LIEUTENANT-EMT</td>\n",
       "      <td>FIRE</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$114324.00</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>CRESPO,  VILMA I</td>\n",
       "      <td>STAFF ASST</td>\n",
       "      <td>LAW</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$76932.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DOLAN,  ROBERT J</td>\n",
       "      <td>SERGEANT</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$111474.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "               Name      Job Titles Department Full or Part-Time  \\\n",
       "0  ALLISON,  PAUL W      LIEUTENANT       FIRE                 F   \n",
       "1   BRUNO,  KEVIN D        SERGEANT     POLICE                 F   \n",
       "2   COOPER,  JOHN E  LIEUTENANT-EMT       FIRE                 F   \n",
       "3  CRESPO,  VILMA I      STAFF ASST        LAW                 F   \n",
       "4  DOLAN,  ROBERT J        SERGEANT     POLICE                 F   \n",
       "\n",
       "  Salary or Hourly  Typical Hours Annual Salary Hourly Rate  \n",
       "0           Salary            NaN    $107790.00         NaN  \n",
       "1           Salary            NaN    $104628.00         NaN  \n",
       "2           Salary            NaN    $114324.00         NaN  \n",
       "3           Salary            NaN     $76932.00         NaN  \n",
       "4           Salary            NaN    $111474.00         NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Get the overview of your data.<br>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32658 entries, 0 to 32657\n",
      "Data columns (total 8 columns):\n",
      "Name                 32658 non-null object\n",
      "Job Titles           32658 non-null object\n",
      "Department           32658 non-null object\n",
      "Full or Part-Time    32658 non-null object\n",
      "Salary or Hourly     32658 non-null object\n",
      "Typical Hours        7883 non-null float64\n",
      "Annual Salary        24775 non-null object\n",
      "Hourly Rate          7883 non-null object\n",
      "dtypes: float64(1), object(7)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that:<br>\n",
    "`Typical Hours` is the only float type. <br>\n",
    "`Typical Hours`, `Annual Salary` and `Hourly Rate` have lots of NaN or null values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. How many 'NaN' you have in each column in your data?**<br>\n",
    "*Hint: use [<code>isnull()</code>](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.isnull.html)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                     0\n",
       "Job Titles               0\n",
       "Department               0\n",
       "Full or Part-Time        0\n",
       "Salary or Hourly         0\n",
       "Typical Hours        24775\n",
       "Annual Salary         7883\n",
       "Hourly Rate          24775\n",
       "dtype: int64"
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. Output the statistics for your dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>top</th>\n",
       "      <td>HERNANDEZ,  JUAN C</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>F</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>10.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                      Name      Job Titles Department Full or Part-Time  \\\n",
       "count                32658           32658      32658             32658   \n",
       "unique               32350            1095         36                 2   \n",
       "top     HERNANDEZ,  JUAN C  POLICE OFFICER     POLICE                 F   \n",
       "freq                     5            9393      12973             30676   \n",
       "mean                   NaN             NaN        NaN               NaN   \n",
       "std                    NaN             NaN        NaN               NaN   \n",
       "min                    NaN             NaN        NaN               NaN   \n",
       "25%                    NaN             NaN        NaN               NaN   \n",
       "50%                    NaN             NaN        NaN               NaN   \n",
       "75%                    NaN             NaN        NaN               NaN   \n",
       "max                    NaN             NaN        NaN               NaN   \n",
       "\n",
       "       Salary or Hourly  Typical Hours Annual Salary Hourly Rate  \n",
       "count             32658    7883.000000         24775        7883  \n",
       "unique                2            NaN          1038         167  \n",
       "top              Salary            NaN     $90024.00      $35.60  \n",
       "freq              24775            NaN          2368        1404  \n",
       "mean                NaN      34.698719           NaN         NaN  \n",
       "std                 NaN       9.145553           NaN         NaN  \n",
       "min                 NaN      10.000000           NaN         NaN  \n",
       "25%                 NaN      20.000000           NaN         NaN  \n",
       "50%                 NaN      40.000000           NaN         NaN  \n",
       "75%                 NaN      40.000000           NaN         NaN  \n",
       "max                 NaN      40.000000           NaN         NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6. What are the maximum, minimum and average Typical Hours? (use 'Typical Hours' column)**<br>\n",
    "*Do you remember, pandas ignore NA/NaN/NULL values by default?* <br>\n",
    "<code>pay['Typical Hours'].dropna().mean()<code> should gives the same mean :) You may want to confirm! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Typical Hours are: 40.0\n",
      "Min Typical Hours are: 10.0\n",
      "Mean/Avg Typical Hours are: 34.69871876189268\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7. How many employees are on salary and how many are working on hourly basis?**<br>\n",
    "*Hint: Group the data on 'Salary or Hourly' column. Instead of whole dataset, you can take the single column as input as well.* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7883</td>\n",
       "      <td>7883</td>\n",
       "      <td>7883</td>\n",
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      ],
      "text/plain": [
       "                   Name  Job Titles  Department  Full or Part-Time  \\\n",
       "Salary or Hourly                                                     \n",
       "Hourly             7883        7883        7883               7883   \n",
       "Salary            24775       24775       24775              24775   \n",
       "\n",
       "                  Typical Hours  Annual Salary  Hourly Rate  \n",
       "Salary or Hourly                                             \n",
       "Hourly                     7883              0         7883  \n",
       "Salary                        0          24775            0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8. Which department has maximum no of employees? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POLICE    12973\n",
       "FIRE       4800\n",
       "Name: Department, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**9. How many employees are on Salary and how many are on Hourly in the Police department?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Salary or Hourly\n",
       "Hourly       32\n",
       "Salary    12941\n",
       "Name: Department, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**10. What are the mean, max and min salaries?**<br>\n",
    "use <code>.str and replace()</code><br>\n",
    "Create a new column name \"Salaries\" and separate the number from dollar sign as float .<br>\n",
    "Hint: [pandas.series.str](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.split.html) and [Stackoverflow link](https://stackoverflow.com/questions/38516481/trying-to-remove-commas-and-dollars-signs-with-pandas-in-python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min: 0.96\n",
      "max: 300000.0\n",
      "mean: 87512.78024137241\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Job Titles</th>\n",
       "      <th>Department</th>\n",
       "      <th>Full or Part-Time</th>\n",
       "      <th>Salary or Hourly</th>\n",
       "      <th>Typical Hours</th>\n",
       "      <th>Annual Salary</th>\n",
       "      <th>Hourly Rate</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALLISON,  PAUL W</td>\n",
       "      <td>LIEUTENANT</td>\n",
       "      <td>FIRE</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$107790.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107790.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRUNO,  KEVIN D</td>\n",
       "      <td>SERGEANT</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$104628.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104628.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Job Titles Department Full or Part-Time Salary or Hourly  \\\n",
       "0  ALLISON,  PAUL W  LIEUTENANT       FIRE                 F           Salary   \n",
       "1   BRUNO,  KEVIN D    SERGEANT     POLICE                 F           Salary   \n",
       "\n",
       "   Typical Hours Annual Salary Hourly Rate    Salary  \n",
       "0            NaN    $107790.00         NaN  107790.0  \n",
       "1            NaN    $104628.00         NaN  104628.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**11. Find an employee who has the maximum salary. **<br>\n",
    "Don't use the max number you got above, write a code for general condition. <br>\n",
    "Try with [<code>idxmax()</code>](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.idxmax.html) as well!}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Job Titles</th>\n",
       "      <th>Department</th>\n",
       "      <th>Full or Part-Time</th>\n",
       "      <th>Salary or Hourly</th>\n",
       "      <th>Typical Hours</th>\n",
       "      <th>Annual Salary</th>\n",
       "      <th>Hourly Rate</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8310</th>\n",
       "      <td>EVANS,  GINGER S</td>\n",
       "      <td>COMMISSIONER OF AVIATION</td>\n",
       "      <td>AVIATION</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$300000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name                Job Titles Department Full or Part-Time  \\\n",
       "8310  EVANS,  GINGER S  COMMISSIONER OF AVIATION   AVIATION                 F   \n",
       "\n",
       "     Salary or Hourly  Typical Hours Annual Salary Hourly Rate    Salary  \n",
       "8310           Salary            NaN    $300000.00         NaN  300000.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**12. Find an employee who has the minimum salary. **<br>\n",
    "Use [<code>idxmin()</code>](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.idxmin.html) as well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                           KOCH,  STEVEN \n",
       "Job Titles           ADMINISTRATIVE SECRETARY\n",
       "Department                     MAYOR'S OFFICE\n",
       "Full or Part-Time                           F\n",
       "Salary or Hourly                       Salary\n",
       "Typical Hours                             NaN\n",
       "Annual Salary                           $0.96\n",
       "Hourly Rate                               NaN\n",
       "Salary                                   0.96\n",
       "Name: 15387, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**13. What are the mean, max and min Hourly Rate?**<br>\n",
    "use <code>.str and replace()</code><br>\n",
    "Hint:\n",
    "Once again, create a new column name \"H_Rate\" and separate the number from dollar sign as float .<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Job Titles</th>\n",
       "      <th>Department</th>\n",
       "      <th>Full or Part-Time</th>\n",
       "      <th>Salary or Hourly</th>\n",
       "      <th>Typical Hours</th>\n",
       "      <th>Annual Salary</th>\n",
       "      <th>Hourly Rate</th>\n",
       "      <th>Salary</th>\n",
       "      <th>H_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALLISON,  PAUL W</td>\n",
       "      <td>LIEUTENANT</td>\n",
       "      <td>FIRE</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$107790.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107790.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRUNO,  KEVIN D</td>\n",
       "      <td>SERGEANT</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$104628.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104628.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COOPER,  JOHN E</td>\n",
       "      <td>LIEUTENANT-EMT</td>\n",
       "      <td>FIRE</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$114324.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114324.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CRESPO,  VILMA I</td>\n",
       "      <td>STAFF ASST</td>\n",
       "      <td>LAW</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$76932.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76932.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DOLAN,  ROBERT J</td>\n",
       "      <td>SERGEANT</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>F</td>\n",
       "      <td>Salary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$111474.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111474.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name      Job Titles Department Full or Part-Time  \\\n",
       "0  ALLISON,  PAUL W      LIEUTENANT       FIRE                 F   \n",
       "1   BRUNO,  KEVIN D        SERGEANT     POLICE                 F   \n",
       "2   COOPER,  JOHN E  LIEUTENANT-EMT       FIRE                 F   \n",
       "3  CRESPO,  VILMA I      STAFF ASST        LAW                 F   \n",
       "4  DOLAN,  ROBERT J        SERGEANT     POLICE                 F   \n",
       "\n",
       "  Salary or Hourly  Typical Hours Annual Salary Hourly Rate    Salary  H_Rate  \n",
       "0           Salary            NaN    $107790.00         NaN  107790.0     NaN  \n",
       "1           Salary            NaN    $104628.00         NaN  104628.0     NaN  \n",
       "2           Salary            NaN    $114324.00         NaN  114324.0     NaN  \n",
       "3           Salary            NaN     $76932.00         NaN   76932.0     NaN  \n",
       "4           Salary            NaN    $111474.00         NaN  111474.0     NaN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see how the data look like now!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min H_Rate : 2.65\n",
      "max H_Rate : 96.0\n",
      "mean H_Rate : 32.86819231257292\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**14. How many employees are getting max Hourly Rate? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                 1\n",
       "Job Titles           1\n",
       "Department           1\n",
       "Full or Part-Time    1\n",
       "Salary or Hourly     1\n",
       "Typical Hours        1\n",
       "Annual Salary        0\n",
       "Hourly Rate          1\n",
       "Salary               0\n",
       "H_Rate               1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**15. Who is getting max Hourly Rate?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Job Titles</th>\n",
       "      <th>Department</th>\n",
       "      <th>Full or Part-Time</th>\n",
       "      <th>Salary or Hourly</th>\n",
       "      <th>Typical Hours</th>\n",
       "      <th>Annual Salary</th>\n",
       "      <th>Hourly Rate</th>\n",
       "      <th>Salary</th>\n",
       "      <th>H_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14317</th>\n",
       "      <td>JONES,  JOHN W</td>\n",
       "      <td>PSYCHIATRIST</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>F</td>\n",
       "      <td>Hourly</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$96.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name    Job Titles Department Full or Part-Time  \\\n",
       "14317  JONES,  JOHN W  PSYCHIATRIST     HEALTH                 F   \n",
       "\n",
       "      Salary or Hourly  Typical Hours Annual Salary Hourly Rate  Salary  \\\n",
       "14317           Hourly           35.0           NaN      $96.00     NaN   \n",
       "\n",
       "       H_Rate  \n",
       "14317    96.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**16. How many employees are earning less than the average Hourly Rate?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2791"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**17. How many employees are paid hourly and they have full-time job?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5906"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**18. Find the full-time employees who are working at hourly rate of $10.00?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Job Titles</th>\n",
       "      <th>Department</th>\n",
       "      <th>Full or Part-Time</th>\n",
       "      <th>Salary or Hourly</th>\n",
       "      <th>Typical Hours</th>\n",
       "      <th>Annual Salary</th>\n",
       "      <th>Hourly Rate</th>\n",
       "      <th>Salary</th>\n",
       "      <th>H_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>AYALA JR,  JESUS</td>\n",
       "      <td>STUDENT INTERN</td>\n",
       "      <td>HUMAN RESOURCES</td>\n",
       "      <td>F</td>\n",
       "      <td>Hourly</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$10.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4522</th>\n",
       "      <td>CERVANTES,  BIANCA A</td>\n",
       "      <td>STUDENT INTERN</td>\n",
       "      <td>BUDGET &amp; MGMT</td>\n",
       "      <td>F</td>\n",
       "      <td>Hourly</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$10.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6476</th>\n",
       "      <td>DAVILA,  ROBERTO A</td>\n",
       "      <td>ALDERMANIC AIDE</td>\n",
       "      <td>CITY COUNCIL</td>\n",
       "      <td>F</td>\n",
       "      <td>Hourly</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$10.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16474</th>\n",
       "      <td>LEVY,  ETHAN</td>\n",
       "      <td>CLERK CITY COUNCIL</td>\n",
       "      <td>CITY COUNCIL</td>\n",
       "      <td>F</td>\n",
       "      <td>Hourly</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$10.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name          Job Titles       Department  \\\n",
       "1232      AYALA JR,  JESUS       STUDENT INTERN  HUMAN RESOURCES   \n",
       "4522   CERVANTES,  BIANCA A      STUDENT INTERN    BUDGET & MGMT   \n",
       "6476     DAVILA,  ROBERTO A     ALDERMANIC AIDE     CITY COUNCIL   \n",
       "16474         LEVY,  ETHAN   CLERK CITY COUNCIL     CITY COUNCIL   \n",
       "\n",
       "      Full or Part-Time Salary or Hourly  Typical Hours Annual Salary  \\\n",
       "1232                  F           Hourly           35.0           NaN   \n",
       "4522                  F           Hourly           35.0           NaN   \n",
       "6476                  F           Hourly           35.0           NaN   \n",
       "16474                 F           Hourly           35.0           NaN   \n",
       "\n",
       "      Hourly Rate  Salary  H_Rate  \n",
       "1232       $10.00     NaN    10.0  \n",
       "4522       $10.00     NaN    10.0  \n",
       "6476       $10.00     NaN    10.0  \n",
       "16474      $10.00     NaN    10.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**19. How many unique Job Titles are there in the data?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1095"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**20. What was the average Salary of the employees in each department? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Department\n",
       "ADMIN HEARNG             78912.947368\n",
       "ANIMAL CONTRL            66089.684211\n",
       "AVIATION                 76140.018777\n",
       "BOARD OF ELECTION        56051.142857\n",
       "BOARD OF ETHICS          94552.500000\n",
       "BUDGET & MGMT            93925.395349\n",
       "BUILDINGS                98864.833534\n",
       "BUSINESS AFFAIRS         80446.425000\n",
       "CITY CLERK               69762.439024\n",
       "CITY COUNCIL             63577.172069\n",
       "COMMUNITY DEVELOPMENT    88363.257143\n",
       "COPA                     98784.705882\n",
       "CULTURAL AFFAIRS         87048.909091\n",
       "DISABILITIES             82431.724138\n",
       "DoIT                     99681.029703\n",
       "FAMILY & SUPPORT         79013.588785\n",
       "FINANCE                  73276.364662\n",
       "FIRE                     97762.348662\n",
       "GENERAL SERVICES         83095.528390\n",
       "HEALTH                   85488.210938\n",
       "HUMAN RELATIONS          93778.588235\n",
       "HUMAN RESOURCES          79851.761194\n",
       "INSPECTOR GEN            84030.666667\n",
       "IPRA                     94429.285714\n",
       "LAW                      84582.814404\n",
       "LICENSE APPL COMM        80568.000000\n",
       "MAYOR'S OFFICE           96165.512308\n",
       "OEMC                     73153.778221\n",
       "POLICE                   87836.025349\n",
       "POLICE BOARD             86136.000000\n",
       "PROCUREMENT              83278.243902\n",
       "PUBLIC LIBRARY           71273.288136\n",
       "STREETS & SAN            84347.775701\n",
       "TRANSPORTN               89976.896061\n",
       "TREASURER                88062.652174\n",
       "WATER MGMNT              89894.111803\n",
       "Name: Salary, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**21. What is the job title of 'AGAR,  BULENT B'?** please note, there are two spaces between AGAR, and BULENT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "230    CHIEF ENGINEER OF SEWERS\n",
       "Name: Job Titles, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**22. What are the top most common job titles?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POLICE OFFICER                            9393\n",
       "FIREFIGHTER-EMT                           1424\n",
       "SERGEANT                                  1118\n",
       "POOL MOTOR TRUCK DRIVER                    996\n",
       "POLICE OFFICER (ASSIGNED AS DETECTIVE)     845\n",
       "Name: Job Titles, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**23. How many people have the word 'officer' in their job title? (This is pretty tricky) **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here please"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_string(title):\n",
    "    if 'officer' in title.lower():\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11101"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pay['Job Titles'].apply(lambda x: find_string(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
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