SF Salaries-Mahvash-GitHub.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SF Salaries\n",
    "\n",
    "dateset: [SF Salaries Dataset](https://www.kaggle.com/kaggle/sf-salaries) from Kaggle! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sal = pd.read_csv('Salaries.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>EmployeeName</th>\n",
       "      <th>JobTitle</th>\n",
       "      <th>BasePay</th>\n",
       "      <th>OvertimePay</th>\n",
       "      <th>OtherPay</th>\n",
       "      <th>Benefits</th>\n",
       "      <th>TotalPay</th>\n",
       "      <th>TotalPayBenefits</th>\n",
       "      <th>Year</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NATHANIEL FORD</td>\n",
       "      <td>GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY</td>\n",
       "      <td>167411.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>400184.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>567595.43</td>\n",
       "      <td>567595.43</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>GARY JIMENEZ</td>\n",
       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
       "      <td>155966.02</td>\n",
       "      <td>245131.88</td>\n",
       "      <td>137811.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>538909.28</td>\n",
       "      <td>538909.28</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>ALBERT PARDINI</td>\n",
       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
       "      <td>212739.13</td>\n",
       "      <td>106088.18</td>\n",
       "      <td>16452.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335279.91</td>\n",
       "      <td>335279.91</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>CHRISTOPHER CHONG</td>\n",
       "      <td>WIRE ROPE CABLE MAINTENANCE MECHANIC</td>\n",
       "      <td>77916.00</td>\n",
       "      <td>56120.71</td>\n",
       "      <td>198306.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>332343.61</td>\n",
       "      <td>332343.61</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>PATRICK GARDNER</td>\n",
       "      <td>DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)</td>\n",
       "      <td>134401.60</td>\n",
       "      <td>9737.00</td>\n",
       "      <td>182234.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>326373.19</td>\n",
       "      <td>326373.19</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id       EmployeeName                                        JobTitle  \\\n",
       "0   1     NATHANIEL FORD  GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY   \n",
       "1   2       GARY JIMENEZ                 CAPTAIN III (POLICE DEPARTMENT)   \n",
       "2   3     ALBERT PARDINI                 CAPTAIN III (POLICE DEPARTMENT)   \n",
       "3   4  CHRISTOPHER CHONG            WIRE ROPE CABLE MAINTENANCE MECHANIC   \n",
       "4   5    PATRICK GARDNER    DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)   \n",
       "\n",
       "     BasePay  OvertimePay   OtherPay  Benefits   TotalPay  TotalPayBenefits  \\\n",
       "0  167411.18         0.00  400184.25       NaN  567595.43         567595.43   \n",
       "1  155966.02    245131.88  137811.38       NaN  538909.28         538909.28   \n",
       "2  212739.13    106088.18   16452.60       NaN  335279.91         335279.91   \n",
       "3   77916.00     56120.71  198306.90       NaN  332343.61         332343.61   \n",
       "4  134401.60      9737.00  182234.59       NaN  326373.19         326373.19   \n",
       "\n",
       "   Year  Notes         Agency  Status  \n",
       "0  2011    NaN  San Francisco     NaN  \n",
       "1  2011    NaN  San Francisco     NaN  \n",
       "2  2011    NaN  San Francisco     NaN  \n",
       "3  2011    NaN  San Francisco     NaN  \n",
       "4  2011    NaN  San Francisco     NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>EmployeeName</th>\n",
       "      <th>JobTitle</th>\n",
       "      <th>BasePay</th>\n",
       "      <th>OvertimePay</th>\n",
       "      <th>OtherPay</th>\n",
       "      <th>Benefits</th>\n",
       "      <th>TotalPay</th>\n",
       "      <th>TotalPayBenefits</th>\n",
       "      <th>Year</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NATHANIEL FORD</td>\n",
       "      <td>GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY</td>\n",
       "      <td>167411.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>400184.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>567595.43</td>\n",
       "      <td>567595.43</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>GARY JIMENEZ</td>\n",
       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
       "      <td>155966.02</td>\n",
       "      <td>245131.88</td>\n",
       "      <td>137811.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>538909.28</td>\n",
       "      <td>538909.28</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Id    EmployeeName                                        JobTitle  \\\n",
       "0   1  NATHANIEL FORD  GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY   \n",
       "1   2    GARY JIMENEZ                 CAPTAIN III (POLICE DEPARTMENT)   \n",
       "\n",
       "     BasePay  OvertimePay   OtherPay  Benefits   TotalPay  TotalPayBenefits  \\\n",
       "0  167411.18         0.00  400184.25       NaN  567595.43         567595.43   \n",
       "1  155966.02    245131.88  137811.38       NaN  538909.28         538909.28   \n",
       "\n",
       "   Year  Notes         Agency  Status  \n",
       "0  2011    NaN  San Francisco     NaN  \n",
       "1  2011    NaN  San Francisco     NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 148654 entries, 0 to 148653\n",
      "Data columns (total 13 columns):\n",
      "Id                  148654 non-null int64\n",
      "EmployeeName        148654 non-null object\n",
      "JobTitle            148654 non-null object\n",
      "BasePay             148045 non-null float64\n",
      "OvertimePay         148650 non-null float64\n",
      "OtherPay            148650 non-null float64\n",
      "Benefits            112491 non-null float64\n",
      "TotalPay            148654 non-null float64\n",
      "TotalPayBenefits    148654 non-null float64\n",
      "Year                148654 non-null int64\n",
      "Notes               0 non-null float64\n",
      "Agency              148654 non-null object\n",
      "Status              0 non-null float64\n",
      "dtypes: float64(8), int64(2), object(3)\n",
      "memory usage: 14.7+ MB\n"
     ]
    }
   ],
   "source": [
    "sal.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "average BasePay?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66325.44884050643"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.BasePay.mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66325.44884050643"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['BasePay'].mean() #correct answer 2 - instructor's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "highest amount of OvertimePay? in the dataset ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245131.88"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.OvertimePay.max() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245131.88"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['OvertimePay'].max() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'EmployeeName', 'JobTitle', 'BasePay', 'OvertimePay', 'OtherPay',\n",
       "       'Benefits', 'TotalPay', 'TotalPayBenefits', 'Year', 'Notes', 'Agency',\n",
       "       'Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=148654, step=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>EmployeeName</th>\n",
       "      <th>JobTitle</th>\n",
       "      <th>BasePay</th>\n",
       "      <th>OvertimePay</th>\n",
       "      <th>OtherPay</th>\n",
       "      <th>Benefits</th>\n",
       "      <th>TotalPay</th>\n",
       "      <th>TotalPayBenefits</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>29353</td>\n",
       "      <td>29354</td>\n",
       "      <td>A BERNARD FATOOH</td>\n",
       "      <td>SHERIFF'S PROPERTY KEEPER</td>\n",
       "      <td>19969.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>70.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20039.91</td>\n",
       "      <td>20039.91</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102843</td>\n",
       "      <td>102844</td>\n",
       "      <td>A Bernard  Fatooh</td>\n",
       "      <td>Sheriff's Property Keeper</td>\n",
       "      <td>23908.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.12</td>\n",
       "      <td>5455.62</td>\n",
       "      <td>23923.62</td>\n",
       "      <td>29379.24</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66371</td>\n",
       "      <td>66372</td>\n",
       "      <td>A Bernard Fatooh</td>\n",
       "      <td>Sheriff's Property Keeper</td>\n",
       "      <td>18472.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.84</td>\n",
       "      <td>5014.66</td>\n",
       "      <td>18500.19</td>\n",
       "      <td>23514.85</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140525</td>\n",
       "      <td>140526</td>\n",
       "      <td>A Bernard Fatooh</td>\n",
       "      <td>Sheriff's Property Keeper</td>\n",
       "      <td>24061.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6091.68</td>\n",
       "      <td>24061.35</td>\n",
       "      <td>30153.03</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28469</td>\n",
       "      <td>28470</td>\n",
       "      <td>A ELIZABETH MARCHASIN</td>\n",
       "      <td>PRINCIPAL CLERK</td>\n",
       "      <td>23591.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2691.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26282.86</td>\n",
       "      <td>26282.86</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>134790</td>\n",
       "      <td>134791</td>\n",
       "      <td>Zuna T Luu</td>\n",
       "      <td>Custodian</td>\n",
       "      <td>54703.00</td>\n",
       "      <td>264.36</td>\n",
       "      <td>1120.55</td>\n",
       "      <td>26063.31</td>\n",
       "      <td>56087.91</td>\n",
       "      <td>82151.22</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97363</td>\n",
       "      <td>97364</td>\n",
       "      <td>Zuna T Luu</td>\n",
       "      <td>Custodian</td>\n",
       "      <td>55026.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1129.44</td>\n",
       "      <td>24776.86</td>\n",
       "      <td>56155.44</td>\n",
       "      <td>80932.30</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93463</td>\n",
       "      <td>93464</td>\n",
       "      <td>Zuri  Jones</td>\n",
       "      <td>Deputy Probation Officer</td>\n",
       "      <td>71986.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22727.21</td>\n",
       "      <td>71986.90</td>\n",
       "      <td>94714.11</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49185</td>\n",
       "      <td>49186</td>\n",
       "      <td>Zuri Jones</td>\n",
       "      <td>Deputy Probation Officer</td>\n",
       "      <td>86588.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35762.34</td>\n",
       "      <td>86588.52</td>\n",
       "      <td>122350.86</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132614</td>\n",
       "      <td>132615</td>\n",
       "      <td>Zuri Jones</td>\n",
       "      <td>Deputy Probation Officer</td>\n",
       "      <td>66667.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22870.94</td>\n",
       "      <td>66667.54</td>\n",
       "      <td>89538.48</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148654 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id           EmployeeName                   JobTitle   BasePay  \\\n",
       "29353    29354       A BERNARD FATOOH  SHERIFF'S PROPERTY KEEPER  19969.37   \n",
       "102843  102844      A Bernard  Fatooh  Sheriff's Property Keeper  23908.50   \n",
       "66371    66372       A Bernard Fatooh  Sheriff's Property Keeper  18472.35   \n",
       "140525  140526       A Bernard Fatooh  Sheriff's Property Keeper  24061.35   \n",
       "28469    28470  A ELIZABETH MARCHASIN            PRINCIPAL CLERK  23591.80   \n",
       "...        ...                    ...                        ...       ...   \n",
       "134790  134791             Zuna T Luu                  Custodian  54703.00   \n",
       "97363    97364             Zuna T Luu                  Custodian  55026.00   \n",
       "93463    93464            Zuri  Jones   Deputy Probation Officer  71986.90   \n",
       "49185    49186             Zuri Jones   Deputy Probation Officer  86588.52   \n",
       "132614  132615             Zuri Jones   Deputy Probation Officer  66667.54   \n",
       "\n",
       "        OvertimePay  OtherPay  Benefits  TotalPay  TotalPayBenefits  Year  \\\n",
       "29353          0.00     70.54       NaN  20039.91          20039.91  2011   \n",
       "102843         0.00     15.12   5455.62  23923.62          29379.24  2013   \n",
       "66371          0.00     27.84   5014.66  18500.19          23514.85  2012   \n",
       "140525         0.00      0.00   6091.68  24061.35          30153.03  2014   \n",
       "28469          0.00   2691.06       NaN  26282.86          26282.86  2011   \n",
       "...             ...       ...       ...       ...               ...   ...   \n",
       "134790       264.36   1120.55  26063.31  56087.91          82151.22  2014   \n",
       "97363          0.00   1129.44  24776.86  56155.44          80932.30  2013   \n",
       "93463          0.00      0.00  22727.21  71986.90          94714.11  2013   \n",
       "49185          0.00      0.00  35762.34  86588.52         122350.86  2012   \n",
       "132614         0.00      0.00  22870.94  66667.54          89538.48  2014   \n",
       "\n",
       "        Notes         Agency  Status  \n",
       "29353     NaN  San Francisco     NaN  \n",
       "102843    NaN  San Francisco     NaN  \n",
       "66371     NaN  San Francisco     NaN  \n",
       "140525    NaN  San Francisco     NaN  \n",
       "28469     NaN  San Francisco     NaN  \n",
       "...       ...            ...     ...  \n",
       "134790    NaN  San Francisco     NaN  \n",
       "97363     NaN  San Francisco     NaN  \n",
       "93463     NaN  San Francisco     NaN  \n",
       "49185     NaN  San Francisco     NaN  \n",
       "132614    NaN  San Francisco     NaN  \n",
       "\n",
       "[148654 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.sort_values('EmployeeName')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding random names' job titles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sal[sal['EmployeeName']] == [\"JOSEPH DRISCOLL\"] #pay attention to the brackets; NOT the right answer --> Next cell!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>EmployeeName</th>\n",
       "      <th>JobTitle</th>\n",
       "      <th>BasePay</th>\n",
       "      <th>OvertimePay</th>\n",
       "      <th>OtherPay</th>\n",
       "      <th>Benefits</th>\n",
       "      <th>TotalPay</th>\n",
       "      <th>TotalPayBenefits</th>\n",
       "      <th>Year</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>JOSEPH DRISCOLL</td>\n",
       "      <td>CAPTAIN, FIRE SUPPRESSION</td>\n",
       "      <td>140546.86</td>\n",
       "      <td>97868.77</td>\n",
       "      <td>31909.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270324.91</td>\n",
       "      <td>270324.91</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id     EmployeeName                   JobTitle    BasePay  OvertimePay  \\\n",
       "24  25  JOSEPH DRISCOLL  CAPTAIN, FIRE SUPPRESSION  140546.86     97868.77   \n",
       "\n",
       "    OtherPay  Benefits   TotalPay  TotalPayBenefits  Year  Notes  \\\n",
       "24  31909.28       NaN  270324.91         270324.91  2011    NaN   \n",
       "\n",
       "           Agency  Status  \n",
       "24  San Francisco     NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['EmployeeName'] == \"JOSEPH DRISCOLL\"] # With Solution help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Id</th>\n",
       "      <th>EmployeeName</th>\n",
       "      <th>JobTitle</th>\n",
       "      <th>BasePay</th>\n",
       "      <th>OvertimePay</th>\n",
       "      <th>OtherPay</th>\n",
       "      <th>Benefits</th>\n",
       "      <th>TotalPay</th>\n",
       "      <th>TotalPayBenefits</th>\n",
       "      <th>Year</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>JOSEPH DRISCOLL</td>\n",
       "      <td>CAPTAIN, FIRE SUPPRESSION</td>\n",
       "      <td>140546.86</td>\n",
       "      <td>97868.77</td>\n",
       "      <td>31909.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270324.91</td>\n",
       "      <td>270324.91</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id     EmployeeName                   JobTitle    BasePay  OvertimePay  \\\n",
       "24  25  JOSEPH DRISCOLL  CAPTAIN, FIRE SUPPRESSION  140546.86     97868.77   \n",
       "\n",
       "    OtherPay  Benefits   TotalPay  TotalPayBenefits  Year  Notes  \\\n",
       "24  31909.28       NaN  270324.91         270324.91  2011    NaN   \n",
       "\n",
       "           Agency  Status  \n",
       "24  San Francisco     NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['EmployeeName'] == \"JOSEPH DRISCOLL\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    CAPTAIN, FIRE SUPPRESSION\n",
       "Name: JobTitle, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['EmployeeName'] == \"JOSEPH DRISCOLL\"]['JobTitle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    270324.91\n",
       "Name: TotalPay, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['EmployeeName'] == \"JOSEPH DRISCOLL\"]['TotalPay']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### code 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    CAPTAIN, FIRE SUPPRESSION\n",
       "Name: JobTitle, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['EmployeeName'] == 'JOSEPH DRISCOLL']['JobTitle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    270324.91\n",
       "Name: TotalPayBenefits, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['EmployeeName'] == \"JOSEPH DRISCOLL\"]['TotalPayBenefits']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### End "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total benefits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    270324.91\n",
       "Name: TotalPayBenefits, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['EmployeeName'] == 'JOSEPH DRISCOLL']['TotalPayBenefits']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "highest paid person (including benefits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "567595.43"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.TotalPayBenefits.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NATHANIEL FORD\n",
       "Name: EmployeeName, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['TotalPayBenefits'] == 567595.43]['EmployeeName'] #my solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NATHANIEL FORD\n",
       "Name: EmployeeName, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['TotalPayBenefits'] == sal['TotalPayBenefits'].max()]['EmployeeName'] #Instructor's solution 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                                                               1\n",
       "EmployeeName                                        NATHANIEL FORD\n",
       "JobTitle            GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY\n",
       "BasePay                                                     167411\n",
       "OvertimePay                                                      0\n",
       "OtherPay                                                    400184\n",
       "Benefits                                                       NaN\n",
       "TotalPay                                                    567595\n",
       "TotalPayBenefits                                            567595\n",
       "Year                                                          2011\n",
       "Notes                                                          NaN\n",
       "Agency                                               San Francisco\n",
       "Status                                                         NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.loc[sal['TotalPayBenefits'].idxmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "idxmax is very similar ro NumPy's arg max. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value of idxmax is the number 0.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['TotalPayBenefits'].idxmax() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value of argmax is the number 0.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mahvash\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: \n",
      "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n",
      "instead.\n",
      "The behavior of 'argmax' will be corrected to return the positional\n",
      "maximum in the future. For now, use 'series.values.argmax' or\n",
      "'np.argmax(np.array(values))' to get the position of the maximum\n",
      "row.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['TotalPayBenefits'].argmax() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mahvash\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: \n",
      "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n",
      "instead.\n",
      "The behavior of 'argmax' will be corrected to return the positional\n",
      "maximum in the future. For now, use 'series.values.argmax' or\n",
      "'np.argmax(np.array(values))' to get the position of the maximum\n",
      "row.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Id                                                               1\n",
       "EmployeeName                                        NATHANIEL FORD\n",
       "JobTitle            GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY\n",
       "BasePay                                                     167411\n",
       "OvertimePay                                                      0\n",
       "OtherPay                                                    400184\n",
       "Benefits                                                       NaN\n",
       "TotalPay                                                    567595\n",
       "TotalPayBenefits                                            567595\n",
       "Year                                                          2011\n",
       "Notes                                                          NaN\n",
       "Agency                                               San Francisco\n",
       "Status                                                         NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.iloc[sal['TotalPayBenefits'].argmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lowest paid person (including benefits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### before and after the advanced method of argmax/argmin from previous problem. Precious problem is similar in for the max, where this problem is for min."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - Before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-618.13"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.TotalPayBenefits.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148653    Joe Lopez\n",
       "Name: EmployeeName, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['TotalPayBenefits'] == -618.13]['EmployeeName']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>148653</td>\n",
       "      <td>148654</td>\n",
       "      <td>Joe Lopez</td>\n",
       "      <td>Counselor, Log Cabin Ranch</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-618.13</td>\n",
       "      <td>-618.13</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id EmployeeName                    JobTitle  BasePay  OvertimePay  \\\n",
       "148653  148654    Joe Lopez  Counselor, Log Cabin Ranch      0.0          0.0   \n",
       "\n",
       "        OtherPay  Benefits  TotalPay  TotalPayBenefits  Year  Notes  \\\n",
       "148653   -618.13       0.0   -618.13           -618.13  2014    NaN   \n",
       "\n",
       "               Agency  Status  \n",
       "148653  San Francisco     NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['EmployeeName'] == \"Joe Lopez\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After; the answer is similar to solution A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mahvash\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: \n",
      "The current behaviour of 'Series.argmin' is deprecated, use 'idxmin'\n",
      "instead.\n",
      "The behavior of 'argmin' will be corrected to return the positional\n",
      "minimum in the future. For now, use 'series.values.argmin' or\n",
      "'np.argmin(np.array(values))' to get the position of the minimum\n",
      "row.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Id                                      148654\n",
       "EmployeeName                         Joe Lopez\n",
       "JobTitle            Counselor, Log Cabin Ranch\n",
       "BasePay                                      0\n",
       "OvertimePay                                  0\n",
       "OtherPay                               -618.13\n",
       "Benefits                                     0\n",
       "TotalPay                               -618.13\n",
       "TotalPayBenefits                       -618.13\n",
       "Year                                      2014\n",
       "Notes                                      NaN\n",
       "Agency                           San Francisco\n",
       "Status                                     NaN\n",
       "Name: 148653, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.iloc[sal['TotalPayBenefits'].argmin()] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "solution B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Id</th>\n",
       "      <th>EmployeeName</th>\n",
       "      <th>JobTitle</th>\n",
       "      <th>BasePay</th>\n",
       "      <th>OvertimePay</th>\n",
       "      <th>OtherPay</th>\n",
       "      <th>Benefits</th>\n",
       "      <th>TotalPay</th>\n",
       "      <th>TotalPayBenefits</th>\n",
       "      <th>Year</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>148653</td>\n",
       "      <td>148654</td>\n",
       "      <td>Joe Lopez</td>\n",
       "      <td>Counselor, Log Cabin Ranch</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-618.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-618.13</td>\n",
       "      <td>-618.13</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id EmployeeName                    JobTitle  BasePay  OvertimePay  \\\n",
       "148653  148654    Joe Lopez  Counselor, Log Cabin Ranch      0.0          0.0   \n",
       "\n",
       "        OtherPay  Benefits  TotalPay  TotalPayBenefits  Year  Notes  \\\n",
       "148653   -618.13       0.0   -618.13           -618.13  2014    NaN   \n",
       "\n",
       "               Agency  Status  \n",
       "148653  San Francisco     NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['TotalPayBenefits']==sal['TotalPayBenefits'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Joe Lopez has negative pay, which means he owes $618.13 to the SF city."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "average (mean) BasePay of all employees per year? (2011-2014) ? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Groupby method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66325.44884050643"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.BasePay.mean() #This answer is not specified for the year 2011-2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Id</th>\n",
       "      <th>BasePay</th>\n",
       "      <th>OvertimePay</th>\n",
       "      <th>OtherPay</th>\n",
       "      <th>Benefits</th>\n",
       "      <th>TotalPay</th>\n",
       "      <th>TotalPayBenefits</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2011</td>\n",
       "      <td>18080.0</td>\n",
       "      <td>63595.956517</td>\n",
       "      <td>4531.065429</td>\n",
       "      <td>3617.081926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71744.103871</td>\n",
       "      <td>71744.103871</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012</td>\n",
       "      <td>54542.5</td>\n",
       "      <td>65436.406857</td>\n",
       "      <td>5023.417824</td>\n",
       "      <td>3653.437583</td>\n",
       "      <td>26439.966967</td>\n",
       "      <td>74113.262265</td>\n",
       "      <td>100553.229232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013</td>\n",
       "      <td>91728.5</td>\n",
       "      <td>69630.030216</td>\n",
       "      <td>5281.641980</td>\n",
       "      <td>3819.969007</td>\n",
       "      <td>23829.076572</td>\n",
       "      <td>77611.443142</td>\n",
       "      <td>101440.519714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014</td>\n",
       "      <td>129593.0</td>\n",
       "      <td>66564.421924</td>\n",
       "      <td>5401.993737</td>\n",
       "      <td>3505.421251</td>\n",
       "      <td>24789.601756</td>\n",
       "      <td>75463.918140</td>\n",
       "      <td>100250.918884</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id       BasePay  OvertimePay     OtherPay      Benefits  \\\n",
       "Year                                                                   \n",
       "2011   18080.0  63595.956517  4531.065429  3617.081926           NaN   \n",
       "2012   54542.5  65436.406857  5023.417824  3653.437583  26439.966967   \n",
       "2013   91728.5  69630.030216  5281.641980  3819.969007  23829.076572   \n",
       "2014  129593.0  66564.421924  5401.993737  3505.421251  24789.601756   \n",
       "\n",
       "          TotalPay  TotalPayBenefits  Notes  Status  \n",
       "Year                                                 \n",
       "2011  71744.103871      71744.103871    NaN     NaN  \n",
       "2012  74113.262265     100553.229232    NaN     NaN  \n",
       "2013  77611.443142     101440.519714    NaN     NaN  \n",
       "2014  75463.918140     100250.918884    NaN     NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.groupby('Year').mean() # This code returns the mean for all columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code returns the mean only for the 'BasePay'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "2011    63595.956517\n",
       "2012    65436.406857\n",
       "2013    69630.030216\n",
       "2014    66564.421924\n",
       "Name: BasePay, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.groupby('Year').mean()['BasePay']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of unique job titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY',\n",
       "       'CAPTAIN III (POLICE DEPARTMENT)',\n",
       "       'WIRE ROPE CABLE MAINTENANCE MECHANIC', ..., 'Conversion',\n",
       "       'Cashier 3', 'Not provided'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.JobTitle.unique() # This code returns an arrays of unique values for JobTitles.\n",
    "                      # The answer is the number of Unique Job Titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The right answer is the number of unique values.\n",
    "\n",
    "'nunique' return the number of Unique values, Job Titles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2159"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal.JobTitle.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using 'len' also returns the same value, but using 'nunique' is an easier way to do this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2159"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sal['JobTitle'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "top 5 most common jobs? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transit Operator                          7036\n",
       "Special Nurse                             4389\n",
       "Registered Nurse                          3736\n",
       "Public Svc Aide-Public Works              2518\n",
       "Police Officer 3                          2421\n",
       "                                          ... \n",
       "Mobile Equipment Supervisor                  1\n",
       "ASSESSOR                                     1\n",
       "EMPLOYMENT AND TRAINING SPECIALIST VI        1\n",
       "DIRECTOR, INFORMATION TECHNOLOGY GROUP       1\n",
       "GLAZIER SUPERVISOR I                         1\n",
       "Name: JobTitle, Length: 2159, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['JobTitle'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the most common jobs: 5 first rows in the head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transit Operator                7036\n",
       "Special Nurse                   4389\n",
       "Registered Nurse                3736\n",
       "Public Svc Aide-Public Works    2518\n",
       "Police Officer 3                2421\n",
       "Name: JobTitle, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['JobTitle'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Job Titles were represented by only one person in 2013? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transit Operator                False\n",
       "Special Nurse                   False\n",
       "Registered Nurse                False\n",
       "Public Svc Aide-Public Works    False\n",
       "Custodian                       False\n",
       "                                ...  \n",
       "Senior Transcriber Typist        True\n",
       "Communications Director          True\n",
       "Claims Process Clerk             True\n",
       "Acupuncturist                    True\n",
       "Window Cleaner Supervisor        True\n",
       "Name: JobTitle, Length: 1051, dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[sal['Year']==2013]['JobTitle'].value_counts() == 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above boolean values if the JobTitle was hold by one person or not. \n",
    "\n",
    "To count the actual number for the accurances, use the 'sum' operation method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "202"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(sal[sal['Year']==2013]['JobTitle'].value_counts() == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transit Operator                          7036\n",
       "Special Nurse                             4389\n",
       "Registered Nurse                          3736\n",
       "Public Svc Aide-Public Works              2518\n",
       "Police Officer 3                          2421\n",
       "                                          ... \n",
       "Mobile Equipment Supervisor                  1\n",
       "ASSESSOR                                     1\n",
       "EMPLOYMENT AND TRAINING SPECIALIST VI        1\n",
       "DIRECTOR, INFORMATION TECHNOLOGY GROUP       1\n",
       "GLAZIER SUPERVISOR I                         1\n",
       "Name: JobTitle, Length: 2159, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['JobTitle'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number of people with the word Chief in their job title?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY\n",
       "1                        CAPTAIN III (POLICE DEPARTMENT)\n",
       "2                        CAPTAIN III (POLICE DEPARTMENT)\n",
       "3                   WIRE ROPE CABLE MAINTENANCE MECHANIC\n",
       "4           DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)\n",
       "                               ...                      \n",
       "148649                                         Custodian\n",
       "148650                                      Not provided\n",
       "148651                                      Not provided\n",
       "148652                                      Not provided\n",
       "148653                        Counselor, Log Cabin Ranch\n",
       "Name: JobTitle, Length: 148654, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['JobTitle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chief_string(title):\n",
    "    if 'chief' in title.lower().split():\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chief_string('TREE TOPPER SUPERVISOR II')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['JobTitle'].iloc[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chief_string('DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4          True\n",
       "          ...  \n",
       "148649    False\n",
       "148650    False\n",
       "148651    False\n",
       "148652    False\n",
       "148653    False\n",
       "Name: JobTitle, Length: 148654, dtype: bool"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal['JobTitle'].apply(lambda x: chief_string(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "477"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(sal['JobTitle'].apply(lambda x: chief_string(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "correlation between length of the Job Title string and Salary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "sal['title_len'] = sal['JobTitle'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>TotalPayBenefits</th>\n",
       "      <th>title_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>TotalPayBenefits</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.036878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>title_len</td>\n",
       "      <td>-0.036878</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  TotalPayBenefits  title_len\n",
       "TotalPayBenefits          1.000000  -0.036878\n",
       "title_len                -0.036878   1.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal[['TotalPayBenefits', 'title_len']].corr()"
   ]
  }
 ],
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