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kjam/sf_salaries_1.ipynb

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sf_salaries_1.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"salaries = pd.read_csv('Salaries.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>NATHANIEL FORD</td>\n",
" <td>GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY</td>\n",
" <td>167411</td>\n",
" <td>0</td>\n",
" <td>400184</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",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>GARY JIMENEZ</td>\n",
" <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
" <td>155966</td>\n",
" <td>245132</td>\n",
" <td>137811</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",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>ALBERT PARDINI</td>\n",
" <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
" <td>212739</td>\n",
" <td>106088</td>\n",
" <td>16452.6</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",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>CHRISTOPHER CHONG</td>\n",
" <td>WIRE ROPE CABLE MAINTENANCE MECHANIC</td>\n",
" <td>77916</td>\n",
" <td>56120.7</td>\n",
" <td>198307</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",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>PATRICK GARDNER</td>\n",
" <td>DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)</td>\n",
" <td>134402</td>\n",
" <td>9737</td>\n",
" <td>182235</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 Year \\\n",
"0 167411 0 400184 NaN 567595.43 567595.43 2011 \n",
"1 155966 245132 137811 NaN 538909.28 538909.28 2011 \n",
"2 212739 106088 16452.6 NaN 335279.91 335279.91 2011 \n",
"3 77916 56120.7 198307 NaN 332343.61 332343.61 2011 \n",
"4 134402 9737 182235 NaN 326373.19 326373.19 2011 \n",
"\n",
" Notes Agency Status \n",
"0 NaN San Francisco NaN \n",
"1 NaN San Francisco NaN \n",
"2 NaN San Francisco NaN \n",
"3 NaN San Francisco NaN \n",
"4 NaN San Francisco NaN "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"salaries.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>TotalPay</th>\n",
" <th>TotalPayBenefits</th>\n",
" <th>Year</th>\n",
" <th>Notes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>148654.000000</td>\n",
" <td>148654.000000</td>\n",
" <td>148654.000000</td>\n",
" <td>148654.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>74327.500000</td>\n",
" <td>74768.321972</td>\n",
" <td>93692.554811</td>\n",
" <td>2012.522643</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>42912.857795</td>\n",
" <td>50517.005274</td>\n",
" <td>62793.533483</td>\n",
" <td>1.117538</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>-618.130000</td>\n",
" <td>-618.130000</td>\n",
" <td>2011.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>37164.250000</td>\n",
" <td>36168.995000</td>\n",
" <td>44065.650000</td>\n",
" <td>2012.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>74327.500000</td>\n",
" <td>71426.610000</td>\n",
" <td>92404.090000</td>\n",
" <td>2013.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>111490.750000</td>\n",
" <td>105839.135000</td>\n",
" <td>132876.450000</td>\n",
" <td>2014.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>148654.000000</td>\n",
" <td>567595.430000</td>\n",
" <td>567595.430000</td>\n",
" <td>2014.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Id TotalPay TotalPayBenefits Year Notes\n",
"count 148654.000000 148654.000000 148654.000000 148654.000000 0.0\n",
"mean 74327.500000 74768.321972 93692.554811 2012.522643 NaN\n",
"std 42912.857795 50517.005274 62793.533483 1.117538 NaN\n",
"min 1.000000 -618.130000 -618.130000 2011.000000 NaN\n",
"25% 37164.250000 36168.995000 44065.650000 2012.000000 NaN\n",
"50% 74327.500000 71426.610000 92404.090000 2013.000000 NaN\n",
"75% 111490.750000 105839.135000 132876.450000 2014.000000 NaN\n",
"max 148654.000000 567595.430000 567595.430000 2014.000000 NaN"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"salaries.describe()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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