sf_salaries_2.ipynb

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  "source": [
   "latest_salaries = salaries[salaries['Year'] == 2014]"
  ]
 },
 {
  "cell_type": "code",
  "execution_count": 6,
  "metadata": {
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  "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>38123.000000</td>\n",
      "      <td>38123.000000</td>\n",
      "      <td>38123.000000</td>\n",
      "      <td>38123.0</td>\n",
      "      <td>0.0</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>mean</th>\n",
      "      <td>129593.000000</td>\n",
      "      <td>75463.918140</td>\n",
      "      <td>100250.918884</td>\n",
      "      <td>2014.0</td>\n",
      "      <td>NaN</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>std</th>\n",
      "      <td>11005.306493</td>\n",
      "      <td>51697.713029</td>\n",
      "      <td>66238.852887</td>\n",
      "      <td>0.0</td>\n",
      "      <td>NaN</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>min</th>\n",
      "      <td>110532.000000</td>\n",
      "      <td>-618.130000</td>\n",
      "      <td>-618.130000</td>\n",
      "      <td>2014.0</td>\n",
      "      <td>NaN</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>25%</th>\n",
      "      <td>120062.500000</td>\n",
      "      <td>33436.210000</td>\n",
      "      <td>44199.990000</td>\n",
      "      <td>2014.0</td>\n",
      "      <td>NaN</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>50%</th>\n",
      "      <td>129593.000000</td>\n",
      "      <td>72359.980000</td>\n",
      "      <td>101105.370000</td>\n",
      "      <td>2014.0</td>\n",
      "      <td>NaN</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>75%</th>\n",
      "      <td>139123.500000</td>\n",
      "      <td>107974.445000</td>\n",
      "      <td>142746.950000</td>\n",
      "      <td>2014.0</td>\n",
      "      <td>NaN</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>max</th>\n",
      "      <td>148654.000000</td>\n",
      "      <td>471952.640000</td>\n",
      "      <td>510732.680000</td>\n",
      "      <td>2014.0</td>\n",
      "      <td>NaN</td>\n",
      "    </tr>\n",
      "  </tbody>\n",
      "</table>\n",
      "</div>"
     ],
     "text/plain": [
      "                  Id       TotalPay  TotalPayBenefits     Year  Notes\n",
      "count   38123.000000   38123.000000      38123.000000  38123.0    0.0\n",
      "mean   129593.000000   75463.918140     100250.918884   2014.0    NaN\n",
      "std     11005.306493   51697.713029      66238.852887      0.0    NaN\n",
      "min    110532.000000    -618.130000       -618.130000   2014.0    NaN\n",
      "25%    120062.500000   33436.210000      44199.990000   2014.0    NaN\n",
      "50%    129593.000000   72359.980000     101105.370000   2014.0    NaN\n",
      "75%    139123.500000  107974.445000     142746.950000   2014.0    NaN\n",
      "max    148654.000000  471952.640000     510732.680000   2014.0    NaN"
     ]
    },
    "execution_count": 6,
    "metadata": {},
    "output_type": "execute_result"
   }
  ],
  "source": [
   "latest_salaries.describe()"
  ]
 }
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