Assignment_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Assignment 2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kL9VPYin9nOD",
        "colab_type": "text"
      },
      "source": [
        "![](https://i.imgur.com/0AUxkXt.png)\n",
        "\n",
        "# SF Salaries Exercise \n",
        "\n",
        "Explore San Francisco city employee salary data.\n",
        "\n",
        "## Overview\n",
        "\n",
        "One way to understand how a city government works is by looking at who it employs and how its employees are compensated. This data contains the names, job title, and compensation for San Francisco city employees on an annual basis from 2011 to 2014.\n",
        "\n",
        "\n",
        "\n",
        "Just follow along and complete the tasks outlined in bold below. The tasks will get harder and harder as you go along.\n",
        "\n",
        "## Resources\n",
        "\n",
        "[Pandas API Reference](https://pandas.pydata.org/pandas-docs/stable/api.html)\n",
        "\n",
        "[NumPy Reference](https://docs.scipy.org/doc/numpy/reference/)\n",
        "\n",
        "[Visualization with Seaborn](https://jakevdp.github.io/PythonDataScienceHandbook/04.14-visualization-with-seaborn.html)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ueY1PMBl9nOF",
        "colab_type": "text"
      },
      "source": [
        "**Import libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnwk5LCx9nOG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "3713f45f-1295-4f32-ef6b-d4aa5f2bb90a"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jiOt1hEM9nOJ",
        "colab_type": "text"
      },
      "source": [
        "**Read `Salaries.csv` as a dataframe called `sal`.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtA68BoY9nOJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "link = \"https://s3-ap-southeast-1.amazonaws.com/intro-to-ml-minhdh/Salaries.csv\"\n",
        "sal = pd.read_csv(link)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QW6hV4iv9nOM",
        "colab_type": "text"
      },
      "source": [
        "**Check the head of the DataFrame.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oa2JHHJ99nON",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "c22a263f-a483-4543-8d5b-49556b8979ae"
      },
      "source": [
        "sal.head(10)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "      <th>0</th>\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",
              "      <th>1</th>\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",
              "      <th>2</th>\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",
              "      <th>3</th>\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",
              "      <th>4</th>\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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>DAVID SULLIVAN</td>\n",
              "      <td>ASSISTANT DEPUTY CHIEF II</td>\n",
              "      <td>118602.00</td>\n",
              "      <td>8601.00</td>\n",
              "      <td>189082.74</td>\n",
              "      <td>NaN</td>\n",
              "      <td>316285.74</td>\n",
              "      <td>316285.74</td>\n",
              "      <td>2011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>ALSON LEE</td>\n",
              "      <td>BATTALION CHIEF, (FIRE DEPARTMENT)</td>\n",
              "      <td>92492.01</td>\n",
              "      <td>89062.90</td>\n",
              "      <td>134426.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>315981.05</td>\n",
              "      <td>315981.05</td>\n",
              "      <td>2011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>DAVID KUSHNER</td>\n",
              "      <td>DEPUTY DIRECTOR OF INVESTMENTS</td>\n",
              "      <td>256576.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>51322.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>307899.46</td>\n",
              "      <td>307899.46</td>\n",
              "      <td>2011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>MICHAEL MORRIS</td>\n",
              "      <td>BATTALION CHIEF, (FIRE DEPARTMENT)</td>\n",
              "      <td>176932.64</td>\n",
              "      <td>86362.68</td>\n",
              "      <td>40132.23</td>\n",
              "      <td>NaN</td>\n",
              "      <td>303427.55</td>\n",
              "      <td>303427.55</td>\n",
              "      <td>2011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>JOANNE HAYES-WHITE</td>\n",
              "      <td>CHIEF OF DEPARTMENT, (FIRE DEPARTMENT)</td>\n",
              "      <td>285262.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>17115.73</td>\n",
              "      <td>NaN</td>\n",
              "      <td>302377.73</td>\n",
              "      <td>302377.73</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  ...         Agency  Status\n",
              "0   1      NATHANIEL FORD  ...  San Francisco     NaN\n",
              "1   2        GARY JIMENEZ  ...  San Francisco     NaN\n",
              "2   3      ALBERT PARDINI  ...  San Francisco     NaN\n",
              "3   4   CHRISTOPHER CHONG  ...  San Francisco     NaN\n",
              "4   5     PATRICK GARDNER  ...  San Francisco     NaN\n",
              "5   6      DAVID SULLIVAN  ...  San Francisco     NaN\n",
              "6   7           ALSON LEE  ...  San Francisco     NaN\n",
              "7   8       DAVID KUSHNER  ...  San Francisco     NaN\n",
              "8   9      MICHAEL MORRIS  ...  San Francisco     NaN\n",
              "9  10  JOANNE HAYES-WHITE  ...  San Francisco     NaN\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgRIzJ_l9nOP",
        "colab_type": "text"
      },
      "source": [
        "**Use the .info() method to find out how many entries there are.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuwgdTfz9nOP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "7ec11447-4b59-45d9-a1af-99bff3e5a5c5"
      },
      "source": [
        "sal.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 148654 entries, 0 to 148653\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count   Dtype  \n",
            "---  ------            --------------   -----  \n",
            " 0   Id                148654 non-null  int64  \n",
            " 1   EmployeeName      148654 non-null  object \n",
            " 2   JobTitle          148654 non-null  object \n",
            " 3   BasePay           148045 non-null  float64\n",
            " 4   OvertimePay       148650 non-null  float64\n",
            " 5   OtherPay          148650 non-null  float64\n",
            " 6   Benefits          112491 non-null  float64\n",
            " 7   TotalPay          148654 non-null  float64\n",
            " 8   TotalPayBenefits  148654 non-null  float64\n",
            " 9   Year              148654 non-null  int64  \n",
            " 10  Notes             0 non-null       float64\n",
            " 11  Agency            148654 non-null  object \n",
            " 12  Status            0 non-null       float64\n",
            "dtypes: float64(8), int64(2), object(3)\n",
            "memory usage: 14.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgO49aNW9nOR",
        "colab_type": "text"
      },
      "source": [
        "**What is the average BasePay ?**\n",
        "\n",
        "Keyword: [mean](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twQu_3xj9nOS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b8a7f42b-3e7e-4805-be59-e7ee7a8965c0"
      },
      "source": [
        "sal['BasePay'].mean()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66325.44884050643"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQc-OERD9nOU",
        "colab_type": "text"
      },
      "source": [
        "**What is the lowest and highest amount of BasePay?**\n",
        "\n",
        "Keyword: \n",
        "[min](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.min.html) , \n",
        "[max](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.max.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22XKRdg49nOV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "14f6db1e-34f2-40c7-c2ab-f5f71b3d18d4"
      },
      "source": [
        "sal['BasePay'].max()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "319275.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-yPM0ZU9nOX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "579c7260-bdcd-45d3-e346-47c18147210e"
      },
      "source": [
        "sal['BasePay'].min()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-166.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MdkKp6U9nOa",
        "colab_type": "text"
      },
      "source": [
        "**How about OvertimePay in the dataset? What is the average, lowest, highest amount?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Og2yxCil9nOb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "278cb6ba-64c1-4679-e276-40e9dfb019dd"
      },
      "source": [
        "sal['OvertimePay'].mean()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5066.059886444622"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVhw0urTDr6b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c8b57390-8e15-4cab-bd71-944a349ff493"
      },
      "source": [
        "sal['OvertimePay'].min()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9si4GhoDvLf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c361bc05-f84f-418c-8655-a6ba806f293f"
      },
      "source": [
        "sal['OvertimePay'].max()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "245131.88"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mF0zrXXh9nOf",
        "colab_type": "text"
      },
      "source": [
        "**What is the job title of  JOSEPH DRISCOLL ? Note: Use all caps, otherwise you may get an answer that doesn't match up (there is also a lowercase Joseph Driscoll).**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbqO05iu9nOh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "a8f91439-65ef-4911-93cd-aaa04b115806"
      },
      "source": [
        "sal.loc[lambda df: df['EmployeeName'] == 'Joseph Driscoll', lambda df: ['JobTitle']]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>JobTitle</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>36198</th>\n",
              "      <td>Captain, Fire Suppression</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        JobTitle\n",
              "36198  Captain, Fire Suppression"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwzxTlI29nOl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "1f7f9a14-326e-40b6-c6cf-23eed6429584"
      },
      "source": [
        "# Now try to select Employees who have name \"GARY JIMENEZ\"\n",
        "# Your Code Here\n",
        "sal.loc[lambda df: df['EmployeeName'] == 'GARY JIMENEZ', lambda df: ['EmployeeName','JobTitle']]"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>EmployeeName</th>\n",
              "      <th>JobTitle</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GARY JIMENEZ</td>\n",
              "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   EmployeeName                         JobTitle\n",
              "1  GARY JIMENEZ  CAPTAIN III (POLICE DEPARTMENT)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0nrHI4iu9nOn",
        "colab_type": "text"
      },
      "source": [
        "**How much does JOSEPH DRISCOLL make (including benefits)?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUZlBSDE9nOo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "8a3c979a-b5b6-479f-dbec-c804a0f1b264"
      },
      "source": [
        "# Your Code Heree\n",
        "sal['sum'] = sal.loc[:, lambda df: ['Benefits','BasePay','OvertimePay','OtherPay']].sum(axis = 1)\n",
        "sal.loc[lambda df: df['EmployeeName'] == 'JOSEPH DRISCOLL', lambda df: ['sum']]\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>270324.91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sum\n",
              "24  270324.91"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1thjd2GC9nOq",
        "colab_type": "text"
      },
      "source": [
        "**What is the name of highest paid person (including benefits)?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msrq-7Ww9nOr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "a90904c5-103d-4ddd-e257-39a3327813bf"
      },
      "source": [
        "sal['sum'].max()\n",
        "sal.loc[lambda df : df['sum'] == df['sum'].max(),lambda df : ['EmployeeName','sum']]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>EmployeeName</th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NATHANIEL FORD</td>\n",
              "      <td>567595.43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     EmployeeName        sum\n",
              "0  NATHANIEL FORD  567595.43"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uF2kX93x9nOu",
        "colab_type": "text"
      },
      "source": [
        "**What is the name of lowest paid person (including benefits)? Do you notice something strange about how much he or she is paid?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "htAfatTl9nOv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "8c60612b-a2d6-463b-a1f3-cc9b343bb46b"
      },
      "source": [
        "sal.loc[lambda df : df['sum'] == df['sum'].min(),lambda df : ['EmployeeName','sum']]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>EmployeeName</th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>148653</th>\n",
              "      <td>Joe Lopez</td>\n",
              "      <td>-618.13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       EmployeeName     sum\n",
              "148653    Joe Lopez -618.13"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCsudHFgIWJ7",
        "colab_type": "text"
      },
      "source": [
        "### His benefits were negative"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WB0HTvjM9nOx",
        "colab_type": "text"
      },
      "source": [
        "**What was the average (mean) BasePay of all employees per year? (2011-2014) ?**\n",
        "Keyword: _groupby_"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Y5mUYir9nOy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "75e7d26b-f112-4c80-bd3b-08fd61c78847"
      },
      "source": [
        "sal.loc[lambda df : np.logical_and(df['Year'] < 2014, df['Year'] >2011),lambda df:['BasePay']].mean()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BasePay    67539.898363\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k8Qzx9MD9nO0",
        "colab_type": "text"
      },
      "source": [
        "**How many unique job titles are there?**\n",
        "\n",
        "Keyword: _unique, nunique_"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvzwZ5Mb9nO1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "950414c8-6576-4410-ad69-0a72ab44961d"
      },
      "source": [
        "sal['JobTitle'].unique()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "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)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0dkyZe-39nO4",
        "colab_type": "text"
      },
      "source": [
        "**What are the top 5 most common jobs?**\n",
        "\n",
        "Keyword: *value_counts*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVSDr3Ww9nO7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "8a1f9edd-0935-4dde-f700-37c62e45a2c3"
      },
      "source": [
        "sal['JobTitle'].value_counts()[0:5]"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25fCX21U9nO-",
        "colab_type": "text"
      },
      "source": [
        "**How many Job Titles were represented by only one person in 2013? (e.g. Job Titles with only one occurence in 2013?)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9vr6BKD9nPA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "14c8160b-a283-4a2b-f859-0ed74d3aa90b"
      },
      "source": [
        "sum(sal[sal['Year']==2013]['JobTitle'].value_counts()==1)\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "202"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HoiNghja9nPD",
        "colab_type": "text"
      },
      "source": [
        "**How many people have the word Chief in their job title?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBc0hzan9nPE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bbd13a48-0c7f-4167-9b2b-d7fe6ebf938e"
      },
      "source": [
        "def chief_string(title):\n",
        "    if 'chief' in title.lower().split():\n",
        "        return True\n",
        "    else:\n",
        "        return False\n",
        "    \n",
        "sum(sal['JobTitle'].apply(lambda x:chief_string(x)))\n",
        "  "
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "477"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5G094gws9nPL",
        "colab_type": "text"
      },
      "source": [
        "## Data Visualization\n",
        "\n",
        "**Implement seaborn's countplot with x='Year'**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56GKG4UD9nPM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "71f0d8b0-68e7-4ad0-c9c5-bac2f3d43c1f"
      },
      "source": [
        "sns.countplot(x = \"Year\", data=sal)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff66720ed68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "orQACfVP9nPP",
        "colab_type": "text"
      },
      "source": [
        "**Implement seaborn's distplot for BasePay of Year 2011**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYPrrLW19nPQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "94f223a5-b398-4c8a-e713-c2ce366a7bdd"
      },
      "source": [
        "sns.distplot(sal[sal[\"Year\"] == 2011][\"BasePay\"], bins = 30);"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ea9OFiqK9nPS",
        "colab_type": "text"
      },
      "source": [
        "**How about other Year**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orRMn0X89nPT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "a7981afd-4837-4b70-9686-985c0ce40a40"
      },
      "source": [
        "sns.distplot(sal[sal[\"Year\"] == 2012][\"BasePay\"], bins = 30);"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAERCAYAAABrWly6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxcZ33v8c9vZiTNaF+t3fsWO3YSx2QnENYklKSQLkk36A1NSwltX13uDa/2UhrKvQVu6UqhKaU0aROglMWF0JBAICFxHCub13i3bMnarF2afea5f8wZZyxrGUlntjO/9+ull2fOOTrnp4nz9aPnPM9zxBiDUkqpwufKdQFKKaXsoYGulFIOoYGulFIOoYGulFIOoYGulFIOoYGulFIOkdNAF5Evi8igiByw6XwxEXnV+tplxzmVUqpQSC7HoYvIzcAU8LAx5nIbzjdljKlcfmVKKVV4ctpCN8Y8A4ykbhORdSLy3yLykog8KyKbc1SeUkoVlHzsQ38I+Kgx5mrgD4F/WMT3ekWkS0ReEJGfzUx5SimVnzy5LiCViFQCNwD/ISLJzWXWvvcDD87ybb3GmHdbr1cZY3pFZC3wIxHZb4w5kem6lVIqH+RVoJP4jWHMGHPlzB3GmG8C35zvm40xvdafJ0Xkx8BVgAa6Uqoo5FWXizFmAjglIj8PIAlXpPO9IlInIsnWfCNwI3AoY8UqpVSeyfWwxceA3cAmEekRkXuBXwbuFZHXgIPAnWme7jKgy/q+p4G/MMZooCulikZOhy0qpZSyT151uSillFq6nN0UbWxsNKtXr87V5ZVSqiC99NJL540xTbPty1mgr169mq6urlxdXimlCpKIdM+1T7tclFLKITTQlVLKITTQlVLKITTQlVLKITTQlVLKITTQlVLKITTQlVLKITTQlVLKITTQlVLKIfJtPfSMe3TPmQWP+aVrV2ahEqWUslfRBbqT6T9WShU37XJRSimH0EBXSimH0EBXSimHWDDQReTLIjIoIgfm2P/LIrJPRPaLyPPpPgNUKaWUvdJpoX8FuHWe/aeAtxhjtgGfBB6yoS61BKFIjCcO9vOln54kHI3nuhylVJYtOMrFGPOMiKyeZ//zKW9fADqWX5ZarKcODfC5p44yGYwC8FL3CNeva8xxVUqpbLK7D/1e4Ptz7RSR+0SkS0S6hoaGbL508YrG4vzhN16jvNTNh9+yjs46H8+dGCauDwBXqqjYFugicguJQP9fcx1jjHnIGLPTGLOzqWnWR+KpJejqHmXMH+Htm5vprC/npg1NjEyHOXRuItelKaWyyJZAF5HtwJeAO40xw3acU6XvqUMDlLpdbGiuBGBrWzV15SX89Pj5HFemlMqmZQe6iKwEvgn8qjHm6PJLUothjOHJwwPcsL6BMo8bAJcIN65v5MyInzMj/hxXqJTKlnSGLT4G7AY2iUiPiNwrIr8lIr9lHfJxoAH4BxF5VUS6MlivmuH44BTdw37ecVnzRduvXlWHxyUc6B3PUWVKqWxLZ5TLPQvs/xDwIdsqUrOaa52WnxwZBGAyGKXGV3Jhe5nHTVutj7PaQleqaOhM0QJ3uH+S9lrfRWGe1Fnno3csQDSuY9KVKgYa6AVsMhjh7Iifza1Vs+5f2VBBNG7oHw9muTKlVC5ooBewsyN+DLChqXLW/Z11PgC9MapUkdBAL2B9E0EEaK7xzrq/tryUaq9HA12pIqGBXsD6x4PUV5ReGK44m876cr0xqlSR0EAvYAMTQVrmaJ0nrawvZ9QfYTIYyVJVSqlc0UAvUOFonOGpMC3VCwc6wNmRQDbKUkrlkAZ6gRqcDGJgwRZ6W60Pt4j2oytVBDTQC1RyKOJCLfQSt4vWWq8GulJFQAO9QPVNBCl1u6irKF3w2PZaH33jAYwup6uUo2mgF6j+8SDN1WW4RBY8trnaSygaZzygN0aVcjIN9AJkTGL250L950nJbpmBCZ0xqpSTaaAXoIlglEAktmD/eVKzdVz/RCiTZSmlckwDvQBduCFa40vreF+pmxpfibbQlXI4DfQClAzmdFvoAM3VZRroSjmcBnoBGpgIUu314Cude8r/TM1VXoYmQ0RjupSuUk6lgV6AhqfDNFSWLep7mmu8ROOG08M6Hl0pp9JAL0DD02Ea0hh/nip5Y/TowGQmSlJK5QEN9AITisaYDkWpX2Sgr6gqQ4DX+zXQlXIqDfQCMzIdBlh0oJe4XTRUlnJUA10px9JALzBLDXRIdLtol4tSzqWBXmCSgd5QsbibopAI9NPD0wQjMbvLUkrlAQ30AjMyHcZb4lrUkMWk5movcQPHB6cyUJlSKtc00AvMyHR4Sa1zSEwuAjii/ehKOdKCgS4iXxaRQRE5MMd+EZG/FZHjIrJPRHbYX6ZKGpkOL6n/HBLdNKUeF0e0H10pR0qnhf4V4NZ59t8GbLC+7gO+sPyy1GxiccOof+mB7nYJ65sqtYWulEMtGOjGmGeAkXkOuRN42CS8ANSKSKtdBao3jAcixM3SRrgkbWqp0pEuSjmUHX3o7cDZlPc91rZLiMh9ItIlIl1DQ0M2XLq4LGfIYtLG5ir6xoOM+/VhF0o5TVZvihpjHjLG7DTG7GxqasrmpR3hjSGLSw/0zS1VABwd1Fa6Uk5jR6D3Ap0p7zusbcpmI9Mh3C6h2ley5HNstAJd+9GVch47An0X8GvWaJfrgHFjTJ8N51UzDE+HqSsvSes5onNpq/FSVebRQFfKgTwLHSAijwFvBRpFpAf4U6AEwBjzReBx4HbgOOAHfj1TxRa70WUMWUwSETa2VOnQRaUcaMFAN8bcs8B+A3zEtorUrIwxDE+HWdlQsexzbWyu4vsH+jDGIMto7Sul8ovOFC0QgUiMUDROffnS+8+TNjVXMuaPMDipD41Wykk00AvEmDXMsLZ8eV0uAJtaqgG9MaqU02igF4g3An35LfSNzZWAPr1IKafRQC8QY4HEGHQ7WugNlWU0VpZxuE8DXSkn0UAvEGP+CB6XULGEZXNns7WtmoPnxm05l1IqP2igF4ixQITa8hLbRqVsa6/h2OCUPuxCKQfRQC8QY/6wLd0tSZe31xCLGw73Tdh2TqVUbmmgF4hxf4TaZUz5n2lbRw0AB3q120Upp9BALwDBSIzJUNSWES5JbTVe6itK2a+BrpRjFHWgR2JxvvlyD/t6xkhMeM1P/eNBAGp99nW5iAiXt9ewv1e7XJRyiqIO9O5hP13do3x171m+8JMTnBsL5LqkWfVaddnZQgfY1l7NsYFJvTGqlEMUdaD3jyeC8j3bWhn1R/jWK/m56u8bgW5fCx0SI12iccPrOmNUKUco7kCfCFJV5uHG9Y1cs7qOc2MBQnnYWu0dDSBAtW/BtdQW5fL2xI1R7UdXyhmKO9DHg7TUeAFY1VCBAc6O5l+3y7mxAFVeDx6Xvf+52mt91JWXcKBHA10pJyjaQI/FDQOToQuBvrK+HAFOD0/ntrBZ9I4FbO9ugdQboxroSjlB0Qb6+akQsbihpToR6N4SNy01XrrzMNDPjQWosXEMeqpt7TUc1RujSjlC0QZ6cihgsoUOiW6XsyMBIrF4rsq6RDxuODcWpM7mES5Jb1pdTzRueKl7NCPnV0plT/EG+kQQtwhNVWUXtq1uKCcci+fVdPjz0yHCsTg1GehyAbhmTT0el/Dc8fMZOb9SKnuKNtD7xgM0VZVddKNxlfV4t67T+dNa7bVu0tZlqMuloszDlZ21PHdiOCPnV0plT9EGev94kNaU7haAGl8JdeUldHWP5KiqS50bS3QN1WSoywXghvWN7O8ZYzwQydg1lFKZZ+/A5gLhD0WZCEYv6j9PWtVQwd7To3nzAOXeMT8AdTZ1uTy658wl2/zhKHEDn/3vI2xpq+aXrl1py7WUUtlVlC30volLb4gmrWooZ2gyRE+ejEc/N5aY/OQtsefBFrNZWVdOiVs4MTSVsWsopTKvKAP9wgiX6ksDva3GB8DBc/lxY7RnNEB7nS+j1/C4XaxuqNBAV6rAFWWgn58K4StxU+W9tF+6udqLCLzenx+Bfm4sQFttZgMdYF1TJYOTISaC2o+uVKFKK9BF5FYROSIix0XkgVn2rxSRp0XkFRHZJyK321+qfaZDUSq9s98+KPW4WNNQkTdDF3vHArRnKdABTgxqK12pQrVgoIuIG/g8cBuwBbhHRLbMOOxPgK8bY64C7gb+we5C7TQVilFZNvf94Mtaq/NiBcKpUJTxQCQrLfTWWi81vhJe6xnL+LWUUpmRTgv9GuC4MeakMSYMfBW4c8YxBqi2XtcA5+wr0X7ToSgV8wT65pYquof9TIWiWazqUsn12TPdhw7gEmHHylqODUzRN54fN4SVUouTTqC3A2dT3vdY21J9AvgVEekBHgc+OtuJROQ+EekSka6hoaEllGuPqVCUitK5R41c1pr4t+lIjlvpyXXQ22svvXmbCTtW1mGAb76cn+vCK6XmZ9dN0XuArxhjOoDbgUdE5JJzG2MeMsbsNMbsbGpqsunSixOLGwKR+btcNrdWAeS8Hz05S7S9tjwr12uoLGNNYwVf7zqb14/kU0rNLp1A7wU6U953WNtS3Qt8HcAYsxvwAo12FGi36XCiG2W+Lpf2Wh9VXk/OA/3cWACP6+L1ZjJt56o6uof9vHgqf2bLKqXSk06g7wU2iMgaESklcdNz14xjzgBvBxCRy0gEeu76VOYxbfWLz9dCFxEua8n9jdHesQCttV7cruzNWN3aVkNlmYevdZ1d+GClVF5ZMNCNMVHgfuAJ4DCJ0SwHReRBEbnDOuwPgN8QkdeAx4APmjz9nT15o3O+FjrAZa1VvN43QTyeux/j3FjgwkSnbCn1uHjfVe3812vnODviz+q1lVLLk1YfujHmcWPMRmPMOmPMp6xtHzfG7LJeHzLG3GiMucIYc6Ux5geZLHo50mmhA2xurWY6HMvpEgC9WZglOpuP3LIelwife/Jo1q+tlFq6opspOhVKPJlnoUBPjnQ5lKN+9GgsTv9EMCuTimZqqfHywRtX8+1Xe3N+H0Eplb6iW21xOhTFJeAtmf/fsk3NVReWALj18pYsVfeG/okgcUNOAv3RPWdYUemlzOPi9776Kh+4YfWsx+mqjErll6JroU+HolSWeRZcGtdX6s7pEgDJddCzMUt0Nr5SN2/ZuIIjA5OcOp9/z1lVSl2q6AJ9aoFZoqlyuQRAch30XPShJ12/toFqr4cnDvbruHSlCkDRBXqyhZ6OXC4BcKGFnuVRLqlKPS7etrmZMyP+nA/hVEotrOgCfbEtdMjNEgA9owEaKkrxzbNEQTZcvaqOhopSnjjYT1xb6UrltaIL9OlQbN51XFLlcgmAbK2DvhC3S3jX1hYGJ0O8ekZXYlQqnxVVoPvDUcKxeNpdLrlcAqBn1J+TES6z2dpWTUu1l90nh3NdilJqHkUV6MNTYWDhWaJJuVoCIB43nB0NsLIhO4tyLSS5tG7vWIChyVCuy1FKzaG4An06EejpttAhN0sADEwGCUfjrKzPj0AH2N5RiwCvntVuF6XyVXEF+lSidZluCx1yswRA93BiyOKqPGmhA1T7SljXVMlrPWM6hFGpPFVkgb6UFnr2lwA4YwV6PrXQAa7orGVkOszZHK5vo5SaW3EF+vTi+tDh4iUAsuXMiB+3S/JilEuqrW3VeFyi3S5K5aniCvSpEKVuF6We9H/sXCwB0D2SGOFS4s6v/zzeEjeXtVazr2eMWA6XFVZKzS6/EiPDhqfDVJQtfqLOZa3VHO7L3kiXM8PTedV/nmprWzX+cOzC806VUvmjqAL9/FRoUf3nSVvaqjkz4mciGMlAVZfqHvHTmWf950nrV1QiwLEBXQpAqXxTVIE+PBVeVP950uXtNQAc6B23u6RLjAcijPkjrMrTQC8v9dBe5+PY4FSuS1FKzVBcgT4dWlKgb7MCfX9P5gM9+di3fO1yAdiwopKeUT/jgez8xqKUSk/RPODCGMPodIRNzQv3oT+658wl22p9JXx3Xx9V3hIgcw93SI5Bz9cuF4D1K6p4+sgQu08M5+ThH0qp2RVNoAciMcKxOL7Spf3IbbU+zmXhRuCZCy30ioxfa6lW1pdT5nHxzLGhBQN9tn8cZ9InHyllj6Lpckl2D5SXLG052o46H8PTYYKRmJ1lXeLMyDQNFaVLunmbLW6XsLapkmeODumsUaXySNEE+pg/EejeJa4vnpzkk+nhet3D+TvCJVWiHz1woYtoKeLGMGJN9lJKLV/RBPqFFvoSAz25lG2mu126h/15fUM0acOKSgCePTa0pO8/fX6aL/z4BP/vB0f4wcF+O0tTqmgVXaD7ltjlUlHmodZXktEWejgap288kLdDFlPVV5TSWe/jJ0fPL/p7nzo8wEPPnmQyGKG+opQ/+69D+MPZf8yfUk6TVqCLyK0ickREjovIA3Mc8wsickhEDorIo/aWuXzj/uUFOiS6XXozuDBV71iAuMnvES5JIsLNG5rYfeI8kVg87e+bDEZ45ugQW9uq+f13buKuHR30jgX4ux8dz2C1ShWHBQNdRNzA54HbgC3APSKyZcYxG4CPATcaY7YCv5eBWpflQgt9Gc/obM/wjdET1mSdtU35O8Il1Zs3NDEdjvHKIh5N9/yJYWJxw7u3tlDqcbGmsYK7dnTwpWdPclwnKym1LOm00K8BjhtjThpjwsBXgTtnHPMbwOeNMaMAxphBe8tcvrFAGLdLKFvEwlwztWf4xugRazr9xuaqjJzfbteva8DtkrT70YORGHtODbO1rZrGyrIL2z92+2a8Hjf/8GNtpSu1HOmkWztwNuV9j7Ut1UZgo4g8JyIviMitdhVol/FAhGqvBxFZ8jk6rEDvGVn6yI75HB2YtJ5jWpKR89utxlfClZ21PHMsvX70F0+NEIzEuXlj00XbGyvLuGXzCp45ej6rT4ZSymnsuinqATYAbwXuAf5JRGpnHiQi94lIl4h0DQ0tbXTEUo0HotSWly7rHOVlHhorSy9M/rHbkf5JNjZXZuTcmfLmDY3s6xljdIHhh9FYnOdOnGdtUwUddZfeI7h5YxPnp0IczuK680o5TTqB3gt0przvsLal6gF2GWMixphTwFESAX8RY8xDxpidxpidTU1NM3dn1Jg/TLVv+S3fzrpyzoz4bZ9QE4nFOTk0zcaWwuhuSbp5YxPGwHMn5m+lHzw3wWQwypvXz/7f/eYNjQA8s4RRM0qphHQCfS+wQUTWiEgpcDewa8Yx3ybROkdEGkl0wZy0sc5lmwhEqLEh0Fc2lDMdjnF2xN5+9NPnpwnH4mwqkP7zpO3tNVR7PTy7QBC/cGqY+opSNszxG8iKai+bW6p45mh2f3NTykkWDHRjTBS4H3gCOAx83RhzUEQeFJE7rMOeAIZF5BDwNPBHxpjhTBW9FOOBCLV2BLo1pPDlM6PLPleqQrshmuRxu3jzhiaeOjxAKDr76J/+8SDdw36uXVOPa557GDdvbKKre4TpkI5JV2op0lowxBjzOPD4jG0fT3ltgN+3vvLSmE0t9OZqL6UeFy+fGeVnr5p5b3hpHt1zhicPDSDAS92j7MvCMr12uvuaTr63v4/H9/fxvqs6Ltm/59QwHpdw9cq6ec9z84YmHnrmJC+cHObtlzVnqlylHKsoZorG44aJQITa8uUHukuEjjqf7S30gYkgDZVlefcc0XTcuK6RtY0V/Ovz3ZfsC0ZivHJ2jO0dtZQvsODYztV1eEtc2u2i1BIVXnoswWQoStxgSwsdEt0uh/smbZ2uPjARpLm6bOED85DLJfzq9at49ewY+3ounmT04qkRwtE4166pX/A83hI3161tSHsYpFLqYkUR6BPWLFE7RrlAItBjcWNb10g4GmdkOkxztdeW8+XCXVd3UF7q5uHdb7TSD52b4KnDA2xuqaKjzpfWeW5c18ip89MMTgYzVapSjlUUgZ6c9m/HTVGAldY46sVMeZ/P0GQIAwUd6NXeEt6/o51dr53jh4cHmA5F+ehjL+MrdfP+HR1pT+i6amVi+sJrZwvrPoJS+SB/n6Jgo+Ra6DW+Es5PLX/97fIyD2sbK3ip255+9IGJRGu0ULtcku578zqefn2Ie/+1iyqvh6lQlF+/Yc2CD+tIfapROBrHJfDvL3QzNBm6sF2faqTUwooi0JMt9BobboomXbOmnsf39xGLG9yupS8nANA/EcTjEhoqCjvQVzaU8+M/eiuP7+/jkd3d3LJ5BXWLnJ1b6nHRUu2lJ4OrWirlVEXW5bK8qf+prl/XwEQwyqFzy5+qfnbUT0uNd9n/MOSDEreLO69s5xsfvoGP3LJ+SefoqCvn7KifuD7eTqlFKYpAHwskulnsGuUCiUAHeH6BKe8LCUZi9IwGWJPHD4XOto46H6FonPNToYUPVkpdUBSBPh6IUOpx4S2x78ddUeVlw4pKnj+xvAmxr50dIxY3rG7UQE9KPuCjx+blFZRyuqII9OQ6LstZOnc2169rYO/pxDjrpdp7egSgIB47ly1NVWWUelycHc3MqpZKOVVRBPqY355p/zPdsK4Bfzh2yWSaxdhzaoTm6rIFZ1EWE5cIHbU+vTGq1CIVRaDbtTDXTNeuaUCEJXe7RGNxXu4eZbX2n1+io66cvvHAop5XqlSxK4pAz1QLva6ilC2t1Uu+MXq4b5LpcEwDfRad9T7iBvrGdcaoUukqit/zxwMRNmfowRE3rGvgX3d3E4zE8JYs7gHUe04lWvaFekM0dUKQ3ZJPNTo74r+wZLFSan5F0UKfCERsnVSU6s0bmghH40taIXDv6RE6630Z+e2h0NX4Sqj2eujRG6NKpc3xgR6NxZkMRTMWmteva6DGV8L3D/Qv6vvicUPX6VGuWd2QkbqcIDHBSG+MKpUuxwf6RDCxxG0mbopCYmbku7Y089ShuZ/YM5sXT48wPB3m5o2NGanLCTrryxmZDuPXJxgplRbHB3om1nGZ6fbtrUyGojx3PP2bo99+pZeKUjfv2tKSsboKXXLJXW2lK5Uexwf6mN/+af8z3biukSqvh8f3p9ftEozE+N7+Pt69tQVf6eJupBaTjlofAtqPrlSaHB/oF1roNi7MNVOpx8U7tzTzg4P9ac0a/fGRQSaDUdueSepUZSVumqrKdMaoUmkqokDP7EiS2y9vZSIYTWtM+rde6aWpqowb1ukN0YV01pfTMxrA6MqLSi3I8YE+Op3ocqnLYB86wE0bGqnxlfBvL1z6oORU4/4IT78+xHu3t+EpwAdCZ1tHnQ9/OMZZXahLqQU5fmLRqD8zLfTZJtW8aXU9Tx0e4DP//fqFiTEzn7Tzta4zhGNxfvaqNlvrcarO5OP+zo6yskEnGCk1H8c3Ecf8Yaq9nqy0hm9c10B5qZunDg/Mur9/PMjfPHWMt25qYlt7TcbrcYLmai8lbtFnjCqVhrRSTkRuFZEjInJcRB6Y57i7RMSIyE77SlyeUX+EuorM3RBNVVbi5uYNTRwdmKJ7ePqS/Z/87iGiccODd1xu+1K+TuV2CW21Pl4+Y8/zW5VysgUDXUTcwOeB24AtwD0ismWW46qA3wX22F3kcoz6w9Qu8rmWy3Hd2gYqyjw8vr+PQPiNiUY/OTrE9/b38ZFb1mvXwSKtaahgf+84UzrBSKl5pdNCvwY4bow5aYwJA18F7pzluE8Cnwbyanm8MX8k4zdEU5V6XPzMtlZ6xwL83Y+O8dShAT753UP85iNdrG2s4DffsjZrtTjF2qZKYnHD3lMjuS5FqbyWTqC3A2dT3vdY2y4QkR1ApzHmezbWZotRf3jRT55fris6a/nNm9fhcgkferiLrzx/mvdsa+Phe6+hzKMTiRZrVUM5pW7Xsp/fqpTTLXuUi4i4gM8BH0zj2PuA+wBWrly5wNH2GPNHqM1iCz2ps76c+29ZjwjcsmlFwS6Rmw9K3C52rKpd9vNblXK6dAK9F+hMed9hbUuqAi4Hfmzd6GsBdonIHcaYrtQTGWMeAh4C2LlzZ8ZnioSjcaZC0ay30JOS66M/f2JYw2iZblzXyOeeOsrodDhrN7mVKjTpdLnsBTaIyBoRKQXuBnYldxpjxo0xjcaY1caY1cALwCVhngtjgexMKlKZd8P6Box546EgSqlLLRjoxpgocD/wBHAY+Lox5qCIPCgid2S6wOUYsyYVZXOUi8qM7R21lJe69TcdpeaRVh+6MeZx4PEZ2z4+x7FvXX5Z9khO+6/XX9ELXonbxTVr6jXQlZqHo2eKjl5ooWuXixPcsK6B44NT9OuDo5WalaMDPbkWeq5uiip7vW1zMwDf3Xcux5UolZ8cHegjGuiOsn5FJVd01PDNl3sXPlipIuToQB/zRyjzuPSpQA7y/h0dHOqb4HDfRK5LUSrvODrQR6ezP0tUZdZ7r2ijxC3850s9uS5Fqbzj7EDP0SxRlTn1FaXcsmkF3371HNHYwo/7U6qYODrQx3KwjovKvLuu7uD8VIhnj+naLkqlcnSgj/rD1FVoC91pbtm0goaKUr7005P6rFGlUjg60BMLc2kL3WlKPS7uf9t6njs+zNNHBnNdjlJ5w7GBboxhLJDdtdBV9vzKdatY21jBp753mIj2pSsFOPgh0RPBKLG40T50h5jtodw3rm/kkRe6+f2vv8b1axsueSC3UsXGsS305CxR7XJxrs0tVaxtrOCpQwNMBiO5LkepnHNsoCfXcdEuF+cSEe64oo1ILM5/vtyjN0hV0XNwoFvT/nWlRUdbUe3ltm2tHB2Y4pEXunNdjlI55dhA14W5isd1a+rZ2FzJp753mOODU7kuR6mccWygj05rl0uxEBHu2tGBt8TNH39rv3a9qKLl2EAf84dxCVR7NdCLQZW3hP956yb2nBrhO6/q8rqqODk20Ef9EWp8JbhckutSVJbc/aaVXNFRw59/7zATOupFFSEHB7qu41Js3C7hz392G8PTIf7qyaO5LkeprHN0oOtKi8VnW0cNv3B1J/++5wznp0K5LkeprHJsoA9OhFhR5c11GSoH7nvLWiKxOA/v1mGMqrg4NtD7x4O01GigF6N1TZW8fXMzj+w+TSAcy3U5SmWNIwN9KhRlMhTVQC9i9928llF/hG+8rE82UsXDkYtz9Y8HAWjVQC8qqRZLq2gAABA9SURBVAt4GWPoqPPx108eRQCXJEY76QJeyskc2UJPBnpLtQZ6sRIRblrfyPB0mGMDk7kuR6msSCvQReRWETkiIsdF5IFZ9v++iBwSkX0i8kMRWWV/qenrGw8AaJdLkdvSVk1FqZu9p0dzXYpSWbFgoIuIG/g8cBuwBbhHRLbMOOwVYKcxZjvwDeAzdhe6GAMTiRZ6s7bQi5rH5WLHyjpe75/Q5XVVUUinhX4NcNwYc9IYEwa+CtyZeoAx5mljjN96+wLQYW+Zi9M3HqSuvARviTuXZag8cPXqOuIGXjkzlutSlMq4dAK9HTib8r7H2jaXe4Hvz7ZDRO4TkS4R6RoaGkq/ykUamAjSUuPL2PlV4VhR5WVVQzl7T4/ool3K8Wy9KSoivwLsBD47235jzEPGmJ3GmJ1NTU12XvoifeNBHeGiLnjT6nqGp8OcHvYvfLBSBSydQO8FOlPed1jbLiIi7wD+GLjDGJPTOdf940HtP1cXXN5WQ5nHxd7TI7kuRamMSifQ9wIbRGSNiJQCdwO7Ug8QkauAfyQR5oP2l5m+UDTG8HRYW+jqglKPiys7aznQO864X2+OKudaMNCNMVHgfuAJ4DDwdWPMQRF5UETusA77LFAJ/IeIvCoiu+Y4XcYNTiR+OdAhiyrVztX1ROOG77x2yS+XSjlGWjNFjTGPA4/P2PbxlNfvsLmuJevTSUVqFu21PtpqvDz24ll+9bpViOg6+cp5HDdTtH9Cp/2r2e1cXc/hvgkO9E7kuhSlMsJ5gW7NEm3WQFczXNFRS5nHxWN7zyx8sFIFyHGB3jcepKLUTVWZI9cdU8vgK3Xznm2tfOeVXsb84VyXo5TtHBfoiUlFXu0jVbO67y1rmQ7H+Oefnsp1KUrZznGB3qcPtlDz2NxSze3bWviX505rK105juMCvX88SEu1TvtXc/vdt29kKhTlS89qK105i6MCPRY3DE6GaKkpy3UpKo9taqniPdta+ZfnTjE6ra105RyOCvTzUyFicaMLc6kF/e47NhCMxnnwu4dyXYpStnFUoB/pTzyZZm1jRY4rUfluY3MVH33ber71Si/feVVnjypncFSg7+8dB+Dy9pocV6IKwf23rOfqVXX8ybcOcHZEV2JUhc9Rgb6vZ4w1jRXU+EpyXYoqAB63i7/+xSsB+M1HXuL8VE4XCVVq2RwV6Ad6J9imrXO1CJ315fz9L+/g5Pkpfv6Lu+kZ1Za6KlyOmU55fipE71iAX79xda5LUXns0T2zT/v/wPWr+dfdp7ntb57lvdvb+NT7LtfJaargOCbQk/3n2kJXS7GqoYL73ryOr3Wd4dEXz3B8aIrffus6blzfSIl79l9kk/84TAYjnBsL4hKo9HqoLy+lzHqe7S9duzJrP4NSzgn0nnFEYKsGulqilhov99+yga7uEX567Dwf/Je91JaXcPOGJja1VLG2sQIRIRSNcWbYz+MH+ukd9TMRjF50nhK3sGNlHTetb8zRT6KKlWMCfV/POGsbK6jURbnUMrhdwrVrGtixso7jg1Ps7x3nx0cG2fXauUuObawsY21TZWKt9VofAkyGohwbmKSre5QXT41wfirEH7x7E9VevVGvMs8x6be/d4wb1mmLSNmjxO3istZqLmutBiAYiTEyHUYEPC4XVV4PXqtbZaZt7TW8Y0szPz4yxMMvdPP9A/38yc9s4b3bW7VfXmWUI0a5DEwEGZgIaf+5yhhviZu2Wh+tNT6aqsrmDPOkam8Jd1zRxrd/+0aaqsr4ncde4f1feJ6XuvVB1SpzHBHo+3sSN0S3d2igq/xyRWctu+6/ic/ctZ3e0QB3fWE3v/DF3fz3gX5icZPr8pTDOKLLZW/3CC6BLW3VuS5FqYukDpP88FvXsffUCM+fHOa3/u0lqrweruqs5X//zBY2NFflsErlFAUf6JPBCI/tOcPbL2umvLTgfxzlYGUeNzdtaOL6dY283j/BS92j/PT4ed75V8+wvaOGu3Z08O6tLbqev1qygk/Ah3d3MxGM8jtv25DrUpRKi9slbG2rYWtbDVOhKG6X8J8v9fCnuw7yp7sOcnl7NTeua2RbRw3b22vprPfpzVSVloIOdH84yj//9BRv3dTENu0/VwWosszDL127kntvWsOxgUmePDzAjw4P8uXnThGJJfrYa3wlbO+oYUtbNZe1VLO5tYq1jZWUehxxC0zZqKAD/dE9ZxiZDvNRbZ2rApbaz17rK+X9Ozq444o2BiYSy1n0jvkZmQ7zLz89TTgWBxKTl9Y1VbK5pYrNrdVsbqnistZqVlSVaWu+iKUV6CJyK/A3gBv4kjHmL2bsLwMeBq4GhoFfNMactrfUNxhj+K99ffzND49xw7oGrl5Vl6lLKZUTHreL9jof7XU+oB5IPJHr/FSI/vEg/RNB+seDPH1kiG+/+sakp7ryEjZbrfiV9eW01vhorfHSWuulsaIMl0vD3skWDHQRcQOfB94J9AB7RWSXMSb1US/3AqPGmPUicjfwaeAXM1Hw+akQ//vbB/j+gX6u7Kzl03dtz8RllMo7bpfQXO2ludrLFSnbA+GYFfAB+ieC9Iz66eoeudBlk+SSxHj6pqoyqrweqspKqPR6KPW4KHO7KHG7KPW88VXidlHmcVHqfuP9hf1usf50W/vkojVvjIG4MRgSDTBjbZvrdbI+l0sQQERwyRt/ukQQSXwGHpfgdrnwuATXhfcyy3vXhXMshrEKStZlZm5P+RkT782F18yyb7ZzlbiFMs/8cxmWIp0W+jXAcWPMSQAR+SpwJ5Aa6HcCn7BefwP4exERY2b+mMv3wslhfnh4kAdu28yHblqDZ46Fk5QqFr5SN2saK1iT8qQuYwzT4RjjgQjj/gjjwQgTgQiBSIxgJEYoEudcIEAoGicaN8TicWJxQ9T6illfTuC2/pFYKJiz6bfeso4Hbtts+3llocwVkZ8DbjXGfMh6/6vAtcaY+1OOOWAd02O9P2Edc37Gue4D7rPebgKO2PWDLEIjcH7Bo/JPIdZdiDVDYdatNWdPruteZYxpmm1HVm+KGmMeAh7K5jVnEpEuY8zOXNawFIVYdyHWDIVZt9acPflcdzr9Fb1AZ8r7DmvbrMeIiAeoIXFzVCmlVJakE+h7gQ0iskZESoG7gV0zjtkFfMB6/XPAjzLRf66UUmpuC3a5GGOiInI/8ASJYYtfNsYcFJEHgS5jzC7gn4FHROQ4MEIi9PNVTrt8lqEQ6y7EmqEw69aasydv617wpqhSSqnCoGP+lFLKITTQlVLKIYoq0EXkVhE5IiLHReSBHNVwWkT2i8irItJlbasXkSdF5Jj1Z521XUTkb61694nIjpTzfMA6/piIfCBl+9XW+Y9b37voud4i8mURGbTmFyS3ZbzGua6xzLo/ISK91uf9qojcnrLvY1YNR0Tk3SnbZ/17Yg0M2GNt/5o1SAARKbPeH7f2r15EzZ0i8rSIHBKRgyLyu/n+ec9Tc75/1l4ReVFEXrPq/rOlXsuun8d2xpii+CJxQ/cEsBYoBV4DtuSgjtNA44xtnwEesF4/AHzaen078H1AgOuAPdb2euCk9Wed9brO2veidaxY33vbEmq8GdgBHMhmjXNdY5l1fwL4w1mO3WL9HSgD1lh/N9zz/T0Bvg7cbb3+IvBh6/VvA1+0Xt8NfG0RNbcCO6zXVcBRq7a8/bznqTnfP2sBKq3XJcAe63NZ1LXs/Hlsz5dMnDQfv4DrgSdS3n8M+FgO6jjNpYF+BGi1XrcCR6zX/wjcM/M44B7gH1O2/6O1rRV4PWX7Rcctss7VXByMGa9xrmsss+5PMHvIXPTfn8Qoruvn+ntihcF5wDPz71Pye63XHus4WeLn/h0S6yYVxOc9o+aC+ayBcuBl4NrFXsvOn8fur2LqcmkHzqa877G2ZZsBfiAiL0liKQSAZmNMn/W6H2i2Xs9V83zbe2bZbods1DjXNZbrfqt74ssp3QqLrbsBGDPGRGep+8L3WPvHreMXxfqV/ioSLceC+Lxn1Ax5/lmLiFtEXgUGgSdJtKgXey07fx5bFVOg54ubjDE7gNuAj4jIzak7TeKf8LweS5qNGm28xheAdcCVQB/wlzac03YiUgn8J/B7xpiJ1H35+nnPUnPef9bGmJgx5koSM96vAexfISuHiinQ01nCIOOMMb3Wn4PAt0j8pRoQkVYA689B6/C5ap5ve8cs2+2QjRrnusaSGWMGrP+J48A/kfi8l1L3MFAriaUtZta9rKUvRKSERDD+uzHmm9bmvP68Z6u5ED7rJGPMGPA0ie6PxV7Lzp/HVsUU6OksYZBRIlIhIlXJ18C7gANcvHTCB0j0SWJt/zVrZMN1wLj1K/ITwLtEpM76tfZdJPrk+oAJEbnOGsnwaynnWq5s1DjXNZYsGViW95H4vJPXutsaybAG2EDi5uGsf0+sFuzTJJa2mO0zWNLSF9Zn8M/AYWPM51J25e3nPVfNBfBZN4lIrfXaR6Lf//ASrmXnz2OvTHTM5+sXiRECR0n0m/1xDq6/lsSd79eAg8kaSPSx/RA4BjwF1FvbhcTDRU4A+4GdKef6H8Bx6+vXU7bvJPE/0gng71naDaPHSPzKHCHR33dvNmqc6xrLrPsRq659JP5HbE05/o+tGo6QMhporr8n1n+/F62f5z+AMmu713p/3Nq/dhE130Siq2Mf8Kr1dXs+f97z1Jzvn/V24BWrvgPAx5d6Lbt+Hru/dOq/Uko5RDF1uSillKNpoCullENooCullENooCullENooCullENooCtHEJGYJFb4e01EXhaRG2w+/1dE5JR1jZdF5Ho7z6+UHTTQlVMEjDFXGmOuILEg0v/NwDX+yCSmjT9AYvErpfKKBrpyompgFBLrjYjID61W9X4RudPaXiEi37Na9AdE5Bet7VeLyE+sxdOemDH7MekZYP08535QRH4vebCIfEqsNcOVyqQFHxKtVIHwWavoeUksBfs2a3sQeJ8xZkJEGoEXRGQXcCtwzhjzHgARqbHWJ/k74E5jzJAV8p8iMQMz1XtJzIic69xfBr4J/LWIuEhMAb8GpTJMA105RcDqDsHq335YRC4nMVX+/1irWsZJLFvaTCKQ/1JEPg181xjzrHX85cCTieVKcJNYSiDpsyLyJ8AQiWUFZj23Mea0iAyLyFXWtV4xxix6ASmlFksDXTmOMWa31WJuIrG2RhNwtTEmIiKnAa8x5qgkHt92O/DnIvJDEqtfHjTGzHXD84+MMd9IvhGRD852bmv3l4APAi0kWuxKZZz2oSvHEZHNJFrXwySWPB20AvcWYJV1TBvgN8b8G/BZEo+uOwI0JUewiEiJiGyd51KzntvyLRLdOm8isRKiUhmnLXTlFMk+dEh0hXzAGBMTkX8H/ktE9gNdwOvWMdtIdKHESazO+GFjTFhEfg74WxGpIfH/x1+TWBlzNnOdG+tcT5N4Uk3M3h9VqdnpaotKZYB1M/Rl4OeNMcdyXY8qDtrlopTNRGQLiXWvf6hhrrJJW+hKKeUQ2kJXSimH0EBXSimH0EBXSimH0EBXSimH0EBXSimH+P9A+jtEE4mkYwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCyvCLkrMfal",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Your Code Here for 2013\n",
        "sns.distplot(sal[sal[\"Year\"] == 2013][\"BasePay\"], bins = 30);"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MkX9mqZMigJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Your Code Here for 2014\n",
        "sns.distplot(sal[sal[\"Year\"] == 2014][\"BasePay\"], bins = 30);"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00EkkG_s9nPV",
        "colab_type": "text"
      },
      "source": [
        "## Bonus\n",
        "\n",
        "**Visualize top 5 Jobs with BasePay, Benefits and OvertimePay**\n",
        "\n",
        "Keyword: *index, isin, groupby, mean, plot*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoZm87Bu9nPV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "04725c01-a430-4299-aa56-27c24f2356f3"
      },
      "source": [
        "# Your Code Here\n",
        "sort = sal.sort_values(by = ['BasePay'],ascending=False)[0:5].head()\n",
        "sns.barplot(x= 'JobTitle',y='BasePay',data = sort)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff66300ec18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-nKGOA-9nPY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Your Code Here"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V2Lz3SGu9nPc",
        "colab_type": "text"
      },
      "source": [
        "**Fun Fact: Is there a correlation between length of the Job Title string and Salary?**\n",
        "*italicized text*\n",
        "\n",
        "1.   List item\n",
        "\n",
        "1.   List item\n",
        "2.   List item\n",
        "\n",
        "\n",
        "2.   List item\n",
        "\n",
        "\n",
        "*Hint: corr()*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgcrtPdJ9nPd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "776c3f56-8b9e-4549-a7c2-f2f4da35435a"
      },
      "source": [
        "sal['title_len'] = sal['JobTitle'].apply(len)\n",
        "sal[['TotalPayBenefits', 'title_len']].corr()\n"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "      <th>TotalPayBenefits</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.036878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_len</th>\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gY4kMupD9nPg",
        "colab_type": "text"
      },
      "source": [
        "# Great Job!"
      ]
    }
  ]
}