Week_2_Assignment___MHieu.ipynb

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      "name": "Week_2_Assignment___MHieu.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "kL9VPYin9nOD",
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      "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": {
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          "height": 71
        },
        "outputId": "c8757bf7-e69d-4e3f-e2ed-7a5f36929cfe"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "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": 2,
      "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",
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          "height": 546
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        "outputId": "398f317d-ca9f-46f7-a30b-33abad2237bc"
      },
      "source": [
        "sal.head(10)"
      ],
      "execution_count": 3,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</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": 3
        }
      ]
    },
    {
      "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": 357
        },
        "outputId": "dba1aa0b-bb02-4844-84c3-26bb82381964"
      },
      "source": [
        "sal.info()"
      ],
      "execution_count": 4,
      "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": "c6d8b2ce-7485-4d86-c444-85d828b69ff4"
      },
      "source": [
        "sal['BasePay'].mean()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66325.44884050643"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "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": "b86024ad-21e7-4b4e-a38e-7c3a1ca345b3"
      },
      "source": [
        "sal['BasePay'].min()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-166.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-yPM0ZU9nOX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d74669c2-7de7-4fb1-b7e6-acdef7e3597a"
      },
      "source": [
        "sal['BasePay'].max()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "319275.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "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": "f9e48746-8de5-4e02-d5ea-bf617255b165"
      },
      "source": [
        "sal['OvertimePay'].mean()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5066.059886444622"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVhw0urTDr6b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "066c100c-e6f0-4b8b-cc8a-ffffd96f5551"
      },
      "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": "42f7fe8f-84df-4eb5-801b-00dcf7c73aa4"
      },
      "source": [
        "sal['OvertimePay'].max()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "245131.88"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "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": 51
        },
        "outputId": "94bdb8ed-b3f8-4da0-8366-86c8688d78b5"
      },
      "source": [
        "sal.query('EmployeeName==\"JOSEPH DRISCOLL\"')['JobTitle']"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24    CAPTAIN, FIRE SUPPRESSION\n",
              "Name: JobTitle, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwzxTlI29nOl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "ebfe4ce2-6f16-4298-a1b9-9b8368f7aea5"
      },
      "source": [
        "# Now try to select Employees who have name \"GARY JIMENEZ\"\n",
        "sal.query('EmployeeName==\"GARY JIMENEZ\"')"
      ],
      "execution_count": 22,
      "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>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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  EmployeeName  ...         Agency  Status\n",
              "1   2  GARY JIMENEZ  ...  San Francisco     NaN\n",
              "\n",
              "[1 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "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": 51
        },
        "outputId": "b557f4bc-d5a4-4a2c-aaaf-cb64b5b628ae"
      },
      "source": [
        "sal.query('EmployeeName==\"JOSEPH DRISCOLL\"')['TotalPayBenefits']"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24    270324.91\n",
              "Name: TotalPayBenefits, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "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": 97
        },
        "outputId": "5b4f78f7-8c45-4f3b-e721-b6ce631d7b41"
      },
      "source": [
        "sal.nlargest(1, ['TotalPayBenefits'])"
      ],
      "execution_count": 79,
      "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>0.0</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id    EmployeeName  ...         Agency  Status\n",
              "0   1  NATHANIEL FORD  ...  San Francisco     NaN\n",
              "\n",
              "[1 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "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": 80
        },
        "outputId": "b20ffd05-719f-41d6-e7b4-642cdfbe3e5a"
      },
      "source": [
        "sal.nsmallest(1, ['TotalPayBenefits'])"
      ],
      "execution_count": 81,
      "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>148653</th>\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  ...         Agency  Status\n",
              "148653  148654    Joe Lopez  ...  San Francisco     NaN\n",
              "\n",
              "[1 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "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": 119
        },
        "outputId": "10a0ef31-5fac-4bdb-af2d-9f8bd7de9db7"
      },
      "source": [
        "sal.groupby('Year').mean()['BasePay']"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "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": 34
        },
        "outputId": "b9c0aa54-a251-4a73-a82b-01f0c23ae30b"
      },
      "source": [
        "sal['JobTitle'].nunique()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2159"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "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": 119
        },
        "outputId": "209cf1ed-3ec5-492c-e653-eb517a22df05"
      },
      "source": [
        "sal['JobTitle'].value_counts().head(5)"
      ],
      "execution_count": 28,
      "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": 28
        }
      ]
    },
    {
      "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": "d93e3cac-3766-4cbc-a075-496a6b9f8f20"
      },
      "source": [
        "sum(sal[sal['Year']==2013]['JobTitle'].value_counts() == 1)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "202"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "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": {}
      },
      "source": [
        "# Your Code Here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "702ca772-110e-409d-f5eb-c142eb2a3f05"
      },
      "source": [
        "sns.countplot(x = \"Year\", data=sal)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3fdba54a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "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": {}
      },
      "source": [
        "sns.distplot(sal[sal[\"Year\"] == 2011][\"BasePay\"], bins = 30);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "eeea8efc-16c1-4543-92af-af0b82aa3b0c"
      },
      "source": [
        "# Your Code Here for 2012\n",
        "sns.distplot(sal[sal[\"Year\"] == 2012][\"BasePay\"], bins = 30);"
      ],
      "execution_count": 56,
      "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": "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": null,
      "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": null,
      "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": 450
        },
        "outputId": "d23c5d22-389c-46c1-8a58-7f34ed17058d"
      },
      "source": [
        "x = sal.groupby(sal['JobTitle'].str.lower())[['OvertimePay','BasePay','Benefits']].mean()\n",
        "x"
      ],
      "execution_count": 64,
      "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>OvertimePay</th>\n",
              "      <th>BasePay</th>\n",
              "      <th>Benefits</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JobTitle</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>account clerk</th>\n",
              "      <td>234.586809</td>\n",
              "      <td>42593.966897</td>\n",
              "      <td>19911.574590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accountant</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>46643.172000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accountant i</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>61777.832500</td>\n",
              "      <td>26086.087500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accountant ii</th>\n",
              "      <td>8.408673</td>\n",
              "      <td>66880.084425</td>\n",
              "      <td>27730.821327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accountant iii</th>\n",
              "      <td>7.197374</td>\n",
              "      <td>78940.945000</td>\n",
              "      <td>28740.340612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worker's compensation supervisor i</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>90966.140000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x-ray laboratory aide</th>\n",
              "      <td>3501.813651</td>\n",
              "      <td>46412.085794</td>\n",
              "      <td>18697.180500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>youth comm advisor</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>39077.957500</td>\n",
              "      <td>18704.242500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>youth commission advisor, board of supervisors</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>52609.910000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zoo curator</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>43148.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1637 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                OvertimePay  ...      Benefits\n",
              "JobTitle                                                     ...              \n",
              "account clerk                                    234.586809  ...  19911.574590\n",
              "accountant                                         0.000000  ...           NaN\n",
              "accountant i                                       0.000000  ...  26086.087500\n",
              "accountant ii                                      8.408673  ...  27730.821327\n",
              "accountant iii                                     7.197374  ...  28740.340612\n",
              "...                                                     ...  ...           ...\n",
              "worker's compensation supervisor i                 0.000000  ...           NaN\n",
              "x-ray laboratory aide                           3501.813651  ...  18697.180500\n",
              "youth comm advisor                                 0.000000  ...  18704.242500\n",
              "youth commission advisor, board of supervisors     0.000000  ...           NaN\n",
              "zoo curator                                        0.000000  ...           NaN\n",
              "\n",
              "[1637 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-nKGOA-9nPY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "deb42a82-ab33-4b59-fef5-54bb9056cfdd"
      },
      "source": [
        "sns.barplot(x='OvertimePay',y='JobTitle', data=x.reset_index().sort_values(by='OvertimePay',ascending=False).head(5))\n",
        "\n"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3f58fe668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "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": "RyoZ4N70tPgo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "6ce0ba81-0c9b-4379-dbe2-4b13a5944e72"
      },
      "source": [
        "sns.barplot(x='BasePay',y='JobTitle', data=x.reset_index().sort_values(by='BasePay',ascending=False).head(5))"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3f0d2c710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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B+1Rqi+y+YNVcDZwbETdI2gLoIWk8sBswmuy/l5mSxkbEnLLjdALZ7BKbv3fzGl2YmVl71Epiyr9avRHfdsCAccD16f5aRMRLuW03RsQG4AFJO1bYdzwwJLd2ZmuyhGB1jf7GAJ9Jfd0haVtJW9UR5zVpnzmStpK0TXvaktQPGBgRN6T6r6fy8Wkci1PVvmkMGyUxETENmAbQd+e+VW/lYWZm7VMriXmNjb9abdYeb+SeV5rVE3BSRMxqQSzliUOjEgkB50TEJQ1qz8zM2qHWmpgXI+Lyao+WRWjd2R3AREnbQrbOpB37zgK+LqlX2vfDkvq0sc/dpDuqSzoYeCEiXqmjr0lpnzFkl7DWtKetiFgL/F7SUal+b0nvSWM4TlLfVD4wrdExM7MWqDUT82bLorBCioiVks4G7pK0nuyyyuQ6d78UGAQskiTgeeCoNvY5E7gsfcX/VeBLdfb1uqTFQC/guA629UXgEknfA94CJkbErZL2BO7NhsA64AvAc3XGZWZmnaCItmfWJX2abA1BAPeU1gaYdXeS7iRb9Nst7sred+e+MfTUoV0dhplZQ809aW5T25e0MCJGlpe3eQNISRcBJwLLgRXA1yT9rPEhmpmZmdWv1uWkknHAnpGmbCRdDqxsalRmDRIRB3d1DGZm1hxtzsQAjwI7515/IJWZmZmZdZmqMzGSfk22BqYf8KCkeWnTaGBetf3MzMzMWqHW5aR/b1kUZmZmZu1UNYmJiLtKz9Mvro5KL+dFhL9CamZmZl2qnm8nfZbs8tFE4LPA/bmfijczMzPrEvV8O+l0YFRp9kXS9sBveecN/czMzMxapp5vJ/Uou3z0Yp37mZmZmTVNPTMxt0iaRboTMHA0cHPzQjIzMzNrW5tJTEScmm47cGAqmhoRNzY3LDMzM7Paqt47SdJast+JAVDZ5teBx4DTI+L25oVntmkZOXJkLFjQLW7jZGZWGNXunVTrK9b9ajTWE9gHuDr9NTMzM2upDi3QjYj1EbEU+GmD4zEzMzOrS6e+ZRQRlzQqEDMzM7P28FelzczMrJCcxJiZmVkhOYkxMzOzQnISY2ZmZoXkJMbMzMwKqZ7bDphZg6xdtYq7xn60q8MwM6vpo3Pu6uoQ6uKZGDMzMyskJzFmZmZWSE5izMzMrJCcxJiZmVkhOYkxMzOzQnISY2ZmZoXkJMbMzMwKyUmMmZmZFZKTGDMzMyskJzFmZmZWSE5izMzMrJCcxJiZmVkhOYkxMzOzQmppEiNpuqQJFcp3kjSjjv3Pl7RS0vl19tdb0m8lLZE0qUa9QZJW1NNmZ0iaLGmn7tSPpFMkHZue75GO1WJJgyX9roExnSlpSqPaq9LHNpL+oZl9VOpH0vaSbml2v2ZmtrFuMRMTEc9ExDuSmwpOAIZExKl1Nr1van9YRFzb4QAbQFJPYDLQ9CSm3n4kbQYcB/x3KjoKmBER+0bEYxFxQJV9up0U1zZA05OY8n4i4nngWUkHtqBvMzNLmpbESDpW0jJJSyVdmds0VtLvJD1empXJz4RI6plmXOan/b+WymcCfYGF5bMqkvpLujHVv0/SEEk7AFcBo9LswuCyfUak2JYC38iVV+v/YElzJP1G0ipJUyX1SNsulrQgzRKdlWvrCUnnSVoEfA4YCVyd4tkybT8nvV4gabikWZIek3Rirp1Tc/GclTtmD0r6eer31tTmhPJ+apymccCiiPiLpMOAU4CvS5qd+liXG/vd6Rw8UO0YVXgPnC7pYUn3ALvnygdLukXSwtTuHql8ejquC9J+h+fGerekRelxQKW4gHOBwWnc56ftd0n6n/R+O1fSMZLmSVpeek+kmZRfpvHMLyUjymaPLpN0Z9r/W2kIG/WTym4EjqlxrM3MrMGa8q9qSXsDZwAHRMQLkvrnNg8AxgB7ADOB8stIxwNrImKUpN7AXEm3RsQRktZFxLAKXZ4FLI6IoySNA66IiGGSvgJMiYjDK+zzX8A3I2KONr48VbH/tG00sBfwJHAL8OkU/+kR8ZKy2ZbbJQ2JiGVpnxcjYng6LqV4FqTXAE+lWC8ApgMHAlsAK4CpksYDu6W+BcyUNBZ4KpV/LiK+Kuk64DMRcZWkb+b7qeFAYCFARPyvpKnAuoj49wp1hwP7RMRqSSdUOkYRsbpUWdII4GhgGNn7bFGpL2AacGJEPCLpb4CLyBIqgEFprIOB2ZJ2BZ4D/i4iXpe0G3ANWaJWHteg9HxYiuFgYCiwJ/AS8DhwaUSMlnQycBJZ4vZj4IKIuEfSzsCstA9k79NDgH7AKkkXA6fl+0kWAN9v43ibmVkDNevSwDjg+oh4ASAiXsptuzEiNpD9i37HCvuOB4bo7bUzW5N9WK+uULdkDPCZ1NcdkraVtFW1ypK2AbaJiDmp6Erg0Db6fxOYFxGPpzauSf3OAD6bPtg3I0vS9gJKSUxbl7Fmpr/Lgb4RsRZYK+mNFOf49Fic6vVN8TwFrI6IJal8IVkC0B4DgAfrrDsvl6TUc44OAm6IiFfhrzNpSOoLHABcn5I4gN65/a5L749HJD1OlkSsBi6UNAxYD3y4SlyVzI+IZ1PfjwGlhHQ5WXIC8DFgr1w8W6U4AX4TEW8Ab0h6Dqj0noUs0ap4CS+9N04A2LF370pVzMysA7pifcMbueeqsF3ASRExq0Xx1NV/+ld9lNUNSbsAU4BREfEnSdPJZlJK/txGf6XjsYGNj80GsvMj4JyIuKQsnkFl9dcDtS4dVfJaWay15MfRmXPUA3i5yowaVDjGwLeBP5LNqvQAXq8SVyXlxzR/vEvv/x7AfhGRb7c0U1Z+jKv9N7MF2fF8h4iYRjb7xO79+pWPz8zMOqhZa2LuACZK2hayNSvt2HcW2bqMXmnfD0vq08Y+d5PWI6Rk44WIeKVa5Yh4GXhZ0phUlF/LUKv/0ZJ2UbYWZhJwD7AV2QfpmjSzdCjVrSW7LNEes4DjSjMDkgYqW+9Ty0b9KFt386kK9R4Edm1nPKWY2jpHc4CjlK3T6Qd8EiCdl9WSJqZ9JWlobr+Jknqk9SofAlaRzfQ8m2Zovgj0rBJXR44vZLMzJ5VepBmfWir182GyS4BmZtYiTZmJiYiVks4G7pK0nuxSyOQ6d7+U7LLIImX/FH6e7FsztZwJXCZpGfAq8KU6+vly2id4+xJDW/3PBy4k++CfTXa5ZIOkxcBDwNPA3Bp9Tidb5/IasH8dMRIRt0raE7g3zQysA75ANitQbz8f4e3LVnk3k11Ka682z1FELJJ0LbCU7FLL/NzmY4CLJZ0B9AJ+kepBdplsHllyeGJaB3MR8EtlXwW/hSqzLxHxoqS5yhaJ3wz8ps7xfAv4WXr/bEaWgJ1YrXJ5P+nbcoe0oz8zM2sARXh2ux5phqfaIuFuTdKsiPh4lW03AP8cEY+0OKxKsUwHboqINn8zqLuRNAc4MiL+VKve7v36xbR9h7coKjOzjvnonLu6OoSNSFoYESPLy7vF78RYc1VLYJLTyBb4WgdJ2h74UVsJjJmZNVa3/OGy7igi7gTu7OIwGi4iVpGtO+lyETG5q2PoiPRjdzd2dRxmZu82nokxMzOzQnISY2ZmZoXkJMbMzMwKyUmMmZmZFZKTGDMzMyskJzFmZmZWSE5izMzMrJCcxJiZmVkhOYkxMzOzQnISY2ZmZoXk2w6YtVC/3XfvdjdWMzMrKs/EmJmZWSE5iTEzM7NCchJjZmZmheQkxszMzArJSYyZmZkVkpMYMzMzKyQnMWZmZlZITmLMzMyskPxjd2Yt9Nzv13DhP/26q8Mws3epb/7wk10dQkN5JsbMzMwKyUmMmZmZFZKTGDMzMyskJzFmZmZWSE5izMzMrJCcxJiZmVkhOYkxMzOzQnISY2ZmZoXkJMbMzMwKyUmMmZmZFZKTGDMzMyskJzFmZmZWSE5izMzMrJCcxHSSpDMlTWlCu9MlTUjPL5W0Vx377CFpiaTFkgZ3sN+6+uosScMkHbYJ9fMvze7DzMw25iSmACLiKxHxQHm5pJ5lRUcBMyJi34h4rK12ldnoPVCtryYYBjQ9uWhkPxWOd56TGDOzFnMS0wGSTpf0sKR7gN1z5YMl3SJpoaS7Je2RyqdLmippQdrv8AptStKFklZJ+i2wQ27bnZJGpufrJP1Q0lJg/1ydw4BTgK9Lmp3K/lHSivQ4JZUNSn1cAawAPlAWR3lfZ0taKuk+STtK2lrSk6XkR1IfSU9L6lVj/BNTDEslzZG0OfA9YFKaOZqUZrQuT/s9KenTkn4gaXlqs1dqa4Sku1IfsyQNyMV9nqR56RgfVKWfj6bnpRmrfm2c642Ot6QvpD6WSLpEUk9J5wJbprKr23j7mJlZgziJaSdJI4Cjeftf+KNym6cBJ0XECGAKcFFu2yBgNPAJYKqkLcqa/hRZQrQXcCxwQJUQ+gD3R8TQiLinVBgR/wtMBS6IiENSnF8G/gbYD/iqpH1T9d2AiyJi74h4ssZw+wD3RcRQYA7w1YhYAywBPprqHA7Mioi3aoz/u8DHUztHRMSbqezaiBgWEdemeoOBccARwFXA7Ij4CPAa8ImUyPwUmJD6uAw4OxfvZhExmiyZ+9cq/UwBvhERw4CDUtu1/PV4Ay8Ck4AD0/7rgWMi4jTgtdTHMeUNSDohJbAL1r26po3uzMysXpt1dQAFdBBwQ0S8CiBpZvrblyzxuF5SqW7v3H7XRcQG4BFJjwN7kCUDJWOBayJiPfCMpDuq9L8e+GUdcY5Jcf45xferFPtM4MmIuK+ONt4EbkrPFwJ/l55fS/ZhPpssobuojfHPBaZLug74VY3+bo6ItyQtB3oCt6Ty5WRJ4O7APsBtqY+ewLO5/UttL0z1K5kL/CjNmPwqIn5fIx7Y+Hj/LTACmJ/63xJ4ro39iYhpZAkeO79vt2irvpmZ1cdJTOP0AF5O/0KvpPzDq6MfZq+nRKcz/lxnvbciohTnet5+v8wE/k1Sf7IP9TvIZiwqjj8iTpT0N2SzUAvTLFElb6T6GyTl+96Q+hawMiL2r7V/WazlsZwr6Tdks2hzJX08Ih6q0h5sfLwFXB4R36lR38zMWsSXk9pvDnCUpC3TeopPAkTEK8BqSRPhr2tchub2myiph7JvDX0IWFWh3UlpjcUA4JBOxnl3ivM9kvqQXa66u5NtAhAR64D5wI+BmyJifa3xSxocEfdHxHeB58nW4awFaq5HqWAVsL2k/VO7vSTt3cY+G/WTYlkeEeelMZTW7dRKZEpuByZI2iHt01/SB9O2t0rrdszMrDWcxLRTRCwiu5yyFLiZ7IOw5Bjg+LQIdCVwZG7bU8C8tM+JEfF6WdM3AI8ADwBXAPc2IM7pqc/7gUsjYnFn2ixzLfCF9Lek2vjPTwt0VwC/Izt2s4G9Sgtu6+kwrXGZAJyX+lhC9bVDJeX9nJIWGS8D3gJulrQd2SxLW/0/AJwB3Jr2vw0YkDZPA5Z5Ya+ZWevo7Rl7axZJ08lmLGZ0dSz2Tsq+LfahiPhJs/va+X27xT8f86Nmd2NmVtE3f/jJrg6hQyQtjIiR5eVeE2PvehFxU9u1zMysu3ES0wIRMbmrYzAzM9vUeE2MmZmZFZKTGDMzMyskJzFmZmZWSE5izMzMrJCcxJiZmVkhOYkxMzOzQnISY2ZmZoXkJMbMzMwKyUmMmZmZFZJ/sdeshXZ4/9aFvXeJmVl345kYMzMzKyQnMWZmZlZITmLMzMyskJzEmJmZWSEpIro6BrN3DUlrgVVdHUcDbQe80NVBNNimNqZNbTyw6Y3J42nbByNi+/JCfzvJrLVWRcTIrg6iUSQt2JTGA5vemDa18cCmNyaPp+N8OcnMzMwKyUmMmZmZFZKTGLPWmtbVATTYpjYe2PTGtKmNBza9MXk8HeSFvWZmZlZInokxMzOzQnISY2ZmZoXkJMasBST9vaRVkh6VdFpXx1NO0hOSlktaImlBKusv6TZJj6S/703lkvSTNJZlkobn2vlSqv+IpC/lykek9h9N+6oJY7hM0nOSVuTKmj6Gan00aTxnSvpDOk9LJB2W2/adFNsqSR/PlVd870naRdL9qfxaSZun8t7p9aNp+6BGjCe1/QFJsyU9IGmlpJNTeSHPU43xFPI8SdpC0jxJS9N4zupoDI0aZ9kn/4UAAAY3SURBVJsiwg8//GjiA+gJPAZ8CNgcWArs1dVxlcX4BLBdWdkPgNPS89OA89Lzw4CbAQH7Afen8v7A4+nve9Pz96Zt81JdpX0PbcIYxgLDgRWtHEO1Ppo0njOBKRXq7pXeV72BXdL7rWet9x5wHXB0ej4V+Hp6/g/A1PT8aODaBp6jAcDw9Lwf8HCKvZDnqcZ4Cnme0jHrm573Au5Px7JdMTRynG09PBNj1nyjgUcj4vGIeBP4BXBkF8dUjyOBy9Pzy4GjcuVXROY+YBtJA4CPA7dFxEsR8SfgNuDv07atIuK+yP4PdUWurYaJiDnAS10whmp9NGM81RwJ/CIi3oiI1cCjZO+7iu+9NDsxDphRIe78eGYAf1uazeisiHg2Ihal52uBB4GBFPQ81RhPNd36PKXjvC697JUe0YEYGjnOmpzEmDXfQODp3OvfU/t/dF0hgFslLZR0QirbMSKeTc//D9gxPa82nlrlv69Q3gqtGEO1Pprlm+nSymW5SyLtHc+2wMsR8Zey8o3aStvXpPoNlS497Ev2r/3Cn6ey8UBBz5OknpKWAM+RJYePdSCGRo6zJicxZgYwJiKGA4cC35A0Nr8x/au20L/H0IoxtKCPi4HBwDDgWeCHTeyraST1BX4JnBIRr+S3FfE8VRhPYc9TRKyPiGHA+8lmTvbo4pBqchJj1nx/AD6Qe/3+VNZtRMQf0t/ngBvI/uf1xzQ9T/r7XKpebTy1yt9fobwVWjGGan00XET8MX3IbAB+TnaeaCPuSuUvkl2a2aysfKO20vatU/2GkNSL7AP/6oj4VSou7HmqNJ5N4TxFxMvAbGD/DsTQyHHW5CTGrPnmA7ul1febky2Am9nFMf2VpD6S+pWeA+OBFWQxlr718SXgf9LzmcCxyuwHrEnT9LOA8ZLem6bPxwOz0rZXJO2Xrn0fm2ur2Voxhmp9NFzpQzj5FNl5KsVwdPq2yC7AbmQLXCu+99JMxGxgQoW48+OZANyR6jcifgH/CTwYET/KbSrkeao2nqKeJ0nbS9omPd8S+DuydT7tjaGR46ytntW/fvjhR+ceZN+yeJjs+vLpXR1PWWwfIvuWwFJgZSk+suvUtwOPAL8F+qdyAT9LY1kOjMy1dRzZIr5HgS/nykeS/Y/8MeBC0q+FN3gc15BN3b9Fdk39+FaMoVofTRrPlSneZemDYkCu/ukptlXkvv1V7b2Xzvu8NM7rgd6pfIv0+tG0/UMNPEdjyC7jLAOWpMdhRT1PNcZTyPMEDAEWp7hXAN/taAyNGmdbD992wMzMzArJl5PMzMyskJzEmJmZWSE5iTEzM7NCchJjZmZmheQkxszMzArJSYyZWQFJWq/sDslLJS2SdECD258uaXXqY5Gk/RvZvlkjOIkxMyum1yJiWEQMBb4DnNOEPk6N7CfoTwMuaUL7Zp3iJMbMrPi2Av4E2X18JN2eZk+WSzoylfeR9Js0c7NC0qRUPkLSXenmn7PKfm22ZA6wa422vyfplFJlSWdLOrnpo7Z3vc3armJmZt3Qluluw1sAA4Bxqfx14FMR8Yqk7YD7JM0E/h54JiI+ASBp63Tfn58CR0bE8ymxOZvs13DzPkn2C7TV2r4M+BXwH5J6kP2c/GjMmsxJjJlZMb2WLvWQ1qtcIWkfsp/q/7d0J/INwEBgR7Ik5IeSzgNuioi7U/19gNuy2wDRk+xWByXnSzoDeJ7stgcV246IJyS9KGnf1NfiiGjYzQjNqnESY2ZWcBFxb5oZ2Z7s3jTbAyMi4i1JTwBbRMTDkoan7d+XdDvZHctXRkS1RbunRsSM0gtJkyu1nTZfCkwG3kc2M2PWdF4TY2ZWcJL2IJtFeRHYGnguJRmHAB9MdXYCXo2Iq4DzgeFkN+fbvvTNI0m9JO1do6uKbSc3kF2yGkV2l2mzpvNMjJlZMZXWxEB2medLEbFe0tXAryUtBxYAD6U6HyG7PLSB7M7YX4+INyVNAH4iaWuyz4T/ILubeSXV2ia1NRt4OSLWN3aoZpX5LtZmZtZpaUHvImBiRDzS1fHYu4MvJ5mZWadI2gt4FLjdCYy1kmdizMzMrJA8E2NmZmaF5CTGzMzMCslJjJmZmRWSkxgzMzMrJCcxZmZmVkj/H9NnEG8f1fgLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XiA5BPy0tURn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "90799f16-65e6-4a48-98ab-64993fb3c1c3"
      },
      "source": [
        "sns.barplot(x='Benefits',y='JobTitle', data=x.reset_index().sort_values(by='Benefits',ascending=False).head(5))"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3f0ccf438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "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": "V2Lz3SGu9nPc",
        "colab_type": "text"
      },
      "source": [
        "**Fun Fact: Is there a correlation between length of the Job Title string and Salary?**\n",
        "\n",
        "*Hint: corr()*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJXoUOaJuXJR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114
        },
        "outputId": "2fd77bd1-eb0e-4dd3-d487-e8b48f6c258c"
      },
      "source": [
        "sal['lenJobTitle'] = sal['JobTitle'].str.len()\n",
        "sal.head(1)"
      ],
      "execution_count": 90,
      "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",
              "      <th>lenJobTitle</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>0.0</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",
              "      <td>46</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id    EmployeeName  ... Status  lenJobTitle\n",
              "0   1  NATHANIEL FORD  ...    NaN           46\n",
              "\n",
              "[1 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgcrtPdJ9nPd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "1a1da321-2a1e-4706-b852-7e31b149000f"
      },
      "source": [
        "\n",
        "sns.jointplot(x='TotalPayBenefits', y='lenJobTitle', data=sal)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fe3eb806dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gY4kMupD9nPg",
        "colab_type": "text"
      },
      "source": [
        "# Great Job!"
      ]
    }
  ]
}