Week_2_Assignment.ipynb

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      "name": "Copy of Assignment 2.ipynb",
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      "cell_type": "markdown",
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        "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": "ff807538-93ed-4475-eb06-5d00a643fd1c"
      },
      "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": 29,
      "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": 821
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        "outputId": "bb8ad437-47fd-4cf8-cea4-cc640d1e118c"
      },
      "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",
              "        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": 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": "59ebb650-2338-4b90-9191-2a9211ee7aa8"
      },
      "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": "a98a9e7a-7d8f-48d6-d8b8-91723685b111"
      },
      "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": "96a27146-24fa-4843-af5b-59a9bc607c0b"
      },
      "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": "46dae962-6669-42f3-9a38-fc62e30f5e0f"
      },
      "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": "bc8cf876-d7a5-4f53-df7d-e6cde1bf1366"
      },
      "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": "9e8927e5-08df-4a88-d849-241f25c2791d"
      },
      "source": [
        "sal['OvertimePay'].min()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9si4GhoDvLf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8045ba3e-c88c-4ba7-9d82-094f3dd18225"
      },
      "source": [
        "sal['OvertimePay'].max()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "245131.88"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "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": 34
        },
        "outputId": "393ab50f-71cc-4fb1-c736-575ebab0a356"
      },
      "source": [
        "for index, row in sal.iterrows():\n",
        "  if (row['EmployeeName'] == 'JOSEPH DRISCOLL'):\n",
        "    print(row['JobTitle'])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CAPTAIN, FIRE SUPPRESSION\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwzxTlI29nOl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "a8d4f99e-0b51-4b1d-f248-6dfa368fdd53"
      },
      "source": [
        "# Now try to select Employees who have name \"GARY JIMENEZ\"\n",
        "# Your Code Here\n",
        "for index, row in sal.iterrows():\n",
        "  if (row['EmployeeName'] == 'GARY JIMENEZ'):\n",
        "    print(row)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Id                                                2\n",
            "EmployeeName                           GARY JIMENEZ\n",
            "JobTitle            CAPTAIN III (POLICE DEPARTMENT)\n",
            "BasePay                                      155966\n",
            "OvertimePay                                  245132\n",
            "OtherPay                                     137811\n",
            "Benefits                                        NaN\n",
            "TotalPay                                     538909\n",
            "TotalPayBenefits                             538909\n",
            "Year                                           2011\n",
            "Notes                                           NaN\n",
            "Agency                                San Francisco\n",
            "Status                                          NaN\n",
            "Name: 1, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 34
        },
        "outputId": "5571cde9-380e-4d47-f58a-16dc6950eecd"
      },
      "source": [
        "for index, row in sal.iterrows():\n",
        "  if (row['EmployeeName'] == 'JOSEPH DRISCOLL'):\n",
        "    print(row['TotalPayBenefits'])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "270324.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1thjd2GC9nOq",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "**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": 68
        },
        "outputId": "283e3874-bf43-4d7b-fa51-77f8b38f448d"
      },
      "source": [
        "maxPay = sal['TotalPayBenefits'].max()\n",
        "print(maxPay)\n",
        "print(sal[sal['TotalPayBenefits'] == maxPay]['EmployeeName'])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "567595.43\n",
            "0    NATHANIEL FORD\n",
            "Name: EmployeeName, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 68
        },
        "outputId": "305fd826-326e-4073-a636-b170151f40d1"
      },
      "source": [
        "minPay = sal['TotalPayBenefits'].min()\n",
        "print(minPay)\n",
        "print(sal[sal['TotalPayBenefits'] == minPay]['EmployeeName'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-618.13\n",
            "148653    Joe Lopez\n",
            "Name: EmployeeName, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "adeb0cdc-1dd2-498d-a74e-3eac8a3e8b42"
      },
      "source": [
        "sal.groupby('Year')['BasePay'].mean()"
      ],
      "execution_count": 16,
      "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": 16
        }
      ]
    },
    {
      "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": "46942af4-322f-447a-9c32-58506d5f8c04"
      },
      "source": [
        "len(sal['JobTitle'].unique())"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2159"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "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": "097d0e28-8d81-4fa0-ef48-c60ab6fe14e7"
      },
      "source": [
        "sal['JobTitle'].value_counts().head(5)"
      ],
      "execution_count": 18,
      "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": 18
        }
      ]
    },
    {
      "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": "a4f901ab-d96d-45da-80c2-4f444f3dd631"
      },
      "source": [
        "my_value = sal[sal['Year'] == 2013]['JobTitle'].value_counts()\n",
        "(my_value == 1).sum()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "202"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "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": "622feb5d-ae0f-49c2-ba52-c64cf3f98861"
      },
      "source": [
        "sal['JobTitle'].str.contains('.*Chief').sum()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "423"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "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": "f896bdda-c23b-4d05-baad-307acf9165f2"
      },
      "source": [
        "sns.countplot(x = \"Year\", data=sal)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f884be5dbe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEKCAYAAADTgGjXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYV0lEQVR4nO3df7BfdX3n8efLBJRVMSB3Kc2PwtTsOJGtQe9CdulsWRwh0B+hXXVgVsm6jHFHsDrjtkL/KAoyo7sqFavM0BIJ1jVSfywpjc1mkerYLZBEIhCQ4RZwSTaSlPBDdIQNvveP7yf6bbgJl8P9fr+53udj5sw9530+53w/50zw5Tnn8z3fVBWSJHXxklF3QJI0cxkikqTODBFJUmeGiCSpM0NEktSZISJJ6mzgIZJkTpI7ktzUlk9IcluSiSRfSnJ4q7+0LU+09cf37eOSVr8vyZl99eWtNpHk4kEfiyTpnxrGlcj7gHv7lj8GXFlVrwEeAy5o9QuAx1r9ytaOJEuAc4HXAcuBz7ZgmgN8BjgLWAKc19pKkoZkoCGSZAHwm8Cft+UApwNfbk3WAOe0+RVtmbb+Ta39CmBtVT1dVQ8CE8DJbZqoqgeq6hlgbWsrSRqSuQPe/58Afwi8si2/Gni8qva25e3A/DY/H3gYoKr2JnmitZ8P3Nq3z/5tHt6vfsrzdeiYY46p448//gUfiCTNZlu2bPnHqhrbvz6wEEnyW8CuqtqS5LRBfc4U+7IKWAWwaNEiNm/ePMruSNKMk+T7k9UHeTvrVOB3kjxE71bT6cCngHlJ9oXXAmBHm98BLGydnQu8Cni0v77fNgeqP0dVXVNV41U1Pjb2nCCVJHU0sBCpqkuqakFVHU/vwfg3quo/ALcAb2nNVgI3tvl1bZm2/hvVezvkOuDcNnrrBGAxcDuwCVjcRnsd3j5j3aCOR5L0XIN+JjKZDwJrk3wEuAO4ttWvBT6fZALYQy8UqKptSW4A7gH2AhdW1bMASS4CNgBzgNVVtW2oRyJJs1xm26vgx8fHy2cikvTCJNlSVeP71/3GuiSpM0NEktSZISJJ6swQkSR1ZohIkjobxRBfSRq5b/7b3xh1Fw4Zv/Gtb3be1isRSVJnhogkqTNvZ0kzxKmfPnXUXThk/N17/27UXVDjlYgkqTNDRJLUmSEiSerMZyIaqP9z2b8cdRcOGYv++K5Rd0Gadl6JSJI6M0QkSZ15O2s/b/yD60fdhUPGlv92/qi7IOkQ55WIJKkzQ0SS1NnAQiTJy5LcnuS7SbYl+XCrX5fkwSRb27S01ZPkqiQTSe5M8oa+fa1Mcn+bVvbV35jkrrbNVUkyqOORJD3XIJ+JPA2cXlVPJTkM+HaSr7d1f1BVX96v/VnA4jadAlwNnJLkaOBSYBwoYEuSdVX1WGvzLuA2YD2wHPg6kqShGNiVSPU81RYPa1MdZJMVwPVtu1uBeUmOA84ENlbVnhYcG4Hlbd2RVXVrVRVwPXDOoI5HkvRcA30mkmROkq3ALnpBcFtbdUW7ZXVlkpe22nzg4b7Nt7fawerbJ6lLkoZkoCFSVc9W1VJgAXBykhOBS4DXAv8KOBr44CD7AJBkVZLNSTbv3r170B8nSbPGUEZnVdXjwC3A8qra2W5ZPQ18Dji5NdsBLOzbbEGrHay+YJL6ZJ9/TVWNV9X42NjYdBySJInBjs4aSzKvzR8BvBn4XnuWQRtJdQ5wd9tkHXB+G6W1DHiiqnYCG4AzkhyV5CjgDGBDW/dkkmVtX+cDNw7qeCRJzzXI0VnHAWuSzKEXVjdU1U1JvpFkDAiwFfjPrf164GxgAvgx8E6AqtqT5HJgU2t3WVXtafPvAa4DjqA3KsuRWZI0RAMLkaq6EzhpkvrpB2hfwIUHWLcaWD1JfTNw4ovrqSSpK7+xLknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOhtYiCR5WZLbk3w3ybYkH271E5LclmQiyZeSHN7qL23LE2398X37uqTV70tyZl99eatNJLl4UMciSZrcIK9EngZOr6rXA0uB5UmWAR8Drqyq1wCPARe09hcAj7X6la0dSZYA5wKvA5YDn00yJ8kc4DPAWcAS4LzWVpI0JAMLkep5qi0e1qYCTge+3OprgHPa/Iq2TFv/piRp9bVV9XRVPQhMACe3aaKqHqiqZ4C1ra0kaUgG+kykXTFsBXYBG4F/AB6vqr2tyXZgfpufDzwM0NY/Aby6v77fNgeqT9aPVUk2J9m8e/fu6Tg0SRIDDpGqeraqlgIL6F05vHaQn3eQflxTVeNVNT42NjaKLkjSL6ShjM6qqseBW4B/DcxLMretWgDsaPM7gIUAbf2rgEf76/ttc6C6JGlIBjk6ayzJvDZ/BPBm4F56YfKW1mwlcGObX9eWaeu/UVXV6ue20VsnAIuB24FNwOI22utweg/f1w3qeCRJzzX3+Zt0dhywpo2ieglwQ1XdlOQeYG2SjwB3ANe29tcCn08yAeyhFwpU1bYkNwD3AHuBC6vqWYAkFwEbgDnA6qraNsDjkSTtZ2AhUlV3AidNUn+A3vOR/es/Ad56gH1dAVwxSX09sP5Fd1aS1InfWJckdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6G1iIJFmY5JYk9yTZluR9rf6hJDuSbG3T2X3bXJJkIsl9Sc7sqy9vtYkkF/fVT0hyW6t/KcnhgzoeSdJzDfJKZC/wgapaAiwDLkyypK27sqqWtmk9QFt3LvA6YDnw2SRzkswBPgOcBSwBzuvbz8favl4DPAZcMMDjkSTtZ2AhUlU7q+o7bf6HwL3A/INssgJYW1VPV9WDwARwcpsmquqBqnoGWAusSBLgdODLbfs1wDmDORpJ0mSG8kwkyfHAScBtrXRRkjuTrE5yVKvNBx7u22x7qx2o/mrg8arau199ss9flWRzks27d++ehiOSJMEQQiTJK4CvAO+vqieBq4FfBZYCO4FPDLoPVXVNVY1X1fjY2NigP06SZo25g9x5ksPoBcgXquqrAFX1SN/6PwNuaos7gIV9my9oNQ5QfxSYl2Ruuxrpby9JGoJBjs4KcC1wb1V9sq9+XF+z3wXubvPrgHOTvDTJCcBi4HZgE7C4jcQ6nN7D93VVVcAtwFva9iuBGwd1PJKk5xrklcipwDuAu5JsbbU/oje6ailQwEPAuwGqaluSG4B76I3surCqngVIchGwAZgDrK6qbW1/HwTWJvkIcAe90JIkDcnAQqSqvg1kklXrD7LNFcAVk9TXT7ZdVT1Ab/SWJGkE/Ma6JKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1NmUQiTJzVOpSZJml4O+OyvJy4B/BhzTfjxq37uwjuTgv1IoSZoFnu8FjO8G3g/8MrCFn4fIk8CfDrBfkqQZ4KAhUlWfAj6V5L1V9ekh9UmSNENM6VXwVfXpJP8GOL5/m6q6fkD9kiTNAFMKkSSfp/e76FuBZ1u5AENEkmaxqf4o1TiwpP0krSRJwNS/J3I38EuD7IgkaeaZaogcA9yTZEOSdfumg22QZGGSW5Lck2Rbkve1+tFJNia5v/09qtWT5KokE0nuTPKGvn2tbO3vT7Kyr/7GJHe1ba5KMtnP8UqSBmSqt7M+1GHfe4EPVNV3krwS2JJkI/AfgZur6qNJLgYuBj4InAUsbtMpwNXAKUmOBi6ld0ut2n7WVdVjrc27gNvo/Qb7cuDrHfoqSepgqqOzvvlCd1xVO4Gdbf6HSe6l9wXFFcBprdka4G/phcgK4Pr23OXWJPOSHNfabqyqPQAtiJYn+VvgyKq6tdWvB87BEJGkoZnq6Kwf0rsKADgcOAz4UVUdOcXtjwdOonfFcGwLGIAfAMe2+fnAw32bbW+1g9W3T1Kf7PNXAasAFi1aNJUuS5KmYKpXIq/cN9+eO6wAlk1l2ySvAL4CvL+qnux/bFFVlWTgI76q6hrgGoDx8XFHmEnSNHnBb/Gtnv8BnPl8bZMcRi9AvlBVX23lR9ptKtrfXa2+A1jYt/mCVjtYfcEkdUnSkEz1Lb6/1ze9JclHgZ88zzYBrgXurapP9q1aB+wbYbUSuLGvfn4bpbUMeKLd9toAnJHkqDaS6wxgQ1v3ZJJl7bPO79uXJGkIpjo667f75vcCD9G7pXUwpwLvAO5KsrXV/gj4KHBDkguA7wNva+vWA2cDE8CPgXcCVNWeJJcDm1q7y/Y9ZAfeA1wHHEHvgboP1SVpiKb6TOSdL3THVfVtfv7W3/29aZL2BVx4gH2tBlZPUt8MnPhC+yZJmh5TvZ21IMnXkuxq01eSLHj+LSVJv8im+mD9c/SeWfxym/6q1SRJs9hUQ2Ssqj5XVXvbdB0wNsB+SZJmgKmGyKNJ3p5kTpveDjw6yI5Jkg59Uw2R/0RvFNUP6L3K5C303oElSZrFpjrE9zJgZXvpIe2liB+nFy6SpFlqqlciv7YvQKD33Q1678KSJM1iUw2Rl+z73Q/42ZXIVK9iJEm/oKYaBJ8A/j7JX7bltwJXDKZLkqSZYqrfWL8+yWbg9Fb6vaq6Z3DdkiTNBFO+JdVCw+CQJP3MC34VvCRJ+xgikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1NrAQSbK6/Qri3X21DyXZkWRrm87uW3dJkokk9yU5s6++vNUmklzcVz8hyW2t/qUkhw/qWCRJkxvklch1wPJJ6ldW1dI2rQdIsgQ4F3hd2+az+367BPgMcBawBDivtQX4WNvXa4DHgAsGeCySpEkMLESq6lvAnik2XwGsraqnq+pBYAI4uU0TVfVAVT0DrAVWJAm9V7B8uW2/BjhnWg9AkvS8RvFM5KIkd7bbXfveDDwfeLivzfZWO1D91cDjVbV3v/qkkqxKsjnJ5t27d0/XcUjSrDfsELka+FVgKb1fSPzEMD60qq6pqvGqGh8b86fhJWm6DPU3QarqkX3zSf4MuKkt7gAW9jVd0GocoP4oMC/J3HY10t9ekjQkQ70SSXJc3+LvAvtGbq0Dzk3y0iQnAIuB24FNwOI2Eutweg/f11VVAbfQ+613gJXAjcM4BknSzw3sSiTJF4HTgGOSbAcuBU5LshQo4CHg3QBVtS3JDfReNb8XuLCqnm37uQjYAMwBVlfVtvYRHwTWJvkIcAdw7aCORZI0uYGFSFWdN0n5gP9DX1VXMMmvJbZhwOsnqT9Ab/SWJGlE/Ma6JKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1NnAQiTJ6iS7ktzdVzs6ycYk97e/R7V6klyVZCLJnUne0LfNytb+/iQr++pvTHJX2+aqJBnUsUiSJjfIK5HrgOX71S4Gbq6qxcDNbRngLGBxm1YBV0MvdIBLgVPo/Z76pfuCp7V5V992+3+WJGnABhYiVfUtYM9+5RXAmja/Bjinr3599dwKzEtyHHAmsLGq9lTVY8BGYHlbd2RV3VpVBVzfty9J0pAM+5nIsVW1s83/ADi2zc8HHu5rt73VDlbfPkl9UklWJdmcZPPu3btf3BFIkn5mZA/W2xVEDemzrqmq8aoaHxsbG8ZHStKsMOwQeaTdiqL93dXqO4CFfe0WtNrB6gsmqUuShmjYIbIO2DfCaiVwY1/9/DZKaxnwRLvttQE4I8lR7YH6GcCGtu7JJMvaqKzz+/YlSRqSuYPacZIvAqcBxyTZTm+U1UeBG5JcAHwfeFtrvh44G5gAfgy8E6Cq9iS5HNjU2l1WVfse1r+H3giwI4Cvt0mSNEQDC5GqOu8Aq940SdsCLjzAflYDqyepbwZOfDF9lCS9OH5jXZLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSepsJCGS5KEkdyXZmmRzqx2dZGOS+9vfo1o9Sa5KMpHkziRv6NvPytb+/iQrR3EskjSbjfJK5N9V1dKqGm/LFwM3V9Vi4Oa2DHAWsLhNq4CroRc6wKXAKcDJwKX7gkeSNByH0u2sFcCaNr8GOKevfn313ArMS3IccCawsar2VNVjwEZg+bA7LUmz2ahCpID/mWRLklWtdmxV7WzzPwCObfPzgYf7tt3eageqS5KGZO6IPvfXq2pHkn8ObEzyvf6VVVVJaro+rAXVKoBFixZN124ladYbyZVIVe1of3cBX6P3TOORdpuK9ndXa74DWNi3+YJWO1B9ss+7pqrGq2p8bGxsOg9Fkma1oYdIkpcneeW+eeAM4G5gHbBvhNVK4MY2vw44v43SWgY80W57bQDOSHJUe6B+RqtJkoZkFLezjgW+lmTf5//3qvqbJJuAG5JcAHwfeFtrvx44G5gAfgy8E6Cq9iS5HNjU2l1WVXuGdxiSpKGHSFU9ALx+kvqjwJsmqRdw4QH2tRpYPd19lCRNzaE0xFeSNMMYIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzmZ8iCRZnuS+JBNJLh51fyRpNpnRIZJkDvAZ4CxgCXBekiWj7ZUkzR4zOkSAk4GJqnqgqp4B1gIrRtwnSZo1ZnqIzAce7lve3mqSpCGYO+oODEOSVcCqtvhUkvtG2Z8pOgb4x1F2IB9fOcqPn04jP5cAXJpR92C6jPx85vd/Yc4lHALnk0zpfP7KZMWZHiI7gIV9ywta7Z+oqmuAa4bVqemQZHNVjY+6H78IPJfTy/M5vWb6+Zzpt7M2AYuTnJDkcOBcYN2I+yRJs8aMvhKpqr1JLgI2AHOA1VW1bcTdkqRZY0aHCEBVrQfWj7ofAzCjbr8d4jyX08vzOb1m9PlMVY26D5KkGWqmPxORJI2QITIkSRYmuSXJPUm2JXlfqx+dZGOS+9vfo1r9tUn+PsnTSf7LfvtanWRXkrtHcSyjNl3n8kD7mW2m8Xy+LMntSb7b9vPhUR3TKE3nf+tt/ZwkdyS5adjHMhWGyPDsBT5QVUuAZcCF7RUtFwM3V9Vi4Oa2DLAH+H3g45Ps6zpg+cB7fOiarnN5oP3MNtN1Pp8GTq+q1wNLgeVJlg3jAA4x0/nfOsD7gHsH2+XuDJEhqaqdVfWdNv9Dev8o5tN7Tcua1mwNcE5rs6uqNgH/b5J9fYveP7xZabrO5UH2M6tM4/msqnqqLR7Wpln30HU6/1tPsgD4TeDPh9D1TgyREUhyPHAScBtwbFXtbKt+ABw7om7NSNN1Lvfbz6z1Ys9nu/WyFdgFbKwqz+eL+/f5J8AfAj8dRP+mgyEyZEleAXwFeH9VPdm/rnpD5Wbd/3PrarrO5cH2M5tMx/msqmeraim9t0ecnOTEgXR2Bnix5zPJbwG7qmrL4Hr54hkiQ5TkMHr/qL5QVV9t5UeSHNfWH0fv/8HpeUzXuTzAfmad6f63WVWPA7cwS5/dTdP5PBX4nSQP0XtD+elJ/mJAXe7MEBmSJAGuBe6tqk/2rVoH7HvT4UrgxmH3baaZrnN5kP3MKtN4PseSzGvzRwBvBr43/T0+tE3X+ayqS6pqQVUdT++VTt+oqrcPoMsvTlU5DWECfp3e5eudwNY2nQ28mt5IjfuB/wUc3dr/Er1X2z8JPN7mj2zrvgjspPcgbjtwwaiPbyaeywPtZ9THN4PP568Bd7T93A388aiPbSafz/32eRpw06iPbbLJb6xLkjrzdpYkqTNDRJLUmSEiSerMEJEkdWaISJI6M0SkAUvPt5Oc1Vd7a5K/GWW/pOngEF9pCNrrP/6S3nuU5tL7PsXyqvqHDvuaW1V7p7mLUieGiDQkSf4r8CPg5e3vrwAn0nvb7Yeq6sb2wr7PtzYAF1XV/05yGnA58Bjw2qr6F8PtvTQ5Q0QakiQvB74DPAPcBGyrqr9orwq5nd5VSgE/raqfJFkMfLGqxluI/DVwYlU9OJojkJ5r7qg7IM0WVfWjJF8CngLeBvx23y/ZvQxYBPxf4E+TLAWeBfqvOG43QHSoMUSk4fppmwL8+6q6r39lkg8BjwCvpzfw5Sd9q380pD5KU+boLGk0NgDvbW98JclJrf4qYGdV/RR4BzBnRP2TpsQQkUbjcnoP1O9Msq0tA3wWWJnku8Br8epDhzgfrEuSOvNKRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqbP/D+NQ+LR0CgyaAAAAAElFTkSuQmCC\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": "924f7967-6adb-4c2d-e410-c496a1289ebf"
      },
      "source": [
        "sns.distplot(sal[sal[\"Year\"] == 2011][\"BasePay\"], bins = 30);"
      ],
      "execution_count": 22,
      "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": "be1cc3d0-6575-4b61-b19e-80741e3040c9"
      },
      "source": [
        "sns.distplot(sal[sal[\"Year\"] == 2012][\"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": "code",
      "metadata": {
        "id": "zCyvCLkrMfal",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "dc143a45-8224-44f0-db82-8af9dd2b8fb6"
      },
      "source": [
        "sns.distplot(sal[sal[\"Year\"] == 2013][\"BasePay\"], bins = 30);"
      ],
      "execution_count": 24,
      "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": "4MkX9mqZMigJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "ca52b7a4-7b95-4d8c-fcbd-7ede9e56f4e2"
      },
      "source": [
        "sns.distplot(sal[sal[\"Year\"] == 2014][\"BasePay\"], bins = 30);"
      ],
      "execution_count": 25,
      "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": "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": 265
        },
        "outputId": "b3dedb2b-5e3b-47a6-e232-347094b8aabf"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "top5 = sal.groupby('JobTitle')['BasePay'].mean().sort_values(ascending=False).head(5)\n",
        "plt.plot(top5)\n",
        "# top5 = top5.keys()\n",
        "# plt.plot(sal[sal['JobTitle'].isin(top5)]['Id'], sal[sal['JobTitle'].isin(top5)]['BasePay'])\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y5BV8smgTVSrEMqBrMrd0a0atKvZrHRNZovV3gvZjeWMKYdnmvQz7cgWfLtpE5XhLhibyWBKMEJYETUmyZGgilSXBCGFJ0JQGS4Ym0lgSjBCWBE1pypkM+5+ZzC3dm1HbkqEpZywJRghLgiYclm85dgONJUNTHlkSjBCWBE04WTI05ZUlwQhhSdCUBZYMTXljSTBCWBI0ZUnOZPj8tadz9sn1wh2WMT8TrUmwSI9NM8YUTcv61XjumtP5fHAPmtSpwsAxc/l88eZwh2WM8SwJGlMKWtSvxpu3ptKqQTVuGzuPTxZuCndIxhgsCRpTampUjueNWzrTPqkmd745j/fnrQ93SMZEPUuCxpSiagnxjL6pE52b1uG+d75l3Owfwx2SMVHNkqAxpaxKxTheu/EMerRIZMj7ixgzc024QzImalkSNCYMEuJjGXFDR85vXZ+/fbiYEVNXhTskY6KSJUFjwqRiXCwvXHs6F7VtwD8/Xcqzk1eEOyRjok5cwYMYY0pKfGwMQ/u0p2JsDP+etJxDWdn8sefJiEi4QzMmKlgSNCbM4mJj+NdV7agQF8PzU1Zx6MhR/npRK0uExpQCS4LGlAExMcI/Lz+NinExjPzmBzKzsnnkklOJibFEaExJsiRoTBkREyM8dEkbEuJjGT51NYezjvLo79oSa4nQmBJjSdCYMkREGNL7FCrGxzJs8goys47y76vaERdr97AZUxIsCRpTxogI957fkopxMTw5cRmHs44ytG8HKsRZIjSmuBV4VIlIkohMEZHvRWSxiNzty9uJyEwRWSQiH4lIdV+eLCIHRWSB/3spMK2OfviVIjJM/Df/IlJbRCaJyAr/v5YvFz/cShFZKCKnl8xqMKbsuf2ck/jrRa2Y8N1m/vDGXA4dyQ53SMZEnMJcWmYB96lqayAVuF1EWgOvAENU9TTgv8D9gXFWqWp7//f7QPmLwK1AC//Xy5cPASaragtgsv8M0Dsw7EA/vjFR45buzfj7ZacyeelWbh2TzsHDlgiNKU4FJkFV3aSq83z3XmAJ0BBoCUz1g00CrshvOiLSAKiuqmnqXmI4BrjM974UGO27R+coH6NOGlDTT8eYqHF9ahOeuKIt36zcxoDXZrMvMyvcIRkTMYr0JYOIJAMdgFnAYlySArgKSAoM2lRE5ovI1yLS3Zc1BIKPzV/vywDqq2ro3TKbgfqBcdblMU4wroEiki4i6RkZGUVZJGPKhavPSOKZPu1JX7uTG0bOYs+hI+EOyZiIUOgkKCJVgfeAwaq6B7gJuE1E5gLVgMN+0E1AY1XtANwL/Cf0fWFh+FpikV53r6ojVDVFVVMSExOLMqox5cal7RvyXL8OLNqwm+temcWuA4cLHskYk69CJUERicclwLGq+j6Aqi5V1Z6q2hF4E1jlyzNVdbvvnuvLWwIbgEaByTbyZQBbQs2c/v9WX76B42uYwXGMiTq9T2vAS9d1ZOmmvfQdkca2fZnhDsmYcq0wd4cKMBJYoqpPBcrr+f8xwF+Bl/znRBGJ9d3NcDe1rPbNnXtEJNVP8wbgQz+58UB/390/R/kN/i7RVGB3oNnUmKh0Xqv6vNI/hTXb99N3RBpb9hwKd0jGlFuFqQl2Ba4Hzg387OFCoJ+ILAeWAhuB1/zwPYCFIrIAeBf4varu8P1uw91VuhJXQ5zgyx8DzheRFcBv/GeAT4HVfviX/fjGRL0eLRMZdWMnNu46SJ/hM9mw62C4QzKmXBL3FVzkSElJ0fT09HCHYUypmLt2BwNenUP1SvG8eWsqjetUDndIppwSkbmqmhLuOEqbPYLCmHKsY5PajL21M/sys7h6+ExWZ+wLd0jGlCuWBI0p59o2qsm4gakcyT7K1cPTWL5lb7hDMqbcsCRoTARo1aA6bw1KJUag74g0Fm/cHe6QjCkXLAkaEyFOqleNtwd1ISEuhn4j0liwble4QzKmzLMkaEwESa5bhbcGdaFG5Xiue2UWc9bsKHgkY6KYJUFjIkxS7cq8PagL9apV5IaRs5mxclu4QzKmzLIkaEwEalCjEuMGpZJUuxI3jprDV8u2FjySMVHIkqAxEapetQTGDexC88SqDBwzl88Xbw53SMaUOZYEjYlgtatU4M1bU2l1YnVuGzuPTxbaUweNCbIkaEyEq1E5njdu7kT7pJrc+eY83p+3vuCRjIkSlgSNiQLVEuIZfVMnOjetw33vfMubs38Md0jGlAmWBI2JElUqxvHajWfQo0UiD7y/iNEz1oQ7JGPCzpKgMVEkIT6WETd05PzW9Xlw/GKGf70q3CEZE1aWBI2JMhXjYnnh2tO5qG0DHp2wlGGTV4Q7JGPCJi7cARhjSl98bAxD+7SnYmwMT01aTmZWNn/seTLufdfGRA9LgsZEqbjYGP51VTsqxMXw/JRVHDpylL9e1MoSoYkqlgSNiWIxMcI/Lz+NinExjPzmBzKzsnnkklOJibFEaKKDJUFjolxMjPDQJW1IiI9l+NTVZB45ymNXtCXWEqGJApYEjTGICEN6n0LF+FiGTV7B4eyj/PuqdsTF2r1zJrJZEjTGAC4R3nt+SyrGxfDkxGUczjrK0L4dqBBnidBErgL3bhFJEpEpIvK9iCwWkbt9eTsRmSkii0TkIxGpHhjnARFZKSLLROSCQHkvX7ZSRIYEypuKyCxf/paIVPDlFf3nlb5/cnEuvDHm524/5yT+9+LWTPhuM394Yy6HjmSHOyRjSkxhLvGygPtUtTWQCtwuIq2BV4Ahqnoa8F/gfgDfry/QBugFvCAisSISCzwP9AZaA/38sACPA0+r6knATuBmX34zsNOXP+2HM8aUsJu7NeXvl53K5KVbuXVMOgcPWyI0kanAJKiqm1R1nu/eCywBGgItgal+sEnAFb77UmCcqmaq6g/ASqCT/1upqqtV9TAwDrhU3P3Y5wLv+vFHA5cFpjXad78LnCd2/7YxpeL61CY8cUVbvlm5jQGvzWZfZla4QzKm2BWpsd83R3YAZgGLcUkK4CogyXc3BNYFRlvvy/IqrwPsUtWsHOXHTcv33+2HzxnXQBFJF5H0jIyMoiySMSYfV5+RxDN92pO+dic3jJzFnkNHwh2SMcWq0ElQRKoC7wGDVXUPcBNwm4jMBaoBh0smxIKp6ghVTVHVlMTExHCFYUxEurR9Q57r14FFG3Zz7cuz2Lk/bIe6McWuUElQROJxCXCsqr4PoKpLVbWnqnYE3gRCT+LdwLFaIUAjX5ZX+XagpojE5Sg/blq+fw0/vDGmFPU+rQEvXdeRZZv30u/lNLbtywx3SMYUi8LcHSrASGCJqj4VKK/n/8cAfwVe8r3GA339nZ1NgRbAbGAO0MLfCVoBd/PMeFVVYApwpR+/P/BhYFr9ffeVwJd+eGNMKTuvVX1GDkhhzfb99B2RxpY9h8IdkjG/WmFqgl2B64FzRWSB/7sQd3fncmApsBF4DUBVFwNvA98DnwG3q2q2/07vDmAi7uaat/2wAH8G7hWRlbjv/Eb68pFAHV9+L/DTzyqMMaWve4tERt3YiY27DtJn+Ew27DoY7pCM+VUk0ipWKSkpmp6eHu4wjIloc9fuYMCrc6heKZ43b02lcZ3K4Q7J/EoiMldVU8IdR2mzR0EYY4qsY5PajL21M/sys7h6+ExWZewLd0jG/CKWBI0xv0jbRjUZNzCVI9lH6TM8jWWb94Y7JGOKzJKgMeYXa9WgOm8NSiVGoO+ImXy3YXe4QzKmSCwJGmN+lZPqVePtQV2oFB/LNS+nsWDdrnCHZEyhWRI0xvxqyXWr8NagLtSoHM91r8xizpod4Q7JmEKxJGiMKRZJtSvz9qAu1KtWkRtGzmbGym3hDsmYAlkSNMYUmwY1KjFuUCpJtStx46g5fLVsa7hDMiZflgSNMcWqXrUExg3sQvPEqtw6Jp3PF28Od0jG5MmSoDGm2NWuUoE3b02l9Yk1uG3sPD5euDHcIRmTK0uCxpgSUaNyPG/c3IkOjWty15vzeX/e+nCHZMzPWBI0xpSYagnxjL6pE6nN6nDfO9/y5uwfwx2SMcexJGiMKVGVK8Tx6oAz6NEikQfeX8ToGWvCHZIxP7EkaIwpcQnxsYy4oSPnt67Pg+MXM/zrVQWPZEwpsCRojCkVFeNieeHa07mobQMenbCUYZNXEGlvsTHlT1zBgxhjTPGIj41haJ/2VIyN4alJyzl0JJv7LzgZ9+5uY0qfJUFjTKmKi43hX1e1o2J8DC98tYrMrKP89aJWlghNWFgSNMaUupgY4Z+Xn0bFuFhGfvMDmVnZPHLJqcTEWCI0pcuSoDEmLESEB3/bmopxMQyfuprMI0d57Iq2xFoiNKXIkqAxJmxEhCG9T6FifCzDJq/gcPZR/n1VO+Ji7Z49UzosCRpjwkpEuPf8llSMi+HJicvIPHKUYf06UCHOEqEpebaXGWPKhNvPOYn/vbg1ny3ezO/fmMuhI9nhDslEgQKToIgkicgUEfleRBaLyN2+vL2IpInIAhFJF5FOvvxsEdntyxeIyN8C0+olIstEZKWIDAmUNxWRWb78LRGp4Msr+s8rff/k4l4Bxpiy4+ZuTfn7Zafy5dKt3DomnX2ZWeEOyUS4wtQEs4D7VLU1kArcLiKtgSeAh1W1PfA3/zlkmqq293+PAIhILPA80BtoDfTz0wF4HHhaVU8CdgI3+/KbgZ2+/Gk/nDEmgl2f2oQnrmzL9JXbuHDoNOau3RnukEwEKzAJquomVZ3nu/cCS4CGgALV/WA1gILeldIJWKmqq1X1MDAOuFTcj4POBd71w40GLvPdl/rP+P7nif2YyJiId3VKEuMGdiH7qHL18Jk8PWk5WdlHwx2WiUBF+k7QN0d2AGYBg4EnRWQd8C/ggcCgXUTkWxGZICJtfFlDYF1gmPW+rA6wS1WzcpQfN47vv9sPnzOugb5JNj0jI6Moi2SMKaM6Na3NhMHduaTdiQydvIIrX5rJmm37wx2WiTCFToIiUhV4DxisqnuAPwD3qGoScA8w0g86D2iiqu2AZ4EPijfkn1PVEaqaoqopiYmJJT07Y0wpqZ4Qz9N92jOsXwdWZ+zjwmHTGDf7R3vmqCk2hUqCIhKPS4BjVfV9X9wfCHW/g2vuRFX3qOo+3/0pEC8idYENQFJgso182XagpojE5SgnOI7vX8MPb4yJIpe0O5HPBvegXaOaDHl/EYNen8uO/YfDHZaJAIW5O1RwtbwlqvpUoNdG4CzffS6wwg9/Quh7O3/HaAwucc0BWvg7QSsAfYHx6i7ppgBX+mn1Bz703eP9Z3z/L9UuAY2JSifWrMTYWzrzPxe24qtlGVzwzFS+WrY13GGZck4Kyiki0g2YBiwCQt9M/wXYAwzF/eD+EHCbqs4VkTtwTaVZwEHgXlWd4ad1IfAMEAu8qqr/8OXNcDfK1AbmA9epaqaIJACv476H3AH0VdXV+cWbkpKi6enpRVoJxpjy5fuNexj81nyWb9lH/y5NeODCViTEx4Y7rHJNROaqakq44yhtBSbB8saSoDHR4dCRbB7/bCmvTV/DSfWq8kyf9pzasEa4wyq3ojUJ2hNjjDHlUkJ8LA/+tg1jburEnoNHuPyF6bz09Sqyj0bWhb0pWZYEjTHlWo+WiUwc3IPzTqnPYxOWcs3LaWzYdTDcYZlywpKgMabcq1WlAi9edzpPXNmW7zbsptczU/lwwYaCRzRRz5KgMSYiiAhXpyTx6d3daVGvKnePW8Dd4+az++CRcIdmyjBLgsaYiNKkThXeHtSFe89vyccLN9H7manMXGU/Lza5syRojIk4cbEx3HVeC979fRcqxMVwzStpPDphCYez7Pmj5niWBI0xEatD41p8cld3+p6RxPCvV3PZ89NZsWVvuMMyZYglQWNMRKtSMY5Hf9eWEdd3ZPOeQ1z87DeMnrHGnj9qAEuCxpgo0bPNCXw2uDtdmtfhwfGLGfDaHLbuORTusEyYWRI0xkSNetUSeG3AGfz90jakrd5Or6HTmLh4c7jDMmFkSdAYE1VEhOu7JPPJXd1oUCOBQa/PZch7C9mfmVXwyCbiWBI0xkSlk+pV47+3deUPZzfnrfR1XDhsGvN/3BnusEwpsyRojIlaFeJi+HOvUxh3aypZ2cqVL81k6BcryMq2n8FH2O4AABmQSURBVFJEC0uCxpio17lZHSYM7s4l7U7k6S+Wc9Xwmazdvj/cYZlSYEnQGGOA6gnxPN2nPcP6dWDl1n30HjqNt+ess59SRDhLgsYYE3BJuxP5bHAP2jaqwZ/eW8gf3pjHjv2Hwx2WKSGWBI0xJoeGNSvxn1tS+cuFpzB56RZ6PTOVr5dnhDssUwIsCRpjTC5iYoSBPZrzwe1dqVEpnv6vzuah8Ys5dCQ73KGZYmRJ0Bhj8tHmxBp8dGc3BpyZzKgZa/jts9+weOPucIdlioklQWOMKUBCfCwPXdKG0Td1YtfBI1z2/HSGf72Ko0ftppnyrsAkKCJJIjJFRL4XkcUicrcvby8iaSKyQETSRaSTLxcRGSYiK0VkoYicHphWfxFZ4f/6B8o7isgiP84wERFfXltEJvnhJ4lIreJfBcYYUzhntUxk4uAenHtKPR6dsJRrXklj466D4Q7L/AqFqQlmAfepamsgFbhdRFoDTwAPq2p74G/+M0BvoIX/Gwi8CC6hAQ8CnYFOwIOBpPYicGtgvF6+fAgwWVVbAJP9Z2OMCZvaVSrw0nUdeeKKtixcv5tez0xl/Lcbwx2W+YUKTIKquklV5/nuvcASoCGgQHU/WA0gtBdcCoxRJw2oKSINgAuASaq6Q1V3ApOAXr5fdVVNU/eDnDHAZYFpjfbdowPlxhgTNiLC1WckMeHu7jSvV5W73pzP4HHz2X3wSLhDM0VUpO8ERSQZ6ADMAgYDT4rIOuBfwAN+sIbAusBo631ZfuXrcykHqK+qm3z3ZqB+HnEN9E2y6RkZdhuzMaZ0NKlThXcGdeGe37Tko4WbuHDoNGat3h7usEwRFDoJikhV4D1gsKruAf4A3KOqScA9wMiSCdHxtcRcv4VW1RGqmqKqKYmJiSUZhjHGHCcuNoa7f9OCd37fhbhYoe/LaTz+2VIOZ9nzR8uDQiVBEYnHJcCxqvq+L+4PhLrfwX3PB7ABSAqM3siX5VfeKJdygC2+uRT/f2th4jXGmNJ2euNafHpXd/qkJPHiV6u4/IXprNy6N9xhmQIU5u5QwdXylqjqU4FeG4GzfPe5wArfPR64wd8lmgrs9k2aE4GeIlLL3xDTE5jo++0RkVQ/rxuADwPTCt1F2j9QbowxZU6VinE8dkVbhl/fkY27DnLRsG8YM3ONPX+0DJOCNo6IdAOmAYuAUP3+L8AeYCgQBxwCblPVuT6RPYe7w/MAcKOqpvtp3eTHBfiHqr7my1OAUUAlYAJwp6qqiNQB3gYaA2uBq1V1R37xpqSkaHp6eqFXgDHGlIStew5x/7sL+Xp5BmefnMgTV7alXrWEcIeVJxGZq6op4Y6jtBWYBMsbS4LGmLJCVRkzcy3//HSJqyX+7jR6tjkh3GHlKlqToD0xxhhjSoiI0P/MZD6+sxsnVE9g4OtzeeD9hRw4nBXu0IxnSdAYY0pYi/rV+OD2rgw6qxnj5qzjomHfsGDdrnCHZbAkaIwxpaJCXAwP9G7Ff25JJfNINle8OINhk1eQlW0/pQgnS4LGGFOKujSvw4TBPbi4bQOemrScq4fP5MftB8IdVtSyJGiMMaWsRqV4hvbtwNC+7VmxdR+9h07lnfR19lOKMLAkaIwxYXJp+4Z8NrgHpzaswf3vLuS2sfPYuf9wuMOKKpYEjTEmjBrWrMR/bk1lSO9T+GLJFi54ZirTVtgzkEuLJUFjjAmz2Bjh92c157+3daV6pXiuHzmbRz76nkNHssMdWsSzJGiMMWXEqQ1r8PGd3RhwZjKvTv+BS577hu837gl3WBHNkqAxxpQhCfGxPHRJG1678Qx2HjjCZc9P5+Wpqzl61G6aKQmWBI0xpgw65+R6fHZ3d84+OZF/fLqE60bOYtPug+EOK+JYEjTGmDKqTtWKDL++I49fcRoL1u3igqen8vHCjeEOK6JYEjTGmDJMROhzRmM+vas7zRKrcsd/5nPvWwvYc+hIuEOLCJYEjTGmHEiuW4V3f9+Fu89rwYffbqT3M9OY/UO+b5YzhWBJ0Bhjyom42BjuOb8lbw/qQlys0HfETJ6cuJTDWfb80V/KkqAxxpQzHZvU4pO7unNVxySen7KKK16cwcqt+8IdVrlkSdAYY8qhqhXjePzKtrx0XUfW7zzAxc9O46Nv7aaZoooLdwDGGGN+uV6nnsDpjWvyl/8uomndKuEOp9yxJGiMMeVcveoJvNL/jHCHUS5Zc6gxxpioZUnQGGNM1CowCYpIkohMEZHvRWSxiNzty98SkQX+b42ILPDlySJyMNDvpcC0OorIIhFZKSLDRER8eW0RmSQiK/z/Wr5c/HArRWShiJxeMqvBGGNMNCpMTTALuE9VWwOpwO0i0lpV+6hqe1VtD7wHvB8YZ1Won6r+PlD+InAr0ML/9fLlQ4DJqtoCmOw/A/QODDvQj2+MMcYUiwKToKpuUtV5vnsvsARoGOrva3NXA2/mNx0RaQBUV9U0VVVgDHCZ730pMNp3j85RPkadNKCmn44xxhjzqxXpO0ERSQY6ALMCxd2BLaq6IlDWVETmi8jXItLdlzUE1geGWc+xZFpfVTf57s1A/cA46/IYJxjXQBFJF5H0jAx7I7MxxpjCKXQSFJGquGbPwaoafMtjP46vBW4CGqtqB+Be4D8iUr2w8/G1xCK9OEtVR6hqiqqmJCYmFmVUY4wxUaxQvxMUkXhcAhyrqu8HyuOA3wEdQ2Wqmglk+u65IrIKaAlsABoFJtvIlwFsEZEGqrrJN3du9eUbgKQ8xjHGGGN+lQKToP/ObySwRFWfytH7N8BSVV0fGD4R2KGq2SLSDHdTy2pV3SEie0QkFdecegPwrB9tPNAfeMz//zBQfoeIjAM6A7sDzaa5mjt37jYRWVvQcuWhLrDtF44bjWx9FY2tr6KzdVY0v2Z9NSnOQMoLca2P+Qwg0g2YBiwCQo8q/4uqfioio4A0VQ3+DOIK4BHgiB/+QVX9yPdLAUYBlYAJwJ2qqiJSB3gbaAysBa72SVOA53B3kR4AblTV9OJY8DyWNV1VU0pq+pHG1lfR2PoqOltnRWPrq+gKTILRxHagorH1VTS2vorO1lnR2PoqOntijDHGmKhlSfB4I8IdQDlj66tobH0Vna2zorH1VUTWHGqMMSZqWU3QGGNM1LIkaIwxJnqpatj/gBOAccAqYC7wKe4H9mcDH+cxzitA6wKm2x1YDCwAKhUylrtwz0cdm6P8bGC3n9YS3E8/8pvOKOBKv2yrgB/DsWzAGtzPWxb4vzN9DDV/xfYKTXMR8D3wf0BCPsPXB/4DrPbrYCZw+S+Y72U514tfzweAaoGyZ3BPHapbiH0sGfguxzTHAU8Epv+g3/b7cA+C2OTX5bt+mIdwD3EIreMXc5mmAH/1sa4FpgBTQ9sht+0UGPdGX6a4nx0dAg7iHjYfk8v+Gfr7je+X7T9/B7wDVPblcUAG8FiOWL8ClgHf+uVdAWz3883w8w7N48rCbINADBv8dIcE5pUeGC/Fl10QmMc+H88C3LOEg9Nb7Kd3H+6ivhLwNRDrt20w1gVABWAA8Fwu2+573BOwQsu/Azjsl3kRMMOPMyOX88J8P85U4GLf7yo/bmaOGGqSz/kEaO/XXS9gX6A8uB0/8tOZ5ct+9PMKzSMZt09Ny7FtF+D3zRwx/ODjCO0zCvw7MN4f/br6n8A8sgPddwF3ADcV9RguwvEf3J7f454/HV/AOAOAE/Md5peeCIvrD3dymAn8PlDWDneSP5s8EkUhp/0ScF0Rx1kKNMql/KdYgCq4E8Pp+UxnFO4EEdZl8wdC3UIMJ/gTalGmCVTFJbjRRdi+TXC/ES3K8saF1mku63lhaF3gToQLcc+ZrZtPDKHtkIw7qcQF+i0ILQ/HkuDHuJNjt5zb3p8c/hj4nMzPk+AduMQ7FXei74lLyAmF3U64ZLAG94PoesAXwMO+37l57U8cfyIdC9zru3sD030cEhjmKyDFd28Dvgps6w+Bnb9gG+zLbTn9vH4EevvPKaH55RZPHsv007oAbgfuzms7+PIBHJ8E/+i7WwB7cEk09JvmvsC/ga/zWLdn+30jzn9u75fxPOAz4NXgvpFzPN8dOp+c4T8/jvtt9ugcyxnsHg38T27LlOM4XQAk+c+t+HkSDMUwisCxhbvg+SGw/f4IPJTXNvCfKwPzC9iHj5tPEc8ByYHYY4EvgWsLGOdn+87PhvklwRTnH+7gnZrPDvYV8C4uOY3l2M08Py0c7oQyE5iHu9KtCtyCu5L7gRy1Oj/OvbiT33e456GCSyyHcVd99+S10/rP43Bvz2gPpOEO+v8CtQIb+yHcSS8Yay8f50pgp1+2ZbgT0WzcFeWiX7NsuRwIdXMr8zvVMtwV1WJccrofmOOX5+HCTBOojruirO0/B6cxBndSSQ5swyV+uSv7nfkbYD/uwJuGS1pnA7twJ4fQ1V9omRcAzf16XYw72e4FzvDj7/J/oQP4a9xV6z6/3u/35aEr2aO42ll9XE35IK7mswD3uMBgEkwJbPuHOHaF/EfcvpScx7KuB5px/L6wj2Mn7AyO1Wpez2O9/5QE/ef9PtZvgTtxrz37h/+chnswPX6Y73z5CuAF3EnzRz/PA8AruZ04cLWkZYF+p/r1FtzW23C142W+rB9u394HLPfLvw9XWwgdX1MC87oT+MZ/LnIS9J+b+W02w3c/6Zf3EDAocAxP9eU7ccf7wxx/AbMZd2EQSoJX4vbRH4B2wXn76S304ywPTOMmXE1ln183XwEJwGt+2ecDg3H71ADck7G2+HEE12LSHNiYY17ZHDsXzvLbsRfuvDAA92CRszmW2Db7bbzeD/Mo8GfcvvY97pyzCrfP5zy2snHH5SbcPvyM/78C+L/A/ngd7ry1ABiOOwd28v2O2xfzmM9dPpaFwLgCzmXJBC5qcE8Y+5Pv7og7zucCE4EGftsFWxFybTErC98JnooLPC8dcDtMa9zO3TXYU0Tq4pqZfqOqpwPpuCvdV3A71/2qem2OcTrimpg6496ReKuIdFD37sONwDmq+nReAfkn3KTiTiBjgD+ralvcDv5gYNCk4LL5R8q9DFyBS2Sxftnew52I7wXOwe0cCb9k2fIwxb/geFYu/VoAL6hqG+Bk/7kTLrl3FJEeBU1c3QPVfwBaiEjPHNM4HXeA46f/gqq2wl1x3wbcjLvKrgLUwG3nm/3wVXEntSq4E3maX+b2qrrKD3MUtw63+3WyFrfzVwVqichVPpYXcQdGXR8TuBrLDlxzVQLuQHnB9/tY3bsy9+ZY3Ddxz8t9BDg/UH4Pbrt9CvTIsawHcUljdY5pZQIni0gbXNOW4JqhWv1sJeeusl/+nrh9LxaXgBRoijvhgWsCvAB3oljuh43HvZGlJy6RXe6f6JRTJb9MIftwiSy4rT/GnczqiciFwCV+efbiTnx7fKw3BabzUqB7JnBYRM4p5HL/jF+3scBJuMc57sa9ik2Bp0Xke+Bu3L7wBq71ojmBde1f2r0C97SrkCdxx3BtXJLJqQWwWFVbBsrm4bZlOu49qy1wyeYSXFLsBzzAsXsyUnD7wlW49fWD37+/8ssUEoPb10/1y7kKVwPujNvGAH2Acf7cUdPP80cfywDcRUB1oA3uOKvLsX0e4IrAsTXHxzUKd778xM97gD8Hip9fV3+sZOMuxLrjjtk0VW3n53mrqs7g2HkrdAwPATr482fw3bP5EpEEv9yf+WdbP4urYXbE1b7/oarv+uW+1s/vYG7TKtQDtMNstvpnk/q31yfjrlBCUnEnzun+RfUVcAdVfroB/1XV/X667+M23PwCxusuIvNxJ57HcFdYNVX1a99/NO6KKy+puFrvDyLSBJilquv9yaQ2ruawE7ezn4DbiYu6bLk5R1Xzep7gWnXvagR3QuzJsfVQFXcATy3EPCSPadTHXViAey3WNSLyAlARt8xZwJkiMhi33PFAF1yNbQ/wqqoe9du+TS7zDb1qayIu+bTFNZNk4U7yfXxsV+O2+1Ggjh9HcVev9YC/A+er6i1+Xjl19/GtB+5S1ZdE5KFA/6dxJ5mLQ3Gp6nTf/ZaPIy/n4mp1PfLZTrnJ5vgT9lGguaqqiPThWJIWXO1hJ249jQSewm37Df7ZvP/0yxh6LOFYEamAuzAZmcf8Q9u6OnCWj6cf7uJnHcces/gGcIOqtheRNeS+P/4f7oLvz/xygkt+PXH7wTW+fAsueRzGbfsMXKvHm7iXdXcVkRtx3xH/lmMv9QZ3wn5XRD7EJc6cluIucnLGEfQ07uL9WVX9EkBEtuDWdxvccf2Iqi4WkedwLQ1wrMUh6GPcfp0JbFXVLBH5DFdZOApcBPwJtz3igddxj6NsjKvxb/XDjcRduH6jqhf5R2B+HEiA4Gqde3EXdFtwtdJMEVmNu8CPw11YzfHnp0q4WteJuHX9sZ/OXI6/YAxaiNvXPgA+yGOYoOb++GwKfKKqC0XkVFxynuTjiMXVYAulLNQEFxN4C0UuMgPd2fw8cQswSY+9yb61qt5MyZimqh1UtaMGnpeaj/UUbtkE9yzVB/0VVRqu2as0lm1/oFuARwPzO0lV8zoBHhtJpBru4mR5zmngvlOp6AdVVb0d931JTVwSisUdPKeoaiXcyTCU2DQQXza576/Z/v+XuCv7SaoaOvnG+niW4x703h53Vft5YPoHAtPJ76JwGu5gvj6w7bNyxJQQ6NZA9wEgyz9QPqgi7qTxS2Xi4g69dUXVtw1x/PIcwNWORuFORtVwF2QNfFKai6uptQhM+1pcy8s+3AVfSBXcSfunbY2/usft671xF6nB5S+QTw6VfFxF5tdtNu7EL7gm1guBVaraVFWD2/y4WQNP+5aQK3DJ4bj9TERigdNwzYE5HcqlrEMew+Y0D9dyNM5fVMX6GP7mt8uzQKw/vgCy/T7cxH++wP8fh/sqoBHuJqO9uHVwENeydCdu3Q7GraPVuATXBVebyksmrhn0Qo5fb0c5tm+NDpwvTgYm+fkeyWNfzOki4Hlc68wc/2ai/Kzy66A5rqXqEr+siwNxnKaqPQuYzk/KQhL8EqgoIgNDBSLSNvAy3oKk4a7kTvLjVhGRlgWMMw24TEQqi0gV4HJfViSquhvYGYj1ely7dMgi3ImuQSDW80QkdHUX7/9PxF25hlQNDF+oZRORpUWNPxcTgZv8uyMRkYYiUi+/EfywLwAfqOrOnNPAXSlXxZ1UG4tIF9wJtwruZPkV7gDd5ce5hmPrJaeDuBN4brb4eb2Qo/wt3BV+NRG5E99UJyJt+bnGIvIox5pHC7KGY02rDXFXp8FpdfHd1+DWyzD8MSciv8HtG+/hjoEqQC3fr3ZBM/ZN6xVxN0MUlHBEVWep6t9wtaBTcOtkp4+/Fe77o+NeWO2nuwtIEZFT/Pb5O7AnuK05doLLwiXMUO0jdPFzDccuVvaS9zb8P1wtJv+FEWlIYPv4dfESPmng7pr9QyguEWnpj3NwzaF1fXcfXFNhaHnH42rCoZd646f3KK5mv7AQsbUF/hd3Yg+ahjsG8MdwPY6/AAV3cbhQVZNUNVlVm+DW6eXBgVT1AK51ppdPGqHv3NtwrBaZhltHTXHf0z2NS36VcTfAfepjDL3rNdftoqo7cHcyN85lcbOAK0PnCL/fdsR9/5yXn+YjIjG4m3am4FoAagBVRaSTiIzJZxr4loQhuGblZUBi6HgTkXj/FUOeyxUU9uZQ33RzOfCMiPwZd2W1BnfV8rO3yOcyfoaIDADeFJHQQfdX3JVqXuPM89X/2b7oFVUtqCk0L/2Bl0SkMm4nuzFH/8tx3zV9gGuq+RF3I0WoqQncieUS4O8icj+uKbTQy+bb/3M2wRSZqn4uIq2Amb5ZIfTF99ZcBp8ibqAY3EH293ymcTvu1uojuFu7K+EOlBdx2/sq3HclWbgm0LwuzmYB94vIXbjv/XL6MUdzDqr6tv9e8Le4E4EAg3BX6zlV8/P/DrjYN33n/B5vrIiEmr92+Jhvw+2zwe2yDLhdRF7FffF/La62NAR3Fb4Wt14P+SbxXcB4ETmMa0oekEt8lXBNTd9wrBb4cKB/bKApt4aPCdxF5iK/7JNx3yctwTUZvYerQYwFbg7sZyHq/2bgEuWX+ObtwLZ+CNe0txG3v2T75W/kx1sExPnYagFLRGSLP8kfm5F7M01GLsudU4PAssbjtsHruCbeRrjv9RrhmuOScU3el/lx5+AuVhvjLpC2cLxHcAlrLJCIS+C7gc1+fp1yiSf0NUll3Da9S1Uni8j/+v734PbvRiJyDW6fegxXOw/qhzuWgrJ8+eM5yrfhWkz6qerrPrYefplD545tuMeohb5XXIjb5k1EZCHu4jTLj1sFd2/E33KJ621+3iwLbr/4K/C5T2hH/HTuy2XYkHHAy/4Y7guMFJEauH1zmKruEpHG/LyJOTcf4Pa9zrjzwTA/rThcDXYxrvXjJX/Mdsnte0F7bFoEEJGLgWaqOizcseRFRJJx3zmcGoZ5V1XVff5CZSowUFXn5TLcG7i7ggtzIi7X/MVViqreUQLTTqYEt7WI3IG74BmfS7/Tcdvw+lz6nY27E/TinP3MryciHXA37v1s3RdxOk/i7pAusOZdHMJeEzS/nqp+XPBQUW2EiLTGNQ+Nzi0BAqjqdaUblvklVDW3uzRD/eaJyBQRiVXV7LyGMyWiLq4p+FdR1fuLIZZCs5qgMcaYqFUWbowxxhhjwsKSoDHGmKhlSdAYY0zUsiRojDEmalkSNMYYE7X+H1yXBe5KfdIPAAAAAElFTkSuQmCC\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": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "595670f3-9a46-43c4-8bfe-38571671f968"
      },
      "source": [
        "top5 = sal.groupby('JobTitle')['OvertimePay'].mean().sort_values(ascending=False).head(5)\n",
        "plt.plot(top5)\n",
        "# top5 = top5.keys()\n",
        "# sns.lineplot(x = sal[sal['JobTitle'].isin(top5)]['JobTitle'], y = sal[sal['JobTitle'].isin(top5)]['OvertimePay'])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f884916a208>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C1Tzy8bfJLo6ISK5RYJS9dnbrOlx4VF2eHvkdI2av2PMMIiIFkAKj7JN7zmpGk4MrcNOgySxduynZxREROeAUGGWflC6RwtMXtWFrxg6ue30S2zTeKCKFjAKj7LNDU8vz99+0YML3a3joo9nJLo6IyAGlwCi/SvdWtel19CH8a9Q8Pp21fM8ziIgUEAqM8qvddUZTmtasyM1vTmGJxhtFpJBQYJRfLXO8MWO702fARLZmaLxRRAo+BUbZL/Wrl+P+c1owaeFaHhz+TbKLIyKy3xQYZb+dcWQtLm5fj+fHzOfjmRpvFJGCTYFRDog7zziC5rUrcvMbk1m0+udkF0dE5FdTYJQDolTxFJ7q1QZ36PP6JI03ikiBpcAoB0y9auX457lHMmXRWu4fqvFGESmYFBjlgOrWoiaXdqjPS1/MZ9j0H5JdHBGRfabAKAfc7ac1oWWdSvxx8BQWrtJ4o4gULAqMcsCVKp7Ck73aYMC1Ayay7udtyS6SiMheU2CUXFG3alkeOq8lM5au4/iHRvDyF/P1wHERKRAUGCXXdGl2MO9f15FmtSryl/dmcuqjo/lk5nLcPdlFExHJkQKj5KqmtSryn8uP5qVL08DgilfTueiFccxYui7ZRRMRyZYCo+Q6M+PEJjUY3rcTfzmrGTOXreeMJz7nT4OnsGL95mQXT0RkF7keGM2sspkNNrNvzGyWmR1jZveY2RIzmxxfpyXkv93M5prZbDM7NSG9a0yba2a3JaQ3MLNxMX2QmZXM7W2SX6dESjF6d6jPqFtO4IrjGvD2pCV0fmgkT3w6h01btye7eCIiQN70GB8Hhrl7E6AlMCumP+rureLrQwAzawpcCDQDugJPm1mKmaUATwHdgKZAz5gX4IG4rEbAGuDyPNgm2Q+VypbgjtOb8slNx9OpcSoPf/wtJz48krcnLWbHDo0/ikhy5WpgNLNKQCfgRQB33+rua3czS3dgoLtvcff5wFygXXzNdfd57r4VGAh0NzMDTgQGx/n7Az1yZ2vkQKtXrRzPXtyWQVe1p3r5Utw4aAo9nv6Cr+evTnbRRKQIy+0eYwNgJfCymU0ysxfMrFyc1sfMpprZS2ZWJabVBhYlzL84puWUXg1Y6+4ZWdKlADn60Gq8e+2xPHJ+S1as38L5//qKq/8zQQ8HEJGkyO3AWBxoAzzj7q2BjcBtwDNAQ6AVsAx4OJfLgZldZWbpZpa+cuXK3F6d7KNixYzftKnDZ7ccz40nH8bI2Ss5+ZFR/P3DWazbpAcEiEjeye3AuBhY7O7j4ufBQBt3X+7u2919B/A84VIpwBKgbsL8dWJaTumrgMpmVjxL+i+4+3PunubuaampqQdg0yQ3lC1ZnBtObszIP3ame6taPD9mHp0fHMGrXy0gQw8IEJE8kKuB0d1/ABaZ2eEx6SRgppnVTMh2NjA9vh8CXGhmpcysAdAY+BoYDzSOd6CWJNygM8TDf4qPAM6N8/cG3s3NbZK8UaNiaR48ryXv9TmOww+uwF3vzqDr42MY8c0KPSBARHKV5XYjY2atgBeAksA84DKgH+EyqgMLgN+7+7KY/w7gd0AG0Nfdh8b004DHgBTgJXe/L6YfSrgZpyowCfitu2/ZXZnS0tI8PT39wG6o5Bp355NZK/j7h7OY/+NGOjauzh2nH0GTgysmu2giRYqZTXD3tGSXI7flemDMjxQYC6atGTv4z9jvefzTOfy0eRsXHFWXG085jIMqlE520USKhKISGPXkGykwShYvxu+Oa8CoP3bm0g4NeDN9MSc8OJKnRsxl8zY9IEBEDgwFRilwKpctyV1nNuWjGzvRoVF1Hhw+m5MeHsW7k5do/FFE9psCoxRYh6aW5/lL0hhw5dFUKlOCGwZO5uynv2TC92uSXTQRKcAUGKXA69CwOu9ddxwPnnskS9du4pxnvqTPgIksWq0HBIjIvlNglEIhpZhxXlpdRtzSmRtOaswns5Zz0iOjuH/oN/y0WQ8IEJG9p8AohUq5UsW58ZTDGHFLZ844sibPjvqOzg+O5LVx3+sBASKyVxQYpVCqWakMj5zfiiF9jqXhQeW54+3pnNZvDKO+1eMARWT3FBilUDuyTmUGXdWeZ3/bli0ZO+j90tf0fulrvl3+U7KLJiL5lAKjFHpmRtfmB/Pxjcdz5+lHMHHhGro+Npo73p7Gjxt2+5AkESmCFBilyChZvBhXdDyUUX88gUuOqc/A8Ys44cGRPDvqOz0gQET+R4FRipyq5Upyz1nNGN63E0cfWpX7h37DyY+M4v2pS/WAABFRYJSiq9FB5Xmh91G8dsXRlC9VnD4DJnHus18xaaEeECBSlCkwSpF3bKPqfHB9Rx44pwXfr/qZs5/+khsGTmLJ2k3JLpqIJIF+XUMkwYYtGfxr1Hc8N3oeAFd0bMDVnRtRvlTxPcwpUvjp1zVEiqDypYpzc5fDGXFLZ7o1P5inRoQHBAz8eiHbdxS9k0iRokiBUSQbtSqX4bELW/POtcdSv1pZbntrGqf3G8Pnc35MdtFEJJcpMIrsRqu6lXnzD8fw9EVt2Lg1g9++OI7fvTKeuSv0gACRwkqBUWQPzIzTWtTk4xuP5/ZuTRg/fzWnPjaGu9+dzuqNW5NdPBE5wBQYRfZS6RIp/P74hoz8Y2d6tTuE/4xbyPEPjuD50fPYkqEHBIgUFgqMIvuoWvlS/LVHc4bd0JG0elW478NZnPLIaIZOW6YHBIgUAgqMIr9S4xoVePmydrz6u3aUKZHC1a9N5IJ/jWXq4rXJLpqI7AcFRpH91OmwVD64/jj+fnYL5v24gbOe/IKbBk1m2To9IECkIMqTwGhmlc1ssJl9Y2azzOwYM6tqZh+b2Zz4t0rMa2bWz8zmmtlUM2uTsJzeMf8cM+udkN7WzKbFefqZmeXFdolkKp5SjF5HH8KIWzpzTeeGvD9tGSc8NJJHPprNxi0ZyS6eiOyDvOoxPg4Mc/cmQEtgFnAb8Km7NwY+jZ8BugGN4+sq4BkAM6sK3A0cDbQD7s4MpjHPlQnzdc2DbRL5hQqlS/Cnrk347ObjOaXpwfT7bC4nPDSSN9IX6QEBIgVErgdGM6sEdAJeBHD3re6+FugO9I/Z+gM94vvuwKsejAUqm1lN4FTgY3df7e5rgI+BrnFaRXcf6+HOh1cTliWSFHWqlOWJnq3579UdqF2lDH8aPJUzn/icL7/TAwJE8ru86DE2AFYCL5vZJDN7wczKATXcfVnM8wNQI76vDSxKmH9xTNtd+uJs0kWSrm29Krx1dQee6NmadZu20ev5cVzRP515Kzcku2gikoO8CIzFgTbAM+7eGtjIzsumAMSeXq5eZzKzq8ws3czSV65cmZurEtmFmXFmy1p8evPx3Nq1CWPnraLLo6P5y3szWPuzHhAgkt/kRWBcDCx293Hx82BCoFweL4MS/66I05cAdRPmrxPTdpdeJ5v0Xbj7c+6e5u5pqamp+71RIvuqdIkUru4cHhBwwVF16f/lAo5/cCQvfj6frRk7kl08EYlyPTC6+w/AIjM7PCadBMwEhgCZd5b2Bt6N74cAl8S7U9sD6+Il1+FAFzOrEm+66QIMj9PWm1n7eDfqJQnLEsl3qpcvxX1nt2DoDZ04sk4l/vr+TLo8OorhM37QAwJE8oE8+T1GM2sFvACUBOYBlxGC8hvAIcD3wPnuvjoGtycJd5b+DFzm7ulxOb8D/hwXe5+7vxzT04BXgDLAUOA6382G6fcYJT8ZOXsF930wizkrNtD+0Krcd3YLGqaWT3axRH6hqPweo36oWCQfyNi+g4HjF/HQR7PZvG07d57elIuOPgT9S67kJ0UlMOrJNyL5QPGUYvy2fT2G9+3EUfWrcuc707mifzo/btiS7KKJFDkKjCL5SI2Kpel/WTvuPrMpY+b+SNfHRvPZN8uTXSyRIkWBUSSfKVbMuOzYBrx/3XFUL1+K372Szp3vTGPTVv20lUheUGAUyacOq1GBd/scy1WdDuU/YxdyxhNjmL5kXbKLJVLoKTCK5GOliqfw59OOYMAVR7Nxy3Z6PPUFT4+cq+euiuQiBUaRAqBDo+oM69uRU5sfzD+Hzabn82NZvObnZBdLpFBSYBQpICqXLcmTPVvz8Hktmbl0Pd0eH8O7k3/xkCcR2U8KjCIFiJlxTts6DL2hI4fXqMANAydz/euTWLdpW7KLJlJoKDCKFEB1q5Zl4FXtuaXLYXw4bRndHhvN2Hmrkl0skUJBgVGkgCqeUow+Jzbmv1d3oFSJFHo+P5b7h36jB5KL7CcFRpECrmXdynxw/XFceNQhPDvqO85++gvmrvgp2cUSKbAUGEUKgbIli/OP37Tg+UvSWLZuM6f3+5xXv1qgX+sQ+RUUGEUKkVOa1mBY344c07Aad707g9+9Mp6VP+l5qyL7QoFRpJA5qEJpXr70KO7t3owvv1tF18dG8/FMPW9VZG8pMIoUQmbGJcfU5/3rjqNGxdJc+Wo6t781jZ+3ZiS7aCL5ngKjSCHWuEYF3rn2WH5//KEMHL+Q0/t9zpRFa5NdLJF8TYFRpJArWbwYt3c7ggFXtGfLtu2c88yXPPnZHD1vVSQHCowiRcQxDasx9IZOdGtRk4c++pYL/vUVi1breasiWSkwihQhlcqW4ImerXnsglbM/uEnuj0+hv9OWKx/6xBJoMAoUgT1aF2boX070rRmRW5+cwp9Xp/E2p+3JrtYIvmCAqNIEVWnSllev6o9f+p6OMOn/0DXx8bw5dwfk10skaRTYBQpwlKKGdd0bsTb1xxL2VIp9HphHPd9MJMtGduTXTSRpMn1wGhmC8xsmplNNrP0mHaPmS2JaZPN7LSE/Leb2Vwzm21mpyakd41pc83stoT0BmY2LqYPMrOSub1NIoVNizqV+OC6jvy2/SE8P2Y+3Z/8gm+X63mrUjTlVY/xBHdv5e5pCWmPxrRW7v4hgJk1BS4EmgFdgafNLMXMUoCngG5AU6BnzAvwQFxWI2ANcHkebZNIoVKmZAp/69GCF3unsfKnLZzxxOe8/MV8dujfOqSIyW+XUrsDA919i7vPB+YC7eJrrrvPc/etwECgu5kZcCIwOM7fH+iRhHKLFBonHVGDYX07cVyj6vzlvZlc+sp4VqzfnOxiieSZvAiMDnxkZhPM7KqE9D5mNtXMXjKzKjGtNrAoIc/imJZTejVgrbtnZEkXkf2QWqEUL/ZO4289mvP1/FWc+thohk3/IdnFEskTeREYj3P3NoTLoNeaWSfgGaAh0ApYBjyc24Uws6vMLN3M0leuXJnbqxMp8MyM37avx/vXdaR2lTL84T8TuHXwVDZu0fNWpXDL9cDo7kvi3xXA20A7d1/u7tvdfQfwPOFSKcASoG7C7HViWk7pq4DKZlY8S3p25XjO3dPcPS01NfXAbJxIEdDooPK8dfWxXNO5IW9MWMRp/cYwaeGaZBdLJNfkamA0s3JmViHzPdAFmG5mNROynQ1Mj++HABeaWSkzawA0Br4GxgON4x2oJQk36Azx8LiOEcC5cf7ewLu5uU0iRVHJ4sX4U9cmDLrqGDK2O+c++xWPfzKHjO07kl00kQMut3uMNYDPzWwKIcB94O7DgH/Gf+GYCpwA3Ajg7jOAN4CZwDDg2tizzAD6AMOBWcAbMS/ArcBNZjaXMOb4Yi5vk0iR1a5BVYb27chZLWvx6Cffcv6/vuL7VRuTXSyRA8qK4jMS09LSPD09PdnFECnQhkxZyh1vT2PHDufus5pxXts6hBvFpbAyswlZ/u2uUMpv/64hIgXEWS1rMaxvJ1rUqcSfBk/lmtcmsmajnrcqBZ8Co4j8arUrl+G1K9pzW7cmfDJrOV0fH82YObrrWwo2BUYR2S8pxYw/HN+Qt685lgqlS3Dxi19z73sz2bxNz1uVgkmBUUQOiOa1K/Fen+PofUw9XvpiPj2e+oJvflif7GKJ7DMFRhE5YMqUTOEv3Zvz8mVH8eOGrZz1xBe8MGaenrcqBYoCo4gccCccfhDD+3ak02Gp/O2DWVz80jh+WKfnrUrBoMAoIrmiWvlSPH9JW/7xmxZM/H4tpz42mg+nLUt2sUT2SIFRRHKNmdGz3SF8cP1x1K9Wlmtem8gtb07hp83bkl00kRwpMIpIrjs0tTyDr3gscNUAABf7SURBVO7AdSc24q2Jizmt3xgmfL862cUSyZYCo4jkiRIpxbi5y+G88ftjcIfznv2KRz6azTY9b1XyGQVGEclTafWrMvSGjvRoXZt+n83l3Ge/Yv6Pet6q5B8KjCKS5yqULsEj57fiyV6tWfDjRk7vN4aBXy+kKD67WfIfBUYRSZozjqzFsL4daVW3Mre9NY3f/3sCq/W8VUkyBUYRSaqalcrwn8uP5o7TjmDk7JWc+thoRs5ekexiSRGmwCgiSVesmHFlp0N559pjqVK2BJe+PJ57hszQ81YlKRQYRSTfaFqrIkP6HMdlx9bnlS8XcOYTnzNj6bpkF0uKGAVGEclXSpdI4e4zm9H/d+1Yu2kbPZ76gudGf6fnrUqeUWAUkXzp+MNSGd63Eyc2OYi/f/gNF70wjqVrNyW7WFIEKDCKSL5VtVxJnv1tWx44pwVTFq+l62OjeW/K0mQXSwo5BUYRydfMjAuOOoQPr+/Ioanlue71Sdw0aDLr9bxVySUKjCJSINSvXo43/3AMN5zUmHcmL6HbY2MYv0DPW5UDz4rikybS0tI8PT092cUQkV9pwvdruHHQZBat+ZnyJYvvnGDZvsXM4t/dT09MT8ybmDszfdf5E3PuKe8uC/5l3j0sa9cy/rLcWT9kzXtx+3r07lA/2zLsiZlNcPe0XzVzAVJ8z1n2n5ktAH4CtgMZ7p5mZlWBQUB9YAFwvruvsfDtPQ6cBvwMXOruE+NyegN3xsX+zd37x/S2wCtAGeBD4AYvihFfpIhoW68KH97QkVe+mM/qjeGSqrPzkM/u6E9sEnyX9My07OfPLi855d1lPt/LZSWsO8e8vyx7zsvNZjsTMlQrXxLZvTwJjNEJ7v5jwufbgE/d/X4zuy1+vhXoBjSOr6OBZ4CjYyC9G0gjfM0TzGyIu6+Jea4ExhECY1dgaN5slogkQ/lSxelzYuNkF0MKoWSOMXYH+sf3/YEeCemvejAWqGxmNYFTgY/dfXUMhh8DXeO0iu4+NvYSX01YloiIyD7Jq8DowEdmNsHMroppNdx9WXz/A1Ajvq8NLEqYd3FM21364mzSRURE9lleXUo9zt2XmNlBwMdm9k3iRHd3M8vVMcEYkK8COOSQQ3JzVSIiUoDlSY/R3ZfEvyuAt4F2wPJ4GZT4N/Nx+kuAugmz14lpu0uvk0161jI85+5p7p6Wmpp6IDZLREQKoVwPjGZWzswqZL4HugDTgSFA75itN/BufD8EuMSC9sC6eMl1ONDFzKqYWZW4nOFx2nozax/vaL0kYVkiIiL7JC8updYA3o7/Q1McGODuw8xsPPCGmV0OfA+cH/N/SPhXjbmEf9e4DMDdV5vZX4HxMd+97p75373XsPPfNYaiO1JFRORX0j/4i4jIXikq/+CvR8KJiIgkKJI9RjNbSbh8+2tUB37cYy7JpP2177TP9o32177Zn/1Vz90L/d2LRTIw7g8zSy8KlxIOFO2vfad9tm+0v/aN9tee6VKqiIhIAgVGERGRBAqM++65ZBeggNH+2nfaZ/tG+2vfaH/tgcYYRUREEqjHKCIikkCBUUREJJG75/gCHgX6JnweDryQ8Plh4CbgLOC23S1rD+u5FFgJTAZWA2/vxTrrA9NjWmdgXZz/G+ChHJad+WpKeKbqdGAaMAnYEadNB94DKicsoxmwjPBw8jnA/7HzMvT9wJY470zgyj2stz7hZ7j+FvMdDLwV01YQHol3WMK6+wKbgUqZ+znL9k4FPgEOivlvITxDNus+XhC3dSWwifB/TOOBBgl57onlaJRl/Q6kJaS1imlds6xjQ/ybuY3XJUx7Mu6TpxL21aaE1xpgY9yXm4ApwJcJ+3IHcGTC8qYD9bMp08tZyuTAwwn7YBmwHPgo7vus9eCWmPcVYH7Cd/dlNvu0LPBanHc68DlQnoS6mWXf3gKMJNSvzGVPJDz2sF9O62TXuvQNcGNMHwGcmrCO7rEsz8QyLI/7c0rcpxnAdmADUCXOUyeWYS3wHfA4UDKb42pOnL4VmA18uJvjeQEwJkva5Kz7JJv5agEL4/KnAy8BJbLJ9+cc5r8BeCzh87+ATxI+bwH6ZTPfPZnfew7LTSP8zuvIuB2zgOcStrX6r2z3egBNs6TdR/h5vY3ZrS8ZL8KxdVoO07I9Bg7gumsBg/eQpz47Y0Fadt9xlry99rjePazwXOCN+L4YMAH4KmH6V0D73cxffC83/lLgyYT3Wwi/pJHjOvllYHw/vi9DaDyOzbrshGV0IzQGteLnUsDmhOn9gTsSlvcdoSE9N1aEocC1cfr9wPz4/iBCA1Yju/UmfDHzCI2wxe0ZEA+AJ4GWQMeE/OOAMcBlCWn/2974+R/AX7KWJ8t6FxB+dmswUCym1SE2kPHzPYRAe2dC2heECp+W+X0CD8Qy9c+yjsTAuJzwvNvMRvZJ4NLsKnPCuscD52atP3FfLgQGJUzLGhgzy7QtS5k2E4JN9bgP7orr+jvhYfNZ60Hmic0riWXJod7eDjyS8PnwuIz6sXyWsK/vYdfAeG5M7wJM3d062fX4qEY4qakbv8+XE/KlEgJXp1iG9XH7/kaoZ58STqwyy1kc+Bp4IX4/KcCLwIPZHFcvxO90WdyXR+5mvywg1Oe68fMR7EVgjHlPi/vNgNeBq7PJsyGHedOArxM+j411KiV+3gZcmLWNYg+BMeYbDnRP+NwiYVt3GxgT60GW9F9834S2rSbhJOYX68vrV9w//6t/e3sM5HEZ6+9N3Yp5/1end/fa06XUL4Fj4vtmhIP9p/gLF6VihZ9oZpea2ZMAZvaKmT1rZuOAf5pZQzMbFn+keIyZNdnDOj+Kf2vGde4AmpvZTDO7Oa5zFeEgr2Nms4C/sPOycFOgKvBvMxtO6GlldTvhQFgK4O5bCBURM7sSOB640cz+S6gUXwBLgZOB0UATwsGU1UZCD+WzWKYGOWzjz4SzwN8TDtZawBuxLFOAymY2zsxmxH3wMNAzcT8DLc2sn5l9SXiIeuaPTJ4H1DKzyWZ2Y5b11gCWufuOuK7F7r4mbvcG4FRCL+pPZpZqZg3jtlYmnDHfYWbfx3VcSvi1k0VmViKbbVxJ+I5657APcnJtlvrzCnARUAI428yuj/mKA4Pidk4HLgZmAMXNbIqZvRbzZRDuwsu6L0YDHQl14T4zm0ZoSOfG6ScR96mZTTKzu+L7e2MdATgdONfMpprZX9x9NqHefkr4sezp7PpTadkZDRxmZu/Hz+eb2UtmNtLM5iVsL2b2W8JJWRlCz+4t4HQzKxmzlCM0wkvj5xLAfwln/OsJDUgnwhn+EsIPhDciNBYXEE4spgB9zawecDXQycymAkcTeo0pcdlzzGyomT1oZqPN7AMzm21mz8bpbwBzzexhQs92eea+MLP6cZ+tM7OJZtYhMx34p4cWrDeh7t9iZnPM7J8xz/1Amfi9Z37HmSbHfVnGzCoResmTgRZxegrwRWyPlprZKsJVmt8k7OP7zGxNLN/XZlbBzDoT2sHFZlbNzD4CBprZC4QrBP+O39cPZrY4s2xxf7xKqIMjzWyFmY01sxpm9jfgQuDBmL8hgLuP9fCLQUbCD7C7+7RYvsQ2ADN7P5YPM9tgZo+a2Qwz+9TMUmP6SDN7PPNYMbN2Mb2qmb0Tt3WsmR0Z0+8xs3+b2RfAv4F7gQvi/Bdk2ec1SfiZP3ef7e5b4nf8TdwPs8xssJmVjctva2ajYkwYbjt/frCRmX0Sj9+JFmJH/Xh8Z9abMXHa/+pNIjPrnHksmdnxscyT4zFcgdBx6GjZt4//s9vAGANHhpkdAnQgnHWOI1SSNGCau2/NZtY6QAd3v4nQKF3n7m0JZ8xP726d7PzFjzWEilOLcPntTuBaQm9rG3AosNrdjyAEpPqxgX6acFbbgXAp5hx2fqmTzWwy0JzQE83OO4SzzN8Sgtd5CXnrE35LsgtQLbPiJXiQcCbekdCDOTVWusx1l0nIOzAuezbh8tbShGmfE84cBxAuk3YknIlVTMhTg3AZsC7hRKFTTH8TWOrurdz90Szl6wX83sw2mdkIM2udMK1cLMM/CQfk44T9v4JwkF7i7n8h9NzWuvt3hJ70bHff9ou9GDxAaNhScpienTSgJ2E/1ohplQgN/F2E3k9m2mh3b0VowL919z8QGqF73f2ihGU+RQiulpB2BiHApBF+K7tFXG9/Mysdt/vPMSgcDvwhztcRGG1mXQiNfWlC/etlZr1ingaEutnM3ff06MEzCXU60ymEIFWF0Nu7m9CgV4npFxIC1EpC7+prwhUQ4rTZhLp/KOHKy1hCD/M4Qv2tQ+h1fk+40vA6ofc8iNAruotQ768knCSNcfcjgYcIdbIicGvM+zrwAeGYuI5wUtqQcFXlv0BJQnuxjHDsl7Hw03MrYrmujdvUL5v9kkLY79cSAtsFZlbX3W8DNsX6nfgd4+4ZhCsxR8Wyjovr6WBmtWOeRTF7KtCa0PutQmg/biAMHZwet/lkQnCF8B19RmgLtgPHEn5btlpczm1x2lrgCsIJfWNCe1SMcFl3PeFE6EqgbVzeH+O2fJdl+7cBn8WTjxvNrHI2+yirckC6uzcDRhHqTqay8Vi5htAuQjh5nxS39c+Ek99MTYGT3b0noU4MiuUclGWdLwG3mtlXZvY3M2ucMO1w4OnYRq8Hrolt9BOEnnLbOP99Mf9rwFPu3pJQh5dlWdcK4BR3b0PO9SbRLYQre60Ix+0mwvc0Jof28X/25menvoyF7AA8QjgT7kAYe/gih3nedPftZlY+5n3T7H9tUqkc5rnAzDoReiiTCBXnFOB9QmXqQKicHvMvJfS8AD4mNOjTgcMIO3AY4eAywpfaJ3NFZpb5c1WJysSgWT/O04RwNrg+Ic8bsbc1x8y2x3VB7KERzr5XESp8VcIXcYG7z0pYd+bbYYSxoBLAf7KUpQ6hoeoUt7M0oaE5irDfITTKt7n7a2Z2K/DXbLYpq47AT8CJ8fWpmZ3n7p8SDuQZMd/ThBORxoRGdXPCMn6OeSHs35LkwN3nWej59copTzbSgVfcvT+EKxCEAAGhcf67mTUg7NvzzGwj4btK7CH2JOyvzHKsj2fu1xGCqBGuTGwmNKCPxnzfWOgRH0bYv5sJB2074JR4xtvA3Wdb6DW2JfS6DiZ83y8QAt1idjaou+yShPcPmtmdhAD3IOEEDkJdnuLu98Xtn0UIRmcR6sFcwhl6Z0LD8TohIL4b/75NOFbmxjJ8TWjY3iD0cEsSjuH3CY1vSULwybw6s5jQ8B9N+G3Uu8xsCqEu9CP0zC8nfPdD43xfu/u8WN7X47yr4vamEALtT4SxzTPjvr+IEFy2sfM4SnQx4WRnWFzuTKAeYfxtdzLbqzKEE/k5hEZ/ZdyuTLPdfWFc9g9x/y4g7PsvIdSbOJ24X04jBNri8W9LwknRKMIJVmtCEHyXUHfWuftYM9tKOMntTTh+m8TXqN1sRwbh6lhXwtjx782s5R62fQeh3YDQpryVMO31uE2jzaxiDLTHEeudu39moTecefI9xN2zq8O7cPfJZnYoobNwMjDezI4h1L1F7p4ZI/4DXE9o95oDH8f9mgIsi7252u7+dlzuZtilvYTQVj5pZq0I32V29SbRF8AjFq4svOXui7MsL0d7c1fqF4SK1oIQeMYSeowdCJUwOxsTlr82RufM1xE5zDMonrl0IOy4kwnd9Myzy2MIAWN+zO9Z5v+RMAY4KX6+NPYCHspmXTMIjVqiTfHMIvMmhOcJZ1QbEvKGAYNQETJvYoDYQyOcrXeJ7+8ijMHNIhuxpz2V0DAPzjL5CcI4lBHOvjsTGr32WfJtiX+HsJe/renuW9x9qLv/kdBT6JFNtszAvjBu5w6A2PNrCRxrZgsJgbpNrNQ5+Tuhh7F3NTLYmOVzRix7BmEM7VZCgD6fcOJwDnC/mS0gnHh1zaZMjwEVCGfFz7p75k03OZ2J/0jo/XQknJhNIpzpZ149MOAf7n6kuzdw90qEs98TiDe3ZFleVXZ9cHNmT+EUdtbpTFsS3m8nNB4TCXW5HaFncLy730NohE8yszaEuvI64RhqC/zs7j8RAmptQoAaS+gt1iPcAHVmTP+Y0PhXIFxCzvwOxsQz+GaEYLaNcLXjJ3Zepch6LGZ+3koYu3w9fl5H+M4eJwT3FoSAssvJlZndTTgZSAwc29m7Op7ZXh1DCIyzCL2fDuw8oYNdT/Y8lqdW1rLsslHhCtpqwhBIBqGdypzfCPcmPEc4ST+JnZcYt8XLwwMJx3BDwgnMbrn7Und/yd27J6wvg13b7dK7W0QO77P7nFXWY3B35dzg7m+5+zWEAHjabtZpwIyEeNDC3bvs5apuJJywtiSbepNNue4n9N7LEC6h72kY73/2JjB+SbjstNrdt3v4ceDKhIqXU2DMLNh6YL6ZnQdgwW7Petw9nXA58xxCo9edUImrEipU5kFWm7DBEILoGkJgqhjz3Bq77bWyWc0/CGfsB8dylWTnQVee0AW/mXA5dR7hzKomoYdSltAz2BTXl2g4cJ3tPC2pvrttjetZRAjoxLIcGcvcmjCOORQY6+61CPs9uyB0HDsP+k3kXGGONLNacT3FgCPZ+SsjxQgNCIQxl8/YeYkj00mEMai3CY3Wc4Se2dk5baC7f8POBvhAyCB83zUIgWYBoVfwb3evT2h838paplhvNxIa90z/IFwhuALAzJoR9sFswv5cRbjc/RVh/O0WQpCE8F1fb2Z14rz1CQfskjjvMjM7MU6rSjjz/3w/tnsxoZ4sJIz73Gpm9dx9A2EM7yVCvZ9FqD9HsbPXuix+XkD4nisSjpdP2XnJ+ntCcDyIcFI2jNCLyqzLRxLGuisSLpGXJZysAbQzswaxTl3AzsCeQQgSw+PnjUAbQo9ybLz6cjE7xy0hnFCcSriakpNtlv24Nuy8OS/V3VfEgLSS0I5sz2Ee4vZfQThGugFYGF/MbBdS4zpHE256qka4VFiOcOKaTqgr5xCCcyVCDyfR24TvoQE7Ty5yOqlMydzG2E5VI9StBUArMytmZnUJJ0qZirGzLenFrvXtgris4wg92XWEOn1RTO8M/JjZS84ix3Ka2bFmViW+L0k4fjLblENi7zGxPLMJ+/KYOE8JM2sWT+AWm1mPmF4qtrWJKrHzHoms9Sa7sjV092nu/gBheKzJ7rYl0d4ExmmEBn5slrR17r43P11yEXB5vBwzg1BB9+RWwk4YRbhz62tCA7bG3UfEPPMI43yzCMFsQeyFnUs4mzg3rq8xvxxjXEs4k/3Ewg0uE9nZAPwf4c68ioQGbnssc+Z179WERuLizO5+gr8SDoaphKByduJ6LctgsbvPIPRITiY0ID0JjfUjhC/+InbtZUyI2wOhMXsw7teL2dkYLQZqmNlWM1tr4WaAOnHai4QbIjYRGv2MuB8gNFq1CScEJ8bty7yEmakn4eAeRDhpGEQIjD3ZvfsIvf29kcbOGxImk30d7Ueok0MJwaAEoRcCIVifQBhkz2od4QQLAHf/kHBjQTcz20xo3AZ5uBkLQoCpSWhs/y9uw5g470eEXuScOO9MQlAaGue9BPi/uA2fEe4azhxHOhn4V/xu3sxSxlMIN35lbn9mvVxDuLz9UczTh3CiSNwHLYHXYyAYF/NnWkE4qepKOEFZQ7g/wAk3UXUkjKH2IgTTo2OZlxNuvpkS811GCASfEr7TDYTe03hCPZpF6P1mDnHg7g/4rvchvE+oZ63jcpuwa++kFqFu/x/huL2LX3oOmGq/vPkGDzeTrWTnsACE7+8gdu0x/oK7jyTU/8EWbvj4mJ09slTCFYYTCZeetxPq2SpC+/F3wr6pSjhJ+Q9ZerixbIsJ/87wNSE4/tHCjSENAczsn2a2OK7353iZdzjhCsMPhKA7n1Df+rFzmAHCfmwXy34ioW5n2mxmk4BnCZfCIZx4t7Uwjn4/Od8oNwJoatnffNMQGGXh5rVJhGMocxhjNuFmulmEE55nEtroB+L3P5nQrkJox66P5fmSMESR6Gmgdw71Jjt9LdxsNJVwpWMooW3ebuEGnxxvvimQj4SLZ+fvu3vzPWSVvWRmG9y9fLLLIQVL7Gnc4u5nJLssyWBmlxL+x7fPnvLmQVmyPYbNbCThO0rPw7LUpwC30XryjYiISIIC2WMUERHJLeoxioiIJFBgFBERSaDAKCIikkCBUUREJIECo4iISIL/B9YD4jcQzTXmAAAAAElFTkSuQmCC\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": "CgcrtPdJ9nPd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "7d75b5ee-c991-4da7-b650-9f00c392ba49"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def relationship(a, b):\n",
        "    v = (a / (b + 1)).sum()\n",
        "    return v\n",
        "link = \"https://s3-ap-southeast-1.amazonaws.com/intro-to-ml-minhdh/Salaries.csv\"\n",
        "sal = pd.read_csv(link)\n",
        "sal = sal[['JobTitle', 'TotalPayBenefits']]\n",
        "sal['JobTitle'] = sal['JobTitle'].str.len()\n",
        "sal.corr(method=relationship)"
      ],
      "execution_count": 28,
      "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",
              "      <th>TotalPayBenefits</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>JobTitle</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>960.666554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TotalPayBenefits</th>\n",
              "      <td>960.666554</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    JobTitle  TotalPayBenefits\n",
              "JobTitle            1.000000        960.666554\n",
              "TotalPayBenefits  960.666554          1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gY4kMupD9nPg",
        "colab_type": "text"
      },
      "source": [
        "# Great Job!"
      ]
    }
  ]
}