weekly_task_2_M_Hazmi_Cokro_Mandiri.ipynb

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  "metadata": {
    "colab": {
      "name": "weekly_task_2_M Hazmi Cokro Mandiri.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hazmicokro/Data-Literate-Bootcamp-2020/blob/main/weekly_task_2_M_Hazmi_Cokro_Mandiri.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55nDd6qHBX8x"
      },
      "source": [
        "# SF Salaries Exercise\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ohprVbEsCPOB"
      },
      "source": [
        "Selamat datang di latihan untuk melatih keterampilan pandas Anda! Kami menggunakan Dataset SF Salaries dari [Kaggle!](https://www.kaggle.com/kaggle/sf-salaries) Cukup ikuti dan selesaikan tugas yang diuraikan di bawah ini. Tugas akan semakin sulit seiring berjalannya waktu."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRcRePdACo9h"
      },
      "source": [
        "**import library pandas as pd**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiIRhjEl842B"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vcJ83G_DKIG2",
        "outputId": "d993a8ca-7854-4a6e-e06c-6c0497c00eb0"
      },
      "source": [
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6AulZq_2Kcmn",
        "outputId": "2052b19c-095b-47d2-fe3f-41a1a181bbb0"
      },
      "source": [
        "%cd '/content/drive/MyDrive/weekly_task_2_M Hazmi Cokro Mandiri'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/weekly_task_2_M Hazmi Cokro Mandiri\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bhNHc4Y0il2D",
        "outputId": "37de83ca-3242-49fc-f962-2e74d86d6ac5"
      },
      "source": [
        "!unzip 'Lemonade Final - Hazmi Cokro Weekly 2.zip' &> /dev/null && rm *.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  Lemonade Final - Hazmi Cokro Weekly 2.zip\n",
            "  inflating: Lemonade Final - Hazmi Cokro Weekly 2.xlsx  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUofQr2iL7Nj",
        "outputId": "7636bb37-86a5-45db-f7a3-e73a0b22565b"
      },
      "source": [
        "!unzip Salaries.zip &> /dev/null && rm *.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  Salaries.zip\n",
            "  inflating: Salaries.csv            \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siD7Mh5LC6ir"
      },
      "source": [
        "**baca file Salaries.csv sebagai dataframe bernama sal.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzJcLfZuC5xC"
      },
      "source": [
        "sal=pd.read_csv('/content/drive/MyDrive/weekly_task_2_M Hazmi Cokro Mandiri/Salaries.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPQNo-f9FNLE"
      },
      "source": [
        "**cek head dari DataFrame**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "0fG0zqGgDVsy",
        "outputId": "eeba04f3-1e50-43fb-a02d-afa4883d1363"
      },
      "source": [
        "sal.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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",
              "  </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",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhdtT6arFh94"
      },
      "source": [
        "Gunakan .info() untuk mengetahui berapa banyak entri yang ada"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QTvAf-fFXNY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e127591e-f49c-4c6b-aa26-8c7ee7d01c7f"
      },
      "source": [
        "sal.info()"
      ],
      "execution_count": null,
      "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": "4mi5RdU5GpT9"
      },
      "source": [
        "**Berapakah rata-rata dari BasePay?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4FX0iVvFs9i",
        "outputId": "59b3e73d-70b0-4267-cf7b-6bb8834a465a"
      },
      "source": [
        "sal['BasePay'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66325.44884050643"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SVPwdt9SG4yv"
      },
      "source": [
        "**Berapakah nilai tertinggi dari OvertimePay?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-8V5PHxGzvW",
        "outputId": "5572fa0e-bb21-4820-def3-9b924e5dd542"
      },
      "source": [
        "sal['OvertimePay'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "245131.88"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rf9GCqp_HUKY"
      },
      "source": [
        "**Apa pekerjaan dari JOSEPH DRISCOLL?**\n",
        "\n",
        "*Catatan: Gunakan huruf besar semua, jika tidak, Anda mungkin mendapatkan jawaban yang tidak cocok (ada juga huruf kecil Joseph Driscoll)*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEBNcMwdHKet",
        "outputId": "c58a208f-d5b4-4d63-d2ed-23526043dd98"
      },
      "source": [
        "sal[sal['EmployeeName']=='JOSEPH DRISCOLL']['JobTitle']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24    CAPTAIN, FIRE SUPPRESSION\n",
              "Name: JobTitle, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjBlDB6aH0Gu"
      },
      "source": [
        "**Berapa penghasilan JOSEPH DRISCOLL (termasuk keuntungannya)?**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gOseiJ2jHt1L",
        "outputId": "88e05f8f-00fb-40c1-b5c5-b3b1591afd0c"
      },
      "source": [
        "sal[sal['EmployeeName']=='JOSEPH DRISCOLL']['TotalPayBenefits']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24    270324.91\n",
              "Name: TotalPayBenefits, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uaGLsoe8ISUT"
      },
      "source": [
        "**Siapa nama orang yang dibayar paling tinggi (termasuk tunjangan)?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "aykjyLytH-2w",
        "outputId": "49a586b1-0fa1-4336-cf75-ea1061f26474"
      },
      "source": [
        "sal[sal['TotalPayBenefits']==sal['TotalPayBenefits'].max()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\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>title_length</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  title_length\n",
              "0   1  NATHANIEL FORD  ...    NaN            46\n",
              "\n",
              "[1 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zONu75_IJisH"
      },
      "source": [
        "**Siapa nama orang yang dibayar paling rendah (termasuk tunjangan)? Apakah Anda memperhatikan sesuatu yang aneh tentang berapa banyak dia dibayar?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "id": "zpKxDBTAJIus",
        "outputId": "0c565d20-7f14-46e5-d22e-0eadf1c56c37"
      },
      "source": [
        "sal[sal['TotalPayBenefits']==sal['TotalPayBenefits'].min()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\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",
              "      <th>title_length</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",
              "      <td>26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Id EmployeeName  ... Status  title_length\n",
              "148653  148654    Joe Lopez  ...    NaN            26\n",
              "\n",
              "[1 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Qd_45phJ3Eh"
      },
      "source": [
        "**Berapa rata-rata BasePay dari semua karyawan per tahun?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iN_Cqb7pJpYf",
        "outputId": "83c3e780-47ad-429a-f37b-e157a3aa5ce1"
      },
      "source": [
        "sal.groupby('Year').mean()['BasePay']"
      ],
      "execution_count": null,
      "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": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2VqNnXeKVjr"
      },
      "source": [
        "**Ada berapa banyak job titles?**\n",
        "\n",
        "*Catatan : Unique job titles*\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T5lfSPocKDn8",
        "outputId": "d7befdb2-8dc8-454b-e125-f251c811e714"
      },
      "source": [
        "sal['JobTitle'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2159"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bH66DQQELjca"
      },
      "source": [
        "**Apa 5 pekerjaan paling umum teratas?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41TtWCy7LbLy",
        "outputId": "3c36bd41-2183-4971-e417-9bcbd3af39d5"
      },
      "source": [
        "sal['JobTitle'].value_counts().head(5)"
      ],
      "execution_count": null,
      "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": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FoHXuxO2L8uS"
      },
      "source": [
        "**Berapa Pekerjaan yang diwakili oleh hanya satu orang pada tahun 2013?**\n",
        "\n",
        "*CATATAN: Pekerjaan dengan hanya satu kemunculan di 2013*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRWDIanKLq4j",
        "outputId": "cadea424-e4a5-4757-f712-0489ebc6592f"
      },
      "source": [
        "sum(sal[sal['Year']==2013]['JobTitle'].value_counts()==1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "202"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n5R3eBaNMXLk"
      },
      "source": [
        "**Berapa banyak orang yang memiliki kata Chief dalam jabatannya?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-DC5CnOJMd82",
        "outputId": "0a165661-82f3-4619-c9e8-ff26a9059408"
      },
      "source": [
        "def kata_chief(kc):\r\n",
        "    if 'chief' in kc.lower():\r\n",
        "        return True\r\n",
        "    else:\r\n",
        "        return False\r\n",
        "\r\n",
        "total_Chief = sum(sal['JobTitle'].apply(lambda kc: kata_chief(kc)))\r\n",
        "print(f\"Jumlah Chief = {total_Chief}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Jumlah Chief = 627\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGrZ6kokM2gO"
      },
      "source": [
        "**Bonus: Apakah ada korelasi antara panjang string Pekerjaan dan Gaji?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "bhil_hLJOFHn",
        "outputId": "95beb540-3763-4aae-db84-fcfc4e5c0897"
      },
      "source": [
        "sal['title_length']=sal['JobTitle'].apply(len)\r\n",
        "sal[['TotalPayBenefits','title_length']].corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TotalPayBenefits</th>\n",
              "      <th>title_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TotalPayBenefits</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.036878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_length</th>\n",
              "      <td>-0.036878</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  TotalPayBenefits  title_length\n",
              "TotalPayBenefits          1.000000     -0.036878\n",
              "title_length             -0.036878      1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    }
  ]
}