Malaysia EDA/Colby/Assignment_Pandas_Colby.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/drshahizan/Python_EDA/blob/main/Malaysia%20EDA/Colby/Assignment_Pandas_Colby.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7F8DQl5dVJJ0"
      },
      "source": [
        "# Malaysia GE-14 Result\n",
        "\n",
        "This project is to examine the Malaysian general election result in 2018. The dataset consists of 1647 rows and 12 columns and is acquired from https://www.kaggle.com/. From this dataset, we could investigate the election result in terms of state results by candidate via data preparation, cleaning, and visualization. "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Group members:**\n",
        "1. Chloe Racquelmae Kennedy A20EC0026\n",
        "2. Lee Cai Xuan A20EC0062"
      ],
      "metadata": {
        "id": "jwvJUDt8yg7Y"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxWzVndBVJJ1"
      },
      "source": [
        "## Downloading the Dataset\n",
        "\n",
        "* Election-Results-2018 - State_Results_By_Candidate   \n",
        "* List of each candidate's results for the parliamentary and state elections in GE14.\n",
        "* A combination of 12 columns and 1647 rows\n",
        "\n",
        "#### Attribute Information:\n",
        "<table>\n",
        "    <thead>\n",
        "        <tr>\n",
        "            <th>Attribute</th>\n",
        "            <th>Acronym</th>\n",
        "            <th>Description</th>\n",
        "        </tr>\n",
        "    </thead>\n",
        "    <tbody>\n",
        "        <tr>\n",
        "            <td rowspan=2>Gender</td>\n",
        "            <td>L</td>\n",
        "            <td>Male (Lelaki)</td>\n",
        "        </tr>\n",
        "        <tr>\n",
        "            <td>P</td>\n",
        "            <td>Female (Perempuan)</td>\n",
        "        </tr>\n",
        "        <tr>\n",
        "            <td rowspan=2>Status</td>\n",
        "            <td>MNG</td>\n",
        "            <td>Win (Menang)</td>\n",
        "        </tr>\n",
        "        <tr>\n",
        "            <td>KLH</td>\n",
        "            <td>Lose (Kalah)</td>\n",
        "        </tr>\n",
        "    </tbody>\n",
        "</table>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GhhO14GEVJJ2"
      },
      "source": [
        "Let's begin by downloading the data, and listing the files within the dataset.\n",
        "\n",
        "There are 2 options:"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Load the dataset directly from Kaggle into Google Colab**\n",
        "\n",
        "  Download the API Credentials first and then use the code below:"
      ],
      "metadata": {
        "id": "4-Gc7BT6rwA4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b915b3f8-e4af-4e8c-ea32-5b456c1d0922"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.8/dist-packages (1.5.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.8/dist-packages (from kaggle) (2022.12.7)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.8/dist-packages (from kaggle) (7.0.0)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from kaggle) (4.64.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.8/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (2.10)\n",
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "Downloading malaysia-ge14-election-results-parliament.zip to /content\n",
            "  0% 0.00/76.2k [00:00<?, ?B/s]\n",
            "100% 76.2k/76.2k [00:00<00:00, 55.3MB/s]\n"
          ]
        }
      ],
      "source": [
        "! pip install kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets download -d terenctb/malaysia-ge14-election-results-parliament"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "  Unzip the dataset file"
      ],
      "metadata": {
        "id": "ODA97e85srjK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oAVmLuq2VJJ3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41e6e935-e1cc-430b-844c-2a87dc93c431"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  malaysia-ge14-election-results-parliament.zip\n",
            "  inflating: Election-Results-2018 - Parlimen_Results_By_Candidate.csv  \n",
            "  inflating: Election-Results-2018 - State_Results_By_Candidate.csv  \n",
            "  inflating: Election-Results-2018 - Total_Votes_by_Seat.csv  \n"
          ]
        }
      ],
      "source": [
        "! unzip malaysia-ge14-election-results-parliament.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Download the dataset directly from the website:**\n",
        "https://www.kaggle.com/datasets/terenctb/malaysia-ge14-election-results-parliament?select=Election-Results-2018+-+State_Results_By_Candidate.csv\n",
        "\n",
        "  From the data explorer in Kaggle, download the dataset named \"Election-Results-2018 - State_Results_By_Candidate\" (141.17kB)"
      ],
      "metadata": {
        "id": "L-wWQ6vCrMoc"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XXnPmr-JVJJ3"
      },
      "source": [
        "The dataset has been downloaded and extracted."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Next step**\n",
        "\n",
        "Upload the downloaded dataset into github which could be access from this link: \n",
        "https://raw.githubusercontent.com/Racquelmae/dataset/main/Election-Results-2018%20-%20State_Results_By_Candidate.csv"
      ],
      "metadata": {
        "id": "2z163ujltoaN"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0vucnGFVJJ5"
      },
      "source": [
        "## Data Preparation and Cleaning\n",
        "\n",
        "In this process, the dataset will be clean and transform to make sure it is prepare for the analysis step and could produce great visualization. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 1:** Load the dataset into a data frame using Pandas"
      ],
      "metadata": {
        "id": "DNGG2-XDui3B"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "df5R-2s3VJJ5",
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          "height": 206
        },
        "outputId": "7566f736-20e3-4258-ff2f-5c49ef7f3f66"
      },
      "outputs": [
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          "data": {
            "text/plain": [
              "   State Seat ID    Seat Name                 Candidate Name Candidate Party  \\\n",
              "0  Johor    N.01  BULOH KASAP                   ZAHARI SARIP              BN   \n",
              "1  Johor    N.01  BULOH KASAP            NORSAMSU MOHD YUSOF             PKR   \n",
              "2  Johor    N.02     JEMENTAH                 CHIAM YOK MENG              BN   \n",
              "3  Johor    N.02     JEMENTAH                 TAN CHEN CHOON             PKR   \n",
              "4  Johor    N.02     JEMENTAH  KHAIRUL FAIZI BIN AHMAD KAMIL             PAS   \n",
              "\n",
              "  Votes for Candidate Total Votes Cast % of total Votes  Candidate Win Gender  \\\n",
              "0               9,186           17,495           52.51%              1      L   \n",
              "1               8,309           17,495           47.49%              0      L   \n",
              "2               9,335           27,657           33.75%              0      L   \n",
              "3              16,336           27,657           59.07%              1      L   \n",
              "4               1,986           27,657            7.18%              0      L   \n",
              "\n",
              "  Status Pekerjaan  \n",
              "0    MNG         0  \n",
              "1    KLH         0  \n",
              "2    KLH         0  \n",
              "3    MNG         0  \n",
              "4     HD         0  "
            ],
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              "      <th>State</th>\n",
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              "      <td>BULOH KASAP</td>\n",
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              "      <td>JEMENTAH</td>\n",
              "      <td>CHIAM YOK MENG</td>\n",
              "      <td>BN</td>\n",
              "      <td>9,335</td>\n",
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              "      <td>33.75%</td>\n",
              "      <td>0</td>\n",
              "      <td>L</td>\n",
              "      <td>KLH</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Johor</td>\n",
              "      <td>N.02</td>\n",
              "      <td>JEMENTAH</td>\n",
              "      <td>TAN CHEN CHOON</td>\n",
              "      <td>PKR</td>\n",
              "      <td>16,336</td>\n",
              "      <td>27,657</td>\n",
              "      <td>59.07%</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Johor</td>\n",
              "      <td>N.02</td>\n",
              "      <td>JEMENTAH</td>\n",
              "      <td>KHAIRUL FAIZI BIN AHMAD KAMIL</td>\n",
              "      <td>PAS</td>\n",
              "      <td>1,986</td>\n",
              "      <td>27,657</td>\n",
              "      <td>7.18%</td>\n",
              "      <td>0</td>\n",
              "      <td>L</td>\n",
              "      <td>HD</td>\n",
              "      <td>0</td>\n",
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          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/Racquelmae/dataset/main/Election-Results-2018%20-%20State_Results_By_Candidate.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 2:** Explore the number of rows & columns, ranges of values etc."
      ],
      "metadata": {
        "id": "O4V6cAzLvW9k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
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        "colab": {
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        "outputId": "dd454041-e93f-46c2-82bf-e6dd1fa97acb"
      },
      "execution_count": 146,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1646 entries, 0 to 1645\n",
            "Data columns (total 12 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   State                1646 non-null   object\n",
            " 1   Seat ID              1646 non-null   object\n",
            " 2   Seat Name            1646 non-null   object\n",
            " 3   Candidate Name       1646 non-null   object\n",
            " 4   Candidate Party      1646 non-null   object\n",
            " 5   Votes for Candidate  1646 non-null   object\n",
            " 6   Total Votes Cast     1646 non-null   object\n",
            " 7   % of total Votes     1646 non-null   object\n",
            " 8   Candidate Win        1646 non-null   int64 \n",
            " 9   Gender               1646 non-null   object\n",
            " 10  Status               1646 non-null   object\n",
            " 11  Pekerjaan            1646 non-null   object\n",
            "dtypes: int64(1), object(11)\n",
            "memory usage: 154.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdV02gVxvbkh",
        "outputId": "84675d3d-74a2-41ce-b88e-91b43e1758ce"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State                  object\n",
              "Seat ID                object\n",
              "Seat Name              object\n",
              "Candidate Name         object\n",
              "Candidate Party        object\n",
              "Votes for Candidate    object\n",
              "Total Votes Cast       object\n",
              "% of total Votes       object\n",
              "Candidate Win           int64\n",
              "Gender                 object\n",
              "Status                 object\n",
              "Pekerjaan              object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Candidate Party'].unique()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "p9ky16xF420B",
        "outputId": "3cff5dbe-8a92-4fab-c387-2ff1ad30dc8d"
      },
      "execution_count": 148,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "array(['BN', 'PKR', 'PAS', 'BEBAS - POKOK', 'BEBAS - KUDA',\n",
              "       'BEBAS - GAJAH', 'BEBAS - MOTOSIKAL', 'BEBAS - KUNCI',\n",
              "       'BEBAS - BEG', 'PRM', 'PSM', 'BEBAS - PEN', 'BEBAS - CAWAN',\n",
              "       'BEBAS - KERETA', 'BEBAS - BUKU', 'PAP', 'BEBAS - RUMAH',\n",
              "       'BEBAS - CINCIN', 'BEBAS - IKAN', 'MU', 'PFP', 'BEBAS - JAM',\n",
              "       'PCM', 'BEBAS - KAPAL TERBANG', 'BERJASA', 'PPRS', 'HR', 'WARISAN',\n",
              "       'SOLIDARITI', 'PKS', 'PCS', 'ANAKNEGERI', 'BEBAS - TRAKTOR',\n",
              "       'SAPP', 'DAP', 'STAR', 'USNO', 'AMANAH'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 3:** Handle missing, incorrect and invalid data"
      ],
      "metadata": {
        "id": "2I252Hl7yc_b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jrWOYMrbzLJN",
        "outputId": "0d1fcf47-7f5e-4620-f673-e106deb5ff89"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      State Seat ID     Seat Name                 Candidate Name  \\\n",
              "0     Johor    N.01   BULOH KASAP                   ZAHARI SARIP   \n",
              "1     Johor    N.01   BULOH KASAP            NORSAMSU MOHD YUSOF   \n",
              "2     Johor    N.02      JEMENTAH                 CHIAM YOK MENG   \n",
              "3     Johor    N.02      JEMENTAH                 TAN CHEN CHOON   \n",
              "4     Johor    N.02      JEMENTAH  KHAIRUL FAIZI BIN AHMAD KAMIL   \n",
              "...     ...     ...           ...                            ...   \n",
              "1641  Sabah    N.59  TANJONG BATU                  ISMAIL SENANG   \n",
              "1642  Sabah    N.60       SEBATIK                 YUSRI HJ YUNUS   \n",
              "1643  Sabah    N.60       SEBATIK             USTAZ ROSLAN RAMLI   \n",
              "1644  Sabah    N.60       SEBATIK   DATUK HJ. ABD MUIS HJ. PICHO   \n",
              "1645  Sabah    N.60       SEBATIK          HASSAN A GANI PG AMIR   \n",
              "\n",
              "     Candidate Party Votes for Candidate Total Votes Cast % of total Votes  \\\n",
              "0                 BN               9,186           17,495           52.51%   \n",
              "1                PKR               8,309           17,495           47.49%   \n",
              "2                 BN               9,335           27,657           33.75%   \n",
              "3                PKR              16,336           27,657           59.07%   \n",
              "4                PAS               1,986           27,657            7.18%   \n",
              "...              ...                 ...              ...              ...   \n",
              "1641         WARISAN               6,552           16,767           39.08%   \n",
              "1642            PPRS                  38            5,285            0.72%   \n",
              "1643             PAS                 504            5,285            9.54%   \n",
              "1644              BN               2,468            5,285           46.70%   \n",
              "1645         WARISAN               2,275            5,285           43.05%   \n",
              "\n",
              "      Candidate Win Gender Status        Pekerjaan  \n",
              "0                 1      L    MNG                0  \n",
              "1                 0      L    KLH                0  \n",
              "2                 0      L    KLH                0  \n",
              "3                 1      L    MNG                0  \n",
              "4                 0      L     HD                0  \n",
              "...             ...    ...    ...              ...  \n",
              "1641              0      L    KLH  BEKERJA SENDIRI  \n",
              "1642              0      L     HD                0  \n",
              "1643              0      L     HD                0  \n",
              "1644              1      L    MNG            TIADA  \n",
              "1645              0      L    KLH  BEKERJA SENDIRI  \n",
              "\n",
              "[1646 rows x 12 columns]"
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              "      <td>PAS</td>\n",
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              "      <td>7.18%</td>\n",
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              "      <th>1642</th>\n",
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              "      <td>SEBATIK</td>\n",
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              "      <td>PPRS</td>\n",
              "      <td>38</td>\n",
              "      <td>5,285</td>\n",
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              "      <td>L</td>\n",
              "      <td>HD</td>\n",
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              "    <tr>\n",
              "      <th>1643</th>\n",
              "      <td>Sabah</td>\n",
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              "      <td>SEBATIK</td>\n",
              "      <td>USTAZ ROSLAN RAMLI</td>\n",
              "      <td>PAS</td>\n",
              "      <td>504</td>\n",
              "      <td>5,285</td>\n",
              "      <td>9.54%</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1644</th>\n",
              "      <td>Sabah</td>\n",
              "      <td>N.60</td>\n",
              "      <td>SEBATIK</td>\n",
              "      <td>DATUK HJ. ABD MUIS HJ. PICHO</td>\n",
              "      <td>BN</td>\n",
              "      <td>2,468</td>\n",
              "      <td>5,285</td>\n",
              "      <td>46.70%</td>\n",
              "      <td>1</td>\n",
              "      <td>L</td>\n",
              "      <td>MNG</td>\n",
              "      <td>TIADA</td>\n",
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              "    <tr>\n",
              "      <th>1645</th>\n",
              "      <td>Sabah</td>\n",
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              "      <td>WARISAN</td>\n",
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              "      <td>5,285</td>\n",
              "      <td>43.05%</td>\n",
              "      <td>0</td>\n",
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              "      <td>BEKERJA SENDIRI</td>\n",
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              "<p>1646 rows × 12 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the output above, it shows that there are still 1646 rows with 12 columns. Therefore, it means that there is no missing values in this dataset.\n",
        "\n"
      ],
      "metadata": {
        "id": "XT1XD1SV0Gj2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df['Total Votes Cast'] != '0']\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7XTOsMvIxH8F",
        "outputId": "14c296cb-624f-4784-cc06-f4b7daf493b0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      State Seat ID     Seat Name                 Candidate Name  \\\n",
              "0     Johor    N.01   BULOH KASAP                   ZAHARI SARIP   \n",
              "1     Johor    N.01   BULOH KASAP            NORSAMSU MOHD YUSOF   \n",
              "2     Johor    N.02      JEMENTAH                 CHIAM YOK MENG   \n",
              "3     Johor    N.02      JEMENTAH                 TAN CHEN CHOON   \n",
              "4     Johor    N.02      JEMENTAH  KHAIRUL FAIZI BIN AHMAD KAMIL   \n",
              "...     ...     ...           ...                            ...   \n",
              "1641  Sabah    N.59  TANJONG BATU                  ISMAIL SENANG   \n",
              "1642  Sabah    N.60       SEBATIK                 YUSRI HJ YUNUS   \n",
              "1643  Sabah    N.60       SEBATIK             USTAZ ROSLAN RAMLI   \n",
              "1644  Sabah    N.60       SEBATIK   DATUK HJ. ABD MUIS HJ. PICHO   \n",
              "1645  Sabah    N.60       SEBATIK          HASSAN A GANI PG AMIR   \n",
              "\n",
              "     Candidate Party Votes for Candidate Total Votes Cast % of total Votes  \\\n",
              "0                 BN               9,186           17,495           52.51%   \n",
              "1                PKR               8,309           17,495           47.49%   \n",
              "2                 BN               9,335           27,657           33.75%   \n",
              "3                PKR              16,336           27,657           59.07%   \n",
              "4                PAS               1,986           27,657            7.18%   \n",
              "...              ...                 ...              ...              ...   \n",
              "1641         WARISAN               6,552           16,767           39.08%   \n",
              "1642            PPRS                  38            5,285            0.72%   \n",
              "1643             PAS                 504            5,285            9.54%   \n",
              "1644              BN               2,468            5,285           46.70%   \n",
              "1645         WARISAN               2,275            5,285           43.05%   \n",
              "\n",
              "      Candidate Win Gender Status        Pekerjaan  \n",
              "0                 1      L    MNG                0  \n",
              "1                 0      L    KLH                0  \n",
              "2                 0      L    KLH                0  \n",
              "3                 1      L    MNG                0  \n",
              "4                 0      L     HD                0  \n",
              "...             ...    ...    ...              ...  \n",
              "1641              0      L    KLH  BEKERJA SENDIRI  \n",
              "1642              0      L     HD                0  \n",
              "1643              0      L     HD                0  \n",
              "1644              1      L    MNG            TIADA  \n",
              "1645              0      L    KLH  BEKERJA SENDIRI  \n",
              "\n",
              "[1645 rows x 12 columns]"
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              "      <td>BULOH KASAP</td>\n",
              "      <td>NORSAMSU MOHD YUSOF</td>\n",
              "      <td>PKR</td>\n",
              "      <td>8,309</td>\n",
              "      <td>17,495</td>\n",
              "      <td>47.49%</td>\n",
              "      <td>0</td>\n",
              "      <td>L</td>\n",
              "      <td>KLH</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Johor</td>\n",
              "      <td>N.02</td>\n",
              "      <td>JEMENTAH</td>\n",
              "      <td>CHIAM YOK MENG</td>\n",
              "      <td>BN</td>\n",
              "      <td>9,335</td>\n",
              "      <td>27,657</td>\n",
              "      <td>33.75%</td>\n",
              "      <td>0</td>\n",
              "      <td>L</td>\n",
              "      <td>KLH</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Johor</td>\n",
              "      <td>N.02</td>\n",
              "      <td>JEMENTAH</td>\n",
              "      <td>TAN CHEN CHOON</td>\n",
              "      <td>PKR</td>\n",
              "      <td>16,336</td>\n",
              "      <td>27,657</td>\n",
              "      <td>59.07%</td>\n",
              "      <td>1</td>\n",
              "      <td>L</td>\n",
              "      <td>MNG</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Johor</td>\n",
              "      <td>N.02</td>\n",
              "      <td>JEMENTAH</td>\n",
              "      <td>KHAIRUL FAIZI BIN AHMAD KAMIL</td>\n",
              "      <td>PAS</td>\n",
              "      <td>1,986</td>\n",
              "      <td>27,657</td>\n",
              "      <td>7.18%</td>\n",
              "      <td>0</td>\n",
              "      <td>L</td>\n",
              "      <td>HD</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1641</th>\n",
              "      <td>Sabah</td>\n",
              "      <td>N.59</td>\n",
              "      <td>TANJONG BATU</td>\n",
              "      <td>ISMAIL SENANG</td>\n",
              "      <td>WARISAN</td>\n",
              "      <td>6,552</td>\n",
              "      <td>16,767</td>\n",
              "      <td>39.08%</td>\n",
              "      <td>0</td>\n",
              "      <td>L</td>\n",
              "      <td>KLH</td>\n",
              "      <td>BEKERJA SENDIRI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1642</th>\n",
              "      <td>Sabah</td>\n",
              "      <td>N.60</td>\n",
              "      <td>SEBATIK</td>\n",
              "      <td>YUSRI HJ YUNUS</td>\n",
              "      <td>PPRS</td>\n",
              "      <td>38</td>\n",
              "      <td>5,285</td>\n",
              "      <td>0.72%</td>\n",
              "      <td>0</td>\n",
              "      <td>L</td>\n",
              "      <td>HD</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1643</th>\n",
              "      <td>Sabah</td>\n",
              "      <td>N.60</td>\n",
              "      <td>SEBATIK</td>\n",
              "      <td>USTAZ ROSLAN RAMLI</td>\n",
              "      <td>PAS</td>\n",
              "      <td>504</td>\n",
              "      <td>5,285</td>\n",
              "      <td>9.54%</td>\n",
              "      <td>0</td>\n",
              "      <td>L</td>\n",
              "      <td>HD</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1644</th>\n",
              "      <td>Sabah</td>\n",
              "      <td>N.60</td>\n",
              "      <td>SEBATIK</td>\n",
              "      <td>DATUK HJ. ABD MUIS HJ. PICHO</td>\n",
              "      <td>BN</td>\n",
              "      <td>2,468</td>\n",
              "      <td>5,285</td>\n",
              "      <td>46.70%</td>\n",
              "      <td>1</td>\n",
              "      <td>L</td>\n",
              "      <td>MNG</td>\n",
              "      <td>TIADA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1645</th>\n",
              "      <td>Sabah</td>\n",
              "      <td>N.60</td>\n",
              "      <td>SEBATIK</td>\n",
              "      <td>HASSAN A GANI PG AMIR</td>\n",
              "      <td>WARISAN</td>\n",
              "      <td>2,275</td>\n",
              "      <td>5,285</td>\n",
              "      <td>43.05%</td>\n",
              "      <td>0</td>\n",
              "      <td>L</td>\n",
              "      <td>KLH</td>\n",
              "      <td>BEKERJA SENDIRI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1645 rows × 12 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the output above, a candidate with zero votes has been removed from the dataset and now the dataset is left with 1645 rows."
      ],
      "metadata": {
        "id": "OPsrDIDtyYKE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 4:** Perform any additional steps"
      ],
      "metadata": {
        "id": "A2OzGJVqvCGO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop columns that are not useful:\n",
        "\n",
        "The \"Candidate Name\" column will be removed."
      ],
      "metadata": {
        "id": "6R2aZPmU1ENl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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          "height": 206
        },
        "outputId": "6402f07b-62b7-4443-8c5a-e10372d95009"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   State Seat ID    Seat Name Candidate Party Votes for Candidate  \\\n",
              "0  Johor    N.01  BULOH KASAP              BN               9,186   \n",
              "1  Johor    N.01  BULOH KASAP             PKR               8,309   \n",
              "2  Johor    N.02     JEMENTAH              BN               9,335   \n",
              "3  Johor    N.02     JEMENTAH             PKR              16,336   \n",
              "4  Johor    N.02     JEMENTAH             PAS               1,986   \n",
              "\n",
              "  Total Votes Cast % of total Votes  Candidate Win Gender Status Pekerjaan  \n",
              "0           17,495           52.51%              1      L    MNG         0  \n",
              "1           17,495           47.49%              0      L    KLH         0  \n",
              "2           27,657           33.75%              0      L    KLH         0  \n",
              "3           27,657           59.07%              1      L    MNG         0  \n",
              "4           27,657            7.18%              0      L     HD         0  "
            ],
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              "      <th>State</th>\n",
              "      <th>Seat ID</th>\n",
              "      <th>Seat Name</th>\n",
              "      <th>Candidate Party</th>\n",
              "      <th>Votes for Candidate</th>\n",
              "      <th>Total Votes Cast</th>\n",
              "      <th>% of total Votes</th>\n",
              "      <th>Candidate Win</th>\n",
              "      <th>Gender</th>\n",
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              "      <th>3</th>\n",
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              "      <td>JEMENTAH</td>\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "cols = ['Candidate Name']\n",
        "df = df.drop(cols, axis=1)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove the comma (,) from the data values in the columns of \"Votes for Candidate\" and \"Total Votes Cast\"."
      ],
      "metadata": {
        "id": "JgmSqFxT3KkP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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        "id": "KribcC_XVJJ5",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a5d00b74-ff1d-4f4b-81ff-d7951d56b2e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   State Seat ID    Seat Name Candidate Party Votes for Candidate  \\\n",
              "0  Johor    N.01  BULOH KASAP              BN                9186   \n",
              "1  Johor    N.01  BULOH KASAP             PKR                8309   \n",
              "2  Johor    N.02     JEMENTAH              BN                9335   \n",
              "3  Johor    N.02     JEMENTAH             PKR               16336   \n",
              "4  Johor    N.02     JEMENTAH             PAS                1986   \n",
              "\n",
              "  Total Votes Cast % of total Votes  Candidate Win Gender Status Pekerjaan  \n",
              "0            17495           52.51%              1      L    MNG         0  \n",
              "1            17495           47.49%              0      L    KLH         0  \n",
              "2            27657           33.75%              0      L    KLH         0  \n",
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          "metadata": {},
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        }
      ],
      "source": [
        "df['Votes for Candidate'] = df['Votes for Candidate'].str.replace(',','')\n",
        "df['Total Votes Cast'] = df['Total Votes Cast'].str.replace(',','')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Change the data type of \"Votes for Candidate\", \"Total Votes Cast\" columns to int64 data type."
      ],
      "metadata": {
        "id": "7ExS0_mX3YZb"
      }
    },
    {
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      "execution_count": 6,
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          "base_uri": "https://localhost:8080/"
        },
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        {
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          "data": {
            "text/plain": [
              "State                  object\n",
              "Seat ID                object\n",
              "Seat Name              object\n",
              "Candidate Party        object\n",
              "Votes for Candidate     Int64\n",
              "Total Votes Cast        int64\n",
              "% of total Votes       object\n",
              "Candidate Win           int64\n",
              "Gender                 object\n",
              "Status                 object\n",
              "Pekerjaan              object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df['Votes for Candidate'] = df['Votes for Candidate'].apply(pd.to_numeric, errors='coerce').fillna(0.0)\n",
        "df['Total Votes Cast'] = df['Total Votes Cast'].apply(pd.to_numeric, errors='coerce').fillna(0.0)\n",
        "df['Votes for Candidate'] = df['Votes for Candidate'].astype('Int64')\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove (%) from the \"% for total Votes\" columns and convert the data type from object to float64."
      ],
      "metadata": {
        "id": "LUNvk5xn36Uf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "h46ilQakVJJ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5d680f5-1d52-4beb-de8f-4164dbe028b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State                   object\n",
              "Seat ID                 object\n",
              "Seat Name               object\n",
              "Candidate Party         object\n",
              "Votes for Candidate      Int64\n",
              "Total Votes Cast         int64\n",
              "% of total Votes       float64\n",
              "Candidate Win            int64\n",
              "Gender                  object\n",
              "Status                  object\n",
              "Pekerjaan               object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df['% of total Votes'] = (df['% of total Votes'].str[:-1].apply(pd.to_numeric, errors='coerce').fillna(0.0))\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert all the strings in the \"State\" column to upper letter."
      ],
      "metadata": {
        "id": "1AaCgTfr4KQy"
      }
    },
    {
      "cell_type": "code",
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            "text/plain": [
              "   State Seat ID    Seat Name Candidate Party  Votes for Candidate  \\\n",
              "0  JOHOR    N.01  BULOH KASAP              BN                 9186   \n",
              "1  JOHOR    N.01  BULOH KASAP             PKR                 8309   \n",
              "2  JOHOR    N.02     JEMENTAH              BN                 9335   \n",
              "3  JOHOR    N.02     JEMENTAH             PKR                16336   \n",
              "4  JOHOR    N.02     JEMENTAH             PAS                 1986   \n",
              "\n",
              "   Total Votes Cast  % of total Votes  Candidate Win Gender Status Pekerjaan  \n",
              "0             17495             52.51              1      L    MNG         0  \n",
              "1             17495             47.49              0      L    KLH         0  \n",
              "2             27657             33.75              0      L    KLH         0  \n",
              "3             27657             59.07              1      L    MNG         0  \n",
              "4             27657              7.18              0      L     HD         0  "
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          "metadata": {},
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      ],
      "source": [
        "df['State'] = df['State'].str.upper()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert the data type of \"Pekerjaan\" column to string and replace the \"-\", \"0\", \"TIADA\" to same value \"NaN\"."
      ],
      "metadata": {
        "id": "7bF5Kf2y4Ql-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
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        "id": "HsDyuPIgVJJ6",
        "colab": {
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          "height": 206
        },
        "outputId": "50dc126d-8499-4b59-9f8b-a375c2c2a1de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   State Seat ID    Seat Name Candidate Party  Votes for Candidate  \\\n",
              "0  JOHOR    N.01  BULOH KASAP              BN                 9186   \n",
              "1  JOHOR    N.01  BULOH KASAP             PKR                 8309   \n",
              "2  JOHOR    N.02     JEMENTAH              BN                 9335   \n",
              "3  JOHOR    N.02     JEMENTAH             PKR                16336   \n",
              "4  JOHOR    N.02     JEMENTAH             PAS                 1986   \n",
              "\n",
              "   Total Votes Cast  % of total Votes  Candidate Win Gender Status Pekerjaan  \n",
              "0             17495             52.51              1      L    MNG       NaN  \n",
              "1             17495             47.49              0      L    KLH       NaN  \n",
              "2             27657             33.75              0      L    KLH       NaN  \n",
              "3             27657             59.07              1      L    MNG       NaN  \n",
              "4             27657              7.18              0      L     HD       NaN  "
            ],
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df['Pekerjaan'] = df['Pekerjaan'].replace(['0', '-', 'TIADA'], 'NaN')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0m5LkbVVJJ6"
      },
      "source": [
        "## Exploratory Analysis and Visualization\n",
        "\n",
        "**TODO** - write some explanation here.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RuPTpsGWVJJ7"
      },
      "source": [
        "Let's begin by importing`matplotlib.pyplot` and `seaborn`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "zOTtwqAMVJJ7"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "matplotlib.rcParams['font.size'] = 14\n",
        "matplotlib.rcParams['figure.figsize'] = (9, 5)\n",
        "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Total number of votes for all candidate party"
      ],
      "metadata": {
        "id": "7JLqlprPQjN6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "party = df.groupby(['Candidate Party'])['Votes for Candidate'].sum()\n",
        "party"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cp8j1UirQuMD",
        "outputId": "3cfc9901-d8c7-4e5d-9190-a3a3463c995f"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Candidate Party\n",
              "AMANAH                       193\n",
              "ANAKNEGERI                  3059\n",
              "BEBAS - BEG                  444\n",
              "BEBAS - BUKU                 905\n",
              "BEBAS - CAWAN               2817\n",
              "BEBAS - CINCIN                42\n",
              "BEBAS - GAJAH               1060\n",
              "BEBAS - IKAN                 145\n",
              "BEBAS - JAM                   23\n",
              "BEBAS - KAPAL TERBANG        189\n",
              "BEBAS - KERETA                25\n",
              "BEBAS - KUDA                 364\n",
              "BEBAS - KUNCI               3904\n",
              "BEBAS - MOTOSIKAL            544\n",
              "BEBAS - PEN                  389\n",
              "BEBAS - POKOK               2927\n",
              "BEBAS - RUMAH                 15\n",
              "BEBAS - TRAKTOR               85\n",
              "BERJASA                      211\n",
              "BN                       3471044\n",
              "DAP                        78901\n",
              "HR                         17713\n",
              "MU                          2366\n",
              "PAP                          933\n",
              "PAS                      2058818\n",
              "PCM                           83\n",
              "PCS                         9311\n",
              "PFP                          631\n",
              "PKR                      4455338\n",
              "PKS                         2018\n",
              "PPRS                        1491\n",
              "PRM                         3505\n",
              "PSM                         5139\n",
              "SAPP                        5031\n",
              "SOLIDARITI                 39803\n",
              "STAR                         109\n",
              "USNO                         147\n",
              "WARISAN                   271446\n",
              "Name: Votes for Candidate, dtype: Int64"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Votes for each winning candidate party"
      ],
      "metadata": {
        "id": "SELL_pHf5zoc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 216,
      "metadata": {
        "id": "c7fugjWzVJJ7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "69b63d5a-cadf-43c8-d208-07fdec2d9a99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Votes for Candidate  Total Votes Cast  % of total Votes  \\\n",
              "Candidate Party                                                            \n",
              "PKR                          3630863           5786569          13580.60   \n",
              "BN                           1201287           2420456           8208.73   \n",
              "PAS                           906151           1816453           4514.83   \n",
              "WARISAN                       161579            288571           1179.27   \n",
              "DAP                            74668            102288            435.78   \n",
              "SOLIDARITI                     10688             26831             79.97   \n",
              "\n",
              "                 Candidate Win  \n",
              "Candidate Party                 \n",
              "PKR                        220  \n",
              "BN                         166  \n",
              "PAS                         90  \n",
              "WARISAN                     21  \n",
              "DAP                          6  \n",
              "SOLIDARITI                   2  "
            ],
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 216
        }
      ],
      "source": [
        "win_party = df[(df['Candidate Win'] == 1)]\n",
        "win_party = win_party.groupby('Candidate Party').sum()\n",
        "win_party.sort_values(by=['Candidate Win'], ascending = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Statistics for top 1 candidate party with most votes: PKR"
      ],
      "metadata": {
        "id": "sOQqkm2RGRzH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "PKR_party = df[(df['Candidate Party'] == 'PKR')]\n",
        "PKR_party.describe().apply(lambda x: round(x, 2))"
      ],
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      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Votes for Candidate  Total Votes Cast  % of total Votes  Candidate Win\n",
              "count               448.00            448.00            448.00         448.00\n",
              "mean               9944.95          21537.86             41.53           0.49\n",
              "std                9491.20          10621.20             24.39           0.50\n",
              "min                 417.00           6164.00              2.81           0.00\n",
              "25%                2939.00          15001.00             23.14           0.00\n",
              "50%                7086.00          19136.00             41.94           0.00\n",
              "75%               13636.50          24239.50             57.80           1.00\n",
              "max               55354.00          62934.00             92.06           1.00"
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>0.50</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>0.00</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>24239.50</td>\n",
              "      <td>57.80</td>\n",
              "      <td>1.00</td>\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Type of job titles among candidates"
      ],
      "metadata": {
        "id": "r5Ci1obXT4Q4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "job = pd.DataFrame(df[\"Pekerjaan\"].value_counts())\n",
        "job = job[(job[\"Pekerjaan\"]>5)]\n",
        "job"
      ],
      "metadata": {
        "colab": {
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          "height": 488
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        "id": "NxH966Oe7OxJ",
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      },
      "execution_count": 172,
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        {
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            "text/plain": [
              "                                                  Pekerjaan\n",
              "NaN                                                    1126\n",
              "AHLI PERNIAGAAN                                          94\n",
              "BERNIAGA                                                 49\n",
              "PENIAGA                                                  40\n",
              "PESARA                                                   38\n",
              "PEGUAM                                                   31\n",
              "BEKERJA SENDIRI                                          26\n",
              "BERNIAGA SENDIRI                                         20\n",
              "USAHAWAN                                                 14\n",
              "KERJA SENDIRI                                            14\n",
              "AHLI POLITIK                                             13\n",
              "SENDIRI                                                  11\n",
              "BEKAS ADUN                                               11\n",
              "AHLI MAJLIS MESYUARAT KERAJAAN NEGERI TERENGGANU          7"
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      "cell_type": "markdown",
      "source": [
        "The **Histogram** below displays the frequency values of PKR party's votes during the general election in 2018."
      ],
      "metadata": {
        "id": "JtpuWGVbXlKf"
      }
    },
    {
      "cell_type": "code",
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        "PKR_party.hist(bins = 20, figsize = (20,15))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 887
        },
        "id": "WkxPCEolX-cj",
        "outputId": "7ffdba0c-1f2b-440a-f923-757f8553f977"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dn_AW-5uVJJ7"
      },
      "source": [
        "This **Pie Chart** presents the percentage of winning candidate in each party and PKR has the most number of winning candidates (43.6%)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(win_party['Candidate Win'], labels=win_party.index, autopct='%1.1f%%')\n",
        "plt.legend(win_party.index, loc='right', bbox_to_anchor=(2,0.5))\n",
        "plt.title('Percentage of Winning Candidate in Each Party')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "mxlfchzghapF",
        "outputId": "be6fbb3b-3d05-44cf-e667-ea7f40a236ac"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fv4NPHasVJJ7"
      },
      "source": [
        "The **bar chart** shows the total number of votes cast in each states where Sabah has the highest number of votes cast."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.State.value_counts().plot.bar(figsize = (10,5))\n",
        "plt.title('Votes Cast by State')\n",
        "plt.ylabel('Number of Votes Cast')\n",
        "plt.xlabel('States')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "X0oTJBAcVTWt",
        "outputId": "482dd7ea-1ebd-4842-dbf0-870c415087e7"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'States')"
            ]
          },
          "metadata": {},
          "execution_count": 175
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLNLw0EOVJJ8"
      },
      "source": [
        "This **boxplot** demonstrates the percentage of total votes for each candidate party where it is positively skewed with no outliers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {
        "id": "61sgf555VJJ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "0dcf5bb5-fb31-45c9-8bb1-f03d554ce949"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1ad6a9f880>"
            ]
          },
          "metadata": {},
          "execution_count": 176
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.boxplot(x=df['% of total Votes'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nt_CVhi-VJJ8"
      },
      "source": [
        "The **scatterplot** exhibits the correlation between votes for candidate and total votes cast.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {
        "id": "Zatc4Si_VJJ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "outputId": "0e779d5a-ad6f-4739-a8d5-982956571daf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "ax.scatter(df['Total Votes Cast'], df['Votes for Candidate'])\n",
        "ax.set_xlabel('Votes for Candidate')\n",
        "ax.set_ylabel('Total Votes Cast')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0U3IWm8VJJ9"
      },
      "source": [
        "## Asking and Answering Questions\n",
        "\n",
        "5 questions about this dataset will be asked and answered by using Pandas, Matplotlib and Seaborn. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Lor5rHMVJJ9"
      },
      "source": [
        "#### Q1: What is the percentage of male and female among the candidates?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "pR1HdQ4JVJJ9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "6aeca30c-8e17-48be-c084-09255eb6f1f5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "label = ['Male', 'Female']\n",
        "plt.pie(df['Gender'].value_counts(), labels=label, autopct='%1.1f%%')\n",
        "plt.legend(labels=label, loc='right', bbox_to_anchor=(2,0.5))\n",
        "plt.title('Percentage of Gender among Candidates')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer:** There are 89.3% of male candidates and 10.7% of female candidates in the general election of 2018."
      ],
      "metadata": {
        "id": "D1m-Ox_NMJTg"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_REVnrhVJJ-"
      },
      "source": [
        "#### Q2: From the winning candidates, what are number of male and female in each party? "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "ShA3QyQpVJJ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "44269c12-9387-4b90-f114-6d1537d01ae9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fea2a949e80>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "win_candidate = df[(df['Candidate Win'] == 1)]\n",
        "ax = win_candidate.groupby(['Candidate Party', 'Gender']).size().unstack().plot(kind='bar', stacked=False)\n",
        "for bar in ax.patches:\n",
        "  ax.annotate(format(bar.get_height(), '.0f'),\n",
        "                   (bar.get_x() + bar.get_width() / 2,\n",
        "                    bar.get_height()), ha='center', va='center',\n",
        "                   size=15, xytext=(0, 8),\n",
        "                   textcoords='offset points')\n",
        "plt.title('Winning Candidates in each Party by Gender')\n",
        "plt.ylabel('Number of Candidates')\n",
        "plt.legend(labels=label)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer:** Among the winning candidates, BN has 144 male and 22 female candidates; \n",
        "\n",
        "DAP has 5 male and 1 female candidates; \n",
        "\n",
        "PAS has 87 male and 3 female candidates; \n",
        "\n",
        "PKR has 188 male and 32 female candidates; \n",
        "\n",
        "SOLIDARITI has 2 male and 0 female candidates; \n",
        "\n",
        "Warisan has 18 male and 3 female candidates. "
      ],
      "metadata": {
        "id": "l9P21u3NRxPX"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ya3o0SurVJJ-"
      },
      "source": [
        "#### Q3: Among the candidates from ANAKNEGERI party, what is the job title with most percentage?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AN_party = df[(df['Candidate Party'] == 'ANAKNEGERI')]\n",
        "plt.pie(AN_party['Pekerjaan'].value_counts(),autopct='%1.1f%%')\n",
        "label = ['Businessman', 'Self-work','Politicians','Retired']\n",
        "plt.legend(labels=label, loc='right', bbox_to_anchor=(2,0.5))\n",
        "plt.title('Job Titles in ANAKNEGERI Party')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "vJgzedxYHx14",
        "outputId": "85f3cd5e-d3b0-4729-c31d-d9a0e5ce056f"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Job Titles in ANAKNEGERI Party')"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer:** In ANAKNEGERI party, most candidates are businessman."
      ],
      "metadata": {
        "id": "wThdIIvrR_XT"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4CcVO1TVJJ-"
      },
      "source": [
        "#### Q4: In Johor, which seat name did most people voted in during the election?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "1y8gWqGcVJJ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "34f7724a-09ca-45d0-e1b4-2deabc306716"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Seat Name')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1440 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "johor_state = df[(df['State'] == 'JOHOR')]\n",
        "johor_state = johor_state.groupby('Seat Name')\n",
        "total_state = johor_state['Votes for Candidate'].sum().sort_values(ascending = True)\n",
        "ts = total_state[1:]\n",
        "plt.figure(figsize=(15,20))\n",
        "plt.barh(ts.index, ts.values)\n",
        "plt.title('Total Votes for Candidate by Seat Name')\n",
        "plt.xlabel('Votes for Candidate')\n",
        "plt.ylabel('Seat Name')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer:** In Johor, most people participated the voting for election in seat name Skudai. "
      ],
      "metadata": {
        "id": "14wNU1ADF7xx"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFuZBqX1VJJ_"
      },
      "source": [
        "#### Q5: In Perlis, Beseri area, what is the votes for the each candidate party?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "xZWIg8jJVJJ_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "70a63c0b-e4ff-448a-c569-65adcaf5dab0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Votes for Candidate')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "perlis_votes = df[(df['State']=='PERLIS') & (df['Seat Name'] == 'BESERI')]\n",
        "perlis_votes = perlis_votes.groupby('Candidate Party')\n",
        "votes = perlis_votes['Votes for Candidate'].sum()\n",
        "splot = sns.barplot(x=votes.index, y=votes.values)\n",
        "for bar in splot.patches:\n",
        "  splot.annotate(format(bar.get_height(), '.0f'),\n",
        "                   (bar.get_x() + bar.get_width() / 2,\n",
        "                    bar.get_height()), ha='center', va='center',\n",
        "                   size=15, xytext=(0, 8),\n",
        "                   textcoords='offset points')\n",
        "plt.title('Votes for Candidate by Party')\n",
        "plt.xlabel('Candidate Party')\n",
        "plt.ylabel('Votes for Candidate')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer:** In Perlis, Beseri area, BN has the most votes with a total of 2879 votes, followed by PAS with 1523 votes and PKR 2463 votes."
      ],
      "metadata": {
        "id": "Bg4-2w5LI3eZ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxoO755tVJJ_"
      },
      "source": [
        "## Inferences and Conclusion\n",
        "\n",
        "The process of cleaning, analyzing, and visualizing the data has been completed. From the questions answered above, we can observe that most of the candidates are male. This shows that men show more interest in politics than women. \n",
        "\n",
        "Moving on, the PKR party has won the majority of votes during the general election with a total of 220 winning candidates, followed by the BN party with the second highest number of winning candidates (166 people) and the PAS party with the third highest number of winning candidates (90 people). \n",
        "\n",
        "Furthermore, 1126 of the candidates have no occupation, which is considered the majority in this dataset. This means that most of the candidates only work as politicians. \n",
        "\n",
        "Besides, throughout this analysis, we found out that the total votes cast in each state has a big difference depends on the the number of citizens in each state. For example, Selangor ia city which near Putrajaya and Kuala Lumpur, the number of total votes cast is the highest among all the states. Compare to Perlis, it is a small state, the number of total votes cast is the lowest among all the states. From this analysis, we can see that a bigger city has a higher population and the small city has a lower population and this factor affects the number of total votes cast. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfuc6sJgVJKA"
      },
      "source": [
        "## References and Future Work\n",
        "\n",
        "Future projects related to the general election of Malaysia could use this dataset to investigate the results and predict the future parliment for the upcoming elections.\n",
        "\n",
        "**Resources**\n",
        "\n",
        "[Exploratory Data Analysis](https://colab.research.google.com/drive/1E99RyuEAZM8j2dvoxRqV8oWpg3kjqfBg#scrollTo=dB_j6LtTTO5j)\n",
        "\n",
        "[Introduction to Exploratory Data Analysis](https://colab.research.google.com/drive/1DjKNfvPGkYyHQWpiEdyK4ozm-nXVa43D#scrollTo=nzh3zNvXEOOn)\n",
        "\n",
        "[Matplotlib](https://colab.research.google.com/drive/1Sk44p68l7wwrldwtZ_jpQ9-CTeDDcpTH)\n",
        "\n",
        "[Seaborn](https://colab.research.google.com/drive/1GWKtrW5GqZ8ifOh2deHLThxCpBE14A2c)\n",
        "\n",
        "[UNDC](https://colab.research.google.com/drive/1bMSLUmU-gSVJh0SF6CVWx1ddM17uBe_n)\n",
        "\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}