taruma_hk73_bmkg.ipynb

taruma_hk73_bmkg.ipynb

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  "metadata": {
    "colab": {
      "name": "taruma_hk73_bmkg.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/taruma/b00880905f297013f046dad95dc2e284/taruma_hk73_bmkg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7uQLNtOfUqQ"
      },
      "source": [
        "Berdasarkan isu [#73](https://github.com/taruma/hidrokit/issues/73): **request: mengolah berkas dari data bmkg**\n",
        "\n",
        "Deskripsi:\n",
        "- mengolah berkas excel yang diperoleh dari data online bmkg untuk siap dipakai\n",
        "- memeriksa kondisi data\n",
        "\n",
        "Fungsi yang diharapkan:\n",
        "\n",
        "__Umum / General__\n",
        "- Memeriksa apakah data lengkap atau tidak? Jika tidak, data apa dan pada tanggal berapa?\n",
        "- Memeriksa apakah data tidak ada data / tidak ada pengukuran (9999) atau data tidak diukur (8888)? Jika ada, data apa dan pada tanggal berapa?\n",
        "- Menampilkan \"potongan\" baris yang tidak memiliki data / tidak melakukan pengukuran?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CAXMkTucfi5r"
      },
      "source": [
        "# DATASET"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BppR0eMif41Y"
      },
      "source": [
        "# Upload berkas excel bmkg\n",
        "FILE_NAME = \"data_bmkg_jawa_barat_96783_1975_2000_stasiun_geofisika_bandung.xlsx\"\n",
        "DATASET_PATH = '/content/' + FILE_NAME"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rzXF7v0BtnYq"
      },
      "source": [
        "# FUNGSI"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "This module provides functions for reading and analyzing\n",
        "    BMKG (Meteorology, Climatology, and Geophysics Agency) data.\n",
        "\n",
        "For more information, refer to the manual:\n",
        "    https://gist.github.com/taruma/b00880905f297013f046dad95dc2e284\n",
        "\n",
        "Functions:\n",
        "- read_bmkg_excel(io): Read BMKG data from an Excel file.\n",
        "- has_nan_values(dataframe): Check if the given dataset contains any NaN values.\n",
        "- get_missing_data_indices(nan_indicator_vector): Get the indices of missing data\n",
        "    in the given nan_indicator_vector.\n",
        "- get_nan_indices_by_column(dataframe): Get the indices of missing values (NaN)\n",
        "    for each column in a DataFrame.\n",
        "- get_unrecorded_indices(dataframe): Get the indices of unrecorded data in the given dataframe.\n",
        "- get_nan_indices_if_exists(dataframe): Returns the indices of NaN values\n",
        "    in the given dataframe if NaN values exist.\n",
        "- get_columns_with_nan_values(dataframe): Get the columns with NaN values in the given dataframe.\n",
        "- group_consecutive_elements(input_list): Groups consecutive elements in the input list.\n",
        "- format_group_indices(group_list, indices=None, format_date=\"%Y%m%d\", date_range_format=\"{}-{}\"):\n",
        "    Formats the group indices based on the given parameters.\n",
        "\n",
        "Deprecated Functions:\n",
        "- _read_bmkg(*args, **kwargs): Deprecated version of read_bmkg_excel.\n",
        "- _have_nan(*args, **kwargs): Deprecated version of has_nan_values.\n",
        "- _get_index1D(*args, **kwargs): Deprecated version of get_missing_data_indices.\n",
        "- _get_nan(*args, **kwargs): Deprecated version of get_nan_indices_by_column.\n",
        "- _get_missing(*args, **kwargs): Deprecated version of get_unrecorded_indices.\n",
        "- _check_nan(*args, **kwargs): Deprecated version of get_nan_indices_if_exists.\n",
        "- _get_nan_columns(*args, **kwargs): Deprecated version of get_columns_with_nan_values.\n",
        "- _group_as_list(*args, **kwargs): Deprecated version of group_consecutive_elements.\n",
        "- _group_as_index(group_list, index=None, date_format=\"%Y%m%d\", format_date=\"{}-{}\"):\n",
        "    Deprecated version of format_group_indices.\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "PVXaK7BEqP93",
        "outputId": "8bed83ae-5c67-479f-ab99-a56447765e2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nThis module provides functions for reading and analyzing \\n    BMKG (Meteorology, Climatology, and Geophysics Agency) data.\\n\\nFor more information, refer to the manual: \\n    https://gist.github.com/taruma/b00880905f297013f046dad95dc2e284\\n\\nFunctions:\\n- read_bmkg_excel(io): Read BMKG data from an Excel file.\\n- has_nan_values(dataframe): Check if the given dataset contains any NaN values.\\n- get_missing_data_indices(nan_indicator_vector): Get the indices of missing data \\n    in the given nan_indicator_vector.\\n- get_nan_indices_by_column(dataframe): Get the indices of missing values (NaN) \\n    for each column in a DataFrame.\\n- get_unrecorded_indices(dataframe): Get the indices of unrecorded data in the given dataframe.\\n- get_nan_indices_if_exists(dataframe): Returns the indices of NaN values \\n    in the given dataframe if NaN values exist.\\n- get_columns_with_nan_values(dataframe): Get the columns with NaN values in the given dataframe.\\n- group_consecutive_elements(input_list): Groups consecutive elements in the input list.\\n- format_group_indices(group_list, indices=None, format_date=\"%Y%m%d\", date_range_format=\"{}-{}\"): \\n    Formats the group indices based on the given parameters.\\n\\nDeprecated Functions:\\n- _read_bmkg(*args, **kwargs): Deprecated version of read_bmkg_excel.\\n- _have_nan(*args, **kwargs): Deprecated version of has_nan_values.\\n- _get_index1D(*args, **kwargs): Deprecated version of get_missing_data_indices.\\n- _get_nan(*args, **kwargs): Deprecated version of get_nan_indices_by_column.\\n- _get_missing(*args, **kwargs): Deprecated version of get_unrecorded_indices.\\n- _check_nan(*args, **kwargs): Deprecated version of get_nan_indices_if_exists.\\n- _get_nan_columns(*args, **kwargs): Deprecated version of get_columns_with_nan_values.\\n- _group_as_list(*args, **kwargs): Deprecated version of group_consecutive_elements.\\n- _group_as_index(group_list, index=None, date_format=\"%Y%m%d\", format_date=\"{}-{}\"): \\n    Deprecated version of format_group_indices.\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "import functools\n",
        "\n",
        "\n",
        "def deprecated(new_func_name):\n",
        "    \"\"\"\n",
        "    Decorator to mark a function as deprecated.\n",
        "\n",
        "    Parameters:\n",
        "    - new_func_name (str): The name of the new function that should be used instead.\n",
        "\n",
        "    Returns:\n",
        "    - wrapper (function): The decorated function.\n",
        "\n",
        "    Example:\n",
        "    @deprecated(\"new_function\")\n",
        "    def old_function():\n",
        "        pass\n",
        "\n",
        "    The above example will generate a warning when `old_function` is called,\n",
        "    suggesting to use `new_function` instead.\n",
        "    \"\"\"\n",
        "\n",
        "    def decorator(func):\n",
        "        @functools.wraps(func)\n",
        "        def wrapper(*args, **kwargs):\n",
        "            warnings.warn(\n",
        "                f\"{func.__name__} is deprecated, use {new_func_name} instead\",\n",
        "                DeprecationWarning,\n",
        "            )\n",
        "            return func(*args, **kwargs)\n",
        "\n",
        "        return wrapper\n",
        "\n",
        "    return decorator"
      ],
      "metadata": {
        "id": "J7Ca2-JWji3I"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MwMIU0bwEwK"
      },
      "source": [
        "from itertools import groupby\n",
        "from operator import itemgetter\n",
        "from typing import List\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def read_bmkg_excel(io):\n",
        "    \"\"\"\n",
        "    Read BMKG data from an Excel file.\n",
        "\n",
        "    Parameters:\n",
        "    - io: str or file-like object\n",
        "        The file path or file-like object to read the Excel data from.\n",
        "\n",
        "    Returns:\n",
        "    - pandas.DataFrame\n",
        "        The data read from the Excel file.\n",
        "\n",
        "    \"\"\"\n",
        "    return pd.read_excel(\n",
        "        io,\n",
        "        skiprows=8,\n",
        "        skipfooter=16,\n",
        "        header=0,\n",
        "        index_col=0,\n",
        "        parse_dates=True,\n",
        "        date_format=\"%d-%m-%Y\",\n",
        "    )\n",
        "\n",
        "\n",
        "def _has_nan_values(dataframe):\n",
        "    \"\"\"\n",
        "    Check if the given dataset contains any NaN values.\n",
        "\n",
        "    Parameters:\n",
        "        dataframe (pandas.DataFrame): The dataset to check for NaN values.\n",
        "\n",
        "    Returns:\n",
        "        bool: True if the dataset contains NaN values, False otherwise.\n",
        "    \"\"\"\n",
        "    return bool(dataframe.isna().any().any())\n",
        "\n",
        "\n",
        "def _get_missing_data_indices(nan_indicator_vector):\n",
        "    \"\"\"\n",
        "    Get the indices of missing data in the given nan_indicator_vector.\n",
        "\n",
        "    Parameters:\n",
        "        nan_indicator_vector (numpy.ndarray): A boolean array indicating missing data.\n",
        "\n",
        "    Returns:\n",
        "        numpy.ndarray: An array of indices where missing data is present.\n",
        "    \"\"\"\n",
        "    return np.argwhere(nan_indicator_vector).reshape(\n",
        "        -1,\n",
        "    )\n",
        "\n",
        "\n",
        "def _get_nan_indices_by_column(dataframe):\n",
        "    \"\"\"\n",
        "    Get the indices of missing values (NaN) for each column in a DataFrame.\n",
        "\n",
        "    Parameters:\n",
        "        dataframe (pandas.DataFrame): The input DataFrame.\n",
        "\n",
        "    Returns:\n",
        "        dict: A dictionary where the keys are the column names and the values are lists of indices\n",
        "            where missing values occur in each column.\n",
        "    \"\"\"\n",
        "    nan = {}\n",
        "    for col in dataframe.columns:\n",
        "        nan[col] = _get_missing_data_indices(dataframe[col].isna().values).tolist()\n",
        "    return nan\n",
        "\n",
        "\n",
        "def _get_unrecorded_indices(dataframe):\n",
        "    \"\"\"\n",
        "    Get the indices of unrecorded data in the given dataframe.\n",
        "\n",
        "    Parameters:\n",
        "        dataframe (pandas.DataFrame): The input dataframe.\n",
        "\n",
        "    Returns:\n",
        "        dict: A dictionary where the keys are the column names and the values are\n",
        "            the indices of unrecorded data in each column.\n",
        "    \"\"\"\n",
        "    unrecorded_indices = {}\n",
        "\n",
        "    for col in dataframe.columns:\n",
        "        masking = (dataframe[col] == 8888) | (dataframe[col] == 9999)\n",
        "        unrecorded_indices[col] = _get_missing_data_indices(masking.values)\n",
        "\n",
        "    return unrecorded_indices\n",
        "\n",
        "\n",
        "def _get_nan_indices_if_exists(dataframe):\n",
        "    \"\"\"\n",
        "    Returns the indices of NaN values in the given dataframe if NaN values exist.\n",
        "\n",
        "    Parameters:\n",
        "        dataframe (pandas.DataFrame): The input dataframe.\n",
        "\n",
        "    Returns:\n",
        "        dict or None: A dictionary with keys as column names and values as lists of\n",
        "            indices where missing values occur, or None if no NaN values exist.\n",
        "    \"\"\"\n",
        "    if _has_nan_values(dataframe):\n",
        "        return _get_nan_indices_by_column(dataframe)\n",
        "    else:\n",
        "        return None\n",
        "\n",
        "\n",
        "def _get_columns_with_nan_values(dataframe):\n",
        "    return dataframe.columns[dataframe.isna().any()].tolist()\n",
        "\n",
        "\n",
        "def _group_consecutive_elements(input_list: List) -> List[List]:\n",
        "    \"\"\"\n",
        "    Groups consecutive elements in the input list.\n",
        "\n",
        "    Args:\n",
        "        input_list (List): The list of elements to be grouped.\n",
        "\n",
        "    Returns:\n",
        "        List[List]: A list of lists, where each inner list contains consecutive elements\n",
        "            from the input list.\n",
        "\n",
        "    Example:\n",
        "        >>> input_list = [1, 2, 3, 5, 6, 8, 9]\n",
        "        >>> _group_consecutive_elements(input_list)\n",
        "        [[1, 2, 3], [5, 6], [8, 9]]\n",
        "    \"\"\"\n",
        "    # based on https://stackoverflow.com/a/15276206\n",
        "    group_list = []\n",
        "    for _, g in groupby(enumerate(input_list), lambda x: x[0] - x[1]):\n",
        "        single_list = sorted(list(map(itemgetter(1), g)))\n",
        "        group_list.append(single_list)\n",
        "    return group_list\n",
        "\n",
        "\n",
        "def _format_group_indices(\n",
        "    group_list, indices=None, format_date=\"%Y%m%d\", date_range_format=\"{}-{}\"\n",
        "):\n",
        "    \"\"\"\n",
        "    Formats the group indices based on the given parameters.\n",
        "\n",
        "    Args:\n",
        "        group_list (list): The list of groups.\n",
        "        indices (pd.Index or pd.DatetimeIndex, optional): The indices to format. Defaults to None.\n",
        "        format_date (str, optional): The date format string. Defaults to \"%Y%m%d\".\n",
        "        date_range_format (str, optional): The format string for date ranges. Defaults to \"{}-{}\".\n",
        "\n",
        "    Returns:\n",
        "        list: The formatted group indices.\n",
        "    \"\"\"\n",
        "    formatted_indices = []\n",
        "    is_date_index = isinstance(indices, pd.DatetimeIndex)\n",
        "\n",
        "    for item in group_list:\n",
        "        if len(item) == 1:\n",
        "            if is_date_index:\n",
        "                formatted_indices.append(indices[item[0]].strftime(format_date))\n",
        "            else:\n",
        "                formatted_indices.append(indices[item[0]])\n",
        "        else:\n",
        "            if is_date_index:\n",
        "                formatted_indices.append(\n",
        "                    date_range_format.format(\n",
        "                        indices[item[0]].strftime(format_date),\n",
        "                        indices[item[-1]].strftime(format_date),\n",
        "                    )\n",
        "                )\n",
        "            else:\n",
        "                formatted_indices.append(\n",
        "                    date_range_format.format(indices[item[0]], indices[item[-1]])\n",
        "                )\n",
        "\n",
        "    return formatted_indices\n",
        "\n",
        "\n",
        "# for backward compatibility\n",
        "@deprecated(\"read_bmkg_excel\")\n",
        "def _read_bmkg(*args, **kwargs):\n",
        "    return read_bmkg_excel(*args, **kwargs)\n",
        "\n",
        "\n",
        "@deprecated(\"_has_nan_values\")\n",
        "def _have_nan(*args, **kwargs):\n",
        "    return _has_nan_values(*args, **kwargs)\n",
        "\n",
        "\n",
        "@deprecated(\"_get_missing_data_indices\")\n",
        "def _get_index1D(*args, **kwargs):\n",
        "    return _get_missing_data_indices(*args, **kwargs)\n",
        "\n",
        "\n",
        "@deprecated(\"_get_nan_indices_by_column\")\n",
        "def _get_nan(*args, **kwargs):\n",
        "    return _get_nan_indices_by_column(*args, **kwargs)\n",
        "\n",
        "\n",
        "@deprecated(\"_get_unrecorded_indices\")\n",
        "def _get_missing(*args, **kwargs):\n",
        "    return _get_unrecorded_indices(*args, **kwargs)\n",
        "\n",
        "\n",
        "@deprecated(\"_get_nan_indices_if_exists\")\n",
        "def _check_nan(*args, **kwargs):\n",
        "    return _get_nan_indices_if_exists(*args, **kwargs)\n",
        "\n",
        "\n",
        "@deprecated(\"_get_columns_with_nan_values\")\n",
        "def _get_nan_columns(*args, **kwargs):\n",
        "    return _get_columns_with_nan_values(*args, **kwargs)\n",
        "\n",
        "\n",
        "@deprecated(\"_group_consecutive_elements\")\n",
        "def _group_as_list(*args, **kwargs):\n",
        "    return _group_consecutive_elements(*args, **kwargs)\n",
        "\n",
        "\n",
        "@deprecated(\"_format_group_indices\")\n",
        "def _group_as_index(\n",
        "    group_list, index=None, date_format=\"%Y%m%d\", format_date=\"{}-{}\"\n",
        "):\n",
        "    return _format_group_indices(\n",
        "        group_list,\n",
        "        indices=index,\n",
        "        format_date=date_format,\n",
        "        date_range_format=format_date,\n",
        "    )\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdCnFSrSgN-p"
      },
      "source": [
        "# PENGGUNAAN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvkIulXMenWc"
      },
      "source": [
        "## Fungsi `_read_bmkg`\n",
        "\n",
        "Tujuan: Impor berkas excel bmkg ke dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_x0pjyAgQZ8",
        "outputId": "996a165b-bdde-4bc4-ffd5-d4ff9da95ceb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "dataset = _read_bmkg(DATASET_PATH)\n",
        "dataset.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _read_bmkg is deprecated, use read_bmkg_excel instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Tn    Tx  Tavg  RH_avg   RR   ss  ff_x  ddd_x  ff_avg ddd_car\n",
              "Tanggal                                                                    \n",
              "1975-01-01  20.0  28.8  23.4    76.0  0.0  7.5   NaN    NaN     NaN     NaN\n",
              "1975-01-02  19.0  28.6  23.7    72.0  0.0  3.3   NaN    NaN     NaN     NaN\n",
              "1975-01-03  20.0  28.9  23.3    78.0  0.0  5.1   NaN    NaN     NaN     NaN\n",
              "1975-01-04  21.0  30.0  23.7    82.0  0.0  5.7   NaN    NaN     NaN     NaN\n",
              "1975-01-05  19.0  28.8  22.2    86.0  8.0  3.1   NaN    NaN     NaN     NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f0d15b8-4684-429c-a6b6-0c4d9e35a8dd\" 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>Tn</th>\n",
              "      <th>Tx</th>\n",
              "      <th>Tavg</th>\n",
              "      <th>RH_avg</th>\n",
              "      <th>RR</th>\n",
              "      <th>ss</th>\n",
              "      <th>ff_x</th>\n",
              "      <th>ddd_x</th>\n",
              "      <th>ff_avg</th>\n",
              "      <th>ddd_car</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tanggal</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1975-01-01</th>\n",
              "      <td>20.0</td>\n",
              "      <td>28.8</td>\n",
              "      <td>23.4</td>\n",
              "      <td>76.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-01-02</th>\n",
              "      <td>19.0</td>\n",
              "      <td>28.6</td>\n",
              "      <td>23.7</td>\n",
              "      <td>72.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>5.7</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1975-01-05</th>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 9497,\n  \"fields\": [\n    {\n      \"column\": \"Tanggal\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1975-01-01 00:00:00\",\n        \"max\": \"2000-12-31 00:00:00\",\n        \"num_unique_values\": 9497,\n        \"samples\": [\n          \"1979-02-22 00:00:00\",\n          \"1998-03-12 00:00:00\",\n          \"1986-02-07 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4858238194831097,\n        \"min\": 11.0,\n        \"max\": 27.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          25.0,\n          22.0,\n          20.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tx\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.454382948790635,\n        \"min\": 16.0,\n        \"max\": 34.2,\n        \"num_unique_values\": 112,\n        \"samples\": [\n          29.0,\n          23.0,\n          26.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tavg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9191089152951978,\n        \"min\": 17.8,\n        \"max\": 28.2,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          21.9,\n          21.4,\n          24.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RH_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.631941314135817,\n        \"min\": 44.0,\n        \"max\": 96.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          44.0,\n          96.0,\n          47.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1777.728251958913,\n        \"min\": 0.0,\n        \"max\": 8888.0,\n        \"num_unique_values\": 443,\n        \"samples\": [\n          29.5,\n          1.1,\n          39.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.199949675862862,\n        \"min\": 0.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          1.1,\n          7.5,\n          4.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ff_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3764084491523396,\n        \"min\": 0.0,\n        \"max\": 44.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          10.0,\n          0.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ddd_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.14385919761148,\n        \"min\": 0.0,\n        \"max\": 360.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          315.0,\n          22.0,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ff_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0996532687360956,\n        \"min\": 0.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8.0,\n          0.0,\n          11.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ddd_car\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"N \",\n          \"NW\",\n          \"W \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PJwY1ODe5XS",
        "outputId": "53ad85f3-0d3e-44a5-cc7e-74dd78b8dda0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "dataset.tail()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Tn    Tx  Tavg  RH_avg   RR   ss  ff_x  ddd_x  ff_avg ddd_car\n",
              "Tanggal                                                                    \n",
              "2000-12-27  19.0  28.6  22.9    79.0  8.8  3.3   3.0  270.0     3.0      W \n",
              "2000-12-28  19.0  30.2  23.3    78.0  3.1  5.2   3.0  270.0     3.0      W \n",
              "2000-12-29  19.0  29.8  23.3    76.0  0.0  6.0   3.0  270.0     3.0      W \n",
              "2000-12-30  21.0  28.4  23.8    77.0  0.0  2.2   0.0    0.0     0.0      N \n",
              "2000-12-31  20.0  29.2  22.6    82.0  9.6  5.4   3.0  360.0     3.0      N "
            ],
            "text/html": [
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-5aefbac1-1133-43f1-94c7-d8c2e6685b65 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-5aefbac1-1133-43f1-94c7-d8c2e6685b65');\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",
              "\n",
              "\n",
              "<div id=\"df-f5e16650-0030-4d23-89c4-fd413caa1e87\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f5e16650-0030-4d23-89c4-fd413caa1e87')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f5e16650-0030-4d23-89c4-fd413caa1e87 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Tanggal\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2000-12-27 00:00:00\",\n        \"max\": \"2000-12-31 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2000-12-28 00:00:00\",\n          \"2000-12-31 00:00:00\",\n          \"2000-12-29 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8944271909999159,\n        \"min\": 19.0,\n        \"max\": 21.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          19.0,\n          21.0,\n          20.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tx\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7668115805072325,\n        \"min\": 28.4,\n        \"max\": 30.2,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          30.2,\n          29.2,\n          29.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tavg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4549725266430931,\n        \"min\": 22.6,\n        \"max\": 23.8,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          23.3,\n          22.6,\n          22.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RH_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3021728866442674,\n        \"min\": 76.0,\n        \"max\": 82.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          78.0,\n          82.0,\n          76.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.657252408878008,\n        \"min\": 0.0,\n        \"max\": 9.6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.1,\n          9.6,\n          8.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.600624877977348,\n        \"min\": 2.2,\n        \"max\": 6.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5.2,\n          5.4,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ff_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3416407864998738,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ddd_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 136.49175799292792,\n        \"min\": 0.0,\n        \"max\": 360.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          270.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ff_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3416407864998738,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ddd_car\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N \",\n          \"W \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qY3RdEtXe885"
      },
      "source": [
        "## Fungsi `_have_nan()`\n",
        "\n",
        "Tujuan: Memeriksa apakah di dalam tabel memiliki nilai yang hilang (np.nan)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6zmDPDupfG7x",
        "outputId": "25128f17-3834-41cf-9532-ddd7ffbe4b21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "_have_nan(dataset)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _have_nan is deprecated, use _has_nan_values instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcj4-4--f8P7"
      },
      "source": [
        "## Fungsi `_get_index1D()`\n",
        "\n",
        "Tujuan: Memperoleh index data yang hilang untuk setiap array"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHdn_BWdgEZc",
        "outputId": "0949ceab-7359-4271-9d71-124a2e2aac3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "_get_index1D(dataset['RH_avg'].isna().values)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _get_index1D is deprecated, use _get_missing_data_indices instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 781,  962,  990,  991, 1010, 1117, 1236, 1264, 2831, 2835, 2839,\n",
              "       2860, 3255, 3346, 3568, 4977, 5028, 5368, 5706, 5786, 5791, 5961,\n",
              "       5962, 6059, 6122, 6131, 6350, 7212, 7217, 8283, 8289, 8290, 8291,\n",
              "       8297, 8304, 8320, 8321, 8322, 8325, 8326, 8328, 8329, 8332, 8338,\n",
              "       8339, 8340, 8405, 8565])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XXFJxVfqfJ8N"
      },
      "source": [
        "## Fungsi `_get_nan()`\n",
        "\n",
        "Tujuan: Memperoleh index data yang hilang untuk setiap kolom dalam bentuk `dictionary`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RrL9iG0fT9P",
        "outputId": "d6c8ef24-c68c-48de-faa3-0f54505211a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "_get_nan(dataset).keys()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _get_nan is deprecated, use _get_nan_indices_by_column instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['Tn', 'Tx', 'Tavg', 'RH_avg', 'RR', 'ss', 'ff_x', 'ddd_x', 'ff_avg', 'ddd_car'])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJG-IJ_8fYVF",
        "outputId": "bcc9c8dc-fb14-47ee-ac7d-43358876387f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(_get_nan(dataset)['RH_avg'])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[781, 962, 990, 991, 1010, 1117, 1236, 1264, 2831, 2835, 2839, 2860, 3255, 3346, 3568, 4977, 5028, 5368, 5706, 5786, 5791, 5961, 5962, 6059, 6122, 6131, 6350, 7212, 7217, 8283, 8289, 8290, 8291, 8297, 8304, 8320, 8321, 8322, 8325, 8326, 8328, 8329, 8332, 8338, 8339, 8340, 8405, 8565]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _get_nan is deprecated, use _get_nan_indices_by_column instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAW_kMxEh_6N"
      },
      "source": [
        "## Fungsi `_get_nan_columns()`\n",
        "\n",
        "Tujuan: Memperoleh nama kolom yang memiliki nilai yang hilang `NaN`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-sHtOb1h_aT",
        "outputId": "83d2183c-a41a-4ee9-d578-352484ff0f88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "_get_nan_columns(dataset)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _get_nan_columns is deprecated, use _get_columns_with_nan_values instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Tn', 'Tavg', 'RH_avg', 'ss', 'ff_x', 'ddd_x', 'ff_avg', 'ddd_car']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L09Uw9DcfkjG"
      },
      "source": [
        "## Fungsi `_check_nan()`\n",
        "\n",
        "Tujuan: Gabungan dari `_have_nan()` dan `_get_nan()`. Memeriksa apakah dataset memiliki `NaN`, jika iya, memberikan nilai hasil `_get_nan()`, jika tidak memberikan nilai `None`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yE2uLd0shma4",
        "outputId": "a5443c09-1626-4069-874f-5a3311c47d79",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "_check_nan(dataset).items()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _check_nan is deprecated, use _get_nan_indices_if_exists instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('Tn', [3255, 3256, 3470, 4876, 4877, 4878, 5521, 5522, 5523, 5524, 5525, 5526, 5527, 5528, 5529, 5530, 5531, 5532, 5533, 5534, 5535, 5536, 5537, 5538, 5539, 5540, 5541, 5542, 5543, 5544, 5545, 5546, 5547, 5548, 5549, 5550, 5551, 6411, 6412, 6413, 6414, 6415, 6416, 6417, 6418, 6419, 6420, 6421, 6422, 6423, 6424, 6425, 6426, 6427, 6428, 6429, 6430, 6431, 6432, 6433, 6434, 6435, 6436, 6437, 6438, 6439, 6440, 6441, 6442, 6443, 6444, 6445, 6446, 6447, 6448, 6449, 6450, 6451, 6452, 6453, 6454, 6455, 6456, 6457, 6458, 6459, 6460, 6461, 6462, 6463, 6464, 6465, 6466, 6467, 6468, 6469, 6470, 6471, 6472, 6473, 6474, 6475, 6476, 6477, 6478, 6479, 6480, 6481, 6482, 6483, 6484, 6485, 6486, 6487, 6488, 6489, 6490, 6491, 6492, 6493, 6494, 6495, 6496, 6497, 6498, 6499, 6500, 6501, 6502, 6503, 6504, 6505, 6506, 6507, 6508, 6509, 6510, 6511, 6512, 6513, 6514, 6515, 6516, 6517, 6518, 6519, 6520, 6521, 6522, 6523, 6524, 6525, 6526, 6527, 6528, 6529, 6530, 6531, 6532, 6533, 6534, 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3145, 3530, 4502, 7056, 7309, 7432, 7435, 7439, 7454, 7455, 7462, 7482, 7483, 7484, 7513, 7559, 7566, 7573, 7578, 7579, 7585, 7587, 7593, 7597, 7609, 7611, 7614, 7615, 7622, 7634, 7682, 7744, 7748, 7759, 7767, 7779, 7800, 7806, 7896, 7904, 7906, 7919, 7922, 7979, 8109, 8156, 8233, 8238, 8279, 8281, 8282, 8300, 8305, 8320, 8324, 8348, 8358, 8410, 8432, 8439, 8499, 8576, 8643, 8998, 9000, 9011, 9013, 9014, 9030, 9034, 9052, 9223, 9313])])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "66nIQJcLhviR",
        "outputId": "bd151b4e-74bc-4ede-bdae-79270ed7c6c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Jika tidak memiliki nilai nan\n",
        "print(_check_nan(dataset.drop(_get_nan_columns(dataset), axis=1)))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "None\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _get_nan_columns is deprecated, use _get_columns_with_nan_values instead\n",
            "  warnings.warn(\n",
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _check_nan is deprecated, use _get_nan_indices_if_exists instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TwvE5jb6iPQp"
      },
      "source": [
        "## Fungsi `_group_as_list()`\n",
        "\n",
        "Tujuan: Mengelompokkan kelompok array yang bersifat kontinu (nilainya berurutan) dalam masing-masing list.\n",
        "\n",
        "Referensi: https://stackoverflow.com/a/15276206 (dimodifikasi untuk Python 3.x dan kemudahan membaca)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YrEFvgtri0UO",
        "outputId": "d925ff68-f944-457c-ecd5-52d426861321",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "missing_dict = _get_nan(dataset)\n",
        "missing_RH_avg = missing_dict['RH_avg']\n",
        "print(missing_RH_avg)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[781, 962, 990, 991, 1010, 1117, 1236, 1264, 2831, 2835, 2839, 2860, 3255, 3346, 3568, 4977, 5028, 5368, 5706, 5786, 5791, 5961, 5962, 6059, 6122, 6131, 6350, 7212, 7217, 8283, 8289, 8290, 8291, 8297, 8304, 8320, 8321, 8322, 8325, 8326, 8328, 8329, 8332, 8338, 8339, 8340, 8405, 8565]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _get_nan is deprecated, use _get_nan_indices_by_column instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHstC8RfjG1H",
        "outputId": "48f9447e-8423-45a4-a9e4-814ca0c35ef8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(_group_as_list(missing_RH_avg))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[781], [962], [990, 991], [1010], [1117], [1236], [1264], [2831], [2835], [2839], [2860], [3255], [3346], [3568], [4977], [5028], [5368], [5706], [5786], [5791], [5961, 5962], [6059], [6122], [6131], [6350], [7212], [7217], [8283], [8289, 8290, 8291], [8297], [8304], [8320, 8321, 8322], [8325, 8326], [8328, 8329], [8332], [8338, 8339, 8340], [8405], [8565]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _group_as_list is deprecated, use _group_consecutive_elements instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Ui1dYZwhFQn"
      },
      "source": [
        "## Fungsi `_group_as_index()`\n",
        "\n",
        "Tujuan: Mengubah hasil pengelompokkan menjadi jenis index dataset (dalam kasus ini dalam bentuk tanggal dibandingkan dalam bentuk angka-index dataset)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lab87rO_jaUJ",
        "outputId": "8c246961-1150-4a04-ae8c-33c9565023a8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "_group_as_index(_group_as_list(missing_RH_avg), index=dataset.index, date_format='%d %b %Y')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _group_as_list is deprecated, use _group_consecutive_elements instead\n",
            "  warnings.warn(\n",
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _group_as_index is deprecated, use _format_group_indices instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['20 Feb 1977',\n",
              " '20 Aug 1977',\n",
              " '17 Sep 1977-18 Sep 1977',\n",
              " '07 Oct 1977',\n",
              " '22 Jan 1978',\n",
              " '21 May 1978',\n",
              " '18 Jun 1978',\n",
              " '02 Oct 1982',\n",
              " '06 Oct 1982',\n",
              " '10 Oct 1982',\n",
              " '31 Oct 1982',\n",
              " '30 Nov 1983',\n",
              " '29 Feb 1984',\n",
              " '08 Oct 1984',\n",
              " '17 Aug 1988',\n",
              " '07 Oct 1988',\n",
              " '12 Sep 1989',\n",
              " '16 Aug 1990',\n",
              " '04 Nov 1990',\n",
              " '09 Nov 1990',\n",
              " '28 Apr 1991-29 Apr 1991',\n",
              " '04 Aug 1991',\n",
              " '06 Oct 1991',\n",
              " '15 Oct 1991',\n",
              " '21 May 1992',\n",
              " '30 Sep 1994',\n",
              " '05 Oct 1994',\n",
              " '05 Sep 1997',\n",
              " '11 Sep 1997-13 Sep 1997',\n",
              " '19 Sep 1997',\n",
              " '26 Sep 1997',\n",
              " '12 Oct 1997-14 Oct 1997',\n",
              " '17 Oct 1997-18 Oct 1997',\n",
              " '20 Oct 1997-21 Oct 1997',\n",
              " '24 Oct 1997',\n",
              " '30 Oct 1997-01 Nov 1997',\n",
              " '05 Jan 1998',\n",
              " '14 Jun 1998']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rZUej-Bl-Rd"
      },
      "source": [
        "## Fungsi `_get_missing()`\n",
        "\n",
        "Tujuan: Memperoleh index yang memiliki nilai tidak terukur (bernilai `8888` atau `9999`) untuk setiap kolomnya"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwNiOYjSl9wE",
        "outputId": "39b49fe8-2006-4659-b8f3-35c73a3ecbfb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "_get_missing(dataset)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _get_missing is deprecated, use _get_unrecorded_indices instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Tn': array([], dtype=int64),\n",
              " 'Tx': array([], dtype=int64),\n",
              " 'Tavg': array([], dtype=int64),\n",
              " 'RH_avg': array([], dtype=int64),\n",
              " 'RR': array([  29,   32,   33,   35,   47,   57,   60,   69,   98,  113,  124,\n",
              "         135,  138,  163,  164,  199,  227,  276,  310,  336,  338,  349,\n",
              "         360,  387,  427,  436,  465,  467,  479,  491,  512,  514,  517,\n",
              "         552,  603,  692,  700,  702,  744,  754,  756,  760,  765,  779,\n",
              "         799,  858,  897,  912,  974, 1019, 1058, 1107, 1108, 1132, 1150,\n",
              "        1179, 1199, 1202, 1214, 1216, 1227, 1237, 1246, 1247, 1258, 1306,\n",
              "        1379, 1407, 1410, 1412, 1440, 1459, 1478, 1522, 1532, 1545, 1588,\n",
              "        1613, 1675, 1809, 1827, 1828, 1830, 1832, 1834, 1853, 1854, 1856,\n",
              "        1859, 1867, 1869, 1871, 1876, 1885, 1905, 1906, 1909, 1913, 1928,\n",
              "        1929, 1966, 1978, 1996, 2024, 2031, 2043, 2045, 2054, 2071, 2075,\n",
              "        2086, 2087, 2100, 2130, 2177, 2189, 2194, 2227, 2231, 2235, 2253,\n",
              "        2271, 2275, 2278, 2295, 2313, 2322, 2323, 2337, 2340, 2341, 2349,\n",
              "        2356, 2367, 2377, 2379, 2388, 2401, 2410, 2423, 2442, 2443, 2453,\n",
              "        2456, 2462, 2549, 2550, 2578, 2582, 2583, 2595, 2682, 2708, 2743,\n",
              "        2759, 2762, 2771, 2785, 2818, 2872, 2931, 2995, 2999, 3028, 3033,\n",
              "        3070, 3071, 3213, 3290, 3296, 3336, 3356, 3472, 3473, 3477, 3495,\n",
              "        3501, 3510, 3521, 3568, 3583, 3608, 3637, 3638, 3652, 3665, 3668,\n",
              "        3729, 3740, 3755, 3765, 3772, 3786, 3792, 3804, 3826, 3833, 3854,\n",
              "        3895, 3943, 3965, 3975, 4018, 4031, 4043, 4080, 4148, 4160, 4172,\n",
              "        4173, 4182, 4331, 4333, 4389, 4418, 4468, 4483, 4496, 4672, 4733,\n",
              "        4741, 4775, 4782, 4832, 4852, 4875, 4928, 4973, 5062, 5064, 5082,\n",
              "        5086, 5135, 5262, 5265, 5280, 5320, 5349, 5350, 5397, 5538, 5574,\n",
              "        5694, 5718, 5805, 5809, 5822, 5849, 5858, 5869, 5904, 5915, 5927,\n",
              "        5934, 5949, 5965, 6133, 6139, 6173, 6196, 6205, 6207, 6251, 6259,\n",
              "        6309, 6313, 6356, 6370, 6484, 6514, 6515, 6516, 6547, 6595, 6606,\n",
              "        6608, 6620, 6676, 6688, 6692, 6700, 6770, 6880, 6924, 6926, 6935,\n",
              "        6946, 7052, 7208, 7263, 7276, 7283, 7294, 7315, 7326, 7336, 7351,\n",
              "        7367, 7375, 7406, 7415, 7417, 7429, 7430, 7549, 7567, 7638, 7646,\n",
              "        7663, 7779, 7809, 7833, 7894, 7915, 8015, 8028, 8031, 8040, 8044,\n",
              "        8057, 8063, 8087, 8090, 8091, 8092, 8147, 8150, 8177, 8185, 8191,\n",
              "        8199, 8200, 8208, 8220, 8255, 8277, 8350, 8351, 8360, 8383, 8388,\n",
              "        8390, 8413, 8454, 8457, 8459, 8486, 8488, 8496, 8501, 8503, 8514,\n",
              "        8519, 8539, 8548, 8565, 8572, 8603, 8611, 8641, 8650, 8701, 8727,\n",
              "        8784, 8798, 8804, 8805, 8807, 8826, 8882, 8923, 8974, 9002, 9081,\n",
              "        9114, 9127, 9149, 9153, 9155, 9171, 9193, 9201, 9230, 9302, 9308,\n",
              "        9312, 9348, 9374, 9398, 9404, 9415, 9442, 9461, 9462, 9476, 9481,\n",
              "        9482]),\n",
              " 'ss': array([], dtype=int64),\n",
              " 'ff_x': array([], dtype=int64),\n",
              " 'ddd_x': array([], dtype=int64),\n",
              " 'ff_avg': array([], dtype=int64),\n",
              " 'ddd_car': array([], dtype=int64)}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGRLHHnFj3b_"
      },
      "source": [
        "# Penerapan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7kzIN8r_mb1Q"
      },
      "source": [
        "## Menampilkan index yang bermasalah\n",
        "\n",
        "Tujuan: Setelah memperoleh index dari hasil `_get_missing()` atau `_get_nan()`, bisa menampilkan potongan index tersebut dalam dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAk3dPyHOYk2",
        "outputId": "8841c973-ecee-4d25-f756-99a5357af3d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        }
      },
      "source": [
        "dataset.iloc[_get_missing(dataset)['RR']]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _get_missing is deprecated, use _get_unrecorded_indices instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Tn    Tx  Tavg  RH_avg      RR   ss  ff_x  ddd_x  ff_avg ddd_car\n",
              "Tanggal                                                                       \n",
              "1975-01-30  20.0  28.0  23.0    89.0  8888.0  4.3   NaN    NaN     NaN     NaN\n",
              "1975-02-02  20.0  26.6  23.0    86.0  8888.0  0.5   NaN    NaN     NaN     NaN\n",
              "1975-02-03  20.0  27.9  23.0    84.0  8888.0  3.8   NaN    NaN     NaN     NaN\n",
              "1975-02-05  19.0  27.6  22.7    84.0  8888.0  2.9   NaN    NaN     NaN     NaN\n",
              "1975-02-17  19.0  24.8  21.9    86.0  8888.0  0.7   NaN    NaN     NaN     NaN\n",
              "...          ...   ...   ...     ...     ...  ...   ...    ...     ...     ...\n",
              "2000-11-26  21.0  25.0  22.6    84.0  8888.0  0.1   3.0  270.0     2.0      W \n",
              "2000-11-27  20.0  29.0  23.3    80.0  8888.0  4.4   5.0  270.0     3.0      W \n",
              "2000-12-11  21.0  29.4  24.3    77.0  8888.0  4.0   4.0  270.0     3.0      W \n",
              "2000-12-16  20.0  29.4  23.8    72.0  8888.0  4.9   5.0  270.0     3.0      W \n",
              "2000-12-17  19.0  28.2  23.7    79.0  8888.0  4.9   5.0  270.0     3.0      W \n",
              "\n",
              "[397 rows x 10 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Tn</th>\n",
              "      <th>Tx</th>\n",
              "      <th>Tavg</th>\n",
              "      <th>RH_avg</th>\n",
              "      <th>RR</th>\n",
              "      <th>ss</th>\n",
              "      <th>ff_x</th>\n",
              "      <th>ddd_x</th>\n",
              "      <th>ff_avg</th>\n",
              "      <th>ddd_car</th>\n",
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              "    <tr>\n",
              "      <th>Tanggal</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1975-01-30</th>\n",
              "      <td>20.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>8888.0</td>\n",
              "      <td>4.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-02-02</th>\n",
              "      <td>20.0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>23.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>8888.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-02-03</th>\n",
              "      <td>20.0</td>\n",
              "      <td>27.9</td>\n",
              "      <td>23.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>8888.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-02-05</th>\n",
              "      <td>19.0</td>\n",
              "      <td>27.6</td>\n",
              "      <td>22.7</td>\n",
              "      <td>84.0</td>\n",
              "      <td>8888.0</td>\n",
              "      <td>2.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-02-17</th>\n",
              "      <td>19.0</td>\n",
              "      <td>24.8</td>\n",
              "      <td>21.9</td>\n",
              "      <td>86.0</td>\n",
              "      <td>8888.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <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>2000-11-26</th>\n",
              "      <td>21.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>22.6</td>\n",
              "      <td>84.0</td>\n",
              "      <td>8888.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-11-27</th>\n",
              "      <td>20.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>8888.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-12-11</th>\n",
              "      <td>21.0</td>\n",
              "      <td>29.4</td>\n",
              "      <td>24.3</td>\n",
              "      <td>77.0</td>\n",
              "      <td>8888.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>W</td>\n",
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              "    <tr>\n",
              "      <th>2000-12-16</th>\n",
              "      <td>20.0</td>\n",
              "      <td>29.4</td>\n",
              "      <td>23.8</td>\n",
              "      <td>72.0</td>\n",
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              "    <tr>\n",
              "      <th>2000-12-17</th>\n",
              "      <td>19.0</td>\n",
              "      <td>28.2</td>\n",
              "      <td>23.7</td>\n",
              "      <td>79.0</td>\n",
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              "      <td>270.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>W</td>\n",
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              "<p>397 rows × 10 columns</p>\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d599fccb-e615-4bd6-9fc7-b640d6230f0d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 397,\n  \"fields\": [\n    {\n      \"column\": \"Tanggal\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1975-01-30 00:00:00\",\n        \"max\": \"2000-12-17 00:00:00\",\n        \"num_unique_values\": 397,\n        \"samples\": [\n          \"1980-12-17 00:00:00\",\n          \"1993-04-24 00:00:00\",\n          \"1989-08-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3514928366078944,\n        \"min\": 13.0,\n        \"max\": 22.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          13.0,\n          19.0,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tx\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.564603788141466,\n        \"min\": 16.0,\n        \"max\": 32.6,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          27.1,\n          28.0,\n          29.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tavg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.845311648836912,\n        \"min\": 20.4,\n        \"max\": 25.7,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          23.8,\n          23.5,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RH_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.253278263801212,\n        \"min\": 56.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          81.0,\n          91.0,\n          90.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 8888.0,\n        \"max\": 8888.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8888.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.191180638991747,\n        \"min\": 0.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ff_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4935572024046992,\n        \"min\": 0.0,\n        \"max\": 13.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ddd_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105.67132331634998,\n        \"min\": 0.0,\n        \"max\": 360.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ff_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1323560570509896,\n        \"min\": 0.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ddd_car\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"W \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QSK1QI3nAK9",
        "outputId": "428b681f-58c2-4648-c6cb-0b3e3f0864e7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "_group_as_list(_get_missing(dataset)['RR'])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _get_missing is deprecated, use _get_unrecorded_indices instead\n",
            "  warnings.warn(\n",
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _group_as_list is deprecated, use _group_consecutive_elements instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xz3aiDZAnBlt",
        "outputId": "77eae914-96cf-4b1c-e266-be0311129b4a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "_group_as_index(_, index=dataset.index, date_format='%d %b %Y', format_date='{} sampai {}')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _group_as_index is deprecated, use _format_group_indices instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " '12 Jun 1978',\n",
              " '30 Jul 1978',\n",
              " '11 Oct 1978',\n",
              " '08 Nov 1978',\n",
              " '11 Nov 1978',\n",
              " '13 Nov 1978',\n",
              " '11 Dec 1978',\n",
              " '30 Dec 1978',\n",
              " '18 Jan 1979',\n",
              " '03 Mar 1979',\n",
              " '13 Mar 1979',\n",
              " '26 Mar 1979',\n",
              " '08 May 1979',\n",
              " '02 Jun 1979',\n",
              " '03 Aug 1979',\n",
              " '15 Dec 1979',\n",
              " '02 Jan 1980 sampai 03 Jan 1980',\n",
              " '05 Jan 1980',\n",
              " '07 Jan 1980',\n",
              " '09 Jan 1980',\n",
              " '28 Jan 1980 sampai 29 Jan 1980',\n",
              " '31 Jan 1980',\n",
              " '03 Feb 1980',\n",
              " '11 Feb 1980',\n",
              " '13 Feb 1980',\n",
              " '15 Feb 1980',\n",
              " '20 Feb 1980',\n",
              " '29 Feb 1980',\n",
              " '20 Mar 1980 sampai 21 Mar 1980',\n",
              " '24 Mar 1980',\n",
              " '28 Mar 1980',\n",
              " '12 Apr 1980 sampai 13 Apr 1980',\n",
              " '20 May 1980',\n",
              " '01 Jun 1980',\n",
              " '19 Jun 1980',\n",
              " '17 Jul 1980',\n",
              " '24 Jul 1980',\n",
              " '05 Aug 1980',\n",
              " '07 Aug 1980',\n",
              " '16 Aug 1980',\n",
              " '02 Sep 1980',\n",
              " '06 Sep 1980',\n",
              " '17 Sep 1980 sampai 18 Sep 1980',\n",
              " '01 Oct 1980',\n",
              " '31 Oct 1980',\n",
              " '17 Dec 1980',\n",
              " '29 Dec 1980',\n",
              " '03 Jan 1981',\n",
              " '05 Feb 1981',\n",
              " '09 Feb 1981',\n",
              " '13 Feb 1981',\n",
              " '03 Mar 1981',\n",
              " '21 Mar 1981',\n",
              " '25 Mar 1981',\n",
              " '28 Mar 1981',\n",
              " '14 Apr 1981',\n",
              " '02 May 1981',\n",
              " '11 May 1981 sampai 12 May 1981',\n",
              " '26 May 1981',\n",
              " '29 May 1981 sampai 30 May 1981',\n",
              " '07 Jun 1981',\n",
              " '14 Jun 1981',\n",
              " '25 Jun 1981',\n",
              " '05 Jul 1981',\n",
              " '07 Jul 1981',\n",
              " '16 Jul 1981',\n",
              " '29 Jul 1981',\n",
              " '07 Aug 1981',\n",
              " '20 Aug 1981',\n",
              " '08 Sep 1981 sampai 09 Sep 1981',\n",
              " '19 Sep 1981',\n",
              " '22 Sep 1981',\n",
              " '28 Sep 1981',\n",
              " '24 Dec 1981 sampai 25 Dec 1981',\n",
              " '22 Jan 1982',\n",
              " '26 Jan 1982 sampai 27 Jan 1982',\n",
              " '08 Feb 1982',\n",
              " '06 May 1982',\n",
              " '01 Jun 1982',\n",
              " '06 Jul 1982',\n",
              " '22 Jul 1982',\n",
              " '25 Jul 1982',\n",
              " '03 Aug 1982',\n",
              " '17 Aug 1982',\n",
              " '19 Sep 1982',\n",
              " '12 Nov 1982',\n",
              " '10 Jan 1983',\n",
              " '15 Mar 1983',\n",
              " '19 Mar 1983',\n",
              " '17 Apr 1983',\n",
              " '22 Apr 1983',\n",
              " '29 May 1983 sampai 30 May 1983',\n",
              " '19 Oct 1983',\n",
              " '04 Jan 1984',\n",
              " '10 Jan 1984',\n",
              " '19 Feb 1984',\n",
              " '10 Mar 1984',\n",
              " '04 Jul 1984 sampai 05 Jul 1984',\n",
              " '09 Jul 1984',\n",
              " '27 Jul 1984',\n",
              " '02 Aug 1984',\n",
              " '11 Aug 1984',\n",
              " '22 Aug 1984',\n",
              " '08 Oct 1984',\n",
              " '23 Oct 1984',\n",
              " '17 Nov 1984',\n",
              " '16 Dec 1984 sampai 17 Dec 1984',\n",
              " '31 Dec 1984',\n",
              " '13 Jan 1985',\n",
              " '16 Jan 1985',\n",
              " '18 Mar 1985',\n",
              " '29 Mar 1985',\n",
              " '13 Apr 1985',\n",
              " '23 Apr 1985',\n",
              " '30 Apr 1985',\n",
              " '14 May 1985',\n",
              " '20 May 1985',\n",
              " '01 Jun 1985',\n",
              " '23 Jun 1985',\n",
              " '30 Jun 1985',\n",
              " '21 Jul 1985',\n",
              " '31 Aug 1985',\n",
              " '18 Oct 1985',\n",
              " '09 Nov 1985',\n",
              " '19 Nov 1985',\n",
              " '01 Jan 1986',\n",
              " '14 Jan 1986',\n",
              " '26 Jan 1986',\n",
              " '04 Mar 1986',\n",
              " '11 May 1986',\n",
              " '23 May 1986',\n",
              " '04 Jun 1986 sampai 05 Jun 1986',\n",
              " '14 Jun 1986',\n",
              " '10 Nov 1986',\n",
              " '12 Nov 1986',\n",
              " '07 Jan 1987',\n",
              " '05 Feb 1987',\n",
              " '27 Mar 1987',\n",
              " '11 Apr 1987',\n",
              " '24 Apr 1987',\n",
              " '17 Oct 1987',\n",
              " '17 Dec 1987',\n",
              " '25 Dec 1987',\n",
              " '28 Jan 1988',\n",
              " '04 Feb 1988',\n",
              " '25 Mar 1988',\n",
              " '14 Apr 1988',\n",
              " '07 May 1988',\n",
              " '29 Jun 1988',\n",
              " '13 Aug 1988',\n",
              " '10 Nov 1988',\n",
              " '12 Nov 1988',\n",
              " '30 Nov 1988',\n",
              " '04 Dec 1988',\n",
              " '22 Jan 1989',\n",
              " '29 May 1989',\n",
              " '01 Jun 1989',\n",
              " '16 Jun 1989',\n",
              " '26 Jul 1989',\n",
              " '24 Aug 1989 sampai 25 Aug 1989',\n",
              " '11 Oct 1989',\n",
              " '01 Mar 1990',\n",
              " '06 Apr 1990',\n",
              " '04 Aug 1990',\n",
              " '28 Aug 1990',\n",
              " '23 Nov 1990',\n",
              " '27 Nov 1990',\n",
              " '10 Dec 1990',\n",
              " '06 Jan 1991',\n",
              " '15 Jan 1991',\n",
              " '26 Jan 1991',\n",
              " '02 Mar 1991',\n",
              " '13 Mar 1991',\n",
              " '25 Mar 1991',\n",
              " '01 Apr 1991',\n",
              " '16 Apr 1991',\n",
              " '02 May 1991',\n",
              " '17 Oct 1991',\n",
              " '23 Oct 1991',\n",
              " '26 Nov 1991',\n",
              " '19 Dec 1991',\n",
              " '28 Dec 1991',\n",
              " '30 Dec 1991',\n",
              " '12 Feb 1992',\n",
              " '20 Feb 1992',\n",
              " '10 Apr 1992',\n",
              " '14 Apr 1992',\n",
              " '27 May 1992',\n",
              " '10 Jun 1992',\n",
              " '02 Oct 1992',\n",
              " '01 Nov 1992 sampai 03 Nov 1992',\n",
              " '04 Dec 1992',\n",
              " '21 Jan 1993',\n",
              " '01 Feb 1993',\n",
              " '03 Feb 1993',\n",
              " '15 Feb 1993',\n",
              " '12 Apr 1993',\n",
              " '24 Apr 1993',\n",
              " '28 Apr 1993',\n",
              " '06 May 1993',\n",
              " '15 Jul 1993',\n",
              " '02 Nov 1993',\n",
              " '16 Dec 1993',\n",
              " '18 Dec 1993',\n",
              " '27 Dec 1993',\n",
              " '07 Jan 1994',\n",
              " '23 Apr 1994',\n",
              " '26 Sep 1994',\n",
              " '20 Nov 1994',\n",
              " '03 Dec 1994',\n",
              " '10 Dec 1994',\n",
              " '21 Dec 1994',\n",
              " '11 Jan 1995',\n",
              " '22 Jan 1995',\n",
              " '01 Feb 1995',\n",
              " '16 Feb 1995',\n",
              " '04 Mar 1995',\n",
              " '12 Mar 1995',\n",
              " '12 Apr 1995',\n",
              " '21 Apr 1995',\n",
              " '23 Apr 1995',\n",
              " '05 May 1995 sampai 06 May 1995',\n",
              " '02 Sep 1995',\n",
              " '20 Sep 1995',\n",
              " '30 Nov 1995',\n",
              " '08 Dec 1995',\n",
              " '25 Dec 1995',\n",
              " '19 Apr 1996',\n",
              " '19 May 1996',\n",
              " '12 Jun 1996',\n",
              " '12 Aug 1996',\n",
              " '02 Sep 1996',\n",
              " '11 Dec 1996',\n",
              " '24 Dec 1996',\n",
              " '27 Dec 1996',\n",
              " '05 Jan 1997',\n",
              " '09 Jan 1997',\n",
              " '22 Jan 1997',\n",
              " '28 Jan 1997',\n",
              " '21 Feb 1997',\n",
              " '24 Feb 1997 sampai 26 Feb 1997',\n",
              " '22 Apr 1997',\n",
              " '25 Apr 1997',\n",
              " '22 May 1997',\n",
              " '30 May 1997',\n",
              " '05 Jun 1997',\n",
              " '13 Jun 1997 sampai 14 Jun 1997',\n",
              " '22 Jun 1997',\n",
              " '04 Jul 1997',\n",
              " '08 Aug 1997',\n",
              " '30 Aug 1997',\n",
              " '11 Nov 1997 sampai 12 Nov 1997',\n",
              " '21 Nov 1997',\n",
              " '14 Dec 1997',\n",
              " '19 Dec 1997',\n",
              " '21 Dec 1997',\n",
              " '13 Jan 1998',\n",
              " '23 Feb 1998',\n",
              " '26 Feb 1998',\n",
              " '28 Feb 1998',\n",
              " '27 Mar 1998',\n",
              " '29 Mar 1998',\n",
              " '06 Apr 1998',\n",
              " '11 Apr 1998',\n",
              " '13 Apr 1998',\n",
              " '24 Apr 1998',\n",
              " '29 Apr 1998',\n",
              " '19 May 1998',\n",
              " '28 May 1998',\n",
              " '14 Jun 1998',\n",
              " '21 Jun 1998',\n",
              " '22 Jul 1998',\n",
              " '30 Jul 1998',\n",
              " '29 Aug 1998',\n",
              " '07 Sep 1998',\n",
              " '28 Oct 1998',\n",
              " '23 Nov 1998',\n",
              " '19 Jan 1999',\n",
              " '02 Feb 1999',\n",
              " '08 Feb 1999 sampai 09 Feb 1999',\n",
              " '11 Feb 1999',\n",
              " '02 Mar 1999',\n",
              " '27 Apr 1999',\n",
              " '07 Jun 1999',\n",
              " '28 Jul 1999',\n",
              " '25 Aug 1999',\n",
              " '12 Nov 1999',\n",
              " '15 Dec 1999',\n",
              " '28 Dec 1999',\n",
              " '19 Jan 2000',\n",
              " '23 Jan 2000',\n",
              " '25 Jan 2000',\n",
              " '10 Feb 2000',\n",
              " '03 Mar 2000',\n",
              " '11 Mar 2000',\n",
              " '09 Apr 2000',\n",
              " '20 Jun 2000',\n",
              " '26 Jun 2000',\n",
              " '30 Jun 2000',\n",
              " '05 Aug 2000',\n",
              " '31 Aug 2000',\n",
              " '24 Sep 2000',\n",
              " '30 Sep 2000',\n",
              " '11 Oct 2000',\n",
              " '07 Nov 2000',\n",
              " '26 Nov 2000 sampai 27 Nov 2000',\n",
              " '11 Dec 2000',\n",
              " '16 Dec 2000 sampai 17 Dec 2000']"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qbv_bFl5nVsx"
      },
      "source": [
        "# Changelog\n",
        "\n",
        "```\n",
        "- 20240413 - 2.0.0 / v0.5.0 - Refactor function names and update deprecated functions\n",
        "- 20190928 - 1.0.0 - Initial\n",
        "```\n",
        "\n",
        "#### Copyright &copy; 2019-2024 [Taruma Sakti Megariansyah](https://taruma.github.io)\n",
        "\n",
        "Source code in this notebook is licensed under a [MIT License](https://choosealicense.com/licenses/mit/). Data in this notebook is licensed under a [Creative Common Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)."
      ]
    }
  ]
}