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taruma/taruma_hk73_bmkg.ipynb

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taruma_hk73_bmkg.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"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",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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",
" </tr>\n",
" <tr>\n",
" <th>1975-01-03</th>\n",
" <td>20.0</td>\n",
" <td>28.9</td>\n",
" <td>23.3</td>\n",
" <td>78.0</td>\n",
" <td>0.0</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975-01-04</th>\n",
" <td>21.0</td>\n",
" <td>30.0</td>\n",
" <td>23.7</td>\n",
" <td>82.0</td>\n",
" <td>0.0</td>\n",
" <td>5.7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975-01-05</th>\n",
" <td>19.0</td>\n",
" <td>28.8</td>\n",
" <td>22.2</td>\n",
" <td>86.0</td>\n",
" <td>8.0</td>\n",
" <td>3.1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f0d15b8-4684-429c-a6b6-0c4d9e35a8dd')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
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" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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"\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",
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" --hover-bg-color: #E2EBFA;\n",
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" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
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" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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" })();\n",
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"</div>\n",
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" </div>\n"
],
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"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 "
],
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" 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, 6535, 6536, 6537, 6538, 6539, 6540, 6541, 6542, 6543, 6544, 6545, 6546, 6547, 6548, 6549, 6550, 6551, 6552, 6553, 6554, 6555, 6556, 6557, 6558, 6559, 6560, 6561, 6562, 6563, 6564, 6565, 6566, 6567, 6568, 6569, 6570, 6571, 6572, 6573, 6574, 6575, 6576, 6577, 6578, 6579, 6580, 6581, 6582, 6583, 6584, 6585, 6586, 6587]), ('Tx', []), ('Tavg', [781, 962, 1117, 1236, 1264, 4977, 8565]), ('RH_avg', [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]), ('RR', []), ('ss', [1103, 1296, 1520, 1526, 1815, 2461, 2512, 2518, 2521, 2551, 2570, 2571, 2580, 2602, 2682, 2683, 2711, 2732, 2733, 2794, 2797, 2817, 2820, 2844, 2920, 2921, 2939, 2943, 2947, 2948, 2999, 3055, 3069, 3121, 3166, 3224, 3247, 3248, 3304, 3309, 3353, 3419, 3423, 3510, 3511, 3512, 3513, 3514, 3515, 3516, 3517, 3518, 3519, 3520, 3521, 3522, 3523, 3524, 3525, 3526, 3527, 3528, 3529, 3530, 3584, 3588, 3600, 3614, 3626, 3633, 3634, 3635, 3697, 3713, 3714, 3735, 3977, 4037, 4048, 4059, 4180, 4207, 4269, 4277, 4299, 4383, 4407, 4421, 4531, 4536, 4730, 4734, 4771, 4909, 4917, 4973, 5036, 5043, 5099, 5118, 5143, 5313, 5415, 5461, 5483, 5501, 5614, 5863, 5871, 5877, 5878, 5892, 5897, 5950, 5951, 5971, 6155, 6185, 6188, 6207, 6229, 6232, 6254, 6322, 6323, 6363, 6389, 6603, 6604, 6758, 6892, 6912, 6955, 6960, 6961, 6986, 7020, 7021, 7022, 7098, 7120, 7235, 7277, 7278, 7289, 7308, 7318, 7368, 7427, 7475, 7634, 7651, 7670, 7673, 7674, 7675, 7685, 7708, 7762, 7774, 7833, 7963, 7997, 8006, 8017, 8018, 8057, 8058, 8069, 8091, 8139, 8148, 8524, 8667, 8671, 8692, 8698, 8706, 8733, 8766, 8767, 8791, 8792, 8798, 8891, 8896, 8899, 8923, 8931, 9008, 9068, 9077, 9112, 9116, 9124, 9125, 9148, 9156, 9157, 9285, 9300, 9303, 9420, 9421, 9431, 9436, 9437, 9460]), ('ff_x', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 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815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 2922, 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",
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"<ipython-input-3-23e254b88fe0>:27: DeprecationWarning: _get_missing is deprecated, use _get_unrecorded_indices instead\n",
" warnings.warn(\n"
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" 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",
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{
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"source": [
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"outputs": [
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"<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",
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{
"cell_type": "code",
"metadata": {
"id": "Xz3aiDZAnBlt",
"outputId": "77eae914-96cf-4b1c-e266-be0311129b4a",
"colab": {
"base_uri": "https://localhost:8080/"
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},
"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"
]
},
{
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},
"metadata": {},
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},
{
"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/)."
]
}
]
}
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