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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "taruma_hk79_baca_excel_jamjaman.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/05dab67fac8313a94134ac02d0398897/taruma_hk79_baca_excel_jamjaman.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "QlYJu4L1xpaL" | |
| }, | |
| "source": [ | |
| "Berdasarkan isu [#79](https://github.com/taruma/hidrokit/issues/79): __request: ambil dataset hujan jam-jaman dari excel__\n", | |
| "\n", | |
| "Referensi isu:\n", | |
| "- `hidrokit.contrib.taruma.hk43` [#43](https://github.com/taruma/hidrokit/issues/43). \\([view notebook / manual](https://nbviewer.jupyter.org/gist/taruma/a9dd4ea61db2526853b99600909e9c50)\\). (Menggunakan fungsi `_get_years()` untuk memperoleh `list` tahun pada berkas excel.\n", | |
| "\n", | |
| "Deskripsi permasalahan:\n", | |
| "- Membaca data jam-jaman dari excel\n", | |
| "- Data dalam excel berupa _pivot table_\n", | |
| "- Ubah _pivot table_ ke _regular table_\n", | |
| "- Mengubah tabel tersebut menjadi `pandas.DataFrame`\n", | |
| "\n", | |
| "Strategi penyelesaian masalah:\n", | |
| "- Periksa _sheet_ didalam berkas excel\n", | |
| "- Baca metadata/konfigurasi excel (nama stasiun)\n", | |
| "- Untuk setiap _sheet_ dengan digit tahun, baca setiap _sheet_ (menggunakan `hk43._get_years()`)\n", | |
| "- Dalam _sheet_ tunggal:\n", | |
| " - Baca informasi tahun di lembar aktif\n", | |
| " - Membaca dan mempraproses dataset setiap bulannya\n", | |
| " - Menggabungkan hasil pengubahan pivot ke tabel reguler\n", | |
| " - Menggabungkan tabel reguler dalam satu tahun\n", | |
| "- Menggabungkan tabel setiap tahun menjadi satu `pandas.DataFrame`\n", | |
| "\n", | |
| "Catatan:\n", | |
| "Untuk prapemrosesan tabel seperti (cek data yang hilang, dlsbnya) akan dikembangkan dengan modul yang terpisah karena beberapa fungsi sudah tersedia di modul `hidrokit.contrib.taruma.hk73` (untuk mengolah berkas dari bmkg)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "rLLirn8y6FRB" | |
| }, | |
| "source": [ | |
| "# PERSIAPAN DAN DATASET" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "_q5GFz-NwQpx" | |
| }, | |
| "source": [ | |
| "# Download sample excel\n", | |
| "!wget -O sample.xlsx \"https://taruma.github.io/assets/hidrokit_dataset/hidrokit_hourly_template.xlsx\" -q" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "__pTGKmWwkuA" | |
| }, | |
| "source": [ | |
| "# Import library\n", | |
| "import pandas as pd\n", | |
| "import numpy as np" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "zFd-3lFW6SIa" | |
| }, | |
| "source": [ | |
| "# KODE" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def extract_years_from_excel(file_path):\n", | |
| " \"\"\"\n", | |
| " Get a list of years from an Excel file.\n", | |
| "\n", | |
| " Parameters:\n", | |
| " file_path (str): The path to the Excel file.\n", | |
| "\n", | |
| " Returns:\n", | |
| " List[int]: A sorted list of years found in the Excel file.\n", | |
| " \"\"\"\n", | |
| " excel = pd.ExcelFile(file_path)\n", | |
| " years = []\n", | |
| " for sheet in excel.sheet_names:\n", | |
| " if sheet.isdigit():\n", | |
| " years.append(int(sheet))\n", | |
| " return sorted(years)" | |
| ], | |
| "metadata": { | |
| "id": "24mCxBY3DTE_" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "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\n" | |
| ], | |
| "metadata": { | |
| "id": "syotTOSIGdwV" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "xw79uxePB_k5" | |
| }, | |
| "source": [ | |
| "from calendar import monthrange\n", | |
| "\n", | |
| "# ref: https://www.reddit.com/r/learnpython/comments/485h1p/\n", | |
| "from collections.abc import Sequence\n", | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "def _index_hourly(year, freq=\"60min\"):\n", | |
| " \"\"\"\n", | |
| " Create a DatetimeIndex with specific year or [year_start, year_end].\n", | |
| "\n", | |
| " Parameters:\n", | |
| " year (int or tuple): The year or range of years to create the index for.\n", | |
| " If an int is provided, the index will be created for that specific year.\n", | |
| " If a tuple is provided, the index will be created for the range of years\n", | |
| " specified by the tuple (inclusive).\n", | |
| " freq (str, optional): The frequency of the index. Defaults to \"60min\".\n", | |
| "\n", | |
| " Returns:\n", | |
| " pd.DatetimeIndex: The generated DatetimeIndex.\n", | |
| "\n", | |
| " \"\"\"\n", | |
| " if isinstance(year, Sequence):\n", | |
| " year_start, year_end = year\n", | |
| " else:\n", | |
| " year_start, year_end = year, year\n", | |
| "\n", | |
| " period = f\"{year_start}0101 00:00,{year_end}1231 23:00\".split(\",\")\n", | |
| " return pd.date_range(*period, freq=freq)\n", | |
| "\n", | |
| "\n", | |
| "def _melt_to_array(dataframe):\n", | |
| " \"\"\"\n", | |
| " Convert a DataFrame to a 1-dimensional array by melting it and extracting the 'value' column.\n", | |
| "\n", | |
| " Parameters:\n", | |
| " df (pandas.DataFrame): The DataFrame to be converted.\n", | |
| "\n", | |
| " Returns:\n", | |
| " numpy.ndarray: A 1-dimensional array containing the values\n", | |
| " from the 'value' column of the melted DataFrame.\n", | |
| " \"\"\"\n", | |
| " return dataframe.melt().drop(\"variable\", axis=1)[\"value\"].values\n", | |
| "\n", | |
| "\n", | |
| "def _get_array_in_month(dataframe, year, month):\n", | |
| " \"\"\"\n", | |
| " Get an array of data for a specific year and month from a dataframe.\n", | |
| "\n", | |
| " Parameters:\n", | |
| " dataframe (pandas.DataFrame): The input dataframe.\n", | |
| " year (int): The year.\n", | |
| " month (int): The month.\n", | |
| "\n", | |
| " Returns:\n", | |
| " numpy.ndarray: The array of data for the specified year and month.\n", | |
| " \"\"\"\n", | |
| " n_days = monthrange(year, month)[1]\n", | |
| " mask_month = slice(None, n_days)\n", | |
| " df_month = dataframe.iloc[mask_month, :].T\n", | |
| " return _melt_to_array(df_month)\n", | |
| "\n", | |
| "\n", | |
| "def _get_array_in_year(df, year):\n", | |
| " \"\"\"\n", | |
| " Get an array of data for a specific year from a DataFrame.\n", | |
| "\n", | |
| " Parameters:\n", | |
| " - df (pandas.DataFrame): The DataFrame containing the data.\n", | |
| " - year (int): The year for which the data is to be extracted.\n", | |
| "\n", | |
| " Returns:\n", | |
| " - numpy.ndarray: The array of data for the specified year.\n", | |
| " \"\"\"\n", | |
| " n_rows, _ = df.shape\n", | |
| "\n", | |
| " # configuration (view the excel)\n", | |
| " n_month = 1 # number of row to monthID\n", | |
| " n_gap = 2 # number of row between month pivot table\n", | |
| " n_lines = 31 + n_gap # number of row each month\n", | |
| "\n", | |
| " data = []\n", | |
| " for row in range(1, n_rows, n_lines):\n", | |
| " mask_start = row + n_month\n", | |
| " mask_end = row + n_lines\n", | |
| "\n", | |
| " month = df.iloc[mask_start, 1]\n", | |
| " mask_row = slice(mask_start, mask_end)\n", | |
| "\n", | |
| " df_month = df.iloc[mask_row, 4:]\n", | |
| " array_month = _get_array_in_month(df_month, year, month)\n", | |
| " data.append(array_month)\n", | |
| "\n", | |
| " return np.hstack(data)\n", | |
| "\n", | |
| "\n", | |
| "def get_info(file, config_sheet=None):\n", | |
| " \"\"\"\n", | |
| " Retrieves information from an Excel file.\n", | |
| "\n", | |
| " Args:\n", | |
| " file (str): The path to the Excel file.\n", | |
| " config_sheet (str, optional): The name of the sheet to read from.\n", | |
| " If not provided, the first sheet will be used.\n", | |
| "\n", | |
| " Returns:\n", | |
| " dict: A dictionary containing the information retrieved from the Excel file.\n", | |
| " The keys are the lowercase values from the first column,\n", | |
| " and the values are the corresponding values from the second column.\n", | |
| " \"\"\"\n", | |
| " excel = pd.ExcelFile(file)\n", | |
| " first_sheet = excel.sheet_names[0]\n", | |
| " config_sheet = first_sheet if config_sheet is None else config_sheet\n", | |
| "\n", | |
| " df = pd.read_excel(excel, sheet_name=config_sheet, header=None, usecols=\"A:B\")\n", | |
| " info = {}\n", | |
| "\n", | |
| " for index, _ in df.iterrows():\n", | |
| " key = df.iloc[index, 0].lower()\n", | |
| " value = df.iloc[index, 1]\n", | |
| " info[str(key)] = value\n", | |
| "\n", | |
| " return info\n", | |
| "\n", | |
| "\n", | |
| "@deprecated(\"get_info\")\n", | |
| "def _get_info(file, config_sheet=None):\n", | |
| " return get_info(file, config_sheet=config_sheet)\n", | |
| "\n", | |
| "\n", | |
| "def read_excel_hourly(file, station=None):\n", | |
| " \"\"\"\n", | |
| " Read hourly data from an Excel file.\n", | |
| "\n", | |
| " Parameters:\n", | |
| " file (str): The path to the Excel file.\n", | |
| " station (str, optional): The name of the station. Defaults to None.\n", | |
| "\n", | |
| " Returns:\n", | |
| " pandas.DataFrame: A DataFrame containing the hourly data.\n", | |
| "\n", | |
| " \"\"\"\n", | |
| " excel = pd.ExcelFile(file)\n", | |
| "\n", | |
| " # CONFIG\n", | |
| " years = extract_years_from_excel(excel)\n", | |
| " station = \"NA\" if station is None else station\n", | |
| "\n", | |
| " # READ DATA\n", | |
| " data = []\n", | |
| " for year in years:\n", | |
| " sheet = pd.read_excel(\n", | |
| " excel, sheet_name=str(year), header=None, nrows=396, usecols=\"A:AB\"\n", | |
| " )\n", | |
| " array = _get_array_in_year(sheet, year)\n", | |
| " df_year = pd.DataFrame(data=array, columns=[station], index=_index_hourly(year))\n", | |
| " data.append(df_year)\n", | |
| "\n", | |
| " return pd.concat(data, axis=0)\n" | |
| ], | |
| "execution_count": 12, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "XzODOG3hWvqX" | |
| }, | |
| "source": [ | |
| "# FUNGSI" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "kRYwmWihXYfk" | |
| }, | |
| "source": [ | |
| "## Fungsi _private_ `_index_hourly(year, freq='60min')`\n", | |
| "\n", | |
| "Tujuan: membuat index menggunakan perintah `pd.date_range()` dengan input `year` yang berupa bilangan ataupun _sequence_." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Iz13fHtnWu9O", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "2d986e62-fbc0-427a-e55a-01624cd9901a" | |
| }, | |
| "source": [ | |
| "_index_hourly(2000) # jika bilangan harus dalam bentuk integer" | |
| ], | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 01:00:00',\n", | |
| " '2000-01-01 02:00:00', '2000-01-01 03:00:00',\n", | |
| " '2000-01-01 04:00:00', '2000-01-01 05:00:00',\n", | |
| " '2000-01-01 06:00:00', '2000-01-01 07:00:00',\n", | |
| " '2000-01-01 08:00:00', '2000-01-01 09:00:00',\n", | |
| " ...\n", | |
| " '2000-12-31 14:00:00', '2000-12-31 15:00:00',\n", | |
| " '2000-12-31 16:00:00', '2000-12-31 17:00:00',\n", | |
| " '2000-12-31 18:00:00', '2000-12-31 19:00:00',\n", | |
| " '2000-12-31 20:00:00', '2000-12-31 21:00:00',\n", | |
| " '2000-12-31 22:00:00', '2000-12-31 23:00:00'],\n", | |
| " dtype='datetime64[ns]', length=8784, freq='60T')" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 13 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "dN9idPu5X5pS", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "92ce3513-e717-4d3b-ca37-d0d01bc3e355" | |
| }, | |
| "source": [ | |
| "_index_hourly(['2000', 2001]) # jika dalam seq-object bisa berupa integer atau string" | |
| ], | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 01:00:00',\n", | |
| " '2000-01-01 02:00:00', '2000-01-01 03:00:00',\n", | |
| " '2000-01-01 04:00:00', '2000-01-01 05:00:00',\n", | |
| " '2000-01-01 06:00:00', '2000-01-01 07:00:00',\n", | |
| " '2000-01-01 08:00:00', '2000-01-01 09:00:00',\n", | |
| " ...\n", | |
| " '2001-12-31 14:00:00', '2001-12-31 15:00:00',\n", | |
| " '2001-12-31 16:00:00', '2001-12-31 17:00:00',\n", | |
| " '2001-12-31 18:00:00', '2001-12-31 19:00:00',\n", | |
| " '2001-12-31 20:00:00', '2001-12-31 21:00:00',\n", | |
| " '2001-12-31 22:00:00', '2001-12-31 23:00:00'],\n", | |
| " dtype='datetime64[ns]', length=17544, freq='60T')" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "HR50WxV0YS6e" | |
| }, | |
| "source": [ | |
| "## Fungsi _private_ `_melt_to_array(df)`\n", | |
| "\n", | |
| "Tujuan: perintah `df.melt().drop('variable', axis=1)['value'].values`\n", | |
| "\n", | |
| "Contoh `pd.melt` bisa lihat pada manual [hk43](https://nbviewer.jupyter.org/gist/taruma/a9dd4ea61db2526853b99600909e9c50)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "F6LCRneQYodb" | |
| }, | |
| "source": [ | |
| "## Fungsi _private_ `_get_array_in_month(df, year, month)`\n", | |
| "\n", | |
| "Tujuan: mengambil pivot tabel satu bulan dan mengubahnya (_melt_) ke bentuk tabel biasa." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Xc1zXpIXZJTP" | |
| }, | |
| "source": [ | |
| "## Fungsi _private_ `_get_array_in_year(df, year)`\n", | |
| "\n", | |
| "Tujuan: serupa dengan `_get_array_in_month()`, fungsi ini mengambil seluruh informasi pada _sheet_ tunggal." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "F7AXuu2JZkOa" | |
| }, | |
| "source": [ | |
| "## Fungsi _private_ `_get_info(file, config_sheet=None)`\n", | |
| "\n", | |
| "Tujuan: mengambil nilai pada _sheet_ pengaturan (_sheet_ pertama pada file) dan mengubahnya ke dalam bentuk `dictionary`." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "FFAqYl0FZi8W", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "b2938fa7-7421-4848-b8c4-be101e7afd8a" | |
| }, | |
| "source": [ | |
| "info_file = _get_info('sample.xlsx', config_sheet='_INFO')\n", | |
| "info_file" | |
| ], | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "<ipython-input-11-23e254b88fe0>:27: DeprecationWarning: _get_info is deprecated, use get_info instead\n", | |
| " warnings.warn(\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "{'key': 'VALUE', 'station_name': 'Aurene'}" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 15 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "LGH5Q8B9aChT", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "outputId": "41a414dc-a673-4288-8283-cef51b9fffaf" | |
| }, | |
| "source": [ | |
| "info_file['station_name']" | |
| ], | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'Aurene'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 16 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "PHb8kiqIbXBC" | |
| }, | |
| "source": [ | |
| "# PENERAPAN" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "JQ25V3gmaRCz" | |
| }, | |
| "source": [ | |
| "## Fungsi _public_ `read_excel_hourly(file, station=None)`\n", | |
| "\n", | |
| "Tujuan: membaca data jam-jaman yang terdapat pada file lalu mengubahnya ke dalam bentuk `pandas.DataFrame` dengan index yang sesuai dengan tahun kejadiannya." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "_ZgcqRAmOHvn", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 424 | |
| }, | |
| "outputId": "6e7f51c6-d320-40c4-f1c6-deaf2cade111" | |
| }, | |
| "source": [ | |
| "data = read_excel_hourly('sample.xlsx', station=info_file['station_name'])\n", | |
| "data" | |
| ], | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Aurene\n", | |
| "2000-01-01 00:00:00 -\n", | |
| "2000-01-01 01:00:00 NaN\n", | |
| "2000-01-01 02:00:00 NaN\n", | |
| "2000-01-01 03:00:00 NaN\n", | |
| "2000-01-01 04:00:00 NaN\n", | |
| "... ...\n", | |
| "2002-12-31 19:00:00 -\n", | |
| "2002-12-31 20:00:00 -\n", | |
| "2002-12-31 21:00:00 -\n", | |
| "2002-12-31 22:00:00 -\n", | |
| "2002-12-31 23:00:00 -\n", | |
| "\n", | |
| "[26304 rows x 1 columns]" | |
| ], | |
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| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
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| " background-color: #3B4455;\n", | |
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| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-3ad16fbc-1c92-4d98-874a-637988bb712a');\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", | |
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| " }\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", | |
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| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
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| " }\n", | |
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| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
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| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "data", | |
| "summary": "{\n \"name\": \"data\",\n \"rows\": 26304,\n \"fields\": [\n {\n \"column\": \"Aurene\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 226,\n \"samples\": [\n 10,\n 24.5,\n 4.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 17 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "oYxhaVyua_Th", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "bab2174c-f042-467f-b1e1-98843fbd79cd" | |
| }, | |
| "source": [ | |
| "data.info()" | |
| ], | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "DatetimeIndex: 26304 entries, 2000-01-01 00:00:00 to 2002-12-31 23:00:00\n", | |
| "Freq: 60T\n", | |
| "Data columns (total 1 columns):\n", | |
| " # Column Non-Null Count Dtype \n", | |
| "--- ------ -------------- ----- \n", | |
| " 0 Aurene 10918 non-null object\n", | |
| "dtypes: object(1)\n", | |
| "memory usage: 411.0+ KB\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ndmVdsMZck2W" | |
| }, | |
| "source": [ | |
| "Catatan: data masih berupa `object`, dan belum diubah ke bentuk angka. Prapemrosesan ini serupa pada modul [hk73](https://nbviewer.jupyter.org/gist/taruma/b00880905f297013f046dad95dc2e284) (untuk membaca berkas bmkg)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "SsWZrgyzMTsj", | |
| "outputId": "17a7cbb6-60bb-4b2d-a57c-2a42164a97d6", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 677 | |
| } | |
| }, | |
| "source": [ | |
| "data.sample(n=20) # menampilkan sampel 20 baris dalam data secara acak" | |
| ], | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
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| " Aurene\n", | |
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| "2002-07-25 21:00:00 -\n", | |
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| "2000-01-08 13:00:00 0.6\n", | |
| "2002-03-08 16:00:00 -\n", | |
| "2001-05-16 07:00:00 NaN\n", | |
| "2001-09-27 02:00:00 NaN\n", | |
| "2001-06-11 03:00:00 NaN\n", | |
| "2000-03-19 06:00:00 2.6" | |
| ], | |
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| " <th>2000-02-15 18:00:00</th>\n", | |
| " <td>NaN</td>\n", | |
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| " <td>0.6</td>\n", | |
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| " <tr>\n", | |
| " <th>2002-03-08 16:00:00</th>\n", | |
| " <td>-</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2001-05-16 07:00:00</th>\n", | |
| " <td>NaN</td>\n", | |
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| " <th>2001-09-27 02:00:00</th>\n", | |
| " <td>NaN</td>\n", | |
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| " <tr>\n", | |
| " <th>2001-06-11 03:00:00</th>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2000-03-19 06:00:00</th>\n", | |
| " <td>2.6</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-48293b64-da43-45fc-99f7-e994e63c9642')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", | |
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| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
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| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " .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-48293b64-da43-45fc-99f7-e994e63c9642 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-48293b64-da43-45fc-99f7-e994e63c9642');\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", | |
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| " 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", | |
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| "</svg>\n", | |
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| "\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", | |
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| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
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| " 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", | |
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| " 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", | |
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| " 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", | |
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| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-27d4a568-7279-4491-9a77-3dcd874b85f2 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\": \"data\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"Aurene\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n 0.6,\n 2.6,\n 2.5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 19 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "3fHMWAqnhCwp", | |
| "outputId": "f68babba-c4ad-4caf-a5c8-e893bb67dbf2", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 424 | |
| } | |
| }, | |
| "source": [ | |
| "data.loc['2001'] # menampilkan data pada tahun tertentu" | |
| ], | |
| "execution_count": 30, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Aurene\n", | |
| "2001-01-01 00:00:00 NaN\n", | |
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| "2001-01-01 02:00:00 NaN\n", | |
| "2001-01-01 03:00:00 NaN\n", | |
| "2001-01-01 04:00:00 NaN\n", | |
| "... ...\n", | |
| "2001-12-31 19:00:00 NaN\n", | |
| "2001-12-31 20:00:00 NaN\n", | |
| "2001-12-31 21:00:00 NaN\n", | |
| "2001-12-31 22:00:00 NaN\n", | |
| "2001-12-31 23:00:00 NaN\n", | |
| "\n", | |
| "[8760 rows x 1 columns]" | |
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| " background-color: #E8F0FE;\n", | |
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| " [theme=dark] .colab-df-convert:hover {\n", | |
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| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
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| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-5a1c2df4-67b9-47d7-9cc3-ea2ac2d6b560');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
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| " 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", | |
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| " --bg-color: #E8F0FE;\n", | |
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| " --bg-color: #3B4455;\n", | |
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| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
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| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
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| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
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| " .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", | |
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| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
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| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
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| " border-bottom-color: var(--fill-color);\n", | |
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| "\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-a2d86c5f-5cc6-48c9-bef5-465286a848df 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\": \"data\",\n \"rows\": 8760,\n \"fields\": [\n {\n \"column\": \"Aurene\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": 0.0,\n \"max\": 70.0,\n \"num_unique_values\": 168,\n \"samples\": [\n 0.13,\n 1.6,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 30 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "pHQIH6rRhFtp", | |
| "outputId": "0f423940-61e1-4d37-82b4-7ba56262ccca", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 363 | |
| } | |
| }, | |
| "source": [ | |
| "data['20020101 07:00': '20020101 16:00'] # menampilkan data diantara jam 7.00 sampai 16.00 pada tanggal 1 januari 2002" | |
| ], | |
| "execution_count": 21, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Aurene\n", | |
| "2002-01-01 07:00:00 0.3\n", | |
| "2002-01-01 08:00:00 -\n", | |
| "2002-01-01 09:00:00 -\n", | |
| "2002-01-01 10:00:00 -\n", | |
| "2002-01-01 11:00:00 -\n", | |
| "2002-01-01 12:00:00 -\n", | |
| "2002-01-01 13:00:00 0.5\n", | |
| "2002-01-01 14:00:00 -\n", | |
| "2002-01-01 15:00:00 1.1\n", | |
| "2002-01-01 16:00:00 -" | |
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| " <td>-</td>\n", | |
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| " <td>-</td>\n", | |
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| " <td>-</td>\n", | |
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| " <th>2002-01-01 13:00:00</th>\n", | |
| " <td>0.5</td>\n", | |
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| " <td>-</td>\n", | |
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| " <tr>\n", | |
| " <th>2002-01-01 15:00:00</th>\n", | |
| " <td>1.1</td>\n", | |
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| " <tr>\n", | |
| " <th>2002-01-01 16:00:00</th>\n", | |
| " <td>-</td>\n", | |
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| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
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| "\n", | |
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| " const buttonEl =\n", | |
| " document.querySelector('#df-9a70ae85-dc9f-4cf4-acf9-da36cb8b87c3 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
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| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-9a70ae85-dc9f-4cf4-acf9-da36cb8b87c3');\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", | |
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| " await google.colab.output.renderOutput(dataTable, element);\n", | |
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| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
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| " --hover-bg-color: #E2EBFA;\n", | |
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| "\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-478f2ea4-6185-4ed9-b878-3dbc1e78e33a 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\": \"data['20020101 07:00': '20020101 16:00'] # menampilkan data diantara jam 7\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"Aurene\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \" -\",\n 1.1,\n 0.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 21 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "IQyRDTQgdf5z" | |
| }, | |
| "source": [ | |
| "# Changelog\n", | |
| "\n", | |
| "```\n", | |
| "- 20240414 - 1.1.0 / 0.5.0 - Documentation\n", | |
| "- 20191129 - 1.0.0 - Initial\n", | |
| "```\n", | |
| "\n", | |
| "#### Copyright © 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/).\n" | |
| ] | |
| } | |
| ] | |
| } |
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