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taruma_hk84_tabel_ringkasan_jamjaman.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "taruma_hk84_tabel_ringkasan_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/cad07f29ffc025ba9e7801e752be3444/taruma_hk84_tabel_ringkasan_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": "PijWaYlBOu6E"
},
"source": [
"Berdasarkan isu [#84](https://github.com/taruma/hidrokit/issues/84): **request: buat ringkasan tabel jam-jaman dengan durasinya**\n",
"\n",
"Referensi isu:\n",
"- `hidrokit.contrib.taruma.hk79` [#79](https://github.com/taruma/hidrokit/issues/79). \\([lihat notebook / manual](https://nbviewer.jupyter.org/gist/taruma/05dab67fac8313a94134ac02d0398897)\\). **request: ambil dataset hujan jam-jaman dari excel**\n",
"- `hidrokit.contrib.taruma.hk73` [#73](https://github.com/taruma/hidrokit/issues/73). \\([lihat notebook / manual](https://nbviewer.jupyter.org/gist/taruma/b00880905f297013f046dad95dc2e284)\\). **request: mengolah berkas dari data bmkg**\n",
"\n",
"Deskripsi Permasalahan:\n",
"- Setelah memperoleh dataset menggunakan `.hk79`, maka data jam-jaman akan diproses lebih lanjut lagi.\n",
"- Dalam isu ini, akan dibuat tabel ringkasan mengenai hujan jam-jaman beserta durasinya.\n",
"- Tabel ringkasan dapat disimpan dalam berbagai format (excel atau csv) dengan menggunakan `pandas`.\n",
"\n",
"Strategi Penyelesaian:\n",
"- Ditentukan/diasumsikan bahwa observasi hanya dilakukan per-24 jam, maka durasi terlama dalam tabel sebesar 24 jam.\n",
"- Mempersiapkan DF (`DataFrame`) yang akan digunakan dan menentukan kolom mana yang akan dibuat ringkasannya.\n",
"- Ambil sub-DF dari DF setiap 24 jam, kemudian diambil informasi kejadian hujan jam-jaman.\n",
" - Membuat `index_grouped` yang merupakan `list of list` index kejadian hujan yang berturut-turut. Fungsi ini telah dikembangkan pada modul `.hk73`.\n",
" - Membuat tiga `list` yang merupakan `list` untuk tanggal observasi, `list` untuk jam observasi, dan `list` untuk nilai hujan observasi.\n",
" - Mengubah tiga `list` tersebut ke dalam bentuk `dictionary`.\n",
"- Ulangi tahap sebelumnya untuk setiap hari (24 jam). Dan menggabungkan seluruh `dictionary` dalam satu peubah.\n",
"- Mengubah `dictionary` menjadi `pandas.DataFrame`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mxXIWs-iRvPM"
},
"source": [
"# PERSIAPAN DAN DATASET"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zIWmJH0_Ociy",
"outputId": "c795dd86-2429-4a13-fea0-5a4afb194ba3",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"try:\n",
" import hidrokit\n",
"except:\n",
" !pip install git+https://github.com/hidrokit/hidrokit.git@243-refactor-hk84\n",
"print(f'hidrokit version: {hidrokit.__version__}')"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"hidrokit version: 0.5.0\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lxLA_9I_R2ej"
},
"source": [
"!wget -O aurene_clean.csv \"https://taruma.github.io/assets/hidrokit_dataset/AURENE_STATION_clean.csv\" -q"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "uahcisYRSaFc"
},
"source": [
"import pandas as pd\n",
"import numpy as np"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5IZgq0Lkgp0i",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "67446548-0998-4910-b4a5-92a508226266"
},
"source": [
"# Load Dataset\n",
"dataset = pd.read_csv('aurene_clean.csv', index_col=0, parse_dates=True)\n",
"dataset.info()"
],
"execution_count": 4,
"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",
"Data columns (total 1 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 AURENE 2759 non-null float64\n",
"dtypes: float64(1)\n",
"memory usage: 411.0 KB\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D8bCLVRfSdjx"
},
"source": [
"# KODE"
]
},
{
"cell_type": "code",
"metadata": {
"id": "AbFHYIzwSc0U"
},
"source": [
"import pandas as pd\n",
"from hidrokit.contrib.taruma import hk73\n",
"\n",
"\n",
"def _time_grouped(df, index_grouped, col, date_fmt=\"%Y-%m-%d\", hour_fmt=\"%H:%M\"):\n",
" \"\"\"\n",
" Return index_grouped as (list of date, list of hour)\n",
"\n",
" Parameters:\n",
" - df: pandas DataFrame\n",
" The DataFrame containing the data.\n",
" - index_grouped: list of tuples\n",
" The index groups to be processed.\n",
" - col: int or str\n",
" The column index or name to extract the date and hour values from.\n",
" - date_fmt: str, optional\n",
" The format string for the date values. Default is '%Y-%m-%d'.\n",
" - hour_fmt: str, optional\n",
" The format string for the hour values. Default is '%H:%M'.\n",
"\n",
" Returns:\n",
" - date: list of lists\n",
" The list of date values for each index group.\n",
" - hour: list of lists\n",
" The list of hour values for each index group.\n",
" \"\"\"\n",
" date = []\n",
" hour = []\n",
" for item in index_grouped:\n",
" date_val = df.iloc[[item[0]], col].index.strftime(date_fmt).to_list()\n",
" hour_val = df.iloc[[item[0]], col].index.strftime(hour_fmt).to_list()\n",
" date.append(date_val)\n",
" hour.append(hour_val)\n",
" return date, hour\n",
"\n",
"\n",
"def _value_grouped(df, index_grouped, col):\n",
" \"\"\"Return index_grouped as a list of value lists.\n",
"\n",
" Args:\n",
" df (pandas.DataFrame): The input DataFrame.\n",
" index_grouped (list): The list of indices to group.\n",
" col (int): The column index to extract values from.\n",
"\n",
" Returns:\n",
" list: A list of value lists corresponding to the grouped indices.\n",
" \"\"\"\n",
" value = []\n",
" for item in index_grouped:\n",
" value_val = df.iloc[item, col].to_list()\n",
" value.append(value_val)\n",
" return value\n",
"\n",
"\n",
"def _dict_grouped(date_list, hour_list, value_list, start=0):\n",
" \"\"\"\n",
" Join three lists and return as a dictionary.\n",
"\n",
" Args:\n",
" date_list (list): List of dates.\n",
" hour_list (list): List of hours.\n",
" value_list (list): List of values.\n",
" start (int, optional): Starting index for the dictionary keys. Defaults to 0.\n",
"\n",
" Returns:\n",
" dict: Dictionary with keys as indices and values as concatenated date, hour, and value.\n",
"\n",
" \"\"\"\n",
" item_list = enumerate(zip(date_list, hour_list, value_list), start=start)\n",
" return {i: date + hour + value for i, (date, hour, value) in item_list}\n",
"\n",
"\n",
"def summary_hourly(\n",
" dataframe,\n",
" column,\n",
" n_hours=24,\n",
" text_date=None,\n",
" return_as_dataframe=True,\n",
" date_format=\"%Y-%m-%d\",\n",
" hour_format=\"%H:%M\",\n",
"): # pylint: disable=too-many-arguments,too-many-locals\n",
" \"\"\"\n",
" Generate a summary of hourly data from a DataFrame.\n",
"\n",
" Args:\n",
" df (pandas.DataFrame): The input DataFrame.\n",
" column (str): The name of the column containing the hourly data.\n",
" n_hours (int, optional): The number of hours to group together. Defaults to 24.\n",
" text_date (list, optional):\n",
" The list of column names to include in the summary. Defaults to ['date', 'hour'].\n",
" as_df (bool, optional): Whether to return the summary as a DataFrame. Defaults to True.\n",
" date_fmt (str, optional): The date format string. Defaults to '%Y-%m-%d'.\n",
" hour_fmt (str, optional): The hour format string. Defaults to '%H:%M'.\n",
"\n",
" Returns:\n",
" pandas.DataFrame or dict:\n",
" The summary of hourly data. If `as_df` is True, a DataFrame is returned.\n",
" Otherwise, a dictionary is returned.\n",
" \"\"\"\n",
" col = dataframe.columns.get_loc(column)\n",
" nrows, _ = dataframe.shape\n",
" results = {}\n",
" text_date = [\"date\", \"hour\"] if text_date is None else text_date\n",
"\n",
" for i in range(0, nrows, n_hours):\n",
" sub_df = dataframe.iloc[i : i + n_hours]\n",
" ix_array = hk73.get_missing_data_indices(~sub_df.iloc[:, col].isna().values)\n",
" ix_grouped = hk73.group_consecutive_elements(ix_array)\n",
" date, hour = _time_grouped(\n",
" sub_df, ix_grouped, col, date_fmt=date_format, hour_fmt=hour_format\n",
" )\n",
" value = _value_grouped(sub_df, ix_grouped, col)\n",
" each_hours = _dict_grouped(date, hour, value, start=i)\n",
" results.update(each_hours)\n",
"\n",
" if return_as_dataframe:\n",
" columns_name = text_date + [i for i in range(1, n_hours + 1)]\n",
" df_results = pd.DataFrame.from_dict(\n",
" results, orient=\"index\", columns=columns_name\n",
" )\n",
" return df_results\n",
" return results\n"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "hUCLu72zUHE1"
},
"source": [
"# PENERAPAN"
]
},
{
"cell_type": "code",
"metadata": {
"id": "g4nxkghGU7GM",
"outputId": "73c4403e-af16-4754-8ce7-cc7be55efd67",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 236
}
},
"source": [
"summary = summary_hourly(dataset, 'AURENE')\n",
"summary.head()"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" date hour 1 2 3 4 5 6 7 8 ... 15 16 17 18 \\\n",
"0 2000-01-01 08:00 0.3 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN \n",
"24 2000-01-02 08:00 7.0 0.5 0.5 NaN NaN NaN NaN NaN ... NaN NaN NaN NaN \n",
"48 2000-01-03 00:00 0.6 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN \n",
"49 2000-01-03 05:00 0.6 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN \n",
"50 2000-01-03 09:00 2.2 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN \n",
"\n",
" 19 20 21 22 23 24 \n",
"0 NaN NaN NaN NaN NaN NaN \n",
"24 NaN NaN NaN NaN NaN NaN \n",
"48 NaN NaN NaN NaN NaN NaN \n",
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"50 NaN NaN NaN NaN NaN NaN \n",
"\n",
"[5 rows x 26 columns]"
],
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"\n",
" <div id=\"df-a3cae008-fef8-4aa0-b46a-763fc7dde792\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" <tr style=\"text-align: right;\">\n",
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" <th>date</th>\n",
" <th>hour</th>\n",
" <th>1</th>\n",
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" <th>24</th>\n",
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" <th>0</th>\n",
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" <th>48</th>\n",
" <td>2000-01-03</td>\n",
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" <td>0.6</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>49</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>50</th>\n",
" <td>2000-01-03</td>\n",
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" <td>2.2</td>\n",
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"</table>\n",
"<p>5 rows × 26 columns</p>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a3cae008-fef8-4aa0-b46a-763fc7dde792')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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" .colab-df-buttons div {\n",
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" 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",
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" element.innerHTML = '';\n",
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" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-c4888e44-dee6-4516-ad33-784dc88f4271 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",
"variable_name": "summary"
}
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cGdS4rZklC8s",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 549
},
"outputId": "2a4b479b-3ff3-4e93-fb58-a664063d0674"
},
"source": [
"summary.sample(n=15)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" date hour 1 2 3 4 5 6 7 8 ... 15 16 17 \\\n",
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"5376 NaN NaN NaN NaN NaN NaN NaN \n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>2002-01-31</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15 rows × 26 columns</p>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b96b974-9b24-49cd-8aff-4799dcc2b1b1')\"\n",
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" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
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"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .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",
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" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-3b96b974-9b24-49cd-8aff-4799dcc2b1b1 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-3b96b974-9b24-49cd-8aff-4799dcc2b1b1');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-d056786c-bbf1-4c9d-86ac-295a05c72465\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d056786c-bbf1-4c9d-86ac-295a05c72465')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
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" width=\"24px\">\n",
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" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
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" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
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" .colab-df-quickchart:hover {\n",
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" fill: var(--button-hover-fill-color);\n",
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"\n",
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" border-left-color: var(--fill-color);\n",
" }\n",
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" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
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" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
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"</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",
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" const charts = await google.colab.kernel.invokeFunction(\n",
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{
"cell_type": "code",
"metadata": {
"id": "vtYDmGTeW0cS",
"outputId": "7aace334-992b-4433-cc64-93fe34f2613f",
"colab": {
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"source": [
"summary2 = summary_hourly(dataset, 'AURENE', n_hours=10, text_date=['Tanggal', 'Jam'], date_format='%d %b %Y', hour_format='%I:%M %p')\n",
"summary2.sample(n=10)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Tanggal Jam 1 2 3 4 5 6 7 8 9 10\n",
"15470 06 Oct 2001 05:00 PM 0.7 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
"18700 18 Feb 2002 04:00 AM 2.2 2.3 NaN NaN NaN NaN NaN NaN NaN NaN\n",
"7350 02 Nov 2000 06:00 AM 2.2 0.2 NaN NaN NaN NaN NaN NaN NaN NaN\n",
"21200 02 Jun 2002 10:00 AM 13.6 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
"17120 14 Dec 2001 08:00 AM 0.9 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
"2710 23 Apr 2000 06:00 AM 4.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
"9790 12 Feb 2001 12:00 AM 3.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
"15850 22 Oct 2001 12:00 PM 9.4 1.4 0.4 0.6 NaN NaN NaN NaN NaN NaN\n",
"18140 26 Jan 2002 05:00 AM 0.2 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
"2880 30 Apr 2000 05:00 AM 40.0 17.0 0.5 NaN NaN NaN NaN NaN NaN NaN"
],
"text/html": [
"\n",
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" <td>06 Oct 2001</td>\n",
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" <tr>\n",
" <th>18700</th>\n",
" <td>18 Feb 2002</td>\n",
" <td>04:00 AM</td>\n",
" <td>2.2</td>\n",
" <td>2.3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>02 Nov 2000</td>\n",
" <td>06:00 AM</td>\n",
" <td>2.2</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21200</th>\n",
" <td>02 Jun 2002</td>\n",
" <td>10:00 AM</td>\n",
" <td>13.6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17120</th>\n",
" <td>14 Dec 2001</td>\n",
" <td>08:00 AM</td>\n",
" <td>0.9</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2710</th>\n",
" <td>23 Apr 2000</td>\n",
" <td>06:00 AM</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>9790</th>\n",
" <td>12 Feb 2001</td>\n",
" <td>12:00 AM</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15850</th>\n",
" <td>22 Oct 2001</td>\n",
" <td>12:00 PM</td>\n",
" <td>9.4</td>\n",
" <td>1.4</td>\n",
" <td>0.4</td>\n",
" <td>0.6</td>\n",
" <td>NaN</td>\n",
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" <th>18140</th>\n",
" <td>26 Jan 2002</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2880</th>\n",
" <td>30 Apr 2000</td>\n",
" <td>05:00 AM</td>\n",
" <td>40.0</td>\n",
" <td>17.0</td>\n",
" <td>0.5</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-002b57fd-a33b-48ee-b056-a8e9473fdb28');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
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" border-left-color: var(--fill-color);\n",
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" border-color: transparent;\n",
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" }\n",
" 80% {\n",
" border-color: transparent;\n",
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" 90% {\n",
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" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
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],
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"metadata": {},
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},
{
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"metadata": {
"id": "xtlMdmTlhShI",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ccffe276-fe40-4c7b-9286-c9546f8684a0"
},
"source": [
"summary3 = summary_hourly(dataset, 'AURENE', n_hours=24, return_as_dataframe=False)\n",
"print(type(summary3))\n",
"print(len(summary3.keys()))\n",
"print(list(summary3.keys())[:10])"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'dict'>\n",
"1093\n",
"[0, 24, 48, 49, 50, 72, 96, 97, 120, 144]\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EhPfCQ6LVZYK"
},
"source": [
"# Changelog\n",
"\n",
"```\n",
"- 20240414 - 1.1.0 / 0.5.0 - Refactor hk84\n",
"- 20191209 - 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/).\n"
]
}
]
}
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