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taruma/taruma_hk98_rekap_deret_waktu.ipynb

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taruma_hk98_rekap_deret_waktu.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "taruma_hk98_rekap_deret_waktu.ipynb",
"provenance": [],
"collapsed_sections": [],
"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/aca7f90c8fbb0034587809883d0d9e92/taruma_hk98_rekap_deret_waktu.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MaSOFhUSmz8A"
},
"source": [
"Berdasarkan isu [#98](https://github.com/taruma/hidrokit/issues/98): **buat ringkasan/rekap data deret waktu**\n",
"\n",
"Deskripsi permasalahan: \n",
"- Membuat ringkasan/rekapitulasi/laporan dari data deret waktu (_time series_).\n",
"\n",
"Strategi Penyelesaian:\n",
"- Membuat fungsi yang memudahkan kostumisasi saat menggunakan fungsi buatan sendiri.\n",
"\n",
"Catatan:\n",
"- Fungsi ini hanya diuji pada data harian dengan kepentingan merekapitulasi setiap bulannya."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cXNT0CMkn-Bf"
},
"source": [
"# PERSIAPAN DAN DATASET"
]
},
{
"cell_type": "code",
"metadata": {
"id": "GaXYLfvUmO5m"
},
"source": [
"import numpy as np\n",
"import pandas as pd"
],
"execution_count": 48,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2nnPlg4toC6M",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7a0ac7a9-f5b5-4913-db9b-6dcca917871b"
},
"source": [
"try:\n",
" import hidrokit\n",
"except ModuleNotFoundError:\n",
" !pip install git+https://github.com/taruma/hidrokit.git@latest -q\n",
" import hidrokit\n",
"\n",
"print(f'hidrokit version: {hidrokit.__version__}')"
],
"execution_count": 49,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"hidrokit version: 0.4.1\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "yZOlKYgwsGzs"
},
"source": [
"!wget -O sample.xlsx \"https://taruma.github.io/assets/hidrokit_dataset/data_daily_sample.xlsx\" -q\n",
"dataset_path = 'sample.xlsx'"
],
"execution_count": 50,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "j6zB5QFetQLs",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 397
},
"outputId": "c81495e0-7db4-496d-ff12-1e4278a158ee"
},
"source": [
"from hidrokit.contrib.taruma import hk88\n",
"\n",
"_data = hk88.read_workbook(dataset_path, ['STA_A', 'STA_B', 'STA_C'])\n",
"dataset = _data.infer_objects()\n",
"dataset.info()\n",
"dataset.head()"
],
"execution_count": 51,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 5478 entries, 2001-01-01 to 2015-12-31\n",
"Freq: D\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 STA_A 5477 non-null float64\n",
" 1 STA_B 5470 non-null float64\n",
" 2 STA_C 5475 non-null float64\n",
"dtypes: float64(3)\n",
"memory usage: 171.2 KB\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" STA_A STA_B STA_C\n",
"2001-01-01 0.0 0.0 0.00\n",
"2001-01-02 0.0 0.0 0.65\n",
"2001-01-03 0.0 45.0 9.16\n",
"2001-01-04 0.0 0.0 0.00\n",
"2001-01-05 0.0 5.0 1.03"
],
"text/html": [
"\n",
" <div id=\"df-a3d8b11d-f70c-4b1c-abbd-7a9d085101e9\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>STA_A</th>\n",
" <th>STA_B</th>\n",
" <th>STA_C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-02</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-03</th>\n",
" <td>0.0</td>\n",
" <td>45.0</td>\n",
" <td>9.16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-04</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-05</th>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>1.03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a3d8b11d-f70c-4b1c-abbd-7a9d085101e9')\"\n",
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" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-a3d8b11d-f70c-4b1c-abbd-7a9d085101e9 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-a3d8b11d-f70c-4b1c-abbd-7a9d085101e9');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 51
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UQU1Nv0moBT5"
},
"source": [
"# KODE"
]
},
{
"cell_type": "code",
"metadata": {
"id": "_OZbv2sDrrQk"
},
"source": [
"def summary_station(dataset, column, ufunc, ufunc_col, n_days='MS'):\n",
" grouped = [dataset.index.year, dataset.index.month]\n",
"\n",
" ufunc = ufunc if isinstance(ufunc, (list, tuple)) else (ufunc,)\n",
" ufunc_col = (ufunc_col\n",
" if isinstance(ufunc_col, (list, tuple)) else (ufunc_col,))\n",
"\n",
" if len(ufunc) != len(ufunc_col):\n",
" raise ValueError('length ufunc and ufunc_col are not matched.')\n",
"\n",
" if n_days.endswith(\"D\") or n_days.endswith(\"MS\") or n_days.endswith(\"M\"):\n",
" ix_month = []\n",
" val_month = []\n",
" for _, x in dataset[column].groupby(by=grouped):\n",
" each_month = x.groupby(pd.Grouper(freq=n_days)).agg(ufunc)\n",
" val_month.append(each_month.values)\n",
" ix_month.append(each_month.index.to_numpy())\n",
" return pd.DataFrame(\n",
" data=np.vstack(val_month),\n",
" index=np.hstack(ix_month),\n",
" columns=pd.MultiIndex.from_product([[column], ufunc_col]),\n",
" ).rename_axis(\"DATE\")\n",
" else:\n",
" summary = dataset[[column]].resample(n_days).agg(ufunc)\n",
" summary.columns = pd.MultiIndex.from_product([[column], ufunc_col])\n",
" return summary\n",
"\n",
"def summary_all(dataset, ufunc, ufunc_col, columns=None, n_days='MS', verbose=False):\n",
" res = []\n",
"\n",
" columns = columns if columns is not None else list(dataset.columns)\n",
" columns = columns if isinstance(columns, (list, tuple)) else [columns]\n",
"\n",
" for column in columns:\n",
" if verbose:\n",
" print('PROCESSING:', column)\n",
" res.append(\n",
" summary_station(dataset, column, ufunc, ufunc_col, n_days=n_days)\n",
" )\n",
" return pd.concat(res, axis=1)"
],
"execution_count": 52,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ck-RQB5kWcJB"
},
"source": [
"# FUNGSI"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l6DX4sTkbQ14"
},
"source": [
"## Fungsi `summary_station()`\n",
"\n",
"Fungsi ini membuat rekap untuk stasiun/kolom tunggal dalam bentuk keluaran `pandas.DataFrame`. Argumen yang dibutuhkan antara lain:\n",
"\n",
"- `dataset`: DataFrame dataset. Isian berupa `pandas.DataFrame`. \n",
"- `column`: kolom tunggal yang akan diproses. Isian berupa _string_.\n",
"- `ufunc`: fungsi atau _list_ fungsi yang akan digunakan. Isian berupa `object` atau _list of `object`_.\n",
"- `ufunc_col`: nama atau _list_ nama dari fungsi `ufunc`. Isian berupa _list of string_.\n",
"- `n_days='M'`: indikator jumlah hari/bulan yang diproses. Isian merupakan isian valid untuk parameter `freq` pada objek `pd.Grouper` ([referensi](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases)). Isian `'M'` berarti setiap bulan (*M*onth), isian `'9D'` berarti setiap 9 hari (*D*ays). "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Bn5O9OEDenXj"
},
"source": [
"### Argumen `ufunc` dan `ufunc_col`\n",
"\n",
"Pengguna diberi kebebasan dalam melakukan perhitungan pada fungsi `summary_station`. Dalam _notebook_ ini akan diberikan contoh menggunakan fungsi yang tersedia pada python, numpy, dan membuatnya sendiri."
]
},
{
"cell_type": "code",
"metadata": {
"id": "LMu8A0I0Wdpp"
},
"source": [
"# Fungsi buatan sendiri\n",
"def n_rain(x):\n",
" \"Jumlah hari hujan\"\n",
" return (x > 0).sum()\n",
"\n",
"myfunc = [np.sum, n_rain, len]\n",
"myfunc_col = ['sum', 'n_rain', 'n_days']"
],
"execution_count": 53,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "BOaPl7t8ffQo"
},
"source": [
"### Penggunaan (_default_)\n",
"\n",
"Jika tidak diberi argumen `n_days` maka fungsi ini akan memproses data setiap bulan."
]
},
{
"cell_type": "code",
"metadata": {
"id": "9Mw-SjOKWdVy",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 486
},
"outputId": "5744fed1-1a2d-40a3-f469-b121e0e9ad7a"
},
"source": [
"summary_station(\n",
" dataset=dataset, column='STA_B', \n",
" ufunc=myfunc, ufunc_col=myfunc_col)"
],
"execution_count": 54,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" STA_B \n",
" sum n_rain n_days\n",
"DATE \n",
"2001-01-01 454.0 18.0 31.0\n",
"2001-02-01 298.0 12.0 28.0\n",
"2001-03-01 475.0 18.0 31.0\n",
"2001-04-01 272.0 12.0 30.0\n",
"2001-05-01 86.0 4.0 31.0\n",
"... ... ... ...\n",
"2015-08-01 0.0 0.0 31.0\n",
"2015-09-01 0.0 0.0 30.0\n",
"2015-10-01 14.0 1.0 31.0\n",
"2015-11-01 165.0 3.0 30.0\n",
"2015-12-01 216.0 11.0 31.0\n",
"\n",
"[180 rows x 3 columns]"
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"\n",
" <div id=\"df-b42ce8c5-10a7-4e44-9fa7-441d4cb480a3\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">STA_B</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>sum</th>\n",
" <th>n_rain</th>\n",
" <th>n_days</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th>2001-01-01</th>\n",
" <td>454.0</td>\n",
" <td>18.0</td>\n",
" <td>31.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-02-01</th>\n",
" <td>298.0</td>\n",
" <td>12.0</td>\n",
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" <tr>\n",
" <th>2001-03-01</th>\n",
" <td>475.0</td>\n",
" <td>18.0</td>\n",
" <td>31.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-04-01</th>\n",
" <td>272.0</td>\n",
" <td>12.0</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-05-01</th>\n",
" <td>86.0</td>\n",
" <td>4.0</td>\n",
" <td>31.0</td>\n",
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" <tr>\n",
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" <td>0.0</td>\n",
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" <tr>\n",
" <th>2015-09-01</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
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" <tr>\n",
" <th>2015-10-01</th>\n",
" <td>14.0</td>\n",
" <td>1.0</td>\n",
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" <th>2015-11-01</th>\n",
" <td>165.0</td>\n",
" <td>3.0</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-01</th>\n",
" <td>216.0</td>\n",
" <td>11.0</td>\n",
" <td>31.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</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",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-b42ce8c5-10a7-4e44-9fa7-441d4cb480a3 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-b42ce8c5-10a7-4e44-9fa7-441d4cb480a3');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 54
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "29sA49LffqAU"
},
"source": [
"### Argumen `n_days`\n",
"\n",
"`n_days` bisa diisi dengan jumlah hari yang ingin diproses **setiap bulan**-nya. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "zaP9x1g7gCkc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 486
},
"outputId": "5cb09ebf-3319-4779-9f48-8c065c2a2b62"
},
"source": [
"# Setiap 8 Hari\n",
"summary_station(\n",
" dataset=dataset, column='STA_B', \n",
" ufunc=myfunc, ufunc_col=myfunc_col,\n",
" n_days='8D')"
],
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" STA_B \n",
" sum n_rain n_days\n",
"DATE \n",
"2001-01-01 90.0 4.0 8.0\n",
"2001-01-09 123.0 5.0 8.0\n",
"2001-01-17 192.0 6.0 8.0\n",
"2001-01-25 49.0 3.0 7.0\n",
"2001-02-01 129.0 5.0 8.0\n",
"... ... ... ...\n",
"2015-11-25 48.0 1.0 6.0\n",
"2015-12-01 78.0 2.0 8.0\n",
"2015-12-09 48.0 4.0 8.0\n",
"2015-12-17 52.0 3.0 8.0\n",
"2015-12-25 38.0 2.0 7.0\n",
"\n",
"[720 rows x 3 columns]"
],
"text/html": [
"\n",
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" <div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">STA_B</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>sum</th>\n",
" <th>n_rain</th>\n",
" <th>n_days</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>90.0</td>\n",
" <td>4.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-09</th>\n",
" <td>123.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-17</th>\n",
" <td>192.0</td>\n",
" <td>6.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-25</th>\n",
" <td>49.0</td>\n",
" <td>3.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-02-01</th>\n",
" <td>129.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-25</th>\n",
" <td>48.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-01</th>\n",
" <td>78.0</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-09</th>\n",
" <td>48.0</td>\n",
" <td>4.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-17</th>\n",
" <td>52.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-25</th>\n",
" <td>38.0</td>\n",
" <td>2.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>720 rows × 3 columns</p>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab4c563a-cd52-4e67-a62a-74dee7ff0b96')\"\n",
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" \n",
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" .colab-df-container {\n",
" display:flex;\n",
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" gap: 12px;\n",
" }\n",
"\n",
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" background-color: #E8F0FE;\n",
" border: none;\n",
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" background-color: #3B4455;\n",
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"\n",
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" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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"\n",
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" const element = document.querySelector('#df-ab4c563a-cd52-4e67-a62a-74dee7ff0b96');\n",
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" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kN49MzTlgLVF",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 486
},
"outputId": "e3163078-5300-4108-81ff-26c2ac8dcb19"
},
"source": [
"# Setiap 15 Hari\n",
"summary_station(\n",
" dataset=dataset, column='STA_C', \n",
" ufunc=myfunc, ufunc_col=myfunc_col,\n",
" n_days='15D')"
],
"execution_count": 56,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" STA_C \n",
" sum n_rain n_days\n",
"DATE \n",
"2001-01-01 158.08 13.0 15.0\n",
"2001-01-16 146.94 14.0 15.0\n",
"2001-01-31 22.96 1.0 1.0\n",
"2001-02-01 157.80 12.0 15.0\n",
"2001-02-16 77.45 11.0 13.0\n",
"... ... ... ...\n",
"2015-11-01 152.00 7.0 15.0\n",
"2015-11-16 76.00 4.0 15.0\n",
"2015-12-01 23.00 1.0 15.0\n",
"2015-12-16 46.00 8.0 15.0\n",
"2015-12-31 0.00 0.0 1.0\n",
"\n",
"[465 rows x 3 columns]"
],
"text/html": [
"\n",
" <div id=\"df-b96d0f22-3fce-4825-8667-782497aec5e3\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">STA_C</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>sum</th>\n",
" <th>n_rain</th>\n",
" <th>n_days</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>158.08</td>\n",
" <td>13.0</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-16</th>\n",
" <td>146.94</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-31</th>\n",
" <td>22.96</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-02-01</th>\n",
" <td>157.80</td>\n",
" <td>12.0</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-02-16</th>\n",
" <td>77.45</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-01</th>\n",
" <td>152.00</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-16</th>\n",
" <td>76.00</td>\n",
" <td>4.0</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-01</th>\n",
" <td>23.00</td>\n",
" <td>1.0</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-16</th>\n",
" <td>46.00</td>\n",
" <td>8.0</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-31</th>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>465 rows × 3 columns</p>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b96d0f22-3fce-4825-8667-782497aec5e3')\"\n",
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" width=\"24px\">\n",
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" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-b96d0f22-3fce-4825-8667-782497aec5e3 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-b96d0f22-3fce-4825-8667-782497aec5e3');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 56
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MqVw8aWIgiPm"
},
"source": [
"## Fungsi `summary_all()`\n",
"\n",
"Fungsi ini hanya melakukan proses `summary_station()` untuk seluruh kolom atau kolom tertentu yang diatur dengan argumen `columns`. Argumen `dataset`, `ufunc`, `ufunc_col`, `n_days='M'` sama dengan `summary_station()`, yang membedakan adalah argumen `columns`.\n",
"\n",
"Baru di versi `0.4.0` (isu [#115](https://github.com/hidrokit/hidrokit/issues/115)): Penambahan argumen `verbose` untuk menampilkan/tidak menampilkan tulisan `PROCESSING: ...`. Nilai default `False` (tidak ditampilkan). "
]
},
{
"cell_type": "code",
"metadata": {
"id": "OKewkFKUhoN-"
},
"source": [
"# Menggunakan fungsi yang lebih banyak\n",
"def n_rain(x):\n",
" \"Jumlah hari hujan\"\n",
" return (x > 0).sum()\n",
"\n",
"def n_dry(x):\n",
" \"Jumlah hari kering\"\n",
" return np.logical_or(x == 0, x.isna()).sum()\n",
"\n",
"myfunc_all = [len, n_rain, n_dry, np.sum, np.mean, np.std]\n",
"myfunc_all_col = ['n_days', 'n_rain', 'n_dry', 'SUM', 'MEAN', 'STD']"
],
"execution_count": 57,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "AgPAPhD7g9Hr"
},
"source": [
"### Seluruh kolom"
]
},
{
"cell_type": "code",
"metadata": {
"id": "WaUftKLovZ97",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 486
},
"outputId": "94c88af2-5674-488c-ed93-3e3fcd363361"
},
"source": [
"summary_all(\n",
" dataset=dataset,\n",
" ufunc=myfunc_all, ufunc_col=myfunc_all_col,\n",
" n_days='7D')"
],
"execution_count": 58,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" STA_A STA_B \\\n",
" n_days n_rain n_dry SUM MEAN STD n_days n_rain \n",
"DATE \n",
"2001-01-01 7.0 0.0 7.0 0.0 0.000000 0.000000 7.0 3.0 \n",
"2001-01-08 7.0 0.0 7.0 0.0 0.000000 0.000000 7.0 4.0 \n",
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"... ... ... ... ... ... ... ... ... \n",
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"\n",
" STA_C \\\n",
" n_dry SUM MEAN STD n_days n_rain n_dry SUM \n",
"DATE \n",
"2001-01-01 4.0 58.0 8.285714 16.499639 7.0 5.0 2.0 15.43 \n",
"2001-01-08 3.0 68.0 9.714286 13.300555 7.0 7.0 0.0 125.30 \n",
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"2001-01-29 1.0 44.0 14.666667 13.650397 3.0 3.0 0.0 37.99 \n",
"... ... ... ... ... ... ... ... ... \n",
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"\n",
" \n",
" MEAN STD \n",
"DATE \n",
"2001-01-01 2.204286 3.384252 \n",
"2001-01-08 17.900000 32.992996 \n",
"2001-01-15 13.340000 12.806884 \n",
"2001-01-22 7.982857 5.429001 \n",
"2001-01-29 12.663333 11.376741 \n",
"... ... ... \n",
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]
},
"metadata": {},
"execution_count": 58
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vvgo6Yg0hMyE"
},
"source": [
"### Kolom tertentu"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iNipfDFMvfkR",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 486
},
"outputId": "ab115a07-8398-4701-8e49-6a751def39bb"
},
"source": [
"summary_all(\n",
" dataset=dataset, columns=['STA_A', 'STA_C'],\n",
" ufunc=myfunc_all, ufunc_col=myfunc_all_col,\n",
" n_days='16D')"
],
"execution_count": 59,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" STA_A STA_C \\\n",
" n_days n_rain n_dry SUM MEAN STD n_days n_rain \n",
"DATE \n",
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"2001-03-01 16.0 0.0 16.0 0.0 0.000000 0.000000 16.0 14.0 \n",
"... ... ... ... ... ... ... ... ... \n",
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"2015-12-01 16.0 10.0 6.0 197.0 12.312500 19.269038 16.0 1.0 \n",
"2015-12-17 15.0 15.0 0.0 200.0 13.333333 10.587504 15.0 8.0 \n",
"\n",
" \n",
" n_dry SUM MEAN STD \n",
"DATE \n",
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"2001-01-17 1.0 142.18 9.478667 9.163421 \n",
"2001-02-01 3.0 157.81 9.863125 15.319503 \n",
"2001-02-17 2.0 77.44 6.453333 7.835151 \n",
"2001-03-01 2.0 9.01 0.563125 0.719228 \n",
"... ... ... ... ... \n",
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"2015-12-01 15.0 23.00 1.437500 5.750000 \n",
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" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>16.0</td>\n",
" <td>0.0</td>\n",
" <td>16.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>16.0</td>\n",
" <td>14.0</td>\n",
" <td>2.0</td>\n",
" <td>185.80</td>\n",
" <td>11.612500</td>\n",
" <td>22.786906</td>\n",
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" <tr>\n",
" <th>2001-01-17</th>\n",
" <td>15.0</td>\n",
" <td>0.0</td>\n",
" <td>15.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>1.0</td>\n",
" <td>142.18</td>\n",
" <td>9.478667</td>\n",
" <td>9.163421</td>\n",
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" <tr>\n",
" <th>2001-02-01</th>\n",
" <td>16.0</td>\n",
" <td>0.0</td>\n",
" <td>16.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>16.0</td>\n",
" <td>13.0</td>\n",
" <td>3.0</td>\n",
" <td>157.81</td>\n",
" <td>9.863125</td>\n",
" <td>15.319503</td>\n",
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" <tr>\n",
" <th>2001-02-17</th>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>12.0</td>\n",
" <td>10.0</td>\n",
" <td>2.0</td>\n",
" <td>77.44</td>\n",
" <td>6.453333</td>\n",
" <td>7.835151</td>\n",
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" <tr>\n",
" <th>2001-03-01</th>\n",
" <td>16.0</td>\n",
" <td>0.0</td>\n",
" <td>16.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>16.0</td>\n",
" <td>14.0</td>\n",
" <td>2.0</td>\n",
" <td>9.01</td>\n",
" <td>0.563125</td>\n",
" <td>0.719228</td>\n",
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" <tr>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <th>2015-10-17</th>\n",
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" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>97.0</td>\n",
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" <td>9.500877</td>\n",
" <td>15.0</td>\n",
" <td>1.0</td>\n",
" <td>14.0</td>\n",
" <td>6.00</td>\n",
" <td>0.400000</td>\n",
" <td>1.549193</td>\n",
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" <tr>\n",
" <th>2015-11-01</th>\n",
" <td>16.0</td>\n",
" <td>12.0</td>\n",
" <td>4.0</td>\n",
" <td>190.0</td>\n",
" <td>11.875000</td>\n",
" <td>14.655488</td>\n",
" <td>16.0</td>\n",
" <td>7.0</td>\n",
" <td>9.0</td>\n",
" <td>152.00</td>\n",
" <td>9.500000</td>\n",
" <td>19.721393</td>\n",
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" <tr>\n",
" <th>2015-11-17</th>\n",
" <td>14.0</td>\n",
" <td>13.0</td>\n",
" <td>1.0</td>\n",
" <td>384.0</td>\n",
" <td>27.428571</td>\n",
" <td>15.360861</td>\n",
" <td>14.0</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>76.00</td>\n",
" <td>5.428571</td>\n",
" <td>13.119066</td>\n",
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" <tr>\n",
" <th>2015-12-01</th>\n",
" <td>16.0</td>\n",
" <td>10.0</td>\n",
" <td>6.0</td>\n",
" <td>197.0</td>\n",
" <td>12.312500</td>\n",
" <td>19.269038</td>\n",
" <td>16.0</td>\n",
" <td>1.0</td>\n",
" <td>15.0</td>\n",
" <td>23.00</td>\n",
" <td>1.437500</td>\n",
" <td>5.750000</td>\n",
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" <th>2015-12-17</th>\n",
" <td>15.0</td>\n",
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" <td>200.0</td>\n",
" <td>13.333333</td>\n",
" <td>10.587504</td>\n",
" <td>15.0</td>\n",
" <td>8.0</td>\n",
" <td>7.0</td>\n",
" <td>46.00</td>\n",
" <td>3.066667</td>\n",
" <td>4.317186</td>\n",
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},
"metadata": {},
"execution_count": 59
}
]
},
{
"cell_type": "markdown",
"source": [
"### Penggunaan Verbose"
],
"metadata": {
"id": "yjWynoCFs550"
}
},
{
"cell_type": "code",
"source": [
"summary_all(\n",
" dataset=dataset,\n",
" ufunc=myfunc_all, ufunc_col=myfunc_all_col,\n",
" n_days='7D', verbose=True)"
],
"metadata": {
"id": "QBWtlbbes-x4",
"outputId": "2497de33-6c2f-4ab9-d05a-11f9bf4c92bc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 538
}
},
"execution_count": 60,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"PROCESSING: STA_A\n",
"PROCESSING: STA_B\n",
"PROCESSING: STA_C\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" STA_A STA_B \\\n",
" n_days n_rain n_dry SUM MEAN STD n_days n_rain \n",
"DATE \n",
"2001-01-01 7.0 0.0 7.0 0.0 0.000000 0.000000 7.0 3.0 \n",
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"2001-01-15 7.0 0.0 7.0 0.0 0.000000 0.000000 7.0 5.0 \n",
"2001-01-22 7.0 0.0 7.0 0.0 0.000000 0.000000 7.0 4.0 \n",
"2001-01-29 3.0 0.0 3.0 0.0 0.000000 0.000000 3.0 2.0 \n",
"... ... ... ... ... ... ... ... ... \n",
"2015-12-01 7.0 3.0 4.0 86.0 12.285714 25.408098 7.0 1.0 \n",
"2015-12-08 7.0 5.0 2.0 55.0 7.857143 12.266874 7.0 3.0 \n",
"2015-12-15 7.0 7.0 0.0 105.0 15.000000 11.503623 7.0 4.0 \n",
"2015-12-22 7.0 7.0 0.0 136.0 19.428571 12.053452 7.0 3.0 \n",
"2015-12-29 3.0 3.0 0.0 15.0 5.000000 2.000000 3.0 0.0 \n",
"\n",
" STA_C \\\n",
" n_dry SUM MEAN STD n_days n_rain n_dry SUM \n",
"DATE \n",
"2001-01-01 4.0 58.0 8.285714 16.499639 7.0 5.0 2.0 15.43 \n",
"2001-01-08 3.0 68.0 9.714286 13.300555 7.0 7.0 0.0 125.30 \n",
"2001-01-15 2.0 224.0 32.000000 37.434387 7.0 7.0 0.0 93.38 \n",
"2001-01-22 3.0 60.0 8.571429 10.906529 7.0 6.0 1.0 55.88 \n",
"2001-01-29 1.0 44.0 14.666667 13.650397 3.0 3.0 0.0 37.99 \n",
"... ... ... ... ... ... ... ... ... \n",
"2015-12-01 6.0 43.0 6.142857 16.252472 7.0 0.0 7.0 0.00 \n",
"2015-12-08 4.0 67.0 9.571429 13.513662 7.0 0.0 7.0 0.00 \n",
"2015-12-15 3.0 55.0 7.857143 9.118271 7.0 5.0 2.0 44.00 \n",
"2015-12-22 4.0 51.0 7.285714 11.954278 7.0 2.0 5.0 18.00 \n",
"2015-12-29 3.0 0.0 0.000000 0.000000 3.0 2.0 1.0 7.00 \n",
"\n",
" \n",
" MEAN STD \n",
"DATE \n",
"2001-01-01 2.204286 3.384252 \n",
"2001-01-08 17.900000 32.992996 \n",
"2001-01-15 13.340000 12.806884 \n",
"2001-01-22 7.982857 5.429001 \n",
"2001-01-29 12.663333 11.376741 \n",
"... ... ... \n",
"2015-12-01 0.000000 0.000000 \n",
"2015-12-08 0.000000 0.000000 \n",
"2015-12-15 6.285714 8.220184 \n",
"2015-12-22 2.571429 5.255383 \n",
"2015-12-29 2.333333 3.214550 \n",
"\n",
"[888 rows x 18 columns]"
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" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>67.0</td>\n",
" <td>9.571429</td>\n",
" <td>13.513662</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-15</th>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>105.0</td>\n",
" <td>15.000000</td>\n",
" <td>11.503623</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>55.0</td>\n",
" <td>7.857143</td>\n",
" <td>9.118271</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>44.00</td>\n",
" <td>6.285714</td>\n",
" <td>8.220184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-22</th>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>136.0</td>\n",
" <td>19.428571</td>\n",
" <td>12.053452</td>\n",
" <td>7.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>51.0</td>\n",
" <td>7.285714</td>\n",
" <td>11.954278</td>\n",
" <td>7.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>18.00</td>\n",
" <td>2.571429</td>\n",
" <td>5.255383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-29</th>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>15.0</td>\n",
" <td>5.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>7.00</td>\n",
" <td>2.333333</td>\n",
" <td>3.214550</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>888 rows × 18 columns</p>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c3f4b04-0365-41be-b6de-562ab25d25d1')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-9c3f4b04-0365-41be-b6de-562ab25d25d1 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-9c3f4b04-0365-41be-b6de-562ab25d25d1');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 60
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5-F6J0TcaDUV"
},
"source": [
"# Changelog\n",
"\n",
"```\n",
"- 20220613 - 1.1.0 - Fix error concat index (live at v0.4.1) & refactor based on new output from hk88. \n",
"- 20220311 - 1.0.1 - Add verbose (#115)\n",
"- 20191217 - 1.0.0 - Initial\n",
"```\n",
"\n",
"#### Copyright &copy; 2022 [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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