{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "taruma-hidrokit-viz-table", "version": "0.3.2", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "_CtZpmBbCbAg", "colab_type": "text" }, "source": [ "# Tutorial `hidrokit.viz.table`\n", "\n", "- **Kategori**: _data visualization_\n", "- __Tujuan__: Menampilkan dataset melalui bentuk tabel\n", "- __Dokumentasi__: [readthedocs](https://hidrokit.readthedocs.io/en/stable/viz.html#module-viz.table)\n", "\n", "## Informasi notebook\n", "\n", "- __notebook name__: `taruma_hidrokit_viz_table`\n", "- __notebook version/date__: `1.0.1`/`20190713`\n", "- __notebook server__: Google Colab\n", "- __hidrokit version__: `0.2.0`\n", "- **python version**: `3.7`" ] }, { "cell_type": "markdown", "metadata": { "id": "BPm5qNh_DQjj", "colab_type": "text" }, "source": [ "## Instalasi hidrokit" ] }, { "cell_type": "code", "metadata": { "id": "aeLepUrl_nxm", "colab_type": "code", "outputId": "64dbd4d3-4858-47d7-ca05-951d060e78c1", "colab": { "base_uri": "https://localhost:8080/", "height": 255 } }, "source": [ "### Instalasi melalui PyPI\n", "!pip install hidrokit\n", "\n", "### Instalasi melalui Github\n", "# !pip install git+https://github.com/taruma/hidrokit.git\n", "\n", "### Instalasi melalui Github (Latest)\n", "# !pip install git+https://github.com/taruma/hidrokit.git@latest" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Collecting hidrokit\n", " Downloading https://files.pythonhosted.org/packages/43/9d/343d2a413a07463a21dd13369e31d664d6733bbfd46276abef5d804c83d1/hidrokit-0.2.0-py2.py3-none-any.whl\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from hidrokit) (3.0.3)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from hidrokit) (1.16.4)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from hidrokit) (0.24.2)\n", "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->hidrokit) (2.5.3)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->hidrokit) (1.1.0)\n", "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->hidrokit) (2.4.0)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->hidrokit) (0.10.0)\n", "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->hidrokit) (2018.9)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->hidrokit) (1.12.0)\n", "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->hidrokit) (41.0.1)\n", "Installing collected packages: hidrokit\n", "Successfully installed hidrokit-0.2.0\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "APb9vC-zDaV4", "colab_type": "text" }, "source": [ "## Import Library" ] }, { "cell_type": "code", "metadata": { "id": "Gx6h8iSxDfQY", "colab_type": "code", "colab": {} }, "source": [ "import numpy as np\n", "import pandas as pd" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Kny2T1itDlz6", "colab_type": "text" }, "source": [ "## Dataset" ] }, { "cell_type": "code", "metadata": { "id": "xdDdm1pbD-AO", "colab_type": "code", "outputId": "9921a41d-670d-4297-c332-dc16dcae1fda", "colab": { "base_uri": "https://localhost:8080/", "height": 357 } }, "source": [ "# Ambil dataset dari data test hidrokit\n", "!wget -O dataset.csv \"https://github.com/taruma/hidrokit/blob/master/tests/data/one_year_three_columns.csv?raw=true\"" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "--2019-07-12 03:02:01-- https://github.com/taruma/hidrokit/blob/master/tests/data/one_year_three_columns.csv?raw=true\n", "Resolving github.com (github.com)... 192.30.253.112\n", "Connecting to github.com (github.com)|192.30.253.112|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: https://github.com/taruma/hidrokit/raw/master/tests/data/one_year_three_columns.csv [following]\n", "--2019-07-12 03:02:01-- https://github.com/taruma/hidrokit/raw/master/tests/data/one_year_three_columns.csv\n", "Reusing existing connection to github.com:443.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: https://raw.githubusercontent.com/taruma/hidrokit/master/tests/data/one_year_three_columns.csv [following]\n", "--2019-07-12 03:02:01-- https://raw.githubusercontent.com/taruma/hidrokit/master/tests/data/one_year_three_columns.csv\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 7242 (7.1K) [text/plain]\n", "Saving to: ‘dataset.csv’\n", "\n", "dataset.csv 100%[===================>] 7.07K --.-KB/s in 0s \n", "\n", "2019-07-12 03:02:01 (90.8 MB/s) - ‘dataset.csv’ saved [7242/7242]\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "nJdvdBzjEH96", "colab_type": "code", "outputId": "e6071183-855a-4123-dc44-5615796198c5", "colab": { "base_uri": "https://localhost:8080/", "height": 359 } }, "source": [ "# Baca dataset\n", "dataset = pd.read_csv('dataset.csv', index_col=0, parse_dates=True)\n", "dataset.head(10)" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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sta_asta_bsta_c
2000-01-0177919
2000-01-02177965
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" ], "text/plain": [ " sta_a sta_b sta_c\n", "2000-01-01 7 79 19\n", "2000-01-02 17 79 65\n", "2000-01-03 79 51 25\n", "2000-01-04 48 75 31\n", "2000-01-05 81 33 80\n", "2000-01-06 26 3 96\n", "2000-01-07 78 75 26\n", "2000-01-08 71 95 65\n", "2000-01-09 48 71 22\n", "2000-01-10 32 89 88" ] }, "metadata": { "tags": [] }, "execution_count": 4 } ] }, { "cell_type": "code", "metadata": { "id": "XDVLNazbGfHC", "colab_type": "code", "outputId": "6bad6d9f-6934-4883-c16d-6d9a6efe7687", "colab": { "base_uri": "https://localhost:8080/", "height": 153 } }, "source": [ "# Info dataset\n", "dataset.info()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "\n", "DatetimeIndex: 366 entries, 2000-01-01 to 2000-12-31\n", "Data columns (total 3 columns):\n", "sta_a 366 non-null int64\n", "sta_b 366 non-null int64\n", "sta_c 366 non-null int64\n", "dtypes: int64(3)\n", "memory usage: 11.4 KB\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "KysAN-JKFs_c", "colab_type": "text" }, "source": [ "# Fungsi `table.pivot()`\n", "\n", "- __Tujuan__: Menampilkan dataset dalam bentuk pivot/ringkasan\n", "- __Sintaks__: `viz.table.pivot(dataframe, column=None, lang=None)`\n", "- __Return__: `DataFrame`\n", "- __Dokumentasi__: [readthedocs](https://hidrokit.readthedocs.io/en/stable/viz.html#viz.table.pivot)" ] }, { "cell_type": "code", "metadata": { "id": "62fs0DjLHJP_", "colab_type": "code", "colab": {} }, "source": [ "from hidrokit.viz import table" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "cxGt4n98GVQE", "colab_type": "text" }, "source": [ "## Argument: None\n", "\n", "Jika tidak ada kolom yang dipilih, maka akan dipilih kolom pertama (`sta_a`)." ] }, { "cell_type": "code", "metadata": { "id": "6Aq3kFPRGStF", "colab_type": "code", "outputId": "1125b183-b82d-444b-e0f7-1d3fbf4ec44c", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "table.pivot(dataset)" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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119.08.08.075.056.034.066.018.041.041.083.010.0
265.036.042.096.028.040.054.053.075.031.046.059.0
325.010.057.067.022.041.076.059.089.044.016.075.0
431.054.081.00.042.069.05.027.021.04.061.017.0
580.060.051.096.084.077.074.035.043.046.026.055.0
696.021.057.097.023.098.029.023.077.052.015.042.0
726.057.049.046.065.035.031.043.022.018.016.04.0
865.093.098.011.074.089.071.068.076.032.013.020.0
922.096.022.015.095.083.016.083.054.061.068.065.0
1088.068.060.075.045.031.017.01.068.086.026.048.0
1155.052.087.065.067.090.020.093.072.054.036.084.0
1280.044.077.098.082.036.059.066.072.055.023.038.0
1368.036.067.061.089.022.039.042.010.086.011.039.0
1490.079.030.086.029.061.043.058.063.00.072.097.0
1573.010.01.079.086.092.025.035.097.047.026.040.0
1612.047.00.092.094.03.089.081.058.067.065.057.0
1772.063.031.085.065.043.086.072.038.044.073.030.0
1837.013.040.067.029.067.02.024.050.053.084.09.0
1945.020.052.031.089.041.029.028.047.016.049.082.0
2092.02.051.033.063.020.089.036.075.029.050.041.0
214.049.026.098.041.065.077.019.015.049.022.052.0
2212.049.033.060.08.059.017.036.041.019.093.081.0
2345.035.038.054.019.037.01.033.039.074.041.00.0
2492.070.055.053.046.090.050.015.056.095.047.066.0
2527.035.014.088.062.033.074.02.044.054.087.04.0
2645.021.010.050.022.096.055.094.044.049.064.048.0
2713.042.062.077.052.043.097.085.088.080.025.070.0
2884.034.019.072.035.077.088.089.016.046.082.056.0
298.096.073.025.027.041.083.032.051.030.01.04.0
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\n", "
" ], "text/plain": [ "month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec\n", "day \n", "1 19.0 8.0 8.0 75.0 56.0 34.0 66.0 18.0 41.0 41.0 83.0 10.0\n", "2 65.0 36.0 42.0 96.0 28.0 40.0 54.0 53.0 75.0 31.0 46.0 59.0\n", "3 25.0 10.0 57.0 67.0 22.0 41.0 76.0 59.0 89.0 44.0 16.0 75.0\n", "4 31.0 54.0 81.0 0.0 42.0 69.0 5.0 27.0 21.0 4.0 61.0 17.0\n", "5 80.0 60.0 51.0 96.0 84.0 77.0 74.0 35.0 43.0 46.0 26.0 55.0\n", "6 96.0 21.0 57.0 97.0 23.0 98.0 29.0 23.0 77.0 52.0 15.0 42.0\n", "7 26.0 57.0 49.0 46.0 65.0 35.0 31.0 43.0 22.0 18.0 16.0 4.0\n", "8 65.0 93.0 98.0 11.0 74.0 89.0 71.0 68.0 76.0 32.0 13.0 20.0\n", "9 22.0 96.0 22.0 15.0 95.0 83.0 16.0 83.0 54.0 61.0 68.0 65.0\n", "10 88.0 68.0 60.0 75.0 45.0 31.0 17.0 1.0 68.0 86.0 26.0 48.0\n", "11 55.0 52.0 87.0 65.0 67.0 90.0 20.0 93.0 72.0 54.0 36.0 84.0\n", "12 80.0 44.0 77.0 98.0 82.0 36.0 59.0 66.0 72.0 55.0 23.0 38.0\n", "13 68.0 36.0 67.0 61.0 89.0 22.0 39.0 42.0 10.0 86.0 11.0 39.0\n", "14 90.0 79.0 30.0 86.0 29.0 61.0 43.0 58.0 63.0 0.0 72.0 97.0\n", "15 73.0 10.0 1.0 79.0 86.0 92.0 25.0 35.0 97.0 47.0 26.0 40.0\n", "16 12.0 47.0 0.0 92.0 94.0 3.0 89.0 81.0 58.0 67.0 65.0 57.0\n", "17 72.0 63.0 31.0 85.0 65.0 43.0 86.0 72.0 38.0 44.0 73.0 30.0\n", "18 37.0 13.0 40.0 67.0 29.0 67.0 2.0 24.0 50.0 53.0 84.0 9.0\n", "19 45.0 20.0 52.0 31.0 89.0 41.0 29.0 28.0 47.0 16.0 49.0 82.0\n", "20 92.0 2.0 51.0 33.0 63.0 20.0 89.0 36.0 75.0 29.0 50.0 41.0\n", "21 4.0 49.0 26.0 98.0 41.0 65.0 77.0 19.0 15.0 49.0 22.0 52.0\n", "22 12.0 49.0 33.0 60.0 8.0 59.0 17.0 36.0 41.0 19.0 93.0 81.0\n", "23 45.0 35.0 38.0 54.0 19.0 37.0 1.0 33.0 39.0 74.0 41.0 0.0\n", "24 92.0 70.0 55.0 53.0 46.0 90.0 50.0 15.0 56.0 95.0 47.0 66.0\n", "25 27.0 35.0 14.0 88.0 62.0 33.0 74.0 2.0 44.0 54.0 87.0 4.0\n", "26 45.0 21.0 10.0 50.0 22.0 96.0 55.0 94.0 44.0 49.0 64.0 48.0\n", "27 13.0 42.0 62.0 77.0 52.0 43.0 97.0 85.0 88.0 80.0 25.0 70.0\n", "28 84.0 34.0 19.0 72.0 35.0 77.0 88.0 89.0 16.0 46.0 82.0 56.0\n", "29 8.0 96.0 73.0 25.0 27.0 41.0 83.0 32.0 51.0 30.0 1.0 4.0\n", "30 45.0 NaN 97.0 92.0 39.0 0.0 69.0 80.0 18.0 26.0 44.0 55.0\n", "31 76.0 NaN 7.0 NaN 69.0 NaN 65.0 26.0 NaN 77.0 NaN 39.0" ] }, "metadata": { "tags": [] }, "execution_count": 9 } ] }, { "cell_type": "markdown", "metadata": { "id": "yHQFwa_nCE9p", "colab_type": "text" }, "source": [ "# Changelog\n", "\n", "```\n", "- 20190713 - 1.0.1 - Update information\n", "- 20190712 - 1.0.0 - Initial\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "gSU3NrNrCKoi", "colab_type": "text" }, "source": [ "#### Copyright © 2019 [Taruma Sakti Megariansyah](https://taruma.github.io)\n", "\n", "Source code in this notebook is licensed under a [MIT License](https://opensource.org/licenses/MIT). Data in this notebook is licensed under a [Creative Common Attribution 4.0 International](https://choosealicense.com/licenses/cc-by-4.0/). " ] } ] }