{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# nbsphinx\n", "\n", "- \n", " - \n", "\n", "`.ipynb` ファイルを sphinx のページとして扱う。最低限、マークダウンで見出しを付けること。\n", "\n", "* `markdown` は `myst` そのものではないようで sphinx のディレクティブ埋め込みは効かないぽい。`jupyter` 独自の `markdown` 拡張があるかも。\n", "* [jupyter](https://jupyter.org/) の実行結果を .doctree 化することで sphinx で扱えるようにするぽい\n", "\n", "わりとフリーダムにいろんなものを埋め込めるようである。\n", "きちんと `.venv` で管理しないと、`github action` のビルドに失敗しそう。\n", "`folium` は `requirements.txt` に含まれていないのに動作しているなど。\n", "`jupyter` カーネル回りはローカルの実行環境にあれば十分？\n", "\n", "- [nbsphinx の紹介](https://qiita.com/kozo2/items/ec032ad80fe9a994f0ea)\n", "\n", "```\n", "> pip install nbsphinx ipykernel\n", "```\n", "\n", "```{warning}\n", "pandoc.exe に PATH を通す必要あり\n", "```\n", "\n", "`conf.py`\n", "\n", "```py\n", "import os\n", "if os.name == 'nt':\n", " os.environ['PATH'] = f\"{os.environ['PATH']};C:\\\\Program Files\\\\Pandoc\"\n", "```\n", "\n", "## jupyter の参考\n", "\n", "- [Visual Studio CodeでJupyter Notebookを動かしてみた](https://dev.classmethod.jp/articles/visual-studio-code-jupyter-notebook/)\n", "- [VS CodeでJupyterしてみよう](https://atmarkit.itmedia.co.jp/ait/articles/2108/06/news030.html)\n", "\n", "## 実行例" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

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	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2
...	...	...	...	...
145	6.7	3.0	5.2	2.3
146	6.3	2.5	5.0	1.9
147	6.5	3.0	5.2	2.0
148	6.2	3.4	5.4	2.3
149	5.9	3.0	5.1	1.8

\n", "

150 rows × 4 columns

\n", "

" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", "0 5.1 3.5 1.4 0.2\n", "1 4.9 3.0 1.4 0.2\n", "2 4.7 3.2 1.3 0.2\n", "3 4.6 3.1 1.5 0.2\n", "4 5.0 3.6 1.4 0.2\n", ".. ... ... ... ...\n", "145 6.7 3.0 5.2 2.3\n", "146 6.3 2.5 5.0 1.9\n", "147 6.5 3.0 5.2 2.0\n", "148 6.2 3.4 5.4 2.3\n", "149 5.9 3.0 5.1 1.8\n", "\n", "[150 rows x 4 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from sklearn import datasets\n", "iris = datasets.load_iris()\n", "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n", "df" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ],\n", " [,\n", " ]], dtype=object)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "

" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.hist()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Make this Notebook Trusted to load map: File -> Trust Notebook

" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import folium\n", "m = folium.Map(\n", " location=[45.372, -121.6972],\n", " zoom_start=12,\n", " tiles='Stamen Terrain'\n", ")\n", "\n", "folium.Marker(\n", " location=[45.3288, -121.6625],\n", " popup='Mt. Hood Meadows',\n", " icon=folium.Icon(icon='cloud')\n", ").add_to(m)\n", "\n", "folium.Marker(\n", " location=[45.3311, -121.7113],\n", " popup='Timberline Lodge',\n", " icon=folium.Icon(color='green')\n", ").add_to(m)\n", "\n", "folium.Marker(\n", " location=[45.3300, -121.6823],\n", " popup='Some Other Location',\n", " icon=folium.Icon(color='red', icon='info-sign')\n", ").add_to(m)\n", "\n", "m" ] } ], "metadata": { "interpreter": { "hash": "2be5faf79681da6f2a61fdfdd5405d65d042280f7fba6178067603e3a2925119" }, "kernelspec": { "display_name": "Python 3.10.0 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.0" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }