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Voilà | JupyterLab |
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# So easy, *voilà*!\n", | |
"\n", | |
"In this example notebook, we demonstrate how Voilà can render Jupyter notebooks with interactions requiring a roundtrip to the kernel." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Jupyter Widgets" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import ipywidgets as widgets\n", | |
"\n", | |
"slider = widgets.FloatSlider(description='$x$', value=4)\n", | |
"text = widgets.FloatText(disabled=True, description='$x^2$')\n", | |
"\n", | |
"def compute(*ignore):\n", | |
" text.value = str(slider.value ** 2)\n", | |
"\n", | |
"slider.observe(compute, 'value')\n", | |
"\n", | |
"widgets.VBox([slider, text])" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Basic outputs of code cells" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import pandas as pd\n", | |
"\n", | |
"iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n", | |
"iris" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"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": "3.8.5" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 4 | |
} |
bqplot | |
ipywidgets~=8.0 | |
jupyterlab~=4.0.5 | |
matplotlib | |
nbconvert>=7 | |
pandas | |
voila==0.5.0 |