diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..70df1728b3b72fa9c94c256d6a8af6e5b78457a5 100644 --- a/module2/exo1/toy_notebook_fr.ipynb +++ b/module2/exo1/toy_notebook_fr.ipynb @@ -1,5 +1,153 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Titre" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exemple de complétion" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "mu, sigma = 100, 15" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.random.normal(loc=mu, scale = sigma, size=10000)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# %matplotlib inline\n", + "plt.hist(x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "x = 10\n", + "plot(cars)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1] 10\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +164,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -