un premier essai

module2/exo1/Module 2 - Test .ipynb

+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

module2/exo1/Tutoriel.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du document "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = x + 10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Petit exemple de completion "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=mu, scale=sigma, size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.hist(x)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

module2/exo1/Untitled.ipynb

+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}