Version initiale

module2/exo1/Essai.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "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": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 96.20165077,  78.505557  ,  90.10754597, ..., 119.81559284,\n",
+       "       109.02727771,  90.26544104])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.normal(loc = mu, scale = sigma, size = 10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "## Utilisation d'autres langages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
+      "  %reload_ext rpy2.ipython\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function (x, y, ...) \n",
+       "UseMethod(\"plot\")\n",
+       "<bytecode: 0x564e3f51ca58>\n",
+       "<environment: namespace:graphics>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Error in plot[cars] : object of type 'closure' is not subsettable\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: Error in plot[cars] : object of type 'closure' is not subsettable\n",
+      "\n",
+      "  warnings.warn(x, RRuntimeWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "plot[cars]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_code_all_hidden": true,
+  "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": 2
+}

module2/exo1/toy_notebook_fr.ipynb

-{
- "cells": [],
- "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": "3.6.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
-