From f98fe0d5955f0c431ef33ba113cefec96c629d8c Mon Sep 17 00:00:00 2001
From: 5956bcdb06c2088a97b35e01dc67db8c
 <5956bcdb06c2088a97b35e01dc67db8c@app-learninglab.inria.fr>
Date: Mon, 14 Dec 2020 19:43:53 +0000
Subject: [PATCH] =?UTF-8?q?Deuxi=C3=A8me=20commit=20de=20l'exo=202=20du=20?=
 =?UTF-8?q?module=202?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 module2/exo2/exercice.ipynb | 173 +++++++++++++++++++++++++++++++++++-
 1 file changed, 170 insertions(+), 3 deletions(-)

diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb
index 0bbbe37..5b11925 100644
--- a/module2/exo2/exercice.ipynb
+++ b/module2/exo2/exercice.ipynb
@@ -1,5 +1,173 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[14. ,  7.6, 11.2, 12.8, 12.5,  9.9, 14.9,  9.4, 16.9, 10.2],\n",
+       "       [14.9, 18.1,  7.3,  9.8, 10.9, 12.2,  9.9,  2.9,  2.8, 15.4],\n",
+       "       [15.7,  9.7, 13.1, 13.2, 12.3, 11.7, 16. , 12.4, 17.9, 12.2],\n",
+       "       [16.2, 18.7,  8.9, 11.9, 12.1, 14.6, 12.1,  4.7,  3.9, 16.9],\n",
+       "       [16.8, 11.3, 14.4, 15.7, 14. , 13.6, 18. , 13.6, 19.9, 13.7],\n",
+       "       [17. , 20.5,  9.9, 12.5, 13.2, 16.1, 13.5,  6.3,  6.4, 17.6],\n",
+       "       [19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1],\n",
+       "       [19.6, 21.7, 11.3, 15. , 14.3, 16.8, 14. ,  6.8,  8.2, 19.9],\n",
+       "       [20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2],\n",
+       "       [20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2,  8.9, 21. ]])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "A = np.array([[14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2],\n",
+    "             [14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4],\n",
+    "             [15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2],\n",
+    "             [16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9],\n",
+    "             [16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7],\n",
+    "             [17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6],\n",
+    "             [19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1],\n",
+    "             [19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9],\n",
+    "             [20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2],\n",
+    "             [20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]])\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.113000000000001"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "moyenne_A = np.mean(A)\n",
+    "moyenne_A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.334094455301447"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "std_A = np.std(A,ddof=1)\n",
+    "std_A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_A = np.min(A)\n",
+    "min_A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.4"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max_A = np.max(A)\n",
+    "max_A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.5"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "med_A = np.median(A) \n",
+    "med_A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 10)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(A)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +184,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
-- 
2.18.1