From d122f0bc69e91e48444f94d3d372ce61c7f71235 Mon Sep 17 00:00:00 2001
From: Lana Scravaglieri <lana.scravaglieri@inria.fr>
Date: Thu, 9 Nov 2023 16:46:01 +0100
Subject: [PATCH] Exo 2

---
 module2/exo2/exercice_python_fr.org | 29 +++++++++++++++++++++++++----
 1 file changed, 25 insertions(+), 4 deletions(-)

diff --git a/module2/exo2/exercice_python_fr.org b/module2/exo2/exercice_python_fr.org
index c7157ba..7c86c49 100644
--- a/module2/exo2/exercice_python_fr.org
+++ b/module2/exo2/exercice_python_fr.org
@@ -1,8 +1,8 @@
-#+TITLE:  Votre titre
-#+AUTHOR: Votre nom
-#+DATE:   La date du jour
+#+TITLE:  Savoir faire un calcul simple soi-même
+#+AUTHOR: Lana Scravagliseri
+#+DATE: 2023-11-09
 #+LANGUAGE: fr
-# #+PROPERTY: header-args :eval never-export
+#+PROPERTY: header-args :eval never-export
 
 #+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/htmlize.css"/>
 #+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/readtheorg.css"/>
@@ -91,3 +91,24 @@ faisant ~<p~, ~<P~ ou ~<PP~ suivi de ~Tab~).
 
 Maintenant, à vous de jouer! Vous pouvez effacer toutes ces
 informations et les remplacer par votre document computationnel.
+
+* Exo 2
+
+#+begin_src python :results output :exports both
+import numpy as np
+values = np.asarray([
+14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])
+
+print("Mean : ", np.mean(values))
+print("Std : ", np.std(values, ddof=1))
+print("Min : ", np.min(values))
+print("Median : ", np.median(values))
+print("Max : ", np.max(values))
+#+end_src
+
+#+RESULTS:
+: Mean :  14.113000000000001
+: Std :  4.334094455301447
+: Min :  2.8
+: Median :  14.5
+: Max :  23.4
-- 
2.18.1