From 80ad57115213e9c9145976f5ee633edc6f768a4e Mon Sep 17 00:00:00 2001 From: 1c81ef35986da123fb66944be6aa226c <1c81ef35986da123fb66944be6aa226c@app-learninglab.inria.fr> Date: Tue, 27 Feb 2024 17:11:57 +0000 Subject: [PATCH] M module2/exo2/exercice_yas.ipynb --- module2/exo2/exercice.ipynb | 218 +++++++++++++++++++++++++++++++++++- 1 file changed, 215 insertions(+), 3 deletions(-) diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb index 0bbbe37..ce6be73 100644 --- a/module2/exo2/exercice.ipynb +++ b/module2/exo2/exercice.ipynb @@ -1,5 +1,218 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.8" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min([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\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "23.4" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max([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])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "100" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len([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])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1411.3000000000006" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum([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])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14.113000000000007" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum([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])/100" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'numpy' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m12.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m22.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m23.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'numpy' is not defined" + ] + } + ], + "source": [ + "a = numpy.average([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])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "a = np.average([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])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14.113000000000001" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "a=np.std( [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])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.312369534258399\n" + ] + } + ], + "source": [ + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.312369534258399\n" + ] + } + ], + "source": [ + "print(np.std([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]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +229,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1