{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercices NumPy - Solutions\n", "Maintenant que nous avons découvert NumPy, testons vos connaissances. Nous commencerons par quelques tâches simples, puis des questions plus complexes.\n", "\n", "** REMARQUE IMPORTANTE : Veillez à ne pas exécuter les cellules directement au-dessus de la sortie de résultat attendu, sinon vous risquez de finir par écrire par dessus ! **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import NumPy as np" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Créer un tableau de 10 zéros " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.zeros(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Créer un tableau de 10 uns" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Créer un tableau de 10 cinq" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones(10) * 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Créer un tableau d'entiers de 10 à 50" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n", " 44, 45, 46, 47, 48, 49, 50])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(10,51)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Créer un tableau de tous les entiers pairs entre 10 et 50" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n", " 44, 46, 48, 50])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(10,51,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Créer une matrice 3x3 avec les valeurs de 0 à 8" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0, 1, 2],\n", " [3, 4, 5],\n", " [6, 7, 8]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(0,9).reshape(3,3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Créer une matrice identité 3x3" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1., 0., 0.],\n", " [0., 1., 0.],\n", " [0., 0., 1.]])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.eye(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Utiliser NumPy pour générer un nombre aléatoire entre 0 et 1" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.0774109])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.rand(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Utiliser NumPy pour générer un tableau de 25 nombres aléatoires échantillonnés à partir d'une distribution normale standard" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.01471837, -0.54119397, 0.61856113, 0.75337696, 0.54470058,\n", " 0.21793039, 1.12443308, 0.77995286, -0.62594303, -0.61483113,\n", " 0.27331136, 0.69654147, 0.56232085, -0.05882004, -0.73255901,\n", " -0.31370757, 1.94487633, -1.27965513, -1.36078982, -0.39200937,\n", " -0.96238637, 0.47766402, -0.84683412, -1.79296081, -0.62813396])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.randn(25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Créer la matrice suivante:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n", " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n", " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n", " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n", " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n", " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n", " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n", " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n", " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n", " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(1,101).reshape(10,10)/100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Créer un tableau de 20 points espacés linéairement entre 0 et 1:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n", " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n", " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n", " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linspace(0,1,20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Indexation et sélection Numpy\n", "\n", "Maintenant vous trouverez quelques matrices, il faut les reproduire:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 2, 3, 4, 5],\n", " [ 6, 7, 8, 9, 10],\n", " [11, 12, 13, 14, 15],\n", " [16, 17, 18, 19, 20],\n", " [21, 22, 23, 24, 25]])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# VOICI LA MATRICE MAT\n", "# A UTILISER POUR LES PROCHAINES TACHES\n", "import numpy as np\n", "mat = np.arange(1,26).reshape(5,5)\n", "mat" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[12, 13, 14, 15],\n", " [17, 18, 19, 20],\n", " [22, 23, 24, 25]])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat[2:,1:]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat[3,4]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2],\n", " [ 7],\n", " [12]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat[:3,1:2]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([21, 22, 23, 24, 25])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat[4,:]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[16, 17, 18, 19, 20],\n", " [21, 22, 23, 24, 25]])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat[3:,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Faire les tâches suivantes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Obtenir la somme de toutes les valeurs de la matrice mat" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "325" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Obtenir l'écart-type des valeurs de la matrice mat" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7.211102550927978" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat.std()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Obtenir la somme de toutes les colonnes de la matrice mat" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([55, 60, 65, 70, 75])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat.sum(axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question Bonus\n", "\n", "Nous avons beaucoup travaillé avec des données aléatoires avec numpy, mais y a-t-il un moyen de nous assurer que nous obtenons toujours les mêmes nombres aléatoires ? [Cliquez ici pour un indice](https://www.google.fr/search?q=numpy+random+seed&rlz=1C1CHBF_enUS747US747&oq=numpy+random+seed&aqs=chrome..69i57j69i60j0l4.2087j0j7&sourceid=chrome&ie=UTF-8)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "np.random.seed(101)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Bon travail!" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 1 }