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