636 lines
12 KiB
Plaintext
636 lines
12 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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"# NumPy Indexing et Selection\n",
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"\n",
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"Dans cette session, nous discuterons de la façon de sélectionner des éléments ou des groupes d'éléments à partir d'un tableau."
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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": 2,
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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": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Création d'un tableau simple\n",
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"arr = np.arange(0,11)"
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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": 4,
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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, 3, 4, 5, 6, 7, 8, 9, 10])"
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]
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},
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"execution_count": 4,
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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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"# afficher\n",
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"arr"
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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\n",
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"La façon la plus simple de choisir un ou plusieurs éléments d'un tableau ressemble beaucoup aux listes python :"
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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": 5,
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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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"8"
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]
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},
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"execution_count": 5,
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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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"# Obtenir une valeur à un index\n",
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"arr[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": 6,
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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])"
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]
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},
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"execution_count": 6,
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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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"# Obtenir des valeurs dans une plage d'entiers\n",
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"arr[1:5]"
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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": 7,
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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, 3, 4])"
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]
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},
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"execution_count": 7,
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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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"# Obtenir des valeurs dans une plage d'entiers\n",
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"arr[0:5]"
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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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"## Broadcasting\n",
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"\n",
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"Les tableaux Numpy diffèrent d'une liste Python normale en raison de leur capacité à diffuser :"
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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": 8,
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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([100, 100, 100, 100, 100, 5, 6, 7, 8, 9, 10])"
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]
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},
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"execution_count": 8,
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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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"# Réglage d'une valeur avec plage d'indice (Broadcasting)\n",
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"arr[0:5]=100\n",
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"\n",
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"# afficher\n",
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"arr"
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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": 9,
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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, 3, 4, 5, 6, 7, 8, 9, 10])"
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]
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},
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"execution_count": 9,
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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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"# Réinitialiser le tableau, nous verrons pourquoi \n",
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"# j'ai dû le réinitialiser plus bas.\n",
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"arr = np.arange(0,11)\n",
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"\n",
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"# afficher\n",
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"arr"
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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": 10,
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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, 3, 4, 5])"
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]
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},
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"execution_count": 10,
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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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"# Remarques importantes sur les tranches (slices)\n",
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"slice_of_arr = arr[0:6]\n",
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"\n",
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"# afficher tranche\n",
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"slice_of_arr"
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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": 11,
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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([99, 99, 99, 99, 99, 99])"
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]
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},
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"execution_count": 11,
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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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"# Changer de tranche\n",
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"slice_of_arr[:] = 99\n",
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"\n",
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"# Afficher de nouveau\n",
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"slice_of_arr"
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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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"Notez maintenant que les changements se produisent aussi dans notre tableau d'origine !"
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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": 12,
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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([99, 99, 99, 99, 99, 99, 6, 7, 8, 9, 10])"
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]
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},
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"execution_count": 12,
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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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"arr"
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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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"Les données ne sont pas copiées, c'est une vue du tableau d'origine ! Cela permet d'éviter les problèmes de mémoire !"
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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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{
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"data": {
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"text/plain": [
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"array([99, 99, 99, 99, 99, 99, 6, 7, 8, 9, 10])"
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]
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},
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"execution_count": 13,
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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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"# Pour en obtenir une copie, il faut être explicite\n",
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"arr_copy = arr.copy()\n",
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"\n",
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"arr_copy"
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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 d'un tableau 2D (matrices)\n",
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"\n",
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"Le format général est le suivant **arr_2d[row][col]** ou **arr_2d[row,col]**. Je recommande habituellement d'utiliser la notation par virgule pour plus de clarté."
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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([[ 5, 10, 15],\n",
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" [20, 25, 30],\n",
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" [35, 40, 45]])"
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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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"arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45]))\n",
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"\n",
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"# afficher\n",
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"arr_2d"
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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([20, 25, 30])"
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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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"# Indexer une ligne\n",
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"arr_2d[1]\n"
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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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"20"
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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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"# Le format est arr_2d[row][col] ou arr_2d[row,col]\n",
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"\n",
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"# Obtenir la valeur d'un élément individuel\n",
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"arr_2d[1][0]"
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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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"20"
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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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"# Obtenir la valeur d'un élément individuel\n",
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"arr_2d[1,0]"
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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, 15],\n",
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" [25, 30]])"
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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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"# découpage en tranches d'un tableau 2D\n",
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"\n",
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"# Forme (2,2) du coin supérieur droit\n",
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"arr_2d[: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": 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([35, 40, 45])"
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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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"# Forme de la rangée du bas\n",
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"arr_2d[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": 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([35, 40, 45])"
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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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"# Forme de la rangée du bas\n",
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"arr_2d[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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"## Plus d'aide pour l'indexation\n",
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"L'indexation d'une matrice 2d peut être un peu confuse au début, surtout lorsque vous commencez à ajouter une taille de pas. Essayez la recherche d'images sur google \"NumPy indexing\" pour trouver des images utiles, comme celle-ci :\n",
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"\n",
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"<img src= 'https://s2.qwant.com/thumbr/0x380/e/f/c01bff98594466ebc0010d2898208f123a92d43849fb809db543c82f2038c9/Numpy1.jpg?u=https%3A%2F%2Fmedia.geeksforgeeks.org%2Fwp-content%2Fuploads%2FNumpy1.jpg&q=0&b=1&p=0&a=1' width=500/>"
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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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"## Sélection conditionnelle\n",
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"\n",
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"C'est un concept très fondamental qui se réglera directement par pandas plus tard, assurez-vous de bien comprendre cette partie !\n",
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"\n",
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"Passons brièvement en revue la façon d'utiliser les parenthèses pour la sélection basée sur des opérateurs de comparaison."
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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": 28,
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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, 6, 7, 8, 9, 10])"
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]
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},
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"execution_count": 28,
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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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"arr = np.arange(1,11)\n",
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"arr"
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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([False, False, False, False, True, True, True, True, True, True], dtype=bool)"
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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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"arr > 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": 31,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"bool_arr = arr>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": 32,
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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([False, False, False, False, True, True, True, True, True, True], dtype=bool)"
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]
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},
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"execution_count": 32,
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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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"bool_arr"
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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": 33,
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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, 6, 7, 8, 9, 10])"
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]
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},
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"execution_count": 33,
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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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"arr[bool_arr]"
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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": 34,
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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([ 3, 4, 5, 6, 7, 8, 9, 10])"
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]
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},
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"execution_count": 34,
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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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"arr[arr>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([ 3, 4, 5, 6, 7, 8, 9, 10])"
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]
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},
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"execution_count": 37,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"x = 2\n",
|
|
"arr[arr>x]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Bon travail!\n"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.7.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|