162 lines
3.2 KiB
Plaintext
162 lines
3.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e1ab7cf7-6cf4-4ba6-b3a1-9c4d3d4aa8e9",
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"metadata": {},
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"source": [
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"# Bienvenue dans Jupyter\n",
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"\n",
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"Cette cellule contient du texte en **Markdown**"
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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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"id": "e7b050ca-32bd-4612-b34e-74b6664fc089",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.0\n",
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"1.0\n"
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]
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}
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],
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"source": [
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"from math import sin, cos\n",
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"print(sin(0))\n",
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"print(cos(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": 1,
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"id": "65ea6cd6-a452-45d3-9274-4247e59a5ac3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 5 8 13 21]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"a1 = np.array([5, 8, 13, 21])\n",
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"print(a1)"
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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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"id": "d6a2aa45-03bd-4e52-8f77-98bd1bb6751a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 1 3 5]\n",
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" [ 8 11 14]\n",
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" [18 22 26]]\n",
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"int32\n",
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"9\n",
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"(3, 3)\n",
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"14\n",
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"[[ 5.2 15.6 26. ]\n",
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" [ 41.6 57.2 72.8]\n",
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" [ 93.6 114.4 135.2]]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"a1 = np.array([[1, 3, 5], [8, 11, 14], [18, 22, 26]], dtype=\"int32\")\n",
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"print(a1)\n",
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"# Afficher le nom du type des données du tableau\n",
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"print(a1.dtype)\n",
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"# Afficher le nombre de cellules au total dans le tableau\n",
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"print(a1.size)\n",
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"# Afficher les dimensions du tableau\n",
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"print(a1.shape)\n",
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"print(a1[1][2])\n",
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"\n",
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"# Appliquer un calcul simple à tous les éléments du tableau\n",
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"a2 = a1 * 5.2\n",
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"print(a2)"
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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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"id": "c6f47454-b849-47ad-abfe-a66ca774d038",
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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])"
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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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"a2 = np.arange(0, 10, 1)\n",
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"a2"
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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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"id": "b9dca432-8fbf-4370-9917-0c8ad5e7ad38",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 34. 71.5]\n",
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" [ 59. 142. ]]\n"
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]
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}
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],
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"source": [
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"m1 = np.array([[5, 8], [4, 17]])\n",
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"m2 = np.array([[2, 1.5], [3, 8]])\n",
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"print(m1 @ m2)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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