{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1ab7cf7-6cf4-4ba6-b3a1-9c4d3d4aa8e9",
   "metadata": {},
   "source": [
    "# Bienvenue dans Jupyter\n",
    "\n",
    "Cette cellule contient du texte en **Markdown**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7b050ca-32bd-4612-b34e-74b6664fc089",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from math import sin, cos\n",
    "print(sin(0))\n",
    "print(cos(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65ea6cd6-a452-45d3-9274-4247e59a5ac3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5  8 13 21]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a1 = np.array([5, 8, 13, 21])\n",
    "print(a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d6a2aa45-03bd-4e52-8f77-98bd1bb6751a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  3  5]\n",
      " [ 8 11 14]\n",
      " [18 22 26]]\n",
      "int32\n",
      "9\n",
      "(3, 3)\n",
      "14\n",
      "[[  5.2  15.6  26. ]\n",
      " [ 41.6  57.2  72.8]\n",
      " [ 93.6 114.4 135.2]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a1 = np.array([[1, 3, 5], [8, 11, 14], [18, 22, 26]], dtype=\"int32\")\n",
    "print(a1)\n",
    "# Afficher le nom du type des données du tableau\n",
    "print(a1.dtype)\n",
    "# Afficher le nombre de cellules au total dans le tableau\n",
    "print(a1.size)\n",
    "# Afficher les dimensions du tableau\n",
    "print(a1.shape)\n",
    "print(a1[1][2])\n",
    "\n",
    "# Appliquer un calcul simple à tous les éléments du tableau\n",
    "a2 = a1 * 5.2\n",
    "print(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6f47454-b849-47ad-abfe-a66ca774d038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = np.arange(0, 10, 1)\n",
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b9dca432-8fbf-4370-9917-0c8ad5e7ad38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 34.   71.5]\n",
      " [ 59.  142. ]]\n"
     ]
    }
   ],
   "source": [
    "m1 = np.array([[5, 8], [4, 17]])\n",
    "m2 = np.array([[2, 1.5], [3, 8]])\n",
    "print(m1 @ m2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}