{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "38091f92",
   "metadata": {},
   "source": [
    "# Einführung in NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f64e9a",
   "metadata": {},
   "source": [
    "NumPy-Operationen führen komplexe Berechnungen auf ganzen Arrays durch, ohne dass Python `for`-Schleifen erforderlich sind, die bei großen Sequenzen langsam sein können. Die Schnelligkeit von NumPy erklärt sich aus den C-basierten Algorithmen, die den Overhead des Python-Codes vermeidet. Um euch einen Eindruck zu vermitteln vom Performance-Unterschied, messen wir den Unterschied zwischen einem NumPy-Array un einer Python-Liste mit hunderttausend Ganzzahlen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "58a6f298",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a1c7b5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "myarray = np.arange(100000)\n",
    "mylist = list(range(100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd429e60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 829 μs, sys: 1.04 ms, total: 1.87 ms\n",
      "Wall time: 1.01 ms\n"
     ]
    }
   ],
   "source": [
    "%time for _ in range(10): myarray2 = myarray ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2747e294",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23.3 ms, sys: 1.99 ms, total: 25.3 ms\n",
      "Wall time: 25.1 ms\n"
     ]
    }
   ],
   "source": [
    "%time for _ in range(10): mylist2 = [x ** 2 for x in mylist]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.13 Kernel",
   "language": "python",
   "name": "python313"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}