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   "source": [
    "# Introduction to NumPy"
   ]
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  {
   "cell_type": "markdown",
   "id": "a2256613",
   "metadata": {},
   "source": [
    "NumPy operations perform complex calculations on entire arrays without the need for Python `for` loops, which can be slow for large sequences. NumPy’s speed is explained by its C-based algorithms, which avoid the overhead of Python code. To give you an idea of the performance difference, we measure the difference between a NumPy array and a Python list with a hundred thousand integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0815cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8764f708",
   "metadata": {},
   "outputs": [],
   "source": [
    "myarray = np.arange(100000)\n",
    "mylist = list(range(100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10dfdb7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15 ms, sys: 76.9 ms, total: 91.9 ms\n",
      "Wall time: 10.4 ms\n"
     ]
    }
   ],
   "source": [
    "%time for _ in range(10): myarray2 = myarray ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38963e40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 67 ms, sys: 181 ms, total: 248 ms\n",
      "Wall time: 40.3 ms\n"
     ]
    }
   ],
   "source": [
    "%time for _ in range(10): mylist2 = [x ** 2 for x in mylist]"
   ]
  }
 ],
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