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   "source": [
    "# Array-oriented programming – vectorisation\n",
    "\n",
    "Using NumPy arrays allows you to express many types of data processing tasks as concise array expressions that would otherwise require writing `for`-loops. This practice of replacing loops with array expressions is also called _vectorisation_. In general, vectorised array operations are significantly faster than their pure Python equivalents."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bca0db79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86c6a95",
   "metadata": {},
   "source": [
    "First we create a NumPy array with one hundred thousand integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "115bba7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "myarray = np.arange(100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef42524a",
   "metadata": {},
   "source": [
    "Then we square all the elements in this array with [numpy.square](https://numpy.org/doc/stable/reference/generated/numpy.square.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eee5c824",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 185 μs, sys: 994 μs, total: 1.18 ms\n",
      "Wall time: 107 μs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([         0,          1,          4, ..., 9999400009, 9999600004,\n",
       "       9999800001])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time np.square(myarray)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e20887d",
   "metadata": {},
   "source": [
    "For comparison, we now measure the time of Python’s quadratic function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0259e4c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.17 ms, sys: 7.11 ms, total: 9.28 ms\n",
      "Wall time: 1.22 ms\n"
     ]
    }
   ],
   "source": [
    "%time for _ in range(10): myarray2 = myarray ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f57a382",
   "metadata": {},
   "source": [
    "And finally, we compare the time with the calculation of the quadratic function of all values of a Python list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc1696f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 116 ms, sys: 365 ms, total: 480 ms\n",
      "Wall time: 47.5 ms\n"
     ]
    }
   ],
   "source": [
    "mylist = list(range(100000))\n",
    "%time for _ in range(10): mylist2 = [x ** 2 for x in mylist]"
   ]
  }
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