Vectorisation Examples GDGDublin DevFest17

Vectorisation_examples.ipynb

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   "source": [
    "# Vectorisation Examples for GDGDublin DevFest 17\n",
    "Using the %timeit function to track and print processing times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "14.6 ns ± 0.139 ns per loop (mean ± std. dev. of 7 runs, 100000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# %timeit examples\n",
    "%timeit 1 + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.7 ns ± 0.113 ns per loop (mean ± std. dev. of 7 runs, 100000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# accessing the results of %timeit\n",
    "result = %timeit -o 1 + 10"
   ]
  },
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   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.53 µs ± 43.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "465 ns ± 6.67 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Create three arrays in Python\n",
    "prices_arr = [9, 12, 14, 8, 6, 11]\n",
    "quantities_arr = [1, 1, 1, 1, 1, 1]\n",
    "total_arr = []\n",
    "\n",
    "# Create three Numpy arrays from the previous ones\n",
    "prices = np.array(prices_arr)\n",
    "quantities = np.array(quantities_arr)\n",
    "total = np.array(total_arr)\n",
    "\n",
    "# Simply multiply prices and quantities\n",
    "def p_times_q_in_loop():\n",
    "    for index, x in enumerate(prices_arr):\n",
    "        total_arr.append(x * quantities_arr[index])\n",
    "\n",
    "# Track time for function and using a vectorised version of the function\n",
    "using_loop = %timeit -o p_times_q_in_loop()\n",
    "using_vectorisaiton = %timeit -o  total = prices * quantities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.92 µs ± 57.8 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "465 ns ± 6.67 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def p_times_q_in_loop(prices, quantities):\n",
    "    for index, x in enumerate(prices):\n",
    "        total_arr.append(x * quantities[index])\n",
    "\n",
    "using_loop_and_parameters = %timeit -o p_times_q_in_loop(prices, quantities)\n",
    "print(using_vectorisaiton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.2 ms ± 374 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "37.1 µs ± 1.08 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# large arrays\n",
    "large_prices = np.random.random_sample(50000)\n",
    "large_quantities = np.random.random_sample(50000)\n",
    "\n",
    "large_using_loop_and_parameters = %timeit -o p_times_q_in_loop(large_prices, large_quantities)\n",
    "large_using_vectorisaiton = %timeit -o  total = large_prices * large_quantities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.4 s ± 568 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "228 ms ± 13.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# large_R_ arrays\n",
    "large_prices = np.random.random_sample(50000000)\n",
    "large_quantities = np.random.random_sample(50000000)\n",
    "\n",
    "large_using_loop_and_parameters = %timeit -o p_times_q_in_loop(large_prices, large_quantities)\n",
    "large_using_vectorisaiton = %timeit -o  total = large_prices * large_quantities\n"
   ]
  }
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