Microbenchmarking iterative vs functional style

iter-vs-func.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "naked-scholarship",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphabet = \"abcdefghijklmnopqrstuvwxyz\"\n",
    "str_user_title = \"Technologist\"\n",
    "ODDS_OF_CORRECT_TITLE = 50\n",
    "NUM_RECORDS = 1000\n",
    "\n",
    "import random\n",
    "def random_title():\n",
    "    if random.randint(0, ODDS_OF_CORRECT_TITLE) == 0:\n",
    "        return str_user_title\n",
    "    else:\n",
    "        return \"\".join(random.choices(alphabet, k=5))\n",
    "def random_record():\n",
    "    return dict(title=random_title(), foo=0, bar=1, baz=2, quux=3)\n",
    "\n",
    "random.seed(0)\n",
    "random_records = [random_record() for x in range(NUM_RECORDS)]\n",
    "ptr_DictReader = random_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "urban-bottle",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This should always return the same result:\n",
    "assert random_records[1]['title'] == \"vzjxf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "innovative-investing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.6 µs ± 158 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit \n",
    "int_counter=0\n",
    "for it_row in ptr_DictReader:\n",
    "    if it_row['title'] == str_user_title:\n",
    "        int_counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "flexible-winter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.8 µs ± 77.8 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "sum(it_row[\"title\"] == str_user_title for it_row in ptr_DictReader)"
   ]
  }
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