smason · June 23, 2020 09:37 · smason · Jun 23, 2020
diff --git a/benchmarking tuple vs list creation.ipynb b/benchmarking tuple vs list creation.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeit import Timer\n",
    "from dis import dis\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(style='ticks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  3           0 LOAD_CONST               1 (1)\n",
      "              2 STORE_FAST               2 (a)\n",
      "\n",
      "  4           4 LOAD_FAST                1 (_timer)\n",
      "              6 CALL_FUNCTION            0\n",
      "              8 STORE_FAST               3 (_t0)\n",
      "\n",
      "  5          10 LOAD_FAST                0 (_it)\n",
      "             12 GET_ITER\n",
      "        >>   14 FOR_ITER                18 (to 34)\n",
      "             16 STORE_FAST               4 (_i)\n",
      "\n",
      "  6          18 LOAD_FAST                2 (a)\n",
      "             20 LOAD_FAST                2 (a)\n",
      "             22 LOAD_FAST                2 (a)\n",
      "             24 LOAD_FAST                2 (a)\n",
      "             26 LOAD_FAST                2 (a)\n",
      "             28 BUILD_TUPLE              5\n",
      "             30 POP_TOP\n",
      "             32 JUMP_ABSOLUTE           14\n",
      "\n",
      "  7     >>   34 LOAD_FAST                1 (_timer)\n",
      "             36 CALL_FUNCTION            0\n",
      "             38 STORE_FAST               5 (_t1)\n",
      "\n",
      "  8          40 LOAD_FAST                5 (_t1)\n",
      "             42 LOAD_FAST                3 (_t0)\n",
      "             44 BINARY_SUBTRACT\n",
      "             46 RETURN_VALUE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "33.17329997662455"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mk(n, brackets):\n",
    "    # trailing comma important for singleton tuples\n",
    "    elems = 'a,' * n\n",
    "    pre,post = brackets\n",
    "    return f'{pre}{elems}{post}'\n",
    "\n",
    "t = Timer(mk(5, '()'), \"a=1\")\n",
    "\n",
    "# make sure it's doing what we think it should be\n",
    "dis(t.inner)\n",
    "\n",
    "t.timeit(10)\n",
    "t.timeit(1_000_000) * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "5\n",
      "10\n",
      "20\n",
      "50\n",
      "100\n",
      "200\n",
      "500\n",
      "1000\n",
      "1\n",
      "2\n",
      "5\n",
      "10\n",
      "20\n",
      "50\n",
      "100\n",
      "200\n",
      "500\n",
      "1000\n"
     ]
    }
   ],
   "source": [
    "def bt(brackets):\n",
    "    result = []\n",
    "    for n in [1,2,5,10,20,50,100,200,500,1000]:\n",
    "        print(n)\n",
    "        t = Timer(mk(n, brackets), \"a=1\")\n",
    "        k = 50_000_000 // (5+n)\n",
    "        # cache warmup\n",
    "        t.timeit(10)\n",
    "        result.extend((\n",
    "            (n, t.timeit(k) / k * 1e9)\n",
    "            for _ in range(3)\n",
    "        ))\n",
    "    return result\n",
    "\n",
    "# spin up CPUs\n",
    "for _ in range(50_000_000):\n",
    "    pass\n",
    "\n",
    "times_tuple = bt('()')\n",
    "times_list = bt('[]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(\n",
    "    [n for (n,ns) in times_tuple],\n",
    "    [ns for (n,ns) in times_tuple],\n",
    "    '.', label='tuples'\n",
    ")\n",
    "plt.loglog(\n",
    "    [n for (n,ns) in times_list],\n",
    "    [ns for (n,ns) in times_list],\n",
    "    '.', label='lists'\n",
    ")\n",
    "plt.legend()\n",
    "sns.despine()\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"time (ns)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
 }
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"from timeit import Timer\n",
	"from dis import dis\n",
	"\n",
	"import matplotlib.pyplot as plt\n",
	"import seaborn as sns\n",
	"\n",
	"sns.set(style='ticks')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	" 3 0 LOAD_CONST 1 (1)\n",
	" 2 STORE_FAST 2 (a)\n",
	"\n",
	" 4 4 LOAD_FAST 1 (_timer)\n",
	" 6 CALL_FUNCTION 0\n",
	" 8 STORE_FAST 3 (_t0)\n",
	"\n",
	" 5 10 LOAD_FAST 0 (_it)\n",
	" 12 GET_ITER\n",
	" >> 14 FOR_ITER 18 (to 34)\n",
	" 16 STORE_FAST 4 (_i)\n",
	"\n",
	" 6 18 LOAD_FAST 2 (a)\n",
	" 20 LOAD_FAST 2 (a)\n",
	" 22 LOAD_FAST 2 (a)\n",
	" 24 LOAD_FAST 2 (a)\n",
	" 26 LOAD_FAST 2 (a)\n",
	" 28 BUILD_TUPLE 5\n",
	" 30 POP_TOP\n",
	" 32 JUMP_ABSOLUTE 14\n",
	"\n",
	" 7 >> 34 LOAD_FAST 1 (_timer)\n",
	" 36 CALL_FUNCTION 0\n",
	" 38 STORE_FAST 5 (_t1)\n",
	"\n",
	" 8 40 LOAD_FAST 5 (_t1)\n",
	" 42 LOAD_FAST 3 (_t0)\n",
	" 44 BINARY_SUBTRACT\n",
	" 46 RETURN_VALUE\n"
	]
	},
	{
	"data": {
	"text/plain": [
	"33.17329997662455"
	]
	},
	"execution_count": 12,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"def mk(n, brackets):\n",
	" # trailing comma important for singleton tuples\n",
	" elems = 'a,' * n\n",
	" pre,post = brackets\n",
	" return f'{pre}{elems}{post}'\n",
	"\n",
	"t = Timer(mk(5, '()'), \"a=1\")\n",
	"\n",
	"# make sure it's doing what we think it should be\n",
	"dis(t.inner)\n",
	"\n",
	"t.timeit(10)\n",
	"t.timeit(1_000_000) * 1000"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"1\n",
	"2\n",
	"5\n",
	"10\n",
	"20\n",
	"50\n",
	"100\n",
	"200\n",
	"500\n",
	"1000\n",
	"1\n",
	"2\n",
	"5\n",
	"10\n",
	"20\n",
	"50\n",
	"100\n",
	"200\n",
	"500\n",
	"1000\n"
	]
	}
	],
	"source": [
	"def bt(brackets):\n",
	" result = []\n",
	" for n in [1,2,5,10,20,50,100,200,500,1000]:\n",
	" print(n)\n",
	" t = Timer(mk(n, brackets), \"a=1\")\n",
	" k = 50_000_000 // (5+n)\n",
	" # cache warmup\n",
	" t.timeit(10)\n",
	" result.extend((\n",
	" (n, t.timeit(k) / k * 1e9)\n",
	" for _ in range(3)\n",
	" ))\n",
	" return result\n",
	"\n",
	"# spin up CPUs\n",
	"for _ in range(50_000_000):\n",
	" pass\n",
	"\n",
	"times_tuple = bt('()')\n",
	"times_list = bt('[]')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.loglog(\n",
	" [n for (n,ns) in times_tuple],\n",
	" [ns for (n,ns) in times_tuple],\n",
	" '.', label='tuples'\n",
	")\n",
	"plt.loglog(\n",
	" [n for (n,ns) in times_list],\n",
	" [ns for (n,ns) in times_list],\n",
	" '.', label='lists'\n",
	")\n",
	"plt.legend()\n",
	"sns.despine()\n",
	"plt.xlabel(\"number of elements\")\n",
	"plt.ylabel(\"time (ns)\")\n",
	"plt.tight_layout()\n",
	"plt.show()"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3",
	"language": "python",
	"name": "python3"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.8.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 4
	}