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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Sebastian Raschka](http://sebastianraschka.com) \n",
"last updated: 06/02/2014\n",
"\n",
"- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day6_string_is_number.ipynb?create=1) \n",
"- [Link to this IPython notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day6_string_is_number.ipynb) \n",
"- [Link to the GitHub Repository One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"I would be happy to hear your comments and suggestions. \n",
"Please feel free to drop me a note via\n",
"[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 6 - One Python Benchmark per Day"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Determining if a string is a number"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"**[-> skip to the results](#Results)**\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
"For this benchmark, we will be testing different approaches to determine is a string is a number. \n",
"\n",
"**Note that methods like `.isdigit()` and `.isnumeric()` are only evaluating if a string in an integer**, \n",
"e.g., both `'1.2'.isdigit()` and `'1.2'.isnumeric()` return `False`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def is_number_tryexcept(s):\n",
" \"\"\" Returns True is string is a number. \"\"\"\n",
" try:\n",
" float(s)\n",
" return True\n",
" except ValueError:\n",
" return False"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from re import match as re_match\n",
"\n",
"def is_number_regex(s):\n",
" \"\"\" Returns True is string is a number. \"\"\"\n",
" if re_match(\"^\\d+?\\.\\d+?$\", s) is None:\n",
" return s.isdigit()\n",
" return True"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from re import compile as re_compile\n",
"\n",
"comp = re_compile(\"^\\d+?\\.\\d+?$\") \n",
"\n",
"def compiled_regex(s):\n",
" \"\"\" Returns True is string is a number. \"\"\"\n",
" if comp.match(s) is None:\n",
" return s.isdigit()\n",
" return True"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def is_number_repl_isdigit(s):\n",
" \"\"\" Returns True is string is a number. \"\"\"\n",
" return s.replace('.','',1).isdigit()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"**Quick note on why I am importing `re.search` as `re_search`: \n",
"To decrease the overhead for the lookup.**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import re\n",
"\n",
"%timeit re_match(\"^\\d+?\\.\\d+?$\", '1.2345')\n",
"%timeit re.match(\"^\\d+?\\.\\d+?$\", '1.2345')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1000000 loops, best of 3: 1.3 \u00b5s per loop\n",
"1000000 loops, best of 3: 1.36 \u00b5s per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing the functions for correct behavior"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_float = '1.1234'\n",
"inv_float = '1.12.34'\n",
"no_number = 'abc123'\n",
"an_int = '12345'\n",
"\n",
"funcs = [\n",
" is_number_tryexcept, \n",
" is_number_regex,\n",
" compiled_regex,\n",
" is_number_repl_isdigit\n",
" ]\n",
"\n",
"for f in funcs:\n",
" assert (f(a_float) == True), 'Error in %s(%s)' %(f.__name__, a_float)\n",
" assert (f(inv_float) == False), 'Error in %s(%s)' %(f.__name__, inv_float)\n",
" assert (f(no_number) == False), 'Error in %s(%s)' %(f.__name__, no_number)\n",
" assert (f(an_int) == True), 'Error in %s(%s)' %(f.__name__, an_int)\n",
"print('ok')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ok\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Special cases where those functions do not work"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_float = '.1234'\n",
"\n",
"print('Float notation \".1234\" is not supported by:')\n",
"for f in funcs:\n",
" if not f(a_float):\n",
" print('\\t -', f.__name__)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Float notation \".1234\" is not supported by:\n",
"\t - is_number_regex\n",
"\t - compiled_regex\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scientific notations"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scientific1 = '1.000000e+50'\n",
"scientific2 = '1e50'\n",
"\n",
"\n",
"print('Scientific notation \"1.000000e+50\" is not supported by:')\n",
"for f in funcs:\n",
" if not f(scientific1):\n",
" print('\\t -', f.__name__)\n",
" \n",
"print('Scientific notation \"1e50\" is not supported by:')\n",
"for f in funcs:\n",
" if not f(scientific2):\n",
" print('\\t -', f.__name__)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Scientific notation \"1.000000e+50\" is not supported by:\n",
"\t - is_number_regex\n",
"\t - compiled_regex\n",
"\t - is_number_repl_isdigit\n",
"Scientific notation \"1e50\" is not supported by:\n",
"\t - is_number_regex\n",
"\t - compiled_regex\n",
"\t - is_number_repl_isdigit\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Timing the functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import timeit\n",
"\n",
"test_cases = ['1.12345', '1.12.345', 'abc12345', '12345']\n",
"times_n = {f.__name__:[] for f in funcs}\n",
"\n",
"for t in test_cases:\n",
" for f in funcs:\n",
" f = f.__name__\n",
" times_n[f].append(min(timeit.Timer('%s(t)' %f, \n",
" 'from __main__ import %s, t' %f)\n",
" .repeat(repeat=3, number=1000000)))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Preparing the plots"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import platform\n",
"import multiprocessing\n",
"\n",
"def print_sysinfo():\n",
" \n",
" print('\\nPython version :', platform.python_version())\n",
" print('compiler :', platform.python_compiler())\n",
" \n",
" print('\\nsystem :', platform.system())\n",
" print('release :', platform.release())\n",
" print('machine :', platform.machine())\n",
" print('processor :', platform.processor())\n",
" print('CPU count :', multiprocessing.cpu_count())\n",
" print('interpreter:', platform.architecture()[0])\n",
" print('\\n\\n')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy import arange\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def plot():\n",
" labels = [('is_number_tryexcept','Try-except method'),\n",
" ('is_number_regex', 'Regular expression'),\n",
" ('compiled_regex', 'Compiled regular expression'),\n",
" ('is_number_repl_isdigit', 'replace-isdigit method')\n",
" ]\n",
"\n",
" x_labels = ['float: \"1.12345\"',\n",
" 'invalid float: \"1.12.345\"',\n",
" 'no number: \"abc12345\"',\n",
" 'integer: \"12345\"']\n",
" \n",
" plt.rcParams.update({'font.size': 12})\n",
"\n",
" ind = arange(len(test_cases)) # the x locations for the groups\n",
" width = 0.2\n",
"\n",
" fig = plt.figure(figsize=(12,8))\n",
" ax = fig.add_subplot(111)\n",
" colors = [(0,'c'), (1,'b'), (2,'g'), (3,'r')]\n",
"\n",
" for l,c in zip(labels,colors):\n",
" ax.bar(ind + c[0]*width,\n",
" times_n[l[0]], \n",
" width,\n",
" alpha=0.5,\n",
" color=c[1],\n",
" label=l[1])\n",
" plt.grid()\n",
" ax.set_ylabel('time in microseconds')\n",
" ax.set_title('Methods for determening if a string is a number')\n",
" ax.set_xticks(ind + width)\n",
" ax.set_xticklabels(x_labels)\n",
" plt.legend(loc='upper right', fontsize=13)\n",
" plt.xlim([-0.2, 3.8])\n",
" plt.ylim([-0, 2])\n",
" plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Results"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print_sysinfo()\n",
"plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Python version : 3.4.0\n",
"compiler : GCC 4.2.1 (Apple Inc. build 5577)\n",
"\n",
"system : Darwin\n",
"release : 13.2.0\n",
"machine : x86_64\n",
"processor : i386\n",
"CPU count : 4\n",
"interpreter: 64bit\n",
"\n",
"\n",
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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XYmNj+fIIZ2dnvmSidBYQsVgMOzs7ZGdn8/9evnwpNUvI1atXERwcjKlTp+Kr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\nAACCRDMdIZitgq3IJmxFNmEz8ukeNNMAAABAkGimIwSzVbAV2YStyCZ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"text": [
""
]
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `try-except` approach appears to be slower than the replace-isdigit method for cases where the string is not a number - executing the `except-block` seems to be very costly. However, we have to consider that it also works for special cases like: \n",
"- '1.000000e+50' \n",
"- '1e50' \n",
"- '.12345' \n",
"\n",
"So it really depends on the data set which method to choose - the more thorough \"try-except\" or the faster \"replace-isdigit\""
]
}
],
"metadata": {}
}
]
}