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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
      "last updated: 05/29/2014\n",
      "\n",
      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day9_string_endswith.ipynb?create=1)  \n",
      "\n",
      "- [Link to this IPython notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day9_string_endswith.ipynb)  \n",
      "\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": [
      "<hr>\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",
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Day 9 - One Python Benchmark per Day"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## The most Pythonic way to check if a string ends with a particular substring"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "**[-> skip to the results](#Results)**\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This benchmark was inspired by the article [\"A Nice Little Bit of Python\"](http://www.jeffknupp.com/blog/2014/05/28/a-nice-little-bit-of-python) for finding the most elegant way to determine if a string ends with a particular substring.  \n",
      "There is the \"old-fashioned\" way using a sequence of \"`or`\"s, e.g.,\n",
      "\n",
      "    if needle.endswith('ly') or needle.endswith('ed') or\\\n",
      "        needle.endswith('ing') or needle.endswith('ers'):\n",
      "        print('Is valid')\n",
      "    else:\n",
      "        print('Invalid')"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or the more elegant way using a list comprehension that was suggested by the author:\n",
      "\n",
      "    if any([needle.endswith(e) for e in ('ly', 'ed', 'ing', 'ers')]):\n",
      "        print('Is valid')\n",
      "    else:\n",
      "        print('Invalid')"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "However, there are two even better solutions. One would be replacing the list comprehension by a generator, and my favorite one: feeding a tuple to the `.endswith()` method. See the different implementations below:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Functions for checking if a string ends with a substring"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from re import search as re_search\n",
      "from re import compile as re_compile\n",
      "\n",
      "def old_fashioned(needle):\n",
      "    return bool(needle.endswith('ly') or needle.endswith('ed') or\\\n",
      "        needle.endswith('ing') or needle.endswith('ers'))\n",
      "\n",
      "def list_comprehension(needle):\n",
      "    return bool(any([needle.endswith(e) for e in ('ly', 'ed', 'ing', 'ers')]))\n",
      "        \n",
      "def generator(needle):\n",
      "    return bool(any(needle.endswith(e) for e in ('ly', 'ed', 'ing', 'ers')))\n",
      "\n",
      "def endswith_tuple(needle):\n",
      "    return bool(needle.endswith(('ly', 'ed', 'ing', 'ers')))\n",
      "\n",
      "def regexpr(needle):\n",
      "     return bool(re_search(r'(?:ly|ed|ing|ers)$', needle))\n",
      "\n",
      "comp = re_compile(r'(?:ly|ed|ing|ers)$')    \n",
      "def compiled_regexpr(needle):\n",
      "    return bool(comp.search(needle))\n",
      "    \n",
      "def map_func(needle):\n",
      "    return any(map(needle.endswith, ('ly', 'ed', 'ing', 'ers')))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\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_search(r'(?:ly|ed|ing|ers)$', 'needlers')\n",
      "%timeit re.search(r'(?:ly|ed|ing|ers)$', 'needlers')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000000 loops, best of 3: 1.63 \u00b5s per loop\n",
        "1000000 loops, best of 3: 1.69 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Verification that all functions work correcltly"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = [old_fashioned, list_comprehension, \n",
      "         generator, endswith_tuple, \n",
      "         regexpr, compiled_regexpr,\n",
      "         map_func\n",
      "        ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for f in funcs:\n",
      "    assert(f('neeeeedly') == True), f.__name__\n",
      "    assert(f('neeeeedlee') == False), f.__name__\n",
      "    assert(f('nelyeedlee') == False), f.__name__\n",
      "print('All functions work correctly.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "All functions work correctly.\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Timing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "test_cases = [\"neeeeedly\", \"neeeeedlers\", \"neeeeedlee\", ]\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=500, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "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": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import arange\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "def plot():\n",
      "\n",
      "    labels = [('old_fashioned','needle.endswith(\"ly\") or needle.endswith(\"ed\") or ...'),\n",
      "          ('list_comprehension', 'any([needle.endswith(e) for e in (\"ly\", \"ed\", ...)])'),\n",
      "          ('generator', 'any(needle.endswith(e) for e in (\"ly\", \"ed\", ...))'),\n",
      "          ('regexpr', 're.search(r\"(?:ly|ed|ing|ers)$\", needle)'),\n",
      "          ('compiled_regexpr', 'same as above, but with compiled regexpr'),\n",
      "          ('map_func', 'any(map(needle.endswith, (\"ly\", \"ed\", \"ing\", \"ers\")))'),\n",
      "          ('endswith_tuple', 'needle.endswith((\"ly\", \"ed\", \"ing\", \"ers\"))'),\n",
      "          \n",
      "          ]\n",
      "\n",
      "    x_labels = ['True (first match): \"neeeeedly\"',\n",
      "                'True (first match): \"neeeeedlers\"',\n",
      "                'False: \"neeeeedlee\"'\n",
      "                ]\n",
      "            \n",
      "\n",
      "    ind = arange(len(test_cases))  # the x locations for the groups\n",
      "    width = 0.12\n",
      "\n",
      "    fig = plt.figure(figsize=(14,8))\n",
      "    ax = fig.add_subplot(111)\n",
      "    colors = [(0,'c'), (1,'b'), (2,'g'), (3,'r'), (4,'y'), (5,'m'), (6, 'k')]\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",
      "        )\n",
      "\n",
      "    ax.set_ylabel('execution time in microseconds', fontsize=16)\n",
      "    ax.set_title('Methods for determening if a string '\\\n",
      "                 'ends with a particular substring',\n",
      "                 fontsize=18)\n",
      "    ax.set_xticks(ind + width + 0.24)\n",
      "    ax.set_xticklabels(x_labels)\n",
      "    leg = ax.legend(loc='upper left', bbox_to_anchor = (0.85, 1.0))\n",
      "    plt.grid()\n",
      "    plt.ylim([0, 0.003])\n",
      "    plt.xlim([-0.2, 3.0])\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "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  : 3.3.5\n",
        "compiler        : GCC 4.0.1 (Apple Inc. build 5493)\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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m+Oyzz9ChQweYmJggKSkJmzdvhoGBAd9QAQDdu3fHuXPnsGbNGjRt2hQcx4nV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\n9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       "text": [
        "<matplotlib.figure.Figure at 0x106484350>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Conclusion"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Clearly, the most elegant and Pythonic way of determining if a string ends with a particular substring is also the most efficient one!"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}