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  "signature": "sha256:a4749ce2a9f843d9846081abaa9265690ebabfaf5a0aa18877f65945b1f56805"
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Sebastian Raschka](http://sebastianraschka.com)\n",
      "last updated: 05/07/2014  \n",
      "\n",
      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb)  \n",
      "- [Link to the GitHub repository](https://github.com/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb)  \n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr>\n",
      "I am really looking forward to your comments and suggestions to improve and extend this collection! Just send me a quick note   \n",
      "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
      "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Python benchmarks via `timeit`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Code was executed in Python 3.4.0"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"sections\"></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Sections\n",
      "- [String operations](#string_operations)\n",
      "    - [String formatting: .format() vs. binary operator %s](#str_format_bin)\n",
      "    - [String reversing: [::-1] vs. `''.join(reversed())`](#str_reverse)\n",
      "    - [String concatenation: `+=` vs. `''.join()`](#string_concat)\n",
      "    - [Assembling strings](#string_assembly)  \n",
      "    - [Testing if a string is an integer](#is_integer)\n",
      "    - [Testing if a string is a number](#is_number)\n",
      "- [List operations](#list_operations)\n",
      "    - [List reversing: [::-1] vs. reverse() vs. reversed()](#list_reverse)\n",
      "    - [Creating lists using conditional statements](#create_cond_list)\n",
      "- [Dictionary operations](#dict_ops) \n",
      "    - [Adding elements to a dictionary](#adding_dict_elements)\n",
      "- [Comprehensions vs. for-loops](#comprehensions)\n",
      "- [Copying files by searching directory trees](#find_copy)\n",
      "- [Returning column vectors slicing through a numpy array](#row_vectors)\n",
      "- [Speed of numpy functions vs Python built-ins and std. lib.](#numpy)\n",
      "    - [`sum()` vs. `numpy.sum()`](#np_sum)\n",
      "    - [`range()` vs. `numpy.arange()`](#np_arange)\n",
      "    - [`statistics.mean()` vs. `numpy.mean()`](#np_mean)\n",
      "- [Cython vs. regular (C)Python](#cython)\n",
      "- [Numba vs. Cython vs. regular (C)Python & NumPy](#numba)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='string_operations'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# String operations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='str_format_bin'></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## String formatting: `.format()` vs. binary operator `%s`\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We expect the string `.format()` method to perform slower than %, because it is doing the formatting for each object itself, where formatting via the binary % is hard-coded for known types. But let's see how big the difference really is..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "n = 10000\n",
      "\n",
      "def test_format(n):\n",
      "    return ['{}'.format(i) for i in range(n)]\n",
      "\n",
      "def test_binaryop(n):\n",
      "    return ['%s' %i for i in range(n)]\n",
      "\n",
      "%timeit test_format(n)\n",
      "%timeit test_binaryop(n)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100 loops, best of 3: 4.01 ms per loop\n",
        "1000 loops, best of 3: 1.82 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['test_format', 'test_binaryop']\n",
      "\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(n)' %f, \n",
      "                      'from __main__ import %s, n' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('test_format', '.format() method'), \n",
      "          ('test_binaryop', 'binary operator %')] \n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of different string formatting methods')\n",
      "max_perf = max( f/b for f,b in zip(times_n['test_format'],\n",
      "                                   times_n['test_binaryop']) )\n",
      "min_perf = min( f/b for f,b in zip(times_n['test_format'],\n",
      "                                   times_n['test_binaryop']) )\n",
      "    \n",
      "ftext = 'The binary op. % is {:.2f}x to {:.2f}x faster than .format()'\\\n",
      "        .format(min_perf, max_perf)    \n",
      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
      "\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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lGDt2LFJTU6GlpVUr+9+8eRMTJkzAvHnz4O3tjbt37+Lrr7+u91xgSB/2jC3Z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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107efc590>"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='str_reverse'></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## String reversing: `[::-1]` vs. `''.join(reversed())`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "def reverse_join(my_str):\n",
      "    return ''.join(reversed(my_str))\n",
      "    \n",
      "def reverse_slizing(my_str):\n",
      "    return my_str[::-1]\n",
      "\n",
      "test_str = 'abcdefg'\n",
      "\n",
      "# Test to show that both work\n",
      "a = reverse_join(test_str)\n",
      "b = reverse_slizing(test_str)\n",
      "assert(a == b and a == 'gfedcba')\n",
      "\n",
      "%timeit reverse_join(test_str)\n",
      "%timeit reverse_slizing(test_str)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000000 loops, best of 3: 1.33 \u00b5s per loop\n",
        "1000000 loops, best of 3: 268 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['reverse_join', 'reverse_slizing']\n",
      "\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "test_strings = (test_str*n for n in orders_n)\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for st,n in zip(test_strings, orders_n):\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(st)' %f, \n",
      "                      'from __main__ import %s, st' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('reverse_join', '\"\".join(reversed(my_str))'), \n",
      "          ('reverse_slizing', 'my_str[::-1]')] \n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot([n*len(test_str) for n in orders_n], \n",
      "             times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of different string reversing methods')\n",
      "max_perf = max( j/s for j,s in zip(times_n['reverse_join'],\n",
      "                                   times_n['reverse_slizing']) )\n",
      "min_perf = min( j/s for j,s in zip(times_n['reverse_join'],\n",
      "                                   times_n['reverse_slizing']) )\n",
      "    \n",
      "ftext = 'my_str[::-1] is {:.2f}x to {:.2f}x faster than \"\".join(reversed(my_str))'\\\n",
      "        .format(min_perf, max_perf)\n",
      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LF8VbuoTxdnR0pIpcTcvLEx+WgoKqhauwUPx6ubmVz6+iVrfyWuzKWmbs2LE4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\ncHAwHnroIWzevPmuN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       "text": [
        "<matplotlib.figure.Figure at 0x105a6fe90>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='string_concat'></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## String concatenation: `+=` vs. `''.join()`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Strings in Python are immutable objects. So, each time we append a character to a string, it has to be created \u201cfrom scratch\u201d in memory. Thus, the answer to the question \u201cWhat is the most efficient way to concatenate strings?\u201d is a quite obvious, but the relative numbers of performance gains are nonetheless interesting."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "def string_add(in_chars):\n",
      "    new_str = ''\n",
      "    for char in in_chars:\n",
      "        new_str += char\n",
      "    return new_str\n",
      "\n",
      "def string_join(in_chars):\n",
      "    return ''.join(in_chars)\n",
      "\n",
      "test_chars = ['a', 'b', 'c', 'd', 'e', 'f']\n",
      "\n",
      "%timeit string_add(test_chars)\n",
      "%timeit string_join(test_chars)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000000 loops, best of 3: 764 ns per loop\n",
        "1000000 loops, best of 3: 321 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['string_add', 'string_join']\n",
      "\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "test_chars_n = (test_chars*n for n in orders_n)\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for st,n in zip(test_chars_n, orders_n):\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(st)' %f, \n",
      "                      'from __main__ import %s, st' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('string_add', 'new_str += char'), \n",
      "          ('string_join', '\"\".join(chars)')] \n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot([len(test_chars)*n for n in orders_n], \n",
      "             times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of different string concatenation methods')\n",
      "max_perf = max( a/j for a,j in zip(times_n['string_add'],\n",
      "                                   times_n['string_join']) )\n",
      "min_perf = min( a/j for a,j in zip(times_n['string_add'],\n",
      "                                   times_n['string_join']) )\n",
      "\n",
      "ftext = '\"\".join(chars) is {:.2f}x to {:.2f}x faster than new_str += char'\\\n",
      "        .format(min_perf, max_perf)\n",
      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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G9u3bQ1dXFxMnTsSrV6/E8u3duxeOjo5QU1ODqakp5syZg9f/P787JiYGKioq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50UcY8d2QqNRoOCugIcLj6M+g79O9HDPMOwNGopgtyDRMqOiIhMwaJ77IgsjftEBNUt1diRuwN7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\n/B8B7aSAfFV1TAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106fa92d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "<a name='string_assembly'></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## Assembling strings\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, I wanted to compare different methods string \u201cassembly.\u201d This is different from simple string concatenation, which we have seen in the previous section, since we insert values into a string, e.g., from a variable."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "n = 1000\n",
      "\n",
      "def plus_operator(n):\n",
      "    my_str = 'a'\n",
      "    for i in range(n):\n",
      "        my_str = my_str + str(1) + str(2)\n",
      "    return my_str \n",
      "    \n",
      "def format_method(n):\n",
      "    my_str = 'a'\n",
      "    for i in range(n):\n",
      "        my_str = '{}{}{}'.format(my_str,1,2)\n",
      "    \n",
      "def binary_operator(n):\n",
      "    my_str = 'a'\n",
      "    for i in range(n):\n",
      "        my_str = '%s%s%s' %(my_str,1,2)\n",
      "    return my_str\n",
      "\n",
      "%timeit plus_operator(n)\n",
      "%timeit format_method(n)\n",
      "%timeit binary_operator(n)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 869 \u00b5s per loop\n",
        "1000 loops, best of 3: 686 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 445 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['plus_operator', 'format_method', 'binary_operator']\n",
      "\n",
      "orders_n = [10**n for n in range(1, 5)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(n)' %f, \n",
      "                      'from __main__ import %s, n' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('plus_operator', 'my_str + str(1) + str(2)'), \n",
      "          ('format_method', '\"{}{}{}\".format(my_str,1,2)'),\n",
      "          ('binary_operator', '\"%s%s%s\" %(my_str,1,2)'),\n",
      "          ] \n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], \n",
      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of different string assembly methods')\n",
      "\n",
      "max_perf = max( p/b for p,b in zip(times_n['plus_operator'],\n",
      "                                   times_n['binary_operator']) )\n",
      "min_perf = min( p/b for p,b in zip(times_n['plus_operator'],\n",
      "                                   times_n['binary_operator']) )\n",
      "\n",
      "ftext = '\"%s%s%s\" %(my_str,1,2) is {:.2f}x to'\\\n",
      "        '{:.2f}x faster than my_str + str(1) + str(2)'\\\n",
      "        .format(min_perf, max_perf)\n",
      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qupbn279/P1q3bl1puaampsRy0kzk/xQQEXr37g1/f/9Ky5o0acL/X0ZGRrAdy7fLhQsX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\nCRV2IiIiInlChZ2IiIhInvgHflbs0wMQQB4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105595650>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "<a name='is_integer'></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## Testing if a string is an integer"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "def string_is_int(a_str):\n",
      "    try:\n",
      "        int(a_str)\n",
      "        return True\n",
      "    except ValueError:\n",
      "        return False\n",
      "\n",
      "an_int = '123'\n",
      "no_int = '123abc'\n",
      "\n",
      "%timeit string_is_int(an_int)\n",
      "%timeit string_is_int(no_int)\n",
      "%timeit an_int.isdigit()\n",
      "%timeit no_int.isdigit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000000 loops, best of 3: 420 ns per loop\n",
        "100000 loops, best of 3: 2.83 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000000 loops, best of 3: 116 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000000 loops, best of 3: 119 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['string_is_int', 'isdigit']\n",
      "t1 = '123'\n",
      "t2 = '123abc'\n",
      "isdigit_method = []\n",
      "string_is_int_method = []\n",
      "\n",
      "for t in [t1,t2]:\n",
      "    string_is_int_method.append(min(timeit.Timer('string_is_int(t)', \n",
      "                      'from __main__ import string_is_int, t')\n",
      "                              .repeat(repeat=3, number=1000000)))\n",
      "    isdigit_method.append(min(timeit.Timer('t.isdigit()', \n",
      "                      'from __main__ import t')\n",
      "                              .repeat(repeat=3, number=1000000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = len(isdigit_method)\n",
      "ind = np.arange(N)  # the x locations for the groups\n",
      "width = 0.25       # the width of the bars\n",
      "\n",
      " \n",
      "fig, ax = plt.subplots()\n",
      "plt.bar(ind, \n",
      "        [i for i in string_is_int_method], \n",
      "        width,\n",
      "        alpha=0.5,\n",
      "        color='g',\n",
      "        label='string_is_int(a_str)')\n",
      "\n",
      "plt.bar(ind + width, \n",
      "        [i for i in isdigit_method], \n",
      "        width,\n",
      "        alpha=0.5,\n",
      "        color='b',\n",
      "        label='a_str.isdigit()')\n",
      "    \n",
      "ax.set_ylabel('time in microseconds')\n",
      "ax.set_title('Time to check if a string is an integer')\n",
      "ax.set_xticks(ind + width)\n",
      "ax.set_xticklabels(['\"%s\"' %t for t in [t1, t2]])\n",
      "plt.xlabel('test strings')\n",
      "plt.xlim(-0.1,1.6)\n",
      "#plt.ylim(0,15)\n",
      "plt.legend(loc='upper left')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fr1UXUFXnFF5NCESYms7PVEw2IxL9/ykmNTU1waOdCqWpqSl4Yp8KjRs3ljkd\npazX9M0338DT01Nq3ejRozFz5kycOXMGvXv3rl3AhLyDXv+h7O/vX2VZhd6HMGPGDCQmJuLo0aP8\nZC+yhISE8FMiJiYmYsWKFRg5cqSiwlS6v//+G05OTtDX14eOjg6cnJwQHR0taNvIyEg4OjpCS0sL\nWlpa6NChA06ePAng/+f77d27N0QiEX8Xrp+fH9q0aYMDBw7AysoKTZo0qdSNExQUhMaNG/PL+fn5\nmDJlCkxMTKCmpgYzMzN8+eWX/PPFxcWYMWMGdHR0oKenh88++wzPnj2TqrOi3Vd98803MDU1haam\nJoYMGYK9e/dCJBLhzp07AF782qlYTk9P5490LCwsIBKJ0KdPHwBAUlISrl69CmdnZ6n61dXVMXDg\nQOzZs0fQ+0nI+0RhCSEjIwM7duxAbGwsjI2NpQYIy8zMhEQi4efaDQsLg4ODA8RiMYYMGYLRo0dj\nyZIligpV6Z48eYJZs2YhKioKFy9eRJs2bTBw4EA8evSo2u1KS0sxfPhwdOvWDTExMYiJiYG/vz80\nNDQAgL+i6/Dhw8jOzpZKMnfu3MG2bdsQHByMhIQENG/evNq2li1bhpiYGBw9ehTJycn49ddfYWNj\nwz+/ePFiHD58GMHBwYiKioKmpia2bt1a7SQ7hw8fxoIFC7Bo0SJcv34d48aNw4IFC6rcxszMDEeO\nHAEAREdHIzs7G4cPHwbwInE0bdoU5ubmlbb78MMPERYWVu3rI+R9pLAuo5YtW1Y77+2rg5ytXbsW\na9euVURY9dLrR0Pff/89Dh06hBMnTmDSpElVbldQUIDHjx9j2LBhsLS0BAD+fwBo2rQpAEBPT69S\nt0txcTGCg4NhamoqKMbMzEx07NgRnTt3BgCYmpqiW7duAF4ktO3bt2PLli0YNmwYgBd/0/DwcH6y\ne1nWr1+PSZMmYfbs2XzsiYmJCAwMlFleJBLxcw8bGBhIvaakpCSZ97cAgLm5OTIyMqQmyiGE0NAV\n9VJaWhrc3NzQpk0baGtrQ1tbG3l5ecjMzKx2O11dXXh5eWHAgAEYPHgwAgMDkZSUJKhNIyMjwckA\nAD777DP89ttvaN++PebOnYsTJ07w/fgpKSl49uwZunfvLrWNo6NjtecvEhIS0LVrV6l1ry8LlZeX\nV+VIpBXzOjx+/PiN6iakoaKEUA8NHToUWVlZ2Lp1Ky5duoRr167B0NAQJSUlNW67Y8cOXL16Ff36\n9cPZs2e7qXC1AAAgAElEQVRhZ2eHHTt21LhdbU/u9u/fH5mZmVi6dCmKi4vh6uqKPn36VHsUKITQ\neZtroqOjU+XQ2hVHKTo6OnXSFiENBSWEeubhw4dISEiAt7c3+vXrx5/kvXfvnuA6bG1tMW/ePBw/\nfhyenp58QqiYeKWsrKxOYtXV1cWECROwfft2HDt2DGfPnkVCQgIsLS2hqqqK8+fPS5U/f/58tV/4\nNjY2uHDhgtS6qKioamOo6jW1adOmyrkMMjIyYG5uTt1FhLzmvftEGBurK/RmMWNj9VqV19XVhYGB\nAXbs2IFWrVrhwYMHWLhwIdTVa64nJSUFO3bswPDhw2Fqaoo7d+7g3Llz+N///gfgxTkEsViM0NBQ\nWFtbo0mTJnwf/Ovc3d3BcRx2794t8/mlS5eiU6dOsLGxgUgkwp49eyCRSGBmZgZNTU1Mnz4dy5Yt\ng5GREdq2bYudO3ciKSlJ5iWjFb788kuMHz8eXbp0wcCBA3HhwgUEBweD47gqE0nLli0hEon4sbGa\nNGkCbW1t9OrVCw8fPkR6enqlE8tRUVF0vwohMrx3CaG+DyMhEolw8OBBfP7557C3t4e5uTlWrlyJ\nRYtqjltTUxPJycmYMGEC7t+/D319fQwdOhTr1q3j6/7uu+/g6+uL9evXo0WLFkhNTZX5hXv79u1K\n615dVldXh4+PD9LT06GiooKOHTsiJCQEEokEALB69WoUFxfDzc0NwIsb22bOnInffvtNqr5X63R2\ndsaaNWuwevVqLFy4EL169YKPjw+mT58udQ/Eq9sYGRlh1apVWL16NebOnYuePXsiLCwM7dq1Q6dO\nnXD48GF88cUXfPmioiKEhobi999/r/H9JOR9Q1Noknrtq6++wpYtW2rVZVZh3759WLlyJeLi4vh1\nwcHBWLt2reB5lesjmkJTOJpCs7Lq9p83OodQVFRU6SYjQt5WaWkpVq9ejevXryMlJQU//vgj1q1b\nBy8vrzeqb9KkSVBXV8fBgwcBvDjP8PXXX2PNmjV1GTYhDYaghPDll1/i0qVLAIBjx45BT08Purq6\nOHr0qFyDI9L27t0rNWnQ6/8qbux7V3Ech7Nnz6Jv376ws7PDxo0bsXTpUqxYseKN67xy5YrUWEYJ\nCQkYOHBgXYVMSIMiqMvI2NgYqamp0NDQQJcuXbBo0SJoa2tj3rx5uHHjhiLilOl96zIqLCystuuk\nZcuWdTJEOKnfqMtIOOoyqqy6/UfQSeWioiJoaGjgwYMHSEtLw+jRowEA6enpdRYkqZlYLK7yZitC\nCHlbghJCmzZt+HHr+/XrBwC4f/8+P0YOIYSQd5+ghLB161bMmTMHqqqq2LlzJwAgNDQU/fv3l2tw\nhBBCFEdQQujSpQsuXrwotc7V1RWurq5yCaou6Orq1tkwCITUJ1XdTEjI26oyIZw+fVrQF2rF+PP1\nTU1DRRNCCJFWZULw9PSUSghZWVkQiUTQ19fHw4cPUV5ezt/pSggh5N1XZUJ49Qqir7/+Gg8fPkRA\nQAA0NDTw9OlT+Pj4QE9PTxExEkIIUQBB9yE0bdoUd+7c4UeWBICSkhI0a9YMDx48kGuA1WmI9xoQ\nUl/QfQgN01sPXaGpqYnLly9LrYuOjq71GPqEEELqL0FXGa1YsQKDBg3CsGHDYGpqitu3b+Ovv/7C\nd999J+/4CCGEKIigIwQ3NzdcunQJVlZWyM/Ph7W1NaKiouDu7i7v+AghhCiI4PkQbGxs4OPjI89Y\nCCGEKJGghPDw4UOsW7cO165dQ2FhIb+e4zhERETILThCCCGKIyghTJo0CSUlJRg3bpzUVI50JzAh\nhDQcghLCxYsXce/ePalpDAkhhDQsgk4q29vbv/OTrxBCCKmeoCOEPn36YNCgQZgyZQqMjY0BAIwx\ncByHqVOnyjVAQgghiiEoIURERKB58+b4+++/Kz1HCYEQQhoGQQkhPDz8rRopKSnBjBkzcPr0aTx6\n9AiWlpZYtWpVlXPbbty4EWvWrMHTp08xZswYbNu2TWrYDEIIIXVP0DkEAMjNzcXu3buxatUq/Pzz\nz7UaXrq0tBRmZmaIiIhAfn4+VqxYgXHjxiEjI6NS2dDQUAQGBiIsLAwZGRlITU2Fr6+v4LYIIYS8\nGUEJ4eLFi7C0tMT333+P69evY/v27WjdujUuXLggqBENDQ34+vrCzMwMADBkyBBYWFjgn3/+qVR2\n9+7d8PLygrW1NXR0dODj44OgoCDhr4gQQsgbEdRlNGfOHGzduhUTJkzg1/3666+YM2cOoqOja91o\nTk4OkpKSYGtrW+m5+Ph4ODs788v29vbIyclBbm4uzRRFCCFyJOgIISkpCePGjZNaN3r0aPz777+1\nbvD58+dwcXGBh4cH2rZtW+n5wsJCaGtr88taWloAgIKCglq3RQghRDhBRwht2rTB/v374eLiwq87\nePAgWrduXavGysvL4ebmBjU1NWzZskVmGbFYjPz8fH45Ly8PACCRSGSW9/Pz4x87OTnBycmpVjER\nQkhDFh4eLvjCIEEJ4dtvv8WQIUOwefNmmJmZISMjA0lJSfjrr78EB8UYg6enJ+7fv4/jx49DRUVF\nZjlbW1tcu3YNY8aMAQDExsbCyMioyu6iVxMCIYQQaa//UPb396+yrKCE0L17d6SkpODYsWO4c+cO\nhg8fjsGDB9dqCs0ZM2YgMTERp06dQpMmTaos5+7uDg8PD7i4uMDY2BgBAQGYMmWK4HYIIYS8GUEJ\nISsrCxoaGnBzc+PXPXr0CHfu3EGzZs1q3D4jIwM7duyAmpoaf6czAOzYsQOOjo6wtbVFQkICTE1N\nMWDAACxcuBC9e/dGUVERxowZU21GI4QQUjcEJYSRI0di165dUkcEWVlZmDZtGi5dulTj9i1btkR5\neXmVz79+wnjevHmYN2+ekNAIIYTUEcFXGbVv315qXfv27ZGQkCCXoAghhCieoIRgaGhY6RLTlJQU\nNG3aVC5BEUIIUTxBCWHq1KkYPXo0/vzzT8THx+Po0aMYPXo0PD095R0fIYQQBRF0DsHb2xuNGzfG\n/PnzkZWVhRYtWsDLywtffPGFvOMjhBCiIIISgkgkwoIFC7BgwQJ5x0MIIURJBI92evLkSUydOhVD\nhw4FAFy5cgVhYWFyC4wQQohiCUoImzdvxowZM9CmTRtEREQAANTU1LBs2TK5BkcIIURxBCWEjRs3\n4tSpU1i8eDE/5IS1tTUSExPlGhwhhBDFEZQQCgsL0aJFC6l1JSUl1Q5BQQgh5N0iKCH06NEDq1ev\nllq3efNm9O7dWy5BEUIIUTxBVxlt3rwZw4YNww8//IDCwkK0bdsWEomkVqOdEkIIqd8EJYRmzZoh\nOjoa0dHRyMjIgJmZGbp06QKRSPBFSoQQQuo5Qd/ojDGIRCJ8+OGHGDduHJ4+fYpz587JOzZCCCEK\nJCgh9OrVC+fPnwcABAYGYuLEiZg4cSJWrlwp1+AIIYQojqCEEBcXh65duwJ4MYdBWFgYLl26hO3b\nt8s1OEIIIYoj6BxCxVwGKSkpAF5Mc8kYQ25urvwiI4QQolCCEoKjoyNmzZqFu3fvwtnZGcCL5GBg\nYCDX4AghhCiOoC6joKAg6OjowMHBgZ/UPjExEXPmzJFnbIQQQhRI0BFC06ZNsWrVKql1FYPcEUII\naRgEHSGUlJTAx8cHFhYWaNKkCSwsLODj44OSkhJ5x0cIIURBBB0hLFq0CJcvX8b3338PMzMzZGZm\n4quvvkJ+fj6++eYbecdICCFEAQQlhAMHDiA2NpafQ9nKygoffPAB7O3tKSEQQkgDQWNPEEIIASAw\nIYwdOxbDhw/HiRMnkJCQgJCQEIwYMQJjx46Vd3yEEEIURFCX0Zo1a7BixQrMmjULd+7cQbNmzTBx\n4kSaMY0QQhqQGhNCaWkppk2bhu+//x5fffWVImIihBCiBDV2GTVq1AgnT57kp84khBDSMAk6hzBv\n3ry3vu9gy5Yt6NSpE9TU1DBlypQqywUFBUFFRQUSiYT/FxER8cbtEkIIEUbQOYRNmzYhJycHGzZs\ngIGBATiOAwBwHIfMzExBDTVv3hzLly9HaGgoioqKqi3r6OhISYAQQhRMUELYs2fPWzdUMSjelStX\nkJWVVW1Zxthbt0cIIaR2BCUEJyenOmuwpi97juMQExMDAwMD6Onpwc3NDYsXL6ZzGIQQImeCziE4\nOztXmjIzIiICY8aMqXWDFd1NVenZsyfi4uJw//59HDp0CPv378fatWtr3Q4hhJDaEXSEcPbsWRw8\neFBqXbdu3TBy5MhaN1jTEYKFhQX/2M7ODj4+Pli7di28vb1llq8Yjht4cSRTl0czhBDyrgsPD0d4\neLigsoISgrq6Op48eQJtbW1+3ZMnT6Cqqlrr4Go6QpCluiTyakIghBAi7fUfyv7+/lWWFdRl1L9/\nf0yfPh15eXkAgLy8PMycORMDBw4UHFRZWRmKi4tRWlqKsrIyPHv2DGVlZZXKhYSEICcnB8CLSXhW\nrFjxRkcihBBCakdQQli/fj3y8/Ohp6fHn+zNy8vDxo0bBTcUEBAADQ0NBAYGYs+ePVBXV8fKlSuR\nmZkJiUTCX3kUFhYGBwcHiMViDBkyBKNHj8aSJUve7NURQggRjGO1uMbz7t27uH37Nlq0aAETExN5\nxiUIx3F0iSohcuIx1wPmI82VHcZbSf8jHUHfBCk7jHqluu/NKs8hMMb4/v7y8nIAgJGREYyMjKTW\niUQ0gjYhhDQEVSYELS0tFBQUvCjUSHYxjuNkngcghBDy7qkyIcTFxfGPU1NTFRIMIYQQ5akyIZiZ\nmfGPzc3NFRELIYQQJRJ0H8Ljx4+xadMmxMTEoLCwkF/PcRxOnjwpt+AIIYQojqCEMHbsWJSXl8PZ\n2Rlqamr8+je5yYwQQkj9JCghXL58Gffu3UOTJk3kHQ8hhBAlEXTNaPfu3ZGYmCjvWAghhCiRoCOE\noKAgDBo0CN26dYORkRF/UwPHcfDx8ZFrgIQQQhRDUEJYsmQJ/vvvP+Tk5CA/P1/eMRFCCFECQQnh\nwIEDuHXrFpo1aybveAghhCiJoHMIFhYWaNy4sbxjIYQQokSCjhDc3d0xYsQIzJ49mx/LqEKfPn3k\nEhghhBDFEpQQtmzZAo7jZA5DnZaWVudBEUIIUTxBCSE9PV3OYRBCCFE2GruaEEIIAEoIhBBCXqKE\nQAghBAAlBEIIIS8JOqlc4d69e1LDXwNAq1at6jQgQgghyiEoIZw4cQKenp64e/eu1HqaQpMQQhoO\nQV1Gn332GZYvX47CwkKUl5fz/ygZEEJIwyF4xrRPP/2UJsQhhJAGTNARgqenJ3766Sd5x0IIIUSJ\nBB0hXLx4Ed9++y1Wr14NY2Njfj3HcYiIiJBbcIQQQhRHUELw8vKCl5dXpfXUhUQIIQ2HoITg4eEh\n5zAIIYQoW5UJITg4GG5ubgCAnTt3Vnk0MHXqVEENbdmyBUFBQbh58yYmTpyIXbt2VVl248aNWLNm\nDZ4+fYoxY8Zg27ZtUFVVFdQOIYSQN1NlQti/fz+fEIKDg986ITRv3hzLly9HaGgoioqKqiwXGhqK\nwMBAnDlzBiYmJnB2doavry9WrVolqB1CCCFvpsqEcPz4cf5xeHj4Wzfk7OwMALhy5QqysrKqLLd7\n9254eXnB2toaAODj44NJkyZRQiCEEDlT+FhGjLFqn4+Pj4eDgwO/bG9vj5ycHOTm5so7NEIIea8p\nPCHUdGVSYWEhtLW1+WUtLS0AQEFBgVzjIoSQ912tBrerCzUdIYjFYuTn5/PLeXl5AACJRCKzvJ+f\nH//YyckJTk5Obx0jIYQ0FOHh4YK7/RWeEGo6QrC1tcW1a9cwZswYAEBsbCyMjIygq6srs/yrCYEQ\nQoi0138o+/v7V1lWcJdRQkICvvrqK8ycORMAkJiYiOvXrwsOqqysDMXFxSgtLUVZWRmePXsmc3A8\nd3d37Ny5EwkJCcjNzUVAQACmTJkiuB1CCCFvRlBCOHjwIHr27In//vsPP//8M4AXffpffPGF4IYC\nAgKgoaGBwMBA7NmzB+rq6li5ciUyMzMhkUj4K48GDBiAhQsXonfv3jA3N4elpWW1GY0QQkjd4FhN\nnfoArKys8Msvv6BDhw7Q1dVFbm4unj9/DhMTEzx48EARccrEcVyN5yQIIW/GY64HzEeaKzuMt5L+\nRzqCvglSdhj1SnXfm4KOEO7fvw97e/vKG4toBk5CCGkoBH2jf/DBBwgODpZa9+uvv6JLly5yCYoQ\nQojiCbrKaPPmzejXrx927tyJp0+fon///khKSsLJkyflHR8hhBAFEZQQrKyskJiYiL/++gtDhw6F\nmZkZhgwZUuW9AYQQQt49gu9D0NTUxPjx4+UZCyGEECUSlBAyMjLg7++PmJgYFBYW8us5jkNSUpLc\ngiOEEKI4ghLC2LFjYW1tjYCAAKipqck7JkIIIUogKCHcunULFy9ehIqKirzjIYQQoiSCLjsdOnQo\nzp49K+9YCCGEKJGgI4Rvv/0W3bp1Q9u2bWFoaMiv5zgOP/30k9yCI4QQojiCEsLUqVOhqqoKa2tr\nqKmp8bc+1zRyKSGEkHeHoIRw5swZ/Pfff/xkNYQQQhoeQecQ7O3t8fDhQ3nHQgghRIkEHSH06dMH\nAwYMwJQpU2BkZAQAfJfR1KlT5RogIYQQxRCUEM6dO4dmzZrJHLuIEgIhhDQMghKC0Pk4CSGEvLuq\nTAivXkVUXl5eZQU0JwIhhDQMVSYELS0tFBQUvCjUSHYxjuNkzotMCCHk3VNlQoiLi+Mfp6amKiQY\nQgghylNlf4+ZmRn/+LfffoO5uXmlf4cPH1ZIkIQQQuRP0AkAf39/mesDAgLqNBhCCCHKU+1VRmFh\nYWCMoaysDGFhYVLPpaSk0J3LhBDSgFSbEKZOnQqO4/Ds2TN4enry6zmOg5GRETZv3iz3AAkhhChG\ntQkhPT0dAODm5obg4GBFxEMIIURJBJ1DoGRACCENH91VRgghBAAlBEIIIS9RQiCEEAJAgQnh0aNH\ncHZ2hlgshrm5Ofbv3y+zXFBQEFRUVCCRSPh/ERERigqTEELeW4JGO60LM2fOhJqaGu7du4eYmBgM\nGTIEDg4OsLGxqVTW0dGRkgAhhCiYQo4Qnjx5gsOHDyMgIAAaGhpwdHTEiBEjqrx6iTGmiLAIIYS8\nQiEJISkpCY0aNULr1q35dQ4ODlID6FXgOA4xMTEwMDBAu3btsGLFChpRlRBCFEAhXUaFhYWVhrmQ\nSCT88Nqv6tmzJ+Li4tCyZUvcvHkT48ePR6NGjeDt7a2IUAkh5L2lkIQgFouRn58vtS4vLw8SiaRS\nWQsLC/6xnZ0dfHx8sHbt2ioTgp+fH//YyckJTk5OdRIzIYQ0BOHh4YJnvVRIQmjbti1KS0uRnJzM\ndxvFxsbCzs5O0PbVnVN4NSEQQgiR9voP5apGrwYUdA5BU1MTo0aNgo+PD54+fYrIyEj8+eefcHNz\nq1Q2JCQEOTk5AIDExESsWLECI0eOVESYhBDyXlPYfQhbt25FUVERDA0N4erqiu3bt8Pa2hqZmZmQ\nSCTIysoC8GLIbQcHB4jFYgwZMgSjR4/GkiVLFBUmIYS8tzj2Dl/jyXEcXaJKiJx4zPWA+UhzZYfx\nVtL/SEfQN0HKDqNeqe57U2E3ptV33n7eyH6creww3pqxjjFW+61WdhiEkHcQJYSXsh9nv/O/hoAX\nv4gIIeRN0OB2hBBCAFBCIIQQ8hIlBEIIIQAoIRBCCHmJEgIhhBAAlBAIIYS8RAmBEEIIAEoIhBBC\nXqKEQAghBAAlBEIIIS9RQiCEEAKAEgIhhJCXKCEQQggBQAmBEELIS5QQCCGEAKCEQAgh5CVKCIQQ\nQgBQQiCEEPISJQRCCCEAKCEQQgh5iRICIYQQAJQQCCGEvNRI2QGQunU1MhUeHn7KDuOtGBurY/Xq\nRcoOgzQADeHzACjuM0EJoYEpeqoKc3M/ZYfxVtLT/ZQdAmkgGsLnAVDcZ4K6jAghhABQYEJ49OgR\nnJ2dIRaLYW5ujv3791dZduPGjTAxMYG2tjY8PT1RUlKiqDAJIeS9pbCEMHPmTKipqeHevXvYu3cv\nZsyYgfj4+ErlQkNDERgYiLCwMGRkZCA1NRW+vr6KCvOdV/QkV9khEFJv0OehdhSSEJ48eYLDhw8j\nICAAGhoacHR0xIgRIxAcHFyp7O7du+Hl5QVra2vo6OjAx8cHQUFBigizQSh6Sh8AQirQ56F2FJIQ\nkpKS0KhRI7Ru3Zpf5+DggLi4uEpl4+Pj4eDgwC/b29sjJycHubn0hyWEEHlSSEIoLCyElpaW1DqJ\nRIKCggKZZbW1tfnliu1klSWEEFJ3FHLZqVgsRn5+vtS6vLw8SCSSGsvm5eUBgMyyDg4O4Diu7gL9\ntu6qUiZ//zp8T5Rk925/ZYdAgAbxmWgInweg7j4Tr/bAvE4hCaFt27YoLS1FcnIy320UGxsLOzu7\nSmVtbW1x7do1jBkzhi9nZGQEXV3dSmWvXbsm38AJIeQ9opAuI01NTYwaNQo+Pj54+vQpIiMj8eef\nf8LNza1SWXd3d+zcuRMJCQnIzc1FQEAApkyZoogwCSHkvaawy063bt2KoqIiGBoawtXVFdu3b4e1\ntTUyMzMhkUiQlZUFABgwYAAWLlyI3r17w9zcHJaWlvD3p+4DQgiRN44xxpQdBCGEKItIJEJycjJa\ntWql7FCUjoauqGcsLCyQkZEBDw8P7N69G9nZ2Rg+fDiaN28OkUiEzMxMqfLz589H27ZtoaWlBWtr\na6l7Ox4+fAhHR0c0bdoU2tra6NixI/744w/+eT8/P/j7++Ps2bPo3bu3wl4jIYrczxX1GhoCSgj1\nVMXVUyKRCIMHD8ahQ4dklhOLxfjrr7+Qn5+P3bt3Y86cObh48SL/3E8//YR79+4hLy8Pfn5+GDdu\nHAoLC6XaIERZFLGfK+o1NASUEOqZV3cujuNgaGiI6dOno1OnTjLL+/n5oW3btgCALl26oEePHvwH\npUmTJmjXrh1EIhHKy8shEonQtGlTqKqqVmqvIe3UpP5T5H5++fJldOvWDbq6umjWrBlmz56N58+f\nS9V/7NgxWFpawsDAAAsXLsSrPek//PADbGxsoKWlxV8FKes1NAiMvBOeP3/OOI5jGRkZVZZ5+vQp\nMzExYaGhoVLr27dvz1RVVZmenh6LioqSd6iEvDF57OdXr15lly5dYmVlZSw9PZ1ZW1uzb775hn+e\n4zjWp08flpubyzIzM1nbtm3Zjz/+yBhj7MCBA6x58+bsypUrjDHGUlJSqo3tXUcJ4R0h5IPi7u7O\nBg0aJPO5Z8+esU2bNrHmzZuzgoICeYVJyFtRxH6+ceNG5uzszC9zHCeVXLZu3co+/vhjxhhj/fv3\nZ5s2bXqTl/JOoi6jBmLBggWIj4/HgQMHZD6vqqqK2bNnQyKR4PTp0wqOjpC68Sb7eVJSEoYOHcoP\nqb906VI8fPhQarsWLVrwj83MzHDnzh0AQFZWFiwtLeX0auofSggNgK+vL0JDQ3Hy5EmIxeJqy5aW\nlkJTU1NBkRFSd950P58xYwZsbGyQnJyMvLw8rFy5EuXl5VLlX72qKTMzE82bNwfwIlEkJyfX8Sup\nvyghvAOKi4tRXFxc6TEArFq1Cvv378fff/9daXiPS5cuITIyEiUlJSgqKkJgYCCKi4vRtWtXhcZP\niBDy2s8LCwshkUigoaGBxMREbNu2rVLb69atw+PHj3H79m1s2rQJ48ePBwB4eXlh3bp1+Oeff8AY\nQ3JycqVLYhsUZfdZkZpxHMc4jmMikYj//9Xn1NTUmFgs5v+tWrWKMcbY2bNnmYODA5NIJKxp06Zs\n8ODB7ObNm8p6GYRUS177eUREBLOysmJisZj16NGD+fj4sB49ekjVvXnzZtaqVSumr6/P5s+fz8rK\nyvjnt2/fztq1a8fEYjFr3749u3btmgLeDeWgO5UJIYQAoC4jQgghL1FCIIQQAoASAiGEkJcoIRBC\nCAFACYEQQshLlBAIIYQAoIRACCHkJUoIhCjRjBkzsGLFCmWHQQgASgikATI3N0dYWNhb1xMUFIQe\nPXrIddtt27Zh2bJlb9QGIXWNEgJpcDiOw7twA/7rA6wRomyUEEiD4ubmhszMTAwbNgwSiQTr1q0D\nAERFRaF79+7Q1dVFhw4dcPbsWX6boKAgWFpaQktLC61atcK+ffuQmJiI6dOn4+LFi5BIJNDT05PZ\nXm229fDwwIwZMzB48GCIxWKcOXMGHh4eWL58OQAgPDwcpqam2LBhA4yMjNCsWTMEBQXxbT18+BDD\nhg2DtrY2unTpgmXLlvFHIYwxzJs3D0ZGRtDW1oa9vT3i4uLk8RaThky5QykRUvfMzc3Z6dOn+eWs\nrCymr6/PQkJCGGOM/f3330xfX589ePCAFRYWMi0tLZaUlMQYYyw7O5vFxcUxxhgLCgpiH330UZXt\n1HbbyZMnM21tbXbhwgXGGGPFxcXMw8ODLV++nDHG2JkzZ1ijRo2Yr68vKy0tZcePH2caGhrs8ePH\njDHGxo8fzyZOnMiKiopYfHw8a9GiBT9I24kTJ9j//vc/lpeXxxhjLDExkd29e/ct3kXyPqIjBNLg\n7dmzB4MHD8bAgQMBAH379kWnTp1w7NgxcBwHkUiEGzduoKioCEZGRrCxsQEAQd1OtdmW4ziMHDkS\n3bp1A/BiLuDXyzZu3Bg+Pj5QUVHBoEGDIBaLcevWLZSVleHw4cPw9/eHmpoarK2tMXnyZH7bxo0b\no6CgAAkJCSgvL0e7du1gbGz8Fu8aeR9RQiANXkZGBg4ePAhdXV3+3/nz55GdnQ0NDQ38+uuv2L59\nO5o1a4ahQ4fi1q1bgurV1NSs9bavzswli76+PkSi//9YamhooLCwEPfv30dpaanU9qampvzjPn36\nYNasWZg5cyaMjIzw6aefoqCgQNDrIKQCJQTS4HAcJ7VsZmYGNzc35Obm8v8KCgqwcOFCAED//v1x\n8lFx22EAAAHVSURBVORJZGdnw8rKCtOmTZNZjyxvs21V8cpiYGCARo0a4fbt2/y6Vx8DwOzZs3Hl\nyhXEx8cjKSkJa9euFRwDIQAlBNIAGRkZISUlhV92dXXFn3/+iZMnT6KsrAzFxcUIDw/Hf//9h3v3\n7uHIkSN48uQJGjduDE1NTaioqPD1ZGVl4fnz5zLbqe22srqRGGOCuqZUVFQwatQo+Pn5oaioCImJ\niQgODuaTyZUrV3Dp0iU8f/4cGhoaUFNT42MhRChKCKTBWbx4MVasWAFdXV1s2LABpqamOHLkCL7+\n+msYGhrCzMwM69evB2MM5eXl2LhxI5o3bw59fX2cO3eOn2Lx448/hq2tLYyNjWFoaFipndpuy3Fc\npaOB19dVd7SwZcsW5OXlwdjYGJMnT8bEiROhqqoKAMjPz8cnn3wCPT09mJubo2nTpliwYMHbvZHk\nvUMzphHyjlq0aBHu3buHXbt2KTsU0kDQEQIh74hbt27h+vXrYIzh8uXL+Omnn+Ds7KzssEgD0kjZ\nARBChCkoKMDEiRNx584dGBkZYf78+Rg+fLiywyINCHUZEUIIAUBdRoQQQl6ihEAIIQQAJQRCCCEv\nUUIghBACgBICIYSQlyghEEIIAQD8H2Cy7wDUzyzIAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107b9de10>"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='is_number'></a>\n",
      "<br>\n",
      "<br>\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## Testing if a string is a number"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "def string_is_number(a_str):\n",
      "    try:\n",
      "        float(a_str)\n",
      "        return True\n",
      "    except ValueError:\n",
      "        return False\n",
      "    \n",
      "a_float = '1.234'\n",
      "no_float = '123abc'\n",
      "\n",
      "a_float.replace('.','',1).isdigit()\n",
      "no_float.replace('.','',1).isdigit()\n",
      "\n",
      "%timeit string_is_number(an_int)\n",
      "%timeit string_is_number(no_int)\n",
      "%timeit a_float.replace('.','',1).isdigit()\n",
      "%timeit no_float.replace('.','',1).isdigit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000000 loops, best of 3: 418 ns per loop\n",
        "1000000 loops, best of 3: 1.28 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000000 loops, best of 3: 500 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000000 loops, best of 3: 427 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = [\"string_is_number\", \"replace('.','',1).isdigit()\"]\n",
      "t1 = '1.234'\n",
      "t2 = '123abc'\n",
      "isdigit_method = []\n",
      "string_is_number_method = []\n",
      "\n",
      "for t in [t1,t2]:\n",
      "    string_is_number_method.append(min(timeit.Timer('string_is_number(t)', \n",
      "                      'from __main__ import string_is_number, t')\n",
      "                              .repeat(repeat=3, number=1000000)))\n",
      "    isdigit_method.append(min(timeit.Timer(\"t.replace('.','',1).isdigit()\", \n",
      "                      'from __main__ import t')\n",
      "                              .repeat(repeat=3, number=1000000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = len(isdigit_method)\n",
      "ind = np.arange(N)  # the x locations for the groups\n",
      "width = 0.25       # the width of the bars\n",
      "\n",
      " \n",
      "fig, ax = plt.subplots()\n",
      "\n",
      "plt.bar(ind , \n",
      "        [i for i in isdigit_method], \n",
      "        width,\n",
      "        alpha=0.5,\n",
      "        color='b',\n",
      "        label=\"a_str.replace('.','',1).isdigit()\")\n",
      "\n",
      "plt.bar(ind + width, \n",
      "        [i for i in string_is_number_method], \n",
      "        width,\n",
      "        alpha=0.5,\n",
      "        color='g',\n",
      "        label='string_is_number(a_str)')\n",
      "\n",
      "    \n",
      "ax.set_ylabel('time in microseconds')\n",
      "ax.set_title('Time to check if a string is a number')\n",
      "ax.set_xticks(ind + width)\n",
      "ax.set_xticklabels(['\"%s\"' %t for t in [t2, t1]])\n",
      "plt.xlabel('test strings')\n",
      "plt.xlim(-0.1,1.6)\n",
      "#plt.ylim(0,15)\n",
      "plt.legend(loc='upper left')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MioqKtW4DAD4+Pli4cCF69OiB0tJS9ticnJwQEBAgcdFaaGgopk+fzp7dEtmi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\nGGNsDCcExhhjAIB/AP0bxvnc1X/jAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10782cfd0>"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "<a name='list_operations'></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "# List operations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='list_reverse'></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## List reversing - `[::-1]` vs. `reverse()` vs. `reversed()`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "import copy\n",
      "\n",
      "def reverse_func(my_list):\n",
      "    return copy.deepcopy(my_list).reverse()\n",
      "    \n",
      "def reversed_func(my_list):\n",
      "    return list(reversed(my_list))\n",
      "\n",
      "def reverse_slizing(my_list):\n",
      "    return my_list[::-1]\n",
      "\n",
      "n = 10\n",
      "test_list = list([i for i in range(n)])\n",
      "\n",
      "%timeit reverse_func(test_list)\n",
      "%timeit reversed_func(test_list)\n",
      "%timeit reverse_slizing(test_list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 13.8 \u00b5s per loop\n",
        "1000000 loops, best of 3: 1.44 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000000 loops, best of 3: 330 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['reverse_func', 'reversed_func',\n",
      "         'reverse_slizing']\n",
      "\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    test_list = list([i for i in range(n)])\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(test_list)' %f, \n",
      "                      'from __main__ import %s, test_list' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('reverse_func', 'copy.deepcopy(my_list).reverse()'), \n",
      "          ('reversed_func', 'list(reversed(my_list))'),\n",
      "          ('reverse_slizing', 'my_list[::-1]'),\n",
      "          ] \n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], \n",
      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of different list reversing approaches')\n",
      "\n",
      "max_perf = max( f/s for f,s in zip(times_n['reverse_func'],\n",
      "                                   times_n['reverse_slizing']) )\n",
      "min_perf = min( f/s for f,s in zip(times_n['reverse_func'],\n",
      "                                   times_n['reverse_slizing']) )\n",
      "\n",
      "ftext = 'my_list[::-1] is {:.2f}x to '\\\n",
      "        '{:.2f}x faster than copy.deepcopy(my_list).reverse()'\\\n",
      "        .format(min_perf, max_perf)\n",
      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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W+OOPP7g8YrEY06ZN49VHRLCwsKh2ejUpKQk9evSAsrIyrKyssHv3bqk8L1++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\nERERkZtgYkdERETkJv4/dDERlOXTSFkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1069c7890>"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='create_cond_list'></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## Creating lists using conditional statements\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "In this test, I attempted to figure out the fastest way to create a new list of elements that meet a certain criterion. For the sake of simplicity, the criterion was to check if an element is even or odd, and only if the element was even, it should be included in the list. For example, the resulting list for numbers in the range from 1 to 10 would be \n",
      "[2, 4, 6, 8, 10].\n",
      "\n",
      "Here, I tested three different approaches:  \n",
      "1) a simple for loop with an if-statement check (`cond_loop()`)  \n",
      "2) a list comprehension (`list_compr()`)  \n",
      "3) the built-in filter() function (`filter_func()`)  \n",
      "\n",
      "Note that the filter() function now returns a generator in Python 3, so I had to wrap it in an additional list() function call."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "def cond_loop(n):\n",
      "    even_nums = []\n",
      "    for i in range(n):\n",
      "        if i % 2 == 0:\n",
      "            even_nums.append(i)\n",
      "    return even_nums\n",
      "\n",
      "def list_compr(n):\n",
      "    even_nums = [i for i in range(n) if i % 2 == 0]\n",
      "    return even_nums\n",
      "    \n",
      "def filter_func(n):\n",
      "    even_nums = list(filter((lambda x: x % 2 != 0), range(n)))\n",
      "    return even_nums\n",
      "\n",
      "%timeit cond_loop(n)\n",
      "%timeit list_compr(n)\n",
      "%timeit filter_func(n)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 18.1 ms per loop\n",
        "100 loops, best of 3: 15.3 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10 loops, best of 3: 22.8 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['cond_loop', 'list_compr',\n",
      "         'filter_func']\n",
      "\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    test_list = list([i for i in range(n)])\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(n)' %f, \n",
      "                      'from __main__ import %s, n' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('cond_loop', 'explicit for-loop'), \n",
      "          ('list_compr', 'list comprehension'),\n",
      "          ('filter_func', 'lambda function'),\n",
      "          ] \n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], \n",
      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of different conditional list creation methods')\n",
      "\n",
      "max_perf = max( f/c for f,c in zip(times_n['filter_func'],\n",
      "                                   times_n['cond_loop']) )\n",
      "min_perf = min( f/c for f,c in zip(times_n['filter_func'],\n",
      "                                   times_n['cond_loop']) )\n",
      "\n",
      "ftext = 'the list comprehension is {:.2f}x to '\\\n",
      "        '{:.2f}x faster than the lambda function'\\\n",
      "        .format(min_perf, max_perf)\n",
      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6dAAAmJiYYMaMGRg1ahSmT58OAIiIiICLiwuGDBmC69evo3379hg+fDhPFhFh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\n4gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106fe4290>"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='dict_ops'></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "# Dictionary operations "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='adding_dict_elements'></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "## Adding elements to a Dictionary\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "All three functions below count how often different elements (values) occur in a list.  \n",
      "E.g., for the list ['a', 'b', 'a', 'c'], the dictionary would look like this:  \n",
      "`my_dict = {'a': 2, 'b': 1, 'c': 1}`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random\n",
      "import timeit\n",
      "from collections import defaultdict\n",
      "\n",
      "\n",
      "def add_element_check1(elements):\n",
      "    \"\"\"if ele not in dict (v1)\"\"\"\n",
      "    d = dict()\n",
      "    for e in elements:\n",
      "        if e not in d:\n",
      "            d[e] = 1\n",
      "        else:\n",
      "            d[e] += 1\n",
      "    return d\n",
      "            \n",
      "def add_element_check2(elements):\n",
      "    \"\"\"if ele not in dict (v2)\"\"\"\n",
      "    d = dict()\n",
      "    for e in elements:\n",
      "        if e not in d:\n",
      "            d[e] = 0\n",
      "        d[e] += 1            \n",
      "    return d\n",
      "        \n",
      "def add_element_except(elements):\n",
      "    \"\"\"try-except\"\"\"\n",
      "    d = dict()\n",
      "    for e in elements:\n",
      "        try:\n",
      "            d[e] += 1\n",
      "        except KeyError:\n",
      "            d[e] = 1\n",
      "    return d\n",
      "            \n",
      "def add_element_defaultdict(elements):\n",
      "    \"\"\"defaultdict\"\"\"\n",
      "    d = defaultdict(int)\n",
      "    for e in elements:\n",
      "        d[e] += 1\n",
      "    return d\n",
      "\n",
      "def add_element_get(elements):\n",
      "    \"\"\".get() method\"\"\"\n",
      "    d = dict()\n",
      "    for e in elements:\n",
      "        d[e] = d.get(e, 1) + 1\n",
      "    return d\n",
      "\n",
      "\n",
      "random.seed(123)\n",
      "\n",
      "print('Results for 100 integers in range 1-10') \n",
      "rand_ints = [random.randrange(1, 10) for i in range(100)]\n",
      "%timeit add_element_check1(rand_ints)\n",
      "%timeit add_element_check2(rand_ints)\n",
      "%timeit add_element_except(rand_ints)\n",
      "%timeit add_element_defaultdict(rand_ints)\n",
      "%timeit add_element_get(rand_ints)\n",
      "\n",
      "print('\\nResults for 1000 integers in range 1-5')            \n",
      "rand_ints = [random.randrange(1, 5) for i in range(1000)]\n",
      "%timeit add_element_check1(rand_ints)\n",
      "%timeit add_element_check2(rand_ints)\n",
      "%timeit add_element_except(rand_ints)\n",
      "%timeit add_element_defaultdict(rand_ints)\n",
      "%timeit add_element_get(rand_ints)\n",
      "\n",
      "print('\\nResults for 1000 integers in range 1-1000')            \n",
      "rand_ints = [random.randrange(1, 1000) for i in range(1000)]\n",
      "%timeit add_element_check1(rand_ints)\n",
      "%timeit add_element_check2(rand_ints)\n",
      "%timeit add_element_except(rand_ints)\n",
      "%timeit add_element_defaultdict(rand_ints)\n",
      "%timeit add_element_get(rand_ints)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Results for 100 integers in range 1-10\n",
        "100000 loops, best of 3: 16.8 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "100000 loops, best of 3: 18.2 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "100000 loops, best of 3: 17.1 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "100000 loops, best of 3: 14.7 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000 loops, best of 3: 20.8 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Results for 1000 integers in range 1-5\n",
        "10000 loops, best of 3: 161 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000 loops, best of 3: 166 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000 loops, best of 3: 128 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000 loops, best of 3: 114 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 197 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Results for 1000 integers in range 1-1000\n",
        "10000 loops, best of 3: 166 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 240 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 354 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 281 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 234 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['add_element_check1', 'add_element_check2',\n",
      "         'add_element_except', 'add_element_defaultdict',\n",
      "         'add_element_get']\n",
      "\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    elements = [random.randrange(1, 100) for i in range(n)]\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(elements)' %f, \n",
      "                      'from __main__ import %s, elements' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('add_element_check1', 'if ele not in dict (v1)'), \n",
      "          ('add_element_check2', 'if ele not in dict (v2)'),\n",
      "          ('add_element_except', 'try-except'),\n",
      "          ('add_element_defaultdict', 'defaultdict'),\n",
      "          ('add_element_get', '.get() method')\n",
      "          ] \n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,10))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], \n",
      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of different methods to count elements in a dictionary')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tClEU8csvv1TIs3DhQqxduxY3b95UOi5jjLH6iTt29YyRkRFMTExkr1NTU0FE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\n7AAAABziG6SlHxRk0gPUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107ebb4d0>"
       ]
      }
     ],
     "prompt_number": 139
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Conclusion"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We see from the results that the `try-except` variant is faster than then the `if element in my_dict` alternative if we have a low number of unique elements (here: 1000 integers in the range 1-5), which makes sense: the `except`-block is skipped if an element is already added as a key to the dictionary. However, in this case the `collections.defaultdict` has even a better performance.  \n",
      "However, if we are having a relative large number of unique entries(here: 1000 integers in range 1-1000), the `if element in my_dict` approach outperforms the alternative approaches."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"comprehensions\"></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Comprehesions vs. for-loops"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Comprehensions are not only shorter and prettier than ye goode olde for-loop,  \n",
      "but they are also up to ~1.2x faster."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "n = 1000"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 140
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Set comprehensions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def set_loop(n):\n",
      "    a_set = set()\n",
      "    for i in range(n):\n",
      "        if i % 3 == 0:\n",
      "            a_set.add(i)\n",
      "    return a_set\n",
      "\n",
      "def set_compr(n):\n",
      "    return {i for i in range(n) if i % 3 == 0}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit set_loop(n)\n",
      "%timeit set_compr(n)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 165 \u00b5s per loop\n",
        "10000 loops, best of 3: 145 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 142
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## List comprehensions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def list_loop(n):\n",
      "    a_list = list()\n",
      "    for i in range(n):\n",
      "        if i % 3 == 0:\n",
      "            a_list.append(i)\n",
      "    return a_list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 143
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def list_compr(n):\n",
      "    return [i for i in range(n) if i % 3 == 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit list_loop(n)\n",
      "%timeit list_compr(n)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 164 \u00b5s per loop\n",
        "10000 loops, best of 3: 143 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['list_loop', 'list_compr']\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(n)' %f, \n",
      "                      'from __main__ import %s, n' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 152
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('list_loop', 'explicit for-loop'), \n",
      "          ('list_compr', 'list comprehension')]\n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], \n",
      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of explicit for-loops vs. list comprehensions')\n",
      "\n",
      "max_perf = max( l/c for l,c in zip(times_n['list_loop'],\n",
      "                                   times_n['list_compr']) )\n",
      "min_perf = min( l/c for l,c in zip(times_n['list_loop'],\n",
      "                                   times_n['list_compr']) )\n",
      "\n",
      "ftext = 'the list comprehension is {:.2f}x to '\\\n",
      "        '{:.2f}x faster than the explicit for-loop'\\\n",
      "        .format(min_perf, max_perf)\n",
      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KMG/ePGzcuBG6urpYsGABPD09kZaWVuPRS1L30HvJiDyovZDPjTp25RQWSh/E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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1055b1910>"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Dictionary comprehensions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dict_loop(n):\n",
      "    a_dict = dict()\n",
      "    for i in range(n):\n",
      "        if i % 3 == 0:\n",
      "            a_dict[i] = i\n",
      "    return a_dict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dict_compr(n):\n",
      "    return {i:i for i in range(n) if i % 3 == 0}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 147
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit dict_loop(n)\n",
      "%timeit dict_compr(n)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 159 \u00b5s per loop\n",
        "10000 loops, best of 3: 151 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"find_copy\"></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Copying files by searching directory trees"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Executing `Unix`/`Linux` shell commands:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import subprocess\n",
      "\n",
      "def subprocess_findcopy(path, search_str, dest):    \n",
      "    query = 'find %s -name \"%s\" -exec cp {} %s \\;' %(path, search_str, dest)\n",
      "    subprocess.call(query, shell=True)\n",
      "    return  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using Python's `os.walk()` to search the directory tree recursively and matching patterns via `fnmatch.filter()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import shutil\n",
      "import os\n",
      "import fnmatch\n",
      "\n",
      "def walk_findcopy(path, search_str, dest):\n",
      "    for path, subdirs, files in os.walk(path):\n",
      "        for name in fnmatch.filter(files, search_str):\n",
      "            try:\n",
      "                shutil.copy(os.path.join(path,name), dest)\n",
      "            except NameError:\n",
      "                pass\n",
      "    return"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "\n",
      "def findcopy_timeit(inpath, outpath, search_str):\n",
      "    \n",
      "    shutil.rmtree(outpath)\n",
      "    os.mkdir(outpath)\n",
      "    print(50*'#')\n",
      "    print('subprocsess call')\n",
      "    %timeit subprocess_findcopy(inpath, search_str, outpath)\n",
      "    print(\"copied %s files\" %len(os.listdir(outpath)))\n",
      "    shutil.rmtree(outpath)\n",
      "    os.mkdir(outpath)\n",
      "    print('\\nos.walk approach')\n",
      "    %timeit walk_findcopy(inpath, search_str, outpath)\n",
      "    print(\"copied %s files\" %len(os.listdir(outpath)))\n",
      "    print(50*'#')\n",
      "\n",
      "print('small tree')\n",
      "inpath = '/Users/sebastian/Desktop/testdir_in'\n",
      "outpath = '/Users/sebastian/Desktop/testdir_out'\n",
      "search_str = '*.png'\n",
      "findcopy_timeit(inpath, outpath, search_str)\n",
      "\n",
      "print('larger tree')\n",
      "inpath = '/Users/sebastian/Dropbox'\n",
      "outpath = '/Users/sebastian/Desktop/testdir_out'\n",
      "search_str = '*.csv'\n",
      "findcopy_timeit(inpath, outpath, search_str)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "small tree\n",
        "##################################################"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "subprocsess call\n",
        "1 loops, best of 3: 268 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "copied 13 files\n",
        "\n",
        "os.walk approach\n",
        "100 loops, best of 3: 12.2 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "copied 13 files\n",
        "##################################################\n",
        "larger tree\n",
        "##################################################\n",
        "subprocsess call\n",
        "1 loops, best of 3: 623 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "copied 105 files\n",
        "\n",
        "os.walk approach\n",
        "1 loops, best of 3: 417 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "copied 105 files\n",
        "##################################################\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I have to say that I am really positively surprised. The shell's `find` scales even better than expected!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<a name='row_vectors'></a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Returning column vectors slicing through a numpy array"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Given a numpy matrix, I want to iterate through it and return each column as a 1-column vector.  \n",
      "E.g., if I want to return the 1st column from matrix A below\n",
      "\n",
      "<pre>\n",
      "A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])\n",
      ">>> A\n",
      "array([[1, 2, 3],\n",
      "       [4, 5, 6],\n",
      "       [7, 8, 9]])</pre>\n",
      "\n",
      "I want my result to be:\n",
      "<pre>\n",
      "array([[1],\n",
      "       [4],\n",
      "       [7]])</pre>\n",
      "\n",
      "with `.shape` = `(3,1)`\n",
      "\n",
      "\n",
      "However, the default behavior of numpy is to return the column as a row vector:\n",
      "\n",
      "<pre>\n",
      ">>> A[:,0]\n",
      "array([1, 4, 7])\n",
      ">>> A[:,0].shape\n",
      "(3,)\n",
      "</pre>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "\n",
      "# 1st column, e.g., A[:,0,np.newaxis]\n",
      "\n",
      "def colvec_method1(A):\n",
      "    for col in A.T:\n",
      "        colvec = row[:,np.newaxis]\n",
      "        yield colvec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1st column, e.g., A[:,0:1]\n",
      "\n",
      "def colvec_method2(A):\n",
      "    for idx in range(A.shape[1]):\n",
      "        colvec = A[:,idx:idx+1]\n",
      "        yield colvec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1st column, e.g., A[:,0].reshape(-1,1)\n",
      "\n",
      "def colvec_method3(A):\n",
      "    for idx in range(A.shape[1]):\n",
      "        colvec = A[:,idx].reshape(-1,1)\n",
      "        yield colvec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1st column, e.g., np.vstack(A[:,0]\n",
      "\n",
      "def colvec_method4(A):\n",
      "    for idx in range(A.shape[1]):\n",
      "        colvec = np.vstack(A[:,idx])\n",
      "        yield colvec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1st column, e.g., np.row_stack(A[:,0])\n",
      "\n",
      "def colvec_method5(A):\n",
      "    for idx in range(A.shape[1]):\n",
      "        colvec = np.row_stack(A[:,idx])\n",
      "        yield colvec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1st column, e.g., np.column_stack((A[:,0],))\n",
      "\n",
      "def colvec_method6(A):\n",
      "    for idx in range(A.shape[1]):\n",
      "        colvec = np.column_stack((A[:,idx],))\n",
      "        yield colvec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1st column, e.g., A[:,[0]]\n",
      "\n",
      "def colvec_method7(A):\n",
      "    for idx in range(A.shape[1]):\n",
      "        colvec = A[:,[idx]]\n",
      "        yield colvec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def test_method(method, A):\n",
      "    for i in method(A): \n",
      "        assert i.shape == (A.shape[0],1), \"{}, {}\".format(i.shape, A.shape[0],1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "A = np.random.random((300, 3))\n",
      "\n",
      "for method in [\n",
      "            colvec_method1, colvec_method2, \n",
      "            colvec_method3, colvec_method4, \n",
      "            colvec_method5, colvec_method6,\n",
      "            colvec_method7]:\n",
      "    print('\\nTest:', method.__name__)\n",
      "    %timeit test_method(colvec_method2, A)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Test: colvec_method1\n",
        "100000 loops, best of 3: 16.6 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Test: colvec_method2\n",
        "10000 loops, best of 3: 16.1 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Test: colvec_method3\n",
        "100000 loops, best of 3: 16.2 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Test: colvec_method4\n",
        "100000 loops, best of 3: 16.4 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Test: colvec_method5\n",
        "100000 loops, best of 3: 16.2 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Test: colvec_method6\n",
        "100000 loops, best of 3: 16.8 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Test: colvec_method7\n",
        "100000 loops, best of 3: 16.3 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name='numpy'></a>\n",
      "<br>\n",
      "<br>\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Speed of numpy functions vs Python built-ins and std. lib."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"np_sum\"></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## `sum()` vs. `numpy.sum()`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import sum as np_sum\n",
      "import timeit\n",
      "\n",
      "samples = list(range(1000000))\n",
      "\n",
      "%timeit(sum(samples))\n",
      "%timeit(np_sum(samples))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 18.2 ms per loop\n",
        "10 loops, best of 3: 138 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['sum', 'np_sum']\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    samples = list(range(n))\n",
      "    times_n['sum'].append(min(timeit.Timer('sum(samples)', \n",
      "                'from __main__ import samples')\n",
      "                    .repeat(repeat=3, number=1000)))\n",
      "    times_n['np_sum'].append(min(timeit.Timer('np_sum(samples)', \n",
      "                'from __main__ import np_sum, samples')\n",
      "                    .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('sum', 'in-built sum() function'), \n",
      "          ('np_sum', 'numpy.sum() function')]\n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], \n",
      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of explicit for-loops vs. list comprehensions')\n",
      "\n",
      "max_perf = max( n/i for i,n in zip(times_n['sum'],\n",
      "                                   times_n['np_sum']) )\n",
      "min_perf = min( n/i for i,n in zip(times_n['sum'],\n",
      "                                   times_n['np_sum']) )\n",
      "\n",
      "ftext = 'the in-built sum() is {:.2f}x to '\\\n",
      "        '{:.2f}x faster than the numpy.sum()'\\\n",
      "        .format(min_perf, max_perf)\n",
      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dHTFs2DD4+voCAFq0aCEz78iRI2FjY4OoqChoaGjg1q1blTo48sQSGBiIZcuW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\nHQAAQIgg2AEAAIQIgh0AAECI+H/3Q2KUDc+3ZgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106783160>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"np_arange\"></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## `range()` vs. `numpy.arange()`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import arange as np_arange\n",
      "\n",
      "n = 1000000\n",
      "\n",
      "def loop_range(n):\n",
      "    for i in range(n):\n",
      "        pass\n",
      "    return\n",
      "\n",
      "def loop_arange(n):\n",
      "    for i in np_arange(n):\n",
      "        pass\n",
      "    return\n",
      "\n",
      "%timeit(loop_range(n))\n",
      "%timeit(loop_arange(n))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 50.9 ms per loop\n",
        "10 loops, best of 3: 183 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['loop_range', 'loop_arange']\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(n)' %f, \n",
      "                'from __main__ import %s, n' %f)\n",
      "                    .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('loop_range', 'in-built range()'), \n",
      "          ('loop_arange', 'numpy.arange()')]\n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], \n",
      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of explicit for-loops vs. list comprehensions')\n",
      "\n",
      "max_perf = max( a/r for r,a in zip(times_n['loop_range'],\n",
      "                                   times_n['loop_arange']) )\n",
      "min_perf = min( a/r for r,a in zip(times_n['loop_range'],\n",
      "                                   times_n['loop_arange']) )\n",
      "\n",
      "ftext = 'the in-built range() is {:.2f}x to '\\\n",
      "        '{:.2f}x faster than numpy.arange()'\\\n",
      "        .format(min_perf, max_perf)\n",
      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zz6usI2nY6L5kRBrUXsjHRiN2Fbx7J36XFBfXfleVlIjftrBQ+jQZY5Uuh1ZW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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1050da390>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"np_mean\"></a>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## `statistics.mean()` vs. `numpy.mean()`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The statistics module has been added to\n",
      "# the standard library in Python 3.4\n",
      "\n",
      "import timeit\n",
      "import statistics as stats\n",
      "import numpy as np\n",
      "\n",
      "def calc_mean(samples):\n",
      "    return sum(samples)/len(samples)\n",
      "\n",
      "def np_mean(samples):\n",
      "    return np.mean(samples)\n",
      "\n",
      "def np_mean_ary(np_array):\n",
      "    return np.mean(np_array)\n",
      "\n",
      "def st_mean(samples):\n",
      "    return stats.mean(samples)\n",
      "\n",
      "n = 1000000\n",
      "samples = list(range(n))\n",
      "samples_array = np.arange(n)\n",
      "\n",
      "assert(st_mean(samples) == np_mean(samples)\n",
      "       == calc_mean(samples) == np_mean_ary(samples_array))\n",
      "\n",
      "%timeit(calc_mean(samples))\n",
      "%timeit(np_mean(samples))\n",
      "%timeit(np_mean_ary(samples_array))\n",
      "%timeit(st_mean(samples))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100 loops, best of 3: 26.2 ms per loop\n",
        "1 loops, best of 3: 144 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "100 loops, best of 3: 3.21 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1 loops, best of 3: 1.12 s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funcs = ['st_mean', 'np_mean', 'calc_mean', 'np_mean_ary']\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    samples = list(range(n))\n",
      "    for f in funcs:\n",
      "        if f == 'np_mean_ary':\n",
      "            samples = np.arange(n)\n",
      "        times_n[f].append(min(timeit.Timer('%s(samples)' %f, \n",
      "                'from __main__ import %s, samples' %f)\n",
      "                    .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('st_mean', 'statistics.mean()'), \n",
      "          ('np_mean', 'numpy.mean() on list'),\n",
      "          ('np_mean_ary', 'numpy.mean() on array'),\n",
      "          ('calc_mean', 'sum(samples)/len(samples)')\n",
      "          ]\n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], \n",
      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "plt.title('Performance of different approaches for calculating sample means')\n",
      "\n",
      "max_perf = max( s/c for s,c in zip(times_n['st_mean'],\n",
      "                                   times_n['np_mean_ary']) )\n",
      "min_perf = min( s/c for s,c in zip(times_n['st_mean'],\n",
      "                                   times_n['np_mean_ary']) )\n",
      "\n",
      "ftext = 'using numpy.mean() on np.arrays is {:.2f}x to '\\\n",
      "        '{:.2f}x faster than statistics.mean() on lists'\\\n",
      "        .format(min_perf, max_perf)\n",
      "plt.figtext(.14,.15, ftext, fontsize=11, ha='left')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qSftpTFv70AYa29jc9wIA1saLFy/iyJEjnLS0tbXx6tUrtn3I4rsDAFavXo158+Zh5MiR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\nXuIfaui5S+SyaUwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1058d4f28>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"cython\"></a>\n",
      "<br>\n",
      "<br>\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Cython vs regular (C)Python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here, we implement a linear regression via least squares fitting (with vertical offsets) by solving to fit *n* points $(x_i, y_i)$ with $i=1,2,...n,$ via linear equation of the form  \n",
      "$f(x) = a\\cdot x + b$.  \n",
      "\n",
      "Therefore we calculate the following parameters as follows:\n",
      "\n",
      "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$  (slope)\n",
      "\n",
      "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)\n",
      "\n",
      "where \n",
      "\n",
      "\n",
      "$S_{xy} = \\sum_{i=1}^{n} (x_i - \\bar{x})(y_i - \\bar{y})\\quad$ (covariance)\n",
      "\n",
      "\n",
      "$\\sigma{_x}^{2} = \\sum_{i=1}^{n} (x_i - \\bar{x})^2\\quad$ (variance)\n",
      "\n",
      "I have described the approach in more detail in this [IPython notebook](http://sebastianraschka.com/IPython_htmls/cython_least_squares.html)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "**First, the implementation in Python (CPython)**:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def py_lstsqr(x, y):\n",
      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
      "\n",
      "    x_avg = sum(x)/len(x)\n",
      "    y_avg = sum(y)/len(y)\n",
      "    var_x = 0\n",
      "    cov_xy = 0\n",
      "    for x_i, y_i in zip(x,y):\n",
      "        temp = (x_i - x_avg)\n",
      "        var_x += temp**2\n",
      "        cov_xy += temp*(y_i - y_avg)\n",
      "    slope = cov_xy / var_x\n",
      "    y_interc = y_avg - slope*x_avg\n",
      "    return (slope, y_interc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "**And now, adding type definitions and compiling the code via Cython**:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "\n",
      "def cy_lstsqr(x, y):\n",
      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
      "    cdef double x_avg, y_avg, temp, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
      "    x_avg = sum(x)/len(x)\n",
      "    y_avg = sum(y)/len(y)\n",
      "    var_x = 0\n",
      "    cov_xy = 0\n",
      "    for x_i, y_i in zip(x,y):\n",
      "        temp = (x_i - x_avg)\n",
      "        var_x += temp**2\n",
      "        cov_xy += temp*(y_i - y_avg)\n",
      "    slope = cov_xy / var_x\n",
      "    y_interc = y_avg - slope*x_avg\n",
      "    return (slope, y_interc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "**A small visual proof of concept that our least squares fit works as intended:**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot as plt\n",
      "\n",
      "import timeit\n",
      "import random\n",
      "random.seed(12345)\n",
      "\n",
      "n = 500\n",
      "x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
      "y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
      "\n",
      "slope, intercept = cy_lstsqr(x, y)\n",
      "\n",
      "line_x = [round(min(x)) - 1, round(max(x)) + 1]\n",
      "line_y = [slope*x_i + intercept for x_i in line_x]\n",
      "\n",
      "plt.figure(figsize=(8,8))\n",
      "plt.scatter(x,y)\n",
      "plt.plot(line_x, line_y, color='red', lw='2')\n",
      "\n",
      "plt.ylabel('y')\n",
      "plt.xlabel('x')\n",
      "plt.title('Linear regression via least squares fit')\n",
      "\n",
      "ftext = 'y = ax + b = {:.3f} + {:.3f}x'\\\n",
      "        .format(slope, intercept)\n",
      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oUKGC+RgfH5/nnp1MUSxFp9OphPU1l5HTTCuKJVg8EUhMTGTt2rWpPvf8PIPEHlWwYEHO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DHxGnAGx86AbwBOQDeWLp1710EAgJ+fH9Onf8O6dYv54IMBNwQBAE5OTsz9aRrpL7blZbGDuztd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\nB2H+zOtjC0adAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10699bb10>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "**Eventually, the benchmark:**:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "import random\n",
      "random.seed(12345)\n",
      "\n",
      "funcs = ['py_lstsqr', 'cy_lstsqr']\n",
      "\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(x,y)' %f, \n",
      "                      'from __main__ import %s, x, y' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('py_lstsqr', 'regular Python (CPython)'), \n",
      "          ('cy_lstsqr', 'Cython implementation')]\n",
      "\n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "max_perf = max( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
      "                                   times_n['cy_lstsqr']) )\n",
      "min_perf = min( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
      "                                   times_n['cy_lstsqr']) )\n",
      "ftext = 'Using Cython is {:.2f}x to '\\\n",
      "        '{:.2f}x faster than regular (C)Python'\\\n",
      "        .format(min_perf, max_perf)\n",
      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
      "plt.title('Performance of least square fit implementations in Cython and (C)Python')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ugo2NDR4+fKjSdWGMMcYYKw1PT09cvny5xPOpfYxc/nbk7du3oampKXdXnKen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qXC/PnzsWrVKvTs2VPK8InoISqNCkcyj+BCwQVxzFHhiInhE/Fs0LNcSiUisjBZllZXrlyJ/Px8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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ca5b4d0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a name=\"numba\"></a>\n",
      "<br>\n",
      "<br>\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Numba vs. Cython vs. regular (C)Python & Numpy"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up the code. If you want to read more about Numba, please see refer to the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here, we implement a linear regression via least squares fitting (with vertical offsets) by solving to fit *n* points $(x_i, y_i)$ with $i=1,2,...n,$ via linear equation of the form  \n",
      "$f(x) = a\\cdot x + b$.  \n",
      "\n",
      "\n",
      "\n",
      "$\\Rightarrow \\pmb a = (\\pmb X^T \\; \\pmb X)^{-1} \\pmb X^T \\; \\pmb y$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I have described the approach in more detail in this [IPython notebook](http://sebastianraschka.com/IPython_htmls/cython_least_squares.html)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Matrix equation "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to obtain the parameters for the linear regression line for a set of multiple points, we can re-write the problem as matrix equation  \n",
      "$\\pmb X \\; \\pmb a = \\pmb y$  \n",
      "\n",
      "$\\Rightarrow\\Bigg[ \\begin{array}{cc}\n",
      "x_1 & 1  \\\\\n",
      "... & 1 \\\\\n",
      "x_n & 1  \\end{array} \\Bigg]$\n",
      "$\\bigg[ \\begin{array}{c}\n",
      "a  \\\\\n",
      "b \\end{array} \\bigg]$\n",
      "$=\\Bigg[ \\begin{array}{c}\n",
      "y_1   \\\\\n",
      "...  \\\\\n",
      "y_n  \\end{array} \\Bigg]$  \n",
      "\n",
      "With a little bit of calculus, we can rearrange the term in order to obtain the parameter vector  \n",
      "$\\pmb a = [a\\;b]^T$  \n",
      "\n",
      "We will implement this matrix equation in  \n",
      "- Python/CPython: `py_mat_lstsqr()`   \n",
      "- Numba: `numba_mat_lstsqrs()`  \n",
      "- Cython: `cy_mat_lstsqr()`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### \"Classic\" approach "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the more \"classic\" approach that is often found in statistics textbooks, we calculate the following parameters as follows:\n",
      "\n",
      "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$  (slope)\n",
      "\n",
      "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)\n",
      "\n",
      "where \n",
      "\n",
      "\n",
      "$S_{xy} = \\sum_{i=1}^{n} (x_i - \\bar{x})(y_i - \\bar{y})\\quad$ (covariance)\n",
      "\n",
      "\n",
      "$\\sigma{_x}^{2} = \\sum_{i=1}^{n} (x_i - \\bar{x})^2\\quad$ (variance)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will implement this \"classic\" approach in\n",
      "- Python/CPython: `py_lstsqr()`  \n",
      "- Numba: `numba_lstsqrs()`  \n",
      "- Cython: `cy_lstsqrs()`  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import scipy.stats\n",
      "from numba import jit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Matrix equation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def py_mat_lstsqr(x, y):\n",
      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
      "    X = np.vstack([x, np.ones(len(x))]).T\n",
      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@jit\n",
      "def numba_mat_lstsqr(x, y):\n",
      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
      "    X = np.vstack([x, np.ones(len(x))]).T\n",
      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "def cy_mat_lstsqr(x, y):\n",
      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
      "    X = np.vstack([x, np.ones(len(x))]).T\n",
      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### \"Classic\" approach:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def py_lstsqr(x, y):\n",
      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
      "    x_avg = sum(x)/len(x)\n",
      "    y_avg = sum(y)/len(y)\n",
      "    var_x = 0\n",
      "    cov_xy = 0\n",
      "    for x_i, y_i in zip(x,y):\n",
      "        temp = (x_i - x_avg)\n",
      "        var_x += temp**2\n",
      "        cov_xy += temp*(y_i - y_avg)\n",
      "    slope = cov_xy / var_x\n",
      "    y_interc = y_avg - slope*x_avg\n",
      "    return (slope, y_interc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@jit\n",
      "def numba_lstsqr(x, y):\n",
      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
      "    x_avg = sum(x)/len(x)\n",
      "    y_avg = sum(y)/len(y)\n",
      "    var_x = 0\n",
      "    cov_xy = 0\n",
      "    for x_i, y_i in zip(x,y):\n",
      "        temp = (x_i - x_avg)\n",
      "        var_x += temp**2\n",
      "        cov_xy += temp*(y_i - y_avg)\n",
      "    slope = cov_xy / var_x\n",
      "    y_interc = y_avg - slope*x_avg\n",
      "    return (slope, y_interc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "def cy_lstsqr(x, y):\n",
      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
      "    cdef double x_avg, y_avg, temp, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
      "    x_avg = sum(x)/len(x)\n",
      "    y_avg = sum(y)/len(y)\n",
      "    var_x = 0\n",
      "    cov_xy = 0\n",
      "    for x_i, y_i in zip(x,y):\n",
      "        temp = (x_i - x_avg)\n",
      "        var_x += temp**2\n",
      "        cov_xy += temp*(y_i - y_avg)\n",
      "    slope = cov_xy / var_x\n",
      "    y_interc = y_avg - slope*x_avg\n",
      "    return (slope, y_interc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### NumPy and SciPy libraries:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def numpy_lstsqr(x, y):\n",
      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
      "    X = np.vstack([x, np.ones(len(x))]).T\n",
      "    return np.linalg.lstsq(X,y)[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def scipy_lstsqr(x,y):\n",
      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
      "    return scipy.stats.linregress(x, y)[0:2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Verifying that the different approaches yield the same results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random\n",
      "random.seed(12345)\n",
      "\n",
      "n = 500\n",
      "x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
      "y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
      "\n",
      "np.testing.assert_array_almost_equal(\n",
      "        py_lstsqr(x, y), py_mat_lstsqr(x, y), decimal=6)\n",
      "np.testing.assert_array_almost_equal(\n",
      "        numpy_lstsqr(x,y), py_lstsqr(x, y), decimal=6)\n",
      "np.testing.assert_array_almost_equal(\n",
      "        scipy_lstsqr(x,y), py_lstsqr(x, y), decimal=6)\n",
      "\n",
      "print('ok')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ok\n"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Visual checking the least square fit"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot as plt\n",
      "\n",
      "slope, intercept = py_mat_lstsqr(x, y)\n",
      "\n",
      "line_x = [round(min(x)) - 1, round(max(x)) + 1]\n",
      "line_y = [slope*x_i + intercept for x_i in line_x]\n",
      "\n",
      "plt.figure(figsize=(7,6))\n",
      "plt.scatter(x,y)\n",
      "plt.plot(line_x, line_y, color='red', lw='2')\n",
      "\n",
      "plt.ylabel('y')\n",
      "plt.xlabel('x')\n",
      "plt.title('Linear regression via least squares fit')\n",
      "\n",
      "ftext = 'y = ax + b = {:.3f} + {:.3f}x'\\\n",
      "        .format(slope, intercept)\n",
      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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c12Aw4OPjw6FDh/D19eXw4cNZ0oOCgjh06JDF2vb+++/TtWtXPDw8CAgIoGLF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\nImIDFaqIiIgNVKgiIiI2UKGKiIjY4C99KgLU7MAmZwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ca76cd0>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Benchmarking:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "import random\n",
      "random.seed(12345)\n",
      "\n",
      "funcs = ['py_mat_lstsqr', 'numba_mat_lstsqr', 'cy_mat_lstsqr', \n",
      "         'py_lstsqr', 'numba_lstsqr', 'cy_lstsqr',\n",
      "         'numpy_lstsqr', 'scipy_lstsqr']\n",
      "\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    x = np.asarray([x_i*np.random.randint(8,12)/10 for x_i in range(n)])\n",
      "    y = np.asarray([y_i*np.random.randint(10,14)/10 for y_i in range(n)])\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(x,y)' %f, \n",
      "                      'from __main__ import %s, x, y' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "labels = [('py_mat_lstsqr', 'matrix equation in reg. (C)Python & NumPy'), \n",
      "          ('numba_mat_lstsqr', 'matrix equation in Numba'),\n",
      "          ('cy_mat_lstsqr', 'matrix equation in Cython & NumPy'),\n",
      "          ('py_lstsqr', '\"classic\" least squares in reg. (C)Python'),\n",
      "          ('numba_lstsqr', '\"classic\" least squares in Numba'),\n",
      "          ('cy_lstsqr', '\"classic\" least squares in Cython'),\n",
      "          ('numpy_lstsqr', 'least squares via np.linalg.lstsq()'),\n",
      "          ('scipy_lstsqr', 'least_squares via scipy.stats.linregress()'),]\n",
      "\n",
      "\n",
      "matplotlib.rcParams.update({'font.size': 12})\n",
      "\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "for lb in labels:\n",
      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
      "plt.xlabel('sample size n')\n",
      "plt.ylabel('time per computation in milliseconds [ms]')\n",
      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
      "plt.legend(loc=2)\n",
      "plt.grid()\n",
      "plt.xscale('log')\n",
      "plt.yscale('log')\n",
      "\n",
      "max_perf = max( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
      "                                   times_n['cy_lstsqr']) )\n",
      "min_perf = min( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
      "                                   times_n['cy_lstsqr']) )\n",
      "\n",
      "ftext = 'Using Cython is {:.2f}x to '\\\n",
      "        '{:.2f}x faster than regular (C)Python'\\\n",
      "        .format(min_perf, max_perf)\n",
      "\n",
      "plt.figtext(.14,.15, ftext, fontsize=11, ha='left')\n",
      "plt.title('Performance of least square fit implementations')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cx1HPnj3p+++/p7S0NNq4cSNxHEd///23kC8sLIz++OMPyszMpKSkJOrXrx9Z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\n6dIlTp48WaFutYk6Tps2jZiYGD7++GOCg4M5fvw4o0ePJj8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AtX3WG33IzL1O29nZVRi+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\nWVncMnCgNkNco0LhgkLqDtUFxqzDrNjH2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xSBC89WYrXyQVERJ3Jz4eBBcDjwdzi41mRi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HLt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\nAICkHiAvheE4HrYHAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a753b90>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}