novice/python/03-loop.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Analyzing Multiple Data Sets"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "We have created a function called `analyze` that creates graphs of the minimum, average, and maximum daily inflammation rates\n",
      "for a single data set:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import numpy as np\n",
      "from matplotlib import pyplot as plt\n",
      "\n",
      "def analyze(filename):\n",
      "    data = np.loadtxt(fname=filename, delimiter=',')\n",
      "    \n",
      "    plt.figure(figsize=(10.0, 3.0))\n",
      "    \n",
      "    plt.subplot(1, 3, 1)\n",
      "    plt.ylabel('average')\n",
      "    plt.plot(data.mean(0))\n",
      "    \n",
      "    plt.subplot(1, 3, 2)\n",
      "    plt.ylabel('max')\n",
      "    plt.plot(data.max(0))\n",
      "    \n",
      "    plt.subplot(1, 3, 3)\n",
      "    plt.ylabel('min')\n",
      "    plt.plot(data.min(0))\n",
      "    \n",
      "    plt.tight_layout()\n",
      "    plt.show()\n",
      "\n",
      "analyze('inflammation-01.csv')"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SncRedu9WJaApKbqTeFdB7/081yvbtm1L0aJFeeihhzAMg48++oj09HSCg4Pp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\nD8Se36QdbJ39AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105f3e110>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "We can use it to analyze other data sets one by one:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "analyze('inflammation-02.csv')"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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63OPHaV0uozXJrVtTqTh7qDBg7b2fkZGBChUq5PvczZs3UVPhEil66rNjxwJ/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\nY4w9hgfJjDHGGGOMPeb/AeKLvR1eWwu1AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105f07d10>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "but we have a dozen data sets right now and more on the way.\n",
      "We want to create plots for all our data sets with a single statement.\n",
      "To do that,\n",
      "we'll have to teach the computer how to repeat things."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "objectives"
      ]
     },
     "source": [
      "#### Objectives\n",
      "\n",
      "*   Explain what a for loop does.\n",
      "*   Correctly write for loops to repeat simple calculations.\n",
      "*   Trace changes to a loop variable as the loop runs.\n",
      "*   Trace changes to other variables as they are updated by a for loop.\n",
      "*   Explain what a list is.\n",
      "*   Create and index lists of simple values.\n",
      "*   Use a library function to get a list of filenames that match a simple wildcard pattern.\n",
      "*   Use a for loop to process multiple files."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "For Loops"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Suppose we want to print each character in the word \"lead\" on a line of its own.\n",
      "One way is to use four `print` statements:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_characters(element):\n",
      "    print element[0]\n",
      "    print element[1]\n",
      "    print element[2]\n",
      "    print element[3]\n",
      "\n",
      "print_characters('lead')"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "l\n",
        "e\n",
        "a\n",
        "d\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "but that's a bad approach for two reasons:\n",
      "\n",
      "1.  It doesn't scale:\n",
      "    if we want to print the characters in a string that's hundreds of letters long,\n",
      "    we'd be better off just typing them in.\n",
      "\n",
      "1.  It's fragile:\n",
      "    if we give it a longer string,\n",
      "    it only prints part of the data,\n",
      "    and if we give it a shorter one,\n",
      "    it produces an error because we're asking for characters that don't exist."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_characters('tin')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IndexError",
       "evalue": "string index out of range",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-13-5bc7311e0bf3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_characters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tin'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m<ipython-input-12-11460561ea56>\u001b[0m in \u001b[0;36mprint_characters\u001b[0;34m(element)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mprint\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint_characters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'lead'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mIndexError\u001b[0m: string index out of range"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "t\n",
        "i\n",
        "n\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here's a better approach:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_characters(element):\n",
      "    for char in element:\n",
      "        print char\n",
      "\n",
      "print_characters('lead')"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "This is shorter---certainly shorter than something that prints every character in a hundred-letter string---and\n",
      "more robust as well:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_characters('oxygen')"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The improved version of `print_characters` uses a [for loop](../../gloss.html#for-loop)\n",
      "to repeat an operation---in this case, printing---once for each thing in a collection.\n",
      "The general form of a loop is:\n",
      "\n",
      "<pre>\n",
      "<strong>for</strong> <em>variable</em> <strong>in</strong> <em>collection</em><strong>:</strong>\n",
      "    <em>do things with variable</em>\n",
      "</pre>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "We can call the [loop variable](../../gloss.html#loop-variable) anything we like,\n",
      "but there must be a colon at the end of the line starting the loop,\n",
      "and we must indent the body of the loop.\n",
      "\n",
      "Here's another loop that repeatedly updates a variable:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "length = 0\n",
      "for vowel in 'aeiou':\n",
      "    length = length + 1\n",
      "print 'There are', length, 'vowels'"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "It's worth tracing the execution of this little program step by step.\n",
      "Since there are five characters in `'aeiou'`,\n",
      "the statement on line 3 will be executed five times.\n",
      "The first time around,\n",
      "`length` is zero (the value assigned to it on line 1)\n",
      "and `vowel` is `'a'`.\n",
      "The statement adds 1 to the old value of `length`,\n",
      "producing 1,\n",
      "and updates `length` to refer to that new value.\n",
      "The next time around,\n",
      "`vowel` is `'e'` and `length` is 1,\n",
      "so `length` is updated to be 2.\n",
      "After three more updates,\n",
      "`length` is 5;\n",
      "since there is nothing left in `'aeiou'` for Python to process,\n",
      "the loop finishes\n",
      "and the `print` statement on line 4 tells us our final answer.\n",
      "\n",
      "Note that a loop variable is just a variable that's being used to record progress in a loop.\n",
      "It still exists after the loop is over,\n",
      "and we can re-use variables previously defined as loop variables as well:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "letter = 'z'\n",
      "for letter in 'abc':\n",
      "    print letter\n",
      "print 'after the loop, letter is', letter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Note also that finding the length of a string is such a common operation\n",
      "that Python actually has a built-in function to do it called `len`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len('aeiou')"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "`len` is much faster than any function we could write ourselves,\n",
      "and much easier to read than a two-line loop;\n",
      "it will also give us the length of many other things that we haven't met yet,\n",
      "so we should always use it when we can."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "challenges"
      ]
     },
     "source": [
      "#### Challenges\n",
      "\n",
      "1.  Python has a built-in function called `range` that creates a list of numbers:\n",
      "    `range(3)` produces `[0, 1, 2]`, `range(2, 5)` produces `[2, 3, 4]`, and `range(2, 10, 3)` produces `[2, 5, 8]`.\n",
      "    Using `range`,\n",
      "    write a function that prints the $N$ natural numbers:\n",
      "    \n",
      "    ~~~python\n",
      "    print_N(3)\n",
      "    1\n",
      "    2\n",
      "    3\n",
      "    ~~~\n",
      "\n",
      "1.  Exponentiation is built into Python:\n",
      "\n",
      "    ~~~python\n",
      "    print 2**4\n",
      "    16\n",
      "    ~~~\n",
      "    \n",
      "    It also has a function called `pow` that calculates the same value.\n",
      "    Write a function called `expo` that uses a loop to calculate the same result.\n",
      "\n",
      "1.  Write a function called `rev` that takes a string as input, and produces a new string with the characters in reverse order:\n",
      "    \n",
      "    ~~~python\n",
      "    print rev('Newton')\n",
      "    notweN\n",
      "    ~~~\n",
      "    \n",
      "    As always, be sure to include a docstring."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Lists"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Just as a `for` loop is a way to do operations many times,\n",
      "a list is a way to store many values.\n",
      "Unlike NumPy arrays,\n",
      "there are built into the language.\n",
      "We create a list by putting values inside square brackets:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "odds = [1, 3, 5, 7]\n",
      "print 'odds are:', odds"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "We select individual elements from lists by indexing them:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'first and last:', odds[0], odds[-1]"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "and if we loop over a list,\n",
      "the loop variable is assigned elements one at a time:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for number in odds:\n",
      "    print number"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "There is one important difference between lists and strings:\n",
      "we can change the values in a list,\n",
      "but we cannot change the characters in a string.\n",
      "For example:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names = ['Newton', 'Darwing', 'Turing'] # typo in Darwin's name\n",
      "print 'names is originally:', names\n",
      "names[1] = 'Darwin' # correct the name\n",
      "print 'final value of names:', names"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "works, but:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "name = 'Bell'\n",
      "name[0] = 'b'"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "does not.\n",
      "\n",
      "> #### Ch-Ch-Ch-Changes\n",
      ">\n",
      "> Data that can be changed is called [mutable](../../gloss.html#mutable),\n",
      "> while data that cannot be is called [immutable](../../gloss.html#immutable).\n",
      "> Like strings,\n",
      "> numbers are immutable:\n",
      "> there's no way to make the number 0 have the value 1 or vice versa\n",
      "> (at least, not in Python&mdash;there actually *are* languages that will let people do this,\n",
      "> with predictably confusing results).\n",
      "> Lists and arrays,\n",
      "> on the other hand,\n",
      "> are mutable:\n",
      "> both can be modified after they have been created.\n",
      ">\n",
      "> Programs that modify data in place can be harder to understand than ones that don't\n",
      "> because readers may have to mentally sum up many lines of code\n",
      "> in order to figure out what the value of something actually is.\n",
      "> On the other hand,\n",
      "> programs that modify data in place instead of creating copies that are almost identical to the original\n",
      "> every time they want to make a small change\n",
      "> are much more efficient.\n",
      "\n",
      "There are many ways to change the contents of in lists besides assigning to elements:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "odds.append(11)\n",
      "print 'odds after adding a value:', odds"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del odds[0]\n",
      "print 'odds after removing the first element:', odds"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "odds.reverse()\n",
      "print 'odds after reversing:', odds"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "challenges"
      ]
     },
     "source": [
      "#### Challenges\n",
      "\n",
      "1.  Write a function called `total` that calculates the sum of the values in a list.\n",
      "    (Python has a built-in function called `sum` that does this for you.\n",
      "    Please don't use it for this exercise.)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Processing Multiple Files"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "We now have almost everything we need to process all our data files.\n",
      "The only thing that's missing is a library with a rather unpleasant name:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import glob"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The `glob` library contains a single function, also called `glob`,\n",
      "that finds files whose names match a pattern.\n",
      "We provide those patterns as strings:\n",
      "the character `*` matches zero or more characters,\n",
      "while `?` matches any one character.\n",
      "We can use this to get the names of all the IPython Notebooks we have created so far:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print glob.glob('*.ipynb')"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['01-numpy.ipynb', '02-func.ipynb', '03-loop.ipynb', '04-cond.ipynb', '05-defensive.ipynb', '06-cmdline.ipynb', 'spatial-intro.ipynb']\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "or to get the names of all our CSV data files:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print glob.glob('*.csv')"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['inflammation-01.csv', 'inflammation-02.csv', 'inflammation-03.csv', 'inflammation-04.csv', 'inflammation-05.csv', 'inflammation-06.csv', 'inflammation-07.csv', 'inflammation-08.csv', 'inflammation-09.csv', 'inflammation-10.csv', 'inflammation-11.csv', 'inflammation-12.csv', 'small-01.csv', 'small-02.csv', 'small-03.csv', 'swc_bc_coords.csv']\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "As these examples show,\n",
      "`glob.glob`'s result is a list of strings,\n",
      "which means we can loop over it\n",
      "to do something with each filename in turn.\n",
      "In our case,\n",
      "the \"something\" we want is our `analyze` function.\n",
      "Let's test it by analyzing the first three files in the list:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filenames = glob.glob('*.csv')\n",
      "filenames = filenames[0:3]\n",
      "for f in filenames:\n",
      "    print f\n",
      "    analyze(f)"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "inflammation-01.csv\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SncRedu9WJaApKbqTeFdB7/081yvbtm1L0aJFeeihhzAMg48++oj09HSCg4Pp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\nD8Se36QdbJ39AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105f4a750>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "inflammation-02.csv\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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63OPHaV0uozXJrVtTqTh7qDBg7b2fkZGBChUq5PvczZs3UVPhEil66rNjxwJ/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\nY4w9hgfJjDHGGGOMPeb/AeKLvR1eWwu1AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105f05610>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "inflammation-03.csv\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cfz0cPKhe1AbS2QQFKXAl2e12Z7d+y/k5b1rB5RvKwa9yc3rtNfj+e/jkE91J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\noky5nmGoX3rPPGPK5YQo0N13w1dfqR6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       "text": [
        "<matplotlib.figure.Figure at 0x106a0ab10>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Sure enough,\n",
      "the maxima of these data sets show exactly the same ramp as the first,\n",
      "and their minima show the same staircase structure."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "challenges"
      ]
     },
     "source": [
      "#### Challenges\n",
      "\n",
      "1.  Write a function called `analyze_all` that takes a filename pattern as its sole argument\n",
      "    and runs `analyze` for each file whose name matches the pattern."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "#### Key Points\n",
      "\n",
      "*   Use `for variable in collection` to process the elements of a collection one at a time.\n",
      "*   The body of a for loop must be indented.\n",
      "*   Use `len(thing)` to determine the length of something that contains other values.\n",
      "*   `[value1, value2, value3, ...]` creates a list.\n",
      "*   Lists are indexed and sliced in the same way as strings and arrays.\n",
      "*   Lists are mutable (i.e., their values can be changed in place).\n",
      "*   Strings are immutable (i.e., the characters in them cannot be changed).\n",
      "*   Use `glob.glob(pattern)` to create a list of files whose names match a pattern.\n",
      "*   Use `*` in a pattern to match zero or more characters, and `?` to match any single character."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "#### Next Steps\n",
      "\n",
      "We have now solved our original problem:\n",
      "we can analyze any number of data files with a single command.\n",
      "More importantly,\n",
      "we have met two of the most important ideas in programming:\n",
      "\n",
      "1.  Use functions to make code easier to re-use and easier to understand.\n",
      "1.  Use lists and arrays to store related values, and loops to repeat operations on them.\n",
      "\n",
      "We have one more big idea to introduce,\n",
      "and then we will be able to go back and create a heat map\n",
      "like the one we initially used to display our first data set."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}