novice/python/01-numpy.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Analyzing Patient Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "We are studying inflammation in patients who have been given a new treatment for arthritis,\n",
      "and need to analyze the first dozen data sets.\n",
      "The data sets are stored in [comma-separated values](../../gloss.html#csv) (CSV) format:\n",
      "each row holds information for a single patient,\n",
      "and the columns represent successive days.\n",
      "The first few rows of our first file look like this:\n",
      "\n",
      "    0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0\n",
      "    0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1\n",
      "    0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1\n",
      "    0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1\n",
      "    0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "We want to:\n",
      "\n",
      "*   load that data into memory,\n",
      "*   calculate the average inflammation per day across all patients, and\n",
      "*   plot the result.\n",
      "\n",
      "To do all that, we'll have to learn a little bit about programming."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "objectives"
      ]
     },
     "source": [
      "#### Objectives\n",
      "\n",
      "*   Explain what a library is, and what libraries are used for.\n",
      "*   Load a Python library and use the things it contains.\n",
      "*   Read tabular data from a file into a program.\n",
      "*   Assign values to variables.\n",
      "*   Select individual values and subsections from data.\n",
      "*   Perform operations on arrays of data.\n",
      "*   Display simple graphs."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Loading Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Words are useful,\n",
      "but what's more useful are the sentences and stories we use them to build.\n",
      "Similarly,\n",
      "while a lot of powerful tools are built into languages like Python,\n",
      "even more lives in the [libraries](../../gloss.html#library) they are used to build.\n",
      "\n",
      "In order to load our inflammation data,\n",
      "we need to [import](../../gloss.html#import) a library called NumPy\n",
      "that knows how to operate on matrices:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Importing a library is like getting a piece of lab equipment out of a storage locker\n",
      "and setting it up on the bench.\n",
      "Once it's done,\n",
      "we can ask the library to read our data file for us:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "array([[ 0.,  0.,  1., ...,  3.,  0.,  0.],\n",
        "       [ 0.,  1.,  2., ...,  1.,  0.,  1.],\n",
        "       [ 0.,  1.,  1., ...,  2.,  1.,  1.],\n",
        "       ..., \n",
        "       [ 0.,  1.,  1., ...,  1.,  1.,  1.],\n",
        "       [ 0.,  0.,  0., ...,  0.,  2.,  0.],\n",
        "       [ 0.,  0.,  1., ...,  1.,  1.,  0.]])"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The expression `numpy.loadtxt(...)` is a [function call](../../gloss.html#function-call)\n",
      "that asks Python to run the function `loadtxt` that belongs to the `numpy` library.\n",
      "This [dotted notation](../../gloss.html#dotted-notation) is used everywhere in Python\n",
      "to refer to the parts of things as `whole.part`.\n",
      "\n",
      "`numpy.loadtxt` has two [parameters](../../gloss.html#parameter):\n",
      "the name of the file we want to read,\n",
      "and the [delimiter](../../gloss.html#delimiter) that separates values on a line.\n",
      "These both need to be character strings (or [strings](../../gloss.html#string) for short),\n",
      "so we put them in quotes.\n",
      "\n",
      "When we are finished typing and press Shift+Enter,\n",
      "the notebook runs our command.\n",
      "Since we haven't told it to do anything else with the function's output,\n",
      "the notebook displays it.\n",
      "In this case,\n",
      "that output is the data we just loaded.\n",
      "By default,\n",
      "only a few rows and columns are shown\n",
      "(with `...` to omit elements when displaying big arrays).\n",
      "To save space,\n",
      "Python displays numbers as `1.` instead of `1.0`\n",
      "when there's nothing interesting after the decimal point."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Our call to `numpy.loadtxt` read our file,\n",
      "but didn't save the data in memory.\n",
      "To do that,\n",
      "we need to [assign](../../gloss.html#assignment) the array to a [variable](../../gloss.html#variable).\n",
      "A variable is just a name for a value,\n",
      "such as `x`, `current_temperature`, or `subject_id`.\n",
      "We can create a new variable simply by assigning a value to it using `=`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weight_kg = 55"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Once a variable has a value, we can print it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print weight_kg"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "55\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "and do arithmetic with it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'weight in pounds:', 2.2 * weight_kg"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "weight in pounds: 121.0\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "We can also change a variable's value by assigning it a new one:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weight_kg = 57.5\n",
      "print 'weight in kilograms is now:', weight_kg"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "weight in kilograms is now: 57.5\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "As the example above shows,\n",
      "we can print several things at once by separating them with commas.\n",
      "\n",
      "If we imagine the variable as a sticky note with a name written on it,\n",
      "assignment is like putting the sticky note on a particular value:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "<img src=\"files/img/python-sticky-note-variables-01.svg\" alt=\"Variables as Sticky Notes\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "This means that assigning a value to one variable does *not* change the values of other variables.\n",
      "For example,\n",
      "let's store the subject's weight in pounds in a variable:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weight_lb = 2.2 * weight_kg\n",
      "print 'weight in kilograms:', weight_kg, 'and in pounds:', weight_lb"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "weight in kilograms: 57.5 and in pounds: 126.5\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"files/img/python-sticky-note-variables-02.svg\" alt=\"Creating Another Variable\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "and then change `weight_kg`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weight_kg = 100.0\n",
      "print 'weight in kilograms is now:', weight_kg, 'and weight in pounds is still:', weight_lb"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "weight in kilograms is now: 100.0 and weight in pounds is still: 126.5\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"files/img/python-sticky-note-variables-03.svg\" alt=\"Updating a Variable\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Since `weight_lb` doesn't \"remember\" where its value came from,\n",
      "it isn't automatically updated when `weight_kg` changes.\n",
      "This is different from the way spreadsheets work.\n",
      "\n",
      "Now that we know how to assign things to variables,\n",
      "let's re-run `numpy.loadtxt` and save its result:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "This statement doesn't produce any output because assignment doesn't display anything.\n",
      "If we want to check that our data has been loaded,\n",
      "we can print the variable's value:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.  0.  1. ...,  3.  0.  0.]\n",
        " [ 0.  1.  2. ...,  1.  0.  1.]\n",
        " [ 0.  1.  1. ...,  2.  1.  1.]\n",
        " ..., \n",
        " [ 0.  1.  1. ...,  1.  1.  1.]\n",
        " [ 0.  0.  0. ...,  0.  2.  0.]\n",
        " [ 0.  0.  1. ...,  1.  1.  0.]]\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "challenges"
      ]
     },
     "source": [
      "#### Challenges\n",
      "\n",
      "1.  Draw diagrams showing what variables refer to what values after each statement in the following program:\n",
      "\n",
      "    ~~~python\n",
      "    mass = 47.5\n",
      "    age = 122\n",
      "    mass = mass * 2.0\n",
      "    age = age - 20\n",
      "    ~~~\n",
      "\n",
      "1.  What does the following program print out?\n",
      "\n",
      "    ~~~python\n",
      "    first, second = 'Grace', 'Hopper'\n",
      "    third, fourth = second, first\n",
      "    print third, fourth\n",
      "    ~~~"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Manipulating Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Now that our data is in memory,\n",
      "we can start doing things with it.\n",
      "First,\n",
      "let's ask what [type](../../gloss.html#data-type) of thing `data` refers to:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print type(data)"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'numpy.ndarray'>\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The output tells us that `data` currently refers to an N-dimensional array created by the NumPy library.\n",
      "We can see what its [shape](../../gloss.html#shape) is like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data.shape"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(60, 40)\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "This tells us that `data` has 60 rows and 40 columns.\n",
      "`data.shape` is a [member](../../gloss.html#member) of `data`,\n",
      "i.e.,\n",
      "a value that is stored as part of a larger value.\n",
      "We use the same dotted notation for the members of values\n",
      "that we use for the functions in libraries\n",
      "because they have the same part-and-whole relationship."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "If we want to get a single value from the matrix,\n",
      "we must provide an [index](../../gloss.html#index) in square brackets,\n",
      "just as we do in math:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'first value in data:', data[0, 0]"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "first value in data: 0.0\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'middle value in data:', data[30, 20]"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "middle value in data: 13.0\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The expression `data[30, 20]` may not surprise you,\n",
      "but `data[0, 0]` might.\n",
      "Programming languages like Fortran and MATLAB start counting at 1,\n",
      "because that's what human beings have done for thousands of years.\n",
      "Languages in the C family (including C++, Java, Perl, and Python) count from 0\n",
      "because that's simpler for computers to do.\n",
      "As a result,\n",
      "if we have an M&times;N array in Python,\n",
      "its indices go from 0 to M-1 on the first axis\n",
      "and 0 to N-1 on the second.\n",
      "It takes a bit of getting used to,\n",
      "but one way to remember the rule is that\n",
      "the index is how many steps we have to take from the start to get the item we want.\n",
      "\n",
      "> #### In the Corner\n",
      ">\n",
      "> What may also surprise you is that when Python displays an array,\n",
      "> it shows the element with index `[0, 0]` in the upper left corner\n",
      "> rather than the lower left.\n",
      "> This is consistent with the way mathematicians draw matrices,\n",
      "> but different from the Cartesian coordinates.\n",
      "> The indices are (row, column) instead of (column, row) for the same reason."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "An index like `[30, 20]` selects a single element of an array,\n",
      "but we can select whole sections as well.\n",
      "For example,\n",
      "we can select the first ten days (columns) of values\n",
      "for the first four (rows) patients like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data[0:4, 0:10]"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.]\n",
        " [ 0.  1.  2.  1.  2.  1.  3.  2.  2.  6.]\n",
        " [ 0.  1.  1.  3.  3.  2.  6.  2.  5.  9.]\n",
        " [ 0.  0.  2.  0.  4.  2.  2.  1.  6.  7.]]\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The [slice](../../gloss.html#slice) `0:4` means,\n",
      "\"Start at index 0 and go up to, but not including, index 4.\"\n",
      "Again,\n",
      "the up-to-but-not-including takes a bit of getting used to,\n",
      "but the rule is that the difference between the upper and lower bounds is the number of values in the slice.\n",
      "\n",
      "We don't have to start slices at 0:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data[5:10, 0:10]"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.  0.  1.  2.  2.  4.  2.  1.  6.  4.]\n",
        " [ 0.  0.  2.  2.  4.  2.  2.  5.  5.  8.]\n",
        " [ 0.  0.  1.  2.  3.  1.  2.  3.  5.  3.]\n",
        " [ 0.  0.  0.  3.  1.  5.  6.  5.  5.  8.]\n",
        " [ 0.  1.  1.  2.  1.  3.  5.  3.  5.  8.]]\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "and we don't have to take all the values in the slice---if we provide a [stride](../../gloss.html#stride),\n",
      "Python takes values spaced that far apart:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data[0:10:3, 0:10:2]"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.  1.  1.  4.  8.]\n",
        " [ 0.  2.  4.  2.  6.]\n",
        " [ 0.  2.  4.  2.  5.]\n",
        " [ 0.  1.  1.  5.  5.]]\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Here,\n",
      "we have taken rows 0, 3, 6, and 9,\n",
      "and columns 0, 2, 4, 6, and 8.\n",
      "(Again, we always include the lower bound,\n",
      "but stop when we reach or cross the upper bound.)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "We also don't have to include the upper and lower bound on the slice.\n",
      "If we don't include the lower bound,\n",
      "Python uses 0 by default;\n",
      "if we don't include the upper,\n",
      "the slice runs to the end of the axis,\n",
      "and if we don't include either\n",
      "(i.e., if we just use ':' on its own),\n",
      "the slice includes everything:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "small = data[:3, 36:]\n",
      "print 'small is:'\n",
      "print small"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "small is:\n",
        "[[ 2.  3.  0.  0.]\n",
        " [ 1.  1.  0.  1.]\n",
        " [ 2.  2.  1.  1.]]\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Arrays also know how to perform common mathematical operations on their values.\n",
      "If we want to find the average inflammation for all patients on all days,\n",
      "for example,\n",
      "we can just ask the array for its mean value"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data.mean()"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "6.14875\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "`mean` is a [method](../../gloss.html#method) of the array,\n",
      "i.e.,\n",
      "a function that belongs to it\n",
      "in the same way that the member `shape` does.\n",
      "If variables are nouns, methods are verbs:\n",
      "they are what the thing in question knows how to do.\n",
      "This is why `data.shape` doesn't need to be called\n",
      "(it's just a thing)\n",
      "but `data.mean()` does\n",
      "(it's an action).\n",
      "It is also why we need empty parentheses for `data.mean()`:\n",
      "even when we're not passing in any parameters,\n",
      "parentheses are how we tell Python to go and do something for us.\n",
      "\n",
      "NumPy arrays have lots of useful methods:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'maximum inflammation:', data.max()\n",
      "print 'minimum inflammation:', data.min()\n",
      "print 'standard deviation:', data.std()"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "maximum inflammation: 20.0\n",
        "minimum inflammation: 0.0\n",
        "standard deviation: 4.61383319712\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "When analyzing data,\n",
      "though,\n",
      "we often want to look at partial statistics,\n",
      "such as the maximum value per patient\n",
      "or the average value per day.\n",
      "One way to do this is to select the data we want to create a new temporary array,\n",
      "then ask it to do the calculation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "patient_0 = data[0, :] # 0 on the first axis, everything on the second\n",
      "print 'maximum inflammation for patient 0:', patient_0.max()"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "maximum inflammation for patient 0: 18.0\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "We don't actually need to store the row in a variable of its own.\n",
      "Instead, we can combine the selection and the method call:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'maximum inflammation for patient 2:', data[2, :].max()"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "maximum inflammation for patient 2: 19.0\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "What if we need the maximum inflammation for *all* patients,\n",
      "or the average for each day?\n",
      "As the diagram below shows,\n",
      "we want to perform the operation across an axis:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "<img src=\"files/img/python-operations-across-axes.svg\" alt=\"Operations Across Axes\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "To support this,\n",
      "most array methods allow us to specify the axis we want to work on.\n",
      "If we ask for the average across axis 0,\n",
      "we get:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data.mean(axis=0)"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  0.           0.45         1.11666667   1.75         2.43333333   3.15\n",
        "   3.8          3.88333333   5.23333333   5.51666667   5.95         5.9\n",
        "   8.35         7.73333333   8.36666667   9.5          9.58333333\n",
        "  10.63333333  11.56666667  12.35        13.25        11.96666667\n",
        "  11.03333333  10.16666667  10.           8.66666667   9.15         7.25\n",
        "   7.33333333   6.58333333   6.06666667   5.95         5.11666667   3.6\n",
        "   3.3          3.56666667   2.48333333   1.5          1.13333333\n",
        "   0.56666667]\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "As a quick check,\n",
      "we can ask this array what its shape is:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data.mean(axis=0).shape"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(40,)\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The expression `(40,)` tells us we have an N&times;1 vector,\n",
      "so this is the average inflammation per day for all patients.\n",
      "If we average across axis 1, we get:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data.mean(axis=1)"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 5.45   5.425  6.1    5.9    5.55   6.225  5.975  6.65   6.625  6.525\n",
        "  6.775  5.8    6.225  5.75   5.225  6.3    6.55   5.7    5.85   6.55\n",
        "  5.775  5.825  6.175  6.1    5.8    6.425  6.05   6.025  6.175  6.55\n",
        "  6.175  6.35   6.725  6.125  7.075  5.725  5.925  6.15   6.075  5.75\n",
        "  5.975  5.725  6.3    5.9    6.75   5.925  7.225  6.15   5.95   6.275  5.7\n",
        "  6.1    6.825  5.975  6.725  5.7    6.25   6.4    7.05   5.9  ]\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "which is the average inflammation per patient across all days."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "challenges"
      ]
     },
     "source": [
      "#### Challenges\n",
      "\n",
      "A subsection of an array is called a [slice](../../gloss.html#slice).\n",
      "We can take slices of character strings as well:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "element = 'oxygen'\n",
      "print 'first three characters:', element[0:3]\n",
      "print 'last three characters:', element[3:6]"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": [
       "challenges"
      ]
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "first three characters: oxy\n",
        "last three characters: gen\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "challenges"
      ]
     },
     "source": [
      "1.  What is the value of `element[:4]`?\n",
      "    What about `element[4:]`?\n",
      "    Or `element[:]`?\n",
      "\n",
      "1.  What is `element[-1]`?\n",
      "    What is `element[-2]`?\n",
      "    Given those answers,\n",
      "    explain what `element[1:-1]` does.\n",
      "\n",
      "1.  The expression `element[3:3]` produces an [empty string](../../gloss.html#empty-string),\n",
      "    i.e., a string that contains no characters.\n",
      "    If `data` holds our array of patient data,\n",
      "    what does `data[3:3, 4:4]` produce?\n",
      "    What about `data[3:3, :]`?"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Plotting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The mathematician Richard Hamming once said,\n",
      "\"The purpose of computing is insight, not numbers,\"\n",
      "and the best way to develop insight is often to visualize data.\n",
      "Visualization deserves an entire lecture (or course) of its own,\n",
      "but we can explore a few features of Python's `matplotlib` here.\n",
      "First,\n",
      "let's tell the IPython Notebook that we want our plots displayed inline,\n",
      "rather than in a separate viewing window:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The `%` at the start of the line signals that this is a command for the notebook,\n",
      "rather than a statement in Python.\n",
      "Next,\n",
      "we will import the `pyplot` module from `matplotlib`\n",
      "and use two of its functions to create and display a heat map of our data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot\n",
      "pyplot.imshow(data)\n",
      "pyplot.show()"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bRP0M5ONA3lL1Wo3stbzivmVAvFGwacyx0kbz1RjQh65JQj0AJgJC2+yEc4nw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\nG5uKPX329IlFj5SQA/+WobfhlJecRNdEdUy1d7jRhzhFbW6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AOmwpZRbodvOma8xDdyTN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ksaZTFbT3l1+5DX8wfMxARVQOMKmgkkfsiKARYNe8wocamMjyoIKGOHm4M9vuQpm0VAlM8Jz+eE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10600f5d0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Blue regions in this heat map are low values, while red shows high values.\n",
      "As we can see,\n",
      "inflammation rises and falls over a 40-day period.\n",
      "Let's take a look at the average inflammation over time:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ave_inflammation = data.mean(axis=0)\n",
      "pyplot.plot(ave_inflammation)\n",
      "pyplot.show()"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x106026750>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Here,\n",
      "we have put the average per day across all patients in the variable `ave_inflammation`,\n",
      "then asked `pyplot` to create and display a line graph of those values.\n",
      "The result is roughly a linear rise and fall,\n",
      "which is suspicious:\n",
      "based on other studies,\n",
      "we expect a sharper rise and slower fall.\n",
      "Let's have a look at two other statistics:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'maximum inflammation per day'\n",
      "pyplot.plot(data.max(axis=0))\n",
      "pyplot.show()\n",
      "\n",
      "print 'minimum inflammation per day'\n",
      "pyplot.plot(data.min(axis=0))\n",
      "pyplot.show()"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "maximum inflammation per day\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x106036bd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "minimum inflammation per day\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x106033550>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The maximum value rises and falls perfectly smoothly,\n",
      "while the minimum seems to be a step function.\n",
      "Neither result seems particularly likely,\n",
      "so either there's a mistake in our calculations\n",
      "or something is wrong with our data."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "challenges"
      ]
     },
     "source": [
      "#### Challenges\n",
      "\n",
      "1.  Why do all of our plots stop just short of the upper end of our graph?\n",
      "    Why are the vertical lines in our plot of the minimum inflammation per day not vertical?\n",
      "\n",
      "1.  Create a plot showing the standard deviation of the inflammation data for each day across all patients."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "Wrapping Up"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "It's very common to create an [alias](../../gloss.html#alias-library) for a library when importing it\n",
      "in order to reduce the amount of typing we have to do.\n",
      "Here are our three plots side by side using aliases for `numpy` and `pyplot`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from matplotlib import pyplot as plt\n",
      "\n",
      "data = np.loadtxt(fname='inflammation-01.csv', 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()"
     ],
     "language": "python",
     "metadata": {
      "cell_tags": []
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SncRedu9WJaApKbqTeFdB7/081yvbtm1L0aJFeeihhzAMg48++oj09HSCg4Pp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\nRf/+/XnqqacuhDLhXe727Wp1b/t2eZc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fl5CQkL3z1sz7xxfsNF4Nw95j1s7j1TCcM2bt\nPF4Nw15j1s7j1TDsPWadMl4Nw7MxK4eJCCGEEEIIcRlLlVsIIYQQQghhBTJJFkIIIYQQ4jIySRZC\nCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jIySRZCCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jL/\nD8Se36QdbJ39AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106062fd0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "The first two lines re-load our libraries as `np` and `plt`,\n",
      "which are the aliases most Python programmers use.\n",
      "The call to `loadtxt` reads our data,\n",
      "and the rest of the program tells the plotting library\n",
      "how large we want the figure to be,\n",
      "that we're creating three sub-plots,\n",
      "what to draw for each one,\n",
      "and that we want a tight layout.\n",
      "(Perversely,\n",
      "if we leave out that call to `plt.tight_layout()`,\n",
      "the graphs will actually be squeezed together more closely.)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "challenges"
      ]
     },
     "source": [
      "#### Challenges\n",
      "\n",
      "1.  Modify the program to display the three plots on top of one another instead of side by side."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": [
       "keypoints"
      ]
     },
     "source": [
      "#### Key Points\n",
      "\n",
      "*   Import a library into a program using `import libraryname`.\n",
      "*   Use the `numpy` library to work with arrays in Python.\n",
      "*   Use `variable = value` to assign a value to a variable in order to record it in memory.\n",
      "*   Variables are created on demand whenever a value is assigned to them.\n",
      "*   Use `print something` to display the value of `something`.\n",
      "*   The expression `array.shape` gives the shape of an array.\n",
      "*   Use `array[x, y]` to select a single element from an array.\n",
      "*   Array indices start at 0, not 1.\n",
      "*   Use `low:high` to specify a slice that includes the indices from `low` to `high-1`.\n",
      "*   All the indexing and slicing that works on arrays also works on strings.\n",
      "*   Use `# some kind of explanation` to add comments to programs.\n",
      "*   Use `array.mean()`, `array.max()`, and `array.min()` to calculate simple statistics.\n",
      "*   Use `array.mean(axis=0)` or `array.mean(axis=1)` to calculate statistics across the specified axis.\n",
      "*   Use the `pyplot` library from `matplotlib` for creating simple visualizations."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "cell_tags": []
     },
     "source": [
      "#### Next Steps\n",
      "\n",
      "Our work so far has convinced us that something's wrong with our first data file.\n",
      "We would like to check the other 11 the same way,\n",
      "but typing in the same commands repeatedly is tedious and error-prone.\n",
      "Since computers don't get bored (that we know of),\n",
      "we should create a way to do a complete analysis with a single command,\n",
      "and then figure out how to repeat that step once for each file.\n",
      "These operations are the subjects of the next two lessons."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}