Split the second episode (#771)

Split 02-numpy.md into 02-numpy.md and 03-matplotlib.md

README.md

@@ -29,16 +29,17 @@ This lesson is also available in [R][R] and [MATLAB][MATLAB].
 | # |  Episode | Time | Question(s) |
 |--:|:---------|:----:|:------------|
 | 1 | [Python Fundamentals][episode01] | 30 | What basic data types can I work with in Python?<br>How can I create a new variable in Python?<br>Can I change the value associated with a variable after I create it? |
-| 2 | [Analyzing Patient Data][episode02] | 90 | How can I process tabular data files in Python? |
-| 3 | [Repeating Actions with Loops][episode03] | 30 | How can I do the same operations on many different values? |
-| 4 | [Storing Multiple Values in Lists][episode04] | 30 | How can I store many values together? |
-| 5 | [Analyzing Data from Multiple Files][episode05] | 20 | How can I do the same operations on many different files? |
-| 6 | [Making Choices][episode06] | 30 | How can my programs do different things based on data values? |
-| 7 | [Creating Functions][episode07] | 30 | How can I define new functions?<br>What’s the difference between defining and calling a function?<br>What happens when I call a function? |
-| 8 | [Errors and Exceptions][episode08] | 30 | How does Python report errors?<br>How can I handle errors in Python programs? |
-| 9 | [Defensive Programming][episode09] | 30 | How can I make my programs more reliable? |
-|10 | [Debugging][episode10] | 30 | How can I debug my program? |
-|11 | [Command-Line Programs][episode11] | 30 | How can I write Python programs that will work like Unix command-line tools? |
+| 2 | [Analyzing Patient Data][episode02] | 60 | How can I process tabular data files in Python? |
+| 3 | [Visualizing Tabular Data][episode03] | 50 | How can I visualize tabular data in Python?<br>How can I group several plots together? |
+| 4 | [Repeating Actions with Loops][episode04] | 30 | How can I do the same operations on many different values? |
+| 5 | [Storing Multiple Values in Lists][episode05] | 30 | How can I store many values together? |
+| 6 | [Analyzing Data from Multiple Files][episode06] | 20 | How can I do the same operations on many different files? |
+| 7 | [Making Choices][episode07] | 30 | How can my programs do different things based on data values? |
+| 8 | [Creating Functions][episode08] | 30 | How can I define new functions?<br>What’s the difference between defining and calling a function?<br>What happens when I call a function? |
+| 9 | [Errors and Exceptions][episode09] | 30 | How does Python report errors?<br>How can I handle errors in Python programs? |
+|10 | [Defensive Programming][episode10] | 30 | How can I make my programs more reliable? |
+|11 | [Debugging][episode11] | 30 | How can I debug my program? |
+|12 | [Command-Line Programs][episode12] | 30 | How can I write Python programs that will work like Unix command-line tools? |
 
 
 ## Contributing
@@ -106,15 +107,16 @@ government. More information can be found [here][cp-about].
 [travis_url]: https://travis-ci.org/swcarpentry/python-novice-inflammation
 [episode01]: https://swcarpentry.github.io/python-novice-inflammation/01-intro/index.html
 [episode02]: https://swcarpentry.github.io/python-novice-inflammation/02-numpy/index.html
-[episode03]: https://swcarpentry.github.io/python-novice-inflammation/03-loop/index.html
-[episode04]: https://swcarpentry.github.io/python-novice-inflammation/04-lists/index.html
-[episode05]: https://swcarpentry.github.io/python-novice-inflammation/05-files/index.html
-[episode06]: https://swcarpentry.github.io/python-novice-inflammation/06-cond/index.html
-[episode07]: https://swcarpentry.github.io/python-novice-inflammation/07-func/index.html
-[episode08]: https://swcarpentry.github.io/python-novice-inflammation/08-errors/index.html
-[episode09]: https://swcarpentry.github.io/python-novice-inflammation/09-defensive/index.html
-[episode10]: https://swcarpentry.github.io/python-novice-inflammation/10-debugging/index.html
-[episode11]: https://swcarpentry.github.io/python-novice-inflammation/11-cmdline/index.html
+[episode03]: https://swcarpentry.github.io/python-novice-inflammation/03-matplotlib/index.html
+[episode04]: https://swcarpentry.github.io/python-novice-inflammation/04-loop/index.html
+[episode05]: https://swcarpentry.github.io/python-novice-inflammation/05-lists/index.html
+[episode06]: https://swcarpentry.github.io/python-novice-inflammation/06-files/index.html
+[episode07]: https://swcarpentry.github.io/python-novice-inflammation/07-cond/index.html
+[episode08]: https://swcarpentry.github.io/python-novice-inflammation/08-func/index.html
+[episode09]: https://swcarpentry.github.io/python-novice-inflammation/09-errors/index.html
+[episode10]: https://swcarpentry.github.io/python-novice-inflammation/10-defensive/index.html
+[episode11]: https://swcarpentry.github.io/python-novice-inflammation/11-debugging/index.html
+[episode12]: https://swcarpentry.github.io/python-novice-inflammation/12-cmdline/index.html
 [community-initiatives]: https://communityin.org
 [cp-about]: https://carpentries.org/about
 [swc-about]: https://software-carpentry.org/about/

_episodes/02-numpy.md

 ---
 title: Analyzing Patient Data
-teaching: 60
-exercises: 30
+teaching: 40
+exercises: 20
 questions:
 - "How can I process tabular data files in Python?"
 objectives:
@@ -10,7 +10,6 @@ objectives:
 - "Read tabular data from a file into a program."
 - "Select individual values and subsections from data."
 - "Perform operations on arrays of data."
-- "Plot simple graphs from data."
 keypoints:
 - "Import a library into a program using `import libraryname`."
 - "Use the `numpy` library to work with arrays in Python."
@@ -18,11 +17,9 @@ keypoints:
 - "Use `array[x, y]` to select a single element from a 2D array."
 - "Array indices start at 0, not 1."
 - "Use `low:high` to specify a `slice` that includes the indices from `low` to `high-1`."
-- "All the indexing and slicing that works on arrays also works on strings."
 - "Use `# some kind of explanation` to add comments to programs."
 - "Use `numpy.mean(array)`, `numpy.max(array)`, and `numpy.min(array)` to calculate simple statistics."
 - "Use `numpy.mean(array, axis=0)` or `numpy.mean(array, axis=1)` to calculate statistics across the specified axis."
-- "Use the `pyplot` library from `matplotlib` for creating simple visualizations."
 ---
 
 Words are useful, but what's more useful are the sentences and stories we build with them.
@@ -496,112 +493,6 @@ print(numpy.mean(data, axis=1))
 
 which is the average inflammation per patient across all days.
 
-## Visualizing data
-The mathematician Richard Hamming once said, "The purpose of computing is insight, not numbers," and
-the best way to develop insight is often to visualize data.  Visualization deserves an entire
-lecture of its own, but we can explore a few features of Python's `matplotlib` library here.  While
-there is no official plotting library, `matplotlib` is the _de facto_ standard.  First, we will
-import the `pyplot` module from `matplotlib` and use two of its functions to create and display a
-heat map of our data:
-
-~~~
-import matplotlib.pyplot
-image = matplotlib.pyplot.imshow(data)
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![Heatmap of the Data](../fig/inflammation-01-imshow.svg)
-
-Blue pixels in this heat map represent low values, while yellow pixels represent high values.  As we
-can see, inflammation rises and falls over a 40-day period.  Let's take a look at the average inflammation over time:
-
-~~~
-ave_inflammation = numpy.mean(data, axis=0)
-ave_plot = matplotlib.pyplot.plot(ave_inflammation)
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![Average Inflammation Over Time](../fig/inflammation-01-average.svg)
-
-Here, we have put the average per day across all patients in the variable `ave_inflammation`, then
-asked `matplotlib.pyplot` to create and display a line graph of those values.  The result is a
-roughly linear rise and fall, which is suspicious: we might instead expect a sharper rise and slower
-fall.  Let's have a look at two other statistics:
-
-~~~
-max_plot = matplotlib.pyplot.plot(numpy.max(data, axis=0))
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![Maximum Value Along The First Axis](../fig/inflammation-01-maximum.svg)
-
-~~~
-min_plot = matplotlib.pyplot.plot(numpy.min(data, axis=0))
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![Minimum Value Along The First Axis](../fig/inflammation-01-minimum.svg)
-
-The maximum value rises and falls smoothly, while the minimum seems to be a step function.  Neither
-trend seems particularly likely, so either there's a mistake in our calculations or something is
-wrong with our data.  This insight would have been difficult to reach by examining the numbers
-themselves without visualization tools.
-
-### Grouping plots
-You can group similar plots in a single figure using subplots.
-This script below uses a number of new commands. The function `matplotlib.pyplot.figure()`
-creates a space into which we will place all of our plots. The parameter `figsize`
-tells Python how big to make this space. Each subplot is placed into the figure using
-its `add_subplot` [method]({{ page.root }}/reference/#method). The `add_subplot` method takes 3
-parameters. The first denotes how many total rows of subplots there are, the second parameter
-refers to the total number of subplot columns, and the final parameter denotes which subplot
-your variable is referencing (left-to-right, top-to-bottom). Each subplot is stored in a
-different variable (`axes1`, `axes2`, `axes3`). Once a subplot is created, the axes can
-be titled using the `set_xlabel()` command (or `set_ylabel()`).
-Here are our three plots side by side:
-
-~~~
-import numpy
-import matplotlib.pyplot
-
-data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-
-fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
-
-axes1 = fig.add_subplot(1, 3, 1)
-axes2 = fig.add_subplot(1, 3, 2)
-axes3 = fig.add_subplot(1, 3, 3)
-
-axes1.set_ylabel('average')
-axes1.plot(numpy.mean(data, axis=0))
-
-axes2.set_ylabel('max')
-axes2.plot(numpy.max(data, axis=0))
-
-axes3.set_ylabel('min')
-axes3.plot(numpy.min(data, axis=0))
-
-fig.tight_layout()
-
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![The Previous Plots as Subplots](../fig/inflammation-01-group-plot.svg)
-
-The [call]({{ page.root }}/reference/#function-call) to `loadtxt` reads our data,
-and the rest of the program tells the plotting library
-how large we want the figure to be,
-that we're creating three subplots,
-what to draw for each one,
-and that we want a tight layout.
-(If we leave out that call to `fig.tight_layout()`,
-the graphs will actually be squeezed together more closely.)
-
 
 > ## Slicing Strings
 >
@@ -671,140 +562,6 @@ the graphs will actually be squeezed together more closely.)
 > {: .solution}
 {: .challenge}
 
-> ## Plot Scaling
->
-> Why do all of our plots stop just short of the upper end of our graph?
->
-> > ## Solution
-> > Because matplotlib normally sets x and y axes limits to the min and max of our data
-> > (depending on data range)
-> {: .solution}
->
-> If we want to change this, we can use the `set_ylim(min, max)` method of each 'axes',
-> for example:
->
-> ~~~
-> axes3.set_ylim(0,6)
-> ~~~
-> {: .language-python}
->
-> Update your plotting code to automatically set a more appropriate scale.
-> (Hint: you can make use of the `max` and `min` methods to help.)
->
-> > ## Solution
-> > ~~~
-> > # One method
-> > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0))
-> > axes3.set_ylim(0,6)
-> > ~~~
-> > {: .language-python}
-> {: .solution}
->
-> > ## Solution
-> > ~~~
-> > # A more automated approach
-> > min_data = numpy.min(data, axis=0)
-> > axes3.set_ylabel('min')
-> > axes3.plot(min_data)
-> > axes3.set_ylim(numpy.min(min_data), numpy.max(min_data) * 1.1)
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
-> ## Drawing Straight Lines
->
-> In the center and right subplots above, we expect all lines to look like step functions because
-> non-integer value are not realistic for the minimum and maximum values. However, you can see
-> that the lines are not always vertical or horizontal, and in particular the step function
-> in the subplot on the right looks slanted. Why is this?
->
-> > ## Solution
-> > Because matplotlib interpolates (draws a straight line) between the points.
-> > One way to do avoid this is to use the Matplotlib `drawstyle` option:
-> >
-> > ~~~
-> > import numpy
-> > import matplotlib.pyplot
-> >
-> > data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-> >
-> > fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
-> >
-> > axes1 = fig.add_subplot(1, 3, 1)
-> > axes2 = fig.add_subplot(1, 3, 2)
-> > axes3 = fig.add_subplot(1, 3, 3)
-> >
-> > axes1.set_ylabel('average')
-> > axes1.plot(numpy.mean(data, axis=0), drawstyle='steps-mid')
-> >
-> > axes2.set_ylabel('max')
-> > axes2.plot(numpy.max(data, axis=0), drawstyle='steps-mid')
-> >
-> > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0), drawstyle='steps-mid')
-> >
-> > fig.tight_layout()
-> >
-> > matplotlib.pyplot.show()
-> > ~~~
-> > {: .language-python}
-> ![Plot with step lines](../fig/inflammation-01-line-styles.svg)
-> {: .solution}
-{: .challenge}
-
-> ## Make Your Own Plot
->
-> Create a plot showing the standard deviation (`numpy.std`)
-> of the inflammation data for each day across all patients.
->
-> > ## Solution
-> > ~~~
-> > std_plot = matplotlib.pyplot.plot(numpy.std(data, axis=0))
-> > matplotlib.pyplot.show()
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
-> ## Moving Plots Around
->
-> Modify the program to display the three plots on top of one another
-> instead of side by side.
->
-> > ## Solution
-> > ~~~
-> > import numpy
-> > import matplotlib.pyplot
-> >
-> > data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-> >
-> > # change figsize (swap width and height)
-> > fig = matplotlib.pyplot.figure(figsize=(3.0, 10.0))
-> >
-> > # change add_subplot (swap first two parameters)
-> > axes1 = fig.add_subplot(3, 1, 1)
-> > axes2 = fig.add_subplot(3, 1, 2)
-> > axes3 = fig.add_subplot(3, 1, 3)
-> >
-> > axes1.set_ylabel('average')
-> > axes1.plot(numpy.mean(data, axis=0))
-> >
-> > axes2.set_ylabel('max')
-> > axes2.plot(numpy.max(data, axis=0))
-> >
-> > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0))
-> >
-> > fig.tight_layout()
-> >
-> > matplotlib.pyplot.show()
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
 > ## Stacking Arrays
 >
 > Arrays can be concatenated and stacked on top of one another,

_episodes/03-matplotlib.md

+---
+title: Visualizing Tabular Data
+teaching: 30
+exercises: 20
+questions:
+- "How can I visualize tabular data in Python?"
+- "How can I group several plots together?"
+objectives:
+- "Plot simple graphs from data."
+- "Group several graphs in a single figure."
+keypoints:
+- "Use the `pyplot` module from the `matplotlib` library for creating simple visualizations."
+---
+
+## Visualizing data
+The mathematician Richard Hamming once said, "The purpose of computing is insight, not numbers," and
+the best way to develop insight is often to visualize data.  Visualization deserves an entire
+lecture of its own, but we can explore a few features of Python's `matplotlib` library here.  While
+there is no official plotting library, `matplotlib` is the _de facto_ standard.  First, we will
+import the `pyplot` module from `matplotlib` and use two of its functions to create and display a
+heat map of our data:
+
+~~~
+import matplotlib.pyplot
+image = matplotlib.pyplot.imshow(data)
+matplotlib.pyplot.show()
+~~~
+{: .language-python}
+
+![Heatmap of the Data](../fig/inflammation-01-imshow.svg)
+
+Blue pixels in this heat map represent low values, while yellow pixels represent high values.  As we
+can see, inflammation rises and falls over a 40-day period.  Let's take a look at the average inflammation over time:
+
+~~~
+ave_inflammation = numpy.mean(data, axis=0)
+ave_plot = matplotlib.pyplot.plot(ave_inflammation)
+matplotlib.pyplot.show()
+~~~
+{: .language-python}
+
+![Average Inflammation Over Time](../fig/inflammation-01-average.svg)
+
+Here, we have put the average per day across all patients in the variable `ave_inflammation`, then
+asked `matplotlib.pyplot` to create and display a line graph of those values.  The result is a
+roughly linear rise and fall, which is suspicious: we might instead expect a sharper rise and slower
+fall.  Let's have a look at two other statistics:
+
+~~~
+max_plot = matplotlib.pyplot.plot(numpy.max(data, axis=0))
+matplotlib.pyplot.show()
+~~~
+{: .language-python}
+
+![Maximum Value Along The First Axis](../fig/inflammation-01-maximum.svg)
+
+~~~
+min_plot = matplotlib.pyplot.plot(numpy.min(data, axis=0))
+matplotlib.pyplot.show()
+~~~
+{: .language-python}
+
+![Minimum Value Along The First Axis](../fig/inflammation-01-minimum.svg)
+
+The maximum value rises and falls smoothly, while the minimum seems to be a step function.  Neither
+trend seems particularly likely, so either there's a mistake in our calculations or something is
+wrong with our data.  This insight would have been difficult to reach by examining the numbers
+themselves without visualization tools.
+
+### Grouping plots
+You can group similar plots in a single figure using subplots.
+This script below uses a number of new commands. The function `matplotlib.pyplot.figure()`
+creates a space into which we will place all of our plots. The parameter `figsize`
+tells Python how big to make this space. Each subplot is placed into the figure using
+its `add_subplot` [method]({{ page.root }}/reference/#method). The `add_subplot` method takes 3
+parameters. The first denotes how many total rows of subplots there are, the second parameter
+refers to the total number of subplot columns, and the final parameter denotes which subplot
+your variable is referencing (left-to-right, top-to-bottom). Each subplot is stored in a
+different variable (`axes1`, `axes2`, `axes3`). Once a subplot is created, the axes can
+be titled using the `set_xlabel()` command (or `set_ylabel()`).
+Here are our three plots side by side:
+
+~~~
+import numpy
+import matplotlib.pyplot
+
+data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
+
+fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
+
+axes1 = fig.add_subplot(1, 3, 1)
+axes2 = fig.add_subplot(1, 3, 2)
+axes3 = fig.add_subplot(1, 3, 3)
+
+axes1.set_ylabel('average')
+axes1.plot(numpy.mean(data, axis=0))
+
+axes2.set_ylabel('max')
+axes2.plot(numpy.max(data, axis=0))
+
+axes3.set_ylabel('min')
+axes3.plot(numpy.min(data, axis=0))
+
+fig.tight_layout()
+
+matplotlib.pyplot.show()
+~~~
+{: .language-python}
+
+![The Previous Plots as Subplots](../fig/inflammation-01-group-plot.svg)
+
+The [call]({{ page.root }}/reference/#function-call) to `loadtxt` reads our data,
+and the rest of the program tells the plotting library
+how large we want the figure to be,
+that we're creating three subplots,
+what to draw for each one,
+and that we want a tight layout.
+(If we leave out that call to `fig.tight_layout()`,
+the graphs will actually be squeezed together more closely.)
+
+
+> ## Plot Scaling
+>
+> Why do all of our plots stop just short of the upper end of our graph?
+>
+> > ## Solution
+> > Because matplotlib normally sets x and y axes limits to the min and max of our data
+> > (depending on data range)
+> {: .solution}
+>
+> If we want to change this, we can use the `set_ylim(min, max)` method of each 'axes',
+> for example:
+>
+> ~~~
+> axes3.set_ylim(0,6)
+> ~~~
+> {: .language-python}
+>
+> Update your plotting code to automatically set a more appropriate scale.
+> (Hint: you can make use of the `max` and `min` methods to help.)
+>
+> > ## Solution
+> > ~~~
+> > # One method
+> > axes3.set_ylabel('min')
+> > axes3.plot(numpy.min(data, axis=0))
+> > axes3.set_ylim(0,6)
+> > ~~~
+> > {: .language-python}
+> {: .solution}
+>
+> > ## Solution
+> > ~~~
+> > # A more automated approach
+> > min_data = numpy.min(data, axis=0)
+> > axes3.set_ylabel('min')
+> > axes3.plot(min_data)
+> > axes3.set_ylim(numpy.min(min_data), numpy.max(min_data) * 1.1)
+> > ~~~
+> > {: .language-python}
+> {: .solution}
+{: .challenge}
+
+> ## Drawing Straight Lines
+>
+> In the center and right subplots above, we expect all lines to look like step functions because
+> non-integer value are not realistic for the minimum and maximum values. However, you can see
+> that the lines are not always vertical or horizontal, and in particular the step function
+> in the subplot on the right looks slanted. Why is this?
+>
+> > ## Solution
+> > Because matplotlib interpolates (draws a straight line) between the points.
+> > One way to do avoid this is to use the Matplotlib `drawstyle` option:
+> >
+> > ~~~
+> > import numpy
+> > import matplotlib.pyplot
+> >
+> > data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
+> >
+> > fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
+> >
+> > axes1 = fig.add_subplot(1, 3, 1)
+> > axes2 = fig.add_subplot(1, 3, 2)
+> > axes3 = fig.add_subplot(1, 3, 3)
+> >
+> > axes1.set_ylabel('average')
+> > axes1.plot(numpy.mean(data, axis=0), drawstyle='steps-mid')
+> >
+> > axes2.set_ylabel('max')
+> > axes2.plot(numpy.max(data, axis=0), drawstyle='steps-mid')
+> >
+> > axes3.set_ylabel('min')
+> > axes3.plot(numpy.min(data, axis=0), drawstyle='steps-mid')
+> >
+> > fig.tight_layout()
+> >
+> > matplotlib.pyplot.show()
+> > ~~~
+> > {: .language-python}
+> ![Plot with step lines](../fig/inflammation-01-line-styles.svg)
+> {: .solution}
+{: .challenge}
+
+> ## Make Your Own Plot
+>
+> Create a plot showing the standard deviation (`numpy.std`)
+> of the inflammation data for each day across all patients.
+>
+> > ## Solution
+> > ~~~
+> > std_plot = matplotlib.pyplot.plot(numpy.std(data, axis=0))
+> > matplotlib.pyplot.show()
+> > ~~~
+> > {: .language-python}
+> {: .solution}
+{: .challenge}
+
+> ## Moving Plots Around
+>
+> Modify the program to display the three plots on top of one another
+> instead of side by side.
+>
+> > ## Solution
+> > ~~~
+> > import numpy
+> > import matplotlib.pyplot
+> >
+> > data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
+> >
+> > # change figsize (swap width and height)
+> > fig = matplotlib.pyplot.figure(figsize=(3.0, 10.0))
+> >
+> > # change add_subplot (swap first two parameters)
+> > axes1 = fig.add_subplot(3, 1, 1)
+> > axes2 = fig.add_subplot(3, 1, 2)
+> > axes3 = fig.add_subplot(3, 1, 3)
+> >
+> > axes1.set_ylabel('average')
+> > axes1.plot(numpy.mean(data, axis=0))
+> >
+> > axes2.set_ylabel('max')
+> > axes2.plot(numpy.max(data, axis=0))
+> >
+> > axes3.set_ylabel('min')
+> > axes3.plot(numpy.min(data, axis=0))
+> >
+> > fig.tight_layout()
+> >
+> > matplotlib.pyplot.show()
+> > ~~~
+> > {: .language-python}
+> {: .solution}
+{: .challenge}
+
+{% include links.md %}