Presentation I threw together for my lab mates as a practical introduction to scientific data plotting with Python.

1. Python plotting for lab folk
Only the stuff you need to know to
make publishable figures of your
data. For all else: ask Sourish

2. Overview
• Introductory stuff
• A simple time series plot
• Plots with multiple panes and axes
• A Keeling plot
• Scatterplots and maps
• Functions, modules and classes

3. What is Python?
• Python is an general-purpose high-level programming
language
• Many, many things are done with Python
• There are many libraries available with modules for
specialized tasks. Like for scientific data plotting...
A general tutorial to Python is available on
http://docs.python.org/tutorial/. Very useful!
This one also seems useful:
http://www.openbookproject.net/thinkcs/python/english
2e/

4. Packages for plotting data
• Matplotlib: for plotting (
http://matplotlib.sourceforge.net/, you’ll
need this webpage often... )
• Numpy: for scientific computing
(http://www.scipy.org/Tentative_NumPy_Tuto
rial )
These libraries/packages are combined in the
pylab package.

5. Starting up
I usually open a terminal, and give the command
“ipython –pylab”. Then this appears:
This may work slightly differently on your computer.

6. Starting up
Now I can give commands, or run some script
that I have on my computer somewhere.

7. Scripts
Scripts are text files with extension ‘.py’ that
contain Python commands. You can edit them in
Komodo or any other text editor that you find
convenient.
In principle, you could build your figure by
typing all your commands in the terminal, but
that is really tedious...
So from now on, I’ll assume that you want a
script that draws your figure.

8. Overview
• Introductory stuff
• A simple time series plot
• Plots with multiple panes
• A Keeling plot
• Scatterplots and maps
• Functions, modules and classes

9. A simple time series plot
In Excel:

10. A simple timeseries plot (step 1)
Save your data in a “clean” Windows Comma Separated
Value (.csv) file (other text formats are also
possible, but this usually works best).

11. A simple timeseries plot (step 2)
Start scripting! First, load the useful packages. Maybe
set some default settings for the graphics as well.
There are different ways to import functions

12. A simple timeseries plot (step 3)
Read the data from the file and get them into a
tidy nested list.
Object-oriented way of calling a function
“List comprehensions”: typical for Python and essentially a
way to write a list-creating loop very compactly

13. A simple timeseries plot (step 4)
Set up the figure, get the values you want to
plot in lists, and plot.
For use in the legend
Formatting string: specifies blue (b)
lines (-) with square (s) markers

14. A simple timeseries plot (first result)

15. A simple timeseries plot (step 5)
Format the axes and embellish your plot with
titles, axis labels, legends, annotations. Save.

16. A simple timeseries plot (end result)

17. Errorbar plot
Suppose you want to have errorbars in your plot that are 2%
of the values. Then you can replace the plot command:
With this command that uses the errorbar function:

18. Errorbar plot

19. Overview
• Introductory stuff
• A simple time series plot
• Plots with multiple panes and axes
• A Keeling plot
• Scatterplots and maps
• Functions, modules and classes

20. Plot with two y-axes
You can make a plot with two y-axes with the twinx() command:

21. Plot with two y-axes

22. Multipane plots
The simplest way to define subplots is with the subplot() or
fig.add_subplot() commands. In the brackets should be the desired
number of rows, columns and the number of the figure.

23. Multipane plots

24. Multipane plots
The distance between the subplots is adjustable, also
to 0. The NullFormatter() can be used to remove the
axis ticklabels. Overlapping ticklabels can be removed.

25. Multipane plots

26. Multipane plots
Even more customizable subplots can be made with
add_axes(), should you want it.

27. Multipane plots

28. Overview
• Introductory stuff
• A simple time series plot
• Plots with multiple panes and axes
• A Keeling plot
• Scatterplots and maps
• Functions, modules and classes

29. A Keeling plot
Python offers more possibilities than Excel for
customized fits to data. There are
scipy.stats.linregress() and
scipy.optimize.curvefit(), but you can also write
your own routines.
I often use a home-made bivariate fit module
based on Cantrell (2008) to fit straight lines to
data with errors in x and y, like in Keeling plots.

30. A Keeling plot

31. Overview
• Introductory stuff
• A simple time series plot
• Plots with multiple panes and axes
• A Keeling plot
• Scatterplots and maps
• Functions, modules and classes

32. Scatter plots
Of all the other plot possibilities that matplotlib offers, I
find the scatter plots quite useful.
In scatter plots, marker color and/or size can depend on
a third variable.

33. Maps
Maps can be made with the basemap package

34. Scatter plots and maps
Maps can be combined with other things, like
plot(), errorbar() and scatter().

35. Overview
• Introductory stuff
• A simple time series plot
• Plots with multiple panes and axes
• A Keeling plot
• Scatterplots and maps
• Functions, modules and classes

36. Functions
When your script gets longer, it can be a good
idea to group some statements into functions.
“def” starts The function needs
function definition this argument
Optional
argument
Body: what the
Return value function does
Calls to the function

37. Modules
Function definitions can be grouped into a file
and then imported into a script (or
interactively). Such a file with definitions is
called a module.
The bivariate fit module that was imported to
the Keeling plot script is an example.
If you’re changing your module while running
your script, you may have to use the reload()
command.

38. Classes
Almost everything in Python is an object of some class or other.
Object classes have “methods” associated with them that can
work on those objects.
You can define your own object classes and methods.
Method: definition of a function that can work
on your object
Another method