This document discusses plotting data with Python and Pylab. It begins by describing a sample data table and the problem of reading and plotting the data. It then reviews options for plotting in Python like Pylab, Enthought, RPy, and Sage. The remainder of the document demonstrates how to use Pylab to read CSV data, and create bar charts, pie charts, line plots, and histograms of the sample data.

1. Barcelona Python Meetup
Plotting data with python and
pylab
Giovanni M. Dall'Olio

2. Problem statement
 Let's say we have a table of data like this:
name country apples pears
Giovanni Italy 31 13
Mario Italy 23 33
Luigi Italy 0 5
Margaret England 22 13
Albert Germany 15 6
 How to read it in python?
 How to do some basic plotting?

3. Alternatives for plotting
data in python
 Pylab (enthought)→ Matlab/Octave approach
 Enthought → extended version of Pylab (free for
academic use)
 rpy/rpy2 → allows to run R commands within
python
 Sage → interfaces python with Matlab, R, octave,
mathematica, ...

4. The Pylab system
 pylab is a system of three libraries, which together
transform python in a Matlab­like environment
 It is composed by:
 Numpy (arrays, matrices, complex numbers, etc.. in
python)
 Scipy (extended scientific/statistics functions)
 Matplotlib (plotting library)
 iPython (extended interactive interpreter)

5. How to install pylab
 There are many alternatives to install PyLab:
 use the package manager of your linux distro
 use enthought's distribution (
http://www.enthought.com/products/epd.php) (free
for academic use)
 compile and google for help!
 Numpy and scipy contains some Fortran libraries,
therefore easy_install doesn't work well with
them

6. ipython -pylab
 Ipython is an extended version of the standard
python interpreter
 It has a modality especially designed for pylab
 The standard python interpreter doesn't support
very well plotting (not multi­threading)
 So if you want an interactive interpreter, use
ipython with the pylab option:
$: alias pylab=”ipython -pylab”
$: pylab
In [1]:

7. Why the python interpreter
is not the best for plotting
Gets blocked when you create a plot

8. How to read a CSV file with
python
 To read a file like this in pylab:
name country apples pears
Giovanni Italy 31 13
Mario Italy 23 33
Luigi Italy 0 5
Margaret England 22 13
Albert Germany 15 6
 → Use the function 'matplotlib.mlab.csv2rec'
>>> data = csv2rec('exampledata.txt',
delimiter='t')

9. Numpy - record arrays
 csv2rec stores data in a numpy recarray object, where
you can access columns and rows easily:
>>> print data['name']
['Giovanni' 'Mario' 'Luigi' 'Margaret'
'Albert']
>>> data['apples']
array([31, 23, 0, 22, 15])
>>> data[1]
('Mario', 'Italy', 23, 33)

10. Alternative to csv2rec
 numpy.genfromtxt (new in 2009)
 More options than csv2rec, included in numpy
 Tricky default parameters: need to specify dtype=None
>>> data = numpy.genfromtxt('datafile.txt',
dtype=None)
>>> data
array....

11. Barchart
>>> data = csv2rec('exampledata.txt', delimiter='t')
>>> bar(arange(len(data)), data['apples'], color='red',
width=0.1, label='apples')
>>> bar(arange(len(data))+0.1, data['pears'],
color='blue', width=0.1, label='pears')
>>> xticks(range(len(data)), data['name'], )
>>> legend()
>>> grid('.')

12. Barchart
>>> data = csv2rec('exampledata.txt',
delimiter='t')
>>> figure()
>>> clf()
Read a CSV file and storing
it in a recordarray object
Use figure() and cls() to
reset the graphic device

13. Barchart
>>> data = csv2rec('exampledata.txt',
delimiter='t')
>>> bar(x=arange(len(data)), y=data['apples'],
color='red', width=0.1, label='apples')
 The bar function creates a
barchart

14. Barchart
>>> data = csv2rec('exampledata.txt',
delimiter='t')
>>> bar(x=arange(len(data)), y=data['apples'],
color='red', width=0.1, label='apples')
>>> bar(arange(len(data))+0.1, data['pears'],
color='blue', width=0.1, label='pears')
 This is the second barchart

15. Barchart
>>> data = csv2rec('exampledata.txt',
delimiter='t')
>>> bar(x=arange(len(data)), y=data['apples'],
color='red', width=0.1, label='apples')
>>> bar(arange(len(data))+0.1, data['pears'],
color='blue', width=0.1, label='pears')
>>> xticks(range(len(data)), data['name'], )
 Re­defining the labels in the X axis
(xticks)

16. Barchart
>>> data = csv2rec('exampledata.txt',
delimiter='t')
>>> bar(x=arange(len(data)), y=data['apples'],
color='red', width=0.1, label='apples')
>>> bar(arange(len(data))+0.1, data['pears'],
color='blue', width=0.1, label='pears')
>>> xticks(range(len(data)), data['name'], )
>>> legend()
>>> grid('.')
>>> title('apples and pears by person')
 Adding legend, grid, title

17. Barchart (result)

18. Pie Chart
>>> pie(data['pears'], labels=data['name'])
>>> pie(data['pears'], labels=['%sn(%s
pears)' % (i,j) for (i, j) in
zip(data['name'], data['pears'])] )

19. Pie chart (result)

20. A plot chart
>>> x = linspace(1,10, 10)
>>> y = randn(10)
>>> plot(x,y, 'r.', ms=15)

21. An histogram
>>> x = randn(1000)
>>> hist(x, bins=40)
>>> title('histogram of random numbers')

22. Matplotlib gallery

23. Scipy Cookbook

24. Thanks for the attention!!
 PyLab ­ http://www.scipy.org/PyLab
 matplotlib ­ http://matplotlib.sourceforge.net/
 scipy ­ http://www.scipy.org/
 numpy ­ http://numpy.scipy.org/
 ipython ­ http://ipython.scipy.org/moin/
 These slides: http://bioinfoblog.it