A short introduction to how to read a CSV file and plot it with python and pylab.

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