Plotting data with python and pylab
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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.
![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]:](https://image.slidesharecdn.com/plottingpylab-091014151337-phpapp02/85/Plotting-data-with-python-and-pylab-6-320.jpg)
![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)](https://image.slidesharecdn.com/plottingpylab-091014151337-phpapp02/85/Plotting-data-with-python-and-pylab-9-320.jpg)
![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('.')](https://image.slidesharecdn.com/plottingpylab-091014151337-phpapp02/85/Plotting-data-with-python-and-pylab-11-320.jpg)
![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](https://image.slidesharecdn.com/plottingpylab-091014151337-phpapp02/85/Plotting-data-with-python-and-pylab-13-320.jpg)
![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](https://image.slidesharecdn.com/plottingpylab-091014151337-phpapp02/85/Plotting-data-with-python-and-pylab-14-320.jpg)
![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)](https://image.slidesharecdn.com/plottingpylab-091014151337-phpapp02/85/Plotting-data-with-python-and-pylab-15-320.jpg)
![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](https://image.slidesharecdn.com/plottingpylab-091014151337-phpapp02/85/Plotting-data-with-python-and-pylab-16-320.jpg)
![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'])] )](https://image.slidesharecdn.com/plottingpylab-091014151337-phpapp02/85/Plotting-data-with-python-and-pylab-18-320.jpg)
Plotting data 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
- 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'])] )
- 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')
- 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