Basic graphing with MatPlotLib

One of the Python modules that has most interested me recently is MatPlotLib which is a sophisticated graphing module which can be used to create journal grade graphs of almost anything.  The official gallery for MatPlotLib is worth checking out to get an idea of the sheer range of graph types it can be used to create.

It is simple enough to get started using MatPlotLib for example to create a line graph of x*x and save it as a PNG file requires only the following:

"""Simple demonstration of MatPlotLib plotting."""

from matplotlib import pyplot

X = range(0,100)
Y = [ i*i for i in X ]

pyplot.plot( X, Y, '-' )
pyplot.title( 'Plotting x*x' )
pyplot.xlabel( 'X Axis' )
pyplot.ylabel( 'Y Axis' )
pyplot.savefig( 'Simple.png' )
pyplot.show()

The above script will produce the following graph:

To plot data over a time period the simplest solution is to convert date/time units to timestamps using MatPlotLibs date2num function and then to plot using the plot_date method as follows:

"""Simple demonstration of MatPlotLib Date plotting."""

from matplotlib import pyplot
from matplotlib.dates import date2num
from datetime import datetime, timedelta

# Generate a series of timestamps from today to today + 100 years.
X = [date2num(datetime.today()+timedelta(days=365*x)) for x in range(0,100)]
Y = [i*i for i in range(0,100)]

pyplot.plot_date( X, Y, '-', xdate=True )
pyplot.title( 'Plotting x*x' )
pyplot.xlabel( 'X Axis' )
pyplot.ylabel( 'Y Axis' )
pyplot.savefig( 'SimpleDates.png' )
pyplot.show()

Which will generate a chart like the following:

As you can see it is fairly simple to graph data using MatPlotLib. This makes Python and MatPlotLib a compelling solution for data analysis when combined with the many available modules for dealing with common data storage formats like text (using RegEx), CSV, XML and JSON files and SQL databases.

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