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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Praise for Python

• Simplified Memory management
  I am so much more productive when I am not having to worry about pointer related errors e.g. pointer math or sweat the subtleties of memory management e.g. memory alignment while writing code.
• Less structural syntax
  After using Python for a while I really appreciate it’s use of indentation to give a program structure, as it makes python source code much more concise than C/C++.
• No compiling or linking
  It is so much easier to stay in the flow when your not waiting 5-30 minutes for compilation and linking.  I’ve recently taken to running PyLint when I miss the feedback from a compiler/linker on my program structure and to learn the coding style outlined in the Python Style Guide.
• Selective imports
  Having worked on large scale C/C++ projects for most of my career I really appreciate the ability to only import what I want from modules and the option to also rename (or alias) what I’ve imported.
• Batteries included philosophy
  The sheer scope of the library of modules included in Python means I can spend more time writing the interesting parts of my programs, as most of the time the utility functionality I need is just an import away.
• Package management
  The Python Package Index (PyPi) and Setup Tools module make installing most python modules as simple as ‘easy_install <module_name>’.
• Duck typing
  Python’s use of Duck Typing emphasizes interfaces over types which makes it so much easier to supply my own classes to standard library functions, as I only have to implement as much of the interface as is required.

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