Easy vectorized parallel plots for multiple data sets

I will share a very quick and straight-forward solution to generate parallel plots in python of multiple groups of data.   The idea is transitioning from the parallel axis plot tool  to a method that enables  the plots to be exported as a vectorized image.   You can also take a look at Matt’s python parallel.py code available in github: https://github.com/matthewjwoodruff/parallel.py .

This is the type of figure that you will get:

parallel3.png

The previous figure was generated with the following lines of code:

import numpy as np
import pandas as pd
from pandas.tools.plotting import parallel_coordinates
import matplotlib.pyplot as plt
import seaborn

data = pd.read_csv('sample_data.csv')

parallel_coordinates(data,'Name', color= ['#225ea8','#7fcdbb','#1d91c0'], linewidth=5, alpha=.8)
plt.ylabel('Direction of Preference $\\rightarrow$', fontsize=12)

plt.savefig('parallel_plot.svg')

Lines 1-4 are the required libraries.  I just threw in the seaborn library to give it the gray background but it is not necessary.  In the parallel_coordinates function, you need to specify the data, ‘Name’ and the color of the different groups.  You can substitute the color  variable for colormap and specify the colormap that you wish to use (e.g. colormap=’YlGnBu’).   I also specified an alpha for transparency to see overlapping lines. If you want to learn more, you can take a look at the parallel_coordinates source code.  I found this stack overflow link very useful,  it shows some examples on editing the source code to enable other capabilities.

Finally, the following snippet shows the format of the input data (the sample_data.csv  file that is read in line 7 ) :

sample_data.png

Columns A-G the different categories to be plotted are specified (e.g. the objectives of a problem) and in Column H the names of the different data groups are specified.  And there you have it, I hope you find this plotting alternative useful.

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3 thoughts on “Easy vectorized parallel plots for multiple data sets

  1. Pingback: Water Programming Blog Guide (Part I) – Water Programming: A Collaborative Research Blog

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