I'm struggling because while I want to show the interrelationship of correlation between my fields, I realize that trying to plot nodes in terms of distance away from each other based on correlation will lead to impossibilities such as a case where A and B are 1 unit apart, B and C are 1 unit apart, but C and A are say, 5 units apart, there is no way to represent this on a 2 dimensional plane.

I simply want to create a visualization that generally clusters things with high correlation together, and moves things that are anti-correlated apart. So far, the closest I've gotten is using python networkx:

import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt

G = nx.Graph()

for ii in range(len(links_filtered)):
    a = data['var1'][ii]
    b = data['var2'][ii]
    c = data['value'][ii]

elarge = [(u,v) for (u,v,d) in G.edges(data=True) if d['weight']>0]
esmall = [(u,v) for (u,v,d) in G.edges(data=True) if d['weight']<=0]

pos = nx.spring_layout(G,k=.2,iterations=10000)

# nx.draw_networkx_labels(G,pos,font_size=8,)

for k,v in pos.iteritems():
    x,y = pos[k]

However, the result is generally ugly, and doesn't actually ensure that clusters that are not correlated are not in close proximity, since it is only drawing edges, not actually calcing a distance between nodes.enter image description here


closed as unclear what you're asking by mkt, Peter Flom Jun 21 at 12:39

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  • 1
    $\begingroup$ I see a few off-topic close votes, but I think there is an interesting data viz question here if you take away the Python aspect: is there a good way to visualize correlation as a node graph? $\endgroup$ – xan Jan 19 '18 at 16:21
  • $\begingroup$ This is too subjective to opinion, I suggest narrowing down the question a bit further. $\endgroup$ – Firebug Feb 18 '18 at 15:29