CODEWITHSUNDEEP
pythonnumpypandascorrelation
mikkom
mikkom

Reputation: 3630

Pandas: How to drop self correlation from correlation matrix

I'm trying to find highest correlations for different columns with pandas. I know can get correlation matrix with

df.corr()

I know I can get the highest correlations after that with

df.sort() 
df.stack() 
df[-5:]

The problem is that these correlation also contain values for column with the column itself (1). How do I remove these columns that contain correlation with self? I know I can remove them by removing all 1 values but I don't want to do that as there might be actual 1 correlations too.

Upvotes: 18

Views: 14068

Answers (4)

Yasir
Yasir

Reputation: 1070

another solution would be a stack.

s = corr.stack(-1)
# remove where corr is 1 
s = s[s != 1]
# convert to matrix again
s.unstack()

or

corr.values[np.tril_indices_from(corr.values, k=0)] = np.nan

Upvotes: 0

Kermit
Kermit

Reputation: 5982

Fill them with NaN rather than a fake number

import numpy as np
np.fill_diagonal(corr_matrix.values, np.nan) # automatically inplace

NaN is supported by both seaborn and plotly correlation matrices

Upvotes: 2

mikkom
mikkom

Reputation: 3630

I recently found even cleaner answer to my question, you can compare multi-index levels by value.

This is what I ended using.

corr = df.corr().stack()
corr = corr[corr.index.get_level_values(0) != corr.index.get_level_values(1)]

Upvotes: 11

Ami Tavory
Ami Tavory

Reputation: 76297

Say you have

corrs = df.corr()

Then the problem is with the diagonal elements, IIUC. You can easily set them to some negative value, say -2 (which will necessarily be lower than all correlations) with

np.fill_diagonal(corrs.values, -2)

Example

(Many thanks to @Fabian Rost for the improvement & @jezrael for the DataFrame)

import numpy as np
df=pd.DataFrame( {
    'one':[0.1, .32, .2, 0.4, 0.8], 
    'two':[.23, .18, .56, .61, .12], 
    'three':[.9, .3, .6, .5, .3], 
    'four':[.34, .75, .91, .19, .21], 
    'zive': [0.1, .32, .2, 0.4, 0.8], 
    'six':[.9, .3, .6, .5, .3],
    'drive':[.9, .3, .6, .5, .3]})
corrs = df.corr()
np.fill_diagonal(corrs.values, -2)
>>> corrs
    drive   four    one six three   two zive
drive   -2.000000   -0.039607   -0.747365   1.000000    1.000000    0.238102    -0.747365
four    -0.039607   -2.000000   -0.489177   -0.039607   -0.039607   0.159583    -0.489177
one -0.747365   -0.489177   -2.000000   -0.747365   -0.747365   -0.351531   1.000000
six 1.000000    -0.039607   -0.747365   -2.000000   1.000000    0.238102    -0.747365
three   1.000000    -0.039607   -0.747365   1.000000    -2.000000   0.238102    -0.747365
two 0.238102    0.159583    -0.351531   0.238102    0.238102    -2.000000   -0.351531
zive    -0.747365   -0.489177   1.000000    -0.747365   -0.747365   -0.351531   -2.000000

Upvotes: 13

Related Questions