List Highest Correlation Pairs from a Large Correlation Matrix in Pandas?

Question

How do you find the top correlations in a correlation matrix with Pandas? There are many answers on how to do this with R (Show correlations as an ordered list, not as a lar

Answer 1

I didn't want to unstack or over-complicate this issue, since I just wanted to drop some highly correlated features as part of a feature selection phase.

So I ended up with the following simplified solution:

# map features to their absolute correlation values
corr = features.corr().abs()

# set equality (self correlation) as zero
corr[corr == 1] = 0

# of each feature, find the max correlation
# and sort the resulting array in ascending order
corr_cols = corr.max().sort_values(ascending=False)

# display the highly correlated features
display(corr_cols[corr_cols > 0.8])

In this case, if you want to drop correlated features, you may map through the filtered corr_cols array and remove the odd-indexed (or even-indexed) ones.