I have a dataset with 24 variables, 21 of them numeric. As part of model building I decided to look into the correlation between features and so what I get is a large correlation matrix (21 * 21).
Now visualising such large matrices becomes a very messy task and you end up hurting your eyes. So what I have done is set a threshold and to slice out those rows that have greater than this value (say 0.60). However, I'm getting a matrix that has now several NaNs. When I try to drop these null values, the matrix loses all data and what I'm left is a 0*0 matrix.
corr_matrix = data.corr() threshold = 0.60 high_corr = corr_matrix.loc[corr_matrix >= 0.60] high_corr.dropna(inplace=True) print(high_corr) Empty DataFrame Columns = 
Visualising the matrix with nans is a good idea but it also results in empty squares. I'm looking for a way where only those rows that have values >= threshold are retained, with no nans. That would make a much smaller matrix which is much less messier when plotted in matplotlib. However I haven't been able to code it that way; can anyone suggest some strategies to deal with such large matrices?