@HYRY's answer is perfect. Just building on that answer by adding a bit more logic to avoid duplicate and self correlations and proper sorting:
import pandas as pd
d = {'x1': [1, 4, 4, 5, 6],
'x2': [0, 0, 8, 2, 4],
'x3': [2, 8, 8, 10, 12],
'x4': [-1, -4, -4, -4, -5]}
df = pd.DataFrame(data = d)
print("Data Frame")
print(df)
print()
print("Correlation Matrix")
print(df.corr())
print()
def get_redundant_pairs(df):
'''Get diagonal and lower triangular pairs of correlation matrix'''
pairs_to_drop = set()
cols = df.columns
for i in range(0, df.shape[1]):
for j in range(0, i+1):
pairs_to_drop.add((cols[i], cols[j]))
return pairs_to_drop
def get_top_abs_correlations(df, n=5):
au_corr = df.corr().abs().unstack()
labels_to_drop = get_redundant_pairs(df)
au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False)
return au_corr[0:n]
print("Top Absolute Correlations")
print(get_top_abs_correlations(df, 3))
That gives the following output:
Data Frame
x1 x2 x3 x4
0 1 0 2 -1
1 4 0 8 -4
2 4 8 8 -4
3 5 2 10 -4
4 6 4 12 -5
Correlation Matrix
x1 x2 x3 x4
x1 1.000000 0.399298 1.000000 -0.969248
x2 0.399298 1.000000 0.399298 -0.472866
x3 1.000000 0.399298 1.000000 -0.969248
x4 -0.969248 -0.472866 -0.969248 1.000000
Top Absolute Correlations
x1 x3 1.000000
x3 x4 0.969248
x1 x4 0.969248
dtype: float64