CODEWITHSUNDEEP
pythonpandas
user5768866
user5768866

Reputation:

How to perform Correlation between two dataframes with different column names

I have a set of columns (col1,col2,col3) in dataframe df1 I have another set of columns (col4,col5,col6) in dataframe df2 Assume this two dataframes has the same number of rows.

How do I generate a correlation table that do pairwise correlation between df1 and df2?

the table will look like

    col1 col2 col3
col4 ..   ..   ..
col5 ..   ..   ..
col6 ..   ..   ..

I use df1.corrwith(df2), it does not seem to generate the table as required.

I have seen the answer at How to check correlation between matching columns of two data sets?, but the main difference is that the col names does not matched.

Upvotes: 9

Views: 12612

Answers (1)

piRSquared
piRSquared

Reputation: 294228

pandas quick and dirty 

pd.concat([df1, df2], axis=1, keys=['df1', 'df2']).corr().loc['df2', 'df1']

numpy clean 

def corr(df1, df2):
    n = len(df1)
    v1, v2 = df1.values, df2.values
    sums = np.multiply.outer(v2.sum(0), v1.sum(0))
    stds = np.multiply.outer(v2.std(0), v1.std(0))
    return pd.DataFrame((v2.T.dot(v1) - sums / n) / stds / n,
                        df2.columns, df1.columns)

corr(df1, df2)

example 

df1 = pd.DataFrame(np.random.rand(10, 4), columns=list('abcd'))

df2 = pd.DataFrame(np.random.rand(10, 3), columns=list('xyz'))

pd.concat([df1, df2], axis=1, keys=['df1', 'df2']).corr().loc['df2', 'df1']

          a         b         c         d
x  0.235624  0.844665 -0.647962  0.535562
y  0.357994  0.462007  0.205863  0.424568
z  0.688853  0.350318  0.132357  0.687038

corr(df1, df2)

          a         b         c         d
x  0.235624  0.844665 -0.647962  0.535562
y  0.357994  0.462007  0.205863  0.424568
z  0.688853  0.350318  0.132357  0.687038

Upvotes: 25

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