Constructing a co-occurrence matrix in python pandas

I know how to do this in R. But, is there any function in pandas that transforms a dataframe to an nxn co-occurrence matrix containing the counts of two aspects co-occurring.

For example a matrix df:

import pandas as pd

df = pd.DataFrame({'TFD' : ['AA', 'SL', 'BB', 'D0', 'Dk', 'FF'],
                    'Snack' : ['1', '0', '1', '1', '0', '0'],
                    'Trans' : ['1', '1', '1', '0', '0', '1'],
                    'Dop' : ['1', '0', '1', '0', '1', '1']}).set_index('TFD')

print df

>>> 
    Dop Snack Trans
TFD                
AA    1     1     1
SL    0     0     1
BB    1     1     1
D0    0     1     0
Dk    1     0     0
FF    1     0     1

[6 rows x 3 columns]

would yield:

    Dop Snack Trans

Dop   0     2     3
Snack 2     0     2
Trans 3     2     0

Since the matrix is mirrored on the diagonal I guess there would be a way to optimize code.

Answer

It’s a simple linear algebra, you multiply matrix with its transpose (your example contains strings, don’t forget to convert them to integer):

>>> df_asint = df.astype(int)
>>> coocc = df_asint.T.dot(df_asint)
>>> coocc
       Dop  Snack  Trans
Dop      4      2      3
Snack    2      3      2
Trans    3      2      4

if, as in R answer, you want to reset diagonal, you can use numpy’s fill_diagonal:

>>> import numpy as np
>>> np.fill_diagonal(coocc.values, 0)
>>> coocc
       Dop  Snack  Trans
Dop      0      2      3
Snack    2      0      2
Trans    3      2      0