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.
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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