Compute co-occurrence matrix by counting values in cells

Question

I have a dataframe like this

df = pd.DataFrame({\'a\' : [1,1,0,0], \'b\': [0,1,1,0], \'c\': [0,0,1,1]})

I want to get

  a         


        

Answer 1

np.einsum

Not as pretty as df.T.dot(df) but how often do you see np.einsum amirite?

pd.DataFrame(np.einsum('ij,ik->jk', df, df), df.columns, df.columns)

   a  b  c
a  2  1  0
b  1  2  1
c  0  1  2

Answer 2

Numpy matmul

np.matmul(df.values.T,df.values)
Out[87]: 
array([[2, 1, 0],
       [1, 2, 1],
       [0, 1, 2]], dtype=int64)

#pd.DataFrame(np.matmul(df.values.T,df.values), df.columns, df.columns)

Answer 3

You can do a multiplication using @ operator for numpy arrays.

df = pd.DataFrame(df.values.T @ df.values, df.columns, df.columns)

Answer 4

You appear to want the matrix product, so leverage DataFrame.dot:

df.T.dot(df)
   a  b  c
a  2  1  0
b  1  2  1
c  0  1  2

---

Alternatively, if you want the same level of performance without the overhead of pandas, you could compute the product with np.dot:

v = df.values
pd.DataFrame(v.T.dot(v), index=df.columns, columns=df.columns)

Or, if you want to get cute,

(lambda a, c: pd.DataFrame(a.T.dot(a), c, c))(df.values, df.columns)

   a  b  c
a  2  1  0
b  1  2  1
c  0  1  2

—piRSquared