import numpy as np

def xtab(*cols):

'''

# References : https://gist.github.com/alexland/d6d64d3f634895b9dc8e

returns:

(i) xt, NumPy array storing the xtab results, number of dimensions is equal to

the len(args) passed in

(ii) unique_vals_all_cols, a tuple of 1D NumPy array for each dimension

in xt (for a 2D xtab, the tuple comprises the row and column headers)

pass in:

(i) 1 or more 1D NumPy arrays of integers

(ii) if wts is True, then the last array in cols is an array of weights

if return_inverse=True, then np.unique also returns an integer index

(from 0, & of same len as array passed in) such that, uniq_vals[idx] gives the original array passed in

higher dimensional cross tabulations are supported (eg, 2D & 3D)

cross tabulation on two variables (columns):

>>> q1 = np.array([7, 8, 8, 8, 5, 6, 4, 6, 6, 8, 4, 6, 6, 6, 6, 8, 8, 5, 8, 6])

>>> q2 = np.array([6, 4, 6, 4, 8, 8, 4, 8, 7, 4, 4, 8, 8, 7, 5, 4, 8, 4, 4, 4])

>>> uv, xt = xtab(q1, q2)

>>> uv

(array([4, 5, 6, 7, 8]), array([4, 5, 6, 7, 8]))

>>> xt

array([[2, 0, 0, 0, 0],

[1, 0, 0, 0, 1],

[1, 1, 0, 2, 4],

[0, 0, 1, 0, 0],

[5, 0, 1, 0, 1]], dtype=uint64)

'''

apply_wt=False

if not all(len(col) == len(cols[0]) for col in cols[1:]):

raise ValueError("all arguments must be same size")

if len(cols) == 0:

raise TypeError("xtab() requires at least one argument")

fnx1 = lambda q: len(q.squeeze().shape)

if not all([fnx1(col) == 1 for col in cols]):

raise ValueError("all input arrays must be 1D")

if apply_wt:

cols, wt = cols[:-1], cols[-1]

else:

wt = 1

uniq_vals_all_cols, idx = zip( *(np.unique(col, return_inverse=True) for col in cols) )

shape_xt = [uniq_vals_col.size for uniq_vals_col in uniq_vals_all_cols]

dtype_xt = 'float' if apply_wt else 'uint'

xt = np.zeros(shape_xt, dtype=dtype_xt)

np.add.at(xt, idx, wt)

return uniq_vals_all_cols, xt

if __name__ == "__main__":

q1 = np.array([2,3,3,4])

q2 = np.array([2,2,3,4])

uv, xt = xtab(q1, q2)

print(q1)

print(q2)

print(uv)

print(xt)