import numpy as np
from pandas.core.dtypes.common import is_list_like
from pandas.core import common as com
def cartesian_product(X):
"""
Numpy version of itertools.product or pandas.compat.product.
Sometimes faster (for large inputs)...
Parameters
----------
X : list-like of list-likes
Returns
-------
product : list of ndarrays
Examples
--------
>>> cartesian_product([list('ABC'), [1, 2]])
[array(['A', 'A', 'B', 'B', 'C', 'C'], dtype='|S1'),
array([1, 2, 1, 2, 1, 2])]
See Also
--------
itertools.product : Cartesian product of input iterables. Equivalent to
nested for-loops.
pandas.compat.product : An alias for itertools.product.
"""
msg = "Input must be a list-like of list-likes"
if not is_list_like(X):
raise TypeError(msg)
for x in X:
if not is_list_like(x):
raise TypeError(msg)
if len(X) == 0:
return []
lenX = np.fromiter((len(x) for x in X), dtype=np.intp)
cumprodX = np.cumproduct(lenX)
a = np.roll(cumprodX, 1)
a[0] = 1
if cumprodX[-1] != 0:
b = cumprodX[-1] / cumprodX
else:
# if any factor is empty, the cartesian product is empty
b = np.zeros_like(cumprodX)
return [np.tile(np.repeat(np.asarray(com.values_from_object(x)), b[i]),
np.product(a[i]))
for i, x in enumerate(X)]