FM / reshape

/usr/local/lib64/python3.6/site-packages/pandas/core/reshape/

/usr/local/lib64/python3.6/site-packages/pandas/core/reshape

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
api.py	365	0644	edit dl rm
concat.py	22218	0644	edit dl rm
melt.py	16531	0644	edit dl rm
merge.py	73623	0644	edit dl rm
pivot.py	23614	0644	edit dl rm
reshape.py	34649	0644	edit dl rm
tile.py	20964	0644	edit dl rm
util.py	1562	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pandas/core/reshape/util.py (1562B)

import numpy as np

from pandas.core.dtypes.common import is_list_like

def cartesian_product(X):
    """
    Numpy version of itertools.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='<U1'), array([1, 2, 1, 2, 1, 2])]

See Also
    --------
    itertools.product : Cartesian product of input iterables.  Equivalent to
        nested for-loops.
    """
    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 [_tile_compat(np.repeat(x, b[i]), np.product(a[i])) for i, x in enumerate(X)]

def _tile_compat(arr, num: int):
    """
    Index compat for np.tile.

Notes
    -----
    Does not support multi-dimensional `num`.
    """
    if isinstance(arr, np.ndarray):
        return np.tile(arr, num)

# Otherwise we have an Index
    taker = np.tile(np.arange(len(arr)), num)
    return arr.take(taker)

import numpy as np from pandas.core.dtypes.common import is_list_like def cartesian_product(X): """ Numpy version of itertools.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='