Discussion: input and output argument type casting about sweatpy HOT 2 OPEN

I have myself second thoughts about supporting all array-like inputs - it makes type transformation very opaque for the user. To put it differently, I'm convinced now that converting lists to ndarrays and back should be a user's headache. If we can design a decorator for it - it would be nice, but not a must.

More interesting question - could we make the ndarray as a default input type, but than still design all algorithms/ function to work directly with pd.Series as well? Basically, repeat what NumPy does already.

from sweatpy.

pandas.Series is in a way a wrapper around numpy.ndarray so my expectation is that in most cases it will just work. E.g.

>>> import numpy as np
>>> import pandas as pd
>>> a = pd.Series([1, 2, 3, 4])
>>> np.power(a, 2)
0     1
1     4
2     9
3    16
dtype: int64
>>>

I want to prevent if-statements in the algorithms like:

if isinstance(power, pd.Series):
    # do this
else:
    # do that

from sweatpy.