np.maximum.reduce(lst) has different behavior from functools.reduce(np.maximum, lst) and also different behavior from np.maximum itself, when one of the elements in the list is a number (e.g. int) instead of an array/pandas Series.

np.maximum.reduce

on one hand,

import pandas as pd
import numpy as np

df = pd.DataFrame({'a': [1, 3, 2]})

np.maximum.reduce([df['a'], 2])

The last line gives the following error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

np.maximum

on the other hand,

np.maximum(df['a'], 2)

yields the expected output

0    2
1    3
2    2
Name: a, dtype: int64

reduce(np.maximum)

on a third hand,

reduce(np.maximum, [df['a'], 2])

also yields the expected output

0    2
1    3
2    2
Name: a, dtype: int64

Versions used

pandas version: 1.2.5
numpy version: 1.19.5
python: 3.7.9

>Solution :

See the docs for ufunc.reduce

.reduce(array, axis=0, dtype=None, out=None, keepdims=False, initial=<no value>, where=True)

Reduces array’s dimension by one, by applying ufunc along one axis.

[df['a'], 2] is not an array with a well-defined 0th axis. I’m not sure how this could work? The other operations are clear element-wise max operations which will operate on each argument after broadcasting against each other but numpy ufunc reduction operates on a single array.