Pandas .any() vs. Python any() on Dataframe

Question

What is the reason to prefer Pandas implementation of .any() instead of Python's builtin any() when used on a DataFrame? Is there a performance reason to this, since Pandas DataFrames are column-major? My hunch is perhaps the Pandas method is implemented in such a way that it is faster for column-based reads, in expectation. Can anyone confirm?

Why this:

if df.any():

instead of this:

if any(df):

Answer 1

Correct me if I am wrong:

1. Performance: the methods of Pandas are highly optimized for operating on Pandas objects (C-level speed optimization).
2. Handling NaN values: NaN values are treated as False.
3. Axis specification: you can specify the axis along which to perform the method on.
4. Different behavior: any(df) checks the truthiness of the columns themselves, not the individual values within the DataFrame.
5. Output is still a Series (or DF).
6. It ensures more consistency when working within the Pandas framework.

Answer 2

These do two completely different things, so you cannot compare them directly. This is easily verifiable:

In [2]: import numpy as np, pandas as pd

In [3]: df = pd.DataFrame(data=np.random.randint(0,2, size=(10,3)), columns=('a','b','c'))

In [4]: df
Out[4]:
   a  b  c
0  0  1  0
1  1  1  1
2  1  1  0
3  1  1  1
4  0  1  1
5  0  0  0
6  1  0  1
7  0  0  0
8  1  1  0
9  1  0  1

In [5]: df.any()
Out[5]:
a    True
b    True
c    True
dtype: bool

In [6]: any(df)
Out[6]: True

pandas.DataFrame.any is a method that does an "or" reduction operation across some dimension (by default, the 0th axis) which results in some pandas.Series object. In contrast, the built-in any takes an iterable, and does this reduction on an iterable. The result is always a bool object. When you iterate over a pandas dataframe, you iterate over the columns. So for the above df, the operation any(df) is equivalent to:

In [8]: list(df)
Out[8]: ['a', 'b', 'c']

In [9]: any(['a', 'b', 'c'])
Out[9]: True

Again, note, you can choose the axis for the .any method, like most methods in pandas:

In [10]: df.any(axis=1)
Out[10]:
0     True
1     True
2     True
3     True
4     True
5    False
6     True
7    False
8     True
9     True
dtype: bool

Note, if you worked with a pd.Series, which iterates over the values in the series, the operation would be (almost) the same:

In [12]: any(df['a'])
Out[12]: True

In [13]: all(df['a'])
Out[13]: False

In [14]: df['a'].any()
Out[14]: True

In [15]: df['a'].all()
Out[15]: False

Barring how in vanilla Python, float('nan') is treated as truthy, whereas in pandas by default, are skipped.

However, you should use the pandas methods for pandas data structures, because they are heavily optimized.