ERR: invalid error reporting when comparing vs. None

The "in" function does not work as expected in regard to a data frame. Let's say we have the following data frame :

df = pd.DataFrame(np.random.randn(6,4), columns=list('ABCD'))

We can test this data frame :

df is None
False

However we cannot test its presence in a list :

df in [None]
TypeError: Could not compare [None] with block values

We would expect to get just False in this case.

(Pandas version 0.17.1)

[jreback]

This is coming from this, while useful (should simply be using df.isnull()) to handle all dtypes.

In [3]: df==None
TypeError: Could not compare [None] with block values

is a bug and causes __nonzero__ to fail I think.

This is an inherently ambiguous operation, see docs here

In [22]: df['A'] in [None]
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().