Pandas Datetimeindex Indexing Dtype: Datetime64 Vs Timestamp

Indexing a pandas DatetimeIndex (with dtype numpy datetime64[ns]) returns either: another DatetimeIndex for multiple indices a pandas Timestamp for single index The confusing par

Solution 1:

You are using numpy functions to manipulate pandas types. They are not always compatible.

The function np.in1d first converts its both arguments to ndarrays. A DatetimeIndex has a built-in conversion and an array of dtype np.datetime64 is returned (it's DatetimIndex.values). But a Timestamp doesn't have such a facility and it's not converted.

Instead, you can use for example a python keyword in (the most natural way):

a_datetimeindex[0] in a_datetimeindex

or an Index.isin method for a collection of elements

a_datetimeindex.isin(a_list_or_index)

If you want to use np.in1d, explicitly convert both arguments to numpy types. Or call it on the underlying numpy arrays:

np.in1d(a_datetimeindex.values[0], a_datetimeindex.values)

Alternatively, it's probably safe to use np.in1d with two collections of the same type:

np.in1d(a_datetimeindex, another_datetimeindex)

or even

np.in1d(a_datetimeindex[[0]], a_datetimeindex)