pandas (original) (raw)

This deprecates the current behvior when converting tz-aware Series or Index to an ndarray. Previously, we converted to M8[ns], throwing away the timezone information. In the future, we will return an object-dtype array filled with Timestamps, each of which has the correct tz.

In [1]: import pandas as pd; import numpy as np

In [2]: ser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))

In [3]: np.asarray(ser)
/bin/ipython:1: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.
        To accept the future behavior, pass 'dtype=object'.
        To keep the old behavior, pass 'dtype="datetime64[ns]"'.
  #!/Users/taugspurger/Envs/pandas-dev/bin/python3
Out[3]:
array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00.000000000'],
      dtype='datetime64[ns]')

xref pandas-dev#23569