[0.24.0rc1] passing dtype='M8' to Index raises #24753
Description
In 0.23.4
In [1]: import pandas as pd In [2]: pd.Index([], dtype='M8') Out[2]: DatetimeIndex([], dtype='datetime64[ns]', freq=None)In 0.24.0rc1
In [2]: pd.Index([], dtype='M8')--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-2-09844a8ae29c> in <module> ----> 1 pd.Index([], dtype='M8') ~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexes/base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs) 306 else: 307 result = DatetimeIndex(data, copy=copy, name=name, --> 308 dtype=dtype, **kwargs) 309 return result 310 ~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in __new__(cls, data, freq, start, end, periods, tz, normalize, closed, ambiguous, dayfirst, yearfirst, dtype, copy, name, verify_integrity) 301 data, dtype=dtype, copy=copy, tz=tz, freq=freq, 302 dayfirst=dayfirst, yearfirst=yearfirst, ambiguous=ambiguous, --> 303 int_as_wall_time=True) 304 305 subarr = cls._simple_new(dtarr, name=name, ~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py in _from_sequence(cls, data, dtype, copy, tz, freq, dayfirst, yearfirst, ambiguous, int_as_wall_time) 366 data, dtype=dtype, copy=copy, tz=tz, 367 dayfirst=dayfirst, yearfirst=yearfirst, --> 368 ambiguous=ambiguous, int_as_wall_time=int_as_wall_time) 369 370 freq, freq_infer = dtl.validate_inferred_freq(freq, inferred_freq, ~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py in sequence_to_dt64ns(data, dtype, copy, tz, dayfirst, yearfirst, ambiguous, int_as_wall_time) 1704 inferred_freq = None 1705 -> 1706 dtype = _validate_dt64_dtype(dtype) 1707 1708 if not hasattr(data, "dtype"): ~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py in _validate_dt64_dtype(dtype) 1991 raise ValueError("Unexpected value for 'dtype': '{dtype}'. " 1992 "Must be 'datetime64[ns]' or DatetimeTZDtype'." -> 1993 .format(dtype=dtype)) 1994 return dtype 1995 ValueError: Unexpected value for 'dtype': 'datetime64'. Must be 'datetime64[ns]' or DatetimeTZDtype'.We want that to raise eventually; ns precision should be specified. But we should maybe deprecate the old behavior first?
The best place to do it is probably before we get to arrays, so in DatetimeIndex.__new__ we can check for M8 specifically, warn, then pass through M8[ns].
We should also check
np.dtype("M8")'m8'np.dtype('m8')
It's less clear what we should do for something like M8[us]. In the past, we used to ignore the precision
In [15]: pd.Index(list(pd.date_range('2000', periods=4).asi8), dtype='M8[us]') Out[15]: DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04'], dtype='datetime64[ns]', freq=None)this should arguably raise now...