[0.24.0rc1] passing dtype='M8' to Index raises

[TomAugspurger]

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...