Implement DatetimeIndex.round(), DatetimeIndex.floor(), and DatetimeIndex.ceil()

[xinrong-meng]

        >>> rng = ks.date_range('1/1/2018 11:59:00', periods=3, freq='min')

        >>> rng
        DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',
                       '2018-01-01 12:01:00'],
                      dtype='datetime64[ns]', freq=None)

        >>> rng.round("H")
        DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
                       '2018-01-01 12:00:00'],
                      dtype='datetime64[ns]', freq=None)

        >>> rng.floor("H")
        DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',
                       '2018-01-01 12:00:00'],
                      dtype='datetime64[ns]', freq=None)

        >>> rng.ceil('H')
        DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
                       '2018-01-01 13:00:00'],
                      dtype='datetime64[ns]', freq=None)

[codecov-io]

Codecov Report

Merging #2082 (0de8fe1) into master (9150ca4) will decrease coverage by 5.51%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master    #2082      +/-   ##
==========================================
- Coverage   95.27%   89.76%   -5.52%     
==========================================
  Files          57       57              
  Lines       13196    13107      -89     
==========================================
- Hits        12573    11765     -808     
- Misses        623     1342     +719     

Impacted Files	Coverage Δ
databricks/koalas/missing/indexes.py	100.00% <ø> (ø)
databricks/koalas/indexes/datetimes.py	96.58% <100.00%> (-1.52%)	⬇️
databricks/koalas/tests/indexes/test_datetime.py	96.66% <100.00%> (-3.34%)	⬇️
databricks/koalas/plot/plotly.py	15.78% <0.00%> (-81.06%)	⬇️
...bricks/koalas/tests/plot/test_frame_plot_plotly.py	23.33% <0.00%> (-76.67%)	⬇️
...ricks/koalas/tests/plot/test_series_plot_plotly.py	26.92% <0.00%> (-71.26%)	⬇️
databricks/koalas/usage_logging/__init__.py	27.58% <0.00%> (-64.92%)	⬇️
databricks/koalas/usage_logging/usage_logger.py	47.82% <0.00%> (-52.18%)	⬇️
databricks/koalas/typedef/typehints.py	65.92% <0.00%> (-28.50%)	⬇️
databricks/koalas/__init__.py	77.33% <0.00%> (-14.67%)	⬇️
... and 28 more

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[itholic]

Otherwise, LGTM.

[itholic]

Nit: floor -> ceil ?

[xinrong-meng]

Good catch! Thanks!

[ueshin]

LGTM.

[ueshin]

Thanks! merging.

[xinrong-meng]

Thank you!