Support freq in DatetimeIndex
#14593
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| @@ -2142,6 +2141,8 @@ def __init__( | |||
| if yearfirst is not False: | |||
| raise NotImplementedError("yearfirst == True is not yet supported") | |||
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| self._freq = _validate_freq(freq) | |||
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While looking on adding freq support before, I found some APIs manipulate freq(to new values) and return new results. (I vaguely remember..but I think that happens in binops?) Should we add a TODO comment here that this is not fully functional yet and freq support needs to be added in rest of the code-base?
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Yes, although maybe the default behaviour could be for DatetimeIndex to infer freq from its values. Then this should just work.
Also, we should probably only do that in compatibility mode for perf reasons.
…hwina/cudf into support-freq-in-datetime-index
Co-authored-by: Bradley Dice <[email protected]>
galipremsagar
added
improvement
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| ), | ||
| reason="Nanosecond offsets being dropped by pandas, which is " |
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Is this better solved by fixing the condition on the parameter, which should be "pandas < 2.0"?
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I wanted to do that but it happens only for a few parameter combinations and we currently xpass/xfail strictly. That's the reason for the current approach.
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I know we have two diverging approaches at the same place but I plan on dropping these in pandas-2.0 feature branch.
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Okay. We can clean it up later.
| @@ -463,13 +463,19 @@ class DateOffset: | |||
| } | |||
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| _CODES_TO_UNITS = { | |||
| "N": "nanoseconds", | |||
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I have some vague recollection that we left these out on purpose... hmm. I think there was some pandas behavior for which "L" and "ms" were okay but "N", "U", "T", etc. were not supported. We'd probably be able to tell if there are any newly failing pandas tests? I'd just check to see where _CODES_TO_UNITS is used and if there are any inconsistencies with this across different APIs.
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There were a bunch of failing tests without these changes, adding these units passed the cudf pytests.
There is only slight increase in pandas-pytest failures:
# This PR:
= 12094 failed, 174794 passed, 3850 skipped, 3314 xfailed, 8 xpassed, 21406 warnings, 102 errors in 1516.39s (0:25:16) =
# `branch-24.02`:
= 11607 failed, 175286 passed, 3849 skipped, 3312 xfailed, 11 xpassed, 21414 warnings, 97 errors in 1493.35s (0:24:53) =
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Sounds good, thanks for checking.
| @@ -463,13 +463,19 @@ class DateOffset: | |||
| } | |||
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| _CODES_TO_UNITS = { | |||
| "N": "nanoseconds", | |||
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Sounds good, thanks for checking.
| } | ||
| ) | ||
| ), | ||
| reason="Nanosecond offsets being dropped by pandas, which is " |
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Okay. We can clean it up later.
Co-authored-by: Bradley Dice <[email protected]>
…hwina/cudf into support-freq-in-datetime-index
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/merge |
When a
DatetimeIndexhas a fixed frequency offset, pandas defaults to it having a.freqattribute. Because we don't support that, we raise in pandas compatible mode.Thus, working with datetimes is practically impossible in pandas compatible mode because so many datetime operations involve setting a datetime column as an index (resample, groupby).
This PR adds rudimentary support for the
freqattribute.