Skip to content

Fixed pd.infer_freq incompatible with Series["timestamp[s][pyarrow]"] #58404

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 4 commits into from
Closed

Fixed pd.infer_freq incompatible with Series["timestamp[s][pyarrow]"] #58404

Show file tree
Hide file tree
Changes from 3 commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
27fdbfd
fixed #58403
randolf-scholz Apr 24, 2024
75cf5bc
added whatsnew entry
randolf-scholz Apr 24, 2024
abe76ea
skip tests if pyarrow not installed
randolf-scholz Apr 24, 2024
6315194
used pytest.importorskip
randolf-scholz Apr 24, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions doc/source/whatsnew/v3.0.0.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -362,6 +362,7 @@ Datetimelike
- Bug in :class:`Timestamp` constructor failing to raise when ``tz=None`` is explicitly specified in conjunction with timezone-aware ``tzinfo`` or data (:issue:`48688`)
- Bug in :func:`date_range` where the last valid timestamp would sometimes not be produced (:issue:`56134`)
- Bug in :func:`date_range` where using a negative frequency value would not include all points between the start and end values (:issue:`56382`)
- Bug in :func:`infer_freq` prevented application to :class:`Series` of with ``pyarrow`` dtype. (:issue:`58403`)
- Bug in :func:`tseries.api.guess_datetime_format` would fail to infer time format when "%Y" == "%H%M" (:issue:`57452`)
- Bug in setting scalar values with mismatched resolution into arrays with non-nanosecond ``datetime64``, ``timedelta64`` or :class:`DatetimeTZDtype` incorrectly truncating those scalars (:issue:`56410`)

Expand Down
27 changes: 27 additions & 0 deletions pandas/tests/tseries/frequencies/test_inference.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
datetime,
timedelta,
)
import importlib

import numpy as np
import pytest
Expand Down Expand Up @@ -554,3 +555,29 @@ def test_infer_freq_non_nano_tzaware(tz_aware_fixture):

res = frequencies.infer_freq(dta)
assert res == "B"


@pytest.mark.skipif(
importlib.util.find_spec("pyarrow") is None, reason="pyarrow not installed"
)
@pytest.mark.parametrize(
("data", "expected"),
[
(["2022-01-01T10:00:00", "2022-01-01T10:00:30", "2022-01-01T10:01:00"], "30s"),
(["2022-01-01T10:00:00", "2022-01-01T10:00:37", "2022-01-01T10:01:00"], None),
(["2022-01-01T10:00:00", "2022-01-01T10:00:01", "2022-01-01T10:00:02"], "s"),
(
[
"2022-01-01T10:00:00",
"2022-01-01T10:00:01",
"2022-01-01T10:00:02",
"2022-01-01T10:00:04",
],
None,
),
],
)
@pytest.mark.parametrize("cls", [Index, Series])
def test_infer_freq_pyarrow(data, expected, cls):
obj = cls(data).astype("timestamp[s][pyarrow]")
assert frequencies.infer_freq(obj) == expected
25 changes: 9 additions & 16 deletions pandas/tseries/frequencies.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,10 +33,7 @@
from pandas.util._decorators import cache_readonly

from pandas.core.dtypes.common import is_numeric_dtype
from pandas.core.dtypes.dtypes import (
DatetimeTZDtype,
PeriodDtype,
)
from pandas.core.dtypes.dtypes import PeriodDtype
from pandas.core.dtypes.generic import (
ABCIndex,
ABCSeries,
Expand Down Expand Up @@ -112,20 +109,16 @@ def infer_freq(
>>> pd.infer_freq(idx)
'D'
"""
from pandas.api.types import is_datetime64_any_dtype
from pandas.core.api import DatetimeIndex

if isinstance(index, ABCSeries):
values = index._values
if not (
lib.is_np_dtype(values.dtype, "mM")
or isinstance(values.dtype, DatetimeTZDtype)
or values.dtype == object
):
raise TypeError(
"cannot infer freq from a non-convertible dtype "
f"on a Series of {index.dtype}"
)
index = values
if isinstance(index, ABCSeries) and not (
is_datetime64_any_dtype(index) or index.dtype == object
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Can you check dtype.kind == "mM" on ArrowDtype instead?

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I am a bit surprised by this comment, isn't pandas.api.types.is_ supposed to be a single source of truth of what is considered a certain data type? How do you remember what are all the conditions to check otherwise?

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

For internal usages, checking dtype.kind is more performant than api.types functions so that is preferred

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Ah I guess I accidentally removed checking for timedelta. Although no tests failed. I am adding some tests for timedeltas as well.

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

There is another bug: when trying with Index["duration[s][pyarrow]"], it complains about the property asi8 missing. asi8 is denoted as deprecated on general index types. Moreover, the FrequencyInferer assumes numpy dtype and makes use of internals (like index._data._ndarray)

the easiest workaround would be to cast to numpy, but I guess we want to avoid that and keep dtype as pyarrow inside the ops.

):
raise TypeError(
"cannot infer freq from a non-convertible dtype "
f"on a Series of {index.dtype}"
)

inferer: _FrequencyInferer

Expand Down
Loading