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

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

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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
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v3.0.0.rst
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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`)

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24 changes: 24 additions & 0 deletions pandas/tests/tseries/frequencies/test_inference.py
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Expand Up @@ -554,3 +554,27 @@ def test_infer_freq_non_nano_tzaware(tz_aware_fixture):

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


@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):
pytest.importorskip("pyarrow")
obj = cls(data).astype("timestamp[s][pyarrow]")
assert frequencies.infer_freq(obj) == expected
25 changes: 9 additions & 16 deletions pandas/tseries/frequencies.py
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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
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Can you check dtype.kind == "mM" on ArrowDtype instead?

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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?

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

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

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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

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