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TST: Remove stacklevel checks for deprecated resample kwargs #37603

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TST: Remove stacklevel checks for deprecated resample kwargs #37603

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91 changes: 55 additions & 36 deletions pandas/core/groupby/grouper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -234,42 +234,7 @@ def __new__(cls, *args, **kwargs):
if kwargs.get("freq") is not None:
from pandas.core.resample import TimeGrouper

# Deprecation warning of `base` and `loffset` since v1.1.0:
# we are raising the warning here to be able to set the `stacklevel`
# properly since we need to raise the `base` and `loffset` deprecation
# warning from three different cases:
# core/generic.py::NDFrame.resample
# core/groupby/groupby.py::GroupBy.resample
# core/groupby/grouper.py::Grouper
# raising these warnings from TimeGrouper directly would fail the test:
# tests/resample/test_deprecated.py::test_deprecating_on_loffset_and_base
# hacky way to set the stacklevel: if cls is TimeGrouper it means
# that the call comes from a pandas internal call of resample,
# otherwise it comes from pd.Grouper
stacklevel = 4 if cls is TimeGrouper else 2
if kwargs.get("base", None) is not None:
warnings.warn(
"'base' in .resample() and in Grouper() is deprecated.\n"
"The new arguments that you should use are 'offset' or 'origin'.\n"
'\n>>> df.resample(freq="3s", base=2)\n'
"\nbecomes:\n"
'\n>>> df.resample(freq="3s", offset="2s")\n',
FutureWarning,
stacklevel=stacklevel,
)

if kwargs.get("loffset", None) is not None:
warnings.warn(
"'loffset' in .resample() and in Grouper() is deprecated.\n"
'\n>>> df.resample(freq="3s", loffset="8H")\n'
"\nbecomes:\n"
"\n>>> from pandas.tseries.frequencies import to_offset"
'\n>>> df = df.resample(freq="3s").mean()'
'\n>>> df.index = df.index.to_timestamp() + to_offset("8H")\n',
FutureWarning,
stacklevel=stacklevel,
)

_check_deprecated_resample_kwargs(kwargs, origin=cls)
cls = TimeGrouper
return super().__new__(cls)

Expand Down Expand Up @@ -867,3 +832,57 @@ def _convert_grouper(axis: Index, grouper):
return grouper
else:
return grouper


def _check_deprecated_resample_kwargs(kwargs, origin):
"""
Check for use of deprecated parameters in ``resample`` and related functions.

Raises the appropriate warnings if these parameters are detected.
Only sets an approximate ``stacklevel`` for the warnings (see #37603, #36629).

Parameters
----------
kwargs : dict
Dictionary of keyword arguments to check for deprecated parameters.
origin : object
From where this function is being called; either Grouper or TimeGrouper. Used
to determine an approximate stacklevel.
"""
from pandas.core.resample import TimeGrouper

# Deprecation warning of `base` and `loffset` since v1.1.0:
# we are raising the warning here to be able to set the `stacklevel`
# properly since we need to raise the `base` and `loffset` deprecation
# warning from three different cases:
# core/generic.py::NDFrame.resample
# core/groupby/groupby.py::GroupBy.resample
# core/groupby/grouper.py::Grouper
# raising these warnings from TimeGrouper directly would fail the test:
# tests/resample/test_deprecated.py::test_deprecating_on_loffset_and_base
# hacky way to set the stacklevel: if cls is TimeGrouper it means
# that the call comes from a pandas internal call of resample,
# otherwise it comes from pd.Grouper
stacklevel = (4 if origin is TimeGrouper else 2) + 1

if kwargs.get("base", None) is not None:
warnings.warn(
"'base' in .resample() and in Grouper() is deprecated.\n"
"The new arguments that you should use are 'offset' or 'origin'.\n"
'\n>>> df.resample(freq="3s", base=2)\n'
"\nbecomes:\n"
'\n>>> df.resample(freq="3s", offset="2s")\n',
FutureWarning,
stacklevel=stacklevel,
)
if kwargs.get("loffset", None) is not None:
warnings.warn(
"'loffset' in .resample() and in Grouper() is deprecated.\n"
'\n>>> df.resample(freq="3s", loffset="8H")\n'
"\nbecomes:\n"
"\n>>> from pandas.tseries.frequencies import to_offset"
'\n>>> df = df.resample(freq="3s").mean()'
'\n>>> df.index = df.index.to_timestamp() + to_offset("8H")\n',
FutureWarning,
stacklevel=stacklevel,
)
38 changes: 19 additions & 19 deletions pandas/tests/resample/test_deprecated.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,20 +43,20 @@ def test_deprecating_on_loffset_and_base():
idx = pd.date_range("2001-01-01", periods=4, freq="T")
df = DataFrame(data=4 * [range(2)], index=idx, columns=["a", "b"])

with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
pd.Grouper(freq="10s", base=0)
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
pd.Grouper(freq="10s", loffset="0s")
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
df.groupby("a").resample("3T", base=0).sum()
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
df.groupby("a").resample("3T", loffset="0s").sum()
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
df.resample("3T", base=0).sum()
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
df.resample("3T", loffset="0s").sum()
msg = "'offset' and 'base' cannot be present at the same time"
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
with pytest.raises(ValueError, match=msg):
df.groupby("a").resample("3T", base=0, offset=0).sum()

Expand All @@ -76,7 +76,7 @@ def test_resample_loffset_arg_type(frame, create_index, arg):
expected_index += timedelta(hours=2)
expected = DataFrame({"value": expected_means}, index=expected_index)

with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result_agg = df.resample("2D", loffset="2H").agg(arg)

if isinstance(arg, list):
Expand All @@ -93,7 +93,7 @@ def test_resample_loffset(loffset):
rng = date_range("1/1/2000 00:00:00", "1/1/2000 00:13:00", freq="min")
s = Series(np.random.randn(14), index=rng)

with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = s.resample(
"5min", closed="right", label="right", loffset=loffset
).mean()
Expand All @@ -112,7 +112,7 @@ def test_resample_loffset(loffset):
# to weekly
result = ser.resample("w-sun").last()
business_day_offset = BDay()
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = ser.resample("w-sun", loffset=-business_day_offset).last()
assert result.index[0] - business_day_offset == expected.index[0]

Expand All @@ -122,7 +122,7 @@ def test_resample_loffset_upsample():
rng = date_range("1/1/2000 00:00:00", "1/1/2000 00:13:00", freq="min")
s = Series(np.random.randn(14), index=rng)

with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = s.resample(
"5min", closed="right", label="right", loffset=timedelta(minutes=1)
).ffill()
Expand All @@ -138,7 +138,7 @@ def test_resample_loffset_count():
rng = date_range(start_time, periods=100, freq="S")
ts = Series(np.random.randn(len(rng)), index=rng)

with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = ts.resample("10S", loffset="1s").count()

expected_index = date_range(start_time, periods=10, freq="10S") + timedelta(
Expand All @@ -150,7 +150,7 @@ def test_resample_loffset_count():

# Same issue should apply to .size() since it goes through
# same code path
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = ts.resample("10S", loffset="1s").size()

tm.assert_series_equal(result, expected)
Expand All @@ -160,7 +160,7 @@ def test_resample_base():
rng = date_range("1/1/2000 00:00:00", "1/1/2000 02:00", freq="s")
ts = Series(np.random.randn(len(rng)), index=rng)

with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
resampled = ts.resample("5min", base=2).mean()
exp_rng = date_range("12/31/1999 23:57:00", "1/1/2000 01:57", freq="5min")
tm.assert_index_equal(resampled.index, exp_rng)
Expand All @@ -174,7 +174,7 @@ def test_resample_float_base():
s = Series(np.arange(3), index=dt)

base = 17 + 43.51 / 60
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = s.resample("3min", base=base).size()
expected = Series(
3, index=pd.DatetimeIndex(["2018-11-26 16:17:43.51"], freq="3min")
Expand All @@ -196,7 +196,7 @@ def test_loffset_returns_datetimeindex(frame, kind, agg_arg):
expected_index += timedelta(hours=2)
expected = DataFrame({"value": expected_means}, index=expected_index)

with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result_agg = df.resample("2D", loffset="2H", kind=kind).agg(agg_arg)
if isinstance(agg_arg, list):
expected.columns = pd.MultiIndex.from_tuples([("value", "mean")])
Expand Down Expand Up @@ -228,7 +228,7 @@ def test_resample_with_non_zero_base(start, end, start_freq, end_freq, base, off
# GH 23882
s = Series(0, index=pd.period_range(start, end, freq=start_freq))
s = s + np.arange(len(s))
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = s.resample(end_freq, base=base).mean()
result = result.to_timestamp(end_freq)

Expand All @@ -239,7 +239,7 @@ def test_resample_with_non_zero_base(start, end, start_freq, end_freq, base, off

# to_timestamp casts 24H -> D
result = result.asfreq(end_freq) if end_freq == "24H" else result
with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = s.to_timestamp().resample(end_freq, base=base).mean()
if end_freq == "M":
# TODO: is non-tick the relevant characteristic? (GH 33815)
Expand All @@ -252,7 +252,7 @@ def test_resample_base_with_timedeltaindex():
rng = timedelta_range(start="0s", periods=25, freq="s")
ts = Series(np.random.randn(len(rng)), index=rng)

with tm.assert_produces_warning(FutureWarning):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
with_base = ts.resample("2s", base=5).mean()
without_base = ts.resample("2s").mean()

Expand Down