DEPR: add deprecation warnings and tests. Update whatsnew

Author: kostyafarber

doc/source/whatsnew/v2.0.0.rst

@@ -642,7 +642,7 @@ Deprecations
 - :meth:`Index.is_object` has been deprecated. Use :func:`pandas.api.types.is_object_dtype` instead (:issue:`50042`)
 - :meth:`Index.is_interval` has been deprecated. Use :func:`pandas.api.types.is_intterval_dtype` instead (:issue:`50042`)
 - Deprecated ``all`` and ``any`` reductions with ``datetime64`` and :class:`DatetimeTZDtype` dtypes, use e.g. ``(obj != pd.Timestamp(0), tz=obj.tz).all()`` instead (:issue:`34479`)
--
+- Deprecated arguments ``*args`` and ``**kwargs`` in :class:`Resampler` (:issue:`50977`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_200.prior_deprecations:
@@ -881,7 +881,6 @@ Removal of prior version deprecations/changes
 - Arguments after ``expr`` in :meth:`DataFrame.eval` and :meth:`DataFrame.query` are keyword-only (:issue:`47587`)
 - Removed :meth:`Index._get_attributes_dict` (:issue:`50648`)
 - Removed :meth:`Series.__array_wrap__` (:issue:`50648`)
-- Removed unused ``*args`` and ``**kwargs`` in :class:`Resampler` (:issue:`50977`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_200.performance:

pandas/core/resample.py

@@ -10,6 +10,7 @@
     final,
     no_type_check,
 )
+import warnings
 
 import numpy as np
 
@@ -37,6 +38,7 @@
     TimestampConvertibleTypes,
     npt,
 )
+from pandas.compat.numpy import function as nv
 from pandas.errors import (
     AbstractMethodError,
     DataError,
@@ -46,6 +48,7 @@
     Substitution,
     doc,
 )
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
@@ -898,54 +901,82 @@ def sum(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
+        *args,
+        **kwargs,
     ):
+        maybe_warn_args_and_kwargs(type(self), "sum", args, kwargs)
+        nv.validate_resampler_func("sum", args, kwargs)
         return self._downsample("sum", numeric_only=numeric_only, min_count=min_count)
 
     @doc(GroupBy.prod)
     def prod(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
+        *args,
+        **kwargs,
     ):
+        maybe_warn_args_and_kwargs(type(self), "prod", args, kwargs)
+        nv.validate_resampler_func("prod", args, kwargs)
         return self._downsample("prod", numeric_only=numeric_only, min_count=min_count)
 
     def min(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
+        *args,
+        **kwargs,
     ):
+        maybe_warn_args_and_kwargs(type(self), "min", args, kwargs)
+        nv.validate_resampler_func("min", args, kwargs)
         return self._downsample("min", numeric_only=numeric_only, min_count=min_count)
 
     def max(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
+        *args,
+        **kwargs,
     ):
+        maybe_warn_args_and_kwargs(type(self), "max", args, kwargs)
+        nv.validate_resampler_func("max", args, kwargs)
         return self._downsample("max", numeric_only=numeric_only, min_count=min_count)
 
     @doc(GroupBy.first)
     def first(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
+        *args,
+        **kwargs,
     ):
+        maybe_warn_args_and_kwargs(type(self), "first", args, kwargs)
+        nv.validate_resampler_func("first", args, kwargs)
         return self._downsample("first", numeric_only=numeric_only, min_count=min_count)
 
     @doc(GroupBy.last)
     def last(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
+        *args,
+        **kwargs,
     ):
+        maybe_warn_args_and_kwargs(type(self), "last", args, kwargs)
+        nv.validate_resampler_func("last", args, kwargs)
         return self._downsample("last", numeric_only=numeric_only, min_count=min_count)
 
     @doc(GroupBy.median)
-    def median(self, numeric_only: bool = False):
+    def median(self, numeric_only: bool = False, *args, **kwargs):
+        maybe_warn_args_and_kwargs(type(self), "median", args, kwargs)
+        nv.validate_resampler_func("median", args, kwargs)
         return self._downsample("median", numeric_only=numeric_only)
 
     def mean(
         self,
         numeric_only: bool = False,
+        *args,
+        **kwargs,
     ):
         """
         Compute mean of groups, excluding missing values.
@@ -964,12 +995,16 @@ def mean(
         DataFrame or Series
             Mean of values within each group.
         """
+        maybe_warn_args_and_kwargs(type(self), "mean", args, kwargs)
+        nv.validate_resampler_func("mean", args, kwargs)
         return self._downsample("mean", numeric_only=numeric_only)
 
     def std(
         self,
         ddof: int = 1,
         numeric_only: bool = False,
+        *args,
+        **kwargs,
     ):
         """
         Compute standard deviation of groups, excluding missing values.
@@ -992,12 +1027,16 @@ def std(
         DataFrame or Series
             Standard deviation of values within each group.
         """
+        maybe_warn_args_and_kwargs(type(self), "std", args, kwargs)
+        nv.validate_resampler_func("std", args, kwargs)
         return self._downsample("std", ddof=ddof, numeric_only=numeric_only)
 
     def var(
         self,
         ddof: int = 1,
         numeric_only: bool = False,
+        *args,
+        **kwargs,
     ):
         """
         Compute variance of groups, excluding missing values.
@@ -1021,26 +1060,40 @@ def var(
         DataFrame or Series
             Variance of values within each group.
         """
+        maybe_warn_args_and_kwargs(type(self), "var", args, kwargs)
+        nv.validate_resampler_func("var", args, kwargs)
         return self._downsample("var", ddof=ddof, numeric_only=numeric_only)
 
     @doc(GroupBy.sem)
     def sem(
         self,
         ddof: int = 1,
         numeric_only: bool = False,
+        *args,
+        **kwargs,
     ):
+        maybe_warn_args_and_kwargs(type(self), "sem", args, kwargs)
+        nv.validate_resampler_func("sem", args, kwargs)
         return self._downsample("sem", ddof=ddof, numeric_only=numeric_only)
 
     @doc(GroupBy.ohlc)
     def ohlc(
         self,
+        *args,
+        **kwargs,
     ):
+        maybe_warn_args_and_kwargs(type(self), "ohlc", args, kwargs)
+        nv.validate_resampler_func("ohlc", args, kwargs)
         return self._downsample("ohlc")
 
     @doc(SeriesGroupBy.nunique)
     def nunique(
         self,
+        *args,
+        **kwargs,
     ):
+        maybe_warn_args_and_kwargs(type(self), "nunique", args, kwargs)
+        nv.validate_resampler_func("nunique", args, kwargs)
         return self._downsample("nunique")
 
     @doc(GroupBy.size)
@@ -2193,3 +2246,38 @@ def _asfreq_compat(index: DatetimeIndex | PeriodIndex | TimedeltaIndex, freq):
     else:  # pragma: no cover
         raise TypeError(type(index))
     return new_index
+
+
+def maybe_warn_args_and_kwargs(cls, kernel: str, args, kwargs) -> None:
+    """
+    Warn for deprecation of args and kwargs in resample functions.
+
+    Parameters
+    ----------
+    cls : type
+        Class to warn about.
+    kernel : str
+        Operation name.
+    args : tuple or None
+        args passed by user. Will be None if and only if kernel does not have args.
+    kwargs : dict or None
+        kwargs passed by user. Will be None if and only if kernel does not have kwargs.
+    """
+    warn_args = args is not None and len(args) > 0
+    warn_kwargs = kwargs is not None and len(kwargs) > 0
+    if warn_args and warn_kwargs:
+        msg = "args and kwargs"
+    elif warn_args:
+        msg = "args"
+    elif warn_kwargs:
+        msg = "kwargs"
+    else:
+        msg = ""
+    if msg != "":
+        warnings.warn(
+            f"Passing additional {msg} to {cls.__name__}.{kernel} has "
+            "no impact on the result and is deprecated. This will "
+            "raise a TypeError in a future version of pandas.",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )

pandas/tests/resample/test_resample_api.py

@@ -4,6 +4,7 @@
 import pytest
 
 from pandas._libs import lib
+from pandas.errors import UnsupportedFunctionCall
 
 import pandas as pd
 from pandas import (
@@ -916,3 +917,43 @@ def test_series_downsample_method(method, numeric_only, expected_data):
         result = func(**kwargs)
         expected = Series(expected_data, index=expected_index)
         tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "method, raises",
+    [
+        ("sum", True),
+        ("prod", True),
+        ("min", True),
+        ("max", True),
+        ("first", False),
+        ("last", False),
+        ("median", False),
+        ("mean", True),
+        ("std", True),
+        ("var", True),
+        ("sem", False),
+        ("ohlc", False),
+        ("nunique", False),
+    ],
+)
+def test_args_kwargs_depr(method, raises):
+    index = date_range("20180101", periods=3, freq="h")
+    df = Series([2, 4, 6], index=index)
+    resampled = df.resample("30min")
+    args = ()
+
+    func = getattr(resampled, method)
+
+    error_msg = "numpy operations are not valid with resample."
+    error_msg_type = "too many arguments passed in"
+    warn_msg = f"Passing additional args to DatetimeIndexResampler.{method}"
+
+    if raises:
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            with pytest.raises(UnsupportedFunctionCall, match=error_msg):
+                func(*args, 1, 2, 3)
+    else:
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            with pytest.raises(TypeError, match=error_msg_type):
+                func(*args, 1, 2, 3)