ENH: Add numeric_only to resampler methods (pandas-dev#46792)

Author: lorentzbao

doc/source/whatsnew/v1.5.0.rst

@@ -96,6 +96,7 @@ Other enhancements
 - :meth:`pd.concat` now raises when ``levels`` contains duplicate values (:issue:`46653`)
 - Added ``numeric_only`` argument to :meth:`DataFrame.corr`, :meth:`DataFrame.corrwith`, and :meth:`DataFrame.cov` (:issue:`46560`)
 - A :class:`errors.PerformanceWarning` is now thrown when using ``string[pyarrow]`` dtype with methods that don't dispatch to ``pyarrow.compute`` methods (:issue:`42613`)
+- Added ``numeric_only`` argument to :meth:`Resampler.sum`, :meth:`Resampler.prod`, :meth:`Resampler.min`, :meth:`Resampler.max`, :meth:`Resampler.first`, and :meth:`Resampler.last` (:issue:`46442`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_150.notable_bug_fixes:

pandas/core/resample.py

@@ -1027,9 +1027,21 @@ def quantile(self, q=0.5, **kwargs):
 # downsample methods
 for method in ["sum", "prod", "min", "max", "first", "last"]:
 
-    def f(self, _method=method, min_count=0, *args, **kwargs):
+    def f(
+        self,
+        _method: str = method,
+        numeric_only: bool | lib.NoDefault = lib.no_default,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        if numeric_only is lib.no_default:
+            if _method != "sum":
+                # For DataFrameGroupBy, set it to be False for methods other than `sum`.
+                numeric_only = False
+
         nv.validate_resampler_func(_method, args, kwargs)
-        return self._downsample(_method, min_count=min_count)
+        return self._downsample(_method, numeric_only=numeric_only, min_count=min_count)
 
     f.__doc__ = getattr(GroupBy, method).__doc__
     setattr(Resampler, method, f)

pandas/tests/resample/test_resample_api.py

@@ -3,6 +3,8 @@
 import numpy as np
 import pytest
 
+from pandas._libs import lib
+
 import pandas as pd
 from pandas import (
     DataFrame,
@@ -771,3 +773,85 @@ def test_end_and_end_day_origin(
     )
 
     tm.assert_series_equal(res, expected)
+
+
+@pytest.mark.parametrize(
+    "method, numeric_only, expected_data",
+    [
+        ("sum", True, {"num": [25]}),
+        ("sum", False, {"cat": ["cat_1cat_2"], "num": [25]}),
+        ("sum", lib.no_default, {"num": [25]}),
+        ("prod", True, {"num": [100]}),
+        ("prod", False, {"num": [100]}),
+        ("prod", lib.no_default, {"num": [100]}),
+        ("min", True, {"num": [5]}),
+        ("min", False, {"cat": ["cat_1"], "num": [5]}),
+        ("min", lib.no_default, {"cat": ["cat_1"], "num": [5]}),
+        ("max", True, {"num": [20]}),
+        ("max", False, {"cat": ["cat_2"], "num": [20]}),
+        ("max", lib.no_default, {"cat": ["cat_2"], "num": [20]}),
+        ("first", True, {"num": [5]}),
+        ("first", False, {"cat": ["cat_1"], "num": [5]}),
+        ("first", lib.no_default, {"cat": ["cat_1"], "num": [5]}),
+        ("last", True, {"num": [20]}),
+        ("last", False, {"cat": ["cat_2"], "num": [20]}),
+        ("last", lib.no_default, {"cat": ["cat_2"], "num": [20]}),
+    ],
+)
+def test_frame_downsample_method(method, numeric_only, expected_data):
+    # GH#46442 test if `numeric_only` behave as expected for DataFrameGroupBy
+
+    index = date_range("2018-01-01", periods=2, freq="D")
+    expected_index = date_range("2018-12-31", periods=1, freq="Y")
+    df = DataFrame({"cat": ["cat_1", "cat_2"], "num": [5, 20]}, index=index)
+    resampled = df.resample("Y")
+
+    func = getattr(resampled, method)
+    result = func(numeric_only=numeric_only)
+
+    expected = DataFrame(expected_data, index=expected_index)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "method, numeric_only, expected_data",
+    [
+        ("sum", True, ()),
+        ("sum", False, ["cat_1cat_2"]),
+        ("sum", lib.no_default, ["cat_1cat_2"]),
+        ("prod", True, ()),
+        ("prod", False, ()),
+        ("prod", lib.no_default, ()),
+        ("min", True, ()),
+        ("min", False, ["cat_1"]),
+        ("min", lib.no_default, ["cat_1"]),
+        ("max", True, ()),
+        ("max", False, ["cat_2"]),
+        ("max", lib.no_default, ["cat_2"]),
+        ("first", True, ()),
+        ("first", False, ["cat_1"]),
+        ("first", lib.no_default, ["cat_1"]),
+        ("last", True, ()),
+        ("last", False, ["cat_2"]),
+        ("last", lib.no_default, ["cat_2"]),
+    ],
+)
+def test_series_downsample_method(method, numeric_only, expected_data):
+    # GH#46442 test if `numeric_only` behave as expected for SeriesGroupBy
+
+    index = date_range("2018-01-01", periods=2, freq="D")
+    expected_index = date_range("2018-12-31", periods=1, freq="Y")
+    df = Series(["cat_1", "cat_2"], index=index)
+    resampled = df.resample("Y")
+
+    func = getattr(resampled, method)
+    if numeric_only and numeric_only is not lib.no_default:
+        with pytest.raises(NotImplementedError, match="not implement numeric_only"):
+            func(numeric_only=numeric_only)
+    elif method == "prod":
+        with pytest.raises(TypeError, match="can't multiply sequence by non-int"):
+            func(numeric_only=numeric_only)
+    else:
+        result = func(numeric_only=numeric_only)
+        expected = Series(expected_data, index=expected_index)
+        tm.assert_series_equal(result, expected)