Doc-groupby-ewm

doc/source/reference/groupby.rst

@@ -79,6 +79,7 @@ Function application
    DataFrameGroupBy.cumsum
    DataFrameGroupBy.describe
    DataFrameGroupBy.diff
+   DataFrameGroupBy.ewm
    DataFrameGroupBy.ffill
    DataFrameGroupBy.first
    DataFrameGroupBy.head
@@ -130,6 +131,7 @@ Function application
    SeriesGroupBy.cumsum
    SeriesGroupBy.describe
    SeriesGroupBy.diff
+   SeriesGroupBy.ewm
    SeriesGroupBy.ffill
    SeriesGroupBy.first
    SeriesGroupBy.head

pandas/core/groupby/groupby.py

@@ -152,7 +152,6 @@ class providing the base-class of operations.
     from pandas.core.indexers.objects import BaseIndexer
     from pandas.core.resample import Resampler
     from pandas.core.window import (
-        ExpandingGroupby,
         ExponentialMovingWindowGroupby,
         RollingGroupby,
     )
@@ -3805,35 +3804,93 @@ def rolling(
     @final
     @Substitution(name="groupby")
     @Appender(_common_see_also)
-    def expanding(self, *args, **kwargs) -> ExpandingGroupby:
+    def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
         """
-        Return an expanding grouper, providing expanding
-        functionality per group.
+        Provide exponential weighted functions for the groupby.
+
+        Parameters
+        ----------
+        *args
+            Arguments to be passed to
+            :meth:`~pandas.core.window.ExponentialMovingWindow`.
+        **kwargs
+            Keyword arguments to be passed to
+            :meth:`~pandas.core.window.ExponentialMovingWindow`.
+            These can include:
+            - com : float, optional
+            - span : float, optional
+            - halflife : float, optional
+            - alpha : float, optional
+            - min_periods : int, default 0
+            - adjust : bool, default True
+            - ignore_na : bool, default False
+            - axis : {0 or 'index', 1 or 'columns'}, default 0
 
         Returns
         -------
-        pandas.api.typing.ExpandingGroupby
-        """
-        from pandas.core.window import ExpandingGroupby
+        pandas.api.typing.ExponentialMovingWindowGroupby
+            Return a new grouper with exponential moving window capabilities.
 
-        return ExpandingGroupby(
-            self._selected_obj,
-            *args,
-            _grouper=self._grouper,
-            **kwargs,
-        )
+        Notes
+        -----
+        Each group is treated independently, and the exponential weighted calculations
+        are applied separately to each group.
 
-    @final
-    @Substitution(name="groupby")
-    @Appender(_common_see_also)
-    def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
-        """
-        Return an ewm grouper, providing ewm functionality per group.
+        When ``adjust=True``, weighted averages are calculated using weights
+        :math:`w_i = (1-\\alpha)^i` where :math:`i` is the number of periods from the
+        observations being weighted to the current period.
 
-        Returns
-        -------
-        pandas.api.typing.ExponentialMovingWindowGroupby
+        When ``adjust=False``, the calculation follows the recursive formula:
+        :math:`y_t = (1 - \\alpha) y_{t-1} + \\alpha x_t`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "Class": ["A", "A", "A", "B", "B", "B"],
+        ...         "Value": [10, 20, 30, 40, 50, 60],
+        ...     }
+        ... )
+        >>> df
+        Class  Value
+        0     A     10
+        1     A     20
+        2     A     30
+        3     B     40
+        4     B     50
+        5     B     60
+
+        >>> df.groupby("Class").ewm(span=2).mean()
+                    Value
+        Class
+        A     0  10.000000
+              1  17.500000
+              2  26.153846
+        B     3  40.000000
+              4  47.500000
+              5  56.153846
+
+        >>> df.groupby("Class").ewm(alpha=0.5, adjust=False).mean()
+                Value
+        Class
+        A     0   10.0
+              1   15.0
+              2   22.5
+        B     3   40.0
+              4   45.0
+              5   52.5
+
+        >>> df.groupby("Class").ewm(com=1.0, min_periods=1).std()
+                    Value
+        Class
+        A     0       NaN
+              1  7.071068
+              2  9.636241
+        B     3       NaN
+              4  7.071068
+              5  9.636241
         """
+
         from pandas.core.window import ExponentialMovingWindowGroupby
 
         return ExponentialMovingWindowGroupby(

pandas/tests/frame/test_query_eval.py

@@ -168,7 +168,7 @@ def test_query_duplicate_column_name(self, engine, parser):
             }
         ).rename(columns={"B": "A"})
 
-        res = df.query('C == 1', engine=engine, parser=parser)
+        res = df.query("C == 1", engine=engine, parser=parser)
 
         expect = DataFrame(
             [[1, 1, 1]],
@@ -1411,7 +1411,7 @@ def test_expr_with_column_name_with_backtick_and_hash(self):
     def test_expr_with_column_name_with_backtick(self):
         # GH 59285
         df = DataFrame({"a`b": (1, 2, 3), "ab": (4, 5, 6)})
-        result = df.query("`a``b` < 2")  # noqa
+        result = df.query("`a``b` < 2")
         # Note: Formatting checks may wrongly consider the above ``inline code``.
         expected = df[df["a`b"] < 2]
         tm.assert_frame_equal(result, expected)