From 337254feb7f0c9d8b8b0eda70990f616a80fe626 Mon Sep 17 00:00:00 2001
From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com>
Date: Mon, 14 Apr 2025 06:18:50 +0000
Subject: [PATCH 01/10] DOC: Add documentation for groupby.ewm()

---
 doc/source/reference/groupby.rst |   2 +
 pandas/core/groupby/groupby.py   | 138 ++++++++++++++++++++++++++++++-
 2 files changed, 138 insertions(+), 2 deletions(-)

diff --git a/doc/source/reference/groupby.rst b/doc/source/reference/groupby.rst
index fc180c8161a7e..ce9aeeb358c19 100644
--- a/doc/source/reference/groupby.rst
+++ b/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
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 9cfeb53821fbc..888595867d98f 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3826,14 +3826,148 @@ def expanding(self, *args, **kwargs) -> ExpandingGroupby:
     @final
     @Substitution(name="groupby")
     @Appender(_common_see_also)
-    def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
+    def ewm(
+        self,
+        com: float | None = None,
+        span: float | None = None,
+        halflife: float | None = None,
+        alpha: float | None = None,
+        min_periods: int = 0,
+        adjust: bool = True,
+        ignore_na: bool = False,
+        axis: Axis = 0,
+    ) -> ExponentialMovingWindowGroupby:
         """
-        Return an ewm grouper, providing ewm functionality per group.
+        Return an exponential weighted moving average grouper, providing ewm functionality per group.
+
+        Parameters
+        ----------
+        com : float, optional
+            Specify decay in terms of center of mass:
+            :math:`\\alpha = 1 / (1 + com)` for :math:`com \\geq 0`.
+            
+            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must be provided.
+
+        span : float, optional
+            Specify decay in terms of span:
+            :math:`\\alpha = 2 / (span + 1)` for :math:`span \\geq 1`.
+            
+            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must be provided.
+
+        halflife : float, optional
+            Specify decay in terms of half-life:
+            :math:`\\alpha = 1 - \\exp(-\\ln(2) / halflife)` for :math:`halflife > 0`.
+            
+            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must be provided.
+
+        alpha : float, optional
+            Specify the smoothing factor :math:`\\alpha` directly, where :math:`0 < \\alpha \\leq 1`.
+            
+            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must be provided.
+
+        min_periods : int, default 0
+            Minimum number of observations in window required to have a value;
+            otherwise, result is ``np.nan``.
+
+        adjust : bool, default True
+            Divide by decaying adjustment factor in beginning periods to account
+            for imbalance in relative weightings (viewing EWMA as a moving average).
+            
+            If ``False``, the exponentially weighted function is:
+            
+            .. math::
+                y_t = (1 - \\alpha) y_{t-1} + \\alpha x_t
+                
+            If ``True``, the exponentially weighted function is:
+            
+            .. math::
+                y_t = \\frac{\\sum_{i=0}^t w_i x_{t-i}}{\\sum_{i=0}^t w_i}
+            
+            where :math:`w_i = (1 - \\alpha)^i`.
+
+        ignore_na : bool, default False
+            If ``True``, missing values are ignored in the calculation.
+            
+            If ``False``, missing values are treated as missing.
+
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to use. The value 0 identifies the rows, and 1 identifies the columns.
 
         Returns
         -------
         pandas.api.typing.ExponentialMovingWindowGroupby
+            Return a new grouper with exponential moving window capabilities.
+
+        See Also
+        --------
+        Series.ewm : Exponential weighted moving window functions for Series.
+        DataFrame.ewm : Exponential weighted moving window functions for DataFrames.
+        Series.groupby : Apply a function groupby to a Series.
+        DataFrame.groupby : Apply a function groupby to a DataFrame.
+
+        Notes
+        -----
+        Each group is treated independently, and the exponential weighted calculations
+        are applied separately to each group.
+
+        The exponential weighted calculation is based on the formula:
+
+        .. math::
+            y_t = (1 - \\alpha) y_{t-1} + \\alpha x_t
+
+        where :math:`\\alpha` is the smoothing factor derived from one of the input
+        decay parameters (``com``, ``span``, ``halflife``, or ``alpha``).
+        
+        Only one of ``com``, ``span``, ``halflife``, or ``alpha`` can be specified.
+
+        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  10.000000
+              1  15.000000
+              2  22.500000
+        B     3  40.000000
+              4  45.000000
+              5  52.500000
+
+        >>> df.groupby("Class").ewm(com=1.0, min_periods=1).std()
+                    Value
+        Class               
+        A     0    0.000000
+              1    7.500000
+              2   10.606602
+        B     3    0.000000
+              4    7.500000
+              5   10.606602
         """
+
         from pandas.core.window import ExponentialMovingWindowGroupby
 
         return ExponentialMovingWindowGroupby(

From bbf5e2ba5c798ac91bd959a52202985d9d578aaf Mon Sep 17 00:00:00 2001
From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com>
Date: Mon, 14 Apr 2025 06:35:55 +0000
Subject: [PATCH 02/10] DOC: Add documentation for groupby.ewm()

---
 pandas/core/groupby/groupby.py | 36 +++++++++++++++++-----------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 888595867d98f..61181fd1fed55 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3929,7 +3929,7 @@ def ewm(
         ...     }
         ... )
         >>> df
-        Class  Value
+          Class  Value
         0     A     10
         1     A     20
         2     A     30
@@ -3938,8 +3938,8 @@ def ewm(
         5     B     60
 
         >>> df.groupby("Class").ewm(span=2).mean()
-                    Value
-        Class              
+                     Value
+        Class             
         A     0  10.000000
               1  17.500000
               2  26.153846
@@ -3948,24 +3948,24 @@ def ewm(
               5  56.153846
 
         >>> df.groupby("Class").ewm(alpha=0.5, adjust=False).mean()
-                    Value
-        Class              
-        A     0  10.000000
-              1  15.000000
-              2  22.500000
-        B     3  40.000000
-              4  45.000000
-              5  52.500000
+                 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    0.000000
-              1    7.500000
-              2   10.606602
-        B     3    0.000000
-              4    7.500000
-              5   10.606602
+        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

From f4635460b49be326a5126b82663d59c58d4260b4 Mon Sep 17 00:00:00 2001
From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com>
Date: Mon, 14 Apr 2025 06:59:11 +0000
Subject: [PATCH 03/10] DOC: Added docs for groupby.ewm() #61283

---
 pandas/core/groupby/groupby.py | 58 ++++++++++++++++++++--------------
 1 file changed, 34 insertions(+), 24 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 61181fd1fed55..d0de8b720bb6f 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3835,35 +3835,38 @@ def ewm(
         min_periods: int = 0,
         adjust: bool = True,
         ignore_na: bool = False,
-        axis: Axis = 0,
+        axis: int = 0,
     ) -> ExponentialMovingWindowGroupby:
         """
-        Return an exponential weighted moving average grouper, providing ewm functionality per group.
+        Return an exponential weighted moving average grouper,
+        providing ewm functionality per group.
 
         Parameters
         ----------
         com : float, optional
             Specify decay in terms of center of mass:
             :math:`\\alpha = 1 / (1 + com)` for :math:`com \\geq 0`.
-            
-            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must be provided.
+            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
+            be provided.
 
         span : float, optional
             Specify decay in terms of span:
             :math:`\\alpha = 2 / (span + 1)` for :math:`span \\geq 1`.
-            
-            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must be provided.
+            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
+            be provided.
 
         halflife : float, optional
             Specify decay in terms of half-life:
             :math:`\\alpha = 1 - \\exp(-\\ln(2) / halflife)` for :math:`halflife > 0`.
-            
-            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must be provided.
+            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
+            be provided.
 
         alpha : float, optional
-            Specify the smoothing factor :math:`\\alpha` directly, where :math:`0 < \\alpha \\leq 1`.
-            
-            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must be provided.
+            Specify the smoothing factor
+            :math:`\\alpha` directly, where :math:`0 < \\alpha \\leq 1`.
+
+            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
+            be provided.
 
         min_periods : int, default 0
             Minimum number of observations in window required to have a value;
@@ -3872,26 +3875,27 @@ def ewm(
         adjust : bool, default True
             Divide by decaying adjustment factor in beginning periods to account
             for imbalance in relative weightings (viewing EWMA as a moving average).
-            
+
             If ``False``, the exponentially weighted function is:
-            
+
             .. math::
                 y_t = (1 - \\alpha) y_{t-1} + \\alpha x_t
-                
+
             If ``True``, the exponentially weighted function is:
-            
+
             .. math::
                 y_t = \\frac{\\sum_{i=0}^t w_i x_{t-i}}{\\sum_{i=0}^t w_i}
-            
+
             where :math:`w_i = (1 - \\alpha)^i`.
 
         ignore_na : bool, default False
             If ``True``, missing values are ignored in the calculation.
-            
+
             If ``False``, missing values are treated as missing.
 
         axis : {0 or 'index', 1 or 'columns'}, default 0
-            The axis to use. The value 0 identifies the rows, and 1 identifies the columns.
+            The axis to use. The value 0 identifies the rows, and 1 identifies the
+            columns.
 
         Returns
         -------
@@ -3917,7 +3921,7 @@ def ewm(
 
         where :math:`\\alpha` is the smoothing factor derived from one of the input
         decay parameters (``com``, ``span``, ``halflife``, or ``alpha``).
-        
+
         Only one of ``com``, ``span``, ``halflife``, or ``alpha`` can be specified.
 
         Examples
@@ -3939,7 +3943,7 @@ def ewm(
 
         >>> df.groupby("Class").ewm(span=2).mean()
                      Value
-        Class             
+        Class
         A     0  10.000000
               1  17.500000
               2  26.153846
@@ -3949,7 +3953,7 @@ def ewm(
 
         >>> df.groupby("Class").ewm(alpha=0.5, adjust=False).mean()
                  Value
-        Class         
+        Class
         A     0   10.0
               1   15.0
               2   22.5
@@ -3959,7 +3963,7 @@ def ewm(
 
         >>> df.groupby("Class").ewm(com=1.0, min_periods=1).std()
                     Value
-        Class            
+        Class
         A     0       NaN
               1  7.071068
               2  9.636241
@@ -3972,9 +3976,15 @@ def ewm(
 
         return ExponentialMovingWindowGroupby(
             self._selected_obj,
-            *args,
+            com=com,
+            span=span,
+            halflife=halflife,
+            alpha=alpha,
+            min_periods=min_periods,
+            adjust=adjust,
+            ignore_na=ignore_na,
+            axis=axis,
             _grouper=self._grouper,
-            **kwargs,
         )
 
     @final

From c7fc3a0ffc224b8c3df8bb0fff43895d7a8274ca Mon Sep 17 00:00:00 2001
From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com>
Date: Mon, 14 Apr 2025 07:53:47 +0000
Subject: [PATCH 04/10] reverted function definition

---
 pandas/core/groupby/groupby.py | 22 +++-------------------
 1 file changed, 3 insertions(+), 19 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index d0de8b720bb6f..4653b0b5952ba 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3826,17 +3826,7 @@ def expanding(self, *args, **kwargs) -> ExpandingGroupby:
     @final
     @Substitution(name="groupby")
     @Appender(_common_see_also)
-    def ewm(
-        self,
-        com: float | None = None,
-        span: float | None = None,
-        halflife: float | None = None,
-        alpha: float | None = None,
-        min_periods: int = 0,
-        adjust: bool = True,
-        ignore_na: bool = False,
-        axis: int = 0,
-    ) -> ExponentialMovingWindowGroupby:
+    def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
         """
         Return an exponential weighted moving average grouper,
         providing ewm functionality per group.
@@ -3976,15 +3966,9 @@ def ewm(
 
         return ExponentialMovingWindowGroupby(
             self._selected_obj,
-            com=com,
-            span=span,
-            halflife=halflife,
-            alpha=alpha,
-            min_periods=min_periods,
-            adjust=adjust,
-            ignore_na=ignore_na,
-            axis=axis,
+            *args,
             _grouper=self._grouper,
+            **kwargs,
         )
 
     @final

From 21cbbc0f4d102eff0555b67c856cb4a588ede233 Mon Sep 17 00:00:00 2001
From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com>
Date: Mon, 14 Apr 2025 13:10:51 +0000
Subject: [PATCH 05/10] remove See Also section

---
 pandas/core/groupby/groupby.py | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 4653b0b5952ba..2be6d5186a40c 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3892,13 +3892,6 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
         pandas.api.typing.ExponentialMovingWindowGroupby
             Return a new grouper with exponential moving window capabilities.
 
-        See Also
-        --------
-        Series.ewm : Exponential weighted moving window functions for Series.
-        DataFrame.ewm : Exponential weighted moving window functions for DataFrames.
-        Series.groupby : Apply a function groupby to a Series.
-        DataFrame.groupby : Apply a function groupby to a DataFrame.
-
         Notes
         -----
         Each group is treated independently, and the exponential weighted calculations

From 53ac271f3a5bf379d950c36ef9d9836cbbaf5251 Mon Sep 17 00:00:00 2001
From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com>
Date: Mon, 14 Apr 2025 13:43:09 +0000
Subject: [PATCH 06/10] improvements

---
 pandas/core/groupby/groupby.py | 48 +++++++++-------------------------
 1 file changed, 12 insertions(+), 36 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 2be6d5186a40c..cc9a694efd365 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3828,8 +3828,7 @@ def expanding(self, *args, **kwargs) -> ExpandingGroupby:
     @Appender(_common_see_also)
     def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
         """
-        Return an exponential weighted moving average grouper,
-        providing ewm functionality per group.
+        Provide exponential weighted functions for the groupby.
 
         Parameters
         ----------
@@ -3838,54 +3837,34 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
             :math:`\\alpha = 1 / (1 + com)` for :math:`com \\geq 0`.
             One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
             be provided.
-
         span : float, optional
             Specify decay in terms of span:
             :math:`\\alpha = 2 / (span + 1)` for :math:`span \\geq 1`.
             One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
             be provided.
-
         halflife : float, optional
             Specify decay in terms of half-life:
             :math:`\\alpha = 1 - \\exp(-\\ln(2) / halflife)` for :math:`halflife > 0`.
             One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
             be provided.
-
         alpha : float, optional
-            Specify the smoothing factor
-            :math:`\\alpha` directly, where :math:`0 < \\alpha \\leq 1`.
-
+            Specify the smoothing factor :math:`\\alpha` directly, where :math:`0 < \\alpha \\leq 1`.
             One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
             be provided.
-
         min_periods : int, default 0
             Minimum number of observations in window required to have a value;
             otherwise, result is ``np.nan``.
-
         adjust : bool, default True
             Divide by decaying adjustment factor in beginning periods to account
             for imbalance in relative weightings (viewing EWMA as a moving average).
-
-            If ``False``, the exponentially weighted function is:
-
-            .. math::
-                y_t = (1 - \\alpha) y_{t-1} + \\alpha x_t
-
-            If ``True``, the exponentially weighted function is:
-
-            .. math::
-                y_t = \\frac{\\sum_{i=0}^t w_i x_{t-i}}{\\sum_{i=0}^t w_i}
-
-            where :math:`w_i = (1 - \\alpha)^i`.
-
         ignore_na : bool, default False
             If ``True``, missing values are ignored in the calculation.
-
             If ``False``, missing values are treated as missing.
-
         axis : {0 or 'index', 1 or 'columns'}, default 0
             The axis to use. The value 0 identifies the rows, and 1 identifies the
             columns.
+        *args, **kwargs
+            Additional arguments and keyword arguments passed to the function.
 
         Returns
         -------
@@ -3897,15 +3876,12 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
         Each group is treated independently, and the exponential weighted calculations
         are applied separately to each group.
 
-        The exponential weighted calculation is based on the formula:
-
-        .. math::
-            y_t = (1 - \\alpha) y_{t-1} + \\alpha x_t
+        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.
 
-        where :math:`\\alpha` is the smoothing factor derived from one of the input
-        decay parameters (``com``, ``span``, ``halflife``, or ``alpha``).
-
-        Only one of ``com``, ``span``, ``halflife``, or ``alpha`` can be specified.
+        When ``adjust=False``, the calculation follows the recursive formula:
+        :math:`y_t = (1 - \\alpha) y_{t-1} + \\alpha x_t`.
 
         Examples
         --------
@@ -3916,7 +3892,7 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
         ...     }
         ... )
         >>> df
-          Class  Value
+        Class  Value
         0     A     10
         1     A     20
         2     A     30
@@ -3925,7 +3901,7 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
         5     B     60
 
         >>> df.groupby("Class").ewm(span=2).mean()
-                     Value
+                    Value
         Class
         A     0  10.000000
               1  17.500000
@@ -3935,7 +3911,7 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
               5  56.153846
 
         >>> df.groupby("Class").ewm(alpha=0.5, adjust=False).mean()
-                 Value
+                Value
         Class
         A     0   10.0
               1   15.0

From cd2c7a5174fd8f750bfd179f6a468243cd28fdef Mon Sep 17 00:00:00 2001
From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com>
Date: Mon, 14 Apr 2025 13:49:07 +0000
Subject: [PATCH 07/10] ruff checks updated

---
 pandas/core/groupby/groupby.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index cc9a694efd365..a1e3632fd08f8 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3848,7 +3848,8 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
             One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
             be provided.
         alpha : float, optional
-            Specify the smoothing factor :math:`\\alpha` directly, where :math:`0 < \\alpha \\leq 1`.
+            Specify the smoothing factor :math:`\\alpha` directly, 
+            where :math:`0 < \\alpha \\leq 1`.
             One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
             be provided.
         min_periods : int, default 0
@@ -3875,11 +3876,9 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
         -----
         Each group is treated independently, and the exponential weighted calculations
         are applied separately to each 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.
-
         When ``adjust=False``, the calculation follows the recursive formula:
         :math:`y_t = (1 - \\alpha) y_{t-1} + \\alpha x_t`.
 

From 4b1b051d04c2f421456de8550ef05a88e17fa585 Mon Sep 17 00:00:00 2001
From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com>
Date: Mon, 14 Apr 2025 13:56:36 +0000
Subject: [PATCH 08/10] ruff checks

---
 pandas/core/groupby/groupby.py        | 6 +++---
 pandas/tests/frame/test_query_eval.py | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index a1e3632fd08f8..4c01444f80f9a 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3848,7 +3848,7 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
             One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
             be provided.
         alpha : float, optional
-            Specify the smoothing factor :math:`\\alpha` directly, 
+            Specify the smoothing factor :math:`\\alpha` directly,
             where :math:`0 < \\alpha \\leq 1`.
             One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
             be provided.
@@ -3876,8 +3876,8 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
         -----
         Each group is treated independently, and the exponential weighted calculations
         are applied separately to each 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 
+        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.
         When ``adjust=False``, the calculation follows the recursive formula:
         :math:`y_t = (1 - \\alpha) y_{t-1} + \\alpha x_t`.
diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py
index f93105498ac79..b599be5d042fe 100644
--- a/pandas/tests/frame/test_query_eval.py
+++ b/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)

From ef2d9dd0dfa3c1f93541d737b126482402fc1b3a Mon Sep 17 00:00:00 2001
From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com>
Date: Mon, 14 Apr 2025 14:27:37 +0000
Subject: [PATCH 09/10] docstring checks

---
 pandas/core/groupby/groupby.py | 78 ++++++++++------------------------
 1 file changed, 22 insertions(+), 56 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 4c01444f80f9a..95c3c895e895c 100644
--- a/pandas/core/groupby/groupby.py
+++ b/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,
     )
@@ -3802,27 +3801,6 @@ def rolling(
             _as_index=self.as_index,
         )
 
-    @final
-    @Substitution(name="groupby")
-    @Appender(_common_see_also)
-    def expanding(self, *args, **kwargs) -> ExpandingGroupby:
-        """
-        Return an expanding grouper, providing expanding
-        functionality per group.
-
-        Returns
-        -------
-        pandas.api.typing.ExpandingGroupby
-        """
-        from pandas.core.window import ExpandingGroupby
-
-        return ExpandingGroupby(
-            self._selected_obj,
-            *args,
-            _grouper=self._grouper,
-            **kwargs,
-        )
-
     @final
     @Substitution(name="groupby")
     @Appender(_common_see_also)
@@ -3832,53 +3810,41 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
 
         Parameters
         ----------
-        com : float, optional
-            Specify decay in terms of center of mass:
-            :math:`\\alpha = 1 / (1 + com)` for :math:`com \\geq 0`.
-            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
-            be provided.
-        span : float, optional
-            Specify decay in terms of span:
-            :math:`\\alpha = 2 / (span + 1)` for :math:`span \\geq 1`.
-            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
-            be provided.
-        halflife : float, optional
-            Specify decay in terms of half-life:
-            :math:`\\alpha = 1 - \\exp(-\\ln(2) / halflife)` for :math:`halflife > 0`.
-            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
-            be provided.
-        alpha : float, optional
-            Specify the smoothing factor :math:`\\alpha` directly,
-            where :math:`0 < \\alpha \\leq 1`.
-            One and only one of ``com``, ``span``, ``halflife``, or ``alpha`` must
-            be provided.
-        min_periods : int, default 0
-            Minimum number of observations in window required to have a value;
-            otherwise, result is ``np.nan``.
-        adjust : bool, default True
-            Divide by decaying adjustment factor in beginning periods to account
-            for imbalance in relative weightings (viewing EWMA as a moving average).
-        ignore_na : bool, default False
-            If ``True``, missing values are ignored in the calculation.
-            If ``False``, missing values are treated as missing.
-        axis : {0 or 'index', 1 or 'columns'}, default 0
-            The axis to use. The value 0 identifies the rows, and 1 identifies the
-            columns.
-        *args, **kwargs
-            Additional arguments and keyword arguments passed to the function.
+        *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.ExponentialMovingWindowGroupby
             Return a new grouper with exponential moving window capabilities.
 
+        See Also
+        --------
+        pandas.DataFrame.ewm : Exponential weighted function for DataFrame.
+        pandas.Series.ewm : Exponential weighted function for Series.
+
         Notes
         -----
         Each group is treated independently, and the exponential weighted calculations
         are applied separately to each 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.
+
         When ``adjust=False``, the calculation follows the recursive formula:
         :math:`y_t = (1 - \\alpha) y_{t-1} + \\alpha x_t`.
 

From a0cf60a1727afe8ac25fd653c58d71802805fccb Mon Sep 17 00:00:00 2001
From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com>
Date: Mon, 14 Apr 2025 14:40:59 +0000
Subject: [PATCH 10/10] update docstring

---
 pandas/core/groupby/groupby.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 95c3c895e895c..12fc6896bdb8f 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3831,11 +3831,6 @@ def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
         pandas.api.typing.ExponentialMovingWindowGroupby
             Return a new grouper with exponential moving window capabilities.
 
-        See Also
-        --------
-        pandas.DataFrame.ewm : Exponential weighted function for DataFrame.
-        pandas.Series.ewm : Exponential weighted function for Series.
-
         Notes
         -----
         Each group is treated independently, and the exponential weighted calculations