fix: resolve all ruff format issues - remove trailing whitespace and fix line lengths

Author: wdyy20041223

pandas/core/generic.py

@@ -9981,7 +9981,7 @@ def align(
             * inner: use intersection of keys from both frames,
               preserve the order of the left keys.
 
-        axis : {0 or 'index', 1 or 'columns'} for Series, 
+        axis : {0 or 'index', 1 or 'columns'} for Series,
                {0 or 'index', 1 or 'columns'} for DataFrame, default None
             Align on index (0), columns (1), or both (None).
         level : int or level name, default None
@@ -12698,8 +12698,9 @@ def ewm(
               [:math:`x_0, x_1, ..., x_t`] would be:
 
             .. math::
-                y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... +
-                (1 - \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}
+                y_t = \\frac{x_t + (1 - \\alpha)x_{t-1} + (1 - \\alpha)^2 x_{t-2} + ... +
+                (1 - \\alpha)^t x_0}{1 + (1 - \\alpha) + (1 - \\alpha)^2 + ... +
+                (1 - \\alpha)^t}
 
             - When ``adjust=False``, the exponentially weighted function is calculated
               recursively:
@@ -12714,23 +12715,24 @@ def ewm(
 
             - When ``ignore_na=False`` (default), weights are based on absolute
               positions.
-              For example, the weights of :math:`x_0` and :math:`x_2` used in calculating
-              the final weighted average of [:math:`x_0`, None, :math:`x_2`] are
-              :math:`(1-\alpha)^2` and :math:`1` if ``adjust=True``, and
+              For example, the weights of :math:`x_0` and :math:`x_2` used in
+              calculating the final weighted average of [:math:`x_0`, None, :math:`x_2`]
+              are :math:`(1-\alpha)^2` and :math:`1` if ``adjust=True``, and
               :math:`(1-\alpha)^2` and :math:`\alpha` if ``adjust=False``.
 
             - When ``ignore_na=True``, weights are based
-              on relative positions. For example, the weights of :math:`x_0` and :math:`x_2`
-              used in calculating the final weighted average of
+              on relative positions. For example, the weights of :math:`x_0` and
+              :math:`x_2` used in calculating the final weighted average of
               [:math:`x_0`, None, :math:`x_2`] are :math:`1-\alpha` and :math:`1` if
-              ``adjust=True``, and :math:`1-\alpha` and :math:`\alpha` if ``adjust=False``.
+              ``adjust=True``, and :math:`1-\alpha` and :math:`\alpha` if
+              ``adjust=False``.
 
         times : np.ndarray, Series, default None
 
             Only applicable to ``mean()``.
 
-            Times corresponding to the observations. Must be monotonically increasing and
-            ``datetime64[ns]`` dtype.
+            Times corresponding to the observations. Must be monotonically increasing
+            and ``datetime64[ns]`` dtype.
 
             If 1-D array like, a sequence with the same shape as the observations.
 
@@ -12748,8 +12750,8 @@ def ewm(
         Returns
         -------
         pandas.api.typing.ExponentialMovingWindow
-            An instance of ExponentialMovingWindow for further exponentially weighted (EW)
-            calculations, e.g. using the ``mean`` method.
+            An instance of ExponentialMovingWindow for further exponentially weighted
+            (EW) calculations, e.g. using the ``mean`` method.
 
         See Also
         --------
@@ -12823,10 +12825,11 @@ def ewm(
 
         **times**
 
-        Exponentially weighted mean with weights calculated with a timedelta ``halflife``
-        relative to ``times``.
+        Exponentially weighted mean with weights calculated with a timedelta
+        ``halflife`` relative to ``times``.
 
-        >>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
+        >>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15',
+        ...          '2020-01-17']
         >>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
                   B
         0  0.000000