Avoided looping and multiple np calls, added api breaking section to whatsnew

Author: rhshadrach

doc/source/whatsnew/v1.1.0.rst

@@ -380,6 +380,35 @@ Assignment to multiple columns of a :class:`DataFrame` when some of the columns
    df[['a', 'c']] = 1
    df
 
+.. _whatsnew_110.api_breaking.as_index_false_with_std_and_sem:
+
+:meth:`DataFrameGroupby.std` and :meth:`DataFrameGroupby.sem` preserve group keys when ``as_index=False``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Using :meth:`DataFrameGroupby.std` and :meth:`DataFrameGroupby.sem` would previously alter the group keys when ``as_index=False``. Now, they are correctly left as the group keys. (:issue:`10355`)
+
+.. ipython:: python
+
+   df = pd.DataFrame({"a": [0, 0, 1, 1, 2, 2], "b": [1, 1, 2, 3, 5, 8]})
+   df
+
+*Previous behavior*:
+
+.. code-block:: ipython
+
+   In [3]: df.groupby("a", as_index=False).std()
+   Out[3]:
+          a         b
+   0  0.000000  0.000000
+   1  1.000000  0.707107
+   2  1.414214  2.121320
+
+*New behavior*:
+
+.. ipython:: python
+
+   df.groupby("a", as_index=False).std()
+
 .. _whatsnew_110.deprecations:
 
 Deprecations

pandas/core/groupby/groupby.py

@@ -1271,17 +1271,19 @@ def std(self, ddof: int = 1):
             Degrees of freedom.
 
         Returns
-        -------
+        -------s
         Series or DataFrame
             Standard deviation of values within each group.
         """
         result = self.var(ddof=ddof)
         if result.ndim == 1:
             result = np.sqrt(result)
         else:
-            for col in result:
-                if col not in self.exclusions:
-                    result[col] = np.sqrt(result[col])
+            cols = result.columns.get_indexer_for(
+                result.columns.difference(self.exclusions).unique()
+            )
+            result.iloc[:, cols] = np.sqrt(result.iloc[:, cols]).values
+
         return result
 
     @Substitution(name="groupby")
@@ -1331,13 +1333,15 @@ def sem(self, ddof: int = 1):
             Standard error of the mean of values within each group.
         """
         result = self.std(ddof=ddof)
-        denom = np.sqrt(self.count())
         if result.ndim == 1:
-            result /= denom
+            result /= np.sqrt(self.count())
         else:
-            for col in result:
-                if col not in self.exclusions:
-                    result[col] /= denom[col]
+            cols = result.columns.get_indexer_for(
+                result.columns.difference(self.exclusions).unique()
+            )
+            result.iloc[:, cols] = (
+                result.iloc[:, cols].values / np.sqrt(self.count().iloc[:, cols]).values
+            )
         return result
 
     @Substitution(name="groupby")