DOC: fix misspellings

doc/source/user_guide/groupby.rst

@@ -1317,7 +1317,7 @@ arbitrary function, for example:
    df.groupby(['Store', 'Product']).pipe(mean)
 
 where ``mean`` takes a GroupBy object and finds the mean of the Revenue and Quantity
-columns repectively for each Store-Product combination. The ``mean`` function can
+columns respectively for each Store-Product combination. The ``mean`` function can
 be any function that takes in a GroupBy object; the ``.pipe`` will pass the GroupBy
 object as a parameter into the function you specify.
 

doc/source/whatsnew/v0.10.0.rst

@@ -370,7 +370,7 @@ Updated PyTables Support
       df1.get_dtype_counts()
 
 - performance improvements on table writing
-- support for arbitrarly indexed dimensions
+- support for arbitrarily indexed dimensions
 - ``SparseSeries`` now has a ``density`` property (:issue:`2384`)
 - enable ``Series.str.strip/lstrip/rstrip`` methods to take an input argument
   to strip arbitrary characters (:issue:`2411`)

doc/source/whatsnew/v0.16.1.rst

@@ -136,7 +136,7 @@ groupby operations on the index will preserve the index nature as well
 reindexing operations, will return a resulting index based on the type of the passed
 indexer, meaning that passing a list will return a plain-old-``Index``; indexing with
 a ``Categorical`` will return a ``CategoricalIndex``, indexed according to the categories
-of the PASSED ``Categorical`` dtype. This allows one to arbitrarly index these even with
+of the PASSED ``Categorical`` dtype. This allows one to arbitrarily index these even with
 values NOT in the categories, similarly to how you can reindex ANY pandas index.
 
 .. code-block:: ipython