vibhatha commented on a change in pull request #155:
URL: https://github.com/apache/arrow-cookbook/pull/155#discussion_r818692078
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File path: python/source/data.rst
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@@ -294,6 +294,146 @@ using :meth:`pyarrow.Table.set_column`
item: [["Potato","Bean","Cucumber","Eggs"]]
new_amount: [[30,20,15,40]]
+Group and Sort a Table
+======================
+
+If you have a table which needs to be grouped by a particular key,
+you can use :meth:`pyarrow.Table.group_by` followed by an aggregation
+operation :meth:`pyarrow.TableGroupBy.aggregate`.
+
+For example, let’s say we have some data with a particular set of keys
+and values associated with that key. And we want to group the data by
+those keys and apply an aggregate function like sum to evaluate
+how many items are for each unique key.
+
+.. testcode::
+
+ import pyarrow as pa
+
+ table = pa.table([
+ pa.array(["a", "a", "b", "b", "c", "d", "e", "c"]),
+ pa.array([11, 20, 3, 4, 5, 1, 4, 10]),
+ ], names=["keys", "values"])
+
+ print(table)
+
+.. testoutput::
+
+ pyarrow.Table
+ keys: string
+ values: int64
+ ----
+ keys: [["a","a","b","b","c","d","e","c"]]
+ values: [[11,20,3,4,5,1,4,10]]
+
+Now we let's apply a groupby operation. Note that a groupby
+operation returns a :class:`pyarrow.TableGroupBy` object which contains
+the aggregate operator as :meth:`pyarrow.TableGroupBy.aggregate`.
+
+.. testcode::
+
+ grouped_table = table.group_by("keys")
+
+ print(type(grouped_table))
+
+.. testoutput::
+
+ <class 'pyarrow.lib.TableGroupBy'>
+
+The output will look something similar to this. Now the table is
+grouped by the field ``key`` and let's apply the aggregate operation
+``sum`` based on the values in the column ``values``. Note that, an
+aggregation operation pairs with a column name.
+
+.. testcode::
+
+ aggregated_table = grouped_table.aggregate([("values", "sum")])
+
+ print(aggregated_table)
Review comment:
Yes, that make sense. I will remove this part and redirect the user to
the API docs for more details.
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