jorisvandenbossche commented on a change in pull request #11624:
URL: https://github.com/apache/arrow/pull/11624#discussion_r753103114
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File path: python/pyarrow/table.pxi
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@@ -2387,3 +2429,60 @@ def _from_pydict(cls, mapping, schema, metadata):
return cls.from_arrays(arrays, schema=schema, metadata=metadata)
else:
raise TypeError('Schema must be an instance of pyarrow.Schema')
+
+
+class TableGroupBy:
+ """
+ A grouping of columns in a table on which to perform aggregations.
+ """
+
+ def __init__(self, table, keys):
+ if isinstance(keys, str):
+ keys = [keys]
+
+ self._table = table
+ self.keys = keys
+
+ def aggregate(self, aggregations):
+ """
+ Perform an aggregation over the grouped columns of the table.
+
+ Parameters
+ ----------
+ aggregations : list[tuple(str, str)] or\
+ list[tuple(str, str, FunctionOptions)]
+ List of tuples made of aggregation functions names followed
+ by column names and optionally aggregation function options.
Review comment:
> Well, in pandas it's reversed because the aggregations are keyed by
the column
That's for the interface when specifying a dictionary. But in pandas you can
also do something like `.agg(b_sum=('b', 'sum'))`, in which case it is a bit
more similar as here.
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