jorisvandenbossche commented on a change in pull request #11624:
URL: https://github.com/apache/arrow/pull/11624#discussion_r752350554
##########
File path: python/pyarrow/table.pxi
##########
@@ -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)]
Review comment:
```suggestion
aggregations : list[tuple(str, str)] or \
list[tuple(str, str, FunctionOptions)]
```
(ugly, but that's what is needed to have the actual docstring look good)
##########
File path: python/pyarrow/table.pxi
##########
@@ -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:
Also, I think it would be good to give a small example here (just to see
it visually, something like ```for example ``[("col_A", "sum"), ("col_B",
"min", options)]`` ```
##########
File path: python/pyarrow/table.pxi
##########
@@ -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:
Would it make more sense to do this the other way around?
Now you have `("func", "col", options)`, but so `("col", "func", options)`.
This way the function name and options are closer together (if the options
are specified). That order also matches the resulting name (which is
"col_func"). And it also matches the order in pandas I think.
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