yeandy commented on a change in pull request #17043:
URL: https://github.com/apache/beam/pull/17043#discussion_r837878183
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -3655,6 +3656,121 @@ def shift(self, axis, freq, **kwargs):
describe = _agg_method(pd.DataFrame, 'describe')
max = _agg_method(pd.DataFrame, 'max')
min = _agg_method(pd.DataFrame, 'min')
+
+ @frame_base.with_docs_from(pd.DataFrame)
+ @frame_base.args_to_kwargs(pd.DataFrame)
+ @frame_base.populate_defaults(pd.DataFrame)
+ def pivot(self, index=None, columns=None, values=None, **kwargs):
+ def verify_all_categorical(all_cols_are_categorical):
+ if not all_cols_are_categorical:
+ raise frame_base.WontImplementError(
+ "pivot() of non-categorical type is not supported because "
+ "the type of the output column depends on the data. Please use "
+ "pd.CategoricalDtype with explicit categories.",
+ reason="non-deferred-columns")
Review comment:
Fixed!
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -3655,6 +3656,121 @@ def shift(self, axis, freq, **kwargs):
describe = _agg_method(pd.DataFrame, 'describe')
max = _agg_method(pd.DataFrame, 'max')
min = _agg_method(pd.DataFrame, 'min')
+
+ @frame_base.with_docs_from(pd.DataFrame)
+ @frame_base.args_to_kwargs(pd.DataFrame)
+ @frame_base.populate_defaults(pd.DataFrame)
+ def pivot(self, index=None, columns=None, values=None, **kwargs):
+ def verify_all_categorical(all_cols_are_categorical):
+ if not all_cols_are_categorical:
+ raise frame_base.WontImplementError(
+ "pivot() of non-categorical type is not supported because "
+ "the type of the output column depends on the data. Please use "
+ "pd.CategoricalDtype with explicit categories.",
+ reason="non-deferred-columns")
+
+ # Construct column index
+ if is_list_like(columns) and len(columns) <= 1:
+ columns = columns[0]
+ selected_cols = self._expr.proxy()[columns]
+ if isinstance(selected_cols, pd.Series):
+ all_cols_are_categorical = isinstance(
+ selected_cols.dtype, pd.CategoricalDtype
+ )
+ verify_all_categorical(all_cols_are_categorical)
+
+ # If values not provided, take all remaining columns of dataframe
+ if not values:
+ values = self._expr.proxy() \
+ .drop(index, axis=1).drop(columns, axis=1).columns.values
+
+ # Take the provided values
+ if is_list_like(values) and len(values) > 1:
+ values_in_col_index = values
+ names = [None, columns]
+ col_index = pd.MultiIndex.from_product(
+ [values_in_col_index,
+ selected_cols.dtypes.categories.astype('category')],
+ names=names
+ )
+ else:
+ col_index = pd.CategoricalIndex(
+ selected_cols.dtype.categories,
+ name=columns
+ )
+ else:
+ all_cols_are_categorical = all(
+ isinstance(c, pd.CategoricalDtype) for c in selected_cols.dtypes
+ )
+ verify_all_categorical(all_cols_are_categorical)
+
+ categories = [
+ c.categories.astype('category') for c in selected_cols.dtypes
+ ]
+ if is_list_like(columns) and len(columns) > 1:
Review comment:
True, good catch!
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