WeichenXu123 commented on a change in pull request #34021:
URL: https://github.com/apache/spark/pull/34021#discussion_r711587577
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File path: python/pyspark/sql/dataframe.py
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@@ -2536,6 +2536,28 @@ def withColumnRenamed(self, existing, new):
"""
return DataFrame(self._jdf.withColumnRenamed(existing, new),
self.sql_ctx)
+ def withMetadata(self, columnName, metadata):
+ """Returns a new :class:`DataFrame` by updating an existing column
with metadata.
+
+ .. versionadded:: 3.3.0
+
+ Parameters
+ ----------
+ columnName : str
+ string, name of the existing column to update the metadata.
+ metadata : dict
+ dict, new metadata to be assigned to df.schema[columnName].metadata
+
+ Examples
+ --------
+ >>> df_meta = df.withMetadata('age', {'foo': 'bar'})
+ >>> df_meta.schema['age'].metadata
+ {'foo': 'bar'}
+ """
+ if not isinstance(metadata, dict):
Review comment:
@HyukjinKwon
>
https://github.com/apache/spark/blob/master/python/pyspark/sql/column.py#L712
The existing API that takes the metadata also specified that the metadata
should be a dict in the docstring. However, I'm also fine with not checking the
dict type.
But in code here
https://github.com/apache/spark/blob/cabc36b54d7f6633d8b128e511e7049c475b919d/python/pyspark/sql/column.py#L747
it doesn't require metadata to be dict , so is it a doc error or code error
there ?
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