WeichenXu123 commented on a change in pull request #34021:
URL: https://github.com/apache/spark/pull/34021#discussion_r710134006
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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):
+ raise TypeError("metadata should be a dict")
+ return DataFrame(self._jdf.withMetadata(columnName, metadata),
self.sql_ctx)
Review comment:
The metadata argument type is a dict , how does it pass to the java
method `def withMetadata(columnName: String, metadata: Metadata)` and the
metadata become a `Metadata` class type ?
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