Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/11347#discussion_r54321647
--- Diff: python/pyspark/sql/dataframe.py ---
@@ -859,14 +905,20 @@ def filter(self, condition):
[Row(age=5, name=u'Bob')]
>>> df.where("age = 2").collect()
[Row(age=2, name=u'Alice')]
+
+ >>> df.filter(lambda row: row.age > 3).collect()
+ [Row(age=5, name=u'Bob')]
+ >>> df.map(lambda row: row.age).filter(lambda age: age >
3).collect()
+ [5]
"""
if isinstance(condition, basestring):
- jdf = self._jdf.filter(condition)
+ return DataFrame(self._jdf.filter(condition), self.sql_ctx)
--- End diff --
DataFrame always have a schema(we have a default), the difference is: a
DataFrame with default schema has custom objects as records, other DataFrames
has rows as records.
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