Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/11347#discussion_r54308608
--- Diff: python/pyspark/sql/dataframe.py ---
@@ -257,53 +264,85 @@ def limit(self, num):
@ignore_unicode_prefix
@since(1.3)
def take(self, num):
- """Returns the first ``num`` rows as a :class:`list` of
:class:`Row`.
+ """Returns the first ``num`` records as a :class:`list`.
>>> df.take(2)
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
"""
- with SCCallSiteSync(self._sc) as css:
- port =
self._sc._jvm.org.apache.spark.sql.execution.python.EvaluatePython.takeAndServe(
- self._jdf, num)
- return list(_load_from_socket(port,
BatchedSerializer(PickleSerializer())))
+ return self.limit(num).collect()
@ignore_unicode_prefix
- @since(1.3)
- def map(self, f):
- """ Returns a new :class:`RDD` by applying a the ``f`` function to
each :class:`Row`.
+ @since(2.0)
+ def applySchema(self, schema=None):
+ """Returns a new :class:`DataFrame` by appling the given schema,
or infer the schema
+ by all of the records if no schema is given.
- This is a shorthand for ``df.rdd.map()``.
+ It is only allowed to apply schema for DataFrame which is returned
by typed operations,
+ e.g. map, flatMap, etc. And the record type of the schema-applied
DataFrame will be row.
- >>> df.map(lambda p: p.name).collect()
+ >>> ds = df.map(lambda row: row.name)
+ >>> ds.collect()
[u'Alice', u'Bob']
+ >>> ds.schema
+ StructType(List(StructField(value,BinaryType,false)))
+ >>> ds2 = ds.applySchema(StringType())
+ >>> ds2.collect()
+ [Row(value=u'Alice'), Row(value=u'Bob')]
+ >>> ds2.schema
+ StructType(List(StructField(value,StringType,true)))
+ >>> ds3 = ds.applySchema()
+ >>> ds3.collect()
+ [Row(value=u'Alice'), Row(value=u'Bob')]
+ >>> ds3.schema
+ StructType(List(StructField(value,StringType,true)))
+ """
+ msg = "Cannot apply schema to a DataFrame which is not returned by
typed operations"
+ raise RuntimeError(msg)
--- End diff --
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