Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/11444#discussion_r54822526
--- Diff: python/pyspark/sql/context.py ---
@@ -63,7 +63,42 @@ def toDF(self, schema=None, sampleRatio=None):
"""
return sqlContext.createDataFrame(self, schema, sampleRatio)
+ def schema(self, datatype):
+ """
+ Converts current :class:`RDD` into a :class:`DataFrame` according
to the given data type.
+ Note that, the given datatype must match the real data, or
exception will be thrown at
+ runtime. If the given datatype is not StructType, it will be
wrapped into a StructType as
+ its only field, and the field name will be "value", each record
will also be wrapped into a
+ list, which can be converted to row later.
+
+ :param datatype:
+ a :class:`DataType` or a data type string. The data type
string format equals to
+ `DataType.simpleString`, except that top level struct type can
omit the `struct<>` and
+ numeric types use `typeName()` as their format, e.g. use
`byte` instead of `tinyint` for
+ ByteType. We can also use `int` as a short name for
IntegerType.
+
+ """
+ if isinstance(datatype, basestring):
+ datatype = _parse_datatype_string(datatype)
+
+ if not isinstance(datatype, DataType):
+ raise TypeError("datatype should be DataType or string, but
got: %s" % datatype)
+ else:
+ def verify(obj):
+ _verify_type(obj, datatype)
--- End diff --
Since Python and Scala/Java have different range of int/long numbers, does
_verify_type check if the actual value is within the allowed range?
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