def createFromArrowRecordBatchesRDD(self, ardd, schema=None, timezone=None):
#from pyspark.sql.types import from_arrow_schema
#from pyspark.sql.dataframe import DataFrame
#from pyspark.serializers import ArrowSerializer, PickleSerializer, AutoBatchedSerializer
from pyspark.sql.pandas.types import from_arrow_schema
from pyspark.sql.dataframe import DataFrame
# Filter out and cache arrow record batches
ardd = ardd.filter(lambda x: isinstance(x, pa.RecordBatch)).cache()
ardd = ardd.map(_arrow_record_batch_dumps)
#schema = pa.schema([pa.field('c0', pa.int16()),
# pa.field('c1', pa.int32())],
# metadata={b'foo': b'bar'})
if (args.aligner == "BWA"):
schema = from_arrow_schema(sam_schema())
else:
schema = from_arrow_schema(_schema())
# Create the Spark DataFrame directly from the Arrow data and schema
jrdd = ardd._to_java_object_rdd()
jdf = self._jvm.PythonSQLUtils.toDataFrame(jrdd, schema.json(),
self._wrapped._jsqlContext)
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
"""
Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
data types will be used to coerce the data in Pandas to Arrow conversion.
"""
from pyspark.serializers import ArrowStreamSerializer, _create_batch
from pyspark.sql.types import from_arrow_schema, to_arrow_type, TimestampType
from pyspark.sql.utils import require_minimum_pandas_version, \
require_minimum_pyarrow_version
require_minimum_pandas_version()
require_minimum_pyarrow_version()
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
# Determine arrow types to coerce data when creating batches
if isinstance(schema, StructType):
arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
elif isinstance(schema, DataType):
raise ValueError("Single data type %s is not supported with Arrow" % str(schema))
else:
# Any timestamps must be coerced to be compatible with Spark
arrow_types = [to_arrow_type(TimestampType())
if is_datetime64_dtype(t) or is_datetime64tz_dtype(t) else None
for t in pdf.dtypes]
# Slice the DataFrame to be batched
step = -(-len(pdf) // self.sparkContext.defaultParallelism) # round int up
pdf_slices = (pdf[start:start + step] for start in xrange(0, len(pdf), step))
# Create Arrow record batches
safecheck = self._wrapped._conf.arrowSafeTypeConversion()
batches = [_create_batch([(c, t) for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)],
timezone, safecheck)
for pdf_slice in pdf_slices]
# Create the Spark schema from the first Arrow batch (always at least 1 batch after slicing)
if isinstance(schema, (list, tuple)):
struct = from_arrow_schema(batches[0].schema)
for i, name in enumerate(schema):
struct.fields[i].name = name
struct.names[i] = name
schema = struct
jsqlContext = self._wrapped._jsqlContext
def reader_func(temp_filename):
return self._jvm.PythonSQLUtils.readArrowStreamFromFile(jsqlContext, temp_filename)
def create_RDD_server():
return self._jvm.ArrowRDDServer(jsqlContext)
# Create Spark DataFrame from Arrow stream file, using one batch per partition
jrdd = self._sc._serialize_to_jvm(batches, ArrowStreamSerializer(), reader_func,
create_RDD_server)
jdf = self._jvm.PythonSQLUtils.toDataFrame(jrdd, schema.json(), jsqlContext)
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def _create_dataframe(self, data, schema, samplingRatio, verifySchema):
if isinstance(schema, StructType):
verify_func = _make_type_verifier(schema) if verifySchema else lambda _: True
def prepare(obj):
verify_func(obj)
return obj
elif isinstance(schema, DataType):
dataType = schema
schema = StructType().add("value", schema)
verify_func = _make_type_verifier(
dataType, name="field value") if verifySchema else lambda _: True
def prepare(obj):
verify_func(obj)
return obj,
else:
prepare = lambda obj: obj
if isinstance(data, RDD):
rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
else:
rdd, schema = self._createFromLocal(map(prepare, data), schema)
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
"""
Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
data types will be used to coerce the data in Pandas to Arrow conversion.
"""
from pyspark.serializers import ArrowStreamSerializer, _create_batch
from pyspark.sql.types import from_arrow_schema, to_arrow_type, TimestampType
from pyspark.sql.utils import require_minimum_pandas_version, \
require_minimum_pyarrow_version
require_minimum_pandas_version()
require_minimum_pyarrow_version()
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
# Determine arrow types to coerce data when creating batches
if isinstance(schema, StructType):
arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
elif isinstance(schema, DataType):
raise ValueError("Single data type %s is not supported with Arrow" % str(schema))
else:
# Any timestamps must be coerced to be compatible with Spark
arrow_types = [to_arrow_type(TimestampType())
if is_datetime64_dtype(t) or is_datetime64tz_dtype(t) else None
for t in pdf.dtypes]
# Slice the DataFrame to be batched
step = -(-len(pdf) // self.sparkContext.defaultParallelism) # round int up
pdf_slices = (pdf[start:start + step] for start in xrange(0, len(pdf), step))
# Create Arrow record batches
batches = [_create_batch([(c, t) for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)],
timezone)
for pdf_slice in pdf_slices]
# Create the Spark schema from the first Arrow batch (always at least 1 batch after slicing)
if isinstance(schema, (list, tuple)):
struct = from_arrow_schema(batches[0].schema)
for i, name in enumerate(schema):
struct.fields[i].name = name
struct.names[i] = name
schema = struct
jsqlContext = self._wrapped._jsqlContext
def reader_func(temp_filename):
return self._jvm.PythonSQLUtils.readArrowStreamFromFile(jsqlContext, temp_filename)
def create_RDD_server():
return self._jvm.ArrowRDDServer(jsqlContext)
# Create Spark DataFrame from Arrow stream file, using one batch per partition
jrdd = self._sc._serialize_to_jvm(batches, ArrowStreamSerializer(), reader_func,
create_RDD_server)
jdf = self._jvm.PythonSQLUtils.toDataFrame(jrdd, schema.json(), jsqlContext)
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
"""
Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
data types will be used to coerce the data in Pandas to Arrow conversion.
"""
from pyspark.serializers import ArrowSerializer, _create_batch
from pyspark.sql.types import from_arrow_schema, to_arrow_type, \
_old_pandas_exception_message, TimestampType
try:
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
except ImportError as e:
raise ImportError(_old_pandas_exception_message(e))
# Determine arrow types to coerce data when creating batches
if isinstance(schema, StructType):
arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
elif isinstance(schema, DataType):
raise ValueError(
"Single data type %s is not supported with Arrow" %
str(schema))
else:
# Any timestamps must be coerced to be compatible with Spark
arrow_types = [
to_arrow_type(TimestampType())
if is_datetime64_dtype(t) or is_datetime64tz_dtype(t) else None
for t in pdf.dtypes
]
# Slice the DataFrame to be batched
step = -(-len(pdf) // self.sparkContext.defaultParallelism
) # round int up
pdf_slices = (pdf[start:start + step]
for start in xrange(0, len(pdf), step))
# Create Arrow record batches
batches = [
_create_batch(
[(c, t)
for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)],
timezone) for pdf_slice in pdf_slices
]
# Create the Spark schema from the first Arrow batch (always at least 1 batch after slicing)
if isinstance(schema, (list, tuple)):
struct = from_arrow_schema(batches[0].schema)
for i, name in enumerate(schema):
struct.fields[i].name = name
struct.names[i] = name
schema = struct
# Create the Spark DataFrame directly from the Arrow data and schema
jrdd = self._sc._serialize_to_jvm(batches, len(batches),
ArrowSerializer())
jdf = self._jvm.PythonSQLUtils.arrowPayloadToDataFrame(
jrdd, schema.json(), self._wrapped._jsqlContext)
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
"""
Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
data types will be used to coerce the data in Pandas to Arrow conversion.
"""
from pyspark.serializers import ArrowSerializer, _create_batch
from pyspark.sql.types import from_arrow_schema, to_arrow_type, \
_old_pandas_exception_message, TimestampType
from pyspark.sql.utils import _require_minimum_pyarrow_version
try:
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
except ImportError as e:
raise ImportError(_old_pandas_exception_message(e))
_require_minimum_pyarrow_version()
# Determine arrow types to coerce data when creating batches
if isinstance(schema, StructType):
arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
elif isinstance(schema, DataType):
raise ValueError("Single data type %s is not supported with Arrow" % str(schema))
else:
# Any timestamps must be coerced to be compatible with Spark
arrow_types = [to_arrow_type(TimestampType())
if is_datetime64_dtype(t) or is_datetime64tz_dtype(t) else None
for t in pdf.dtypes]
# Slice the DataFrame to be batched
step = -(-len(pdf) // self.sparkContext.defaultParallelism) # round int up
pdf_slices = (pdf[start:start + step] for start in xrange(0, len(pdf), step))
# Create Arrow record batches
batches = [_create_batch([(c, t) for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)],
timezone)
for pdf_slice in pdf_slices]
# Create the Spark schema from the first Arrow batch (always at least 1 batch after slicing)
if isinstance(schema, (list, tuple)):
struct = from_arrow_schema(batches[0].schema)
for i, name in enumerate(schema):
struct.fields[i].name = name
struct.names[i] = name
schema = struct
# Create the Spark DataFrame directly from the Arrow data and schema
jrdd = self._sc._serialize_to_jvm(batches, len(batches), ArrowSerializer())
jdf = self._jvm.PythonSQLUtils.arrowPayloadToDataFrame(
jrdd, schema.json(), self._wrapped._jsqlContext)
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def _create_dataframe(
self,
data: Union["RDD[Any]", Iterable[Any]],
schema: Optional[Union[DataType, List[str]]],
samplingRatio: Optional[float],
verifySchema: bool,
) -> DataFrame:
if isinstance(schema, StructType):
verify_func = _make_type_verifier(schema) if verifySchema else lambda _: True
@no_type_check
def prepare(obj):
verify_func(obj)
return obj
elif isinstance(schema, DataType):
dataType = schema
schema = StructType().add("value", schema)
verify_func = (
_make_type_verifier(dataType, name="field value")
if verifySchema
else lambda _: True
)
@no_type_check
def prepare(obj):
verify_func(obj)
return (obj,)
else:
def prepare(obj: Any) -> Any:
return obj
if isinstance(data, RDD):
rdd, struct = self._createFromRDD(data.map(prepare), schema, samplingRatio)
else:
rdd, struct = self._createFromLocal(map(prepare, data), schema)
assert self._jvm is not None
jrdd = self._jvm.SerDeUtil.toJavaArray(
rdd._to_java_object_rdd() # type: ignore[attr-defined]
)
jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), struct.json())
df = DataFrame(jdf, self)
df._schema = struct
return df
def createDataFrame(sqlc, data, schema, samplingRatio=None):
""" Our own version of spark.sql.session.createDataFrame which doesn't validate the schema.
See https://issues.apache.org/jira/browse/SPARK-16700
"""
# pylint: disable=protected-access
self = sqlc.sparkSession
if isinstance(data, RDD):
rdd, schema = self._createFromRDD(data, schema, samplingRatio)
else:
rdd, schema = self._createFromLocal(data, schema)
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def createDataFrame(sqlc, data, schema, samplingRatio=None):
""" Our own version of spark.sql.session.createDataFrame which doesn't validate the schema.
See https://issues.apache.org/jira/browse/SPARK-16700
"""
# pylint: disable=protected-access
self = sqlc.sparkSession
if isinstance(data, RDD):
rdd, schema = self._createFromRDD(data, schema, samplingRatio)
else:
rdd, schema = self._createFromLocal(data, schema)
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def createDataFrame(self, data, schema=None, samplingRatio=None):
"""
Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
When ``schema`` is a list of column names, the type of each column
will be inferred from ``data``.
When ``schema`` is ``None``, it will try to infer the schema (column names and types)
from ``data``, which should be an RDD of :class:`Row`,
or :class:`namedtuple`, or :class:`dict`.
When ``schema`` is :class:`DataType` or datatype string, it must match the real data, or
exception will be thrown at runtime. If the given schema 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 tuple, which can be converted to row later.
If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
:param data: an RDD of any kind of SQL data representation(e.g. row, tuple, int, boolean,
etc.), or :class:`list`, or :class:`pandas.DataFrame`.
:param schema: a :class:`DataType` or a datatype string or a list of column names, default
is None. The data type string format equals to `DataType.simpleString`, except that
top level struct type can omit the `struct<>` and atomic 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.
:param samplingRatio: the sample ratio of rows used for inferring
:return: :class:`DataFrame`
.. versionchanged:: 2.0
The schema parameter can be a DataType or a datatype string after 2.0. If it's not a
StructType, it will be wrapped into a StructType and each record will also be wrapped
into a tuple.
>>> l = [('Alice', 1)]
>>> spark.createDataFrame(l).collect()
[Row(_1=u'Alice', _2=1)]
>>> spark.createDataFrame(l, ['name', 'age']).collect()
[Row(name=u'Alice', age=1)]
>>> d = [{'name': 'Alice', 'age': 1}]
>>> spark.createDataFrame(d).collect()
[Row(age=1, name=u'Alice')]
>>> rdd = sc.parallelize(l)
>>> spark.createDataFrame(rdd).collect()
[Row(_1=u'Alice', _2=1)]
>>> df = spark.createDataFrame(rdd, ['name', 'age'])
>>> df.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql import Row
>>> Person = Row('name', 'age')
>>> person = rdd.map(lambda r: Person(*r))
>>> df2 = spark.createDataFrame(person)
>>> df2.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql.types import *
>>> schema = StructType([
... StructField("name", StringType(), True),
... StructField("age", IntegerType(), True)])
>>> df3 = spark.createDataFrame(rdd, schema)
>>> df3.collect()
[Row(name=u'Alice', age=1)]
>>> spark.createDataFrame(df.toPandas()).collect() # doctest: +SKIP
[Row(name=u'Alice', age=1)]
>>> spark.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP
[Row(0=1, 1=2)]
>>> spark.createDataFrame(rdd, "a: string, b: int").collect()
[Row(a=u'Alice', b=1)]
>>> rdd = rdd.map(lambda row: row[1])
>>> spark.createDataFrame(rdd, "int").collect()
[Row(value=1)]
>>> spark.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
Py4JJavaError: ...
"""
if isinstance(data, DataFrame):
raise TypeError("data is already a DataFrame")
if isinstance(schema, basestring):
schema = _parse_datatype_string(schema)
try:
import pandas
has_pandas = True
except Exception:
has_pandas = False
if has_pandas and isinstance(data, pandas.DataFrame):
if schema is None:
schema = [str(x) for x in data.columns]
data = [r.tolist() for r in data.to_records(index=False)]
if isinstance(schema, StructType):
def prepare(obj):
_verify_type(obj, schema)
return obj
elif isinstance(schema, DataType):
datatype = schema
def prepare(obj):
_verify_type(obj, datatype)
return (obj, )
schema = StructType().add("value", datatype)
else:
prepare = lambda obj: obj
if isinstance(data, RDD):
rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
else:
rdd, schema = self._createFromLocal(map(prepare, data), schema)
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=True):
"""
Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
When ``schema`` is a list of column names, the type of each column
will be inferred from ``data``.
When ``schema`` is ``None``, it will try to infer the schema (column names and types)
from ``data``, which should be an RDD of :class:`Row`,
or :class:`namedtuple`, or :class:`dict`.
When ``schema`` is :class:`pyspark.sql.types.DataType` or a datatype string, it must match
the real data, or an exception will be thrown at runtime. If the given schema is not
:class:`pyspark.sql.types.StructType`, it will be wrapped into a
:class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value",
each record will also be wrapped into a tuple, which can be converted to row later.
If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
:param data: an RDD of any kind of SQL data representation(e.g. row, tuple, int, boolean,
etc.), or :class:`list`, or :class:`pandas.DataFrame`.
:param schema: a :class:`pyspark.sql.types.DataType` or a datatype string or a list of
column names, default is ``None``. The data type string format equals to
:class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`. We can also use
``int`` as a short name for ``IntegerType``.
:param samplingRatio: the sample ratio of rows used for inferring
:param verifySchema: verify data types of every row against schema.
:return: :class:`DataFrame`
.. versionchanged:: 2.1
Added verifySchema.
.. note:: Usage with spark.sql.execution.arrow.enabled=True is experimental.
>>> l = [('Alice', 1)]
>>> spark.createDataFrame(l).collect()
[Row(_1=u'Alice', _2=1)]
>>> spark.createDataFrame(l, ['name', 'age']).collect()
[Row(name=u'Alice', age=1)]
>>> d = [{'name': 'Alice', 'age': 1}]
>>> spark.createDataFrame(d).collect()
[Row(age=1, name=u'Alice')]
>>> rdd = sc.parallelize(l)
>>> spark.createDataFrame(rdd).collect()
[Row(_1=u'Alice', _2=1)]
>>> df = spark.createDataFrame(rdd, ['name', 'age'])
>>> df.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql import Row
>>> Person = Row('name', 'age')
>>> person = rdd.map(lambda r: Person(*r))
>>> df2 = spark.createDataFrame(person)
>>> df2.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql.types import *
>>> schema = StructType([
... StructField("name", StringType(), True),
... StructField("age", IntegerType(), True)])
>>> df3 = spark.createDataFrame(rdd, schema)
>>> df3.collect()
[Row(name=u'Alice', age=1)]
>>> spark.createDataFrame(df.toPandas()).collect() # doctest: +SKIP
[Row(name=u'Alice', age=1)]
>>> spark.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP
[Row(0=1, 1=2)]
>>> spark.createDataFrame(rdd, "a: string, b: int").collect()
[Row(a=u'Alice', b=1)]
>>> rdd = rdd.map(lambda row: row[1])
>>> spark.createDataFrame(rdd, "int").collect()
[Row(value=1)]
>>> spark.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
Py4JJavaError: ...
"""
SparkSession._activeSession = self
self._jvm.SparkSession.setActiveSession(self._jsparkSession)
if isinstance(data, DataFrame):
raise TypeError("data is already a DataFrame")
if isinstance(schema, basestring):
schema = _parse_datatype_string(schema)
elif isinstance(schema, (list, tuple)):
# Must re-encode any unicode strings to be consistent with StructField names
schema = [x.encode('utf-8') if not isinstance(x, str) else x for x in schema]
try:
import pandas
has_pandas = True
except Exception:
has_pandas = False
if has_pandas and isinstance(data, pandas.DataFrame):
from pyspark.sql.utils import require_minimum_pandas_version
require_minimum_pandas_version()
if self._wrapped._conf.pandasRespectSessionTimeZone():
timezone = self._wrapped._conf.sessionLocalTimeZone()
else:
timezone = None
# If no schema supplied by user then get the names of columns only
if schema is None:
schema = [str(x) if not isinstance(x, basestring) else
(x.encode('utf-8') if not isinstance(x, str) else x)
for x in data.columns]
if self._wrapped._conf.arrowEnabled() and len(data) > 0:
try:
return self._create_from_pandas_with_arrow(data, schema, timezone)
except Exception as e:
from pyspark.util import _exception_message
if self._wrapped._conf.arrowFallbackEnabled():
msg = (
"createDataFrame attempted Arrow optimization because "
"'spark.sql.execution.arrow.enabled' is set to true; however, "
"failed by the reason below:\n %s\n"
"Attempting non-optimization as "
"'spark.sql.execution.arrow.fallback.enabled' is set to "
"true." % _exception_message(e))
warnings.warn(msg)
else:
msg = (
"createDataFrame attempted Arrow optimization because "
"'spark.sql.execution.arrow.enabled' is set to true, but has reached "
"the error below and will not continue because automatic fallback "
"with 'spark.sql.execution.arrow.fallback.enabled' has been set to "
"false.\n %s" % _exception_message(e))
warnings.warn(msg)
raise
data = self._convert_from_pandas(data, schema, timezone)
if isinstance(schema, StructType):
verify_func = _make_type_verifier(schema) if verifySchema else lambda _: True
def prepare(obj):
verify_func(obj)
return obj
elif isinstance(schema, DataType):
dataType = schema
schema = StructType().add("value", schema)
verify_func = _make_type_verifier(
dataType, name="field value") if verifySchema else lambda _: True
def prepare(obj):
verify_func(obj)
return obj,
else:
prepare = lambda obj: obj
if isinstance(data, RDD):
rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
else:
rdd, schema = self._createFromLocal(map(prepare, data), schema)
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def createDataFrame(self,
data,
schema=None,
samplingRatio=None,
verifySchema=True):
"""
Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
When ``schema`` is a list of column names, the type of each column
will be inferred from ``data``.
When ``schema`` is ``None``, it will try to infer the schema (column names and types)
from ``data``, which should be an RDD of :class:`Row`,
or :class:`namedtuple`, or :class:`dict`.
When ``schema`` is :class:`pyspark.sql.types.DataType` or a datatype string, it must match
the real data, or an exception will be thrown at runtime. If the given schema is not
:class:`pyspark.sql.types.StructType`, it will be wrapped into a
:class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value",
each record will also be wrapped into a tuple, which can be converted to row later.
If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
:param data: an RDD of any kind of SQL data representation(e.g. row, tuple, int, boolean,
etc.), or :class:`list`, or :class:`pandas.DataFrame`.
:param schema: a :class:`pyspark.sql.types.DataType` or a datatype string or a list of
column names, default is ``None``. The data type string format equals to
:class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`. We can also use
``int`` as a short name for ``IntegerType``.
:param samplingRatio: the sample ratio of rows used for inferring
:param verifySchema: verify data types of every row against schema.
:return: :class:`DataFrame`
.. versionchanged:: 2.1
Added verifySchema.
.. note:: Usage with spark.sql.execution.arrow.enabled=True is experimental.
>>> l = [('Alice', 1)]
>>> spark.createDataFrame(l).collect()
[Row(_1=u'Alice', _2=1)]
>>> spark.createDataFrame(l, ['name', 'age']).collect()
[Row(name=u'Alice', age=1)]
>>> d = [{'name': 'Alice', 'age': 1}]
>>> spark.createDataFrame(d).collect()
[Row(age=1, name=u'Alice')]
>>> rdd = sc.parallelize(l)
>>> spark.createDataFrame(rdd).collect()
[Row(_1=u'Alice', _2=1)]
>>> df = spark.createDataFrame(rdd, ['name', 'age'])
>>> df.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql import Row
>>> Person = Row('name', 'age')
>>> person = rdd.map(lambda r: Person(*r))
>>> df2 = spark.createDataFrame(person)
>>> df2.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql.types import *
>>> schema = StructType([
... StructField("name", StringType(), True),
... StructField("age", IntegerType(), True)])
>>> df3 = spark.createDataFrame(rdd, schema)
>>> df3.collect()
[Row(name=u'Alice', age=1)]
>>> spark.createDataFrame(df.toPandas()).collect() # doctest: +SKIP
[Row(name=u'Alice', age=1)]
>>> spark.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP
[Row(0=1, 1=2)]
>>> spark.createDataFrame(rdd, "a: string, b: int").collect()
[Row(a=u'Alice', b=1)]
>>> rdd = rdd.map(lambda row: row[1])
>>> spark.createDataFrame(rdd, "int").collect()
[Row(value=1)]
>>> spark.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
Py4JJavaError: ...
"""
if isinstance(data, DataFrame):
raise TypeError("data is already a DataFrame")
if isinstance(schema, basestring):
schema = _parse_datatype_string(schema)
elif isinstance(schema, (list, tuple)):
# Must re-encode any unicode strings to be consistent with StructField names
schema = [
x.encode('utf-8') if not isinstance(x, str) else x
for x in schema
]
try:
import pandas
has_pandas = True
except Exception:
has_pandas = False
if has_pandas and isinstance(data, pandas.DataFrame):
from pyspark.sql.utils import require_minimum_pandas_version
require_minimum_pandas_version()
if self.conf.get("spark.sql.execution.pandas.respectSessionTimeZone").lower() \
== "true":
timezone = self.conf.get("spark.sql.session.timeZone")
else:
timezone = None
# If no schema supplied by user then get the names of columns only
if schema is None:
schema = [
str(x) if not isinstance(x, basestring) else
(x.encode('utf-8') if not isinstance(x, str) else x)
for x in data.columns
]
if self.conf.get("spark.sql.execution.arrow.enabled", "false").lower() == "true" \
and len(data) > 0:
try:
return self._create_from_pandas_with_arrow(
data, schema, timezone)
except Exception as e:
from pyspark.util import _exception_message
if self.conf.get("spark.sql.execution.arrow.fallback.enabled", "true") \
.lower() == "true":
msg = (
"createDataFrame attempted Arrow optimization because "
"'spark.sql.execution.arrow.enabled' is set to true; however, "
"failed by the reason below:\n %s\n"
"Attempting non-optimization as "
"'spark.sql.execution.arrow.fallback.enabled' is set to "
"true." % _exception_message(e))
warnings.warn(msg)
else:
msg = (
"createDataFrame attempted Arrow optimization because "
"'spark.sql.execution.arrow.enabled' is set to true, but has reached "
"the error below and will not continue because automatic fallback "
"with 'spark.sql.execution.arrow.fallback.enabled' has been set to "
"false.\n %s" % _exception_message(e))
warnings.warn(msg)
raise
data = self._convert_from_pandas(data, schema, timezone)
if isinstance(schema, StructType):
verify_func = _make_type_verifier(
schema) if verifySchema else lambda _: True
def prepare(obj):
verify_func(obj)
return obj
elif isinstance(schema, DataType):
dataType = schema
schema = StructType().add("value", schema)
verify_func = _make_type_verifier(
dataType,
name="field value") if verifySchema else lambda _: True
def prepare(obj):
verify_func(obj)
return obj,
else:
prepare = lambda obj: obj
if isinstance(data, RDD):
rdd, schema = self._createFromRDD(data.map(prepare), schema,
samplingRatio)
else:
rdd, schema = self._createFromLocal(map(prepare, data), schema)
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
jdf = self._jsparkSession.applySchemaToPythonRDD(
jrdd.rdd(), schema.json())
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def createDataFrame(self, data, schema=None, samplingRatio=None):
"""
Creates a :class:`DataFrame` from an :class:`RDD` of :class:`tuple`/:class:`list`,
list or :class:`pandas.DataFrame`.
When ``schema`` is a list of column names, the type of each column
will be inferred from ``data``.
When ``schema`` is ``None``, it will try to infer the schema (column names and types)
from ``data``, which should be an RDD of :class:`Row`,
or :class:`namedtuple`, or :class:`dict`.
If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
:param data: an RDD of :class:`Row`/:class:`tuple`/:class:`list`/:class:`dict`,
:class:`list`, or :class:`pandas.DataFrame`.
:param schema: a :class:`StructType` or list of column names. default None.
:param samplingRatio: the sample ratio of rows used for inferring
:return: :class:`DataFrame`
>>> l = [('Alice', 1)]
>>> sqlContext.createDataFrame(l).collect()
[Row(_1=u'Alice', _2=1)]
>>> sqlContext.createDataFrame(l, ['name', 'age']).collect()
[Row(name=u'Alice', age=1)]
>>> d = [{'name': 'Alice', 'age': 1}]
>>> sqlContext.createDataFrame(d).collect()
[Row(age=1, name=u'Alice')]
>>> rdd = sc.parallelize(l)
>>> sqlContext.createDataFrame(rdd).collect()
[Row(_1=u'Alice', _2=1)]
>>> df = sqlContext.createDataFrame(rdd, ['name', 'age'])
>>> df.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql import Row
>>> Person = Row('name', 'age')
>>> person = rdd.map(lambda r: Person(*r))
>>> df2 = sqlContext.createDataFrame(person)
>>> df2.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql.types import *
>>> schema = StructType([
... StructField("name", StringType(), True),
... StructField("age", IntegerType(), True)])
>>> df3 = sqlContext.createDataFrame(rdd, schema)
>>> df3.collect()
[Row(name=u'Alice', age=1)]
>>> sqlContext.createDataFrame(df.toPandas()).collect() # doctest: +SKIP
[Row(name=u'Alice', age=1)]
>>> sqlContext.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP
[Row(0=1, 1=2)]
"""
if isinstance(data, DataFrame):
raise TypeError("data is already a DataFrame")
if isinstance(data, RDD):
rdd, schema = self._createFromRDD(data, schema, samplingRatio)
else:
rdd, schema = self._createFromLocal(data, schema)
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
jdf = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
df = DataFrame(jdf, self)
df._schema = schema
return df
def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
"""
Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
data types will be used to coerce the data in Pandas to Arrow conversion.
"""
from distutils.version import LooseVersion
from pyspark.serializers import ArrowStreamPandasSerializer
from pyspark.sql.types import from_arrow_type, to_arrow_type, TimestampType
from pyspark.sql.utils import require_minimum_pandas_version, \
require_minimum_pyarrow_version
require_minimum_pandas_version()
require_minimum_pyarrow_version()
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
import pyarrow as pa
# Create the Spark schema from list of names passed in with Arrow types
if isinstance(schema, (list, tuple)):
if LooseVersion(pa.__version__) < LooseVersion("0.12.0"):
temp_batch = pa.RecordBatch.from_pandas(pdf[0:100], preserve_index=False)
arrow_schema = temp_batch.schema
else:
arrow_schema = pa.Schema.from_pandas(pdf, preserve_index=False)
struct = StructType()
for name, field in zip(schema, arrow_schema):
struct.add(name, from_arrow_type(field.type), nullable=field.nullable)
schema = struct
# Determine arrow types to coerce data when creating batches
if isinstance(schema, StructType):
arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
elif isinstance(schema, DataType):
raise ValueError("Single data type %s is not supported with Arrow" % str(schema))
else:
# Any timestamps must be coerced to be compatible with Spark
arrow_types = [to_arrow_type(TimestampType())
if is_datetime64_dtype(t) or is_datetime64tz_dtype(t) else None
for t in pdf.dtypes]
# Slice the DataFrame to be batched
step = -(-len(pdf) // self.sparkContext.defaultParallelism) # round int up
pdf_slices = (pdf[start:start + step] for start in xrange(0, len(pdf), step))
# Create list of Arrow (columns, type) for serializer dump_stream
arrow_data = [[(c, t) for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)]
for pdf_slice in pdf_slices]
jsqlContext = self._wrapped._jsqlContext
safecheck = self._wrapped._conf.arrowSafeTypeConversion()
col_by_name = True # col by name only applies to StructType columns, can't happen here
ser = ArrowStreamPandasSerializer(timezone, safecheck, col_by_name)
def reader_func(temp_filename):
return self._jvm.PythonSQLUtils.readArrowStreamFromFile(jsqlContext, temp_filename)
def create_RDD_server():
return self._jvm.ArrowRDDServer(jsqlContext)
# Create Spark DataFrame from Arrow stream file, using one batch per partition
jrdd = self._sc._serialize_to_jvm(arrow_data, ser, reader_func, create_RDD_server)
jdf = self._jvm.PythonSQLUtils.toDataFrame(jrdd, schema.json(), jsqlContext)
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
"""
Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
data types will be used to coerce the data in Pandas to Arrow conversion.
"""
from pyspark.sql import SparkSession
from pyspark.sql.dataframe import DataFrame
assert isinstance(self, SparkSession)
from pyspark.sql.pandas.serializers import ArrowStreamPandasSerializer
from pyspark.sql.types import TimestampType
from pyspark.sql.pandas.types import from_arrow_type, to_arrow_type
from pyspark.sql.pandas.utils import require_minimum_pandas_version, \
require_minimum_pyarrow_version
require_minimum_pandas_version()
require_minimum_pyarrow_version()
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
import pyarrow as pa
# Create the Spark schema from list of names passed in with Arrow types
if isinstance(schema, (list, tuple)):
arrow_schema = pa.Schema.from_pandas(pdf, preserve_index=False)
struct = StructType()
prefer_timestamp_ntz = is_timestamp_ntz_preferred()
for name, field in zip(schema, arrow_schema):
struct.add(name,
from_arrow_type(field.type, prefer_timestamp_ntz),
nullable=field.nullable)
schema = struct
# Determine arrow types to coerce data when creating batches
if isinstance(schema, StructType):
arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
elif isinstance(schema, DataType):
raise ValueError(
"Single data type %s is not supported with Arrow" %
str(schema))
else:
# Any timestamps must be coerced to be compatible with Spark
arrow_types = [
to_arrow_type(TimestampType())
if is_datetime64_dtype(t) or is_datetime64tz_dtype(t) else None
for t in pdf.dtypes
]
# Slice the DataFrame to be batched
step = -(-len(pdf) // self.sparkContext.defaultParallelism
) # round int up
pdf_slices = (pdf.iloc[start:start + step]
for start in range(0, len(pdf), step))
# Create list of Arrow (columns, type) for serializer dump_stream
arrow_data = [[(c, t)
for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)
] for pdf_slice in pdf_slices]
jsqlContext = self._wrapped._jsqlContext
safecheck = self._wrapped._conf.arrowSafeTypeConversion()
col_by_name = True # col by name only applies to StructType columns, can't happen here
ser = ArrowStreamPandasSerializer(timezone, safecheck, col_by_name)
def reader_func(temp_filename):
return self._jvm.PythonSQLUtils.readArrowStreamFromFile(
jsqlContext, temp_filename)
def create_RDD_server():
return self._jvm.ArrowRDDServer(jsqlContext)
# Create Spark DataFrame from Arrow stream file, using one batch per partition
jrdd = self._sc._serialize_to_jvm(arrow_data, ser, reader_func,
create_RDD_server)
jdf = self._jvm.PythonSQLUtils.toDataFrame(jrdd, schema.json(),
jsqlContext)
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
def createDataFrame(self, data, schema=None, samplingRatio=None):
"""
Creates a :class:`DataFrame` from an :class:`RDD` of :class:`tuple`/:class:`list`,
list or :class:`pandas.DataFrame`.
When ``schema`` is a list of column names, the type of each column
will be inferred from ``data``.
When ``schema`` is ``None``, it will try to infer the schema (column names and types)
from ``data``, which should be an RDD of :class:`Row`,
or :class:`namedtuple`, or :class:`dict`.
If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
:param data: an RDD of :class:`Row`/:class:`tuple`/:class:`list`/:class:`dict`,
:class:`list`, or :class:`pandas.DataFrame`.
:param schema: a :class:`StructType` or list of column names. default None.
:param samplingRatio: the sample ratio of rows used for inferring
:return: :class:`DataFrame`
>>> l = [('Alice', 1)]
>>> sqlContext.createDataFrame(l).collect()
[Row(_1=u'Alice', _2=1)]
>>> sqlContext.createDataFrame(l, ['name', 'age']).collect()
[Row(name=u'Alice', age=1)]
>>> d = [{'name': 'Alice', 'age': 1}]
>>> sqlContext.createDataFrame(d).collect()
[Row(age=1, name=u'Alice')]
>>> rdd = sc.parallelize(l)
>>> sqlContext.createDataFrame(rdd).collect()
[Row(_1=u'Alice', _2=1)]
>>> df = sqlContext.createDataFrame(rdd, ['name', 'age'])
>>> df.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql import Row
>>> Person = Row('name', 'age')
>>> person = rdd.map(lambda r: Person(*r))
>>> df2 = sqlContext.createDataFrame(person)
>>> df2.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql.types import *
>>> schema = StructType([
... StructField("name", StringType(), True),
... StructField("age", IntegerType(), True)])
>>> df3 = sqlContext.createDataFrame(rdd, schema)
>>> df3.collect()
[Row(name=u'Alice', age=1)]
>>> sqlContext.createDataFrame(df.toPandas()).collect() # doctest: +SKIP
[Row(name=u'Alice', age=1)]
>>> sqlContext.createDataFrame(pandas.DataFrame([[1, 2]]).collect()) # doctest: +SKIP
[Row(0=1, 1=2)]
"""
if isinstance(data, DataFrame):
raise TypeError("data is already a DataFrame")
if isinstance(data, RDD):
rdd, schema = self._createFromRDD(data, schema, samplingRatio)
else:
rdd, schema = self._createFromLocal(data, schema)
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
jdf = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
df = DataFrame(jdf, self)
df._schema = schema
return df
def createDataFrame(self,
data,
schema=None,
samplingRatio=None,
verifySchema=True):
"""
Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
When ``schema`` is a list of column names, the type of each column
will be inferred from ``data``.
When ``schema`` is ``None``, it will try to infer the schema (column names and types)
from ``data``, which should be an RDD of :class:`Row`,
or :class:`namedtuple`, or :class:`dict`.
When ``schema`` is :class:`pyspark.sql.types.DataType` or a datatype string, it must match
the real data, or an exception will be thrown at runtime. If the given schema is not
:class:`pyspark.sql.types.StructType`, it will be wrapped into a
:class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value",
each record will also be wrapped into a tuple, which can be converted to row later.
If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
:param data: an RDD of any kind of SQL data representation(e.g. row, tuple, int, boolean,
etc.), or :class:`list`, or :class:`pandas.DataFrame`.
:param schema: a :class:`pyspark.sql.types.DataType` or a datatype string or a list of
column names, default is ``None``. The data type string format equals to
:class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`. We can also use
``int`` as a short name for ``IntegerType``.
:param samplingRatio: the sample ratio of rows used for inferring
:param verifySchema: verify data types of every row against schema.
:return: :class:`DataFrame`
.. versionchanged:: 2.1
Added verifySchema.
>>> l = [('Alice', 1)]
>>> spark.createDataFrame(l).collect()
[Row(_1=u'Alice', _2=1)]
>>> spark.createDataFrame(l, ['name', 'age']).collect()
[Row(name=u'Alice', age=1)]
>>> d = [{'name': 'Alice', 'age': 1}]
>>> spark.createDataFrame(d).collect()
[Row(age=1, name=u'Alice')]
>>> rdd = sc.parallelize(l)
>>> spark.createDataFrame(rdd).collect()
[Row(_1=u'Alice', _2=1)]
>>> df = spark.createDataFrame(rdd, ['name', 'age'])
>>> df.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql import Row
>>> Person = Row('name', 'age')
>>> person = rdd.map(lambda r: Person(*r))
>>> df2 = spark.createDataFrame(person)
>>> df2.collect()
[Row(name=u'Alice', age=1)]
>>> from pyspark.sql.types import *
>>> schema = StructType([
... StructField("name", StringType(), True),
... StructField("age", IntegerType(), True)])
>>> df3 = spark.createDataFrame(rdd, schema)
>>> df3.collect()
[Row(name=u'Alice', age=1)]
>>> spark.createDataFrame(df.toPandas()).collect() # doctest: +SKIP
[Row(name=u'Alice', age=1)]
>>> spark.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP
[Row(0=1, 1=2)]
>>> spark.createDataFrame(rdd, "a: string, b: int").collect()
[Row(a=u'Alice', b=1)]
>>> rdd = rdd.map(lambda row: row[1])
>>> spark.createDataFrame(rdd, "int").collect()
[Row(value=1)]
>>> spark.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
Py4JJavaError: ...
"""
if isinstance(data, DataFrame):
raise TypeError("data is already a DataFrame")
if isinstance(schema, basestring):
schema = _parse_datatype_string(schema)
try:
import pandas
has_pandas = True
except Exception:
has_pandas = False
if has_pandas and isinstance(data, pandas.DataFrame):
if schema is None:
schema = [str(x) for x in data.columns]
data = [r.tolist() for r in data.to_records(index=False)]
verify_func = _verify_type if verifySchema else lambda _, t: True
if isinstance(schema, StructType):
def prepare(obj):
verify_func(obj, schema)
return obj
elif isinstance(schema, DataType):
dataType = schema
schema = StructType().add("value", schema)
def prepare(obj):
verify_func(obj, dataType)
return obj,
else:
if isinstance(schema, list):
schema = [
x.encode('utf-8') if not isinstance(x, str) else x
for x in schema
]
prepare = lambda obj: obj
if isinstance(data, RDD):
rdd, schema = self._createFromRDD(data.map(prepare), schema,
samplingRatio)
else:
rdd, schema = self._createFromLocal(map(prepare, data), schema)
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
jdf = self._jsparkSession.applySchemaToPythonRDD(
jrdd.rdd(), schema.json())
df = DataFrame(jdf, self._wrapped)
df._schema = schema
return df
