Aki Ariga created SPARK-30966:
---------------------------------
Summary: spark.createDataFrame fails with pandas DataFrame
including pandas.NA
Key: SPARK-30966
URL: https://issues.apache.org/jira/browse/SPARK-30966
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 2.4.5
Reporter: Aki Ariga
As of pandas 1.0.0, pandas.NA was introduced, and that breaks createDataFrame
function as the following:
{code:python}
In [5]: from pyspark.sql import SparkSession
In [6]: spark = SparkSession.builder.getOrCreate()
In [7]: spark.conf.set("spark.sql.execution.arrow.enabled", "true")
In [8]: import numpy as np
...: import pandas as pd
In [12]: pdf = pd.DataFrame(data=[{'a':1,'b':2}, {'a':3,'b':4,'c':5}],
dtype=pd.Int64Dtype())
In [16]: pdf
Out[16]:
a b c
0 1 2 <NA>
1 3 4 5
In [13]: sdf = spark.createDataFrame(pdf)
/Users/ariga/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/session.py:714:
UserWarning: createDataFrame attempted Arrow optimization because
'spark.sql.execution.arrow.enabled' is set to true; however, failed by the
reason below:
Did not pass numpy.dtype object
Attempting non-optimization as 'spark.sql.execution.arrow.fallback.enabled' is
set to true.
warnings.warn(msg)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-13-ad13ba53b87e> in <module>
----> 1 sdf = spark.createDataFrame(df2)
~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/session.py in
createDataFrame(self, data, schema, samplingRatio, verifySchema)
746 rdd, schema = self._createFromRDD(data.map(prepare),
schema, samplingRatio)
747 else:
--> 748 rdd, schema = self._createFromLocal(map(prepare, data),
schema)
749 jrdd =
self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
750 jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(),
schema.json())
~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/session.py in
_createFromLocal(self, data, schema)
414
415 if schema is None or isinstance(schema, (list, tuple)):
--> 416 struct = self._inferSchemaFromList(data, names=schema)
417 converter = _create_converter(struct)
418 data = map(converter, data)
~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/session.py in
_inferSchemaFromList(self, data, names)
346 warnings.warn("inferring schema from dict is deprecated,"
347 "please use pyspark.sql.Row instead")
--> 348 schema = reduce(_merge_type, (_infer_schema(row, names) for row
in data))
349 if _has_nulltype(schema):
350 raise ValueError("Some of types cannot be determined after
inferring")
~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/types.py in
_merge_type(a, b, name)
1099 fields = [StructField(f.name, _merge_type(f.dataType,
nfs.get(f.name, NullType()),
1100
name=new_name(f.name)))
-> 1101 for f in a.fields]
1102 names = set([f.name for f in fields])
1103 for n in nfs:
~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/types.py in
<listcomp>(.0)
1099 fields = [StructField(f.name, _merge_type(f.dataType,
nfs.get(f.name, NullType()),
1100
name=new_name(f.name)))
-> 1101 for f in a.fields]
1102 names = set([f.name for f in fields])
1103 for n in nfs:
~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/types.py in
_merge_type(a, b, name)
1092 elif type(a) is not type(b):
1093 # TODO: type cast (such as int -> long)
-> 1094 raise TypeError(new_msg("Can not merge type %s and %s" %
(type(a), type(b))))
1095
1096 # same type
TypeError: field c: Can not merge type <class 'pyspark.sql.types.StructType'>
and <class 'pyspark.sql.types.LongType'>
In [15]: pyspark.__version__
Out[15]: '2.4.5'
{code}
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