Ruifeng Zheng created SPARK-41855:
-------------------------------------
Summary: `createDataFrame` doesn't handle None properly
Key: SPARK-41855
URL: https://issues.apache.org/jira/browse/SPARK-41855
Project: Spark
Issue Type: Sub-task
Components: Connect, PySpark
Affects Versions: 3.4.0
Reporter: Ruifeng Zheng
{code:python}
data = [Row(id=1, value=float("NaN")), Row(id=2, value=42.0), Row(id=3,
value=None)]
# +---+-----+
# | id|value|
# +---+-----+
# | 1| NaN|
# | 2| 42.0|
# | 3| null|
# +---+-----+
cdf = self.connect.createDataFrame(data)
sdf = self.spark.createDataFrame(data)
print()
print()
print(cdf._show_string(100, 100, False))
print()
print(cdf.schema)
print()
print(sdf._jdf.showString(100, 100, False))
print()
print(sdf.schema)
self.compare_by_show(cdf, sdf)
{code}
{code:java}
+---+-----+
| id|value|
+---+-----+
| 1| null|
| 2| 42.0|
| 3| null|
+---+-----+
StructType([StructField('id', LongType(), True), StructField('value',
DoubleType(), True)])
+---+-----+
| id|value|
+---+-----+
| 1| NaN|
| 2| 42.0|
| 3| null|
+---+-----+
StructType([StructField('id', LongType(), True), StructField('value',
DoubleType(), True)])
{code}
this issue is due to that `createDataFrame` can't handle None properly:
1, in the conversion from local data to pd.DataFrame, it automatically converts
None to NaN
2, then in the conversion from pd.DataFrame to pa.Table, it always converts NaN
to null
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