[
https://issues.apache.org/jira/browse/SPARK-41855?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ruifeng Zheng updated SPARK-41855:
----------------------------------
Description:
{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/NaN properly:
1, in the conversion from local data to pd.DataFrame, it automatically converts
both None and NaN to NaN
2, then in the conversion from pd.DataFrame to pa.Table, it always converts NaN
to null
was:
{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/NaN 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
> `createDataFrame` doesn't handle None/NaN 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
> Priority: Major
>
> {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/NaN properly:
> 1, in the conversion from local data to pd.DataFrame, it automatically
> converts both None and NaN to NaN
> 2, then in the conversion from pd.DataFrame to pa.Table, it always converts
> NaN to null
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