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https://issues.apache.org/jira/browse/SPARK-54302?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Maxim Martynov updated SPARK-54302:
-----------------------------------
Description:
I have DataFrame with schema like this:
{code:python}
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
df = spark.createDataFrame([{"a": 1},{"a": None}], schema="a:int")
df.printSchema()
"""
root
|-- a: integer (nullable = true)
"""
df.where(df.a.isNotNull()).printSchema()
"""
root
|-- a: integer (nullable = true)
"""
{code}
Currently filters applied to dataframe doesn't change it's schema. To make
colum non-nullable I have to use coalesce:
{code:python}
import pyspark.sql.functions as F
df.where(df.a.isNotNull()).select(F.coalesce(df.a, F.lit(0))).printSchema()
"""
root
|-- coalesce(a, 0): integer (nullable = false)
"""
{code}
But I have to choose {{F.lit(...)}} value based on column type, even if it will
never be used.
was:
I have DataFrame with schema like this:
{code:python}
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
df = spark.createDataFrame([{"a": 1},{"a": None}], schema="a:int")
df.printSchema()
"""
root
|-- a: integer (nullable = true)
"""
df.where(df.a.isNotNull()).printSchema()
"""
root
|-- a: integer (nullable = true)
"""
{code}
Currently filters applied to dataframe doesn't change it's schema. To make
colum non-nullable I have to use coalesce:
{code:python}
import pyspark.sql.functions as F
df..where(df.a.isNotNull())select(F.coalesce(df.a, F.lit(0))).printSchema()
"""
root
|-- coalesce(a, 0): integer (nullable = false)
"""
{code}
But I have to choose {{F.lit(...)}} value based on column type, even if it will
never be used.
> Filtering by isNotNull should return DataFrame with nullable=False
> ------------------------------------------------------------------
>
> Key: SPARK-54302
> URL: https://issues.apache.org/jira/browse/SPARK-54302
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 3.5.7
> Reporter: Maxim Martynov
> Priority: Major
>
> I have DataFrame with schema like this:
> {code:python}
> from pyspark.sql import SparkSession
> spark = SparkSession.builder.getOrCreate()
> df = spark.createDataFrame([{"a": 1},{"a": None}], schema="a:int")
> df.printSchema()
> """
> root
> |-- a: integer (nullable = true)
> """
> df.where(df.a.isNotNull()).printSchema()
> """
> root
> |-- a: integer (nullable = true)
> """
> {code}
> Currently filters applied to dataframe doesn't change it's schema. To make
> colum non-nullable I have to use coalesce:
> {code:python}
> import pyspark.sql.functions as F
> df.where(df.a.isNotNull()).select(F.coalesce(df.a, F.lit(0))).printSchema()
> """
> root
> |-- coalesce(a, 0): integer (nullable = false)
> """
> {code}
> But I have to choose {{F.lit(...)}} value based on column type, even if it
> will never be used.
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