Maxim Martynov created SPARK-54302:
--------------------------------------
Summary: 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
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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