I have this command for all columns in my dataframe to round to 2 decimal places:
data = data.withColumn("columnName1", func.round(data["columnName1"], 2)) I have no idea how to round all Dataframe by the one command (not every column separate). Could somebody help me, please? I don't want to have the same command 50times with different column name.
There is not a function or command for applying all functions to the columns but you can iterate.
+-----+-----+ | col1| col2| +-----+-----+ |1.111|2.222| +-----+-----+ df = spark.read.option("header","true").option("inferSchema","true").csv("test.csv") for c in df.columns: df = df.withColumn(c, f.round(c, 2)) df.show() +----+----+ |col1|col2| +----+----+ |1.11|2.22| +----+----+ Updated
from pyspark.sql import functions as f df.select(*[f.round(c, 2).alias(c) for c in df.columns]) \ .show() +----+----+ |col1|col2| +----+----+ |1.11|2.22| +----+----+ 
df = df.withColumn(c, round(df.col(c), 2))
To avoid converting non-FP columns:
import pyspark.sql.functions as F for c_name, c_type in df.dtypes: if c_type in ('double', 'float'): df = df.withColumn(c_name, F.round(c_name, 2)) 