Pyspark UDF evaluation

So I have a simple function which takes in two strings and converts them into float(consider it is always possible) and returns the max of them.

def val_estimate(amount_1: str, amount_2: str) -> float:

   return max(float(amount_1), float(amount_2))

When I evaluate the function on the following arguments, I get the output as expected:

val_estimate("2000000","90125900")

Output: 
90125900.0

Now, when I register the function above as a UDF and use it on a spark dataframe with same arguments, I get the following results.

val_estimate_udf = F.udf(val_estimate, returnType = FloatType())

df = spark.createDataFrame( [["2000000","90125900"]], ['sale_amt', 'total_value'])

df = df.withColumn("check",val_estimate_udf(F.col("sale_amt"),F.col("total_value")))
display(df)

Output:

Why am I getting this result? Please ignore lack of error handling, etc. and the fact that I can use native spark function to do the same, but I can't understand this result.

import pyspark.sql.functions as F
from pyspark.sql.types import  DoubleType

@udf(returnType = DoubleType()) 
def val_estimate(amount_1: str, amount_2: str) -> float:
   return max(float(amount_1), float(amount_2))
  
df = spark.createDataFrame( [('2000000', '90125900')], ['sale_amt', 'total_value'])
df2 = df.withColumn("check", val_estimate(F.col("sale_amt"), F.col("total_value")))
df2.show()