In Databricks, encountering values like “0E-10” typically happens when dealing with decimal values with a large scale (number of digits after the decimal point) in Spark DataFrames. This is a form of scientific notation, where “0E-10” represents 0.0000000000.

Here’s why you might see this and how to address it:

Why “0E-10” Occurs

• Scientific Notation: Spark automatically switches to scientific notation for decimal values when the scale exceeds 6. This is to maintain precision and avoid rounding errors.
• Data Type Conversion: If you cast a string value like “0” to a decimal with a large scale (e.g., decimal(38,16)), it might be represented as “0E-10”.

Solutions

1. Change the Decimal Scale:

   • If you don’t need such a high precision, cast your decimal column to a smaller scale (e.g., decimal(10,2)). This will prevent scientific notation for smaller values.
   Python

   from pyspark.sql import functions as F
   
   df = df.withColumn("my_decimal_col", F.col("my_decimal_col").cast("decimal(10,2)"))
   

    

    

2. Format Numbers:

   • Use the format_number function to display the values in a more readable way:
   Python

   from pyspark.sql import functions as F
   
   df = df.withColumn("formatted_col", F.expr("format_number(my_decimal_col, '0.0000000000')")) 
   

    

    

3. Custom Function (For Large Scale):

   • If you need to maintain the large scale but avoid scientific notation specifically for zeros, you can create a custom function (UDF):
   Python

   from pyspark.sql import functions as F
   from pyspark.sql.types import StringType, DecimalType
   
   def handle_decimal_zeros(decimal_val):
       if decimal_val == 0:
           return "0"
       else:
           return str(decimal_val)
   
   handle_decimal_zeros_udf = F.udf(handle_decimal_zeros, StringType())
   
   df = df.withColumn("formatted_col", handle_decimal_zeros_udf(F.col("my_decimal_col")))
   

    

    

Example

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, DecimalType

spark = SparkSession.builder.getOrCreate()

schema = StructType([
    StructField("decimal_col", DecimalType(38, 16), True)
])

data = [(0.0,), (1.00001234,)]

df = spark.createDataFrame(data, schema)

# Apply the formatting solution
df = df.withColumn("formatted_col", F.expr("format_number(decimal_col, '0.0000000000')"))

df.show()