Replacing NaN (Not a Number) values with a specified value in a column : nanvl

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The nanvl function in PySpark is used to replace NaN (Not a Number) values with a specified value in a column. This is particularly useful in datasets where NaN values need to be handled differently from regular null values. This article explores the nanvl function in PySpark, its advantages, and demonstrates its application through a practical use case.

from pyspark.sql.functions import nanvl

Advantages of using nanvl

Data Integrity: Ensures meaningful handling of NaN values, preserving the integrity of the dataset.

Flexibility: Provides the ability to replace NaN values with a specific value, which can be crucial in statistical computations or data visualizations.

Simplicity: Offers a straightforward and effective solution for NaN value replacement.

Example : Handling employee attendance records

Consider a dataset representing the attendance records of employees, where NaN values indicate days not recorded. We want to replace these NaN values with a default value.


Name Attendance Days
Sachin NaN
Ram 22
Raju NaN
David 18
Wilson 20


Replace NaN values in the ‘Attendance Days’ column with the default value of 0.

Implementation in PySpark

Setting up the PySpark environment and creating the DataFrame:

from pyspark.sql import SparkSession
from pyspark.sql.functions import nanvl
# Initialize Spark Session
spark = SparkSession.builder.appName("Nanvl Example").getOrCreate()
# Sample Data
data = [("Sachin", float('nan')), ("Ram", 22.0), ("Raju", float('nan')), ("David", 18.0), ("Wilson", 20.0)]
df = spark.createDataFrame(data, ["Name", "Attendance Days"])


|  Name|Attendance Days|
|Sachin|            NaN|
|   Ram|           22.0|
|  Raju|            NaN|
| David|           18.0|
|Wilson|           20.0|

Applying the nanvl function:

from pyspark.sql.functions import lit
nanvl_df = df.withColumn("Adjusted Attendance", nanvl(df["Attendance Days"], lit(0)))


|  Name|Attendance Days|Adjusted Attendance|
|Sachin|            NaN|                0.0|
|   Ram|           22.0|               22.0|
|  Raju|            NaN|                0.0|
| David|           18.0|               18.0|
|Wilson|           20.0|               20.0|

Spark important urls to refer

  1. Spark Examples
  2. PySpark Blogs
  3. Bigdata Blogs
  4. Spark Interview Questions
  5. Official Page
Author: user