PySpark fillna() & fill() – Replace NULL/None Values

In PySpark,fillna() from DataFrame class or fill() from DataFrameNaFunctions is used to replace NULL/None values on all or selected multiple columns with either zero(0), empty string, space, or any constant literal values.

While working on PySpark DataFrame we often need to replace null values since certain operations on null values return errors. Hence, we need to graciously handle nulls as the first step before processing. Also, while writing to a file, it’s always best practice to replace null values, not doing this results in nulls on the output file.

As part of the cleanup, sometimes you may need to Drop Rows with NULL/None Values in PySpark DataFrame and Filter Rows by checking IS NULL/NOT NULL conditions.

In this article, I will use both fill() and fillna() to replace null/none values with an empty string, constant value, and zero(0) on Dataframe columns integer, string with Python examples.

• PySpark fillna() and fill() Syntax
• Replace NULL/None Values with Zero (0)
• Replace NULL/None Values with Empty String

Before we start, Let’s read a CSV into PySpark DataFrame file, Note that the reading process automatically assigns null values for missing data.

The file we are using here is available at GitHub small_zipcode.csv

# Create SparkSession and read csv
from pyspark.sql import SparkSession
spark = SparkSession.builder \
    .master("local[1]") \
    .appName("SparkByExamples.com") \
    .getOrCreate()

filePath="resources/small_zipcode.csv"
df = spark.read.options(header='true', inferSchema='true') \
          .csv(filePath)

df.printSchema()
df.show(truncate=False)

This yields the below output. Note that the columns city, and population have null values.

# Output
+---+-------+--------+-------------------+-----+----------+
|id |zipcode|type    |city               |state|population|
+---+-------+--------+-------------------+-----+----------+
|1  |704    |STANDARD|null               |PR   |30100     |
|2  |704    |null    |PASEO COSTA DEL SUR|PR   |null      |
|3  |709    |null    |BDA SAN LUIS       |PR   |3700      |
|4  |76166  |UNIQUE  |CINGULAR WIRELESS  |TX   |84000     |
|5  |76177  |STANDARD|null               |TX   |null      |
+---+-------+--------+-------------------+-----+----------+

Now, let’s see how to replace these null values.

PySpark fillna() & fill() Syntax

PySpark provides DataFrame.fillna() and DataFrameNaFunctions.fill() to replace NULL/None values. These two are aliases of each other and returns the same results.

fillna(value, subset=None)
fill(value, subset=None)

• value – Value should be the data type of int, long, float, string, or dict. Value specified here will be replaced with NULL/None values.
• subset – This is optional, when used it should be the subset of the column names where you wanted to replace NULL/None values.

PySpark Replace NULL/None Values with Zero (0)

PySpark fill(value:Long) signatures that are available in DataFrameNaFunctions is used to replace NULL/None values with numeric values either zero(0) or any constant value for all integer and long datatype columns of PySpark DataFrame or Dataset.

#Replace 0 for null for all integer columns
df.na.fill(value=0).show()

#Replace 0 for null on only population column 
df.na.fill(value=0,subset=["population"]).show()

Above both statements yield the same output, since we have just an integer column population with null values Note that it replaces only Integer columns since our value is 0.

# Output
+---+-------+--------+-------------------+-----+----------+
|id |zipcode|type    |city               |state|population|
+---+-------+--------+-------------------+-----+----------+
|1  |704    |STANDARD|null               |PR   |30100     |
|2  |704    |null    |PASEO COSTA DEL SUR|PR   |0         |
|3  |709    |null    |BDA SAN LUIS       |PR   |3700      |
|4  |76166  |UNIQUE  |CINGULAR WIRELESS  |TX   |84000     |
|5  |76177  |STANDARD|null               |TX   |0         |
+---+-------+--------+-------------------+-----+----------+

PySpark Replace Null/None Value with Empty String

Now let’s see how to replace NULL/None values with an empty string or any constant values String on all DataFrame String columns.

df.na.fill("").show(false)

Yields below output. This replaces all String type columns with empty/blank string for all NULL values.

# Output
+---+-------+--------+-------------------+-----+----------+
|id |zipcode|type    |city               |state|population|
+---+-------+--------+-------------------+-----+----------+
|1  |704    |STANDARD|                   |PR   |30100     |
|2  |704    |        |PASEO COSTA DEL SUR|PR   |null      |
|3  |709    |        |BDA SAN LUIS       |PR   |3700      |
|4  |76166  |UNIQUE  |CINGULAR WIRELESS  |TX   |84000     |
|5  |76177  |STANDARD|                   |TX   |null      |
+---+-------+--------+-------------------+-----+----------+

Now, let’s replace NULLs on specific columns, below example replace a column type with an empty string and a column city with the value “unknown”.

df.na.fill("unknown",["city"]) \
    .na.fill("",["type"]).show()

Yields below output. This replaces null values with an empty string for type column and replaces with a constant value “unknown” for city column.

# Output
+---+-------+--------+-------------------+-----+----------+
|id |zipcode|type    |city               |state|population|
+---+-------+--------+-------------------+-----+----------+
|1  |704    |STANDARD|unknown            |PR   |30100     |
|2  |704    |        |PASEO COSTA DEL SUR|PR   |null      |
|3  |709    |        |BDA SAN LUIS       |PR   |3700      |
|4  |76166  |UNIQUE  |CINGULAR WIRELESS  |TX   |84000     |
|5  |76177  |STANDARD|unknown            |TX   |null      |
+---+-------+--------+-------------------+-----+----------+

Alternatively, write the above statement as below.

df.na.fill({"city": "unknown", "type": ""}) \
    .show()

Complete Code

Below is the complete code with a python example. You can use it by copying it from here or using GitHub to download the source code.

from pyspark.sql import SparkSession
spark = SparkSession.builder \
    .master("local[1]") \
    .appName("SparkByExamples.com") \
    .getOrCreate()

filePath="resources/small_zipcode.csv"
df = spark.read.options(header='true', inferSchema='true') \
          .csv(filePath)

df.printSchema()
df.show(truncate=False)


df.fillna(value=0).show()
df.fillna(value=0,subset=["population"]).show()
df.na.fill(value=0).show()
df.na.fill(value=0,subset=["population"]).show()


df.fillna(value="").show()
df.na.fill(value="").show()

df.fillna("unknown",["city"]) \
    .fillna("",["type"]).show()

df.fillna({"city": "unknown", "type": ""}) \
    .show()

df.na.fill("unknown",["city"]) \
    .na.fill("",["type"]).show()

df.na.fill({"city": "unknown", "type": ""}) \
    .show()

Frequently Asked Questions on fillna() and fill()

Conclusion

In this PySpark article, you have learned how to replace null/None values with zero or an empty string on integer and string columns respectively using fill() and fillna() transformation functions.

Happy Learning !!

Related Articles

• PySpark Group by on Multiple Columns
• PySpark Drop Rows with NULL or None Values
• PySpark Parse JSON from String Column | TEXT File
• PySpark Replace Empty Value With None/null on Data Frame
• PySpark count() – Different Methods Explained
• PySpark isNull() & isNotNull()
• PySpark Add a New Column to DataFrame
• PySpark – Find Count of null, None, NaN Values
• PySpark How to Filter Rows with NULL Values

Reference:

• https://spark.apache.org/docs/3.0.0/api/python/pyspark.sql.html