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DataFrameNaFunctions class

Functionality for working with missing data in a DataFrame.

Supports Spark Connect

Syntax​

Python
DataFrame.na

Methods​

Method

Description

drop(how, thresh, subset)

Returns a new DataFrame omitting rows with null or NaN values.

fill(value, subset)

Returns a new DataFrame with null values replaced by the specified value.

replace(to_replace, value, subset)

Returns a new DataFrame replacing a value with another value.

Examples​

Drop rows with null values​

Python
from pyspark.sql import Row

df = spark.createDataFrame([
    Row(age=10, height=80.0, name="Alice"),
    Row(age=5, height=None, name="Bob"),
    Row(age=None, height=None, name="Tom"),
])

df.na.drop().show()
Output
+---+------+-----+
|age|height| name|
+---+------+-----+
| 10|  80.0|Alice|
+---+------+-----+

Fill null values​

Python
df = spark.createDataFrame([
    (10, 80.5, "Alice"),
    (5, None, "Bob"),
    (None, None, "Tom")],
    schema=["age", "height", "name"])

df.na.fill({'age': 50, 'name': 'unknown'}).show()
Output
+---+------+-------+
|age|height|   name|
+---+------+-------+
| 10|  80.5|  Alice|
|  5|  NULL|    Bob|
| 50|  NULL|unknown|
+---+------+-------+

Replace values​

Python
df = spark.createDataFrame([
    (10, 80, "Alice"),
    (5, None, "Bob"),
    (None, 10, "Tom")],
    schema=["age", "height", "name"])

df.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
Output
+----+------+----+
| age|height|name|
+----+------+----+
|  10|    80|   A|
|   5|  NULL|   B|
|NULL|    10| Tom|
+----+------+----+