count_distinct

Returns a new Column for distinct count of col or cols.

Syntax

from pyspark.sql import functions as sf

sf.count_distinct(col, *cols)

Parameters

Parameter	Type	Description
col	pyspark.sql.Column or column name	First column to compute on.
cols	pyspark.sql.Column or column name	Other columns to compute on.

Returns

pyspark.sql.Column: distinct values of these two column values.

Examples

Example 1: Counting distinct values of a single column

from pyspark.sql import functions as sf
df = spark.createDataFrame([(1,), (1,), (3,)], ["value"])
df.select(sf.count_distinct(df.value)).show()

+---------------------+
|count(DISTINCT value)|
+---------------------+
|                    2|
+---------------------+

Example 2: Counting distinct values of multiple columns

from pyspark.sql import functions as sf
df = spark.createDataFrame([(1, 1), (1, 2)], ["value1", "value2"])
df.select(sf.count_distinct(df.value1, df.value2)).show()

+------------------------------+
|count(DISTINCT value1, value2)|
+------------------------------+
|                             2|
+------------------------------+

Example 3: Counting distinct values with column names as strings

from pyspark.sql import functions as sf
df = spark.createDataFrame([(1, 1), (1, 2)], ["value1", "value2"])
df.select(sf.count_distinct("value1", "value2")).show()

+------------------------------+
|count(DISTINCT value1, value2)|
+------------------------------+
|                             2|
+------------------------------+