pyspark.sql.functions.count_distinct#

pyspark.sql.functions.count_distinct(col, *cols)[source]#

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

Added in version 3.2.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters:

colColumn or column name: first column to compute on.
colsColumn or column name: other columns to compute on.

Returns:

Column: distinct values of these two column values.

See also

pyspark.sql.functions.approx_count_distinct()

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|
+------------------------------+