pyspark.sql.functions.count#
- pyspark.sql.functions.count(col)[source]#
- Aggregate function: returns the number of items in a group. - New in version 1.3.0. - Changed in version 3.4.0: Supports Spark Connect. - Parameters
- colColumnor column name
- target column to compute on. 
 
- col
- Returns
- Column
- column for computed results. 
 
 - See also - Examples - Example 1: Count all rows in a DataFrame - >>> from pyspark.sql import functions as sf >>> df = spark.createDataFrame([(None,), ("a",), ("b",), ("c",)], schema=["alphabets"]) >>> df.select(sf.count(sf.expr("*"))).show() +--------+ |count(1)| +--------+ | 4| +--------+ - Example 2: Count non-null values in a specific column - >>> from pyspark.sql import functions as sf >>> df.select(sf.count(df.alphabets)).show() +----------------+ |count(alphabets)| +----------------+ | 3| +----------------+ - Example 3: Count all rows in a DataFrame with multiple columns - >>> from pyspark.sql import functions as sf >>> df = spark.createDataFrame( ... [(1, "apple"), (2, "banana"), (3, None)], schema=["id", "fruit"]) >>> df.select(sf.count(sf.expr("*"))).show() +--------+ |count(1)| +--------+ | 3| +--------+ - Example 4: Count non-null values in multiple columns - >>> from pyspark.sql import functions as sf >>> df.select(sf.count(df.id), sf.count(df.fruit)).show() +---------+------------+ |count(id)|count(fruit)| +---------+------------+ | 3| 2| +---------+------------+