pyspark.sql.functions.array_size#

pyspark.sql.functions.array_size(col)[source]#

Array function: returns the total number of elements in the array. The function returns null for null input.

Added in version 3.5.0.

Parameters:

colColumn or str: The name of the column or an expression that represents the array. A column that evaluates to an array.

Returns:

Column: A new column that contains the size of each array. Returns a column that evaluates to an integer.

See also

pyspark.sql.functions.cardinality()
pyspark.sql.functions.size()

Examples

Example 1: Basic usage with integer array

>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([2, 1, 3],), (None,)], ['data'])
>>> df.select(sf.array_size(df.data)).show()
+----------------+
|array_size(data)|
+----------------+
|               3|
|            NULL|
+----------------+

Example 2: Usage with string array

>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['apple', 'banana', 'cherry'],)], ['data'])
>>> df.select(sf.array_size(df.data)).show()
+----------------+
|array_size(data)|
+----------------+
|               3|
+----------------+

Example 3: Usage with mixed type array

>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['apple', 1, 'cherry'],)], ['data'])
>>> df.select(sf.array_size(df.data)).show()
+----------------+
|array_size(data)|
+----------------+
|               3|
+----------------+

Example 4: Usage with array of arrays

>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([[2, 1], [3, 4]],)], ['data'])
>>> df.select(sf.array_size(df.data)).show()
+----------------+
|array_size(data)|
+----------------+
|               2|
+----------------+

Example 5: Usage with empty array

>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([
...   StructField("data", ArrayType(IntegerType()), True)
... ])
>>> df = spark.createDataFrame([([],)], schema=schema)
>>> df.select(sf.array_size(df.data)).show()
+----------------+
|array_size(data)|
+----------------+
|               0|
+----------------+