pyspark.sql.functions.try_sum

pyspark.sql.functions.try_sum(col)

Returns the sum calculated from values of a group and the result is null on overflow.

New in version 3.5.0.

Parameters

colColumn or str

Examples

Example 1: Calculating the sum of values in a column

>>> from pyspark.sql import functions as sf
>>> df = spark.range(10)
>>> df.select(sf.try_sum(df["id"])).show()
+-----------+
|try_sum(id)|
+-----------+
|         45|
+-----------+

Example 2: Using a plus expression together to calculate the sum

>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, 2), (3, 4)], ["A", "B"])
>>> df.select(sf.try_sum(sf.col("A") + sf.col("B"))).show()
+----------------+
|try_sum((A + B))|
+----------------+
|              10|
+----------------+

Example 3: Calculating the summation of ages with None

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1982, None), (1990, 2), (2000, 4)], ["birth", "age"])
>>> df.select(sf.try_sum("age")).show()
+------------+
|try_sum(age)|
+------------+
|           6|
+------------+

Example 4: Overflow results in NULL when ANSI mode is on

>>> from decimal import Decimal
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
...     df = spark.createDataFrame([(Decimal("1" * 38),)] * 10, "number DECIMAL(38, 0)")
...     df.select(sf.try_sum(df.number)).show()
... finally:
...     spark.conf.set("spark.sql.ansi.enabled", origin)
+---------------+
|try_sum(number)|
+---------------+
|           NULL|
+---------------+