pyspark.sql.functions.min_by#

pyspark.sql.functions.min_by(col, ord)[source]#

Returns the value from the col parameter that is associated with the minimum value from the ord parameter. This function is often used to find the col parameter value corresponding to the minimum ord parameter value within each group when used with groupBy().

New in version 3.3.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

colColumn or str: The column representing the values that will be returned. This could be the column instance or the column name as string.
ordColumn or str: The column that needs to be minimized. This could be the column instance or the column name as string.

Returns

Column: Column object that represents the value from col associated with the minimum value from ord.

Examples

Example 1: Using min_by with groupBy:

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([
...     ("Java", 2012, 20000), ("dotNET", 2012, 5000),
...     ("dotNET", 2013, 48000), ("Java", 2013, 30000)],
...     schema=("course", "year", "earnings"))
>>> df.groupby("course").agg(sf.min_by("year", "earnings")).sort("course").show()
+------+----------------------+
|course|min_by(year, earnings)|
+------+----------------------+
|  Java|                  2012|
|dotNET|                  2012|
+------+----------------------+

Example 2: Using min_by with different data types:

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([
...     ("Marketing", "Anna", 4), ("IT", "Bob", 2),
...     ("IT", "Charlie", 3), ("Marketing", "David", 1)],
...     schema=("department", "name", "years_in_dept"))
>>> df.groupby("department").agg(
...     sf.min_by("name", "years_in_dept")
... ).sort("department").show()
+----------+---------------------------+
|department|min_by(name, years_in_dept)|
+----------+---------------------------+
|        IT|                        Bob|
| Marketing|                      David|
+----------+---------------------------+

Example 3: Using min_by where ord has multiple minimum values:

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([
...     ("Consult", "Eva", 6), ("Finance", "Frank", 5),
...     ("Finance", "George", 9), ("Consult", "Henry", 7)],
...     schema=("department", "name", "years_in_dept"))
>>> df.groupby("department").agg(
...     sf.min_by("name", "years_in_dept")
... ).sort("department").show()
+----------+---------------------------+
|department|min_by(name, years_in_dept)|
+----------+---------------------------+
|   Consult|                        Eva|
|   Finance|                      Frank|
+----------+---------------------------+