width_bucket

Returns the bucket number into which the value of this expression would fall after being evaluated. Note that input arguments must follow conditions listed below; otherwise, the method will return null. Supports Spark Connect.

For the corresponding Databricks SQL function, see width_bucket function.

Syntax

from pyspark.databricks.sql import functions as dbf

dbf.width_bucket(v=<v>, min=<min>, max=<max>, numBucket=<numBucket>)

Parameters

Parameter	Type	Description
v	pyspark.sql.Column or column name	value to compute a bucket number in the histogram
min	pyspark.sql.Column or column name	minimum value of the histogram
max	pyspark.sql.Column or column name	maximum value of the histogram
numBucket	pyspark.sql.Column, column name or int	the number of buckets

Returns

pyspark.sql.Column: the bucket number into which the value would fall after being evaluated

Examples

from pyspark.databricks.sql import functions as dbf
df = spark.createDataFrame([
    (5.3, 0.2, 10.6, 5),
    (-2.1, 1.3, 3.4, 3),
    (8.1, 0.0, 5.7, 4),
    (-0.9, 5.2, 0.5, 2)],
    ['v', 'min', 'max', 'n'])
df.select("*", dbf.width_bucket('v', 'min', 'max', 'n')).show()

+----+---+----+---+----------------------------+
|   v|min| max|  n|width_bucket(v, min, max, n)|
+----+---+----+---+----------------------------+
| 5.3|0.2|10.6|  5|                           3|
|-2.1|1.3| 3.4|  3|                           0|
| 8.1|0.0| 5.7|  4|                           5|
|-0.9|5.2| 0.5|  2|                           3|
+----+---+----+---+----------------------------+