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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|
+----+---+----+---+----------------------------+