pyspark.sql.Column.between#

Column.between(lowerBound, upperBound)[source]#

Check if the current column’s values are between the specified lower and upper bounds, inclusive.

Added in version 1.3.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters:

lowerBoundColumn, int, float, string, bool, datetime, date or Decimal: The lower boundary value, inclusive.
upperBoundColumn, int, float, string, bool, datetime, date or Decimal: The upper boundary value, inclusive.

Returns:

Column: A new column of boolean values indicating whether each element in the original column is within the specified range (inclusive).

Examples

Using between with integer values.

>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], ["age", "name"])
>>> df.select(df.name, df.age.between(2, 4)).show()
+-----+---------------------------+
| name|((age >= 2) AND (age <= 4))|
+-----+---------------------------+
|Alice|                       true|
|  Bob|                      false|
+-----+---------------------------+

Using between with string values.

>>> df = spark.createDataFrame([("Alice", "A"), ("Bob", "B")], ["name", "initial"])
>>> df.select(df.name, df.initial.between("A", "B")).show()
+-----+-----------------------------------+
| name|((initial >= A) AND (initial <= B))|
+-----+-----------------------------------+
|Alice|                               true|
|  Bob|                               true|
+-----+-----------------------------------+

Using between with float values.

>>> df = spark.createDataFrame(
...     [(2.5, "Alice"), (5.5, "Bob")], ["height", "name"])
>>> df.select(df.name, df.height.between(2.0, 5.0)).show()
+-----+-------------------------------------+
| name|((height >= 2.0) AND (height <= 5.0))|
+-----+-------------------------------------+
|Alice|                                 true|
|  Bob|                                false|
+-----+-------------------------------------+

Using between with date values.

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame(
...     [("Alice", "2023-01-01"), ("Bob", "2023-02-01")], ["name", "date"])
>>> df = df.withColumn("date", sf.to_date(df.date))
>>> df.select(df.name, df.date.between("2023-01-01", "2023-01-15")).show()
+-----+-----------------------------------------------+
| name|((date >= 2023-01-01) AND (date <= 2023-01-15))|
+-----+-----------------------------------------------+
|Alice|                                           true|
|  Bob|                                          false|
+-----+-----------------------------------------------+
>>> from datetime import date
>>> df.select(df.name, df.date.between(date(2023, 1, 1), date(2023, 1, 15))).show()
+-----+-------------------------------------------------------------+
| name|((date >= DATE '2023-01-01') AND (date <= DATE '2023-01-15'))|
+-----+-------------------------------------------------------------+
|Alice|                                                         true|
|  Bob|                                                        false|
+-----+-------------------------------------------------------------+

Using between with timestamp values.

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame(
...     [("Alice", "2023-01-01 10:00:00"), ("Bob", "2023-02-01 10:00:00")],
...     schema=["name", "timestamp"])
>>> df = df.withColumn("timestamp", sf.to_timestamp(df.timestamp))
>>> df.select(df.name, df.timestamp.between("2023-01-01", "2023-02-01")).show()
+-----+---------------------------------------------------------+
| name|((timestamp >= 2023-01-01) AND (timestamp <= 2023-02-01))|
+-----+---------------------------------------------------------+
|Alice|                                                     true|
|  Bob|                                                    false|
+-----+---------------------------------------------------------+
>>> df.select(df.name, df.timestamp.between("2023-01-01", "2023-02-01 12:00:00")).show()
+-----+------------------------------------------------------------------+
| name|((timestamp >= 2023-01-01) AND (timestamp <= 2023-02-01 12:00:00))|
+-----+------------------------------------------------------------------+
|Alice|                                                              true|
|  Bob|                                                              true|
+-----+------------------------------------------------------------------+