pyspark.sql.functions.convert_timezone#

pyspark.sql.functions.convert_timezone(sourceTz, targetTz, sourceTs)[source]#

Converts the timestamp without time zone sourceTs from the sourceTz time zone to targetTz.

Added in version 3.5.0.

Parameters:

sourceTzColumn, optional: The time zone for the input timestamp. If it is missed, the current session time zone is used as the source time zone. A column that evaluates to a string.
targetTzColumn: The time zone to which the input timestamp should be converted. A column that evaluates to a string.
sourceTsColumn or column name: A timestamp without time zone. A column that evaluates to a timestamp.

Returns:

Column: A new column that contains a timestamp for converted time zone. Returns a column that evaluates to a timestamp.

See also

pyspark.sql.functions.current_timezone()

Examples

>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")

Example 1: Converts the timestamp without time zone sourceTs.

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08 00:00:00',)], ['ts'])
>>> df.select(
...     '*',
...     sf.convert_timezone(None, sf.lit('Asia/Hong_Kong'), 'ts')
... ).show()
+-------------------+--------------------------------------------------------+
|                 ts|convert_timezone(current_timezone(), Asia/Hong_Kong, ts)|
+-------------------+--------------------------------------------------------+
|2015-04-08 00:00:00|                                     2015-04-08 15:00:00|
+-------------------+--------------------------------------------------------+

Example 2: Converts the timestamp with time zone sourceTs.

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08 15:00:00',)], ['ts'])
>>> df.select(
...     '*',
...     sf.convert_timezone(sf.lit('Asia/Hong_Kong'), sf.lit('America/Los_Angeles'), df.ts)
... ).show()
+-------------------+---------------------------------------------------------+
|                 ts|convert_timezone(Asia/Hong_Kong, America/Los_Angeles, ts)|
+-------------------+---------------------------------------------------------+
|2015-04-08 15:00:00|                                      2015-04-08 00:00:00|
+-------------------+---------------------------------------------------------+

>>> spark.conf.unset("spark.sql.session.timeZone")