pyspark.sql.functions.try_to_timestamp#

pyspark.sql.functions.try_to_timestamp(col, format=None)[source]#

Parses the col with the format to a timestamp. The function always returns null on an invalid input with/without ANSI SQL mode enabled. The result data type is consistent with the value of configuration spark.sql.timestampType.

New in version 3.5.0.

Parameters

colColumn or column name: column values to convert.
format: literal string, optional: format to use to convert timestamp values.

See also

pyspark.sql.functions.to_date()
pyspark.sql.functions.to_timestamp()
pyspark.sql.functions.to_utc_timestamp()
pyspark.sql.functions.date_format()

Examples

Example 1: Convert string to a timestamp

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(sf.try_to_timestamp(df.t).alias('dt')).show()
+-------------------+
|                 dt|
+-------------------+
|1997-02-28 10:30:00|
+-------------------+

Example 2: Convert string to a timestamp with a format

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(sf.try_to_timestamp(df.t, sf.lit('yyyy-MM-dd HH:mm:ss')).alias('dt')).show()
+-------------------+
|                 dt|
+-------------------+
|1997-02-28 10:30:00|
+-------------------+

Example 3: Converion failure results in NULL when ANSI mode is on

>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
...     df = spark.createDataFrame([('malformed',)], ['t'])
...     df.select(sf.try_to_timestamp(df.t)).show()
... finally:
...     spark.conf.set("spark.sql.ansi.enabled", origin)
+-------------------+
|try_to_timestamp(t)|
+-------------------+
|               NULL|
+-------------------+