pyspark.sql.functions.try_to_time#

pyspark.sql.functions.try_to_time(str, format=None)[source]#

Converts a Column into pyspark.sql.types.TimeType using the optionally specified format. Specify formats according to datetime pattern. By default, it follows casting rules to pyspark.sql.types.TimeType if the format is omitted. Equivalent to col.cast("time"). The function always returns null on an invalid input.

Added in version 4.1.0.

Parameters:

strColumn or column name: string to be parsed to time. A column that evaluates to a string.
format: :class:`~pyspark.sql.Column` or column name, optional: time format pattern to follow. A column that evaluates to a string.

Returns:

Column: time value as pyspark.sql.types.TimeType type. Returns a column that evaluates to a time.

See also

pyspark.sql.functions.to_time()
pyspark.sql.functions.try_to_timestamp()

Examples

Example 1: Convert string to a time

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("10:30:00",)], ["str"])
>>> df.select(sf.try_to_time(df.str).alias("time")).show()
+--------+
|    time|
+--------+
|10:30:00|
+--------+

Example 2: Convert string to a time with a format

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("10:30:00", "HH:mm:ss")], ["str", "format"])
>>> df.select(sf.try_to_time(df.str, df.format).alias("time")).show()
+--------+
|    time|
+--------+
|10:30:00|
+--------+

Example 3: Converion failure results in NULL

>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("malformed",)], ["str"])
>>> df.select(sf.try_to_time(df.str).alias("time")).show()
+----+
|time|
+----+
|NULL|
+----+