How to Get the Time From a Timestamp Column in PySpark DataFrame

Published Jan 9, 2022  ∙  Updated May 2, 2022

How can we extract the time from a timestamp column in a PySpark DataFrame?

Suppose we have a DataFrame df with the column datetime, which is of type timestamp.

Column of type timestamp

We might have casted this column to be of type timestamp using cast().

df = df.withColumn("datetime", col("datetime").cast("timestamp"))

We also could have used to_timestamp().

from pyspark.sql.functions import to_timestamp
from pyspark.sql.types import TimestampType
df = df.withColumn("datetime", to_timestamp("datetime", TimestampType())) 

Either way, we have a timestamp column called datetime.

Get the time using date_format()

We can extract the time into a new column using date_format().

We can then specify the the desired format of the time in the second argument.

from pyspark.sql.functions import date_format
df = df.withColumn("time", date_format('datetime', 'HH:mm:ss'))

This would yield a DataFrame that looks like this.

+-------------------+--------+
|           datetime|    time|
+-------------------+--------+
|2022-01-09T01:00:00|01:00:00|
|2022-01-09T06:00:00|06:00:00|
|2022-01-09T20:00:00|20:00:00|
+-------------------+--------+

Read more about date_format() in the PySpark documentation.