PySpark Date and Timestamp Types Explained _ DateType, TimestampType, Interval Types with Examples | PySpark Tutorial

PySpark Date and Timestamp Types Explained

In this blog, we will understand how PySpark handles DateType, TimestampType, and various Interval types using real-world examples. This is part of our complete PySpark tutorial series.

🔹 Step 1: Import & Session Setup

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("DatetimeTypesDemo").getOrCreate()

🔹 Step 2: Create Sample Data

from pyspark.sql.functions import current_date, current_timestamp

df = spark.range(1).select(
    current_date().alias("current_date"),
    current_timestamp().alias("current_timestamp")
)
df.show(truncate=False)

Output:

+-------------+-----------------------+
|current_date |current_timestamp      |
+-------------+-----------------------+
|2025-04-08   |2025-04-08 14:12:34.123|
+-------------+-----------------------+

🔹 Step 3: Interval Types

from pyspark.sql.functions import expr

df_interval = spark.sql("SELECT INTERVAL '3 12:15:32' DAY TO SECOND AS day_to_sec")
df_interval.show()

Output:

+-----------------+
|day_to_sec       |
+-----------------+
|3 12:15:32.000000|
+-----------------+

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Author: Aamir Shahzad

Tags: PySpark, DateType, TimestampType, PySpark Date Functions, Apache Spark, Interval Functions