How to Convert PySpark Date to String Type in Databricks?

This recipe will guide you through the precise steps to seamlessly convert PySpark datetime to string types for effective data handling. | ProjectPro

Recipe Objective - How to Convert Date to String in PySpark? 

When working with PySpark in Databricks, handling date formats is a common task. There are various scenarios where you might need to convert PySpark Datetime to String types for better analysis and visualization. Check out this recipe to explore different methods to seamlessly convert PySpark Date to String in a Databricks environment.

The Need for Convert Date to String in PySpark 

Before diving into the methods, it's crucial to understand why converting PySpark Date to String is necessary. Typically, it's needed for tasks like generating human-readable reports, interfacing with external systems, or transforming data for visualization tools. PySpark provides multiple ways to achieve this conversion, ensuring flexibility in handling diverse use cases. 

System Requirements

  • Python (3.0 version)

  • Apache Spark (3.1.1 version)

How to Convert PySpark Datetime to String Type? 

There are several ways to convert PySpark datetime to string type. Let’s explore each of these below - 

The date_format() function in Apache PySpark is popularly used to convert the DataFrame column from the Date to the String format. The date_format() function supports all the Java Date formats. The "date_format(column, format)" is the syntax of the date_format() function where the first argument specifies the input of the Date that is the column of the dataframe, and the Second argument specifies an additional Date argument which further defines the format of the input Date in the PySpark.

# Importing package

import pyspark.sql

from pyspark.sql import SparkSession

from pyspark.sql.functions import *

Importing packages

The SparkSession and all packages are imported in the environment to perform conversion of Date to String in PySpark.

# Implementing the date_format() function in Databricks in PySpark

spark = SparkSession.builder \

               .appName('PySpark date_format()') \

               .getOrCreate()

# Defining dataframe

dataframe = spark.createDataFrame([["1"]],["id"])

# Using date_format() format

dataframe.select(current_date().alias("Current_Date"), \

      date_format(current_date(),"yyyy MM dd").alias("yyyy MM dd"), \

      date_format(current_timestamp(),"MM/dd/yyyy hh:mm").alias("MM/dd/yyyy"), \

      date_format(current_timestamp(),"yyyy MMM dd").alias("yyyy MMMM dd"), \

      date_format(current_timestamp(),"yyyy MMMM dd F").alias("yyyy MMMM dd F") \

   ).show()

date_format() function in Spark SQL

PySpark date to string output

The "dataframe" value is created in which the data is defined. The date_format() function converts the DataFrame column from the Date to the String format. Further, alias like "MM/dd/yyyy," "yyyy MMMM dd F," etc., are also defined to quickly identify the column names and the generated outputs by date_format() function.

Another approach is to use the cast function, which allows you to explicitly cast a PySpark column to a different data type. In this case, casting the Date column to String is the objective.

from pyspark.sql.functions import col

# Assuming 'date_column' is the PySpark Date column

df = df.withColumn('string_date', col('date_column').cast('string'))

This code snippet demonstrates the use of the cast function to convert a PySpark Date column to a String column.

Combining the to_date and date_format functions provides a powerful way to convert PySpark Date to String with custom formatting. This method is particularly useful when you need to transform the date into a specific pattern.

from pyspark.sql.functions import col, date_format, to_date

# Assuming 'date_column' is the PySpark Date column

df = df.withColumn('formatted_date', date_format(to_date(col('date_column')), 'yyyy-MM-dd'))

Here, the to_date function is used to convert the Date column to the DateType, and then date_format formats it into the desired String pattern.

Build Practical Expertise in PySpark with ProjectPro! 

Converting PySpark Date to String in Databricks is not just about understanding the syntax and methods; it's about applying this knowledge in real-world scenarios. Practical experience is crucial for any data professional looking to excel in their field. By working on real-world projects, you gain a deeper understanding of the challenges and nuances involved in data manipulation tasks. Platforms like ProjectPro offer an invaluable opportunity to bridge the gap between theoretical knowledge and practical application. With a vast repository of over 270+ projects based on data science and big data, ProjectPro becomes a one-stop destination for honing your skills. These projects are designed to simulate real-world challenges, providing a hands-on experience that goes beyond the confines of traditional learning.

What Users are saying..

profile image

Savvy Sahai

Data Science Intern, Capgemini
linkedin profile url

As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of... Read More

Relevant Projects

Retail Analytics Project Example using Sqoop, HDFS, and Hive
This Project gives a detailed explanation of How Data Analytics can be used in the Retail Industry, using technologies like Sqoop, HDFS, and Hive.

Building Real-Time AWS Log Analytics Solution
In this AWS Project, you will build an end-to-end log analytics solution to collect, ingest and process data. The processed data can be analysed to monitor the health of production systems on AWS.

dbt Snowflake Project to Master dbt Fundamentals in Snowflake
DBT Snowflake Project to Master the Fundamentals of DBT and learn how it can be used to build efficient and robust data pipelines with Snowflake.

Implementing Slow Changing Dimensions in a Data Warehouse using Hive and Spark
Hive Project- Understand the various types of SCDs and implement these slowly changing dimesnsion in Hadoop Hive and Spark.

Build a Spark Streaming Pipeline with Synapse and CosmosDB
In this Spark Streaming project, you will learn to build a robust and scalable spark streaming pipeline using Azure Synapse Analytics and Azure Cosmos DB and also gain expertise in window functions, joins, and logic apps for comprehensive real-time data analysis and processing.

Build Serverless Pipeline using AWS CDK and Lambda in Python
In this AWS Data Engineering Project, you will learn to build a serverless pipeline using AWS CDK and other AWS serverless technologies like AWS Lambda and Glue.

PySpark Project-Build a Data Pipeline using Hive and Cassandra
In this PySpark ETL Project, you will learn to build a data pipeline and perform ETL operations by integrating PySpark with Hive and Cassandra

Airline Dataset Analysis using PySpark GraphFrames in Python
In this PySpark project, you will perform airline dataset analysis using graphframes in Python to find structural motifs, the shortest route between cities, and rank airports with PageRank.

Flask API Big Data Project using Databricks and Unity Catalog
In this Flask Project, you will use Flask APIs, Databricks, and Unity Catalog to build a secure data processing platform focusing on climate data. You will also explore advanced features like Docker containerization, data encryption, and detailed data lineage tracking.

Learn to Build Regression Models with PySpark and Spark MLlib
In this PySpark Project, you will learn to implement regression machine learning models in SparkMLlib.