You can use the following syntax to group rows by year in a PySpark DataFrame:

from pyspark.sql.functions import year, sum df.groupBy(year('date').alias('year')).agg(sum('sales').alias('sum_sales')).show()

This particular example groups the rows of the DataFrame by year based on the date in the **date** column and then calculates the sum of the values in the **sales** column by year.

The following example shows how to use this syntax in practice.

**Example: How to Group by Year in PySpark**

Suppose we have the following PySpark DataFrame that contains information about the sales made on various days at some company:

**from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
#define data
data = [['2021-04-11', 22],
['2021-04-15', 14],
['2021-04-17', 12],
['2022-05-21', 15],
['2022-05-23', 30],
['2023-10-26', 45],
['2023-10-28', 32],
['2023-10-29', 47]]
#define column names
columns = ['date', 'sales']
#create dataframe using data and column names
df = spark.createDataFrame(data, columns)
#view dataframe
df.show()
+----------+-----+
| date|sales|
+----------+-----+
|2021-04-11| 22|
|2021-04-15| 14|
|2021-04-17| 12|
|2022-05-21| 15|
|2022-05-23| 30|
|2023-10-26| 45|
|2023-10-28| 32|
|2023-10-29| 47|
+----------+-----+**

Suppose we would like to calculate the sum of the sales, grouped by year.

We can use the following syntax to do so:

from pyspark.sql.functions import year, sum #calculate sum of sales by year df.groupBy(year('date').alias('year')).agg(sum('sales').alias('sum_sales')).show() +----+---------+ |year|sum_sales| +----+---------+ |2021| 48| |2022| 45| |2023| 124| +----+---------+

The resulting DataFrame shows the sum of sales by year.

For example, we can see:

- The sum of sales for 2021 is
**48**. - The sum of sales for 2022 is
**45**. - The sum of sales for 2023 is
**124**.

Note that you can also aggregate sales by a different metric if you’d like.

For example, you could use the following syntax to calculate the total count of sales, grouped by year:

from pyspark.sql.functions import year, count #calculate count of sales by year df.groupBy(year('date').alias('year')).agg(count('sales').alias('cnt_sales')).show() +----+---------+ |year|cnt_sales| +----+---------+ |2021| 3| |2022| 2| |2023| 3| +----+---------+

The resulting DataFrame now shows the count of sales by year.

**Additional Resources**

The following tutorials explain how to perform other common tasks in PySpark:

How to Add Days to a Date Column in PySpark

How to Convert String to Date in PySpark

How to Convert Timestamp to Date in PySpark