In this article, We will see how to order data in a Pyspark dataframe based on one or more columns with the help of examples.
How to order data in a Pyspark dataframe?
You can use the Pyspark dataframe orderBy function to order (that is, sort) the data based on one or more columns. The following is the syntax –
DataFrame.orderBy(*cols, **kwargs)
The orderBy function takes the following parameters –
- cols – The column or list of column names to sort by.
- ascending – Boolean or list of boolean. Use a list for multiple sort orders. By default, it sorts in ascending order.
Examples
Let’s look at some examples of using the orderBy function to sort a Pyspark dataframe. First, let’s create a dataframe that we will be using throughout this tutorial.
#import the pyspark module
import pyspark
# import the sparksession class from pyspark.sql
from pyspark.sql import SparkSession
# create an app from SparkSession class
spark = SparkSession.builder.appName('datascience_parichay').getOrCreate()
# books data as list of lists
df = [[1, "PHP", "Sravan", 250],
[2, "SQL", "Chandra", 300],
[3, "Python", "Harsha", 250],
[4, "R", "Rohith", 1200],
[5, "Hadoop", "Manasa", 700],
]
# creating dataframe from books data
dataframe = spark.createDataFrame(df, ['Book_Id', 'Book_Name', 'Author', 'Price'])
# display the dataframe
dataframe.show()
Output:
Highlighted programs for you
Flatiron School
Flatiron School
University of Maryland Global Campus
University of Maryland Global Campus
Creighton University
Creighton University
+-------+---------+-------+-----+ |Book_Id|Book_Name| Author|Price| +-------+---------+-------+-----+ | 1| PHP| Sravan| 250| | 2| SQL|Chandra| 300| | 3| Python| Harsha| 250| | 4| R| Rohith| 1200| | 5| Hadoop| Manasa| 700| +-------+---------+-------+-----+
We now have a dataframe containing information about books and their prices in a particular store.
Sort dataframe in ascending order
Let’s sort the above dataframe on the column “Price” in ascending order. We pass the column name, “Price” as an argument to the orderBy() function.
# order by Price in ascending order
dataframe.orderBy("Price").show()
Output:
+-------+---------+-------+-----+ |Book_Id|Book_Name| Author|Price| +-------+---------+-------+-----+ | 3| Python| Harsha| 250| | 1| PHP| Sravan| 250| | 2| SQL|Chandra| 300| | 5| Hadoop| Manasa| 700| | 4| R| Rohith| 1200| +-------+---------+-------+-----+
You can see that the output contains dataframe rows sorted by the “Price” column in ascending order. Note that the orderBy() function sorts a column in ascending order by default and hence we didn’t need to explicitly specify it.
Sort dataframe in descending order
Let’s now sort the same dataframe on the “Price” column in descending order. This time, we pass False as the value for the ascending parameter.
# order by Price in descending order
dataframe.orderBy("Price", ascending=False).show()
Output:
+-------+---------+-------+-----+ |Book_Id|Book_Name| Author|Price| +-------+---------+-------+-----+ | 4| R| Rohith| 1200| | 5| Hadoop| Manasa| 700| | 2| SQL|Chandra| 300| | 1| PHP| Sravan| 250| | 3| Python| Harsha| 250| +-------+---------+-------+-----+
You can see that the output contains dataframe rows sorted by the “Price” column in descending order.
Order dataframe by more than one column
You can also use the orderBy() function to sort a Pyspark dataframe by more than one column. For this, pass the columns to sort by as a list. You can also pass sort order as a list to the ascending parameter for custom sort order for each column.
Let’s sort the above dataframe by “Price” and “Book_Id” both in descending order.
# order by Price and Book_Id in descending order dataframe.orderBy(["Price", "Book_Id"], ascending=[False, False]).show()
Output:
+-------+---------+-------+-----+ |Book_Id|Book_Name| Author|Price| +-------+---------+-------+-----+ | 4| R| Rohith| 1200| | 5| Hadoop| Manasa| 700| | 2| SQL|Chandra| 300| | 3| Python| Harsha| 250| | 1| PHP| Sravan| 250| +-------+---------+-------+-----+
You can see that the output is sorted first on the “Price” column in descending order and then on the “Book_Id” column in descending order.
You might also be interested in –
- Display DataFrame in Pyspark with show()
- Pandas – Sort a DataFrame
- Randomly Shuffle Pandas DataFrame Rows
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