PySpark – Sort dataframe by multiple columns
In this article, we are going to see how to sort the PySpark dataframe by multiple columns.
It can be done in these ways:
- Using sort()
- Using orderBy()
Creating Dataframe for demonstration:
Method 1: Using sort() function
This function is used to sort the column.
Syntax: dataframe.sort([‘column1′,’column2′,’column n’],ascending=True)
- dataframe is the dataframe name created from the nested lists using pyspark
- where columns are the llst of columns
- ascending = True specifies order the dataframe in increasing order, ascending=False specifies order the dataframe in decreasing order
Example 1: Python code to sort dataframe by passing a list of multiple columns(2 columns) in ascending order.
Example 2: Python program to sort the data frame by passing a list of columns in descending order
Method 2: Using orderBy() function.
orderBy() function that sorts one or more columns. By default, it orders by ascending.
Syntax: orderBy(*cols, ascending=True)
- cols: Columns by which sorting is needed to be performed.
- ascending: Boolean value to say that sorting is to be done in ascending order
Example 1: Python program to show dataframe by sorting the dataframe based on two columns in descending order using orderby() function
Example 2: Python program to show dataframe by sorting the dataframe based on two columns in ascending order using orderby() function