Drop One or Multiple Columns From PySpark DataFrame
Last Updated :
17 Jun, 2021
In this article, we will discuss how to drop columns in the Pyspark dataframe.
In pyspark the drop() function can be used to remove values/columns from the dataframe.
Syntax: dataframe_name.na.drop(how=”any/all”,thresh=threshold_value,subset=[“column_name_1″,”column_name_2”])
- how – This takes either of the two values ‘any’ or ‘all’. ‘any’, drop a row if it contains NULLs on any columns and ‘all’, drop a row only if all columns have NULL values. By default it is set to ‘any’
- thresh – This takes an integer value and drops rows that have less than that thresh hold non-null values. By default it is set to ‘None’.
- subset – This parameter is used to select a specific column to target the NULL values in it. By default it’s ‘None
Python code to create student dataframe with three columns:
Python3
import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName( 'sparkdf' ).getOrCreate()
data = [[ "1" , "sravan" , "company 1" ],
[ "3" , "bobby" , "company 3" ],
[ "2" , "ojaswi" , "company 2" ],
[ "1" , "sravan" , "company 1" ],
[ "3" , "bobby" , "company 3" ],
[ "4" , "rohith" , "company 2" ],
[ "5" , "gnanesh" , "company 1" ]]
columns = [ 'Employee ID' , 'Employee NAME' , 'Company Name' ]
dataframe = spark.createDataFrame(data,columns)
dataframe.show()
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Output:
+-----------+-------------+------------+
|Employee ID|Employee NAME|Company Name|
+-----------+-------------+------------+
| 1| sravan| company 1|
| 3| bobby| company 3|
| 2| ojaswi| company 2|
| 1| sravan| company 1|
| 3| bobby| company 3|
| 4| rohith| company 2|
| 5| gnanesh| company 1|
+-----------+-------------+------------+
Example 1: Delete a single column.
Here we are going to delete a single column from the dataframe.
Syntax: dataframe.drop(‘column name’)
Code:
Python3
dataframe = dataframe.drop( 'Employee ID' )
dataframe.show()
|
Output:
+-------------+------------+
|Employee NAME|Company Name|
+-------------+------------+
| sravan| company 1|
| bobby| company 3|
| ojaswi| company 2|
| sravan| company 1|
| bobby| company 3|
| rohith| company 2|
| gnanesh| company 1|
+-------------+------------+Example 2:
Example 2: Delete multiple columns.
Here we will delete multiple columns from the dataframe.
Syntax: dataframe.drop(*(‘column 1′,’column 2′,’column n’))
Code:
Python3
dataframe = dataframe.drop( * ( 'Employee NAME' ,
'Employee ID' ))
dataframe.show()
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Output:
+------------+
|Company Name|
+------------+
| company 1|
| company 3|
| company 2|
| company 1|
| company 3|
| company 2|
| company 1|
+------------+
Example 3: Delete all columns
Here we will delete all the columns from the dataframe, for this we will take column’s name as a list and pass it into drop().
Python3
list = [ 'Employee ID' , 'Employee NAME' , 'Company Name' ]
dataframe = dataframe.drop( * list )
dataframe.show()
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Output:
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