Pyspark drop duplicates

What is the difference between PySpark distinct vs dropDuplicates methods? Both these methods are used pyspark drop duplicates drop duplicate rows from the DataFrame and return DataFrame with unique values. The main difference is distinct performs on all columns whereas dropDuplicates is used on selected columns.

Project Library. Project Path. In PySpark , the distinct function is widely used to drop or remove the duplicate rows or all columns from the DataFrame. The dropDuplicates function is widely used to drop the rows based on the selected one or multiple columns. RDD Transformations are also defined as lazy operations that are none of the transformations get executed until an action is called from the user. Learn to Transform your data pipeline with Azure Data Factory! This recipe explains what are distinct and dropDuplicates functions and explains their usage in PySpark.

Pyspark drop duplicates

In this article, we are going to drop the duplicate rows by using distinct and dropDuplicates functions from dataframe using pyspark in Python. We can use the select function along with distinct function to get distinct values from particular columns. Syntax : dataframe. Skip to content. Change Language. Open In App. Related Articles. Solve Coding Problems. Convert PySpark dataframe to list of tuples How to verify Pyspark dataframe column type? How to select a range of rows from a dataframe in PySpark? How to drop all columns with null values in a PySpark DataFrame? Concatenate two PySpark dataframes. Drop duplicate rows in PySpark DataFrame. Improve Improve. Like Article Like.

In this article, you have learned what is the difference between PySpark distinct and dropDuplicate functions, both these functions are from DataFrame class and return a DataFrame after eliminating duplicate rows, pyspark drop duplicates. Interview Experiences.

In this article, you will learn how to use distinct and dropDuplicates functions with PySpark example. We use this DataFrame to demonstrate how to get distinct multiple columns. In the above table, record with employer name James has duplicate rows, As you notice we have 2 rows that have duplicate values on all columns and we have 4 rows that have duplicate values on department and salary columns. On the above DataFrame, we have a total of 10 rows with 2 rows having all values duplicated, performing distinct on this DataFrame should get us 9 after removing 1 duplicate row. This example yields the below output. Alternatively, you can also run dropDuplicates function which returns a new DataFrame after removing duplicate rows.

In this article, we are going to drop the duplicate rows based on a specific column from dataframe using pyspark in Python. Duplicate data means the same data based on some condition column values. For this, we are using dropDuplicates method:. Syntax : dataframe. Skip to content. Change Language. Open In App. Related Articles. Solve Coding Problems.

Pyspark drop duplicates

Related: Drop duplicate rows from DataFrame. Below explained three different ways. To use a second signature you need to import pyspark. You can use either one of these according to your need. This uses an array string as an argument to drop function.

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ExecutorResourceRequest pyspark. Admission Experiences. In your example, you call out Robert as being the one that is duplicated, but in your data example, it is James that is duplicated. Interview Experiences. AccumulatorParam pyspark. Float64Index pyspark. In this blog, he shares his experiences with the data as he come across. Report issue Report. BarrierTaskInfo pyspark. Leave a Reply Cancel reply Comment.

Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. For a static batch DataFrame , it just drops duplicate rows.

Concatenate two PySpark dataframes. The distinct function treats NULL values as equal, so if there are multiple rows with NULL values in all columns, only one of them will be retained after applying distinct. If you want to modify the original DataFrame, you need to assign the result distinct to a new variable or use the inPlace parameter if available. Hi Abdulsattar, I have updated the article when it was pointed out the first time. Save Article. Create Improvement. How to drop duplicates and keep one in PySpark dataframe. In this PySpark SQL article, you have learned distinct the method that is used to get the distinct values of rows all columns and also learned how to use dropDuplicates to get the distinct and finally learned to use dropDuplicates function to get distinct multiple columns. How to drop multiple column names given in a list from PySpark DataFrame? The distinct function returns a new DataFrame with distinct rows, leaving the original DataFrame unchanged. Save my name, email, and website in this browser for the next time I comment. View Project Details. To guarantee the original order we should perform additional sorting operations after distinct.