Pyspark drop duplicates

Project Library. Project Path. In PySparkthe distinct function is widely used to drop or remove the duplicate rows or all columns from the DataFrame. The dropDuplicates function pyspark drop duplicates widely used to drop the rows based on the selected one or multiple columns.

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.

Pyspark drop duplicates

What is the difference between PySpark distinct vs dropDuplicates methods? Both these methods are used to 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. The main difference between distinct vs dropDuplicates functions in PySpark are the former is used to select distinct rows from all columns of the DataFrame and the latter is used select distinct on selected columns. Following is the syntax on PySpark distinct. Returns a new DataFrame containing the distinct rows in this DataFrame. It returns a new DataFrame with duplicate rows removed, when columns are used as arguments, it only considers the selected columns. Following is a complete example of demonstrating the difference between distinct vs dropDuplicates functions. 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. Save my name, email, and website in this browser for the next time I comment. PySpark distinct PySpark dropDuplicates.

Vote for difficulty :. The distinct function on DataFrame returns the new DataFrame after removing the duplicate records.

Determines which duplicates if any to keep. API Reference. SparkSession pyspark. Catalog pyspark. DataFrame pyspark.

We can use 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?

Pyspark drop duplicates

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.

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I come from Northwestern University, which is ranked 9th in the US. DatetimeIndex pyspark. Both these methods are used to drop duplicate rows from the DataFrame and return DataFrame with unique values. Similar Reads. Float64Index pyspark. In this Snowflake Azure project, you will ingest generated Twitter feeds to Snowflake in near real-time to power an in-built dashboard utility for obtaining popularity feeds reports. In this hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to solve the hadoop small file problem. In this blog, he shares his experiences with the data as he come across. This recipe explains what are distinct and dropDuplicates functions and explains their usage in PySpark. Last Updated : 29 Aug, InheritableThread pyspark. The complete example is available at GitHub for reference. Hi Abdulsattar, I have updated the article when it was pointed out the first time.

Related: Drop duplicate rows from DataFrame. Below explained three different ways.

How does distinct handle NULL values? VersionUtils pyspark. Observation pyspark. Please Login to comment Suggest Changes. How to drop multiple column names given in a list from PySpark DataFrame? The dropDuplicates function is executed on selected columns. Report issue Report. Save my name, email, and website in this browser for the next time I comment. StreamingContext pyspark. TaskContext pyspark. Open In App. AccumulatorParam pyspark. DatetimeIndex pyspark. What is the difference between PySpark distinct vs dropDuplicates methods?