Pyspark drop duplicates

What is the difference between PySpark distinct vs dropDuplicates methods?

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

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. 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. The complete example is available at GitHub for reference. PySpark does not support specifying multiple columns with distinct in order to remove the duplicates. We can use the dropDuplicates transformation on specific columns to achieve the uniqueness of the columns. To guarantee the original order we should perform additional sorting operations after distinct. 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.

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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.

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.

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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Enter your name or username to comment. Contribute to the GeeksforGeeks community and help create better learning resources for all. NNK June 22, Reply. 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. Learn to Transform your data pipeline with Azure Data Factory! Campus Experiences. This article is being improved by another user right now. Suggest changes. You can suggest the changes for now and it will be under the article's discussion tab. RDD Transformations are also defined as lazy operations that are none of the transformations get executed until an action is called from the user.

We can use select function along with distinct function to get distinct values from particular columns. Syntax : dataframe. Skip to content.

Suggest Changes. Hire With Us. MultiIndex pyspark. Project Library. RDD Transformations are also defined as lazy operations that are none of the transformations get executed until an action is called from the user. DataFrameNaFunctions pyspark. Report issue Report. Catalog pyspark. DataStreamReader pyspark. Naveen journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. Float64Index pyspark. Please go through our recently updated Improvement Guidelines before submitting any improvements. Enter your website URL optional. 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. Follow Naveen LinkedIn and Medium.