Drop duplicates pyspark

Determines which duplicates if any to keep.

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.

Drop duplicates pyspark

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. The distinct function returns a new DataFrame with distinct rows, leaving the original DataFrame unchanged. 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. 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.

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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. Related Articles.

In this tutorial, we will look at how to drop duplicate rows from a Pyspark dataframe with the help of some examples. You can use the Pyspark dropDuplicates function to drop duplicate rows from a Pyspark dataframe. The following is the syntax —. Apply the function on the dataframe you want to remove the duplicates from. It returns a Pyspark dataframe with the duplicate rows removed. We now have a dataframe containing the name, country, and team information of some students participating in a case-study competition. Note that there are duplicate rows present in the data. For this, apply the Pyspark dropDuplicates function on the dataframe created above.

Drop duplicates pyspark

There are three common ways to drop duplicate rows from a PySpark DataFrame:. The following examples show how to use each method in practice with the following PySpark DataFrame:. We can use the following syntax to drop rows that have duplicate values across all columns in the DataFrame:. We can use the following syntax to drop rows that have duplicate values across the team and position columns in the DataFrame:.

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Save my name, email, and website in this browser for the next time I comment. Please share your company email to get customized projects. Project Library. SparkConf pyspark. Improved By :. Skip to content Menu. DataFrameWriter pyspark. The Sparksession, expr is imported in the environment to use distinct function and dropDuplicates functions in the PySpark. InheritableThread pyspark. Relevant Projects. Add Other Experiences. Explore offer now. This recipe explains what are distinct and dropDuplicates functions and explains their usage in PySpark. SparkUpgradeException pyspark. Removing duplicate columns after DataFrame join in PySpark.

Determines which duplicates if any to keep. Spark SQL pyspark.

Enter your name or username to comment. We used this dataset to generate a pandas data frame. InheritableThread pyspark. Hands on Labs. 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. In this article, we are going to drop the duplicate rows by using distinct and dropDuplicates functions from dataframe using pyspark in Python. Big Data Projects. BarrierTaskContext pyspark. NNK November 1, Reply. Like Article.