Pyspark drop duplicates

What is the difference between PySpark distinct pyspark drop duplicates dropDuplicates methods? Both these methods are used to drop duplicate rows from the DataFrame and return DataFrame with unique values, pyspark drop duplicates. 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.

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

Determines which duplicates if any to keep. API Reference. SparkSession pyspark. Catalog pyspark. DataFrame pyspark. Column pyspark. Observation pyspark. Row pyspark. GroupedData pyspark. PandasCogroupedOps pyspark. DataFrameNaFunctions pyspark.

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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.

As a Data Engineer, I collect, extract and transform raw data in order to provide clean, reliable and usable data. In this tutorial, we want to drop duplicates from a PySpark DataFrame. In order to do this, we use the the dropDuplicates method of PySpark. Before we can work with Pyspark, we need to create a SparkSession. A SparkSession is the entry point into all functionalities of Spark. Next, we create the PySpark DataFrame "df" with some example data from a list. To do this, we use the method createDataFrame and pass the data and the column names as arguments.

Pyspark drop duplicates

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.

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Imports from pyspark. You will be notified via email once the article is available for improvement. Naveen journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. PySpark does not support specifying multiple columns with distinct in order to remove the duplicates. Please Login to comment AccumulatorParam pyspark. The dropDuplicates function is widely used to drop the rows based on the selected one or multiple columns. Skip to content. Drop rows containing specific value in PySpark dataframe. Thanks for the great article. ExecutorResourceRequest pyspark. The complete example is available at GitHub for reference.

Related: Drop duplicate rows from DataFrame.

ExecutorResourceRequest pyspark. Following is the syntax on PySpark distinct. Observation pyspark. Please Login to comment Share your suggestions to enhance the article. ExecutorResourceRequests pyspark. How to drop multiple column names given in a list from PySpark DataFrame? Previous Concatenate two PySpark dataframes. API Reference. PythonModelWrapper pyspark. RDDBarrier pyspark.