Pyspark drop columns with same name after join. g. More detail can be refer to below Spark Dataframe API: pyspark. Oct 26, 2017 · After I've joined multiple tables together, I run them through a simple function to drop columns in the DF if it encounters duplicates while walking from left to right. Here, we will explore effective solutions to cleanly handle this scenario without cumbersome iterations to remove duplicates. This is particularly relevant when performing self-joins or joins on multiple columns. This article and notebook demonstrate how to perform a join so that you don’t have duplicated columns. Sep 5, 2024 · When working with PySpark, it's common to join two DataFrames. , id, dept_id), the result includes both, distinguished by their origin but not automatically resolved, leading to duplicates. This makes it harder to select those columns. Jul 21, 2023 · One common operation in PySpark is joining two DataFrames. If both DataFrames have columns with the same name (e. Apr 17, 2025 · Handling duplicate column names after a join in PySpark is a vital skill for clear, error-free data integration. after that i need to drop all columns of second table. Create the first dataframe for demonstration: Nov 23, 2024 · This error signifies that columns with the same name (in this case, id) are causing confusion within the DataFrame. Feb 21, 2017 · I am trying to join two dataframes with the same column names and compute some new values. Jan 30, 2025 · If both tables contain the same column name, Spark appends suffixes like _1, _2, leading to messy datasets that are difficult to work with. Nov 18, 2015 · After digging into the Spark API, I found I can first use alias to create an alias for the original dataframe, then I use withColumnRenamed to manually rename every column on the alias, this will do the join without causing the column name duplication. join( Jul 21, 2023 · In the world of big data, PySpark has emerged as a powerful tool for processing and analyzing large datasets. In this blog post, we'll explore how to perform a join in PySpark without creating duplicate columns. sql. The number of columns is huge. One common operation in PySpark is joining two DataFrames. Jul 23, 2025 · The merge or join can be inner, outer, left, right, etc. . withColumnRenamed However, I think May 11, 2018 · I want to use join with 3 dataframe, but there are some columns we don't need or have some duplicate name with other dataframes, so I want to drop some columns like below: result_df = (aa_df. However, this operation can often result in duplicate columns, which can be problematic. However, if the DataFrames contain columns with the same name (that aren't used as join keys), the resulting DataFrame can have duplicate columns. alias pyspark. Thus, we have explained in this article, how to rename duplicated columns after join in Pyspark data frame. Dec 29, 2021 · In this article, we will discuss how to remove duplicate columns after a DataFrame join in PySpark. , but after join, if we observe that some of the columns are duplicates in the data frame, then we will get stuck and not be able to apply functions on the joined data frame. From basic column selection to advanced renaming, nested data, SQL expressions, null handling, and performance optimizations, you’ve got a comprehensive toolkit. Oct 13, 2022 · If you perform a join in Spark and don’t specify your join correctly you’ll end up with duplicate column names. DataFrame. In this guide, we’ll explore practical techniques to resolve duplicate columns after a JOIN in Databricks, separately for Spark SQL and PySpark. boabm zwdz vnp ljg vnuoswz icafgzk nlwo btkcj styrd reph