Concatenating Two PySpark DataFrames: A Step-by-Step Guide to Sum Columns earnings and profit

Learn how to left join two PySpark DataFrames, sum their columns, and manage missing data effectively. This guide simplifies the process with clear examples. --- This video is based on the question https://stackoverflow.com/q/62280681/ asked by the user 'madu' ( https://stackoverflow.com/u/455048/ ) and on the answer https://stackoverflow.com/a/62281409/ provided by the user 'anky' ( https://stackoverflow.com/u/9840637/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: PySpark: Concat two dataframes with columns sums Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Concatenating Two PySpark DataFrames: A Step-by-Step Guide to Sum Columns earnings and profit Data manipulation is a key component of data analysis, especially when working with big data frameworks like PySpark. One common challenge is how to effectively concatenate two DataFrames while also performing arithmetic operations like summing specific columns. In this post, we'll address a practical example: how to left join two PySpark DataFrames and sum the earnings from one DataFrame and the profit from another. The Problem Let's consider two PySpark DataFrames: Prev_table user_idearningsstart_dateend_date1102020-06-012020-06-102202020-06-012020-06-103302020-06-012020-06-10New_table user_idprofit110022005500The objective is to create a resultant DataFrame that looks like this: user_idearningsstart_dateend_date11102020-06-012020-06-1022202020-06-012020-06-103302020-06-012020-06-105500Process Overview Rename profit to earnings in the New_table. Align the schema of the two DataFrames by filling in missing columns. Use union to combine the two DataFrames. Group by user_id and sum the earnings. Handle missing data appropriately. Implementation Steps Step 1: Import Required Libraries First, ensure you have the necessary PySpark functions imported: [[See Video to Reveal this Text or Code Snippet]] Step 2: Creating DataFrames Assuming you have already created Prev_table as df1 and New_table as df2, the first operation is to select the required columns from df2 and rename profit to earnings: [[See Video to Reveal this Text or Code Snippet]] Step 3: Fill Missing Columns To align the two DataFrames, it is important to fill in any missing columns from df3. Here’s how you can append None for the missing columns in df2: [[See Video to Reveal this Text or Code Snippet]] Step 4: Combine the DataFrames Now we can perform the union of the two DataFrames: [[See Video to Reveal this Text or Code Snippet]] Step 5: Group and Aggregate Next, group by user_id and sum the earnings, while also retaining the first valid start_date and end_date: [[See Video to Reveal this Text or Code Snippet]] Step 6: Show Result Finally, display the resultant DataFrame: [[See Video to Reveal this Text or Code Snippet]] The Result Executing the above code will provide you with the combined DataFrame displaying the desired sums and handling of None values correctly: [[See Video to Reveal this Text or Code Snippet]] Conclusion In this guide, we've tackled the challenge of concatenating two PySpark DataFrames while summing specific columns. Following a structured approach makes the task much more manageable. Now you're equipped with a clear method to confidently handle similar scenarios in your data manipulation tasks with PySpark. Let us know if you have any questions or need further assistance!