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Horizontally Concatenate Data in Pandas Based on Column Conditions

A step-by-step guide on how to concatenate values to specific rows in a Pandas DataFrame based on a given condition, using the example of concatenating values beside "Romance" genres. --- This video is based on the question https://stackoverflow.com/q/70463137/ asked by the user 'LonanKeane' ( https://stackoverflow.com/u/17748926/ ) and on the answer https://stackoverflow.com/a/70463622/ provided by the user 'mozway' ( https://stackoverflow.com/u/16343464/ ) 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: Pandas concat only beside certain column values 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. --- Horizontally Concatenate Data in Pandas Based on Column Conditions When working with data manipulation in Python, especially using libraries like Pandas, you may encounter situations where you want to concatenate new data based on specific conditions. For instance, suppose you have a DataFrame containing a "genres" column with values like "Romance" and "Fantasy", and you want to add corresponding values from another DataFrame only next to the "Romance" entries. This problem can appear complex at first, but with Pandas, it's quite manageable. Understanding the Problem Let's set the scene with a brief example: You have a DataFrame, df, with 100 rows and a column named "genres". The "genres" column contains either "Romance" or "Fantasy". You also have a second DataFrame, other_df, that has 50 values you wish to append to the first DataFrame but only next to the rows where "genres" equals "Romance". In this scenario, your main goal is to find a way to efficiently add a new column to df where the new column includes values from other_df only in the rows where the "genres" is "Romance". Solution Breakdown Here’s how you can accomplish it using Pandas. We will break down the process step-by-step. Step 1: Set Up Your DataFrames Make sure you have your DataFrames set up correctly. Here’s a simple representation of what they might look like: [[See Video to Reveal this Text or Code Snippet]] Step 2: Use Conditional Selection with loc Pandas provides a very powerful method called .loc[] that allows us to access a group of rows and columns by labels or a boolean array. We can use this to target only the rows where "genres" is "Romance". Here’s how to use it: [[See Video to Reveal this Text or Code Snippet]] Explanation of the Code df['genres'].eq('Romance'): This creates a boolean Series that is True for each row where the genre is "Romance" and False otherwise. loc[]: This method is used to set the values in the new_column only for those rows where the boolean condition (from the previous step) is True. other_df['col'].values: This retrieves the values from the second DataFrame that you want to concatenate into df. Step 3: Verify Your Results After executing the above code, it's always a good idea to check if the concatenation was performed correctly. You can print out the resulting DataFrame: [[See Video to Reveal this Text or Code Snippet]] In this output, you'll see that only the rows in df with "Romance" in the genres column will have corresponding values populated in new_column, whereas rows with "Fantasy" remain empty for that column. Conclusion This method effectively allows you to concatenate values conditionally in Pandas, maintaining organization and ensuring your DataFrame reflects the relationships within your data accurately. By leveraging the power of the loc method, you can customize how and where data is added based on your specific requirements. So next time you need to manipulate your DataFrames selectively, remember this approach! With these steps, you should be able to easily manage DataFrame operations even with conditions applied. Happy coding!