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How to Concatenate N Rows Side-by-Side in Pandas

Learn how to efficiently combine multiple rows in a Pandas DataFrame side-by-side, creating a compiled dataset from smaller segments. --- This video is based on the question https://stackoverflow.com/q/72105167/ asked by the user 'bigci10' ( https://stackoverflow.com/u/13537778/ ) and on the answer https://stackoverflow.com/a/72105729/ provided by the user 'Cameron Riddell' ( https://stackoverflow.com/u/14278448/ ) 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: How to concatenate n rows side-by-side in pandas 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. --- How to Concatenate N Rows Side-by-Side in Pandas: A Step-by-Step Guide Combining multiple rows in a Pandas DataFrame into a single row can be a desirable operation for data analysis and manipulation. In this guide, we'll explore how to concatenate every five rows of a dataset side-by-side, effectively transforming your DataFrame into a new format. This is particularly useful when you have a structured dataset, and you want to create a broader view of the information encompassed in those rows. Understanding the Problem Imagine you have a dataset with 700 rows and 7 columns, where each row contains certain values. Your goal is to take every five rows and combine them side by side into a new row while maintaining the column structure. For example, if your initial five rows look like this: [[See Video to Reveal this Text or Code Snippet]] You want your combined first row to look like: [[See Video to Reveal this Text or Code Snippet]] Setting Up the Environment To accomplish this task, we will use Python's Pandas and NumPy libraries. If you haven't already, you can install them via pip: [[See Video to Reveal this Text or Code Snippet]] The Solution Step 1: Import Required Libraries First, you need to import the necessary libraries in your Python environment: [[See Video to Reveal this Text or Code Snippet]] Step 2: Create the DataFrame You can create a Pandas DataFrame for demonstration purposes. Here's a quick way to generate a sample dataset: [[See Video to Reveal this Text or Code Snippet]] Step 3: Concatenate Rows Next, the main operation will be reshaping your DataFrame to concatenate every five rows side-by-side. This can be done using NumPy's reshape function with the following code: [[See Video to Reveal this Text or Code Snippet]] Explanation of the Code df.to_numpy(): Converts the DataFrame into a NumPy array. reshape(-1, df.shape[1] * 5): Reshapes the array into a new shape where the number of columns is 5 times the original number of columns, effectively stacking the relevant data side by side. *columns=[df.columns] * 5: Sets the column names for the new DataFrame, repeating the original column names for clarity. Output After running the above code, you will get a new DataFrame where every 5 rows from the original DataFrame have been concatenated into one row. The resulting DataFrame will have 2 rows with new concatenated columns, producing a much broader representation of your data. Conclusion In summary, concatenating rows in a Pandas DataFrame is a powerful data manipulation technique that can help streamline your dataset for analysis. Leveraging NumPy's capabilities alongside Pandas makes this process efficient and simple. By following the steps in this guide, you can transform your data efficiently and effectively combine rows side-by-side for enhanced visibility into your datasets. Whether you're working on data clean-up or preparing data for analysis, mastering this technique will significantly enhance your data manipulation skills in Python. Happy coding!