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How to Reverse Engineer a Binned Column in Pandas

Discover how to effortlessly reverse engineer binned columns in pandas using numpy functions for cleaner data analysis. --- This video is based on the question https://stackoverflow.com/q/71132734/ asked by the user 'Giampaolo Levorato' ( https://stackoverflow.com/u/8964393/ ) and on the answer https://stackoverflow.com/a/71132814/ provided by the user 'Corralien' ( https://stackoverflow.com/u/15239951/ ) 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 reverse engineer a binned column 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 Reverse Engineer a Binned Column in Pandas Pandas is a powerful data manipulation library in Python. Sometimes, while working with data, you may encounter binned columns, which make analysis and interpretation a bit challenging. In this article, we will explore how to reverse engineer a binned column in pandas, converting those complex binning intervals into a simpler list of numeric values. Understanding the Problem Consider the following example where we have a pandas DataFrame with a binned column named col1. The binned data consists of ranges, such as (-99999.0, -99998.0) and (1.0, 10.0), and we would like to transform this data into a cleaner format that lists just the edges of the bins. Here’s what our dataset looks like: [[See Video to Reveal this Text or Code Snippet]] Output: [[See Video to Reveal this Text or Code Snippet]] Our goal is to create a list named myBinner, which contains unique numbers at the edges of these bins. Solution Steps To achieve our goal, we can take advantage of the numpy library, specifically the np.ravel and np.unique functions. Below are the steps on how to implement this solution. Step 1: Import Necessary Libraries First, you need to import the required libraries. Make sure you have both pandas and numpy installed in your Python environment. [[See Video to Reveal this Text or Code Snippet]] Step 2: Create Your DataFrame Next, create the DataFrame containing your binned data: [[See Video to Reveal this Text or Code Snippet]] Step 3: Utilize np.ravel and np.unique Now, you can reverse engineer the binned column by using np.ravel and np.unique. Here's how you can do it: [[See Video to Reveal this Text or Code Snippet]] Step 4: Check Your Output Upon running the above code snippet, you should get the desired output: [[See Video to Reveal this Text or Code Snippet]] Conclusion Understanding how to reverse engineer binned columns in pandas is essential for effective data analysis. By using numpy's capabilities, you can easily extract the endpoints of bins and create clearer datasets for further processing. With our final list myBinner, you're now equipped to carry out more insightful analysis on your numeric data. Happy coding!