Applying Cubic Root Transformation and StandardScaler in Pandas for Grouped Data

Learn how to efficiently apply cubic root transformation and standard scaling on grouped dataframe columns in Python using Pandas. --- This video is based on the question https://stackoverflow.com/q/67331531/ asked by the user 'melik' ( https://stackoverflow.com/u/1574551/ ) and on the answer https://stackoverflow.com/a/67332611/ provided by the user 'Shubham Sharma' ( https://stackoverflow.com/u/12833166/ ) 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: Apply cubic root transformation and StandardScaler to some specific columns in pandas dataframe 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. --- Applying Cubic Root Transformation and StandardScaler in Pandas for Grouped Data Introduction In data analysis, it’s often necessary to normalize or transform datasets to improve the results of machine learning algorithms. One common approach is using a cubic root transformation followed by standardization (using StandardScaler). This is particularly useful when dealing with grouped data, such as monthly observations. Imagine you have a pandas dataframe with multiple columns and you want to apply these transformations to specific columns for each month. However, you might encounter some errors if you don’t do it correctly. Let’s take a closer look at how we can tackle this problem effectively. The Problem You have a pandas dataframe containing data structured by months, including multiple columns of observations. When attempting to apply cubic root transformations and standard scaling, you face challenges, particularly with grouping by months. Here’s a snippet of your dataframe for context: [[See Video to Reveal this Text or Code Snippet]] You would like to apply the following operations: Cubic root transformation to the first two columns. Standard scaling of these transformed columns, grouped by the month. However, the challenge arises when trying to execute both operations together, particularly while grouping by the 'month' column. The Solution To achieve the desired transformation and scaling, we can break down the solution into two main parts: Step 1: Apply Cubic Root Transformation The cubic root transformation can be calculated using the numpy function np.cbrt(). This function computes the element-wise cube root of the inputs. Step 2: Standardize Using Grouping For standardization, we will use the zscore() function from the scipy.stats library, which helps to transform our data into a standardized score for each month. Complete Code Example Here’s how you can implement both transformations in one streamlined approach: [[See Video to Reveal this Text or Code Snippet]] Final Output After running the above code, your dataframe will now include new columns representing the scaled values: [[See Video to Reveal this Text or Code Snippet]] Conclusion By following the steps outlined above, you can efficiently apply cubic root transformations and standard scaling to specific grouped columns in a pandas dataframe. This method not only enhances your data preparation process but also prepares your data for better analysis and modeling. If you have further questions or need additional transformations, feel free to explore the libraries mentioned or dig deeper into the capabilities of pandas and scipy.