How to Properly Remove Categorical Columns in a Pipeline with OneHotEncoder in Scikit-Learn

Learn how to effectively use `ColumnTransformer` with `Pipeline` in Scikit-Learn to preprocess your data and eliminate categorical columns that can interfere with other transformations like StandardScaler. --- This video is based on the question https://stackoverflow.com/q/70443956/ asked by the user 'Huy Huy' ( https://stackoverflow.com/u/17094977/ ) and on the answer https://stackoverflow.com/a/70445817/ provided by the user 'Antoine Dubuis' ( https://stackoverflow.com/u/4574633/ ) 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: OneHotEncoder doesn't remove categorical in pipeline Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/licensing The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- How to Properly Remove Categorical Columns in a Pipeline with OneHotEncoder in Scikit-Learn In the world of data science and machine learning, preprocessing data is a crucial step before building models. One common challenge that arises is how to effectively manage categorical features when using tools like Scikit-Learn. Specifically, many users encounter issues with OneHotEncoder not removing categorical columns when working within a pipeline. In this post, we'll break down the problem and provide a straightforward solution. The Problem: Categorical Features Persisting As you work with data, you may find yourself using a ColumnTransformer coupled with a pipeline to preprocess your dataset. For example, you might have implemented the following code to handle a dataset containing both categorical and numerical columns: [[See Video to Reveal this Text or Code Snippet]] However, when running this pipeline, you notice that the categorical columns remain in the output, creating confusion. You might want to use a transformer like StandardScaler next, but the presence of these categorical values complicates matters. The missing conversion might lead to errors, and you may be left wondering what went wrong. Understanding Why It Happened The key to this issue lies in how the ColumnTransformer operates. It performs the first operation defined for the specified columns and does not sequentially apply additional transformations to the same columns. As a result, when you specify a OneHotEncoder for your categorical columns, the intended transformation does not occur as you might expect. In short, categorical columns will only be imputed, and the OneHotEncoder will not modify them. The Solution: Utilize Nested Pipelines To resolve this issue, you should structure your preprocessing using nested pipelines. This way, you will ensure that both imputation and one-hot encoding are applied in sequence. Below is an effective way to implement this solution. Step-by-Step Implementation Create input data: For demonstration, we will use a simple DataFrame. [[See Video to Reveal this Text or Code Snippet]] Build pipelines for categorical and numerical preprocessing: [[See Video to Reveal this Text or Code Snippet]] Combine them in a ColumnTransformer: [[See Video to Reveal this Text or Code Snippet]] Transform your data: [[See Video to Reveal this Text or Code Snippet]] Example Output After running the preprocessing steps, you can expect an output like the following: [[See Video to Reveal this Text or Code Snippet]] Here, you can see that categorical features have been transformed successfully, paving the way for further analysis or model building. Conclusion By employing nested pipelines in conjunction with ColumnTransformer, you can efficiently manage and preprocess your data, ensuring that categorical columns are appropriately encoded and removed from the output of the pipeline. This clarity in data preprocessing not only reduces errors but also enhances your modeling capabilities. If you're facing similar problems in your own projects, remember to check how transformations are structured and utilize the flexibility of pipelines to make your data preprocessing process smoother!