How to Successfully Use OrdinalEncoder from Scikit-learn on Multiple Columns

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Unlocking the Power of Ordinal Encoding with Scikit-learn

Working with categorical data in machine learning can be challenging, especially when it comes to transforming these features into a numerical format that models can consume. Fortunately, the OrdinalEncoder from Scikit-learn provides an efficient way to encode categorical features to ordinal integers. However, many users encounter issues when attempting to apply it to multiple columns simultaneously.

The Problem with Multi-column Encoding

If you have attempted to use OrdinalEncoder on multiple categorical columns, you may have faced a common error:

[[See Video to Reveal this Text or Code Snippet]]

This error arises because OrdinalEncoder expects a specific format when mapping categories from different columns. Let's delve into the solution to work around this issue.

Solution: Correctly Set Up Your Categorical Columns

To successfully use OrdinalEncoder on multiple columns, we need to define the categorical mappings for each column precisely. Here's how you can do that step by step:

Step 1: Define Your Categorical Columns

Start by identifying the categorical columns in your data. For example, if you have a dataset consisting of colors, shapes, and sizes, your categorical columns could look something like below:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Specify Mappings as Lists

Instead of using a dictionary for mappings, you need to create a list of lists, where each inner list contains the unique category values for each respective column. Here's how the change can be implemented:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Set Up ColumnTransformer with a Pipeline

Now, we can integrate our new category lists into the ColumnTransformer, which organizes the preprocessing steps neatly:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Load Your Data and Transform

At this point, we can load our sample data and apply the preprocessor:

[[See Video to Reveal this Text or Code Snippet]]

Expected Output

When you execute the above code, you should see an output similar to:

[[See Video to Reveal this Text or Code Snippet]]

Bonus: Starting from 1 Instead of 0

If you'd prefer the integers to start from 1 instead of 0, you can pad the category lists with None values as the first element. This shift will yield indices beginning from one:

[[See Video to Reveal this Text or Code Snippet]]

This way, running the transformation will give you:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

In summary, using OrdinalEncoder effectively across multiple columns involves careful preparation of your category mappings. By utilizing a list of lists for categorical values and integrating a ColumnTransformer, you can seamlessly transform multiple columns of categorical data into a suitable format for machine learning models. If you encounter any issues along the way, revisiting these steps should help pave your path to success!

Happy encoding!