How to create one-hot encodings for categorical variables in Python?

Hello, I’m currently working on a project that involves a dataset containing categorical variables. My objective is to create one-hot encodings for these variables. I attempted to use the get_dummies() function from the pandas library for this purpose. While it performs well with smaller datasets, I encountered significant slowdowns and memory usage as the dataset size increased.

Consequently, I’m seeking alternative methods to efficiently generate one-hot encodings for categorical variables. I’m specifically looking for approaches that offer both speed and minimal memory consumption. Any assistance or suggestions on how to address this issue more efficiently would be greatly appreciated. Thank you!

Here is the code I used:

You can utilize the widely used OneHotEncoder class from the sklearn library to generate one-hot encodings for categorical variables. It offers greater versatility compared to the pd.get_dummies() function, as it enables you to specify additional parameters, including the treatment of unknown categories. Here is an example code that uses this class:

Regarding the performance of OneHotEncoder, it generally offers good speed and memory efficiency. However, the specific performance characteristics may vary depending on the size and complexity of your dataset. It is generally considered to be a more efficient alternative for large datasets compared to using get_dummies() from pandas.

I hope this helps you!