Uploaded December 2018 | Updated September 2026, 2 weeks ago

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With this video tutorial I tried to demonstrate how to create dummy categorical features, in another words columns or variables, by defining universal procedure in Python.

This procedure will use Pandas Concat method that merge different dataframes to single one. In this case by columns. This procedure is easily adopted to any project or Machine learning (ML) project. Just add or change arguments of procedure with your owns.

This can be a part of Feature Engineering or Data preprocessing for your Real World Project.

This way of handling dummy variables can be useful for data scientist, data analytics, who is preparing Machine Learning projects or just coding in Python.

No sklearn, no sckitlearn, no One Hot Encoding or any very special library for handling categorical data. Just Pandas.

What you have to know: get_dummies function, pd.concat function, what mean axis equal to one, what is Inplace argument.

If you don't know, just write to comments bellow.

Source to check Pandas Concat method: pandas.pydata.org/pandas-docs/stable/merging.html

#dummyvariables #onehotencoding #pandas