That method is the get_dummies Series method, which differs a lot from Pandas’ general function with the same name. Luckily, Pandas has an out-of-the-box method for achieving both transformations at once. Most data science packages (including Pandas, Numpy, scikit-learn, and Keras) include specific functions for one-hot encoding data. To break it down, this can be achieved by doing two transformations:Īlthough it looks seemingly easy, I had a hard time imagining how one goes from the intermediate state (columns with the first, second and third string after splitting them) to the final state. One-hot encoding is the process by which categorical data are converted into numerical data for use in machine learning. For each feature value, the one-hot transformation creates a new feature demarcating the presence or absence of feature value. However, it took me quite some time to figure out how to do it elegantly. One-hot Encoding is a feature encoding strategy to convert categorical features into a numerical vector. Today, I tried a data transformation that seemed so obvious: splitting the string values of a Pandas column on a delimiter and one-hot encode the resulting strings.
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