scikit-learn : Machine Learning Simplified

Imputing missing values through various strategies

Data imputation is critical in practice, and thankfully there are many ways to deal with it. In this recipe, we'll look at a few of the strategies. However, be aware that there might be other approaches that fit your situation better.

This means scikit-learn comes with the ability to perform fairly common imputations; it will simply apply some transformations to the existing data and fill the NAs. However, if the dataset is missing data, and there's a known reason for this missing data—for example, response times for a server that times out after 100ms—it might be better to take a statistical approach through other packages such as the Bayesian treatment via PyMC, the Hazard Models via Lifelines, ...