Matthew Mayo knows that time is a flat circle:
Feature engineering is one of the most important steps when it comes to building effective machine learning models, and this is no less important when dealing with time-series data. By being able to create meaningful features from temporal data, you can unlock predictive power that is unavailable when applied to raw timestamps alone.
Fortunately for us all, Pandas offers a powerful and flexible set of operations for manipulating and creating time-series features.
Click through for seven things you can do in Pandas to extend or work with time series data.
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