We will explore possibilities for making our data analyses and transformations in Pandas robust and production ready. We will see how advanced group-by, resample or rolling aggregations work on large time series weather data. (As a bonus, you will learn about Prague climate.) We will use type annotations and schema validations with the Pandera library to make our code more readable and robust. We will also show the potential of property-based testing using the Hypothesis package, with strategies generated from Pandera schemas. We will show how to avoid issues with time zones when working with time series data. By the end of the tutorial, you will have a deeper understanding of advanced Pandas aggregations and be able to write robust, production ready Pandas code.

We will explore possibilities for making our data analyses and transformations in Pandas robust and production ready. We will see how advanced group-by, resample or rolling aggregations work on large time series weather data. (As a bonus, you will learn about Prague climate.) We will use type annotations and schema validations with the Pandera library to make our code more readable and robust. We will also show the potential of property-based testing using the Hypothesis package, with strategies generated from Pandera schemas. We will show how to avoid issues with time zones when working with time series data. By the end of the tutorial, you will have a deeper understanding of advanced Pandas aggregations and be able to write robust, production ready Pandas code.