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Data Statistics and Analysis With Java and Python

Java and Python are two of the most popular computer languages in use today. Both are very mature and provide the tools and technology ecosystems to support developing solutions to the challenging problems that arise in the world of data science. Each has its idiosyncrasies. It’s important to understand how they compare tackling different problems, whether they shine or lack the required flexibility to handle the assigned tasks.  When one is preferable over the other or when they work in tandem complementing each other.

Python is a dynamically typed language, very straightforward to work with, and is certainly the language of choice to do complex computations if we don't have to worry about intricate program flows. It provides excellent libraries (Pandas, NumPy, Matplotlib, ScyPy, PyTorch, TensorFlow, etc.) to support logical, mathematical, and scientific operations on data structures or arrays.

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Grouping and Aggregations With Java Streams

When we group elements from a list, we can subsequently aggregate the fields of the grouped elements to perform meaningful operations that help us analyze the data. Some examples are addition, averages, or max/min values. These aggregations of single fields can be easily done with Java Streams and Collectors. The documentation provides simple examples of how to do these types of calculations.

However, there are more sophisticated aggregations like weighted averages, geometric means. Additionally, there might be the need to do simultaneous aggregations of several fields. In this article, we are going to show a straightforward path to solve these kinds of problems using Java Streams. Using this framework allows us to process large amounts of data quickly and efficiently.