50 of the most popular Python libraries and frameworks that are used in data science

This article introduces a landscape diagram which shows 50 or so of the most popular Python libraries and frameworks used in data science. Landscape diagrams illustrate components within a technology stack alongside their complementary technologies. In other words, “How do the parts fit together?”

Landscape diagrams provide useful learning materials, helping people conceptualize and discuss complex technology topics. Of course it’s important to keep this diagram curated and updated as the Python ecosystem evolves. We’ll do that.

Python data landscape

One caveat: trying to fit lots of complex, interconnected parts into a neatly formatted 2D grid is a challenge. Any diagram must “blur the lines” of definitions to simplify the illustration, and those definitions could be debated at length. On the one hand, the diagram does not include an exhaustive list. We chose popular libraries among widely-used categories, but had to skip some. For example we didn’t go into the varied universe of audio processing libraries, which get used in speech-to-text recognition work. On the other hand, let’s talk about that! Let us know about any updates that you would suggest. We’ll start a forum discussion here.

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