Why should I know the Python ecosystem?

The programming language Python is on everyone’s lips and is used in almost every scientific discipline. Creating complex data and analysis pipelines is easier than ever before. You’ll be inundated with tutorials online. You can learn the language at every turn. Keeping track of it all is not so easy. Learning the programming basics is easy, but keeping track of the technology possibilities only grows with experience. On this page we give you an overview of the most important Python frameworks and Python features. So you can learn more targeted and therefore faster.

Why Python?

There are many reasons why you should learn Python.
Here are the most important ones:

Python is one of the most widely used programming languages for Data Science and Machine Learning.

The programming language is cross-platform and free. You are offered a variety of programming paradigms. You can do object-oriented and functional programming. But most importantly, Python is easy to learn and user-friendly.

This figure shows the most important Python features
Awesome Python – Features

Trapped in the Python Frameworks-Jungle?
We’ll give you the big picture.

Python offers many solutions for every scientific problem. Frameworks can do a lot of work for you. You can extend the programming language modularly according to your needs. But which Python framework do you use for which use case? The overview will grow with your experience.

We offer you a shortcut!


You have been working with Python for years and are an experienced programmer? But you still want to stay up to date in this fast moving industry?

We offer quick and clear summaries of the most important features.

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Photo by David Riaño Cortés, Pexels

Like a good craftsman, you should know your tools:

Awesome Python - The figure gives an overview of The main Python frameworks and libraries.
Awesome Python – The most important Python frameworks and Libraries

We want to help you keep track:

  • SciPy (mathematical algorithms and convenience functions)
  • NumPy (array-based calculations)
  • Seaborn (visualization library)
  • Pandas (data analysis and manipulation)
  • Theano (machine learning, calculation of mathematical expressions)
  • PyTorch BigGraph (PBG) (system for learning BigData graph embeddings)
  • PyGraph (graph manipulation)
  • scikit-learn (machine learning in Python)
  • Matplotlib (mathematical representations)