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This article is to share with you about what data science libraries are available in python. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

Standard:

1. Numpy is the most commonly used library for data scientists.

At the heart of this library is the ndarray data structure (Python's standard library does not have array data structures), which allows programmers to actually do mathematical and scientific things. It also has some (but not much) support for linear algebra.

2. Scipy is based on Numpy.

It provides a lot of support for statistics, linear algebra and calculus.

3. Scikit-learn is a machine learning library.

In addition to the cross-validation we need on a daily basis, it also contains the code for most common classifiers.

4 、 Pandas .

Different formats of input need to be handled.

5 、 matplotlib .

A good data scientist needs to visualize his results in a way that others can understand, and matplotlib is the best tool in Python.

6 、 Cython .

Cython comes in handy when you need to get parts of your code running fast.

7. Jupyter notebook.

A notebook where you can write code, execute code and view results, draw graphics, explain things in plain text, and so on. Used to prototype and explain things. When I found it, I liked to play with it.

Computer vision related:

Opencv-it has most of the traditional computer vision algorithms you might need. If you are doing computer vision, you really need it.

PIL, Pillow-mainly for opening images, but you can do much more than that.

Thank you for reading! This is the end of the article on "what data Science Library does python have". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, you can share it for more people to see!

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