Shulou(Shulou.com)06/02 Report--

This article introduces the relevant knowledge of "what Python data science software packages are available". In the operation process of actual cases, many people will encounter such difficulties. Next, let Xiaobian lead you to learn how to deal with these situations! I hope you can read carefully and learn something!

№1：Gleam

You've heard of Plotly, you've heard of Matplotlib, and maybe Seaborn, but you probably haven't heard of Gleam. Gleam is an excellent tool for creating interactive visualizations with pages, panels and buttons. These interactive Web visualizations are also fully integrated with the Web, meaning you can put them into anything from websites to endpoints! Gleam uses wtforms to interact and can use any range of different visualization tools to actually display data.

If you want a cool interactive pane that basically works with any graphics library, Gleam is the module for you!

№2：Table

Okay, I know what you're thinking,

"Why not just Pandas? "

This is certainly a viable argument. The Table package makes this list a lightweight alternative to Pandas. It's much easier to read massive datasets with Table.py than with Pandas.py, which is actually the only reason it's such a list. Overall, I would say that in some cases this may be the package that works, but for the most part I will just stick with it.

import pandas as pd

№3：Shogun

> (src = https://www.shogun-toolbox.org/)

Shogun is a machine learning library written in C++ that also happens to have a Python port. One of Shogun's great strengths is that it can be used in many different programming languages and is relatively uniform in this regard. If you're going to learn Shogun using Python, it's amazing that you can leverage that knowledge and apply it to any other language it supports!

Shogun has a wide range of cutting-edge machine learning algorithms that no doubt any scientist can appreciate. Of course, it's also open source and released under the GNU General License, which is always a plus!

№4：OpenCV

OpenCV is another great machine learning package that was actually originally developed by Intel. Despite its proprietary roots, OpenCV is open source and distributed under the FreeBSD license. One of the really great things about OpenCV is that it focuses on real-time computer vision. Like Shogun, OpenCV was originally written in C++, but has interfaces to Python and other languages.

№5：Mlpy

> (src = http://mlpy.sourceforge.net/)

Mlpy provides a wide range of up-to-date machine learning methods for supervised and unsupervised learning. Unlike its peers, it aims to provide an all-in-one approach to data science. Although it's a bit dated at this point, it's certainly a good starter for the huge package explosion caused by Pythonic machine learning. Despite its age, I think it's still worth a try. This is due to its ease of use, interesting algorithms, and inclusiveness.

Mlpy tries to create a package that strikes a balance between reproducibility, modulation and efficiency. I think the package was successful because although many of its counterparts were updated more frequently, including industry standards, many of those ideas seemed to be lost in translation in one way or another. This makes sense because these are huge packages written in C by a lot of developers, but I would say this is probably the downside of using them.

"What Python data science software packages are available" is introduced here. Thank you for reading. If you want to know more about industry-related knowledge, you can pay attention to the website. Xiaobian will output more high-quality practical articles for everyone!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.