Data science and machine learning-related researchers and developers can’t live without Python on an almost daily basis. Here are 12 Python libraries that you can share with your friends.

**** I. **** Core library and statistical data

1. NumPy (Commits: 17911, Contributors: 641)

NumPy is the main software package for a scientific application library for handling large multidimensional arrays and matrices. It has a collection of high-level mathematical functions and implementations that aid in object execution operations.

2. SciPy (Commits: 19150, Contributors: 608)

SciPy is one of the core libraries of scientific computing. Its main data structure is a multi-dimensional array based on NumPy. It has tools to help solve linear algebra, probability theory, integral calculation, and many new BLAS and LAPACK functions.

3. Pandas (Commits: 17144, Contributors: 1165)

Pandas has advanced data structures anda variety of analytical tools. Its best features are its ability to turn complex data operations into one or two commands, its many built-in methods for grouping, filtering, and combining data, and its time series capabilities.

4. StatsModels (Commits: 10067, Contributors: 153)

Statsmodels helps with statistical data analysis to a large extent, such as estimating statistical models, performing statistical tests, and so on. With this, many machine learning methods can be implemented and various drawing possibilities can be explored.

Machine learning

5****. Eli5 (Commits: 922, Contributors: 6)****

General machine learning models predict results that are not entirely clear, and Eli5 solves this problem. It is a software package for visualizing and debugging machine learning models, and tracking algorithms as they work. It provides support for sciKit-learn, SkLear-CrfSuite, XGBoost, LightGBM, Lightning libraries.

6. Scikit-learn (Commits: 22753, Contributors: 1084)

It is based on NumPy and SciPy and is ideal for handling data. It provides algorithms for many standard machine learning and data mining tasks, such as clustering, regression, classification, reduction, and model selection.

7****. XGBoost / LightGBM / CatBoost (Commits: 3277 / 1083 / 1509, Contributors: 280 / 79 / 61)****

The gradient enhancement algorithm should be well known, and it can build a basic model of continuous improvement. XGBoost, LightGBM, and CatBoost are all contenders for solving common problems and are used in much the same way. These libraries provide highly optimized, scalable, and fast implementation of gradient enhancement.

3. Visualization

8****. Seaborn (Commits: 2044, Contributors: 83)****

Seaborn is a high-level API based on the Matplotlib library. It has features suitable for working with charts. It also has a good library of visualizations, including complex types such as time series, joint distributions, violins, etc.

9****. Matplotlib (Commits: 25747, Contributors: 725)****

Matplotlib is used to create two-dimensional diagrams and graphs, and there are many popular drawing libraries that can be used in conjunction with Matplotlib. You can use it to build a variety of ICONS, from histograms and scatter plots to Fiscarian coordinates.

10****. Plotly (Commits: 2906, Contributors: 48)****

Plotly is designed to help users build complex graphics. The software package is suitable for interactive Web applications, which can realize contour, ternary and three-dimensional visual effects.

11****. Bokeh (Commits: 16983, Contributors: 294)****

The Bokeh library leveragesjavascript widgets to create interactive and scalable visualizations in the browser. It has multiple collections of charts, styles, link diagrams, the ability to interact in the form of adding widgets and defining callbacks, and so on.

Iv. Data collection

12. Scrapy (Commits: 6625, Contributors: 281)

Scrapy is commonly used to create web crawlers, scan web pages and collect structured data. And data can be extracted from the API. It is easy to use because it is extensible and portable.

Do you have 12 great Python libraries in your collection? If there’s anything else you’d like to know, or if you have other Python libraries that you can use, feel free to discuss them in the comments below. Python7762 contains python, PythonWeb, crawler, data analysis and other Python skills, as well as artificial intelligence, big data, data mining, automation and other learning methods. Build from zero to the project development hands-on combat all-round analysis!