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This article introduces the knowledge of "what are the excellent Python libraries". In the operation of actual cases, many people will encounter such a dilemma. Then let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1. Open source Pandas library

It is one of the popular choices for data analysis and data processing in Python. If you plan to pursue a career as a data scientist or data analyst and use Python, this is a very important tool to learn.

Pandas provides a high-performance data structure that makes data processing easy, fast and intuitive. The main data structures of the library (series (1D) and DataFrame (2D)) are often used in finance, statistical computing, social sciences and engineering.

What special features does Pandas provide?

Facilitate data cleaning, conversion and analysis.

Import data from various file formats.

Easily overcome lost data.

Delete and insert data columns in the data box.

Flexible grouping according to function.

Easily convert the data into other structures.

Slices, indexes and subsets based on smart tags.

Intuitively merge, join, reshape, and rotate datasets.

Pandas can be used in conjunction with other libraries such as NumPy,SciPy and Matplotlib. Learn more about pandas on its official website.

2. NumPy

NumPy is another useful Python library, which is the foundation of scientific computing. This is an open source tool designed for efficient numerical computation. NumPy provides high-performance multidimensional arrays and matrices and tools for manipulating them. It also contains useful functions for linear algebra, Fourier transform and random numbers.

NumPy integrates with various databases. For more information about this library, see the official NumPy website.

3.SciPy

SciPy is an open source library designed for scientific computing. It includes functions to promote linear algebra, integration, image processing and optimization. It is a good tool for a variety of scientific, mathematical and engineering tasks that require some digital manipulation.

SciPy depends on NumPy. Find more information on the official SciPy website.

4.Matplotlib

Matplotlib is a popular open source library for data visualization. With just a few lines of code, it can generate many types of graphs-including bars, scatters, and histograms.

The library also provides API for embedding diagrams into the application. Matplotlib allows programmers to visualize large amounts of data and generate high-quality images in a variety of formats. For more information about Matplotlib, please visit the official website.

5.Pygal

Pygal is designed to create vector graphics, mainly on websites. It allows users to make attractive interactive charts in a fairly simple way. It is specifically used to create scalable vector graphics (SVG) that integrate well with Python frameworks such as flask or Django. Pygal also allows you to save files in PNG format using the CairoSVG converter.

Check out Pygal's official website for more information.

6. Seaborn

Seaborn is an open source library designed for data visualization. The library is based on Matplotlib, but it provides users with an easier way to create complex diagrams. Seaborn has many built-in styles, which means you can quickly change the appearance of the chart. (it also integrates well with Pandas data structures.) Seaborn provides automatic estimation and mapping of linear regression models.

Learn more about Seaborn here.

7.Scrapy

Scrapy is an open source Python framework designed to crawl websites and extract their data. With Scrapy, these tasks are simple and fast. It is also easy to insert new features into this framework. Because of this flexibility, Scrapy can also be used for data mining, automated testing and information processing.

Visit Scrapy.org for more information.

8.Flask

Flask is another very popular Python framework. It is used to deploy data science models. This open source tool is lightweight and designed to deploy complex applications easily and quickly. Because it is a microframework, no specific libraries or tools are required. You can add more functionality through many of its extensions.

You can find more information about Flask on its official website.

9.SQLAlchemy

SQLAlchemy is open source and one of the most useful Python libraries. It provides an object-relational mapper that allows classes to be mapped to a database. With SQLAlchemy, you can do efficient, high-performance work on the database and automatically perform redundant tasks. There is a reason why SQLAlchemy has become a popular object-relational mapping tool among Python developers.

For more information, visit the official SQLAlchemy website.

10.TensorFlow

Another most useful Python library is TensorFlow. It is open source and is designed for data flow, machine learning and neural network projects. It helps developers build and deploy machine learning applications easily and quickly, and provides a very intuitive high-level API for building and training models. You can deploy the model in the cloud, browser or device. TensorFlow's simple and flexible architecture makes it an ideal tool for researchers because it allows researchers to quickly convert it from ideas to code for release. TensorFlow improves the workflow of preprocessing data, building, training and evaluating the accuracy of the model.

11.PyTorch

PyTorch is a popular open source framework for machine learning and deep learning. This is a good tool for building and training neural networks. It optimizes the tensor for deep learning using CPU and GPU, and speeds up the process from research to prototyping to production deployment.

Learn more about PyTorch on the official website.

12.Scikit-learn

Scikit-learn is an excellent open source library for novice machine learning and professionals. It supports support vector machine, KNN mapping, KNN classifier and regression algorithm. It is an effective tool for predictive data analysis, statistical modeling, classification and clustering. For small projects, this is also a good choice. Learn more on Scikit-Learn.org.

13.Keras

Keras is another popular Python library. This open source deep learning library allows users to quickly build prototypes and create neural network projects. It also includes algorithms for normalization, optimizer and activation layer. This user-friendly extensible tool makes it easier to develop deep learning models. It is also a good and useful tool for beginners to run seamlessly on CPU and GPU.

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