Python is considered to be one of the most in-demand programming languages around the world. Almost every specialist in this field of activity knows it. The popularity of the language is fully explainable by its versatility and the colossal number of libraries that keep growing. In this article, we will try to take a closer look at the best python libraries.

Python benefits

First and foremost, Python is a common programming language widely used for web development, machine learning, artificial intelligence, scientific computing, and more. Before proceeding to the best libraries of this language, we will briefly describe its main advantages, thanks to which Python has become so popular among programmers.

The benefits include:

• simple high-level syntax, especially when compared with competitors;
• elementariness – Python is successfully used by both experts and beginners;
• huge library - it has a large library for all programming languages.

This programming language focuses on the readability of code using English keywords. It is used on such well-known resources as Reddit, NASA, PBS. The language gained its popularity largely due to its simplicity. It is this quality that drives developers to constantly create new libraries.

List of the best Python libraries

Now we will analyze the most popular libraries, their functional features and use.

Keras

It is an open source neural network library written in Python. It is ideal for people wishing to develop in the creation of neural networks. Keras has an effective network data validation policy.

The features of the library include:

• a lot of options to support the neural network, including: optimization, levels, and so on;
• the ability of users to work with documents related to text, images;
• support for not only a neural network, but also a conversion, re-current neural network environment.

It is Keras that is considered one of the most powerful neural network libraries that can work on many platforms. Using it, it is also possible to create deep models for mobile devices on Android, iOS.

Keras is used by such global companies like Netflix, Uber and Zocdoc. In addition, it is well known among NASA and CERN organizations.

Numpy

This library is widely used for machine learning. That is why it is also used by other libraries (Tensor Flow). The most useful option provided by this tool is the array interface.

Now let's take a look at the most useful Numpy options, including:

• using the library to simplify the most complex mathematical operations;
• receipt of an impressive amount of open source materials;
• simplicity and clarity at an intuitive level.

As far as directly using the library, Numpy can be useful for transferring sound waves, images, and binary streams. Full stack developers must know it to perform machine learning operations, as a rule, it is added to simplify the task.

Tensor Flow

Another open source library used more for machine learning. Tensor Flow is easy to learn and it has several useful toolboxes. However, this library is not limited to machine learning, it is used for differentiable programming.

Tensor Flow features include:

• simple architecture that allows you to quickly learn how to program and create machine learning models;
• it is possible to move the created training models to any device, cloud storage;
• the presence of an active execution option, which makes it possible to create models for machine learning, debug and handle the latter.

Tensor Flow has all the solutions for debugging, launching and other tasks, problems related to machine learning.

SciPy

This library is popular among engineers, developers due to the fact that there are various modules here. These are: statistics, linear algebra and optimization.

Below are some interesting features of SciPy:

• the development of this machine learning library was carried out using Numpy;
• it supports various numerical programs, integration and optimization;
• SciPy submodules are well documented.

SciPy can be used to solve various differential equations and linear algebra.

PyTorch

Direct competitor to Tensor Flow, developed and released in 2017 by the Facebook artificial intelligence research group. It is capable of performing complex mathematical operations related to tensors and dynamic graph control.

Main features of PyTorch:

• support for a simple and easy-to-use interface;
• tons of other tools and libraries that can be used with PyTorch;
• good compatibility with distributive learning;
• the ability to integrate with Cython and Numba.

As mentioned above, PyTorch is a direct rival to Tensor Flow, which means it can successfully act as an alternative to the latter. It is popular with specialists because it can be exploited to process natural language applications (NLP). In this area, PyTorch has achieved much better performance than the previously mentioned competitor.

LightGBM

Gradient Boosting is one of the best known machine learning libraries to help developers create new algorithms. There are special libraries that make it possible to quickly and efficiently implement this method, one of which is LightGBM.

The features of the library include:

• fast computation and high performance;
• intuitive and user-friendly interface;
• learning faster than in other similar libraries;
• there is no possibility of error in the case of NaN values.

Binary and multiclassification support, scalability and optimization support make LightGBM quite popular with developers.

Eli5

A machine learning library called Eli5 was created to eliminate inaccurate results from different prediction models. It is engaged in tracking algorithms, contributes to the debugging of training models using a variety of visualizations.

Main features of Eli5:

• support for other libraries, including: XGBoost, lightning;
• support for debugging training classifiers;
• helping developers understand the reason for any prediction.

Eli5 is used for mathematical operations in order to reduce the time of the latter. It is also used for legacy applications and various Python-dependent packages.

Pandas

It is a machine learning library representing high-level data structures and a wide range of analysis tools. A distinctive feature of Pandas is the ability to transfer the most complex operations with information using just one or two commands. This library contains many ways to combine data, group and filter it.

Features of Pandas include:

• the ability to simplify data manipulation;
• support for sorting, visualization and other options.

Pandas provides a lot of flexibility and functionality when used with other libraries. In addition, users can use it in various operations, data sorting applications, etc.

Seaborn

Seaborn uses other libraries (NumPy, Pandas) and is used for data visualization. It contains graph plotting options based on datasets.

The features and benefits of Seaborn include:

• aggregation and display - functions built into the library to help create graphs;
• high-level interface for building statistical graphs (for easy understanding and study of data);
• diagrams and pie charts, histograms, error messages;
• tools for choosing color gamma - to find patterns in the data.

Seaborn is in demand among regression models. It is effective for creating a variety of Matplotlib-style themes and for plotting time series statistic data. It is also used to visualize uni- and bivariate data.

Matplotlib

Using this library, it is possible to create various graphics at the same time (including two-dimensional). It is also used for operations with figures. Engineers and developers often use Matplotlib to work and edit quality drawings. The teaching of this library is quite simple and straightforward, which adds it popularity.

Features of Matplotlib:

• the ability to use with different tools (graphical interfaces and Python scripts, Jupyter notebooks, Ipython shells);
• integration with Seaborn;
• the ability to develop high quality images for publications;
• printed formats of pictures.

If the user needs help, there is a huge community dedicated to this library. There you can get comprehensive information and learning Matplotlib.

The use of this library is quite extensive. These are object-oriented APIs for embedded charting in a variety of applications. Also Matplotlib introduces the pylab procedural interface, supports different toolsets (Excel and natgrid, cartography).

Pillow

The PIL or Python Image Library section was first available with the Python Image structure and codes. However, after several years of research, a more convenient, optimal coding method was developed. It is said in the Python community that Pillow is nothing more than a modern take on PIL. Overall, it is a very user-friendly and reliable image processing platform.

Let's take a look at the most interesting features of Pillow:

• the ability to create sketches;
• image filters;
• compatibility with GIF, PNG, PDF and other formats;
• the ability to make changes to images.

It is possible to use Pillow to find out information about an image. It handles blurry pictures efficiently. The library is operated for batch processing and image archiving applications.

Theano

This is not only another Python library, but also a computer ad compiler that supports a lot of mathematical operations - analysis, optimization, etc. In addition, Theano provides support for optimal exploitation of multidimensional arrays.

Features of Theano:

• speed and ensuring stable optimization;
• the ability to perform fast calculations with an impressive amount of information;
• support for effective symbolic differentiation;
• automatic detection of errors and malfunctions.

This library is in demand for use in deep learning of a neural network. It can also be used for software development.

Scikit-Learn

This library is considered one of the best for working with complex data. It integrates with NumPy and SciPy. Scikit-Learn is frequently modified and improved. One of such optimizations is cross-validation option that allows you to apply more than one metric.

Features of Scikit-Learn:

• the ability to extract elements from texts and pictures;
• cross-validation - many different methods of checking the accuracy of the controlled model on invisible information;
• many machine learning algorithms and the ability to implement expensive tasks (clustering, factor analysis, etc.).

Scikit-Learn is used to implement common machine learning and data mining tasks.

Bokeh

Here the users are given a very useful special tools package. It is really possible to access the graphics using HTML and javascript. We can say that Bokeh is reliable for a variety of dashboards and web applications.

Some useful features of the library include:

• the ability to get the results of work in a notebook (HTML media);
• available to easily convert Matplotlib visualizations;
• simple commands are used to build statistical scenarios;
• custom visualizations can be used to implement the style.

This library supports the Bokeh server. It can be used to transform a large amount of information. Different graphs can be updated with automatic streaming.

OpenCV Python

Popular Python library for computer face recognition, image processing. It regulates all kinds of options related to instant computer vision. It is believed that due to the lack of documentation, OpenCV is difficult to learn. However, due to the fact that it comes with most of the pre-written built-in options, it is quite possible to learn how to process images.

Features of OpenCV:

• computer vision, which makes it possible to interrupt, understand and rebuild a three-dimensional environment from a two-dimensional one;
• the ability to capture and save different moments of the video for further investigation of the background, movements;
• the ability to simultaneously read and write images;
• available to easily find and study various objects in the video.

OpenCV is used for computer vision and machine learning algorithms. It is in demand for object detection and camera movement monitoring. This library is successfully used by such global companies as Microsoft, Google, Intel, etc.

Fire

Open source library with the ability to generate command line interfaces (CLI). This is an effective tool that allows you to extract the CLI from any object of the programming language. Mostly used by Google.

Fire features:

• the ability to work with dictionaries, lists, objects, modules and other elements of Python;
• command line interfaces created by this tool can be adapted for any changes, at any time;
• thanks to the line-out, after using the Fire, there is no need for a docstring.

Fire is a simple and reliable tool that allows the user to write and send commands at any time.

Arrow

Simple and practical date and time library. Its smart API supports tons of schemes. Users with basic programming knowledge can quickly learn how to use Arrow.

Arrow features include:

• the ability to delete, change, update and generate time and date;
• simple and easy conversion of time zones.

Support for various Python versions and simple creation of common input scripts make Arrow an affordable and popular tool.

XGBoost

Optimized tool for high efficiency, flexibility and portability. This is where machine learning algorithms are implemented as part of Gradient Boosting. XGBoost provides parallel tree amplification, solving a lot of data science problems with fast and accurate methods.

Request

A large HTTP library focused on making this protocol more efficient and user-friendly. It will be useful for beginners, with the help of Request it is possible to perform a lot of tasks - setting and checking codes, authorization, etc.

The features of Request include:

• the ability to address custom header, SSL certificate validation, and URL deployment details;
• adding headers and footers, parameters, data formatting;
• loading multiple files at the same time to save time and optimize processes.

Request supports the HTTP proxy method, allowing users to access a file or page.

Conclusion

Python is a useful and productive programming language that is constantly growing and evolving. Its libraries play one of the most important roles in the work and advancement of programmers. They are widely used in data science, machine learning and more. Fortunately, over the years, developers have created a great variety of libraries for Python. We hope this article will help the user navigate the variety of tools and choose the ones that are worth focusing on.

Best Python Libraries You Should Know in 2022 key takeaways:

#1. Python benefits
#2. List of the best Python libraries
#3. Keras
#4. Numpy
#5. Tensor Flow
#6. SciPy
#7. PyTorch
#8. LightGBM
#9. Eli5
#10. Pandas
#11. Seaborn
#12. Matplotlib
#13. Pillow
#14. Theano
#15. Scikit-Learn
#16. Bokeh
#17. OpenCV Python
#18. Fire
#19. Arrow
#20. XGBoost
#21. Request
#22. Conclusion