Python is often labeled a “batteries-included programming language.” This simply means that it comes with a number of prepackaged libraries developers can use to make their jobs much easier. As you may expect, there is a sea of libraries available for this interpreted, high-level, general-purpose programming language.
There’s no doubt that one of the biggest reasons Python is so popular is the fact that there are over a hundred thousand libraries available to choose from. The more libraries and packages a programming language has at its disposal, the more diverse use cases it can have.
Out of all the thousands and thousands of Python libraries available, it can be difficult to tell which ones are good and which ones are forgettable. To help you out, we’ve listed some of the best Python libraries in this article. Is your favorite on this list? Read on to find out!
What is a Python Library?
Before we can answer this question, first, we must talk about what a library is — at least in terms of programming. Libraries are comprised of classes, utility methods, and modules. As you code your applications, these components can come in quite handy. Rather than having to write code from scratch, you can use library components to perform certain tasks within your code. As a result, you save a lot of time and effort. Plus, libraries make code reusable while also establishing a standard among developers.
So what exactly are libraries in Python?
As one of the most widely used programming languages in recent years, Python is used for a huge range of purposes and applications. One of the biggest reasons why Python is so popular is because it comes with a massive variety of open-source libraries that are not just free, but also easy to use.
Python libraries are collections of helpful modules, functions, classes, and more. These libraries help developers speed up their processes by working with preexisting code without the need to reinvent the wheel. Needless to say, libraries allow developers to focus on the important parts of their applications since they no longer have to code everything from scratch.
It’s worth noting that because Python is used in such a wide variety of industries, there are top Python libraries for just about any purpose you can think of.
What to Consider When Choosing a Python Library to Use
Now that you know what a library in Python can do for you, the next question on your mind might be, “how do I choose the right one?” It’s completely understandable to wonder this — after all, with more than 137,000 Python libraries available to date, how do you decide which one is the best for your needs?
When you’re faced with such a massive selection, it can be hard to make a decision. Some might even feel paralyzed, unsure how to go about making their choice. And to some, the end result might even be them choosing to simply code what they need from scratch. You don’t need to go through that.
Here are some things to consider when choosing from the best Python libraries:
- What is your intended purpose? Knowing the primary purpose or intent of your project is important to help you narrow down the list of viable Python libraries. To further shrink your pool of selections, consider any additional fields, purposes, and specialties that may intersect with this primary purpose. For example, if your project is data science-focused, you’ll likely want a library that can also support data management and data visualization.
- What version of Python are you using? These days, there are different versions of Python you can use for your projects. If you’ve chosen a certain version for your application, you must then make sure that any libraries you use are compatible with the said version of Python.
- Will this library work with the other libraries you are using? If you are using multiple libraries, it’s a good idea to make sure that they all work well with each other. Incompatible or overlapping libraries may cause you more trouble than they are worth.
- Will the library fit your budget? There is an abundance of open-source Python libraries you can use entirely for free. If you can find some that suit your project perfectly, you might not even need to pay for any libraries at all. However, there are some libraries that will require you to pay for access. You may want to consider a library’s cost before you proceed with your decision.
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Primary Intent: Making HTTP requests simpler
Secondary Intent(s): None
One of the most popular general Python libraries is Requests, which aims to make HTTP requests simpler and more human-friendly. Licensed under the Apache2 license and written in Python, Requests is the de facto standard used by developers for making HTTP requests in Python.
In addition to using the Requests library for sending HTTP requests to a server, it also allows adding form data, content, header, multi-part files, etc. with them. With the library, developers need not add a query to the URL or form-encode the POST data manually.
The Requests library abstracts the numerous complexities of making HTTP requests in a simple API so that developers can focus more on interacting with services. The library offers official support for Python 2.7, 3.4, and above and works great on PyPy too.
- Allows multipart file uploads and streaming downloads
- Automatic content decoding and automatic decompression
- Browser-style SSL verification
- Features can be customized and optimized as per requirements
- Keep-Alive & Connection Pooling
- Supports international domains and URLs
Primary Intent: Image manipulation
Secondary Intent(s): Image archival, image display
Python Imaging Library or PIL is a free Python library that adds an image processing ability to the Python interpreter. In simple terms, PIL allows manipulating, opening, and saving various image file formats in Python. Created by Alex Clark and other contributors, Pillow is a fork of the PIL library.
In addition to offering powerful image processing capabilities, Pillow offers an effective internal representation and extensive file format support. The core Python library is designed to offer fast access to data stored in a few basic pixel formats.
- Effective debugging support using the show() method
- Ideal for batch processing applications
- Identifies and reads a vast range of image file formats
- Offers BitmapImage, PhotoImage, and Window DIB interfaces
- Supports arbitrary affine transforms, color space conversions, filtering with a set of built-in convolution kernels, image resizing and rotation, and point operations
- The histogram method allows pulling some statistics out of an image, can be used for automatic contrast enhancement and global statistical analysis
Primary Intent: Web scraping
Secondary Intent(s): Automated testing, data mining, web crawling
Scrapy is a free and open-source Python framework that is widely used for web scraping and a number of other tasks, including automated testing and data mining.
Initially, Scrapy was developed for web scraping but has evolved to fulfill other purposes over the years. The library offers a fast and high-level method for crawling websites and extracting structured data from web pages.
Written in Python, Scrapy is built around spiders that are basically self-contained crawlers, which are provided a set of instructions. Abiding by the DRY (don’t repeat yourself) principle, Scrapy makes it easier to build and scale full-fledged web crawling projects.
- Easy to write a spider to crawl a website and extract data
- Follows the DRY principle
- Offers a web-crawling shell that allows developers to test a website’s behavior
- Supports exporting scraped data using the command line
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Primary Intent: Working with the asynchronous code
Secondary Intent(s): None
Numerous Python developers around the world make use of the asyncio library for writing concurrent code using the async/await syntax. In most cases, the asyncio library is ideal for IO-bound and high-level structured network code.
asyncio has been used for building various Python asynchronous frameworks that offer database connection libraries, distributed task queues, high-performance network and web servers, and much more. The library comes with a number of high-level and low-level APIs.
- Allows controlling subprocesses, distributing tasks via queues, performing network IO and IPC, and synchronizing concurrent code
- Bridge callback-based libraries and code with async/await syntax using low-level APIs
- Comes with a set of high-level APIs for concurrently running Python coroutines and having full control over their execution
- Eases working with asynchronous code
- Supports the creation and management of event loops, implementing effective protocols using transports
Primary Intent: GUI development
Secondary Intent(s): None
When used with Tkinter, Python offers an easy and fast way of creating GUI applications. Tkinter is the standard GUI library for the Python programming language. It offers a powerful object-oriented interface for the Tk GUI toolkit.
Creating a GUI application using Tkinter is very easy. All you need to do is to follow these simple steps:
- Import Tkinter
- Create the main window for the GUI application under development
- Add one or more Tkinter Widgets
- Enter the main event loop for taking action for each user-triggered event
Tkinter offers over 15 types of widgets, including buttons, labels, and text boxes. Each of them has access to some specific geometry management methods that serve the purpose of organizing widgets throughout the parent widget area.
- Comes with a range of widgets that support geometry management methods
- Eases the development of GUI applications
- Supports an effective object-oriented interface
Primary Intent: Compatibility library (wrapping over differences between Python 2 and Python 3)
Secondary Intent(s): None
Although simplistic, Six is a powerful Python library that is meant to smooth out the differences between various Python 2 and Python 3 versions. Six is intended for supporting codebases that can operate on both Python 2 and Python 3 without the need for modifications.
The Six library is super-easy to use thanks to being offered as a single Python file. Hence, it is ridiculously easy to copy the library into a Python project. The name Six reflects (Python) 2 x (Python) 3.
- Simple utility functions for making Python code compatible with Python 2 and Python 3
- Supports every version since Python 2.6
- Too simple to use as contained in a single Python file
Primary Intent: Serve as an asynchronous HTTP Client/Server
Secondary Intent(s): None
Another simple yet widely used Python library is the aiohttp. It is basically meant to be an asynchronous HTTP client or server in Python. Beyond this and offering out-of-the-box support for Client WebSockets and Server WebSockets, there’s nothing more to this Python library.
- Offers a web server that has middlewares, pluggable routing, and signals
- Provides out-of-the-box support for both Client WebSockets and Server WebSockets
- Supports both Client and HTTP Server
Primary Intent: 2D game development
Secondary Intent(s): Multimedia app development
Pygame is a free and open-source Python library that is meant for accomplishing multimedia application development in Python, especially two-dimensional gaming projects. Hence, it is widely used by both casual and professional Python game developers.
Under the hood, Pygame makes use of the SDL (Simple DirectMedia Layer) library. Like the SDL library, the Pygame library is highly portable and thus provides support for a wide number of platforms and operating systems.
It is possible to port applications developed using Pygame on Android-powered devices, like smartphones and tablets. For this very purpose, pgs4a (Pygame subset for Android) needs to be used.
- Doesn’t demand OpenGL
- Makes easy for using multi-core CPUs
- No GUI is required for using all available functions
- Provides support for a wide range of platforms and operating systems
- Simple and easy to use
- Uses Assembly code and optimized C code for implementing core functions
Primary Intent: Application development (with innovative user interfaces)
Secondary Intent(s): None
For building mobile apps and multi-touch application software with a NUI (Natural User Interface), Python developers rely on the Kivy library. The free and open-source Python library is distributed under the MIT license and runs on Android, iOS, Linux, macOS, and Windows.
In actuality, Kivy is the evolution of the PyMT project. It contains all the necessary elements for building an intuitive multi-touch application, namely a graphics library, a wide range of widgets with multi-touch support, an intermediate language (Kv), and extensive input support.
Kv, or the Kivy language, is an intermediate language dedicated to describing user interactions and interface. It makes it very easy to create a complete UI and add interaction(s) to it. Kivy also provides support for the Raspberry Pi.
- Ability to natively use most devices, inputs, and protocols
- Offers over 20 highly-extensible widgets
- Support graphics engine built over the OpenGL ES 2
Primary Intent: Developing visualization-based applications
Secondary Intent(s): Data visualization, data science
An interactive visualization library for the Python programming language, Bokeh allows visualizing data in a beautiful and meaningful way inside contemporary web browsers. The data visualization library eases the creation of dashboards, data applications, and interactive plots.
In addition to offering concise and elegant construction of versatile graphics, the Bokeh library also extends its capability with high-performance interactivity over streaming or very large datasets.
- Allows building complex statistical plots with simple commands
- Bokeh visualizations can be easily embedded into two of the most popular Python frameworks, Django and Flask
- Capable of producing elegant and interactive data visualizations
- Multiple language bindings (Julia, Lua, Python, and R)
- Various output formats
Primary Intent: Scientific and numerical computing
Secondary Intent(s): Data analyses, forms foundations of other Python libraries like SciPy
NumPy is one of the best open source Python modules for scientific and numerical computing and data analyses. In fact, it even provides the foundation for a few other Python libraries, such as SciPy and Sci-Kit Learn. NumPy is most often used for mathematical operations with matrices and arrays. Thanks to its efficient yet quick computations, NumPy is the Python library of choice for many scientists performing data analyses.
NumPy can also process multidimensional arrays, which is why so many developers and data scientists use it for AI (artificial intelligence) and ML (machine learning) projects.
- More efficient thanks to array-oriented computing
- Uses vectorization for compact yet faster computations
- Supports an object-oriented or OO approach
- Provides numerical routines in fast and precompiled functions
Primary Intent: Data visualization and manipulation
Secondary Intent(s): Linear algebra, optimization algorithms, image operations (multidimensional)
Like NumPy, SciPy is free and open source, making it accessible for everyone. SciPy is based on NumPy and can also be used for technical and scientific computing with large data sets. It plays a critical role in engineering and scientific analysis, which is why it’s also considered an important library in Python. Some even call it a foundational library for the programming language.
SciPy works well for the purposes of image manipulation. It has high-level commands often used for data manipulation and visualization.
- Collections of functions and algorithms all built upon NumPy
- Has inbuilt functions meant to solve differential equations
- SciPy ndimage submodule for image processing (multidimensional)
13. Sci-Kit Learn
Primary Intent: Machine learning applications
Secondary Intent(s): Statistical modeling
Sci-Kit Learn is based on both NumPy and SciPy and was formerly known as Sklearn. This free Python library is often considered by many as a SciPy extension. Sci-Kit Learn was designed specifically for the purposes of developing algorithms for machine learning and for data modeling.
Many consider Sci-Kit Learn one of the top Python libraries thanks to its consistent, simple, and intuitive interface. Thanks to how user-friendly this library is, many consider it to be an excellent choice for beginners.
- Machine-learning library
- Offers practically all of the algorithms you need for machine learning
- Built on SciPy, NumPy, and Matplotlib
Primary Intent: Machine and Deep Learning
Secondary Intent(s): Evaluating, analyzing, and manipulating mathematical expressions
The numerical computation library known as Theano was created for its express use in machine learning. Many developers use Theano in creating deep learning models thanks to the library’s features. The great majority of Theano users are deep learning and machine learning developers.
Theano also offers the ability to integrate with NumPy, should you ever have the need to. When Theano is used with a GPU (graphics processing unit, such as a video card) instead of a CPU (central processing unit, like the Intel Core i5 or i7 or the AMD Ryzen equivalents), it can perform its intensive data computations as much as 140 times faster.
- Integrates with NumPy
- Works with CPUs but works much more efficiently with GPUs; can perform intensive operations much faster using your GPU
- Optimized for stability and speed
- Employs multidimensional arrays for creating deep learning models
Primary Intent: Deep learning and traditional machine learning; large numerical computations
Secondary Intent(s): Text-based apps, video detection, speech/image recognition, time-series analyses
TensorFlow is an open-source library originally developed by researchers at Google.
Its specialty appears to be in differentiable programming, but its main purpose was intended to be machine and deep learning as well as other workloads in predictive and statistical analytics.
TensorFlow’s collection of resources and built-in tools help developers have a much easier time building their machine learning and deep learning models. TensorFlow also helps make building neural networks much more straightforward for developers, whether they are beginner-level or professional.
The framework and architecture of TensorFlow are highly flexible, allowing the library to be used with both CPUs and GPUs. However, if you wish to unlock TensorFlow’s full power, you’ll need to work with a TPU (Tensor processing unit). This library also isn’t limited to desktop devices — you can also use it on smartphones and servers.
- Frequent updates and new releases guarantee fresh features and clean code
- Backed by Google
- Better visualizations for computational graphs
Primary Intent: Data science
Secondary Intent(s): Deep learning research
PyTorch is another open-source library often used for data science. This library, which was based Torch (a framework for the C programming language), can also integrate with other Python libraries like NumPy. PyTorch is capable of seamlessly creating computational graphs that can be changed as long as the Python program is currently running.
PyTorch is most often used in DL and ML applications, including NLP (natural language processing) and computer vision. This library is well-known for being able to execute fast, even when handling heavy loads. PyTorch is also flexible, allowing it to work on CPUs, GPUs, and even simplified processors.
Users can expand PyTorch using its collection of APIs.
- Tensor computations using GPU acceleration for faster and more efficient processing
- Simple, easy-to-use API
- Uses dynamic computation graphs
- Has a strong community behind it
Primary Intent: Deep Learning and Machine Learning
Secondary Intent(s): Data Visualization
The open-source Python library Keras was designed primarily for the development and evaluation of neural networks inside machine learning and deep learning models. This library can function on top of TensorFlow and Theano, which means developers can start training their neural networks with not much code.
Keras is flexible and extensible while also being modular, which is why it’s a great choice even for beginners. This library is also portable, which means you can use it in various environments and on both GPUs and CPUs.
Developers also often use Keras for data visualization or modeling.
- Supports Theano and TensorFlow backends
- Provides prelabeled data sets developers can use directly to load/import
- Offers simple and consistent APIs
- Easy-to-learn and use, smaller learning curve
Primary Intent: Data Science
Secondary Intent(s): Data Analysis and machine learning
Pandas is one of the most popular Python libraries today, at least in the field of data science. Pandas is yet another library that was built on top of NumPy. This library allows users to build seamless yet intuitive high-level data structures. Pandas is used in a variety of industries, ranging from statistics to engineering and even in finance.
One thing that makes Pandas great is its flexibility and the ability to use it alongside other numerical and scientific Python libraries.
- Used in many commercial fields, including finance, neuroscience, and statistics
- Also used in academic fields
- Has eloquent syntax
- Built on top of NumPy
- Helps take care of a lot of the tedious and time-consuming tasks related to data
Primary Intent: Data Visualization
Secondary Intent(s): Machine Learning
Matplotlib is an open-source Python library often touted as an alternative to the paid solution MATLAB. Matplotlib, a SciPy extension, was made for the purpose of data visualization as it's used to create graphs and plots. Matplotlib can also work with the complex data models outputted by Pandas as well as data structures created by NumPy.
Matplotlib does have a limit — it can only do 2D plotting. Despite this fact, this library remains highly capable of producing publish-ready data visualizations in the form of plots, diagrams, histograms, plots, scatter plots, error charts, and of course, bar charts.
Because of how simple and intuitive Matplotlib is, many beginners choose to work with it when starting out in data visualization. It’s also the choice of many developers who already have plenty of experience with other data visualization tools.
- Open source, makes for a good replacement for MATLAB (a paid solution)
- Low memory usage
- Strong community support
- Offers various types of data visualizations (boxplots, scatterplots, bar charts, histograms, error charts, and more)
Primary Intent: Data visualization
Secondary Intent(s): Machine learning
Much like Matplotlib, Seaborn is a Python library made for plotting and data visualization. In fact, this open source library was based on Matplotlib itself, although Seaborn also includes some of Pandas’s extensive data structures. Seaborn has a high-level interface full of features that allows users to create statistical graphs that are not just accurate but also informative.
Many developers and Seaborn users will agree that this library creates some of the best-looking data visualizations, which is why this library is perfect for use in marketing and publishing applications.
Users also enjoy Seaborn for its ability to create these plots and graphs with simple commands and minimal code, making it a time-saver for many.
- Built upon Matplotlib
- Allows developers to create attractive, informative graphs using the high-level interface
- Create a range of plots such as pairwise plots, histograms, bar plots, scatter plots, and more
Primary Intent: Data Science
Secondary Intent(s): Web scraping
Beautiful Soup received its name thanks to its ability to parse HTML and XML documents (even with malformed markup called “tag soup”). This Python package scrapes the web and collects data, preparing them for future manipulation. As an incredibly versatile package, Beautiful Soup is one of the tools of choice for many data analysts and scientists. Machine learning and deep learning developers also use Beautiful Soup to obtain data for training their ML/DL models.
- Allows data extraction from HTML and XML, even from documents with malformed or incomplete markup (like non-closed tags)
- Began as a parser for HTML that could make “tag soup” (malformed markup) workable or even ‘beautiful’
Primary Intent: Machine Learning
PyCaret got its name from being a Python library based on Caret, a machine learning library in the programming language R. This open source library was also created for machine learning, and as a result, it offers some features to help simplify and automate ML programs.
Although it has a bit of a learning curve, PyCaret is relatively easy to use.
- High-level and low-code library
- Works to automate workflows in machine learning
- Helps to speed up experimental cycles, increasing productivity
- Allows developers to deploy ML models using very little code
Primary Intent: Computer Vision and Image Processing
Secondary Intent(s): Machine Learning
As a Python library, OpenCV is comprised of various functions, making it a great tool for computer vision programs in real-time. This highly efficient library can process various visual inputs not just from images but also from video data. OpenCV can identify faces, handwriting, and objects.
- Performs tasks like objection tracking, face detection, landmark detection, and more
- Provides developers with access to more than 2,500 classic, state-of-the-art algorithms
- Used extensively even by tech giants like Google, IBM, Toyota, and more
- Also used in image/video analysis
Primary Intent: Machine Learning
LightGBM stands for Light Gradient Boosting Machine. It is a free gradient boosting framework that was developed by Microsoft for the purposes of machine learning. It is user-friendly and intuitive and can be learned much more easily than some other libraries for deep learning.Stop
- Offers plenty of memory-efficient yet fast computational power
- Originally developed by Microsoft
- Capable of dealing with large amounts of data
- Offers high accuracy results
Out of the hundreds of thousands of Python libraries available, the list above includes some of the very best. It’s good to know that these libraries often get upgrades and enhancements to help them keep up with Python’s growth and booming popularity.
Knowing one of these popular libraries can help further your learning of the language while also helping to make you a better Python developer all around. Think we missed a top library on this list? Let us know in the comments below!
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Frequently Asked Questions
1. What are Python libraries?
Python libraries are collections of functions, modules, and other components that allow developers to use preexisting code for certain tasks. Libraries can be general or more for specific purposes. They can save developers a ton of time and effort by preventing the need to code a huge chunk of an application from scratch.
2. How many libraries are there in Python?
There are more than 137,000 Python libraries in existence. However, not all of them are created equally, and you’ll find that some are much better than others.
3. What is a Python library example?
There are many hugely popular python packages and libraries. If you’re looking for Python library examples, consider some of the big names below:
- Numpy, SciPy, SciKit-Learn
4. How do I get a list of Python libraries?
If you mean listing all of the modules, packages, or libraries currently installed in your version of Python, you can follow the guide here. The guide contains all the instructions you need for making a Python libraries list.
5. What are Python libraries used for?
Python libraries are used to make a developer’s job much easier and more convenient. Instead of needing to code portions of projects from scratch, developers can take modules and bundled code from libraries and use those in their projects instead. Libraries can also establish coding standards, making code maintenance easier to do.
6. Are all Python libraries free?
Most major libraries do allow free commercial use. However, not all libraries are as easy to use and figure out. Thus, although a library may be free, you may need to pay for licensed or paid modules or software to make debugging and maintenance easier in the long term. Additionally, although many libraries are free for commercial use, you may have to pay if you’d like to include certain modules in your applications for distribution to future customers.
7. How do libraries work in Python?
Python libraries allow developers like you to take modules and bundles of code and use them repeatedly for various projects and purposes. Libraries prevent the need for you to code things from scratch repeatedly, as you can simply take preexisting code and add it to yours.
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