Best Python Libraries for Beginners and Intermediate Developers(2022)
Tags: python, library, beginners, intermediate
Published: 2022-03-04 08:35:00

With the rapid growth in artificial intelligence and machine learning, Python has gained widespread popularity in the past few years. Its simple syntax helps developers to implement complex algorithms with ease. Python is not only suitable for machine learning and data science, but its huge number of libraries allows you to use Python seamlessly for web development, scripting, game development, and much more. Here in this article, we have mentioned the top 10 Python libraries which are trending among the developer community.

But before discussing the best Python libraries, let's try to understand what a library is all about.

## What is a Library?

A library is a collection of pre-written code that you can easily import for adding certain functionality to your project. We use libraries so that we do not have to write code from the scratch. A library allows us to leverage the pre-written code, functions, or classes to enhance the functionality implemented by our code.

## Top Python Libraries

Python offers a large number of libraries for different types of development, ranging from web development to machine learning. Here, we have curated a list of the most popular Python libraries used in different areas of development. Also, all the libraries listed ahead are fully compatible with Python 3. We haven't included any library that is exclusively compatible with Python 2 as it is not in use anymore.

Now, let's get familiar with the top Python libraries.


## 1. Request

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/11nawo4q23ptcnl0786u.png)
Request library is among the most useful Python libraries. Almost every Python web framework uses this library to send  HTTP requests to servers. The request library helps us to send organic HTTP requests without any manual intervention.
**Request Features**
1. International Domains and URLs.
2. Comes with Keep-Alive & connection pooling.
3. Enables sessions with cookie persistence.
4. Supports browser-style SSL verification.

## 2. Beautiful Soup

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/vzzk32zg9wyjqm9tzw2u.png)
Beautiful Soup library is an ideal choice for developers to scrape information from a web page, make HTTP requests using Beautiful Soup library and Request library, and retrieve data from a web page in the form of HTML or XML.
It can also retrieve API data if API data is present on the web page. This library is easy to use and you can go through its documentation to explore all its capabilities.
**Beautiful Soup Features**
1. It can use the API of a website.
2. It can access the HTML of a web page to extract useful information.

## 3. Pillow
Developed by Alex Clark and the team, Pillow is a Python Image Library (PIL). This library helps to add support for opening, manipulating, and saving different image file formats. Many Python libraries and frameworks use this library if they need to handle image files
**Pillow Features**
1. It is an open-source library.
2. It can handle any image format.
3. Allows you to filter images, create thumbnails,  and blur images.

## 4. Pygame

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/brt86ajlxu4c6ar30n9q.png)
If you want to use Python to develop games, you can use the Pygame library. This library allows you to develop games for the Windows and Linux platforms.
Though this library doesn't support the creation of 3D games, it is a great option for intermediate-level Python developers looking to level up their game development skills.
**PyGame Features**
1. It has good documentation.
2. You can create 2D games.
3. It is easy to learn and implement.

## 5. NumPy

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/odn8a9cdo2w8038d235t.png)
NumPy is also one of the most famous Python libraries. If you are an intermediate-level Python developer, you must have heard of this library because it provides one of the most basic Data Structures that every high-level programming language should have.
Python does not have a built-in array Data Structure. NumPy has a Data Structure called list, which looks similar to an array but does not perform all the operations that an array is supposed to do. Apart from the array, it also provides many mathematical functionalities, such as matrix (which is also a part of the array).
**NumPy Features**
1. This library has good documentation.
2. It provides many mathematical functionalities to Python.
3. It is easy to learn and use.
4. Comes in handy if you are using Python for Data Science.

## 6. Pandas

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/9k33tc20reky08qmcfs1.png)
Pandas is a data analysis library used extensively for data science applications. It is an open-source library and is among the most famous  Python libraries. Its large global community provides extensive help to individuals who are using this library to create new projects.
This Library is also used to create machine learning models because of its data handling features. Also, it uses high-level structure and many algorithms to analyze the data. Even many developers use Pandas library to sort complex data.
**Pandas Features**
1. Offers high performance.
2. Easy to learn and use.
3. It has many built-in methods.
4. Comes with a graphical interface.

## 7. SciPy

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/xfrojrqa21q1rmmm67vy.png)
SciPy is similar to NumPy with some distinction. Though NumPy and SciPy both support many common features, we use SciPy when we deal with a large number of data and stats.
Developers highly rely on SciPy if they want to use Python for Data Science. It is an open-source library with huge community support. Basically, SciPy is a collection of many mathematical algorithms that are ideal for solving statistics problems. There are many other libraries available for data science, but SciPy is the simplest amongst them.
**SciPy Features**
1. Large community support.
2. Rapid development.
3. Simple data science tools.
4. Easy to get started.

## 8. Keras

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/bo1qr7754rayohlkoepd.png)
It is an open-source and high-level neural network Python library that is commonly used for developing deep learning algorithms and performing experiments. This library is written in Python itself, which makes it capable of running on top of other machine learning libraries for Python.
Keras also use Theano and TensorFlow at the backend to perform many tasks. The main drawback of Keras is that its speed is a little slow as compared to other machine learning libraries.
**Keras Features**
1. It offers many features like compiling models, processing data-sets, visualization of graphs, and much more.
2. It provides a modular approach for creating artificial neural networks.
3. Being created using Python itself, it is easy to debug.

## 9. Theano

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ob4iygg1deutpcfbrh5i.png)
It is another powerful Python library for machine learning, deep learning, and data analysis. It offers many mathematical algorithms and also comes with multi-dimensional array support that can be used for handling complex data.
**Theano Features**
1. Supports integration with NumPy.
2. It can perform many complex mathematical functions.
3. It can evaluate expression faster as compared to other Python machine learning libraries.

## 10. Scikit-Learn

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/4yftff51yliaa6uzc3sd.png)
Scikit-Learn is among the widely used Python library for machine learning and data science. When it comes to managing and processing huge amounts of complex data, many developers consider this library as the ideal option.
This Python library comes with many built-in algorithms and training methods, including regression. Scikit-Learn is built using NumPy, SciPy, and matplotlib libraries and is known for its exceptional data handling power.
**Scikit-Learn Features**
1. It has many inbuilt methods for cross-validation.
2. Allows development of unsupervised learning models.
3. It also provides a graphical interface to it easier for developers to work with it. 

## 11. Matplotlib

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/qr9rowyen5hwzaekcsat.png)
This Python library is used to provide a graphical interface to represent the data. Matplotlib is used with NumPy and SciPy libraries because the main job of this library is to plot 2D graphs for various filtered data.
First, we use the NumPy and SciPy or any other data analysis library to sort the data, and then we use matplotlib to get a proper graphical view of that data. This library helps to analyze the data in a fun way so even non-programmers can also read and interpret the data easily.
**Matplotlib Features**
1. It helps to plot a graphical interface for the generated data.
2. This library is fun to use.
3. It can plot various kinds of graphs like a bar graph, a pie graph, and more.
4. This library is very easy to learn.


## 12. TensorFlow

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/cyveneg2x975d2iw3xg1.png)
Machine Learning is not hype anymore, it has become a reality. Also, the future is all about artificial intelligence and machine learning. TensorFlow is the most popular library that is used to build machine learning models.
It is an end-to-end open-source library supported by many programming languages, including Python. It is developed by Google and Google itself use TensorFlow to build their AI models.
TensorFlow can store an algorithm in a cube and array-like structure and perform a tensor operation on them. Even in neural computation, developers use TensorFlow because it is very good with the new algorithms.

**TensorFlow Features**
1. It is an open-source library.
2. With TensorFlow, we can develop ML models easily because it uses high-level APIs.
3. It can create ML models for different platforms.
4. TensorFlow comes with a simple and flexible architecture.
5. It has a large expert community.


## Conclusion

In this article, we have mentioned the top Python libraries that are mostly used for data science, artificial intelligence, machine learning, and deep learning. While working on projects, it is essential to choose libraries that not only allow you to add the desired functionalities but also speed up the development process. So, make sure to choose Python libraries having detailed documentation and extensive community support.