Best Python Libraries You Need to Use in 2024

Technology is now a vital part of everyday lives, and it only makes sense to use a programming language that handles real-world problems seamlessly.

Python is a popular programming language that is used in multiple industries, including machine learning, data science, software engineering, etc.

Python offers a host of libraries that are collections of modules, pre-written functions, and classes that you can employ in your Python projects. In this article, I will help you explore some of the best Python libraries as per my research.

What is a Python Library?

Python was developed by Guido Van Rossum in 1980, and it has been a game-changer ever since. Python is popular for multiple reasons, including:

• Python is a beginner-friendly programming language owing to its simplicity and ease of use.
• Python comes with tons of libraries.
• Python allows developers to become more productive during the development, maintenance, and deployment of applications.
• It has a straightforward programming syntax, which comes with a high level of abstraction and is easy to learn.

Python libraries come with a collection of advanced functions that enable you to write code without starting from scratch. It has over 137,000 libraries that can be used to create models and applications in multiple fields, such as machine learning, data manipulation, data science, image, data visualization, etc.

Here are some of the top Python libraries that I have found to be more robust and flexible:

TensorFlow

TensorFlow is a robust, high-performance numerical calculation Python library. This open-source library is used in machine learning algorithms and deep learning algorithms. Google Brain team researchers collaborated with the Google AI organization to develop this advanced library to deal with complex mathematical computations. It employs techniques such as Accelerated Linear Algebra to perform linear algebra computation faster and more effectively.

It is developed completely in C++ and C and has a refined Python front end. The Python code is combined, and it runs in the distributed execution engine of TensorFlow.

Features:

• Flexibility: When it comes to Machine Learning operation models, TensorFlow offers flexibility. It has modularity and enables you to make sections of it individually.
• Responsive Construct: TensorFlow allows you to visualize each element of the graph.
• Train Machine Learning Models Easily: Developers can easily train Machine Learning models on both the GPU and CPU for distributed computing.
• Open Source Library: It is developed by Google experts, enduring constant stability improvements. This open-source library enables everyone with internet access to use it.
• Parallel Neural Network and GPU Training: TensorFlow enables developers to train multiple GPUs and neural networks simultaneously.

NumPy

This open-source library comes with built-in mathematical functions and scientific computation. It handles multidimensional data and big matrices efficiently. It can be used as a random number generator and multidimensional container in linear algebra. NumPy Array is a popular Python object that characterizes an N-dimensional array with columns and rows. It is faster and uses less memory, which makes it more popular compared to other lists.

NumPy interface allows developers to represent sound waves, images, and multiple binary raw streams for visualization as an N-dimensional real value array.

Features:

• Simplification: NumPy facilitates the implementation of complex mathematical equations.
• Interactive: It is a very user-friendly and interactive Python library.
• Open-Source: This open-source library allows a lot of interaction, which helps developers when they face any difficulties.
• Intuitive: It makes mathematical equations and coding easy and simple to understand.

Pandas

Pandas is a Berkeley Software Distribution open-source, licensed library. This library is used for data manipulation, cleaning, analysis, etc. Pandas facilitate simple data analysis operations and data modelling without the requirement to switch to another programming language.

With Pandas, developers can perform various tasks such as:

• Pandas can allow you to slice the data frame
• It facilitates data frame merging and joining
• Index values in a data frame can be changed with Pandas
• Data conversion into multiple forms can be done
• The headers in a column can also be changed

Keras

This open-source, Python-based neural network library allows developers to explore deep neural networks. It is an Application Programming Language (API) that makes deep learning more simple. However, as per my experience, I recommend you install the TensorFlow backend engine prior to installing Keras.

Keras comes with multiple standard neural network-building components such as activation functions, layers, optimizers, objectives, and other tools for working with text data and pictures.

Features:

• Keras runs on both the Graphics Processing Unit and Central Processing Unit efficiently owing to its deep learning features. Also, the modular design of Keras facilitates research on grounds of better adaptability to pre-trained models as well as pre-processed data sets.
• This Python-based library makes it easier for developers to explore multiple models and debug.
• It supports various neural network models such as embedding, recurrent, fully connected, pooling, and convolutional models. Developers can also merge these models to develop more refined models.

Scikit Learn

Scikit Learn is a Python open-source library that developers extensively use for machine learning algorithms. You can use it with supervised as well as unsupervised learning algorithms.

It comes with a plethora of algorithms that allow you to perform multiple tasks, such as:

• Data mining
• Machine learning tasks
• Clustering
• Dimensionality reduction
• Regression
• Classification
• Model selection, etc

Features:

• Cross-Validation: With Scikit Learn, you can effectively check the precision of supervised models on any unseen data.
• Feature Extraction: It has a collection of Python-based tools that can be employed to extract elements effortlessly from text and photos easily.
• Unsupervised Learning: It has multiple unsupervised learning algorithms that are extensively used in machine learning, such as: Factor analysis, Unsupervised neural networks, Principal component analysis, Clustering.

SciPy

SciPy is an open-source, free Python library that facilitates high-performance computing, scientific computing, and data processing. It comes with a plethora of user-friendly elements and is based on the popular NumPy extension. With SciPy, you can perform visualization, data processing, and high-level commands. It can be used for indexing and sorting of array data, storing numerical code, signal processing, etc.

Features:

• It is written in NumPy and uses specialized submodules to deliver efficient numerical algorithms such as numerical integration, optimization, etc.
• The functions in the submodules of NumPy are extensively documented.
• SciPy handles tasks such as integration, linear algebra, and ordinary differential equations with ease.

Requests

This is another user-friendly yet powerful tool for effortlessly dealing with HTTP requests. This library stands out owing to its efficiency and simplicity. It enables you to interact with various web development services and fetch data without involving you in complex HTTP protocols.

Features:

• Seamless HTTP Requests: It makes sending HTTP requests effortless and removes the requirement of manual form-encoding or query string manipulation.
• Connection Management: It manages connections with components such as Keep-Alive and Connection Pooling. It also enhances resource usage and performance.
• Global Reach: It supports international URLs and domains, making it a robust library for globally connected applications.

Conclusion

Python is a dynamically typed, high-level interpreted language that is extensively used for error debugging. In this article, I have listed the top Python libraries that can assist you with robust programming and coding needs. When choosing the right Python library, you need to understand your project needs and check the Python version compatibility, performance, scalability, community and support, budget considerations, etc.