Python is still one of the most popular programming languages that developers flock to. Not only is it an easy-to-understand language for newer users, but its board libraries allow for diverse programming in a multitude of fields. In this blog, we’re going to take a look at some of the top Python libraries of 2023 and see what exactly makes them tick. Some are well-known names, and others are known within their communities.
Top Python Libraries of 2023 and 2024
NumPy is the gold standard for scientific computing in Python and is always considered amongst top Python libraries. It is a library for array manipulation that has been downloaded hundreds of times per month and stands at over 25,000 stars on GitHub. What makes it popular is that it is used in a wide variety of fields, including data science, machine learning, and computational physics.
NumPy arrays are similar to lists in Python, but they are optimized for performance. NumPy arrays are stored in contiguous memory, which makes them much faster to access than lists. NumPy also provides a number of mathematical functions that can be used to operate on arrays, such as addition, subtraction, multiplication, and division.
A machine learning powerhouse, Scikit-learn provides a vast collection of algorithms and tools, making it a go-to library for many data scientists. It is easy to use, with a well-documented API and a wide range of tutorials and examples available. Scikit-learn is also open-source, which makes it a popular choice for both academic and commercial use.
Some of the most popular algorithms in Scikit-learn include:
- Linear regression
- Logistic regression
- Support vector machines (SVMs)
- Decision trees
- Random forests
- k-nearest neighbors (kNN)
Originally developed by researchers at Google Brain, TensorFlow is leading the deep learning charge, TensorFlow’s popularity stems from its robust capabilities and extensive community support. With the explosion of AI across industries TensorFlow has also grown in popularity due to its robust ecosystem of tools, libraries, and community that keeps pushing machine learning advances.
Of course, no list of the top Python libraries would be complete without pandas. Without this library, data analysis wouldn’t be the same without pandas, which reign supreme with its powerful data structures and manipulation tools.
Pandas provides a fast and efficient way to work with tabular data. It is widely used in data science, finance, and other fields where data analysis is essential. It includes several data structures and tools that are specifically designed for data analysis, such as:
- Series: A one-dimensional array of data.
- DataFrame: A two-dimensional array of data, with rows and columns.
- Index: A unique identifier for each row or column in a DataFrame.
- MultiIndex: A hierarchical index for a DataFrame.
- GroupBy: A tool for grouping data based on common values.
- Data Manipulation: A variety of tools for cleaning, transforming, and aggregating data.
Django is a mature and popular Python web framework that is known for its ease of use and its ability to build complex web applications. It has a well-established ecosystem with a large number of third-party libraries and tools. What really makes Django are a few things. First, it’s easy to use, the code is easy to learn and it has a well-documented API. It’s also a powerful framework. It allows for the creation of web applications within the ecosystem and even provides templates, routing, and security features.
And one of the secret sauces of this library is its scalability. As anyone knows it’s important to have the ability to handle high traffic, and Django allows for easy scalability for web applications. Currently, Django is still at over 74,000 stars on GitHub.
This essential library is an open-source ML framework capable of speeding up research prototyping, allowing companies to enter the production deployment phase.
Key PyTorch features include robust cloud support, a rich ecosystem of tools, distributed training and native ONNX (Open Neural Network Exchange) support. PyTorch also has TorchServe, an easy-to-use tool that helps deploy PyTorch models at scale. Another advantage is that PyTorch is environment- and cloud-agnostic, which can save time and money and enable collaboration.
The main benefit of Matplotlib is its stunning visualizations. It’s a plotting library with a vibrant community of around 700 contributors. Programmers most frequently utilize Matplotlib for data visualization projects. The data visualization market could reach approximately $7.76 billion by 2023, with a compound annual growth rate of 9.47%.
Matplotlib is highly useful and has low memory consumption. Some professionals will use Matplotlib to replace MATLAB, considering it’s free and open-source. You can also use this library with toolkits like Python scripts, IPython Shells, Jupyter Notebook, and more.
The Seaborn data visualization library in Python provides a simple and intuitive interface for making beautiful plots directly from a Pandas DataFrame. When users arrange their data in tidy form, the Seaborn plotting functions perform the heavy lifting by grouping, splitting, aggregating, and plotting data, often with a single line of code. In this article, I will provide my top five reasons for using the Seaborn library to create data visualizations with Python.
What’s next for me and these top Python libraries?
Not a bad list right? As expected with the rise of AI machine learning libraries and data science-focused libraries would become the most popular ones of 2023. But we also saw a few, such as Django, that focused on web development. What do you think of this list? Have you worked with any of these libraries? And did any of your favorites make it in?
If you’re looking to start building up your skills in these important Python libraries, especially for those that are used in machine & deep learning, NLP, and analytics, then be sure to check out everything that ODSC East has to offer. Coming up this April 23rd to 25th in Boston or virtually from your own home, ODSC East mostly focuses on Python programming, meaning there’s something there for everyone if you’re a fan of these top Python libraries.