At the forefront of development in the tech industry, Python is one of the most used programming languages in the world. It’s also at the top of list for popular languages in the data science industry. Alongside the language itself, Python developers utilize libraries as tools to help with development.
Here are 12 of the most popular Python libraries:
- Dear PyGui
Often associated with video games, Dear PyGui can handle complicated interfaces, has asynchronous features, and uses an immediate mode paradigm.
Numerical Python is its full name and NumPy is primarily used in the Machine or Deep Learning industries. It’s open-sourced, functions exceptionally well with mathematical data, and even includes random number generators.
Up there with popular languages for handling complex data, Scikit-learn is great with predictive analyses of data and can be used commercially with a BSD license.
Containing importable datasets that are prelabeled, Keras also can be run without a hitch on CPU and GPU. Additionally, it’s primarily all Python-based.
This is the most common Python library and utilizes a heavy data visualization structure. Data is presented exceptionally well with various visual designs like graphs and charts in pleasant appearances as well.
Best for machine learning, PyTorch is speedy and flexible. The learning curve is small, Python features are integrated well, and it has good interface capabilities with APIs.
This is one of the top Python libraries for the engineering, math, and science industries. It’s user-friendly and supports the NumPy library—having been built on it. Similarly, SciPy has features to help with signal processing and statistics.
The Python, as well as Django, frameworks were used to create Plotly, which helps make professional data visualization charts. Importing data into charts easily with Plotly is one of its smoothest features too.
Developed by Google in 2015, TensorFlow has become known for its quick debugging features, prediction capabilities, and hassle-free implementation of machine learning elements.
Some of Scalene’s best features include its multi-threaded system, high performance speed, and support of memory data.
Used primarily with machine learning, LightGBM helps improve accuracy of commands, can withstand apps that have a lot of data, and supports learning of GPU.
This is another library that works great with machine learning implementations. PyCaret uses less code to decrease learning difficulty and save developers time. It also helps generate prototypes quickly.
The tech industry has an incredible amount of available resources for all areas of development. Seasoned developers tend to gravitate toward certain tools due to many factors but, with so many useful ones to choose from, there really is something for everyone. Python as a programming language continues to expand its reach in the world of web and app development. As such, the number of supporting tools such as libraries grow as well. For any developers unsure of what libraries to use, or maybe just want to try something new, this list of popular Python libraries is sure to be of some help!