Machine learning and artificial intelligence libraries are available in almost every language, but Python remains the most popular programming language of all. One of the most important aspects that makes the language the top choice for developers and enthusiasts is its large community and the fact that it has more than 137,000 libraries for data science.
The communities on GitHub contribute almost every day to make the libraries even better and to overcome the existing problems and challenges in AI/ML.
Here is a list of the Best Python Libraries, Most Contributed and Used Most in 2022!
Developed by the Google Brain team in 2015, TensorFlow is the most popular open source library for building deep learning applications. The repository specializes in differential programming and neural networks, allowing beginners and professionals to design and architect with CPUs and GPUs.
TensorFlow hosts a machine learning ecosystem of tools, libraries, and a GitHub community with more than 3,200 contributors and 169,000 stars.
Developed for rapid testing of deep neural networks, Keras is an open source library interface from TensorFlow. It allows developers to construct models, analyze datasets, and visualize charts. It also runs on ‘Theano’ and allows training neural networks with very little code. Being highly scalable and flexible, it is used by organizations such as NASA and YouTube, among others.
Keras has 1,000+ contributors and 56,000 stars with new releases and improvements almost weekly on GitHub.
NumPy or Numerical Python, also created in 2015, is one of the key libraries for mathematical and scientific computing. It is widely used by scientists to analyze data due to its ability to perform various mathematical operations such as linear algebra, Fourier transform, and matrix calculation functions. NumPy is also used to increase the performance of ML models without much complexity and using much less memory with multidimensional arrays.
With more than 1,400 contributors and 22,000 stars, the GitHub community is actively making improvements. NumPy is also the basis for other libraries like Matplotlib, SciPy and Pandas.
Based on Torch, a programming language framework for C, PyTorch is an open source Python library for creating computational graphs that are modifiable in real time. It is very popular with data scientists and machine learning enthusiasts developing NLP or computer vision based applications.
Developed by Meta AI, PyTorch is very similar to TensorFlow and has processing power like NumPy. It hosts more than 2,500 contributors and 60,000 stars.
Pandas is a flexible and powerful Python library for data analysis and manipulation, providing data structures for easier work with relational, multidimensional, and tagged data. Managing data is easier with this library as it provides series and data frames for precise data alignment and merging. Installation requires NumPy, dateutil and pytz.
The GitHub repository is an active community with 36,000+ stars and 2,700+ contributors, updated every few days.
SciPy, another actively used machine learning library designed to work with NumPy arrays, is used for scientific and engineering computations on large datasets. It is used for data visualization and manipulation and is considered one of the best for scientific analysis. It is considered a more user-friendly repository than NumPy.
Besides Python, it is also very popular in C and Fortran. The GitHub repository has more than 1,200 contributors and 10,000 stars.
Matplotlib is a plotting library for Python, which basically means it’s used to create static, animated, and interactive visualizations. It was designed to eliminate the need for the MATLAB statistical language and works as a unit of NumPy and SciPy. The library can create publication-quality diagrams and relies on the Python GUI to draw them using object-oriented APIs.
The GitHub repository for Matplotlib has 1,200+ contributors and 16,500 stars.
Scikit-learn builds on top of SciPy, NumPy, and Matplotlib and provides gradient enhancement, vector machine support, and random forests for regression, classification, and clustering. It is used for data mining and traditional ML applications. Key features include deriving information from imagery and textual data and merging predictions from supervised models using ensemble approaches.
This GitHub repository for machine learning has more than 52,000 stars and 2,500 contributors.
XGBoost, a distributed gradient boosting library, is optimized to build ML algorithms that use its parallel tree boosting algorithm to tackle various data science problems accurately and quickly. The library is also available for R, Julia, C++, Java and Scala along with Python.
XGBoost has 500+ contributors and 23,000+ stars on GitHub.
Read: 15 most popular R libraries you need to know about in 2022