Python is a great programming language for first-time users, but it’s also widely used for professional purposes. This guide takes a practical approach to introducing you to Python and its vast collection of relevant projects. Written with beginners in mind, this quick reference guides you through the language step by step, covering useful features such as:
Are you a Python developer? Do you want to choose the best open source projects from the Python community? This report was prepared by the technical editors of Coder Magazine and contains a selection of great open source Python libraries. The book includes the most popular software packages, and is familiarizing with other cool open-source libraries.
Computer vision is among the most in-demand applications of artificial intelligence. Computer vision focuses on AI models that can analyze images and videos for specific implementations such as object identification, face recognition, etc.
If you want to work on computer vision projects, you must get familiar with OpenCV. OpenCV is a Python library to solve computer vision problems. Gary Brodsky had started OpenCV in 1999, and since then, it has become one of the most popular Python projects with source code Github.
OpenCV allows you to perform image analysis and forms the basis for many sophisticated computer vision algorithms. To use OpenCV, you should be familiar with Numpy, a popular library to perform numerical operations through Python code. You’d have to integrate OpenCV with other libraries to work on computer vision projects, that’s why we’ve kept it at the top of our list.
This is a micro web framework written in Python. It does not have form validation and a database abstraction layer, but it lets you use third-party libraries for common functions. And that is why it’s a microframework. Flask is designed to make creating apps easy and fast and is scalable and lightweight. It is based on the projects Werkzeug and Jinja2. You can learn more about it at DataFlair’s latest article on Python Flask.
Keras is probably the most popular tool for deep learning in Python. It’s a library that simplifies deep learning for Python developers due to which it has become widely popular among AI professionals. Keras offers you consistent and straightforward APIs that minimize the total actions you have to perform.
It automates many necessary use cases, so you only have to focus on your neural network’s advanced sections. Keras is also famous for its accurate and quick error messages that alert you when you make any mistakes.
If you want to become a deep learning professional, you should be familiar with Keras and its applications. Keras was among the top-5 winning teams on Kaggle and ranked no. 1 among most used deep learning frameworks.
Because Keras uses TensorFlow, it is accessible from anywhere and provides a wide range of additional benefits. NASA, LHC, CERN, and many other top organizations use Keras for research. So it’s certainly worth looking into.
This is a module for fast and easy implementation of statistical learning on NeuroImaging data. This makes use of scikit-learn for multivariate statistics for predictive modeling, classification, decoding, and connectivity analysis. Nilearn is a part of the NiPy ecosystem, which is a community devoted to using Python for analyzing neuroimaging data.
Python has many applications in artificial intelligence, and that’s why it has many libraries and frameworks for AI-specific needs. If you plan to become an AI professional, then this project is undoubtedly the best for you. spaCy is an open-source library for high-level NLP (Natural Language Processing) in Python. NLP is the fundamental technology behind many advanced AI applications, such as text analysis, sentiment analysis, and others.
The primary use of spaCy is to build production-grade applications that can process large quantities of text. You can use spaCy to create natural language understanding solutions, information extraction systems, or pre-processing an enhanced deep learning model. One of many reasons behind its high popularity is its long list of features.
You can assign word types to tokens (like segmenting figures of speech or voice), lemmatize the text (converting words into their base forms), find and label individual sentences, and perform many other tasks with ease. spaCy is a must-have Python project Github. You should be familiar with it if you want to become a proficient AI-professional or if you want to work on NLP projects.
PyTorch is another open-source machine learning library written in and for Python. This is based on the Torch library, and is great for domains like computer vision and natural language processing (NLP). It also has a C++ frontend. Among many other features, PyTorch offers two high-level ones:
- Tensor computing with strong acceleration using GPU
- Deep neural networks
Librosa is one of the best python library for music and audio analysis. It provides the necessary building blocks which are used to retrieve information from music. The library is well documented and has several tutorials and examples to make your task easier.
Gensim is another prevalent project among Python developers. It’s a free library you can use to analyze text documents for semantic structure. Gensim is famous for topic modeling. If you want to work on text analysis projects, you should get familiar with the library. It will help you in completing various NLP projects. A topic model is a statistical model that discovers abstract topics in a group of text documents.
You’d have to perform topic modeling for text-mining as it helps you in finding hidden semantic structures with ease. Gensim is probably the best Python solution for text modeling, as it’s capable of handling small and large volumes of text without difficulty.
Face Recognition is a popular project on GitHub- it easily recognizes and manipulates faces using Python/command line and uses the world’s simplest face recognition library for this. This uses dlib with deep learning to detect faces with an accuracy of 99.38% on the Labeled Faces in the Wild benchmark.
One of the most popular Python frameworks, Django, lets you create web apps with fewer code lines and in less time. It simplifies web app building substantially as it takes care of web development basics, and you only have to focus on the technicalities of your web app. Django comes with many built-in tools and facilities that can speed-up your web app development.
For example, it has RSS feeds, site maps, user authentication, and content administration tools. You can quickly add them to your web app by using Django and save a lot of time and effort. By using Django, you can become a Python-based web app developer quickly. Django has a massive community of developers and users where you can find answers to your queries and doubts rapidly.
Pandas is a must-have Python library for data scientists and enthusiasts of data science. Pandas entered the industry in 2008, and since then, it has become a potent tool for any data professional. It provides you with data structures and tools that you can use for data manipulation. Pandas has means for reading and writing data between different formats. It also offers fancy indexing, subsetting, and slicing of big data sets. Here are some additional tasks you can perform with Pandas:
- Merge and join data sets with high performance
- Perform hierarchical axis indexing to work efficiently with high-dimensional data.
- Generate date range and convert frequencies for better time series-functionality
There are many other features present in Pandas, and that’s why it’s a necessity for any data science professional. It is open-source so that you can use it for free. If you’re a data science student, you must be familiar with Pandas.
Kivy is a Python library for development of mobile applications and other multitouch application software with a natural user interface (NUI). It has a graphic library, multiple widget options, the intermediate language Kv to design custom widgets, and input support for mouse, keyboard, TUIO, and multitouch events. This is an open-source library for rapid development of applications with innovative UIs. It is cross-platform, business-friendly, and GPU accelerated.
This is an implementation of Mask R-CNN on Python 3, TensorFlow, and Keras. The model takes each instance of an object in the image and creates bounding boxes and segmentation masks for it. It uses the Feature Pyramid Network (FPN) and a ResNet101 backbone. The code is easy to extend. This project also offers the Matterport3D dataset of 3D-reconstructed spaces captured by customers.
Pipenv is a tool every Python developer should have in their arsenal. It will make your job as a developer much simpler. Pipenv is a tool that focuses on providing you with the benefits of various packaging sectors in Python. It’s for dev workflow and allows users to set up work environments in Python with ease.
Pipenv can create and handle a virtualenv for your task. It can add the packages or remove them from a Pipfile when you install or uninstall them. Pipenv produces the pipfile.lock for creating deterministic builds. With Pipenv, you wouldn’t have to use virtualenv and pip separately. The pipfile.lock helps you in avoiding dependency errors between different packages.
Whether you’re just getting started with Python, or a seasoned developer searching for a new project, these are some of the best open source Python projects to try. Most of these projects have an active community that welcomes new contributors.