Python is among the most popular programming languages on the planet, and there are many reasons behind this fame. One of those reasons is a large number of open-source projects and libraries available for this language. From machine learning to animation, there’s a Python project for nearly everything. If you want to become a proficient Python developer, you should be familiar with some of these projects (if not all).
That’s why in this article, we’ll discuss different Python projects with source code Github. Because Python has applications in various industries, you might find many projects to help you complete your tasks. You should choose projects according to your interests and your experience. You can bookmark this article for future reference. Let’s get started.
Here are a few of the Python Open Source Project Ideas –
Python Open Source Project Ideas
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. Find out more on OpenCV here.
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. You can find out more about Keras here.
Flask is a mini framework because it doesn’t have any specific libraries or tools. Additionally, it doesn’t have any form of validation or database abstraction layer so that the developer can choose them according to their requirements. Flask started as a wrapper around Jinja and Wekrzeug but became one of the most popular Python projects with source code Github.
Apart from being famous, Flask also has one of the most active communities of developers. So if you face any issues, you can ask in the community and quickly get helpful responses. Flask’s community has added numerous extensions to it, which enhances its functionality considerably. Due to these reasons, Flask is an essential framework for any Python web developer. Find out more on Flask here.
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. You can find out more about spaCy here.
This is a fantastic project for any deep learning professional. Nilearn is a Python module that focuses on performing statistical learning on NeuroImaging data. To use Nilearn, you should be familiar with scikit-learn, a prominent Python tool for scientific applications. You can perform numerous tasks on NeuroImaging data by using nilearn. For example, you can perform decoding, classification, connectivity analysis, predictive modeling, and various other jobs.
NeuroImaging is a prominent medical field, and Nilearn’s capabilities solve many problems present in this sector. By using Nilearn, you can make the analysis of Neuroimages relatively easy and effective. AI finds many applications in the healthcare sector, and if you’ve been interested in using your AI-skills to work in that industry, you should get familiar with Nilearn. Find out more on Nilearn here.
PyTorch is a must-have for any machine learning professional. If you aspire to enter the machine learning industry, you should look into PyTorch as it’s one of the most popular frameworks for ML applications. It facilitates numerous ML implementations from production deployment to research prototyping.
PyTorch is open-source, which means it’s free to use. It also has multiple features with which you can quickly work on ML projects and avoid many hassles. PyTorch’s ecosystem is filled with many libraries and tools of various machine learning sections, including reinforcement learning and computer vision.
Similarly, it has native ONNX (open neural network exchange) support. There’s a long list of features due to which PyTorch is a necessity for any Python professional. Its widespread popularity helped it in creating a fantastic community of helpful and innovative developers and researchers. You can find out more about PyTorch here.
Scikit-learn is among the most popular Python libraries for machine learning professionals. It provides you with multiple tools for predictive data analysis. It is commercially usable and is open-source. Based on SciPy, matplotlib, and NumPy, scikit-learn is a must-have for anyone who wants to work on machine learning projects. Scikit-learn enables you to perform numerous machine learning implementations.
With its algorithms and tools, you can perform regression, classification, model selection, dimensionality reduction, pre-processing of data, and many other ML tasks. Scikit-learn’s tools are easy to learn. It is also a famous Python project Github for fundamental machine learning implementations, so learning about it will surely help you become a professional in this sector.
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. Find out more on Gensim here.
A popular field of AI applications is speech and audio analysis. If you are interested in performing those tasks, then you should try Librosa. It is a Python package that focuses on audio and music analysis. By using Librosa, you can easily create a music information retrieval system. Audio analysis has become highly popular thanks to AI assistants, smart home devices, and music streaming platforms. Librosa provides you with the necessary basics you need to build a music analysis solution. Find out more about Librosa here.
9. Face Recognition
As the name suggests, Face Recognition is a Python library to perform face recognition. It simplifies face recognition in images and videos by providing specific commands. Using this library, you can perform face recognition on images with only one or two code lines. Face Recognition is based on a deep learning model. Its model scored 99.38% in accuracy on the LFW (Labeled Faces in the Wild) benchmark.
It has a simple <face_recognition> command-line tool that you can use to perform face recognition efficiently. Another excellent quality of this library is you can combine it with other popular Python libraries to create advanced face identification solutions. It’s undoubtedly an excellent tool for any AI professional. Find out more on Face Recognition here.
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. Find out more Django here.
SimpleCoin is a fantastic project for cryptocurrency enthusiasts. It’s a simple, incomplete, and insecure implementation of a cryptocurrency blockchain in Python. The project focuses on building a fully-functional blockchain currency while ensuring that it remains as simple as possible.
This project is for educational purposes, so whether you’re a Python professional or a blockchain enthusiast, looking into it would be helpful. SimpleCoin will help you get familiar with the basics of blockchain and cryptocurrencies. You can explore how nodes in a blockchain interact and how users execute transactions in one. SimpleCoin is among the most popular Python projects with source code Github with more than 1,500 stars when writing this article. You can find out more on SimpleCoin here.
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. Learn more about Pandas here.
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. Find out more about Pipenv here.
MicroPython allows you to run Python on microcontrollers. It also has the MicroPython pyboard, a small circuit board that can run MicroPython and gives you a low-level Python-based operating system. MicroPython comes packed with features such as generators, exception management, interactive prompts, and others.
You can run with just 256k of code space. If you’re interested in microchips and microcontrollers, you should certainly get familiar with this Python project, Github. You can learn more about MicroPython here.
If you were looking for a unique and creative project, Pyray is the one for you. Pyray is an open-source project that allows users to perform 3D rendering through simple Python code. So, if you take an interest in Python’s applications in arts and related fields, you should look into Pyray. You can create 3D animations, 2D animations, 3D objects, and many other things with Pyray. It only entered the market in 2018, and within a mere two years, it has become one of the most popular Python projects on Github. You can find out more about Pyray here.
This is an excellent project for data science professionals. If you’ve been studying data science for some time, you might have already heard its name. Dash is a product from Plotly and is a framework for developing data science and ML web apps. Apart from Python, you can use Julia and R with Dash. Dash facilitates web app development for data scientists by removing the need for dedicated back-end and front-end development.
Dash is based on Flask, a widely popular Python framework which we’ve discussed earlier in this article. You can use it for free through its open-source version. On the other hand, you can also use its paid version if you have more requirements. Dash has more than 12,000 stars on Github, so it’s certainly a major project you should be familiar with. Find out more on Dash here.
Kivy is a library for the fast development of mobile applications with complex UI. It is open-source and cross-platform compatible, which makes it very accessible for Python developers. UI development is one of the most important aspects of any application, and Kivy helps you substantially in tackling any issues present in that process.
You can run it on Windows, Android, OS X, Linux, Raspberry Pi, and iOS. Its toolkit has more than 20 widgets, so you have a wide range of options to choose from. Kivy is undoubtedly a great tool to add to your arsenal in terms of effectiveness and functionality. You can use Kivy to develop apps with multi-touch functionality as well. Find out more about Kivy here.
18. TensorFlow Models
Probably one of the essential Python projects with source code Github, TensorFlow models is a repository with various SOTA (state of the art) models for TensorFlow. TensorFlow is among the most popular tools for machine learning professionals. It is a product from Google with a large pool of libraries and tools.
TensorFlow facilitates model building in machine learning through its high-level APIs. Moreover, you can access TensorFlow from anywhere as it remains available on the cloud. You can deploy ML on-premise, in your browser, or on your device by using TensorFlow.
The TensorFlow Models project will help you in understanding the capabilities of this powerful technology. You can efficiently work on various TensorFlow projects once you’re familiar with the different sorts of things you can do with TensorFlow. TensorFlow Models helps you know the best practices for this technology, so you would make mistakes less often and work with better accuracy. Find out more on TensorFlow Models here.
This is one of the best Python projects with source code Github for AI enthusiasts. Magenta focuses on exploring the applications of artificial intelligence and machine learning in music and art generation. It allows you to develop reinforcement learning and deep learning algorithms to produce songs, drawings, and other artistic products.
It’s a product of engineers and researchers of the Google Brain team along with many other experts. The purpose of Magenta is to help artists and musicians by providing them with enhanced tools. After going through this project, you can learn about the various ways AI can help creative fields.
They release Magenta models through TensorFlow. If you want to use your Python skills in AI and ML, you should focus on this project. It would help you get familiar with reinforcement learning algorithms and neural networks, two of the most prominent AI sections. Magenta is available as an open-source Python library on TensorFlow. You can visit their blog to find out more about Magenta.
If you’re planning to enter cybersecurity, then this is the perfect project for you. Snallygaster is a solution to identify security issues on HTTP servers, such as finding file leaks. This tool enables you to find files on web servers that unwanted parties can access, thus causing a considerable security risk. Some examples of these files are backup files (that may have passwords), git repositories, and others. You can install snallygaster through PyPI.
At the time of writing this article, snallygaster had more than 1,700 stars on Github.
21. Mask R-CNN
According to its creators, Mask R-CNN is a flexible, simple, and general framework you can use to segment object instances. It enhances Faster R-CNN, a prominent computer vision technology for image recognition and object identification. Mask R-CNN can detect an object in an image and generate a segmentation mask for the event instantaneously. Training and adding Mask R-CNN to your code is easy. It adds very little overhead to Faster R-CNN while offering many advantages.
Even though Mask R-CNN is quite simple, it has outperformed many similar models in object instance segmentation, due to which it’s a famous open-source project. If you want to work on computer vision projects in the future, you should get familiar with Mask R-CNN. Go to this link to find out more about Mask R-CNN.
To work on statistical models, you’d have to get familiar with statsmodels, a popular Python module. It allows you to explore statistical data, estimate various statistical models, and perform statistical tests. Statsmodels would produce a detailed list of results for every estimator, which you can test against existing packages to check their accuracy.
You should be familiar with Python packages, statistical analysis, and pandas before working with this solution. Statsmodels is among the most popular Python projects with source code Github because of its functionality and simplicity. Its primary features include using discrete models (negative binomial regression, MNLogit, etc.), linear regression models, time series analysis, etc. To find out more about this project, you can visit their website or link to this Python project Github. There, you’ll find the documentation for this project along with its source code.
Learn More About Python and its Projects
That’s it. We’ve now reached the end of our list of open-source Python projects. We hope you found this list of projects helpful. If you have any questions or thoughts to share, you can let us know through the comment section below.
You can head to our blog and learn more about Python and get more project ideas. Here are some resources for additional reading:
- 42 Python Project Ideas and Topics
- How to Run a Python Project?
- Python Banking Project (With Source Code)
- Python Projects for Beginners
Getting familiar with these projects would help you in becoming a better Python developer. After all, without these tools and libraries, doing specific tasks with Python is a herculean task.
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On the other hand, you can also get a dedicated and comprehensive learning experience by getting a Python course. The course will allow you to learn from industry experts through videos. Which Python project Github did you like the most? Let us know!