Open Source Code Projects in Python

This article is a “living” grab-bag of open source Python projects — Python code that people can read, fork, modify, and use for their own purposes. The goal is to give new developers an easy place to find high quality open source projects that could provide good starting points for their own work. All these projects were written with beginners in mind (although they don’t assume any particular background or expertise), and all of them include clear and thorough documentation.

1. Flask

Python flask

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.

2. Keras

Python keras

Keras is a neural network library that is open-source and written in Python. It is user-friendly, modular, and extensible, and can run on top of TensorFlow, Theano, PlaidML, or Microsoft Cognitive Toolkit (CNTK). Keras has it all- layers, objectives, activation functions, optimizers, and much more. It also supports convolutional and recurrent neural networks.

Work on the latest Keras based python open-source project – Breast Cancer Classification 

3. spaCy

Python Spacy

This is an open-source software library that deals with Natural Language Processing and is written in Python and Cython. While NLTK is more for teaching and research purposes, spaCy’s job is to provide software for production. Also, Thinc is spaCy’s machine learning library featuring CNN models for part-of-speech tagging, dependency parsing, and named entity recognition.

4. Sentry

top python projects

It offers hosted error monitoring that is also open-source so you can discover and triage errors in real-time. Simply install the SDK for your language(s) or framework(s) and get started. It lets you capture unhandled exceptions, examine the stack trace, analyze the impact of each problem, track errors across different projects, assign issues, and much more. Using Sentry means fewer bugs and more shipped code.

5. OpenCV

OpenCV is an open-source computer vision and machine learning library. The library has more than 2500 optimized algorithms for computer vision tasks like detecting and recognizing objects, classifying different human activities, tracking movements with the camera, producing 3d models of objects, stitching images to get the high-resolution images and a lot more tasks. The library is available for many languages like Python, C++, Java, etc.

Number of stars on Github: 39585

6. Nilearn

Python Nilearn

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.

Number of stars on Github: 549

7. scikit-learn

Python scikit-learn projects

Scikit-learn is another python open-source project. This is a very famous machine learning library for Python. Often used with NumPy and SciPy, scikit-learn offers classification, regression, and clustering- it has support for SVM (Support Vector Machines), random forests, gradient boosting, k-means, and DBSCAN. This library is written in Python and Cython for performance.

Number of stars on Github: 37,144

8. PyTorch

Python pytorch - interesting projects in python

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

Number of stars on Github: 31,779

9. Librosa

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.

Number of stars on Github: 3107

Implement Python Open-source Project with Librosa – Speech Emotion Recognition 

10. Gensim

Python libraries

Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora. It targets the NLP and information retrieval communities. Gensim is short for ‘generate similar’. Earlier, this would generate a shortlist of articles similar to a given article. Gensim is clear, efficient, and scalable. This implements efficient and hassle-free realization of unsupervised semantic modeling from plain text.

11. SimpleCoin

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.

12. Pandas

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 Panda.

Conclusion

Python is a complex, freely available language that’s used by some of the most advanced technology companies today–and it’s constantly being improved and updated. For many outside the tech world, this may seem like an unusual choice, but learning this kind of sophisticated code isn’t just a great way to improve your tech skills–it’s also a gateway to understanding some of the world’s leading programming languages.

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