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13 Exciting Python Projects on Github You Should Try Today [2023]

Python is one of the top choices in programming languages among professionals worldwide. Its straightforward syntax allows software developers and data scientists to pick up new skills with ease. You can also find many Python projects on GitHub to practice and learn while doing. 

Why Python Projects?

The job market has a high demand for professionals with Python skills, but not many candidates pay attention to the advantages of using it. It has extensive support libraries and user-friendly data structures. And over the years, it has emerged as an excellent tool for building command-line applications. Learning python is an integral part of a good data science course. 

You will find various open-source examples if you take a look at the Python projects on GitHub. The repository has something for everyone – from creating a simple password generator to automating routine tasks and mining Twitter Data. For beginners, an activity-based learning approach can do wonders. It can help you understand the ins and outs of the language, such as the Pandas and Django web frameworks and the multiprocess architecture. So, let’s dive in. 

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Python Projects on GitHub

1. Magenta

This Python research project approaches to machine learning through artistic expression. Started by the team at Google Brain, Magenta is centered on deep learning and reinforcement learning algorithms that can create drawings, music, and such. Its collaborative notebooks will introduce you to the technical details of this smart tool that aims to amplify the works of original creators. 

Modiply is another example of an extensible music server that you can find freely on GitHub. 

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2. Photon

It is a fast crawler designed for open-source intelligence (OSINT) tools. The OSINT concept involves collecting data from publicly available sources to be used in an intelligence context. With Photon, you can perform several data crawling functions, including the extraction of the following:

  • In-scope and out-of-scope URLs
  • URLs with parameters
  • Emails and social media accounts
  • XML, pdf, png, and other files
  • Amazon buckets, etc. 

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3. Mailpile

This GitHub project is known for its state-of-the-art encryption functionality. It is a privacy tool backed by a large community. Primarily, it allows you to send and receive PGP encrypted electronic mails. 

Mailpile’s speedy search engine can handle huge volumes of email data and organize it in a clean web interface. It uses static rules or bayesian classifiers for automatic tagging. Go through the free software and live demos on its website to find out more!

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4. XSStrike

Cross-site scripting or XSS is a security vulnerability found in web applications. XSS attacks inject client-side, often malicious, scripts into otherwise benign web pages. So, the XSStrike suite was developed to detect and exploit such attacks. This open-source tool is equipped with the following features: 

  • Four handwritten parsers
  • An intelligent payload generator
  • An effective fuzzing engine
  • A fast crawler

With the above parts, it analyzes the response and crafts payloads. It can also perform efficient context analysis with integrated fuzzers. 

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5. Google Images Download

This command-line python program can search and download hundreds of Google images. The script can look for keywords and phrases and optionally download the image files. Google Images Download is compatible with the 2.x and 3.x versions of Python. You can replicate the source code of this project to hone your programming skills and to understand its real-world applicability. 

6. Pandas Project

When it comes to performing flexible data analysis and manipulation, the Pandas library proves to be an excellent resource. Its expressive data structures offer many benefits over other alternatives. Have a look at some of them below.

  • Flexibility in working with relational/labeled data
  • Convenient handling of missing data and size mutability
  • Intuitive data set operations, including merging, reshaping, and pivoting 
  • Automatic data alignment features with additional capabilities

While looking through the panda’s codebase, you will come across various issues in the documentation. This may prompt you to contribute your own ideas and improve the existing tool. You can find the open-source package on GitHub along with other packages like Django and Keras that enable fast experimentation. 

7. Xonsh

Interactive applications require command-line interpreters like Unix. Such operating systems control the execution using shell scripts. Now, instead of making a trade-off, wouldn’t it be more convenient if your shell could understand a more scalable programming language? Herein enters Xonsh (pronounced ‘Konk’).

It is a Python-powered shell language and commands promptly. This cross-platform language is easily scriptable and comes with a vast standard library and types of variables. Xonsh also has its own virtual environment management system called vox. 

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8. Manim

Manim is short for Mathematical Animation Engine. This project is about programmatically creating video explainers. The program runs on Python 3.7 and produces animated video content, covering complex topics with the aid of illustrations and display graphs. You can watch these videos on the 3Blue1Brown YouTube channel. 

The source code for Manim is freely available on GitHub. You can also refer to online tutorials to learn how to install the package, run a project, and create simple personal projects. 

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9. AI Basketball Analysis

This project is built on the concept of object detection. The artificial intelligence application digs into the collected data to analyze basketball shots. You can easily find the AI web app and API under Python Projects on GitHub. Let us look at how the tool works:

  • You upload files to the web app
  • Alternatively, you can submit a POST request to the API
  • The OpenPose library implements calculations
  • The web app produces results based on the shooting pose data

10. Rebound

It is common for computer program source codes to encounter compiler errors. Rebound can instantly fetch StackOverflow results in such a scenario. It is a command-line tool written in Python and built on the Urwid console user interface. If you choose to implement this project, you can learn how the Beautiful Soup package scrapes StackOverflow content. You can also familiarize yourself with the subprocess that catches the compiler errors. 

11. NeutralTalk

You can fine-tune your knowledge of multimodal recurrent neural networks with NeutralTalk. It is a Python and NumPy project which focuses on describing images. 

Typically, image caption generation methods involve a fusion of computer vision and natural language processing. The system can understand scenes and produce descriptions of the content observed in a picture. 

If you are looking for the latest captioning code, you can refer to NeutralTalk2. Written in Lua, a lightweight and high-level programming language, this project is faster than the original version. 

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12. TensorFlow Projects

TensorFlow is a Python library used for building deep learning models. The Model Garden repository centralizes many code examples for TensorFlow users in one place. It aims to showcase best practices for research and product development while providing ready-to-use pre-trained models. With the help of this official resource, you can explore how to implement distributed training and solve computer vision and NLP problems.  

13. Maps Models Importer

Maps Models Importer works by importing 3D models from extensive maps. It is an experimental tool containing only a Blender add-on and the process requires 3D content software, such as Google Maps. In this project, you can get the hang of importing models from Google Maps.

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How to build your GitHub Python Project?

Making GitHub projects with source code in Python is a skill that every software engineer and data scientist should master. You’ll discover the steps as you go. Just be certain you have enough time.

Although creating free python projects with source code GitHub, may seem difficult, you don’t have to be an experienced pro. Additionally, you don’t require a complex product concept. You do require patience and determination. With any luck, this advice will enable you to use neither as much and help you create your best GitHub Python project

Here are some steps that will help you build your perfect GitHub Python projects – 

Step 1 – Create the plan basics 

Our final goal is to create a very basic library that can be included in a Python programme. Our package’s initial version will let a user understand the prospects of the project and use it further.

Step 2 – Choose the perfect name for your GitHub python projects

This is also quite important in building a project python Github. An easy-to-pronounce, an impactful name can have a huge influence on the popularity of your Python package. Naming anything is challenging. Names must be distinctive, succinct, and memorable. Additionally, they must be entirely lowercase and must not contain any dashes or other punctuation. Underscores can be avoided. Ensure the name is accessible on GitHub and Google while you construct your project python GitHub.

Step 3 – Create a GitHub account 

To make a free python projects with source code GitHub, you must first create an account on the platform. You can create a free account and install GitHub on your system to make your process easier. 

Step 4 – Create an organization on GitHub

Create a brand-new Github organisation.  The purpose behind this is to understand how to set up an open source project for the community; however, you may establish the source on your personal account for your GitHub projects with source code in Python

Step 5 – Set up the repo 

Start with creating a new repository. Choose a ‘.gitignore’ to add from the dropdown menu. Elect Python as your repository language. Your ‘.gitignore’ file’s content will match the directories and file types you want to exclude from your Git repository. Later, you may modify your ‘.gitignore’ file to remove more pointless or delicate items.

You may select a licence from the dropdown list labelled ‘Add a licence’. What users of your repository material may do is outlined in the licence. Different licences offer different levels of flexibility. If no licence is specified, default copyright laws will be followed. You may choose the licence perfect for your project. 

Step 6 – Add Directories

Choose where you want to clone the repository. You may also create a subfolder of your primary files. Ensure that the name does not have any spaces in the middle. Initially, this file may remain empty, and the files placed in the folder need to be imported. You may also create another file that can be referred to while using your package. 

Step 7 – Create setup.py

The build script for your package is contained in the setup.py file. Your package will be built using Setuptools’ setup function before being uploaded to PyPI. Details about your program, its version number, and any additional packages needed by users are all included in Setuptools.

Step 8 – Build the first version of your project 

This is a very important step. You must finish the coding and create a draft version of your project that must include all the functions. Once built, you must test all the functions, and if it works well, you are ready to launch!

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Future Scope for Python

The modern industry is increasingly looking to discover hidden patterns from data pools. Moreover, emerging technologies like artificial intelligence and machine learning add new capabilities and complexities to the landscape. And high-level language like Python is integral to software development and analytics procedures.

Naturally, present-day recruiters place immense value on Python skills when they hire for roles like data scientist, Data/research analyst, Python developer, DevOps engineer, etc. Technology bigwigs like Google, Facebook, Spotify, Netflix, Dropbox, and Reddit offer lucrative career options to candidates with practical training. 

We hope that you can polish your programming skills with the above list on Python projects on GitHub. As the big data market evolves and expands further, Python’s open source community is expected to release even more libraries in the coming years. So, stay up to date and keep learning!

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What are some Machine Learning project ideas for beginners?

Below are some interesting Ml projects that use Python as the main programming language: Some of the tweets can be a bit offensive for a respective audience and the Tweets Sorting Tool can be used to avoid them. This machine learning project filters the tweets based on some keywords. Working on the neural network is one of the best domains to test your machine learning concepts. Handwritten characters classifier works on neural networks to identify handwritten English alphabets from A-Z. The Sentiment Analysis Model is used to detect and identify a person’s feelings and sentiments behind a post or picture posted on social media. This is a good beginner-level project and you can get the data from Reddit or Twitter for it.

Describe the major components that a Python project should have.

The following components highlight the most general architecture of a Python project - Problem Statement is the fundamental component on which the whole project is based. It defines the problem that your model is going to solve and discusses the approach that your project will follow. Dataset is a very crucial component for your project and should be chosen carefully. Only large enough datasets from trusted sources should be used for the project. The algorithm you are using to analyze your data and predict the results. Popular algorithmic techniques include Regression Algorithms, Regression Trees, Naive Bayes Algorithm, and Vector Quantization.

Can Python be used for image processing projects and if yes which Python libraries can be used?

The following are some of the top Python libraries that make building image processing projects very convenient. OpenCV is hands down the most popular and widely used Python library for vision tasks such as image processing and object and face detection. The conversation over Python image processing libraries is incomplete without Sci-Kit Image. It is a simple and straightforward library that can be used for any computer vision task. SciPy is majorly used for mathematical computations but it is also capable of performing image processing. Face Detection, Convolution, and Image Segmentation are some of the features provided by SciPy.

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