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. 

If you are looking for Python projects with source code from GitHub, this article is for you. Whether you have been searching for “Python projects for students GitHub”or “advanced Python projects GitHub” on Google, we have got you covered. We will cover multiple popular Python projects on GitHub that you can try out for yourself. 

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. 

Learn Data Science Courses online at upGrad

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. 

Also read: Excel online course free!

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. 

This is one of the easiest beginner python projects GitHub.

upGrad’s Exclusive Data Science Webinar for you –

How upGrad helps for your Data Science Career?

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!

Read about: Git vs Github: Difference Between Git and Github

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. 

Our learners also read – python free courses!

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. 

Check out all trending Python tutorial concepts in 2024.

This is also one of the easier Python projects for beginners GitHub. GitHub Python projects for beginners like these are really fun to work on.

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. 

pandas is a great data analysis project in Python GitHub that you can try out. You can also use pandas for other EDA projects in Python GitHub.

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. 

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. 

Read: Data Science Project Ideas and Topics

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

This is one of the most popular artificial intelligence projects with source code in Python (GitHub) among young developers.

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. 

This is also a project that can seem interesting for developers who are looking for chatbot project in python with source code from GitHub.

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.

Also Read: Python Project Ideas and Topics for Beginners

How to build your Python Project GitHub?

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 your Python project GitHub

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!

Setting Up Your Python Projects (GitHub)

Magenta

Automated Install (with Anaconda):

We can try using an automated installation script If we are running Ubunty or Mac OS X. Here is the command:

curl https://raw.githubusercontent.com/tensorflow/magenta/main/magenta/tools/magenta-install.sh > /tmp/magenta-install.sh

bash /tmp/magenta-install.sh

We have to open a new terminal window once the script completes itself so that the environment variable changes can take effect. Now, the Magenta libraries are available to us for use within Jupyter notebooks Python programs and Python programs, and the Magenta scripts have been installed in our system’s path.

Note: We will need to run conda activate magenta (or source activate magenta for older conda versions) to use Magenta every time we are opening a new terminal window.

Manual Install (without Anaconda):

If the automated script fails for any reason, or you prefer installing it by hand, do the following steps.

Installing the Magenta pip package:

pip install magenta

Note: In order for us to install the rtmidi package that Magenta depends on, we may need to install headers for certain sound libraries.

Command for Ubuntu Linux for installing the necessary packages:

sudo apt-get install build-essential libasound2-dev libjack-dev portaudio19-dev

Command for Fedora Linux for installing the necessary packages:

sudo dnf group install “C Development Tools and Libraries”

sudo dnf install SAASound-devel jack-audio-connection-kit-devel portaudio-devel

The Magenta libraries can now be for used by us within Python programs and Jupyter notebooks, and the Magenta scripts are installed in our system’s path. We can now train various models and use them to generate music, audio, and images. Instructions for each model can be found in the ‘models’ directory.

We first need to set up the full Development Environment if we want to work on Magenta. 

Here are the steps:

Cloning the magenta repository:

git clone https://github.com/tensorflow/magenta.git

Installing the dependencies by changing to the base directory and executing the setup command:

pip install -e .

You can now edit the files and run scripts by calling Python as usual. For example, this is how you would run the melody_rnn_generate script from the base directory:

python magenta/models/melody_rnn/melody_rnn_generate –config=…

You can also install the (potentially modified) package with:

pip install .

Before creating a pull request, please also test your changes with:

pip install pytest-pylint

pytest

PIP Release:

To build a new version for PIP, bump the version and then run:

python setup.py test

python setup.py bdist_wheel –universal

twine upload dist/magenta-2.1.0-py2.py3-none-any.whl

Note: We are using magenta-2.1.0 but this will be replaced with the actual version number you are releasing.

If you are looking for this Python project with source code GitHub, we have got your back. 

You can check out this link: magenta by Magenta, Github

Pandas

Installing pandas is a very easy process. You can install it using pip.

Here are the steps:

Installing pandas: First open your terminal or command prompt and run the following command to install pandas using pip:

pip install pandas

If you’re using a virtual environment, make sure it’s activated before running this command. If you’re using a Jupyter notebook, you can run the installation commands directly in a notebook cell.

Verifying installation: After the installation is complete, you can verify it by importing pandas in a Python script, interactive shell, or Jupyter notebook:

import pandas as pd

Checking version: To check the installed version, you can run the following in a Python environment:

import pandas as pd

print(pd.__version__)

This will print the version of pandas that you have installed.

Now, let us look at a simple example program using pandas to create a pandas DataFrame and perform basic operations:

import pandas as pd

# Creating a DataFrame

data = {‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’],

        ‘Age’: [25, 30, 35],

        ‘City’: [‘New York’, ‘San Francisco’, ‘Los Angeles’]}

df = pd.DataFrame(data)

# Displaying the DataFrame

print(“Original DataFrame:”)

print(df)

print(“\n”)

# Accessing columns

print(“Column ‘Name’:”)

print(df[‘Name’])

print(“\n”)

# Adding a new column

df[‘Occupation’] = [‘Engineer’, ‘Developer’, ‘Designer’]

# Displaying the modified DataFrame

print(“Modified DataFrame with new column ‘Occupation’:”)

print(df)

print(“\n”)

# Filtering rows

print(“People older than 30:”)

print(df[df[‘Age’] > 30])

print(“\n”)

# Basic statistics

print(“Basic statistics:”)

print(df.describe())

This above program is involves the creation of a DataFrame, accessing columns, adding a new column, filtering rows based on a condition, and computing basic statistics. You can run this script in a Python environment or save it as a .py file and execute it using the command line.

Pandas is one of the most popular data science projects in python with source code GitHub.

Photon

Here are the general steps for installing the Photon web crawler from GitHub:

Clone the Repository: Open your terminal or command prompt and use the git clone command to clone the Photon repository:

git clone https://github.com/s0md3v/Photon.git

Navigate to the Project Directory: Change into the newly cloned directory:

cd Photon

Install Dependencies: Check if there is a requirements.txt file in the project and install dependencies using pip:

pip install -r requirements.txt

Run Photon: You can run the program with the command below and a link such as:

python photon.py -u http://example.com

(I am using ‘google.com’ but you can replace this with the target URL you wish to crawl.

Note: Depending on your Python environment, you might need to use python3 instead of python if you have both Python 2 and Python 3 installed.

Photon has various options and parameters. You can explore them by running:

python photon.py –help

We should always check the project’s official documentation or README for any specific instructions or configurations. Additionally, ensure that you have Python and pip installed on your system before attempting to install and run Photon.

If you are looking for this Python project with source code GitHub, you can check out this link: Photon Repository

Google Images Download

Let us learn how to use google_images_download, one of the most popular tools for downloading google images.

Here are the steps for installing and using it:

Install the Package: Like in the other programs, we open the terminal or command prompt and use the pip command to install google_images_download:

pip install google_images_download

Run the Tool: After installation, we can run the tool using the following command:

googleimagesdownload

Follow the Prompts: The tool will prompt us to enter various parameters, such as the search query, the number of images to download, and other options. We can customize our search by providing specific keywords, image size, file types, etc.

Here is an example of me running the following command to download 5 images of “cats” automatically from Google Images:

googleimagesdownload –keywords “cats” –limit 5

The downloaded images will be saved in a folder named downloads by default.

Explore Additional Options: The tool provides various options that we can explore by running:

googleimagesdownload –help

Mailpile

Here is how you can install Mailpile:

Clone the Repository: Use the git clone command to clone the Mailpile repository:

git clone https://github.com/mailpile/Mailpile.git

Navigate to the Project Directory: Change into the newly cloned directory:

cd Mailpile

Install Dependencies: Check if there is any documentation or README file specifying dependencies and install dependencies accordingly. This might involve using a package manager or installing required Python packages.

Run Mailpile: Start Mailpile.

./mp

(This command might be different based on the project’s structure or setup.)

1. Access Mailpile: Once Mailpile is running, access it through your web browser. By default, it might be available at http://127.0.0.1:33411/.

If you are looking for more Python projects with source code from GitHub, you can follow our blogs.Setting Up Your Python Projects (GitHub)

Magenta

Automated Install (with Anaconda):

We can try using an automated installation script If we are running Ubunty or Mac OS X. Here is the command:

curl https://raw.githubusercontent.com/tensorflow/magenta/main/magenta/tools/magenta-install.sh > /tmp/magenta-install.sh

bash /tmp/magenta-install.sh

We have to open a new terminal window once the script completes itself so that the environment variable changes can take effect. Now, the Magenta libraries are available to us for use within Jupyter notebooks Python programs and Python programs, and the Magenta scripts have been installed in our system’s path.

Note: We will need to run conda activate magenta (or source activate magenta for older conda versions) to use Magenta every time we are opening a new terminal window.

Manual Install (without Anaconda):

If the automated script fails for any reason, or you prefer installing it by hand, do the following steps.

Installing the Magenta pip package:

pip install magenta

Note: In order for us to install the rtmidi package that Magenta depends on, we may need to install headers for certain sound libraries.

Command for Ubuntu Linux for installing the necessary packages:

sudo apt-get install build-essential libasound2-dev libjack-dev portaudio19-dev

Command for Fedora Linux for installing the necessary packages:

sudo dnf group install “C Development Tools and Libraries”

sudo dnf install SAASound-devel jack-audio-connection-kit-devel portaudio-devel

The Magenta libraries can now be for used by us within Python programs and Jupyter notebooks, and the Magenta scripts are installed in our system’s path. We can now train various models and use them to generate music, audio, and images. Instructions for each model can be found in the ‘models’ directory.

We first need to set up the full Development Environment if we want to work on Magenta. 

Here are the steps:

Cloning the magenta repository:

git clone https://github.com/tensorflow/magenta.git

Installing the dependencies by changing to the base directory and executing the setup command:

pip install -e .

You can now edit the files and run scripts by calling Python as usual. For example, this is how you would run the melody_rnn_generate script from the base directory:

python magenta/models/melody_rnn/melody_rnn_generate –config=…

You can also install the (potentially modified) package with:

pip install .

Before creating a pull request, please also test your changes with:

pip install pytest-pylint

pytest

PIP Release:

To build a new version for PIP, bump the version and then run:

python setup.py test

python setup.py bdist_wheel –universal

twine upload dist/magenta-2.1.0-py2.py3-none-any.whl

Note: We are using magenta-2.1.0 but this will be replaced with the actual version number you are releasing.

If you are looking for this Python project with source code GitHub, we have got your back. 

You can check out this link: magenta by Magenta, Github

Pandas

Installing pandas is a very easy process. You can install it using pip.

Here are the steps:

Installing pandas: First open your terminal or command prompt and run the following command to install pandas using pip:

pip install pandas

If you’re using a virtual environment, make sure it’s activated before running this command. If you’re using a Jupyter notebook, you can run the installation commands directly in a notebook cell.

Verifying installation: After the installation is complete, you can verify it by importing pandas in a Python script, interactive shell, or Jupyter notebook:

import pandas as pd

Checking version: To check the installed version, you can run the following in a Python environment:

import pandas as pd

print(pd.__version__)

This will print the version of pandas that you have installed.

Now, let us look at a simple example program using pandas to create a pandas DataFrame and perform basic operations:

import pandas as pd

# Creating a DataFrame

data = {‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’],

        ‘Age’: [25, 30, 35],

        ‘City’: [‘New York’, ‘San Francisco’, ‘Los Angeles’]}

df = pd.DataFrame(data)

# Displaying the DataFrame

print(“Original DataFrame:”)

print(df)

print(“\n”)

# Accessing columns

print(“Column ‘Name’:”)

print(df[‘Name’])

print(“\n”)

# Adding a new column

df[‘Occupation’] = [‘Engineer’, ‘Developer’, ‘Designer’]

# Displaying the modified DataFrame

print(“Modified DataFrame with new column ‘Occupation’:”)

print(df)

print(“\n”)

# Filtering rows

print(“People older than 30:”)

print(df[df[‘Age’] > 30])

print(“\n”)

# Basic statistics

print(“Basic statistics:”)

print(df.describe())

This above program is involves the creation of a DataFrame, accessing columns, adding a new column, filtering rows based on a condition, and computing basic statistics. You can run this script in a Python environment or save it as a .py file and execute it using the command line.

Pandas is one of the most popular data science projects in python with source code GitHub.

Photon

Here are the general steps for installing the Photon web crawler from GitHub:

Clone the Repository: Open your terminal or command prompt and use the git clone command to clone the Photon repository:

git clone https://github.com/s0md3v/Photon.git

Navigate to the Project Directory: Change into the newly cloned directory:

cd Photon

Install Dependencies: Check if there is a requirements.txt file in the project and install dependencies using pip:

pip install -r requirements.txt

Run Photon: You can run the program with the command below and a link such as:

python photon.py -u http://example.com

(I am using ‘google.com’ but you can replace this with the target URL you wish to crawl.

Note: Depending on your Python environment, you might need to use python3 instead of python if you have both Python 2 and Python 3 installed.

Photon has various options and parameters. You can explore them by running:

python photon.py –help

We should always check the project’s official documentation or README for any specific instructions or configurations. Additionally, ensure that you have Python and pip installed on your system before attempting to install and run Photon.

If you are looking for this Python project with source code GitHub, you can check out this link: Photon Repository

Google Images Download

Let us learn how to use google_images_download, one of the most popular tools for downloading google images.

Here are the steps for installing and using it:

Install the Package: Like in the other programs, we open the terminal or command prompt and use the pip command to install google_images_download:

pip install google_images_download

Run the Tool: After installation, we can run the tool using the following command:

googleimagesdownload

Follow the Prompts: The tool will prompt us to enter various parameters, such as the search query, the number of images to download, and other options. We can customize our search by providing specific keywords, image size, file types, etc.

Here is an example of me running the following command to download 5 images of “cats” automatically from Google Images:

googleimagesdownload –keywords “cats” –limit 5

The downloaded images will be saved in a folder named downloads by default.

Explore Additional Options: The tool provides various options that we can explore by running:

googleimagesdownload –help

Mailpile

Here is how you can install Mailpile:

Clone the Repository: Use the git clone command to clone the Mailpile repository:

git clone https://github.com/mailpile/Mailpile.git

Navigate to the Project Directory: Change into the newly cloned directory:

cd Mailpile

Install Dependencies: Check if there is any documentation or README file specifying dependencies and install dependencies accordingly. This might involve using a package manager or installing required Python packages.

Run Mailpile: Start Mailpile.

./mp

(This command might be different based on the project’s structure or setup.)

1. Access Mailpile: Once Mailpile is running, access it through your web browser. By default, it might be available at http://127.0.0.1:33411/.

If you are looking for more Python projects with source code from GitHub, you can follow our blogs.

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!

If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.