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How to Push Jupyter Notebooks to GitHub Using Pycharm



Introduction:

In this blog, we'll break down the concepts of Git and GitHub in simple terms, making it easy to understand and connect with the basics. With the help of step-by-step instructions and included images, we aim to provide an accessible guide that enhances your learning experience.



What is Git?

Understanding Git took me a while, but through this blog, I hope to make it clearer for you as well. Git is a distributed version control system designed to track changes in source code during software development. It offers an organized method for multiple developers to collaborate on projects by keeping a record of modifications, additions, and deletions to files over time.

Git serves as a version control management system and operates as an open-source tool.

It's utilized by various team members located in different places, enabling them to effectively monitor project progress.



What is GitHub?

GitHub is a platform that simplifies these concepts further. It's a space where you can host, share, and manage your files, fostering collaboration and efficient project management.



Let's explore these ideas with a real-world example, making it easier for everyone to grasp and retain.


Applying Git and GitHub to Jupyter Python Projects

In the context of our Jupyter Python projects, Git and GitHub prove incredibly valuable. Imagine a team of four or five members collaborating. Git allows us to review and verify code changes collectively. By creating a repository, loading notebooks, and sharing links with end-users, we leverage the powerful capabilities of GitHub.


The properties of GitHub include repository creation, notebook loading, and link sharing, which are all crucial for effective project management.



Pushing Code Using PyCharm

To push code to GitHub, I use the PyCharm IDE.

Why PyCharm? And

What's a Jupyter Notebook?

The Jupyter Notebook is an open-source web application enabling the creation and sharing of documents containing live code, equations, visualizations, and text. It's an essential tool for interactive data science project development and presentations.

PyCharm, on the other hand, is an Integrated Development Environment (IDE). This tool facilitates the process of pushing Jupyter Notebooks to GitHub seamlessly.



Step-by-Step Guide:



Sign Up on GitHub:




Visit github.com, create an account, and log in.




Click "New" to create a repository. Input a name, description (if desired), select public/private, and hit "Create Repository."






Setting Up PyCharm: Launch PyCharm on your desktop.


Enable Version Control Integration: In the menu bar, click "VCS" and follow the provided screenshots. Enable version control integration by choosing the Git option and clicking "OK."












Cloning the Repository







In the Git menu, select "Clone" and provide the URL. Copy the repository link from GitHub and paste it here. This action will create a new repository in PyCharm.






URL you should copy from GitHub(select HTTPS)





Adding Files to Git: Copy your Jupyter Notebook to the PyCharm repository.

By following these steps, you'll successfully push your Jupyter Notebook to GitHub using PyCharm.



Click "Commit," select the project, and choose "Commit and Push."

Always add message before commit and push







So here after juypter notebook with message in our GitHub repository





Conclusion In the world of collaborative software development and data science, version control and collaboration platforms play a crucial role. Git and GitHub have emerged as essential tools that empower developers and analysts to work efficiently, manage projects, and share their work seamlessly.


Through this blog, we've explored the fundamental concepts of Git and GitHub, demystified the process of pushing Jupyter Notebooks to GitHub, and discussed how PyCharm, a versatile IDE, can streamline this process. By understanding these tools and following the step-by-step guide, you're now equipped to confidently manage your code, collaborate effectively, and showcase your data science projects with ease. Remember, learning to use these tools effectively might take time, but the rewards in terms of project organization, collaboration, and professional growth are well worth the effort. So, whether you're a software developer or a data analyst, incorporating Git, GitHub, and PyCharm into your workflow can make a significant difference in your productivity and project management.

As you continue your journey in the world of software development and data science, keep experimenting, exploring, and refining your skills. The ability to master tools like Git, GitHub, and PyCharm opens up endless opportunities for you to contribute to projects, learn from others, and build a strong online presence in the developer and data science communities.


Thank you for reading, and happy coding!


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