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<divclass="well see-also">Jupyter Notebook is an implementation of the <ahref="/text-editors-ides.html">text editors and IDEs</a> concept. Learn how these parts fit together in the <ahref="/development-environments.html">development environments</a> chapter or view <ahref="/table-of-contents.html">all topics</a>.</div>
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### Jupyter Notebook tutorials
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### Jupyter Notebook beginner tutorials
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Jupyter Notebook's powerful analysis and visualization environment can be
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intimidating even for experienced developers that are new to the tool. The
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following tutorials will explain the basics so you can quickly figure out
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through some of the more confusing bits as you are getting up
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and running.
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*[First Python Notebook](http://www.firstpythonnotebook.org/) is a free
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guide on analyzing data with Python and Jupyter Notebook. It covers
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many "Hello, World!"-style examples in both data analysis topics and
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more general software development areas like Git, GitHub and Markdown.
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### Intermediate to advanced Jupyter Notebook tutorials
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Once you get the hang of the basics there are a slew of ways to connect
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your notebooks to third party [APIs](/application-programming-interfaces.html)
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and use more advanced Python libraries with your code. These walkthroughs
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cover a range of topics from niche tricks to common but advanced situations
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like advanced interactive visualizations.
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*[Advanced Jupyter Notebook Tricks — Part I](https://blog.dominodatalab.com/lesser-known-ways-of-using-notebooks/)
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and
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[Building Interactive Dashboards with Jupyter (Part 2)](https://blog.dominodatalab.com/interactive-dashboards-in-jupyter/)
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have a ton more details on ways to set up Jupyter Notebooks as dashboards
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and export results to other formats.
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*[28 Jupyter Notebook tips, tricks and shortcuts](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/)
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explains many of the lesser-known keyboard shortcuts and mechanisms
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to output settings.
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*[Reproducible Data Analysis in Jupyter](https://jakevdp.github.io/blog/2017/03/03/reproducible-data-analysis-in-jupyter/)
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is a fantastic series of videos by
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[Jake Vanderplas](https://github.com/jakevdp) that shows how to move your
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code from the interactive Jupyter environment into packaged, tested Python
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code that is suitable for [deployment](/deployment.html) to a production
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environment.
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*[Hacking my way to a Jupyter notebook powered blog](https://nipunbatra.github.io/blog/2017/Jupyter-powered-blog.html)
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explores how the author created a blog using Jupyter but ran into some
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issues along the way, along with how to solve those problems.
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* PyData has an extensive
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[list of Jupyter Notebook talks](https://www.youtube.com/user/PyDataTV/search?query=jupyter)
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