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Hi Reader, I'm so excited to share this week's tip with you! It has been in my head for months, but I finally put it in writing βοΈ It's longer than usual, so if you prefer, you can read it as a blog post instead: Jupyter & IPython terminology explainedβ π Link of the weekβPython Problem-Solving Bootcamp (April 1-21) Want to improve your Python skills quickly? There's no better way than solving problems, reviewing alternative solutions, and exchanging ideas with others. That's the idea behind Rodrigo GirΓ£o SerrΓ£o's bootcamp, which I joined back in December! You'll get:
I personally benefitted from the bootcamp, and I recommend checking it out if you're an intermediate Python user! FYI, this is an affiliate link, which means that I may earn a small commission if you sign up using my link, at no extra cost to you. π Tip #42: Jupyter & IPython terminology explainedAre you trying to understand the differences between Jupyter Notebook, JupyterLab, IPython, Colab, and other related terms? You're in the right place! I'll explain by walking through a brief history of the IPython and Jupyter projects:
IPythonIPython was first released in 2006 as an "interactive" version of the Python shell. Whereas the Python shell uses the >>> prompt, you can recognize IPython from its use of In [1] and Out [1] notation to indicate input/output and line numbers: IPython includes many features not present in the default Python shell, such as object introspection, "magic" commands, system shell access, and more. IPython NotebookIn 2011, the IPython Notebook was released. It was known as a "computational notebook" because it allowed you to weave together code, plots, and narrative text into a single document: It was called the IPython Notebook (and not the Python Notebook) because it used IPython as the "kernel", which is the language-specific process that runs the code in a notebook. Jupyter NotebookIn 2015, the IPython Notebook introduced support for programming languages other than Python. Also in 2015, IPython split into two projects: IPython (for Python-specific components) and Jupyter (for language-agnostic components). As part of that split, the IPython Notebook was renamed the Jupyter Notebook. The name "Jupyter" was inspired by the open languages of science: Julia, Python, and R: To be clear, "Jupyter Notebook" was the name of both the coding environment and the files created by that environment. In other words, you would open "the Jupyter Notebook" to create "a Jupyter notebook". Jupyter notebook files used the extension ".ipynb", which was the extension (and file format) originally created for IPython notebooks. JupyterLabAt this point, the Jupyter Notebook was a lightweight coding environment, with far less features than a traditional IDE (integrated development environment). In 2018, JupyterLab (one word) was released as a more full-featured alternative to the Jupyter Notebook: Notebooks created within JupyterLab are still called "Jupyter notebooks", they still use the extension ".ipynb", and they're compatible with notebooks created by the Jupyter Notebook. JupyterLab was originally designed to replace the Jupyter Notebook environment. However, due to the continued popularity of the "classic" Notebook environment, JupyterLab and Jupyter Notebook continue to be developed as separate applications (as of 2024). Summary
Here are a few related terms that I didn't mention above:
Are there any other Jupyter-related terms you want me to explain? Reply and let me know! π See you next Tuesday!Did you like this weekβs tip? Please forward it to a friend or share this link with your favorite online community. It really helps me out! π - Kevin P.S. Google Meet post-meeting filler word breakdown (parody) Did someone AWESOME forward you this email? Sign up here to receive Data Science tips every week! |
Kevin Markham
Join 25,000+ aspiring Data Scientists and receive Python & Data Science tips every Tuesday!
Hi Reader, Last week, I recorded the FINAL 28 LESSONS π for my upcoming course, Master Machine Learning with scikit-learn. That's why you didn't hear from me last week! π I edited one of those 28 videos and posted it on YouTube. That video is today's tip, which I'll tell you about below! π Tip #45: How to read the scikit-learn documentation In order to become truly proficient with scikit-learn, you need to be able to read the documentation. In this video lesson, Iβll walk you through the five...
Hi Reader, happy Tuesday! My recent tips have been rather lengthy, so I'm going to mix it up with some shorter tips (like today's). Let me know what you think! π¬ π Link of the week A stealth attack came close to compromising the world's computers (The Economist) If you haven't heard about the recent "xz Utils backdoor", it's an absolutely fascinating/terrifying story! In short, a hacker (or team of hackers) spent years gaining the trust of an open-source project by making helpful...
Hi Reader, Today's tip is drawn directly from my upcoming course, Master Machine Learning with scikit-learn. You can read the tip below or watch it as a video! If you're interested in receiving more free lessons from the course (which won't be included in Tuesday Tips), you can join the waitlist by clicking here: Yes, I want more free lessons! π Tip #43: Should you discretize continuous features for Machine Learning? Let's say that you're working on a supervised Machine Learning problem, and...