5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
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In this video we walk through some of my favorite tips & tricks for doing data science with Jupyter Notebooks. Many of these tips have helped me become more efficient writing Python code for my data science projects.
Topics covered:
Running bash commands from a jupyter notebook. You can use “!” in a code cell to run any terminal command that you want. You can also try using the “%” to execute magic lines. This includes some of the same bash commands that you might gravitate to as well as some Jupyter specific commands.
The most useful shortcuts that I think you should know. Ctrl+enter to run a cell, shift+enter to run a cell and skip to the next, B to create new cells below, M to turn a cell to markdown, and several more. We will also look at find & replace in this section.
Changing pandas default display settings (number of rows to display, columns, decimal points, etc.)
Turning on OS alerts for running Jupyter Notebook cells. This is particularly useful if you have a long machine learning model training job going on and you want to know exactly when it finishes so that you can play around with the results.
Creating slideshows within a Jupyter notebook!
Bonus topic:
Changing your Jupyter Notebook theme! Monokai is a good one to use if you like dark-themes
Source code:
https://github.com/KeithGalli/Data-Sc...
If you have any questions about these tips & tricks, or if you have additional recommendations, please let me know in the comments!
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Resources:
https://towardsdatascience.com/8-comm...
Learn more about magic commands "%": https://ipython.readthedocs.io/en/sta...
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Video timeline!
0:00 - Introduction & video overview
0:25 - Shoutout to this video’s sponsor, Brilliant.org!
2:11 - 1. Running terminal commands such as “pip” directly in a notebook
5:17 - Magic lines in Jupyter
6:11- 2. Shortcuts that you need to know for Jupyter!
9:25 - 3. Changing default Pandas options to improve results display
14:00 - 4. Setting up notifications for when a cell finishes execution
18:08 - 5. Creating slideshows from a IPython notebook!
23:00 - Conclusion (and link to bonus tip!)
