Jupyter Notebooks are interactive, web-based tools that allow developers and data scientists to combine code, visualizations, and narrative text in a single document.
They are ideal for data analysis, experimentation, and educational purposes. The notebook format enables users to write and execute code in cells, view outputs immediately, and create a narrative around their work, making it accessible and understandable for both technical and non-technical audiences. This makes Jupyter Notebooks a powerful platform for collaboration, teaching, and sharing insights.
Useful for:
- EDA (exploratory data analysis)
- creating AI model POCs
- anything where you want visibility of every step of the process or want to build up and adjust a process without needing to rerun the whole flow from the start
Less useful for:
- creating production ready code
- working on code that a number of people will want to be editing at the same time
Notebooks and example data for an intro presentation on Jupyter notebooks and when they might be a useful tool.
This repo has been set up to be able to run in a number ways:
- as a poetry project. Run
poetry install
and thenpoetry run jupyter notebook
to get up and running with this - as a colab notebook. For this to work you should:
- open Google colab
- upload
notebook-example/notebook-intro.ipynb
to colab - set the config param
in_colab
near the top to be True - follow the instructions in the notebook to get the other dependent files uploaded
- there's also a
requirements.txt
for you to use if you prefer
Files:
notebook-example/notebook-example.ipynb
is a more complete annotated file that we'll be looking through.notebook-example/notebook-tips.ipynb
is a reference file that contains some tips and recommendations to help you get the most out of your notebooks.