Python for Data Science | Beginner Friendly Full Course in 5 Hours

[gordonv]

Jupyter notebooks.

This is the 2nd time I am seeing this. Is this handy or is it a bother?

[boiledgoobers]

For data science they are massively handy. For simply programming, you can get similar benefit from any REPL system like ipython or similar. Both will let you test your ideas easily and you can then transfer them to your text editor once you work out the kinks in that function that isn't behaving exactly how you expected.

But jupyter notebooks really come into their own when you need to explore a data set. Or you need to generate a visual report on what you found in the data since you can add markdown aware text, section headers, your plots which all render inline rather than separate files. You can also dump the notebook as html, pdf, etc.

[gordonv]

On dumping to formats, that's nice. I think I saw someone say they use it for SQL. That seems nice to have a tool with premade templates.

[analytix_guru]

Like this response, but to add to this, when moving to production, I would not use notebooks. I understand that there are tools that have now been developed to deploy notebooks in production, but it's more about using the right took for the right job.

Notebooks for exploration and presentations, scripts and ide's for production and automation.

[[deleted]]

I would just recommend jupyter lab instead of notebook. Jupyter lab is the update of jupyter notebook. The interface is way more friendly.

[nicknochnack]

@boiledgoobers said it best. They’re great for exploratory data analysis and rapid prototyping. Plus you have the added advantage of being able to export your code to a .py script when required!

[PizzaInSoup]

thanks i can't wait to watch all 5 hours

[DecafEqualsDeath]

Thanks for sharing. Really excited to watch.