What do Python's pandas/matplotlib/seaborn bring to the table that Tableau does not? I spent the past year learning Python. As a person who thought coding was impossible to learn for those outside of the CS/IT sphere, I was obviously gobsmacked by the power of a few lines of Python code!

Having arrived at an intermediate level overall, I was pretty proud of myself as it greatly expands my possibilities in data analysis and visualization compared to Excel (aside from the millions of other uses there are for Python).

Purely in terms of data analysis and visualization:

what does approaching the same data set with pandas/matplotlib/seaborn/numpy bring to the table as opposed to using Tableau?

(sidenote: I was greatly disappointed to see all my hard-earned Python data wrangling skills were available in such a user-friendly GUI... :'( )
Don't worry - your hard-earned Python skills are still important ;)

Tableau is not a replacement - it is essentially a means of sharing your insights/findings. It is a wrapper around your normal toolkit (Pandas, Scikit-Learn, Keras, etc.). It can do some basic analysis (just using basic models from sklearn), but the powerful thing is it can deploy your models to allow people to run inference on stored data/new data, and then play around with it in an interactive dashboard.

Watch this video for a good overview of everything it can do, and how it connects to Python (and R/MatLab). There is just a bit of boiler plate code around your normal Python code.

Tableau also offer TabPy to set up a server, allowing nice deployments of your work, but in the end you need their desktop application to view the results (i.e. your customers need it to look at the results). This is not free: https://www.tableau.com/pricing/individual

In summary, I'd say Tableau is more of a business intelligence tool, allowing e.g. your non-data-scientist boss or other stakeholders to interactively explore the data and the results of your modelling. Similar to Microsoft's PowerBI.