I resisted pandas for a long time, then found myself spending way too much time writing data processing routines from scratch on a recommender systems project. It reminded me of when I used to manually compute neural network gradients before discovering Theano.
Two resources that worked well for getting up to speed quickly:
Kaggle Learn micro-courses — well-organised, beginner-friendly, and the pandas course pairs nicely with their visualisation and embedding courses.
Pandas exercises on GitHub by Guilherme Samora — a comprehensive exercise collection I worked through in Jupyter Notebook. Practice-based learning is far faster than reading documentation.
Within a couple of days I was applying pandas to my own project. Happy to hear what resources others have found useful.