No surprise I use python, but I’ve recently started experimenting with polars instead of pandas. I’ve enjoyed it so far, but Im not sure if the benefits for my team’s work will be enough to outweigh the cost of moving from our existing pandas/numpy code over to polars.
I’ve also started playing with grafana, as a quick dashboarding utility to make some basic visualizations on some live production databases.
I like pandas but sometimes figuring out the simplest of shit is so complicated
I learned SQL before pandas. It’s still tabular data, but the mechanisms to mutate/modify/filter the data are different methodologies. It took a long time to get comfy with pandas. It wasnt until I understood that the way you interact with a database table and a dataframe are very different, that I started to finally get a grasp on pandas.
Wow, I feel seen. Currently fighting this battle, any tips or resources you found helpful?
I think it’s the index(?), aggregation, and order of operations I’m struggling with the most.
First off, understanding the different data structure from a high level is mandatory. I would understand the difference between a dataframe, series, and index are. Further, learn how numpy’s ndarrays play a role.
From there, unfortunately, I had to learn by doing…or rather struggling. It was one question at a time to stack overflow, like “how to filter on a column in pandas”. Maybe in the modern era of LLMs, this part might be easier. And eventually, I learned some patterns and internalized the data structures.
Thank you for taking the time, the perspective is helpful. Same answer as everything else then.
Just be more stubborn than the problem. =)