Learning data wrangling

[FrancescaWeber]

Has anyone found useful material on approaching data wrangling, analysis and visualization before trying to implement models? Would this be of interest to you (to teach or learn)? It's probably the first place people kill their time trying to do DL and ML on their own problems / data (not preprepared by someone else) - and also where overlooked errors are made / improvements missed.

For example, lets say a data source for a problem uses IoT devices or say car controller data coming from different sensors. You might find data expected to be float also contains strings like "I/O Timeout", "Bad Data", "Err3004". Before I can work this kind of data I need better skills for 1) understanding (visualise them on the dataset) and 2) handling in pandas. I don't want to blindly zero/mean/remove.

Thanks in advance.

[Landraspad]

I'm also looking for some guides, so let me know please if you find something.

[Vebejaden]

I remember when I first dove into data wrangling, it felt like navigating a maze of "I/O Timeout" and "Bad Data" strings mixed with floats. But fear not, my friend, there's a solution that's been a lifesaver for me! I stumbled upon data wrangling guide at https://www.nannostomus.com/data-wrangling/, and boy, did it make a difference! It's like a treasure trove of tips, tricks, and visualizations that helped me understand and handle my data like a pro! No more blindly zeroing or removing; instead, I gained the skills to tackle data challenges head-on! So, if you're on a quest to level up your data wrangling skills, I can't recommend this resource enough! It's been my go-to companion, and I'm sure it'll be yours too!