Pandas Comprehensive Tutorial: From Zero To Hero in ONE Hour

[@chyvajustice5729]

Thank you Martin for your video

[@martinbel]

I hope it helps!!!

[@StevenWang82]

I am very grateful to the teacher. It’s really a good tutorial for a bigginer

[@martinbel]

Thanks Steven!! Glad to hear that!

[@pandabzzz276]

Wow a full pandas tutorial! Thanks Martin!

[@martinbel]

🚀🚀🚀

[@MateusOliveira-jg4qo]

Thanks Martin!

[@martinbel]

You are welcome Mateus!!

[@realninja357]

Great pandas tutorial! The chaning methodology needs far more popularity. In my opinion the code is far more intuitive, readable and cleaner. I can tell you really know your stuff with the nuances you are aware of. I am learning a lot from you. Keep it up. I have ambition to be a Kaggle 'Master'

[@martinbel]

I wouldn't recommend having as an objective being a kaggle master today. Kaggle is pretty much dead IMO. It was fun many many years ago but since ~2016 it started dying. Deep learning competitions are close to useless IMO. I won't say doing some kaggle is bad but I wouldn't recommend focusing only on that. If you are interested in kaggle, focus on the older competitions and study good and single model solutions, at least you can learn to work with tabular data. Solutions that involve 20 ensembles will never get deployed in a real world scenario. That's just useless. I'd just ignore those. Once you learn how to use the "traditional ML" models (linear models and tree ensembles) kaggle might not be very useful.

[@realninja357]

@@martinbel Thank you so much man. I can tell you just saved me a whole load of time. This is why you're a valuable channel to follow, you know your stuff. I will take this information into account and try to get the best from Kaggle rather than try to become a master. I have a masters in Data Science but I need lots of project practice.

[@realninja357]

Do we need to import the 're' module for the str.contains method?

[@martinbel]

No need IMO. str.contains is inside pandas

[@raffaelegirodoangelin6926]

Hi Martin,interesting course, but where I can find this files? files = ["/mnt/yt_videos/polars/airlines-full/2007.csv", "/mnt/yt_videos/polars/airlines-full/2008.csv"] to move forward in the course.

[@martinbel]

Those files are a bit larger (I can't upload them to github), but you can download all of them here: dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HG7NV7 The rest you should be able to read it if you follow the video. the can be read directly from github.com/martinbel/datasets.