your level of delivery of the material is one of the greatest I've seen. can't stress this enough. I'd prefer your videos over pandas documentation any day:). especially when describing edge cases, thanks
Hi Reuven! what happens if I have unique values, and I I want to know they qtys of each of them, how do I reshape in order to calculate that? by the way your Oreilly course was great!
Thanks so much, and great to see you here! I'm not 100% sure what you're asking here, but you can always group on a column and then use the "count" aggregate function to find out how many times each value appeared. If that's not what you meant, then let me know!
Hi Reuven! for even more flexibility when selecting from multi-index data frames, you can use the 'get_level_values' method, for example like here: df[ df.index.get_level_values('Year').isin([1993, 1920, 2010]) & df.index.get_level_values('Sport').isin(['Archery', 'Judo']) & (df.Age < 24.0) ]
You make this so much easier to understand
I'm delighted to hear it helped!
your level of delivery of the material is one of the greatest I've seen. can't stress this enough. I'd prefer your videos over pandas documentation any day:). especially when describing edge cases, thanks
Thanks so much; I really appreciate it!
OMG! OMG! OMG! What a content! I am speachless! After buying so many online courses I found you for free? OMG! OMG! OMG!
I'm so delighted you enjoyed it!
@@ReuvenLerner I was wondering if you teach on any other platforms? Anywhere to look at your courses? Thanks
@@CHeRKeSSS00 Yup, check out LernerPython.com -- lots of courses there!
Amazing..one of the most underrated Python/Pandas content creators.
Awesome stuff Sir!
I'm so delighted you enjoy it!
Absolutely awesome
Glad to know you enjoyed it!
Hi Reuven! what happens if I have unique values, and I I want to know they qtys of each of them, how do I reshape in order to calculate that? by the way your Oreilly course was great!
Thanks so much, and great to see you here!
I'm not 100% sure what you're asking here, but you can always group on a column and then use the "count" aggregate function to find out how many times each value appeared.
If that's not what you meant, then let me know!
tNice tutorials doesn't seem to work.
What part didn't work for you? BTW, you can get the Jupyter notebook I used from GitHub, if that'll help.
Hi Reuven!
for even more flexibility when selecting from multi-index data frames, you can use the 'get_level_values' method, for example like here:
df[
df.index.get_level_values('Year').isin([1993, 1920, 2010]) &
df.index.get_level_values('Sport').isin(['Archery', 'Judo']) &
(df.Age < 24.0)
]
Excellent! I really like using xs, too.