THANKS for watching! 🙌 Which trick are you most excited to start using? CLICK REPLY and let me know!

That was really Helpful, man! Glad that I saw your tweet in my timeline!

I was becoming sad that you were no longer making pandas videos. What a come back like that of Real Madrid :)). Thanks Kevin!!

Thank you so much! Your tutorials are the best

What a great video! Thanks!

Lots of time savers here thanks well done

love it. I really enjoyed this video. thanks for the quality content

excellent as always

Thanks a lot for the excellent tricks (25 + 21) ! Really amazing and useful.

Great tips, holy! there is always something new to learn.

I love these videos. I always learn so much.

Thanks so much,really helpful

Even after re-watching them, your videos are outstanding.

I completed 36 videos in this series. Thanks a lot Kevin for such amazing tutorial.

Great tutorial ❤

amazing content!!

Cool. pandas and numpy seem to have a ton of functions and it’s hard to remember them all. Would appreciate a video focused on multilevel data frames, as I always forget how to index, etc those.

Hi Kevin, Thank you for creating such amazing contents. These videos are really helpful for doing real time projects. I wanted to request you to make video on particular topic. if you can make video on how to use pandas to write, read and edit google sheets, that would be very helpful. It can include putting values in range of cells as well as one cell in google sheet, reading data, etc. If there is already any video you have made on this topic, let me know.

I miss your videos , man. Best like to best tutor. 👌

Thank you for you amazing videos! Super informative and helpful, as always.

Hi Kevin. The most important question of all: How to remember all of those things? Do you have any means? Any way that lets you retain the tricks/knowledge in your mind for longer? Would you please share any thoughts on that? Thanks.

How to use text classification approach where target column is bigram word...? Can you please show us ..?

Thank you 😊😊

Great tips and tricks as always! do you have a tutorial video about writing efficient pandas code? for example implementing vectorization? I've watched plenty of your videos but I think I haven't seen one about the topic

Hi Kevin, Thanks for the insightful video. I love your videos and courses they are so subtle and impactful. Would it be possible for you to make a video on Python Class/objects? Its a daunting concept for someone like me (not from coding background) who has limited understanding of OOPs. Additionally I have observed that many coders use python classes for ML scripts/pipeline, scripting files on Github. So it would be helpful if you could make an video on the same. Thanks in advance !!

Sir , please make a video on user defined functions in pandas dataframe

This resample methood is incredibly useful Thanks a ton

Just doing your datacamp RI Police course. It's a really well laid out course and I think you're adorable. :-)

Hi Kevin, I hear a lot about API's. Maybe one day you can demo how to create a basic custom API.

Thanks for the tips. Q: How crosstab provided the results compared to pivot where no target value 'Survived' is provided Unable to view the Notebook from link provided. Please re upload

Hi, for example I have a table, which I've got by left outer join: person - vehicle dad - car dad - motorcycle dad - bicycle mom - car mom - bicycle son - None/NA/NaN/NaT How to group by person and count with condition (car and motorcycle)? When I use for example: df = df.groupby(['person'])['vehicle'].apply(lambda x: x[x == 'car'].count()) But I can't use a list in condition lambda x: x[x in ['car']], pandas says: ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

why below code is not giving any output pd.testing.assert_series_equal(df.c, df.d, check_names=False, check_dtype=False)

good

The meaning of 🐼 is 46!

jupyter notebook link broken

On tips 14, I think a better solution is to use df.style to display all columns and rows of your DataFrame. If you have a lot of rows and are only interested in the columns just use df.head().style

Hi Kevin, a question question, why the code (titanix.SibSp > 0).astype(' int ') can set different integers into 0 and 1?