21 more pandas tricks

[@dataschool]

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

[@TimePasser]

No. 7 and no 15

[@rezaghari828]

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

[@dataschool]

That’s great to hear, thanks Reza! 🙌

[@pivotai525]

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

[@dataschool]

You are very welcome!

[@carloseduardocorreacoimbra4040]

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

[@dataschool]

You're very welcome! 🙌

[@nothingDone-ei2fj]

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

[@dataschool]

That is awesome to hear! Congratulations!

[@horoshuhin]

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

[@dataschool]

That’s awesome to hear, thank you! 🙏

[@hyakushiki23]

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

[@dataschool]

Thank you so much! 🙏

[@frankgiardina205]

Lots of time savers here thanks well done

[@dataschool]

You're very welcome, Frank!

[@Kristina_Tsoy]

Thank you so much! Your tutorials are the best

[@dataschool]

Thank you, Kris! 🙏

[@pabloxD75]

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

[@dataschool]

Agreed!

[@fj0n452]

What a great video! Thanks!

[@dataschool]

You’re very welcome!

[@dariuszspiewak5624]

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.

[@dataschool]

Great question! I don't have a system for memorization, rather it just comes naturally the more I use something. However, I also don't worry about forgetting, because I usually remember where to look in order to refresh my memory. Thus, my advice is (1) practice, and (2) keep track of good resources so that you can look up things easily whenever you forget. Hope that helps!

[@yd_]

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.

[@dataschool]

I actually have a video about the MultiIndex! Here it is: kzbin.info/www/bejne/qpS1eJRoqNSWY8U

[@sheikirfan2652]

Great tutorial ❤

[@dataschool]

Thank you!

[@immanuelsuleiman7550]

excellent as always

[@dataschool]

Thank you!

[@gabrielreilly7010]

I love these videos. I always learn so much.

[@dataschool]

What a nice thing to say, thank you so much! 🙏

[@Yuri-to8uj]

amazing content!!

[@dataschool]

Thank you!

[@amitnikhate630]

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.

[@dataschool]

Thanks for your suggestion!

[@kissnicky7001]

Thanks so much,really helpful

[@dataschool]

Glad it was helpful to you! 🙌

[@pietraderdetective8953]

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

[@dataschool]

Thanks very much! I think this video might be of interest to you: kzbin.info/www/bejne/moHafZ-AoLh7gtE

[@pietraderdetective8953]

Awesome, will check that out!

[@yerriswamym323]

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 !!

[@dataschool]

Thanks for your suggestion! I'll consider it for the future. For now, maybe start here: kzbin.info/aero/PL-osiE80TeTsqhIuOqKhwlXsIBIdSeYtc Hope that helps!

[@shubhi2194]

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

[@dataschool]

That's so nice of you to say! You're very welcome! 🙌

[@faizanwar_]

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

[@dvinkster]

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

[@dataschool]

Great question! With the pivot table, I just selected a column with no missing values (Survived in this case) and counted them. With crosstab, it automatically does a count, so you don't need to select a specific column. Regarding the notebook, you can view it here: github.com/justmarkham/pandas-videos/blob/master/21_more_pandas_tricks.ipynb Hope that helps!

[@sachinrathi4197]

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

[@dataschool]

Great question! The assertion passed, thus no error was raised, thus no output was generated. If you're new to assertions, just try running assert(1==1) and assert(1==2) in Python, and you'll see that when an assertion passes, there is no output. Hope that helps!

[@TimePasser]

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

[@dataschool]

Thank you!

[@DartEremite]

Thank you 😊😊

[@dataschool]

You’re welcome!

[@hyakushiki23]

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

[@dataschool]

Thanks for your suggestion!

[@georgiyveter6391]

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()

[@petroliumengineer]

This resample methood is incredibly useful Thanks a ton

[@dataschool]

I agree! It really helped me once I started thinking of resample as a groupby for datetime data.

[@Mortz76]

jupyter notebook link broken

[@dataschool]

You can use this link instead: github.com/justmarkham/pandas-videos/blob/master/21_more_pandas_tricks.ipynb

[@nguyenhuuuc2311]

The meaning of 🐼 is 46!

[@dataschool]

😂

[@tramphone6414]

good

[@dataschool]

Thanks!

[@正经的一哥]

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

[@dataschool]

Great question! The boolean value True gets converted to 1, and False gets converted to 0. Hope that helps!

[@firefouuu]

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

[@dataschool]

Love it! Thank you for sharing! 🙌