Advanced Use of groupby(), aggregate, filter, transform, apply - Beginner Python Pandas Tutorial #5

This is beginner Python Pandas tutorial #5 and in this video, we’ll be diving into advanced use of groupby() method in pandas python. We’ll be covering the aggregate(), filter(), transform(), and apply() methods and showing a few interactive examples with each method. These methods are applied in conjunction with a groupby() on a pandas dataframe.

Documentation can be found here:
Pandas transform():
Pandas apply():
Pandas filter():

Let me know if you have any questions and please offer feedback on how I can improve to help you better.

If you enjoyed this video, please throw in a like and subscribe to my channel. I’ll be posting a lot more videos on data science concepts that cover all things python and SQL.

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Follow along interactively and execute code with me as I walk through the concepts!

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[henrygraterol]

The way you explain and manually breakdown the methods is amazing. I do not have a software background, only basic experience with for, while, and if-else loops in C. I am able to easily understand each method due to the structure of your presentation. I subscribed to your channel after this video. Hope to see more of you and see your channel grow.

[StefanoVerugi]

you are truly talented, the superb teaching quality of your method is by far better (and more effective with entry level people like myself) than most of what can be found in the YT sphere these days
most grateful, immediately subscribed

[immanuelsuleiman]

That display 3 dataframes function is incredibly useful
thanks

[iaroslavd.]

Great tutorial! Very detailed. Thank you!

[craftykidsclub]

the way it is explaning everything is really awesome. Thanks you for nice vedio!!!

[shahidkarim]

looking forward sql and other python vid, thanks for the content

[tarast]

Thank you for this information. The apply method example has helped me with my project

[Bakhiet]

Thank you so much!! I was fighting with groupby and apply!

[jaysonjaylen]

Great video, if you could increase the volume somehow that'd be great though.

[Sheshagiriksrao]

Nice one, I was not able to understand groupby section from Jake Vandreplas's python data science handbook but your video helped me out, could you please take the planet data set example and use two keys to groupby, it is a bit tricky to understand, Thank you

[eseclpt]

Content is interesting, it’s a carbon copy of material being described in the “Data Science Handbook” from Jake VanderPlas, now you have the option to read or watch the video.

[davida]

Love the videos. I became a premium member on SS and subbed to the channel. I've seen a huge improvement in my sql AND python skills !

It would be nice to add more questions like leetcode does where maybe you restrict some questions to only using UPDATE or DELETE FROM or even some practice questions where we create tables

[mohammadyahya]

Thank you very much. Hopefully you can also do more advanced pandas videos. This is very helpful. Not sure what 36:22 `str.lower` means please and how it knows that this refer to `key` column?

[Sam-tgii]

Hard to read the screen. Plz zoom in when recording.
Clear explanations. Thanks

[yadali]

What is x in x['data2'] ? is it df? if so why when x is the cell value in the transform sectiom

[kirubababu]

HI Bro,
My requirement is, I have to group by key and key column with column name 'key' and data2 column with name data2 and I need sum value of data1.
Kindly share your ideas

[manavsaxena]

Hi Nate,

I was practicing '3 Bed Minimum Problem' on the website and although I managed to solve the question in SQL, I am really struggling with the Pandas Solution. Could you please make a video on it or provide the solution? Also, I would really appreciate if in your future videos you could solve the same problem in both SQL and Pandas.

[soojinkim]

Thank you for the explanation. What's the difference between transform() and apply()?

[Konzor]

Hi Nate,

I have a question @25:56 when you do groupby apply: How do you use groupby().apply(function) if you have multiple input parameters of the function? E.g. if in your example "norm_by_data2" would have 2 inputs (x, y).

[Fallen]

I came from the pandas data science handbook to youtube to learn more but this is the same thing lol