Pandas Group By | Python Pandas Tutorial #4 | Split Apply Combine | Python Pandas Aggregate Date

In this video, you'll learn how to use the Pandas groupby method to aggregate data and apply different functions! This is often called split, apply, and combine and is similar to the SQL Group By function.

0:00 Introduction
0:40 Split Apply Combine Overview
2:18 Loading libraries
3:12 Pandas Groupby Overview
4:16 Groups attribute for Pandas Groupby
4:43 Get Groups method for Pandas Groupby
5:12 Apply and Combine with Pandas Groupby
6:54 Other Pandas Groupby Functions
7:53 Multiple Aggregation Functions
8:42 Challenge for Video

Learn Python programming the right way!

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[johnspivack]

You are good, you explain things well. You pace things well too. You take enough time to go through the detail clearly and not rush it, but you keep the video under 10mins. Thanks.

[fallenandfalling]

Alright man, this is better than most of the 1mil club programming channels' content, I swear, I'm soon gonna help you get the views and subs you deserve.
Also, the way of explaining you have is certainly very smooth and easy to understand.
Thanks for doing all of this. I mean most of the people would just quit after not making any significant progress in terms of subs, but you man, you just keep on going, and I appreciate it.
Once again, thank you for this content and God bless you.

[gvvlogsriderunrefresh]

Your teaching skill is so good. step by Step you build up the difficulty level and it all looks so simple in the end. Way to go Nik !

[CAROLUK]

Thank you for making this video. I finally understand how to do groupby.

I have a question related to “agg”. Why did you apply numpy with aggregation functions?

[majddabour]

God Bless u Dear... it was a fruitful video with helpful technique

[the_street_coder]

Sir, Your videos are simple to understand. I wonder why it has not received many eyeballs. Share this video guys with your friends we support him.

[LookNumber]

This shows the mean of Major_category, but not sure how to sort it or how to isolate the max.

[lalafendy]

hi, how do i want to keep it as a new index the column that we groupby

[wackabump]

Hi, thanks a lot for these tutorials. For the challenge question:

What major category has the highest proportion of women?

group = df.groupby('MajorCategory')
women = group['Women'].sum()
total = group['Total'].sum()

L = []
for i in total.index:


women_pcts = pd.Series(L)
women_pcts

[kashif]

Hi, I have one doubt
if I want to store number of times united_states in native_country category (41) into x
that is x=41, how can I do that?

[aashaypratap]

Is it
group['Women'].max()