Session 19 - GroupBy Object in Pandas | Data Science Mentorship Program (DSMP) 2022-23

Data Science Mentorship Program (DSMP) 2022-23

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Datasets used in the session -

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| Time stamp |
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0:00 Start
2:44 Website Update
5:33 Session Start
8:15 GroupBy
14:20 # Applying builtin aggregation fuctions on groupby objects

21:02 # find the top 3 genres by total earning
25:49 # find the genre with highest avg IMDB rating
27:23 # find director with most popularity
29:32 # find the highest rated movie of each genre
31:49 # find number of movies done by each actor
35:00 Doubts

# GroupBy Attributes and Methods
37:34 # find total number of groups -- len
39:01 # find items in each group -- size
40:36 # first()/last() / nth item
43:04 # get_group / vs filtering
45:35 # groups attribute
47:20 # describe / # sample / # nunique
52:55 Doubt Clearance

55:17 # agg method - passing dict
59:28 # agg method - passing list
1:02:26 Doubts

1:03:00 # looping on groups
1:07:24 # find the highest rated movie of each genre
1:11:20 Doubts

1:13:04 # apply -- builtin function
1:15:37 # find number of movies starting with A for each group
1:21:40 # find ranking of each movie in the group according to IMDB score
1:25:38 # find normalized IMDB rating group wise
1:30:57 Doubts

1:32:45 # groupby on multiple cols
1:35:47 # find the most earning actor -- director combo
1:37:26 # find the best(in-terms of metascore(avg)) actor -- genre combo
1:40:01 # agg on multiple groupby
1:42:17 Doubts

1:43:52 IPL Dataset
1:46:30 # find the top 10 batsman in terms of runs
1:49:42 # find the batsman with max no of sixes
1:52:30 # find batsman with most number of 4's and 6's in last 5 overs
1:56:12 Doubts
1:57:09 # find V Kohli's record against all teams
2:00:40 # Create a function that can return the highest score of any batsman

2:05:00 Doubts

[ayushmansingh]

Thanks sir for giving a best contant❤❤❤❤

[TheKumarAshwin]

Groupby works with any data type.
Categorical columns provide significant performance and memory advantages for groupby operations.

[TheKumarAshwin]

22:53 If you can see closely to your sorted data, you can see the sort value methods is not working, hence this are not top genres

[Mtashqain]

Plz provide me solutiion of this seesion task plz

[TheKumarAshwin]

18:44 Hi nitish correct me if im wrong here.. film 300 director is Zack Snyder or Abhishek Chubey, i mean check whole row its completly irrelevant. i think you should revise it. ⚠⚠

[idkwhat]

@14:46 genres.sum() is also including categorical columns. is there some update ?

[kashifchaudhary]

Amazing Session sir, Im from pakistan and im watching your videos, can i buy your course from pakistan?

[anandtalware]

hello, good morning sir and campusx team, I want to ask that if I am not a paid student, then can I access that python practice question set and interview question set ? if not, then what if I paid for only on youtube not on campusx website, in this case can I get that question set ?

[TheKumarAshwin]

31:20

i dont think nitish, you cleared all concepts and the answer would be :

result = imdb.groupby('Genre')[['Series_Title', 'IMDB_Rating']].sum().sort_values(by='IMDB_Rating', ascending=False)

this give Series_title concatenated to avoid i did this:

imdb[imdb['Genre'].isin(result.index)][['Genre', 'Series_Title', 'IMDB_Rating']].head()

this might bit complicated: isin(result.index): This checks whether each element in the "Genre" column is present in the index

[ankitsaurabh_]

'DataFrame' object has no attribute 'append'. How can I do this?

[virendratrivedi]

where is the csv file ??? please tell me

[KhushbuSharma-lxyn]

imdb.groupby('Genre').max().sort_values(["IMDB_Rating"], ascending = False) i used this query to find highest rated movies of each genre so is it right or not