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

[@ayushmansingh7302]

Thanks sir for giving a best contant❤❤❤❤

[@idkwhat337]

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

[@swayamswarupbarik2214]

inside the sum(numeric_only=True)

[@sanchi1095]

Guru jii..❤

[@bhavikdudhrejiya852]

Awesome content on groupby function

[@princeprasad5733]

Maja aa gya Guru ji❤

[@Adilnawazkhan7509]

Great work

[@Shaikh-Ji]

sir ji

[@sajjaduddin8188]

You are the best

[@TheKumarAshwin]

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

[@yudhveersingh8177]

Bhut badhiya bhai ji 🎉

[@rohinisingh6916]

Thank you sir 😊

[@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

[@code-with-me-2002]

pass numeric_only=True inside sum() method

[@AmbarGharat]

df = movies.groupby(['Genre'])['IMDB_Rating'].max().reset_index()
result = movies.merge(df, on=['Genre', 'IMDB_Rating'], how='inner')[['Genre','Series_Title','IMDB_Rating']].sort_values(by = 'Genre')

[@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

[@AmbarGharat]

If you can see closely to your data for e.g. 3.540997e+10 its e raise to 10 i.e. 3.540997×10 ^10 and other values from 5th rank are e+09 that's why they are smaller compared to e+10.

[@Taraprasaddash1994]

thank you sir

[@uttamgupta5653]

THANKS SIR

[@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. ⚠⚠

[@AmbarGharat]

You're interpreting it wrong. The result is showing according specific Genres which is the minimum value in each column.

[@khushalparmar8822]

great

[@ankitsaurabh_]

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

[@curiousboy7015]

df=pd.DataFrame(columns=imdb.columns)
for group,data in genre:
max_rating_data = data[data['IMDB_Rating'] == data['IMDB_Rating'].max()]
df = pd.concat([df, max_rating_data])

[@Mtashqain]

Plz provide me solutiion of this seesion task plz

[@lokeshagarwal6373]

Can i purchase it now.???

[@anandtalware2283]

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 ?

[@campusx-official]

Pay on the website to access it

[@anandtalware2283]

@@campusx-official ok. Thank you

[@virendratrivedi3975]

where is the csv file ??? please tell me

[@WafazAli-b4u]

IN THE DESCRIPTION

[@ganeshandhale2126]

when the new batch will starts....

[@karankeshav1]

Abbey yeh wala toh khtm hone de

[@varunkumar9530]

Sir I want to purchase the course

[@kashifchaudhary680]

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

[@Ahmad-cdx]

Payment issue ata bro, try kia ni hota 🤧

[@KhushbuSharma-lx5yn]

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

[@swayamswarupbarik2214]

this is wrong max() will return row wise implementation

[@siddhigolatkar8558]

Thank you Sir

[@AkriteePathak]

thank you sir