@Kwuasimoto
The "I've spent years researching this" at the start killed me 😂
@ganeshsanjeevkumar9515
Those who are getting error in 2:23 use the numeric_only = True argument in mean function like group_by_frames.mean(numeric_only = True) this will work. The cause of this error was the mean function in pandas automatically calculate values in non numeric column also so we have explicitly specify it in which it should work....
@pancakez2181
“Squiggly bracket” > “curly bracket” 😂 Also the first time I’ve seen describe() with groupby(). Makes total sense, thank you!
@sj1795
Great run through of the popular aggregate functions. Really appreciate the little summary definitions you do at the beginning of your videos. Example: "Group by: groups together values in a column and displays them all on the same row", etc. As always, THANK YOU ALEX!! Happy New Year!
@prabeshrai7060
6:07 we can do group_by_frame.sum(numeric_only = True) to get the sum of only numeric values.
@Merrickul
Thanks Alex this was a great video. Short, concise and to the point !
@ahmadch9275
Hey Alex, Hope you are doing well. First to let you know that you have been a great help for me in navigating the data analytics domain. You once mentioned in one of your videos that cloud computing is now a necessary skills for Data analysts and that you would explain this in detail in one of your video. we are eagerly waiting for this video. Please make one when feasible.
@GhulamHussainKhuhro
Dear Alex, I'm writing to express my sincere gratitude for your video tutorial on groupby in Jupyter Notebook. It was very helpful in understanding the basics of calculating means for grouped data. However, I noticed that the tutorial didn't explicitly address how group_by.mean() handles non-numeric data. In the current version of pandas, attempting to calculate the mean of a column with non-numeric values will raise a TypeError. I found solution that by passing numeric_only = True in mean () the issue is resolved. I would be grateful if you could consider updating the video to include a note to pass numeric_only = True in mean() Thank you again for your excellent tutorials ❤❤
@toygar8699
For those who get error in mean,
df.groupby('Base Flavor').mean(['Flavor Rating','Texture Rating','Total Rating'])
@furrywarrior8765
I really like this series and many thanks to Alex for sharing such useful knowledge.
@LeonardDim
love this thank you, 2 years ago and still commenting
@ecstazyrm
Excellent. Much better than edx ibm course
@何文煜-z6i
really clear and helpful!😃😃
@Niranga.555
Thanks for your great work Alex..!
@theweirdfriend562
Thank you very much! as always, your videos are very helpful!
@wkorn89
Thanks Alex, you saved me with this video
@rafaeldeavila1210
Thanks a lot Alex! First one🎉🎉
@beyond1994
Where did you collect this data from? It looks incredibly thorough and well-organized for its size
@TheFootballPlaya
very clear tutorial. nice work!
@pstefanics
3:27 note that to avoid a FutureWarning (and an error later), you need to specify df.groupby('Base Flavor').mean(numeric_only = True)