How to remove outliers in Python? | For multiple columns | Step by step ♥

[@jingyiwang5113]

I am really grateful for this video. I am doing research with my professor. And this is really an essential skill for me to conduct research with him. Thank you so much! I do appreciate your wisdom!

[@amandacorreia2625]

Your voice, the music and the explanation: everything is amazing! Thanks a lot ♥

[@rajendranayak8018]

Dear Eigen B, Please upload videos on machine learning & higher stats. I found this video, which helps me a lot. Your way of teaching is good.

[@yohanneskebede1573]

clear and simple! well explained with no error! Bravo!

[@Christopher-xr9kq]

Wow. Watched entire video. So peaceful. good job!!!!

[@jayanthimallela8842]

This video Really help me a lot for outliers. thankful to you and very clean and decent explanation, please do more videos on machine learning. Thanks a lot

[@Mandelbrot567]

This video is excellent, I tried the method on another data set , it worked a treat.

[@robertaraujo347]

I loved to watch this video! it goes to the main point, your explanation was very clear and you've taken ur time to avoid letting any detail out. At the beginning I was considering if I should see ur video cause it lasted 13 minutes and I don't like to see videos longer than 5 minutes xd but I'll leave happy cause I've understood this topic and now I'll be able to apply this in futures data cleaning.

[@jamesjulius7726]

excellent explanation and pace! so calm, will never forget these part #removing outliers

[@123arskas]

Nice work. Liked the simplicity and the soothing voice + music.

[@eugenevlaxos92]

thank you so much you saved my data mining project

[@selsabillekkaf5724]

Every thing is amazing ! , More than very helpful. thank you

[@priyanshugupta2104]

बहुत अच्छा सिखाया बहिनी

[@jesusparra9840]

Excelente video, estuve buscando bastante y tu lo explicaste super bien todo

[@chandrasm009]

Thanks alot Eigen B. Its really helpful.

[@nurulfadillah1248]

this really helps me, thank you so much!

[@josiahadesola]

Awesome....Thanks I love the method of teaching and background music

[@christopherfreyre744]

This is amazing thanks for sharing and such a lovely explanation

[@manojnaik8720]

Sweet voice....Nicely explained.... Thanks

[@hizokadarkwolf]

I was doing something similar, with no results... Guess what: I used & instead of | when finding the lower and upper bounds. Thanks a lot for making this video!

[@ft_smile]

I wish i could show you how much thankful am i
🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏

[@bobozaimee]

Thank you! Your video was really helpful for me :)

[@مريم-مناف-عدنان]

thank great video i have question if i have about 446 feature how can i deal with it like in your example i tried to store the features in a variable X then use your code but it did not work any help please

[@oipseismic7621]

i tried these codes and it doesn't work. it shows(an only compare identically-labeled Series objects)

[@rokeyasiddiqua9375]

Great tutorial

[@mihirthakkar6902]

Very nicely explained. great work. Thanks.

[@MrYnitram]

great video! One question though: what if you only wanted to drop the outlier values and not the whole row in which the outlier is found?

[@prashantshrivastava01]

not possible.. but you can replace outliers with NaN but again.. no point of doing that

[@jayanthimallela8842]

It won't be like that; we can't remove only outlier we can remove entire row only.

[@surajsalunkhe2348]

Thanks for the help

[@peopleonemillion4283]

Thank you!!!! you are amazing

[@chiranjeebmahanta1215]

Thanks a lot!

[@zarynooi5669]

Thank You! Very helpful !

[@PulkitKumar-fd8rb]

Instead of removing, how can we impute median values ?

[@t.farias9336]

thanks, you helped me a lot!

[@manish17788]

what if data has no outlier. In that case we will loose tiny data? how to know if not outlier removal is needed in big dataset?

[@souravsinghbhandari9699]

I used the same technique for my dataset but outliers are still persistent any suggestions what to do?
I tried rerunning the loop it removed some outliers but that reduced the original dataset i was working on.
Anyone has any better suggestions?

[@IngenieriaEstructural7]

Genia me ayudaste mucho

[@stephanie_ong]

Thank you so much!

[@cse048harshkumawat6]

Is there any way to replace those outliers rows with upper_bound or lower_bound please help

[@gebremedhnmehari8451]

How we can determine the value of the quantile?

[@KP-oi4ee]

index_list = []
for feature in ['feature1', 'feature2']:
index_list.extend(outliers(data, feature))
index_list = []
----- > For this i am getting an error : Boolean array expected for the condition, not float64 ,
How can i fix it ?

[@aartiahluwalia4104]

index_list = []
for feature in ['feature1', 'feature2']:
index_list.extend(outliers(data, feature))
index_list = [] --> seem to have created two index_list so modify this line as
index_list

[@divina.glitch]

Thanks!

[@sudhanshusingh5594]

thnx u so much.... really tqqq

[@kyleroach2581]

This should be titled Pandas ASMR

[@AhmedDaoud2]

Thanks, can I get the test.csv file?

[@shahadewadh606]

❤❤❤❤

[@VishwasPatki]

Error: TypeError: Cannot perform 'ror_' with a dtyped [float64] array and scalar of type [bool]

[@jenirex1944]

what will be the output of In[8].. can anyone explain?

[@trangdtt30]

Hi. I have one error: "Name 'dt' is not defined" when i ran cell [9]. can you help me

[@aravinthanseenu1237]

Dear Eigen B,
Instead of removing the outliers kindly help to code- how to replace them with mean value of respective column.

[@jorgeeg2668]

No entiendo ingles, pero entendi el video :D

[@9881847751]

what is ft? here?

[@muhammadabdullahbaig6932]

'ft' is short form for feature.

[@AlAhlyLy]

Hello, I write your code And nothing happend, thank you for the video anyway

[@jamesjayanth7926]

Define outliers error is coming

[@Master_of_Chess_Shorts]

great coding but operation should be column wise not row wise, you are removing a possible valid adjacent value by using the index, imagine a large dataset with 500 columns...

[@modhua4497]

Could you share your code? Thanks

[@WEMELONs]

where vids mazafaka