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<speak> Since you do not need the serial number, <break strength="weak"/>you can drop that one from the data1.<break strength="x-strong"/> Here axis=1 means<break strength="weak"/> we are dropping the column. <break strength="x-strong"/> In the NumPy, <break strength="weak"/>quantile(0.25), <break strength="weak"/>will give the first quartile Q1. <break strength="x-strong"/> Quantile(0.75) will give the third quartile.<break strength="strong"/> The difference between Q3 and Q1 is the interquartile range.<break strength="strong"/> Below values will show the interquartile range. <break strength="strong"/>GRE is having an interquartile range of 17. You can see here Q1 minus 1.5 into IQR we have calculated Q1 and also IQR similarly Q3 and IQR . so this is the this Q1 -1.5 into IQR is the lower limit of the outlier and Q3 plus 1.5 into IQR is the upper limit for the outliers. <break strength="x-strong"/> Any value outside this first boundary or above the second boundary we call it outliers. The values below q1 and minus 1.5 into IQR and the values above that Q3 plus 1.5 into IQR are called outliers. <break strength="x-strong"/> In data1, <break strength="weak"/>we find the indices of the data values<break strength="weak"/> which are above and below the boundaries.<break strength="strong"/> So after executing this method,<break strength="weak"/> all the outliers are saved in the df_out1 variable.<break strength="strong"/> If you use the head method, <break strength="weak"/>it will display all the values. <break strength="strong"/>Here there are only three outliers. <break strength="strong"/>The outliers are the student with serial number 92,<break strength="weak"/> the student with serial number 347, <break strength="weak"/>and the student with serial number 376. </speak>