Outlier detection and removal using Zscore in Python:

This method is used for normal distribution or close to normal distribution.

Importing Libraries:

Loading dataset:

Printing first 10 rows:

 
Statistically describing the dataset:

 
Visualizing outliers by boxplot:

 
Visualizing outliers by seaborn boxplot:

Visualizing outliers by hist plot:

 
Histogram with density plot:

Mean and Standard deviation of the age column:

Setting upper and lower limit as 1 std:

You can set 1,2 or 3 standard deviation as upper limit and lower limit.

Printing the rows below Lower Limit and above Upper Limit(Outliers):

Data within the lower and upper limit( Without outliers):

 
Adding Zscore column to the data:

 
Rows with Zscore greater than 1 and lesser than 1(outliers):