def DeleteExtremeOutliers(
self,
mylist): #Delete only the extreme ouliers in a given data set
A = BasicStatisticalMeasures()
Q1 = A.quantile(mylist)[1]
Q3 = A.quantile(mylist)[3]
IQ = A.IQR(mylist)
LOF = Q1 - 3 * IQ
UOF = Q3 + 3 * IQ
i = 0
listx = []
for value in mylist:
if not (
value < LOF or value > UOF
): #If the value is beyond the outer fence ([LOF,UOF]) on either side (extreme outlier) doesn't pass the control and deleted
listx.append(value)
i += 1
return listx
def DeleteOutliers(
self, mylist
): #Delete the ouliers (both mild and extreme) in a given data set
A = BasicStatisticalMeasures(
) #Call the BasicStatisticalMeasures to calculate the quantiles and interquartile range
Q1 = A.quantile(mylist)[1]
Q3 = A.quantile(mylist)[3]
IQ = A.IQR(mylist)
LIF = Q1 - 1.5 * IQ #Calculate the lower inner fence
UIF = Q3 + 1.5 * IQ #Calculate the upper inner fence
LOF = Q1 - 3 * IQ #Calculate the lower outer fence
UOF = Q3 + 3 * IQ #Calculate the upper outer fence
i = 0
listx = []
for value in mylist:
if not (
(value < LOF or value > UOF) or (value < LIF or value > UIF)
): #If the value is beyond the inner fence ([LIF,UIF]) on either side (mild outlier) or beyond the outer fence ([LOF,UOF]) on either side (extreme outlier) doesn't pass the control and deleted
listx.append(value)
i += 1
return listx
