A picture is worth a thousand words. Note that the outliers (the + markers in your plot) are simply points outside of the wide [(Q1-1.5 IQR), (Q3+1.5 IQR)] margin below. However, the picture is only an example for a normally distributed data set. It is important to understand that matplotlib does not estimate a normal distribution first and calculates the quartiles from the estimated ...
The choice depends on what you consider as outliers and the occurrence pattern. In your case, the four points you consider outliers are sporadic, so running median will easily identify them. If several outliers are consecutive, representing a transient shift, however, some of the running median variants may have difficulties recognizing the points in middle as outliers.
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Steve Jobs: ¿Por qué unas empresas fracasan y otras tienen éxito? #negocios #apple #empleo
I have a pandas dataframe with few columns. Now I know that certain rows are outliers based on a certain column value. For instance column Vol has all values around 12xx and one value is 4000 (outl...
With scipy.stats.linregress I am performing a simple linear regression on some sets of highly correlated x,y experimental data, and initially visually inspecting each x,y scatter plot for outliers....
Linear outliers can be found by numpy std function, however, if the data is non-linear, for example, a parabola or cubic function, standard deviation will not handle the task well, since it needs regression to help working out the outliers.
Identifying the outliers in a data set in R - Stack Overflow