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Functional Python Programming - Second Edition
book

Functional Python Programming - Second Edition

by Steven F. Lott
April 2018
Intermediate to advanced content levelIntermediate to advanced
408 pages
10h 42m
English
Packt Publishing
Content preview from Functional Python Programming - Second Edition

Using filter() to identify outliers

In the previous chapter, we defined some useful statistical functions to compute mean and standard deviation and normalize a value. We can use these functions to locate outliers in our trip data. What we can do is apply the mean() and stdev() functions to the distance value in each leg of a trip to get the population mean and standard deviation.

We can then use the z() function to compute a normalized value for each leg. If the normalized value is more than 3, the data is extremely far from the mean. If we reject these outliers, we have a more uniform set of data that's less likely to harbor reporting or measurement errors.

The following is how we can tackle this:

from stats import mean, stdev, zdist_data ...
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Functional Python Programming - Third Edition

Functional Python Programming - Third Edition

Steven F. Lott

Publisher Resources

ISBN: 9781788627061Supplemental Content