The job of the filter() function is to use and apply a decision function called a predicate to each value in a collection. A decision of True means that the value is passed; otherwise, the value is rejected. The itertools module includes filterfalse() as variations on this theme. Refer to Chapter 8, The Itertools Module to understand the usage of the itertools module's filterfalse() function.

We might apply this to our trip data to create a subset of legs that are over 50 nautical miles long, as follows:

long= list(filter(lambda leg: dist(leg) >= 50, trip)))

The predicate lambda will be True for long legs, which will be passed. Short legs will be rejected. The output is the 14 legs that pass this distance test.

This kind of processing clearly segregates the filter rule (lambda leg: dist(leg) >= 50) from any other processing that creates the trip object or analyzes the long legs.

For another simple example, look at the following code...