We've already covered zip in the previous chapters, so let's just define it properly and then I want to show you how you could combine it with map.

According to the Python documentation:

Let's see an example:

# zip.grades.py
>>> grades = [18, 23, 30, 27]
>>> avgs = [22, 21, 29, 24]
>>> _(zip(avgs, grades))
[(22, 18), (21, 23), (29, 30), (24, 27)]
>>> _(map(lambda *a: a, avgs, grades))  # equivalent to zip
[(22, 18), (21, 23), (29, 30), (24, 27)]

In the preceding code, we're zipping together the average and the grade for the last exam, for each student. Notice how easy it is to reproduce zip using map (last two instructions of the example). Here as well, to visualize results we have to use our _ alias.

A simple example on the combined use of map and zip could be a way of calculating the element-wise maximum amongst sequences, that is, the maximum of the first element of each sequence, then the maximum of the second one, and so on:

# maxims.py
>>> a = [5, 9, 2, 4, 7]
>>> b = [3, 7, 1, 9, 2]
>>> c = [6, 8, 0, 5, 3]
>>> maxs = map(lambda n: max(*n), zip(a, b, c))
>>> _(maxs)
[6, 9, 2, 9, 7]

Notice how easy it is to calculate the max values of three sequences. zip is not strictly needed of course, we could just use map. Sometimes it's hard, when showing a simple example, to grasp why using a technique might be good or bad. We forget that we aren't always in control of the source code, we might have to use a third-party library, which we can't change the way we want. Having different ways to work with data is therefore really helpful.