16.4. Mapping lists revisited
Description: Python from novice to pro
Keywords: Python, Dive Into Python, tutorial, object-oriented, programming, documentation, book, free

# Dive Into Python Python from novice to pro | Find:

## 16.4. Mapping lists revisited

You're already familiar with using list comprehensions to map one list into another. There is another way to accomplish the same thing, using the built-in `map` function. It works much the same way as the `filter` function.

### Example 16.10. Introducing `map`

    >>> def double(n):
    ...     return n*2
    ...
    >>> li = [1, 2, 3, 5, 9, 10, 256, -3]
    >>> map(double, li)
    [2, 4, 6, 10, 18, 20, 512, -6]
    >>> [double(n) for n in li]
    [2, 4, 6, 10, 18, 20, 512, -6]
    >>> newlist = []
    >>> for n in li:
    ...     newlist.append(double(n))
    ...
    >>> newlist
    [2, 4, 6, 10, 18, 20, 512, -6]

| `map` takes a function and a list[8] and returns a new list by calling the function with each element of the list in order. In this case, the function simply multiplies each element by 2.
| You could accomplish the same thing with a list comprehension. List comprehensions were first introduced in Python 2.0; `map` has been around forever.
| You could, if you insist on thinking like a Visual Basic programmer, use a `for` loop to accomplish the same thing.

### Example 16.11. `map` with lists of mixed datatypes

    >>> li = [5, 'a', (2, 'b')]
    >>> map(double, li)
    [10, 'aa', (2, 'b', 2, 'b')]

| As a side note, I'd like to point out that `map` works just as well with lists of mixed datatypes, as long as the function you're using correctly handles each type. In this case, the `double` function simply multiplies the given argument by 2, and Python Does The Right Thing depending on the datatype of the argument. For integers, this means actually multiplying it by 2; for strings, it means concatenating the string with itself; for tuples, it means making a new tuple that has all of the elements of the original, then all of the elements of the original again.

All right, enough play time. Let's look at some real code.

### Example 16.12. `map` in `regression.py`

        filenameToModuleName = lambda f: os.path.splitext(f)[0]
        moduleNames = map(filenameToModuleName, files)

| As you saw in Section 4.7, “Using lambda Functions”, `lambda` defines an inline function. And as you saw in Example 6.17, “Splitting Pathnames”, `os.path.splitext` takes a filename and returns a tuple `(name, extension)`. So `filenameToModuleName` is a function which will take a filename and strip off the file extension, and return just the name.
| Calling `map` takes each filename listed in `files`, passes it to the function `filenameToModuleName`, and returns a list of the return values of each of those function calls. In other words, you strip the file extension off of each filename, and store the list of all those stripped filenames in `moduleNames`.

As you'll see in the rest of the chapter, you can extend this type of data-centric thinking all the way to the final goal, which is to define and execute a single test suite that contains the tests from all of those individual test suites.

### Footnotes

[8] Again, I should point out that `map` can take a list, a tuple, or any object that acts like a sequence. See previous footnote about `filter`.