Lists

Lists in Python are one of the most versatile collection object types available. The other workhorses are dictionaries and tuples, but they are really more like variations of lists. Python lists do the work of most of the data collection structures found in other languages, and since they are built in, you don't have to worry about manually creating them. Lists can be used for any type of object, from numbers and strings to other lists. They are accessed just like strings (since strings are just specialized lists), so they are simple to use. Lists are variable in length; that is, they grow and shrink automatically as they're used, and they can be changed in place; that is, a new list isn't created every time, unlike strings. In reality, Python lists are C arrays inside the Python interpreter and act just like an array of pointers.

The following screenshot shows the creation of a list and a few examples of how to use it:

After the list is created in line 42, lines 43 and 44 show different ways of getting the values in a list; line 43 returns the list object while line 44 actually prints the items that are in the list. The difference is subtle, but will be more noticeable with more complicated code.

Line 45 returns the first item in the list, while line 46 pops out the last item. Returning an item doesn't modify the list, but popping an item does, as shown in line 47, where the list is visibly shorter.

The biggest thing to remember is that lists are series of objects written inside square brackets, separated by commas. Dictionaries and tuples will look similar except they have different types of brackets.

Lists are most often used to store homogeneous values; that is, a list usually holds names, numbers, or other sequences that are all one data type. They don't have to; they can be used with whatever data types you want to mix and match. It's just usually easier to think of a list as holding a standard sequence of items.

The most common use of a list is to iterate over the list and perform the same action to each object within the list, hence the use of similar data types. This simple iteration is shown in the following screenshot:

Line 48 defines the list as a sequence of string values. Line 49 creates a for loop that iterates through the list, printing out a phrase for each item.

Lines 50 and 51 show alternative ways of iterating through and creating lists. This method is called list comprehension and is frequently found in code as a shortcut to writing a normal for loop to make a new list. Line 51 demonstrates that additional information can be provided to the returned values, much like the values returned in line 49.

One thing to note right now, however, is that you can use whatever word for the placeholder that you want; that is, if you wanted to use the name number instead of item in the preceding examples, you can do that. This is key because it was a weird concept for me when I first encountered it in Python. In other languages, loops like this are either hardwired into the language and you have to use its format or you have to expressly create the x value beforehand so you can call it in the loop. Python's way is much easier because you can use whatever name makes the most sense.

In the next post we'll further explore lists and see how new items are added to a list.