You’re asking one question: “What makes Python different?” The short answer is: lots of things. The longer answers starts by stating that there’s lots that’s familiar, too. Python is a lot like any other general-purpose programming language, with statements, expressions, operators, functions, modules, methods, and classes. All the usual stuff, really. And then there’s the other stuff Python provides that makes the programmer’s life—your life—that little bit easier. You’ll start your tour of Python by learning about lists. But, before getting to that, there’s another important question that needs answering...
Lots. Rather than tell you, this book’s goal is to show you the greatness that is Python.
Before diving head first into Python, let’s get a bit of housekeeping out of the way.
To work with and execute the Python code in this book, you need a copy of the Python 3 interpreter on your computer. Like a lot of things to do with Python, it’s not difficult to install the interpreter. Assuming, of course, it’s not already there...
Before you write and run Python code, you need to make sure the Python interpreter is on your computer. In this book, you’ll start out with Release 3 of Python, the very latest (and best) version of the language.
A release of Python might already be on your computer. Mac OS X comes with Python 2 preinstalled, as do most versions of Linux (which can also ship with Release 3). Windows, in contrast, doesn’t include any release of Python. Let’s check your computer for Python 3. Open up a command-line prompt and, if you are using Mac OS X or Linux, type:
On Windows, use this command:
Do this!
If Python 3 is missing from your computer, download a copy for your favorite OS from the www.python.org website.
When you install Python 3, you also get IDLE, Python’s simple—yet surprisingly useful—integrated development environment. IDLE includes a color syntax-highlighting editor, a debugger, the Python Shell, and a complete copy of Python 3’s online documentation set.
Let’s take a quick look at IDLE.
IDLE lets you write code in its full-featured code editor as well as experiment with code at the Python Shell. You’ll use the code editor later in this book but, when learning Python, IDLE’s shell really rocks, because it lets you try out new Python code as you go.
When you first start IDLE, you are presented with the “triple chevron” prompt (>>>) at which you enter code. The shell takes your code statement and immediately executes it for you, displaying any results produced on screen.
IDLE knows all about Python syntax and offers “completion hints” that pop up when you use a built-in function like print(). Python programmers generally refer to built-in functions as BIFs. The print() BIF displays messages to standard output (usually the screen).
IDLE uses colored syntax to highlight your code. By default, built-in functions are purple, strings are green, and language keywords (like if) are orange. Any results produced are in blue. If you hate these color choices, don’t worry; you can easily change them by adjusting IDLE’s preferences.
IDLE also knows all about Python’s indentation syntax, which requires code blocks be indented. When you start with Python, this can be hard to get used to, but IDLE keeps you straight by automatically indenting as needed.
IDLE knows Python’s syntax and helps you conform to the Python indentation rules.
IDLE has lots of features, but you need to know about only a few of them to get going.
Start to type in some code, and then press the TAB key. IDLE will offer suggestions to help you complete your statement.
Press Alt-P to recall the previous code statement entered into IDLE or press Alt-N to move to the next code statement (assuming there is one). Both key combinations can be used to cycle rapidly through all of the code you’ve entered into IDLE, re-executing any code statements as needed.
Alt-P for Previous
Alt-N for Next
Once you recall your code statement, you can edit it and move around the statement using the arrow keys. It’s possible to edit any statement that you’ve previously entered, even code statements that span multiple lines.
IDLE’s preferences dialog lets you adjust its default behavior to your tastes. There are four tabs of settings to tweak. You can control font and tab behavior, the colors used to syntax highlight, the behavior of certain key-combinations, and IDLE’s start-up settings. So, if shocking pink strings is really your thing, IDLE gives you the power to change how your code looks on screen.
Any program of any worth that you create has to work with data. Sometimes, the data is simple and straightforward—easy to work with. Other times, the data you have to work with is complex in its structure and meaning, forcing you to work hard to make sense of it all, let alone write code to process it.
To tame complexity, you can often arrange your data as a list: there’s the list of customers, your friend’s list, the shopping list, and your to-do list (to name a few). Arranging data in lists is so common that Python makes it easy for you to create and process lists in code.
Let’s look at some complex data before learning how to create and process list data with Python.
On first glance, this collection of data does indeed look quite complex. However, the data appears to conform to some sort of structure: there’s a line for a list of basic movie facts, then another line for the lead actor(s), followed by a third line listing the movie’s supporting actors.
This looks like a structure you can work with...
Let’s start with the following simple list of movie titles and work up from there:
Here’s the same list written in a way that Python understands:
To turn the human-friendly list into a Python-friendly one, follow this four-step process:
Convert each of the names into strings by surrounding the data with quotes.
Separate each of the list items from the next with a comma.
Surround the list of items with opening and closing square brackets.
Assign the list to an identifier (
moviesin the preceding code) using the assignment operator (=).
It’s perfectly OK to put your list creation code all on one line, assuming, of course, that you have room:
No, because Python’s variable identifiers don’t have a type.
Many other programming languages insist that every identifier used in code has type information declared for it. Not so with Python: identifiers are simply names that refer to a data object of some type.
Think of Python’s list as a high-level collection. The type of the data items is not important to the list. It’s OK to state that your movies list is a “collection of strings,” but Python doesn’t need to be told this. All Python needs to know is that you need a list, you’ve given it a name, and the list has some data items in it.
When you create a list in Python, the interpreter creates an array-like data structure in memory to hold your data, with your data items stacked from the bottom up. Like array technology in other programming languages, the first slot in the stack is numbered 0, the second is numbered 1, the third is numbered 2, and so on:
As with arrays, you can access the data item in a list slot using the standard square bracket offset notation:
Let’s use IDLE to learn a bit about how lists work.
An IDLE Session
Lists in Python might look like arrays, but they are much more than that: they are full-blown Python collection objects. This means that lists come with ready-to-use functionality in the form of list methods.
Let’s get to know some of Python’s list methods. Open up IDLE and follow along with the code entered at the >>> prompt. You should see exactly the same output as shown here.
Start by defining a list of names, which you then display on screen using the print() BIF. Then, use the len() BIF to work out how many data items are in the list, before accessing and displaying the value of the second data item:
With your list created, you can use list methods to add a single data item to the end of your list (using the append() method), remove data from the end of your list (with the pop() method), and add a collection of data items to the end of your list (thanks to the extend() method):
Finally, find and remove a specific data item from your list (with the remove() method) and then add a data item before a specific slot location (using the insert() method):
With your list of movie names created, now you need to add more of the movie buff’s complex data to it. You have a choice here:
Either strategy works. Which works best for you depends on what you are trying to do. Let’s recall what the movie buff’s data looks like:
The next piece of data you need to add to your list is a number (which represents the year the movie was released), and it must be inserted after each movie name. Let’s do that and see what happens.
No, not madness, just the way Python works.
Python lists can contain data of mixed type. It’s perfectly OK to mix strings with numbers within the same Python list. In fact, you can mix more than just strings and numbers; you can store data of any type in a single list, if you like.
Recall that a Python list is a high-level collection, designed from the get-go to store a collection of “related things.” What type those things have is of little interest to the list, because the list exists merely to provide the mechanism to store data in list form.
So, if you really need to store data of mixed type in a list, Python won’t stop you.
Exercise
Let’s take a bit of time to try to work out which strategy to use when adding data to your list in this case.
Given the following list-creation code:
Work out the Python code required to insert the numeric year data into the preceding list, changing the list so that it ends up looking like this:
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Now write the Python code required to re-create the list with the data you need all in one go:
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In this case, which of these two methods do you think is best? (Circle your choice).
1 or 2
Exercise Solution
Let’s take a bit of time to try and work out which strategy to use when adding data to your list in this case.
Given the following list-creation code:
You were to work out the Python code required to insert the numeric year data into the preceding list:
You were also to write the Python code required to recreate the list with the data you need all in one go:
In this case, which of these two methods do you think is best? (You were to circle your choice.)
You often need to iterate over your list and perform some action on each item as you go along. Of course, it is always possible to do something like this, which works but does not scale:
This code works as expected, making the data from the list appear on screen. However, if the code is later amended to add another favorite movie to the list, the list-processing code stops working as expected, because the list-processing code does not mention the third item.
Big deal: all you need to do is add another print() statement, right?
Yes, adding one extra print() statement works for one extra movie, but what if you need to add another hundred favorite movies? The scale of the problem defeats you, because adding all those extra print() statements becomes such a chore that you would rather find an excuse not to have to do.
Python’s for loop exists to process lists and other iterations in Python. Lists are the most common iterated data structure in Python, and when you need to iterate a list, it’s best to use for:
The list-processing code is referred to by Python programmers as the suite.
The target identifier is like any other name in your code. As your list is iterated over, the target identifier is assigned each of the data values in your list, in turn. This means that each time the loop code executes, the target identifier refers to a different data value. The loop keeps iterating until it exhausts all of your list’s data, no matter how big or small your list is.
An alternative to using for is to code the iteration with a while loop. Consider these two snippets of Python code, which perform the same action:
These while and for statements do the same thing.
As you’ve seen, lists can hold data of mixed type. But it gets even better than that: lists can hold collections of anything, including other lists. Simply embed the inner list within the enclosing list as needed.
Looking closely at the movie buff’s data, it is possible to determine a structure which looks much like a list of lists:
In Python, you can turn this real list of data into code with little or no effort. All you need to remember is that every list is a collection of items separated from each other with commas and surrounded with square brackets. And, of course, any list item can itself be another list:
So, a list within a list is possible, as is a list within a list within a list (as this example code demonstrates). In fact, it’s possible to nest lists within lists to most any level with Python. And you can manipulate every list with its own list methods and access it with the square bracket notation:
An IDLE Session
Creating a list that contains another list is straightforward. But what happens when you try to process a list that contains another list (or lists) using the for loop from earlier in this chapter?
Let’s use IDLE to work out what happens. Begin by creating the list of the movie data for “The Holy Grail” in memory, display it on screen, and then process the list with your for loop:
Yes, that’s correct: the loop code isn’t complete.
At the moment, the code within the loop simply prints each list item, and when it finds a list at a slot, it simply displays the entire list on screen. After all, the inner list is just another list item as far as the outer enclosing list is concerned. What’s we need here is some mechanism to spot that an item in a list is in fact another list and take the appropriate action.
That sounds a little tricky. But can Python help?
Each time you process an item in your list, you need to check to see if the item is another list. If the item is a list, you need to process the nested list before processing the next item in your outer list. Deciding what to do when in Python follows the familiar if... else... pattern:
No surprises here, as the if statement in Python works pretty much as expected. But what condition do you need to check? You need a way to determine if the item currently being processed is a list. Luckily, Python ships with a BIF that can help here: isinstance().
What’s cool about the isinstance() BIF is that it lets you check if a specific identifier holds data of a specific type:
Exercise
Here’s a copy of the current list-processing code. Your task is to rewrite this code using an if statement and the isinstance() BIF to process a list that displays another list.
The movie buff’s data is complex. Let’s take another look at a subset of the data and your Python code that processes it.
The data and your code are not in sync.
The movie buff’s data is a list that contains a nested list that itself contains a nested list. The trouble is that your code knows only how to process a list nested inside an enclosing list.
The solution, of course, is to add more code to handle the additionally nested list. By looking at the existing code, it’s easy to spot the code you need to repeat:
That’s more list data and more Python code.
The data has to be embedded as another nested list within the already deeply nested list of supporting actors. That’s possible to do, even though it makes your head hurt just to think about a list of lists of lists of lists! Amending your code is just a matter of adding another for loop and an if statement.
That doesn’t sound like too much trouble, does it?
Adding another nested loop is a huge pain.
Your data is getting more complex (that mind-bending list of lists of lists of lists) and, as a consequence, your code is getting overly complex, too (that brain-exploding for loop inside a for loop inside a for loop). And overly complex code is rarely a good thing...
Take a look at the code that you’ve created so far, which (in an effort to save you from having your brain explode) has already been amended to process yet another nested list. Notice anything?
Your code now contains a lot of repeated code. It’s also a mess to look at, even though it works with the movie buff’s amended data. All that nesting of for loops is hard to read, and it’s even harder to ensure that the else suites are associated with the correct if statement.
There has to be a better way...but what to do?
When code repeats in this way, most programmers look for a way to take the general pattern of the code and turn it into a reusable function. And Python programmers think this way, too. Creating a reusable function lets you invoke the function as needed, as opposed to cutting and pasting existing code.
So, let’s turn the repeating code into a function.
A function in Python is a named suite of code, which can also take an optional list of arguments if required.
You define a Python function using the def statement, providing a name for your function and specifying either an empty or populated argument list within parentheses. The standard form looks something like this:
Your function needs to take a list and process each item in the list. If it finds a nested list within the first list, the function needs to repeat. It can do this by invoking itself on the nested list. In other words, the function needs to recur—that is, invoke itself from within the funtion code suite.
The use of a recursive function has allowed you to reduce 14 lines of messy, hard-to-understand, brain-hurting code into a six-line function. Unlike the earlier code that needs to be amended to support additional nested lists (should the movie buff require them), the recursive function does not need to change to process any depth of nested lists properly.
Python 3 defaults its recursion limit to 1,000, which is a lot of lists of lists of lists of lists...and this limit can be changed should you ever need even more depth than that.
By taking advantage of functions and recursion, you’ve solved the code complexity problems that had crept into your earlier list-processing code.
By creating print_lol(), you’ve produced a reusable chunk of code that can be put to use in many places in your (and others) programs.
You’re well on your way to putting Python to work!
You’ve got Chapter 1 under your belt and you’ve added some key Python goodies to your toolbox.
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