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Your first Python program

❝ Don’t bury your burden in saintly silence. You have a problem? Great. Rejoice, dive in, and investigate. ❞
— Ven. Henepola Gunararatana

Diving in
Declaring functions
1. How Python's datatypes compare to other programming languages
Writing readable code
Everything is an object
1. The import search path
2. What's an object?
Indenting code
Running scripts

Diving in

You know how other books go on and on about programming fundamentals and finally work up to building something useful? Let's skip all that. Here is a complete, working Python program. It probably makes absolutely no sense to you. Don't worry about that, because you're going to dissect it line by line. But read through it first and see what, if anything, you can make of it.

[download]

SUFFIXES = {1000: ('KB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB'),
            1024: ('KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB', 'ZiB', 'YiB')}

def approximate_size(size, a_kilobyte_is_1024_bytes=True):
    """Convert a file size to human-readable form.

    Keyword arguments:
    size -- file size in bytes
    a_kilobyte_is_1024_bytes -- if True (default), use multiples of 1024
                                if False, use multiples of 1000

    Returns: string

    """
    if size < 0:
        raise ValueError('number must be non-negative')

    multiple = 1024 if a_kilobyte_is_1024_bytes else 1000
    for suffix in SUFFIXES[multiple]:
        size /= multiple
        if size < multiple:
            return "{0:.1f} {1}".format(size, suffix)

    raise ValueError('number too large')

if __name__ == "__main__":
    print(approximate_size(1000000000000, False))
    print(approximate_size(1000000000000))

Now let's run this program on the command line. On Windows, it will look something like this:

c:\home\diveintopython3> c:\python30\python.exe humansize.py
1.0 TB
931.3 GiB

On Mac OS X or Linux, it would look something like this:

you@localhost:~$ python3 humansize.py
1.0 TB
931.3 GiB

Declaring functions

Python has functions like most other languages, but it does not have separate header files like C++ or interface/implementation sections like Pascal. When you need a function, just declare it, like this:

def approximate_size(size, a_kilobyte_is_1024_bytes=True):

The keyword def starts the function declaration, followed by the function name, followed by the arguments in parentheses. Multiple arguments are separated with commas.

Also note that the function doesn't define a return datatype. Python functions do not specify the datatype of their return value; they don't even specify whether or not they return a value. (In fact, every Python function returns a value; if the function ever executes a return statement, it will return that value, otherwise it will return None, the Python null value.)

☞In some languages, functions (that return a value) start with function, and subroutines (that do not return a value) start with sub. There are no subroutines in Python. Everything is a function, all functions return a value (even if it's None), and all functions start with def.

The approximate_size function takes the two arguments — size and a_kilobyte_is_1024_bytes — but neither argument specifies a datatype. (As you might guess from the =True syntax, the second argument is a boolean. You'll learn what that syntax does in [FIXME xref-was-#apihelper].) In Python, variables are never explicitly typed. Python figures out what type a variable is and keeps track of it internally.

☞In Java and other statically-typed languages, you must specify the datatype of the function return value and each function argument. In Python, you never explicitly specify the datatype of anything. Based on what value you assign, Python keeps track of the datatype internally.

How Python's datatypes compare to other programming languages

An erudite reader sent me this explanation of how Python compares to other programming languages:

statically typed language: A language in which types are fixed at compile time. Most statically typed languages enforce this by requiring you to declare all variables with their datatypes before using them. Java and C are statically typed languages.
dynamically typed language: A language in which types are discovered at execution time; the opposite of statically typed. JavaScript and Python are dynamically typed, because they figure out what type a variable is when you first assign it a value.
strongly typed language: A language in which types are always enforced. Java and Python are strongly typed. If you have an integer, you can't treat it like a string without explicitly converting it.
weakly typed language: A language in which types are “automagically” coerced to other types as needed; the opposite of strongly typed. PHP is weakly typed. In PHP, you can concatenate the string '12' and the integer 3 to get the string '123', then treat that as the integer 123, all without any explicit conversion. [FIXME double-check this]

So Python is both dynamically typed (because it doesn't use explicit datatype declarations) and strongly typed (because once a variable has a datatype, it actually matters).

If you have experience in other programming languages, this table may help you visualize how Python compares to them:

	Statically typed	Dynamically typed
Weakly typed	C, Objective-C	JavaScript, Perl 5, PHP
Strongly typed	Pascal, Java	Python, Ruby

Writing readable code

I won't bore you with a long finger-wagging speech about the importance of documenting your code. Just know that code is written once but read many times, and the most important audience for your code is yourself, six months after writing it (i.e. after you've forgotten everything but need to fix something). Python makes it easy to write readable code, so take advantage of it. You'll thank me in six months.

Documentation strings

You can document a Python function by giving it a documentation string (docstring for short). In this program, the approximate_size function has a docstring:

def approximate_size(size, a_kilobyte_is_1024_bytes=True):
    """Convert a file size to human-readable form.

    Keyword arguments:
    size -- file size in bytes
    a_kilobyte_is_1024_bytes -- if True (default), use multiples of 1024
                                if False, use multiples of 1000

    Returns: string

    """

Triple quotes signify a multi-line string. Everything between the start and end quotes is part of a single string, including carriage returns, leading white space, and other quote characters. You can use them anywhere, but you'll see them most often used when defining a docstring.

☞Triple quotes are also an easy way to define a string with both single and double quotes, like qq/.../ in Perl 5.

Everything between the triple quotes is the function's docstring, which documents what the function does. A docstring, if it exists, must be the first thing defined in a function (that is, on the next line after the function declaration). You don't technically need to give your function a docstring, but you always should. I know you've heard this in every programming class you've ever taken, but Python gives you an added incentive: the docstring is available at runtime as an attribute of the function.

☞Many Python IDEs use the docstring to provide context-sensitive documentation, so that when you type a function name, its docstring appears as a tooltip. This can be incredibly helpful, but it's only as good as the docstrings you write.

Function annotations

FIXME

Style conventions

FIXME

Everything is an object

In case you missed it, I just said that Python functions have attributes, and that those attributes are available at runtime. A function, like everything else in Python, is an object.

Run the interactive Python shell and follow along:

>>> import humansize                               ①
>>> print(humansize.approximate_size(4096, True))  ②
4.0 KiB
>>> print(humansize.approximate_size.__doc__)      ③
Convert a file size to human-readable form.

    Keyword arguments:
    size -- file size in bytes
    a_kilobyte_is_1024_bytes -- if True (default), use multiples of 1024
                                if False, use multiples of 1000

    Returns: string

The first line imports the humansize program as a module -- a chunk of code that you can use interactively, or from a larger Python program. (You'll see examples of multi-module Python programs in [FIXME xref].) Once you import a module, you can reference any of its public functions, classes, or attributes. Modules can do this to access functionality in other modules, and you can do it in the Python interactive shell too. This is an important concept, and you'll see a lot more of it throughout this book.
When you want to use functions defined in imported modules, you need to include the module name. So you can't just say approximate_size; it must be humansize.approximate_size. If you've used classes in Java, this should feel vaguely familiar.
Instead of calling the function as you would expect to, you asked for one of the function's attributes, __doc__.

☞import in Python is like require in Perl. Once you import a Python module, you access its functions with module.function; once you require a Perl module, you access its functions with module::function.

The `import` search path

Before this goes any further, I want to briefly mention the library search path. Python looks in several places when you try to import a module. Specifically, it looks in all the directories defined in sys.path. This is just a list, and you can easily view it or modify it with standard list methods. (You'll learn more about lists later in this chapter.)

>>> import sys                       ①
>>> sys.path                         ②
['', '/usr/lib/python30.zip', '/usr/lib/python3.0', '/usr/lib/python3.0/plat-linux2@EXTRAMACHDEPPATH@', '/usr/lib/python3.0/lib-dynload', '/usr/lib/python3.0/dist-packages', '/usr/local/lib/python3.0/dist-packages']
>>> sys                              ③
<module 'sys' (built-in)>
>>> sys.path.append('/my/new/path')  ④

Importing the sys module makes all of its functions and attributes available.
sys.path is a list of directory names that constitute the current search path. (Yours will look different, depending on your operating system, what version of Python you're running, and where it was originally installed.) Python will look through these directories (in this order) for a .py file whose name matches what you're trying to import.
Actually, I lied; the truth is more complicated than that, because not all modules are stored as .py files. Some, like the sys module, are "built-in modules"; they are actually baked right into Python itself. Built-in modules behave just like regular modules, but their Python source code is not available, because they are not written in Python! (The sys module is written in C.)
You can add a new directory to Python's search path at runtime by appending the directory name to sys.path, and then Python will look in that directory as well, whenever you try to import a module. The effect lasts as long as Python is running. (You'll learn more about append() and other list methods in [FIXME xref-was-#datatypes].)

What's an object?

Everything in Python is an object, and almost everything has attributes and methods. All functions have a built-in attribute __doc__, which returns the docstring defined in the function's source code. The sys module is an object which has (among other things) an attribute called path. And so forth.

Still, this doesn't answer the more fundamental question: what is an object? Different programming languages define “object” in different ways. In some, it means that all objects must have attributes and methods; in others, it means that all objects are subclassable. In Python, the definition is looser; some objects have neither attributes nor methods (more on this in [FIXME xref-was-#datatypes]), and not all objects are subclassable (more on this in [FIXME xref-was-#fileinfo]). But everything is an object in the sense that it can be assigned to a variable or passed as an argument to a function (more in this in [FIXME xref-was-#apihelp]).

This is so important that I'm going to repeat it in case you missed it the first few times: everything in Python is an object. Strings are objects. Lists are objects. Functions are objects. Even modules are objects.

Indenting code

Python functions have no explicit begin or end, and no curly braces to mark where the function code starts and stops. The only delimiter is a colon (:) and the indentation of the code itself.


def approximate_size(size, a_kilobyte_is_1024_bytes=True):  ①
    if size < 0:                                            ②
        raise ValueError('number must be non-negative')     ③
                                                            ④
    multiple = 1024 if a_kilobyte_is_1024_bytes else 1000
    for suffix in SUFFIXES[multiple]:                       ⑤
        size /= multiple
        if size < multiple:
            return "{0:.1f} {1}".format(size, suffix)

    raise ValueError('number too large')

Code blocks are defined by their indentation. By "code block," I mean functions, if statements, for loops, while loops, and so forth. Indenting starts a block and unindenting ends it. There are no explicit braces, brackets, or keywords. This means that whitespace is significant, and must be consistent. In this example, the function code is indented four spaces. It doesn't need to be four spaces, it just needs to be consistent. The first line that is not indented marks the end of the function.
In Python, an if statement is followed by a code block. If the if expression evaluates to true, the indented block is executed, otherwise it falls to the else block (if any). (Note the lack of parentheses around the expression.)
This line is inside the if code block. This raise statement will raise an exception (of type ValueError), but only if size < 0.
This is not the end of the function. Completely blank lines don't count. The function continues on the next line.
The for loop also marks the start of a code block. Code blocks can contain multiple lines, as long as they are all indented the same amount. This for loop has three lines of code in it. There is no other special syntax for multi-line code blocks. Just indent and get on with your life.

After some initial protests and several snide analogies to Fortran, you will make peace with this and start seeing its benefits. One major benefit is that all Python programs look similar, since indentation is a language requirement and not a matter of style. This makes it easier to read and understand other people's Python code.

☞Python uses carriage returns to separate statements and a colon and indentation to separate code blocks. C++ and Java use semicolons to separate statements and curly braces to separate code blocks.

Running scripts

Python modules are objects and have several useful attributes. You can use this to easily test your modules as you write them, by including a special block of code that executes when you run the Python file on the command line. Take the last few lines of humansize.py:


if __name__ == "__main__":
    print(approximate_size(1000000000000, False))
    print(approximate_size(1000000000000))

☞Like C, Python uses == for comparison and = for assignment. Unlike C, Python does not support in-line assignment, so there's no chance of accidentally assigning the value you thought you were comparing.

So what makes this if statement special? Well, modules are objects, and all modules have a built-in attribute __name__. A module's __name__ depends on how you're using the module. If you import the module, then __name__ is the module's filename, without a directory path or file extension.

>>> import humansize
>>> humansize.__name__
'humansize'

But you can also run the module directly as a standalone program, in which case __name__ will be a special default value, __main__. Python will evaluate this if statement, find a true expression, and execute the if code block. In this case, to print two values.

c:\home\diveintopython3> c:\python30\python.exe humansize.py
1.0 TB
931.3 GiB