Python 2

Functions

A function is a reusable section of code written to perform a specific task in a program. We gave you a taste of functions in Unit 3; here, you’ll learn how to create your own.

What Good are Functions?

You might have considered the situation where you would like to reuse a piece of code, just with a few different values. Instead of rewriting the whole code, it’s much cleaner to define a function, which can then be used repeatedly.

def tax(bill):
    “””Adds 8% tax to a restaurant bill.”””
    bill *= 1.08
    print “With tax: %f” % bill
    return bill

def tip(bill):
    “””Adds 15% tip to a restaurant bill.”””
    bill *= 1.15
    print “With tip: %f” % bill
    return bill
    
meal_cost = 100
meal_with_tax = tax(meal_cost)
meal_with_tip = tip(meal_with_tax)

Function Junction

Functions are defined with three components:

The header, which includes the def keyword, the name of the function, and any parameters the function requires. Here’s an example:

def hello_world(): // There are no parameters

An optional comment that explains what the function does.

“””Prints ‘Hello World!’ to the console.”””

The body, which describes the procedures the function carries out. The body is indented, just like for conditional statements.

print “Hello World!”

Here’s the full function pieced together:

def hello_world():
    “””Prints ‘Hello World!’ to the console.”””
    print “Hello World!”

Call and Response

After defining a function, it must be called to be implemented. In the previous exercise, spam() in the last line told the program to look for the function called spam and execute the code inside it.

Parameters and Arguments

Let's reexamine the first line that defined square in the previous exercise:

def square(n):

n is a parameter of square. A parameter acts as a variable name for a passed in argument. With the previous example, we called square with the argument 10. In this instance the function was called, n holds the value 10.

A function can require as many parameters as you'd like, but when you call the function, you should generally pass in a matching number of arguments.

Functions Calling Functions

We've seen functions that can print text or do simple arithmetic, but functions can be much more powerful than that. For example, a function can call another function:

def fun_one(n):
    return n * 5

def fun_two(m):
    return fun_one(m) + 7

Practice Makes Perfect

Let's create a few more functions just for good measure.

def shout(phrase):
    if phrase == phrase.upper():
        return "YOU'RE SHOUTING!"
    else:
        return "Can you speak up?"

shout("I'M INTERESTED IN SHOUTING")

The example above is just there to help you remember how functions are structured.

Don't forget the colon at the end of your function definition!

I Know Kung Fu

Remember import this from the first exercise in this course? That was an example of importing a module. A module is a file that contains definitions—including variables and functions—that you can use once it is imported.

Generic Imports

Did you see that? Python said: "NameError: name 'sqrt' is not defined." Python doesn't know what square roots are—yet.

There is a Python module named math that includes a number of useful variables and functions, and sqrt() is one of those functions. In order to access math, all you need is the import keyword. When you simply import a module this way, it's called a generic import.

Function Imports

Nice work! Now Python knows how to take the square root of a number.

However, we only really needed the sqrt function, and it can be frustrating to have to keep typing math.sqrt().

It's possible to import only certain variables or functions from a given module. Pulling in just a single function from a module is called a function import, and it's done with the from keyword:

from module import function

Now you can just type sqrt() to get the square root of a number—no more math.sqrt()!

Universal Imports

Great! We've found a way to handpick the variables and functions we want from modules.

What if we still want all of the variables and functions in a module but don't want to have to constantly type math.?

Universal import can handle this for you. The syntax for this is:

from module import *

Here Be Dragons

Universal imports may look great on the surface, but they're not a good idea for one very important reason: they fill your program with a ton of variable and function names without the safety of those names still being associated with the module(s) they came from.

If you have a function of your very own named sqrt and you import math, your function is safe: there is your sqrt and there is math.sqrt. If you do from math import *, however, you have a problem: namely, two different functions with the exact same name.

Even if your own definitions don't directly conflict with names from imported modules, if you import * from several modules at once, you won't be able to figure out which variable or function came from where.

For these reasons, it's best to stick with either import module and type module.name or just import specific variables and functions from various modules as needed.

import math            # Imports the math module
everything = dir(math) # Sets everything to a list of things from math
print everything       # Prints 'em all!

['__doc__', '__name__', '__package__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'hypot', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']

On Beyond Strings

Now that you understand what functions are and how to import modules, let's look at some of the functions that are built in to Python (no modules required!).

You already know about some of the built-in functions we've used with strings, such as .upper(), .lower(), str(), and len(). These are great for doing work with strings, but what about something a little more analytic?

max()

The max() function takes any number of arguments and returns the largest one. ("Largest" can have odd definitions here, so it's best to use max() on integers and floats, where the results are straightforward, and not on other objects, like strings.)

For example, max(1,2,3) will return 3 (the largest number in the set of arguments).

min()

min() then returns the smallest of a given series of arguments.

abs()

The abs() function returns the absolute value of the number it takes as an argument—that is, that number's distance from 0 on an imagined number line. For instance, 3 and -3 both have the same absolute value: 3. The abs() function always returns a positive value, and unlike max() and min(), it only takes a single number.

type()

Finally, the type() function returns the type of the data it receives as an argument. If you ask Python to do the following:

print type(42)
print type(4.2)
print type('spam')

Python will output:

<type 'int'>
<type 'float'>
<type 'str'>

Review: Functions

Okay! Let's review functions.

def speak(message):
    return message

if happy():
    speak("I'm happy!")
elif sad():
    speak("I'm sad.")
else:
    speak("I don't know what I'm feeling.")

Again, the example code above is just there for your reference!

Review: Modules

Good work! Now let's see what you remember about importing modules (and, specifically, what's available in the math module).

Review: Built-In Functions

Perfect! Last but not least, let's review the built-in functions you've learned about in this lesson.

def is_numeric(num):
    return type(num) == int or type(num) == float:

max(2, 3, 4) # 4
min(2, 3, 4) # 2

abs(2) # 2
abs(-2) # 2