6 Functions, Namespaces, and Modules

6_Functions

Notebook 6 - Functions, Namespaces, and Modules¶

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Things you'll learn in this lesson:

what functions are and why they useful
how to define and use functions
the global and local namespaces
what modules are and how to use them
Integrated Developer Environments (IDEs) for Python

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Functions¶

Functions are flexible software building blocks¶

So far, we’ve been writing small programs.
Things get much more complicated when we write large programs, especially with multiple authors.
Ideally, we'd like to build software like snapping lego pieces together.
What would that buy us?
- abstraction
- reuse
- modularity
- maintainability

Abstraction - You don't need to do everything¶

When building a house, you don't do everything yourself.
- You hire an architect, a carpenter, an electrician, a roofer, a plumber, a mason.
- You might hire a contractor to hire and manage all those people.
In our programs we delegate tasks to certain functions, like print(), so that we don't have to worry about all the details.
- It's a bit like hiring an electrician so that we don't have to worry about the details of electrical wiring in our house.

Reuse - Don't Reinvent the Wheel¶

it's ok to reuse other people's work
- it's not stealing
- it makes you more efficient and more productive
Very few people build a house from scratch
- so don't try to build programs from scratch

Reuse Example¶

You can count the number of words in a string the hard way...

In [ ]:

mystr = 'This      is a test.'
cnt = 0
for i in mystr:
  if i.isspace():
    cnt += 1
cnt += 1
print('number of words =', cnt)

Or the easy way, by calling a string method...

In [ ]:

mystr = 'This       is a test.'
cnt = len(mystr.split())
print('number of words =', cnt)

Which would you rather use? Which is more reliable?

The first approach is great for learning.
The second approach is great for getting real work done.

Modularity - Divide and Conquer¶

So far, our programs have been monoliths - one continous sequence of Python statements.
Real programs are often much bigger than the ones we've written.
- Google's software repository has billions of lines of source code (Why Google Stores Billions of Lines of Code in a Single Repository)
- No one person can write a program that big.
- Large programs are built by teams.
- In order to build large, complex programs, we need the ability to divide program logic into manageable pieces.
We call this modularity - dividing software into pieces or modules.

Maintainability - Keeping your code DRY (Don't Repeat Yourself)¶

Imagine that you need to do roughly the same thing in ten different places so you copy the code to those ten locations.
- What happens when you find a bug or want to improve that piece of code?
- You need to make the change ten times.
- Will you remember to do that?
- If you do remember, will you catch all ten locations?
- Copying code is a bad thing - it leads to bugs.

Functions solve all of these Problems¶

Functions give us the ability to:
- Hide low level details (abstraction)
- Share and reuse pieces of functionality (reuse)
- Split programs into manageable pieces (modularity)
- Write one copy of an algorithm and use it anywhere (maintainability)
We've already used several functions
- print(), input(), int(), len(), range(), etc.
Now let's see how to define our own functions.

Defining Functions¶

example:

def function_name(arg1, arg2):
    '''This is a docstring.'''  # optional but a good idea
    statement1
    statement2
    ...

Not surprisingly, we define the scope of the function body using indentation (just like how we define blocks for if statements, for loops, etc.).
This is a bit like an assignment statement in that it assigns a block of code (the function body) to the function name.
- Function names have the same rules as variable names.
- This only defines a function - it doesn't execute it.

In [ ]:

def speak():
    '''
        This function generates hello in cat language.
        Don't try this near a dog.
    '''
    print('meow')

Notice how nothing seemed to happen in that last cell. But something did - we defined a function. However, that function doesn't get executed unless and until we call it. Let's do that now.

In [ ]:

speak()

Docstrings¶

a string defined immediately after the def line
usually triple quoted since it may be multi-line
not required but a good way to document your functions
IDEs use the docstring to make your life easier
automates output of help(function)

Example Function¶

In [ ]:

# The factorial of N is defined as 1*2*...*N.
# Here's an example function definition which prints the factorial of 10.
def fact10():
    '''
    Print the factorial of 10.
    Factorial of 10 is defined as 1*2*...*10.
    '''
    limit = 10
    result = 1
    for i in range(1, limit+1):
        result *= i # result = result * i
    print(result)

In [ ]:

# Get help about this function...
help(fact10)

In [ ]:

# And here's how we would call this function...
fact10()

Passing Values to a Function¶

fact10() is limited.
It only prints the factorial for one value (10).
It would be nice if we could define a more flexible version, that could print the factorial of any number, like this...

In [ ]:

def fact(num):
    '''Print the factorial of any number.'''
    result = 1
    for i in range(1, num+1):
        result *= i
    print(result)
fact(20)

Parameters and Arguments¶

The variables we define in a function to take on the values passed by the caller are called parameters.
In this code, a, b and c are parameters:

def sum(a, b, c):
    return a + b + c

The values supplied by the caller when calling a function are called arguments.
In this code, 1, 2, and 3 are arguments:

sum(1, 2, 3)

Passing Arguments¶

Functions can define any number of parameters, including zero.
Multiple parameters are separated by commas, like this...

def product(a, b, c):
    return a * b * c

If you pass the wrong number of arguments, you'll hear about it:

product(1, 2)
...
TypeError: product() takes exactly 3 positional argument (2 given)
...

Return Values¶

Instead of printing the result, we can also have the function return a result to the caller so that the caller can print it or use it in a calculation.

def fact(num):
    ''' compute factorial of any number '''
    fact = 1
    for i in range(1, num+1):
        fact *= i
    return fact

Here's how we would call this function:

f = fact(5)
print('5! = ', f)

Functions return a value to the caller via the return statement.
The return statement causes two things to happen...
- the function ends and program execution resumes just after the place where the function was called
- the returned value is passed back to the caller
You can have as many return statements as you like (including zero).
If the caller wants to do something with a returned value, it needs to save it or use it in an expression...

resp = input('Yes or No (y/n)? ')

If the caller ignores the return value, it's lost...

input('Yes or No (y/n)? ') # this loses the answer

Returning Multiple Values¶

Functions can return multiple values.
Simply specify more than one comma separated value in the return statement.
This function returns two values...

def divide(dividend, divisor):
    quotient = int(dividend / divisor)
    remainder = dividend % divisor
    return quotient, remainder

You could receive the results like this:

(q, r) = divide(100, 9)
print(f'quotient={q}, remainder={r}')

In [ ]:

def divide(dividend, divisor):
  quotient = int(dividend / divisor)
  remainder = dividend % divisor
  return quotient, remainder
(q, r) = divide(100, 9)
print(f“quotient={q}, remainder={r}")

Challenge¶

Can you think of a function we've used which...

takes no arguments?
takes only one argument?
takes a variable number of arguments?
returns no return values?
returns one return value?
returns a sequence of values?

In [ ]:

#@title Double click here to reveal answers...
# takes no arguments?
print()
# takes only one argument?
input(‘Enter your name: ‘)
# takes a variable number of arguments?
print(‘hello’)
print(‘hello’, ‘world’)
# returns no return values?
print()
# returns one return value?
len(‘test’)
# returns a sequence of values?
range(5)

Namespaces¶

A namespace is an abstract collection of variables that exist in a particular context.
- Kind of like a company's ID numbers.
- My id is only valid within the scope of my company.
We've seen an example of a namespace in this code...

import random
x = random.randrange(10)

In this case, the random module has it's own namespace, which is completely separate from the rest of your program and is accessed via "dot" notation.
If you neglect the "random." prefix, you're referring to a different namespace: your program's global namespace.

Global Namespace¶

Every Python source file has its own global name space.
The global namespace includes all names (variables and functions) defined outside of any functions, in the Python source file.
In the following code, count, mystr and req are all global variables, i.e. they all reside in the global namespace.

count = 0
while True:
    mystr = input('? ')
    if (mystr == '1'):
        req = 'add'
    else:
        break
    count += 1

Local Namespace¶

When you define a function, Python creates a local namespace for that function, encompassing all parameters and variables created inside the function body.
Variables in a local namespace are separate from, and independent of, variables in the global name space.
Inside a function, local variables supersede global variables with the same name.
Local variables are transient - they exist only during the lifetime of function execution.

Example illustrating local variable superseding a global variable¶

In [ ]:

var = 1
def myfunc():
    var = 2  # local scope
    print('local var:', var)
myfunc()
print('global var:', var)

Example illustrating the transient nature of the local namespace¶

In [ ]:

def func():
    var2 = 2  # local scope
    print('local var:', var2)
func()
print('global var:', var2)

Modules¶

A module is a file of reusable Python code.
The module's name is the file without the .py extension.
We've already seen how modules are imported...

import random

In order for Python to see one your modules, the module file (with .py extension) needs to be in the same folder as the program you are running or in a special, predefined system location.
- sys.path in Python
- PYTHONPATH in shell environment

Module Namespace¶

Every module has its own namespace, which is independent of the main source file's global namespace
You can access objects in a module's namespace using this general syntax:

module-name.variable
module-name.function(<args>)

For example, as we've already seen...

rand_val = random.randrange(0, 10)

In [ ]:

import random
print(random.random())
print(random.randint(1, 10))

The `from` Statement¶

You can also import code using this syntax...

from module-name import *

This says loads all the names (*) from the designated module into the global namespace.
With this kind of import, the module names get loaded into the global namespace, which means you don't need to qualify your accesses with the module-name. prefix.
For example, you could do this...

from random import *
rand_val = randrange(0, 10)
# I didn't need to use random.randrange(0, 10)

You can also import selected names from a module

from random import randrange, randint

This says load only those names explicitly listed (randrange and randint, in this case) from the designated module into the global namespace.
As in the previous example, after this import the names are loaded into the common global namespace so there is no need to qualify them...

from random import randrange, randint
rand_val = randrange(0, 10)

In [ ]:

from random import random, randint
print(random())
print(randint(1, 10))

When to use `import` vs. `from`¶

Generally, it's better to use import because...
- less risk of name clashes and other surprises
- makes your code more explicit and clear
Occasionally, you may find that you use a module’s functions so frequently that it pays to import it directly into the global namespace with from.
That’s fine but do so carefully and watch out for name conflicts.

Integrated Developer Environments (IDEs)¶

Integrated Development Environments
Programs to help you write programs
Some recommended ones to check out...
- Microsoft Visual Studio Code - the dominant IDE nowadays
- IDX- cloud hosted VS Code from Google
- repl.it - cloud based multi-lingual IDE, great for learning

Homework¶

Question 1¶

Write a function called total that returns the sum of a range of integers starting with the first passed value and ending at one less than the second passed value.

For example, total(4, 7) returns 15 (4+5+6).

In [ ]:

def total(a, b):
    # Add your code here

In [ ]:

# Run this cell to test your code...
test_data = (
  (4, 7),
  (10, 12),
  (1, 1),
  (0, 0),
  (0, 1),
  (1, 0),
  (-5, 2),
  (-2, 2),
)
for i, (a, b) in enumerate(test_data):
computed = total(a, b)
actual = sum([i for i in range(a, b)])
assert computed == actual, f‘Test {i} failed: {computed} != {actual}’
print(‘All tests passed!’)

Question 2¶

Write a function called reverse that returns a reversed copy of a passed string. For example, reverse('Obama') returns amabO.

In [ ]:

# Add your code here

In [ ]:

# Run this cell to test your code...
import random
import string
def reverse_test(a, b):
rev = reverse(a)
assert rev == b, f’{rev} != {b}’
# Some basic tests.
reverse_test(‘Obama’, ‘amabO’)
reverse_test(‘Marc’, ‘craM’)
reverse_test(’’, ’’)
reverse_test(‘aaa’, ‘aaa’)
reverse_test(‘A a’, ‘a A’)
# Test a very long string.
random_str = ’’
reverse_random_str = ’’
for i in range(1000):
ch = random.choice(string.ascii_lowercase)
random_str += ch
reverse_random_str = ch + reverse_random_str
reverse_test(random_str, reverse_random_str)
print(‘tests passed’)

Question 3¶

Import only the random() function from the random module. Write a function called rando that returns, on average, True half the time and False half the time.

In [ ]:

from random import random
def rando():
    # Add your code here
    return random() >= 0.5

In [ ]:

# Run this cell to test your code...
import seaborn as sns
from random import randint
num_trials = 10000
width = 97
num_steps = 50
li = []
for i in range(num_trials):
position = width // 2
for j in range(num_steps):
if rando():
position += .5
else:
position -= .5
li.append(position)
#print(li)
sns.displot(li)

Question 4¶

Try some exercises on codingbat.com/python.

Last modified October 5, 2024: update notebooks (7635eec)

6 Functions, Namespaces, and Modules

Notebook 6 - Functions, Namespaces, and Modules¶

Functions¶

Functions are flexible software building blocks¶

Abstraction - You don't need to do everything¶

Reuse - Don't Reinvent the Wheel¶

Reuse Example¶

Modularity - Divide and Conquer¶

Maintainability - Keeping your code DRY (Don't Repeat Yourself)¶

Functions solve all of these Problems¶

Defining Functions¶

Docstrings¶

Example Function¶

Passing Values to a Function¶

Parameters and Arguments¶

Passing Arguments¶

Return Values¶

Returning Multiple Values¶

Challenge¶

Namespaces¶

Global Namespace¶

Local Namespace¶

Example illustrating local variable superseding a global variable¶

Example illustrating the transient nature of the local namespace¶

Modules¶

Module Namespace¶

The from Statement¶

When to use import vs. from¶

Integrated Developer Environments (IDEs)¶

Homework¶

Question 1¶

Question 2¶

Question 3¶

Question 4¶

The `from` Statement¶

When to use `import` vs. `from`¶