Course Content

Python Functions Tutorial

1. What is a Function in Python?

What Is Function?Challenge: Building Functions Arguments Return Value Function Body Built-in Functions

2. Positional and Optional Arguments

Positional Arguments Challenge: Positional Arguments Optional Arguments Challenge: Optional Arguments

3. Arbitrary Arguments

Arbitrary Arguments Arbitrary Keyword Arguments Combination of Positional and Optional Arguments

4. Function Return Value Specification

Single Return Value Multiple Return Values None Return Value Generator Functions Challenge: Traffic lights

5. Recursion and Lambda Functions

Recursion Challenge: Search Engine Lambda Functions Immediately Invoked Lambda Expression Writing Effective and Maintainable Functions

Generator Functions

A generator function is a special type of function that uses the yield keyword instead of return to generate a sequence of values. When a generator function is called, it returns an iterator object, which can be iterated over to retrieve values one at a time.
The main advantage of generator functions is their memory efficiency. Generator functions generate values on-the-fly as they are needed, rather than generating the entire sequence upfront. This makes them memory efficient, especially when dealing with large datasets or infinite sequences.

Let's look at the example of the simple generator. This function is a generator that yields logins one by one from the given list:


              12345678910111213141516
            
def unique_logins_from_list(login_list):
    # Iterate over each login in the list
    for login in login_list: 
        yield login  # `yield` the current login

# A predefined list of available logins
login_list = ["user1", "user2", "user3", "user4", "user5"]

# Creating a generator instance from the login list
login_generator = unique_logins_from_list(login_list)

# Generate and print 5 logins, one at a time
for _ in range(5):
    # Each call to `next()` gives the next login
    print(next(login_generator))

The principle of a generator is that it allows values to be returned one at a time using the yield keyword, without storing them all in memory at once. In our example, the unique_logins_from_list generator iterates through the list of logins, returning each one on yield and pausing at that point. When next() is called, the generator resumes from where it left off, efficiently yielding values without needing to store the entire list in memory simultaneously. This makes generators particularly useful for handling large data sets or streams of data.

Task

Swipe to show code editor

Create a generator that generates unique identifiers. The identifiers can be, for example, strings in the format "ID_1", "ID_2", and so on. Each time you call next(), the generator should return a new unique identifier.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 4

Generator Functions

Let's look at the example of the simple generator. This function is a generator that yields logins one by one from the given list:


              12345678910111213141516
            
def unique_logins_from_list(login_list):
    # Iterate over each login in the list
    for login in login_list: 
        yield login  # `yield` the current login

# A predefined list of available logins
login_list = ["user1", "user2", "user3", "user4", "user5"]

# Creating a generator instance from the login list
login_generator = unique_logins_from_list(login_list)

# Generate and print 5 logins, one at a time
for _ in range(5):
    # Each call to `next()` gives the next login
    print(next(login_generator))

Task

Swipe to show code editor

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 4

Switch to desktop for real-world practiceContinue from where you are using one of the options below