Python: Higher-Order Functions and Lambda

Last updated: 2026-09-22

In Python, functions are also "values" — they can be assigned to variables, passed to other functions, just like numbers and strings. This enables very concise and elegant code. Higher-order functions are functions that take other functions as arguments or return functions. This lesson will open a new world for you.

Related Built-in Functions：filter() sorted() list() lambda map() max() sum() False True None

1. Functions Are First-Class Citizens

In Python, functions can be manipulated like any other value:

▶ Example: Functions as Values

Output:

# Function defined successfully

2. map(): Batch Transformation

map() applies a function to every element in a sequence and returns a new iterator:

▶ Example: map Batch Transform

Output:

# Executed successfully

map() is equivalent to a list comprehension: map(f, seq) ≈ [f(x) for x in seq]. Which to use is a matter of preference — comprehensions are more "Pythonic."

3. filter(): Batch Filtering

filter() selects elements for which the function returns True:

▶ Example: filter Data

Output:

# Executed successfully

filter() is also equivalent to a comprehension: filter(f, seq) ≈ [x for x in seq if f(x)]. Using None as the function is equivalent to removing all falsy values ("", 0, None, False, etc.).

4. Advanced sorted() Usage

Lesson 11 covered sorted()'s key parameter. Let's go deeper:

▶ Example: Custom Sorting with sorted

Output:

# Executed successfully

▶ Example: Multi-Level Sorting (Difficulty ⭐⭐)

Output:

Bob: 92
Diana: 92
Alice: 85
Charlie: 85

5. reduce(): Cumulative Computation

reduce() accumulates elements in a sequence, applying a function to two elements at a time. It's not a built-in; import from functools:

▶ Example: reduce Cumulative Computation

Output:

# Executed successfully

(1) Execution Process of reduce()

# reduce(lambda x, y: x + y, [1, 2, 3, 4])
# Step 1: x=1, y=2 → 3
# Step 2: x=3, y=3 → 6
# Step 3: x=6, y=4 → 10
# Result: 10

6. Lambda Expressions Deep Dive

Lambda, named after the Greek letter λ, is essentially an anonymous function — a small function without a name, used and discarded.

(1) Lambda vs Regular Functions

▶ Example: Lambda vs Regular

Output:

# Function defined successfully

(2) Complete Lambda Syntax

lambda parameters: expression
# ↑ Equivalent to
def anonymous_function(parameters):
    return expression

(3) Multiple Parameters

▶ Example: Lambda with Multiple Parameters

Output:

# Executed successfully

(4) When to Use Lambda

▶ Example: Lambda Use Cases

Output:

# Function defined successfully

7. Common Use Cases

• Data pipelines: Chain map() → filter() → sorted() for data processing.
• Custom sorting: Use lambda as the key parameter for various sort rules.
• Event callbacks: Pass functions as arguments to GUI or web frameworks.
• Closure factories: Outer function returns inner function for "parameterized" functionality.
• Lazy evaluation: Higher-order functions defer computation until needed.

Related Built-in Functions：filter() sorted() list() lambda map() max() sum() False True None

❓ FAQ

📖 Summary

• Functions are first-class citizens in Python — can be assigned, passed, and returned
• map(f, seq) applies a function to each element; use list() to convert to a list
• filter(f, seq) selects elements where the function returns True
• sorted(seq, key=f) accepts a function for custom sorting
• reduce(f, seq) accumulates values; import from functools
• Lambda lambda params: expression is an anonymous single-line function for simple scenarios

📝 Exercises

1. Basic (Difficulty ⭐): Given nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], use map() and filter() to: filter odd numbers and compute their squares. Hint: First filter for odds, then map to square.

2. Intermediate (Difficulty ⭐⭐): Given a list of dicts products = [{"name": "A", "price": 100}, {"name": "B", "price": 80}, {"name": "C", "price": 120}], use sorted() to sort by price descending and output names and prices.

3. Challenge (Difficulty ⭐⭐⭐): Use reduce() to implement a flatten(nested_list) function that flattens a nested list by one level. For example, flatten([[1, 2], [3, 4], [5]]) returns [1, 2, 3, 4, 5]. Hint: In reduce, use + to merge two lists each time. Then implement the same with a list comprehension and compare the approaches.

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