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How to Apply all() with List Comprehension in Python

The all() function in Python is a built-in tool that returns True only if every element in an iterable is "truthy". When combined with list comprehensions (or generator expressions), it becomes a powerful one-liner for validating data, checking conditions across collections, and ensuring data integrity.

This guide explains how to effectively use all() with comprehensions, while highlighting the critical performance distinction between using brackets [] and parentheses ().

Understanding the all() Function​

The all(iterable) function iterates through a sequence. It returns:

  • True if every item is True (or truthy).
  • False if at least one item is False (or falsy).

It typically stops executing (short-circuits) as soon as it encounters the first False value.

Method 1: Using Standard List Comprehension​

You can pass a list comprehension, defined by square brackets [], directly into all(). This creates a boolean list first, which all() then evaluates.

numbers = [2, 4, 6, 8, 10]

# ✅ Check if all numbers are even
# This creates a list: [True, True, True, True, True]
result = all([n % 2 == 0 for n in numbers])

print(f"Are all numbers even? {result}")

numbers_with_odd = [2, 4, 5, 8]
result_odd = all([n % 2 == 0 for n in numbers_with_odd])
print(f"Are all numbers even? {result_odd}")

Output:

Are all numbers even? True
Are all numbers even? False

The Downside of Square Brackets []​

While the syntax above is valid, it is inefficient for large datasets. Python must construct the entire list of booleans in memory before all() starts checking them. If the list has 1 million items and the first one is False, Python still builds the list of 1 million booleans before returning False.

Method 2: Using Generator Expressions (Best Practice)​

To improve performance, remove the square brackets []. This converts the list comprehension into a generator expression.

This allows all() to short-circuit. If the first element fails the condition, all() returns False immediately without checking the rest of the items.

numbers = [1, 3, 5, 8, 9] # 8 is the "bad" apple here

# ⛔️ Less Efficient: Builds full list [False, False, False, False, False]
# (Note: Logic checking if they are odd)
full_list_check = all([n % 2 != 0 for n in numbers])

# ✅ Efficient: Stops checking immediately when it hits '8'
generator_check = all(n % 2 != 0 for n in numbers)

print(f"All odd? {generator_check}")

Output:

All odd? False
tip

Always prefer generator expressions all(x for x in y) over list comprehensions all([x for x in y]). It saves memory and processing time.

Practical Examples​

Validating User Strings​

Check if a list of strings meets specific criteria, such as being alphanumeric.

usernames = ["user1", "admin2", "guest3"]
invalid_usernames = ["user1", "admin!", "guest3"] # '!' is invalid

# Check if all usernames are alphanumeric
is_valid_1 = all(name.isalnum() for name in usernames)
is_valid_2 = all(name.isalnum() for name in invalid_usernames)

print(f"List 1 Valid: {is_valid_1}")
print(f"List 2 Valid: {is_valid_2}")

Output:

List 1 Valid: True
List 2 Valid: False

Checking Matrix Rows​

You can nest all() to validate multi-dimensional lists (matrices).

# A matrix where every element is positive
matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]

# Check if every number in every row is positive
all_positive = all(n > 0 for row in matrix for n in row)

print(f"Is the matrix entirely positive? {all_positive}")

Output:

Is the matrix entirely positive? True

Common Pitfall: The Empty List​

A specific behavior of all() that often confuses beginners is "vacuous truth." If you apply all() to an empty list, it returns True, not False.

empty_list = []

# Logic: "Are there any False elements?" No. Therefore, True.
result = all(x > 0 for x in empty_list)

print(f"Result for empty list: {result}")

Output:

Result for empty list: True
warning

If your validation logic requires that data must exist (e.g., "all users are valid" implies there is at least one user), you must explicitly check if the list is not empty: if my_list and all(...):.

Conclusion​

Using all() with comprehension syntax is the Pythonic way to perform collection-wide validation.

  1. Use Generator Syntax: Omit square brackets all(cond for x in list) for memory efficiency and short-circuiting.
  2. Use List Comprehension: Use all([cond for x in list]) only if you specifically need the boolean list for debugging purposes.
  3. Watch for Empty Lists: Remember that all([]) returns True.

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