Implementation of built-in Python function all

Python built-in function all() is used to judge whether all elements in an iterable object are Truthy and is an important tool for logical judgment.

1. Basic syntax

all(iterable) 

Parameters: iterable must be an iterable object (such as lists, tuples, sets, dictionary values, etc.).

Return value:

• True : All elements are true values, or the iterable object is empty.
• False: There is at least one false value element.

2. Truth value judgment rules

The following values ​​in Python are considered false values ​​(Falsy), and the others are true values:

• Numbers: 0 , 0.0 , -0.0 , 0j , NaN , False .
• Empty containers: '' (empty string),   (empty list),  () (empty tuple), {} (empty dictionary/set).
• Special value: None.

Example:

print(all([1, 2, 3])) # True (all nonzero numbers are true)print(all([True, 0, "hello"])) # False (0 is false)print(all("")) # True (empty string is treated as true)print(all({1: False, 2: 5})) # True（Check the dictionary's keys，key1For false）

3. Typical usage scenarios

(1) Conditional batch verification

Check whether all of the conditions are met:

# Check whether all user inputs are non-emptyuser_inputs = ["yes", "2024", "male"]
is_valid = all(() != "" for input in user_inputs)
print(is_valid) # Output: True

(2) Data integrity check

Verify that all elements in the dataset meet the requirements:

# Check whether all values ​​in the list are positivedata = [5, 10, 3, 0] # 0 is a false valueprint(all(x > 0 for x in data)) # Output: False

(3) Short circuit evaluation optimization

Return False immediately when the first false value is encountered to avoid unnecessary traversal:

large_list = [True] * 1000000 + [False] + [True] * 1000000
print(all(large_list)) # Quick returnFalse，No need to traverse subsequent elements

4. Comparison with other functions

Example:

print(all([])) # True
print(any([])) # False

5. Things to note

Non-iterable objects report an error:

If a non-iterable object (such as an integer) is passed in, a TypeError will be triggered:

all(123) # TypeError: 'int' object is not iterable

Dictionary processing:

all() checks its keys instead of values ​​for dictionaries:

print(all({0: "a", 1: "b"})) # False（key0For false）

Efficiency of generator expressions

Combined with generator expressions, it can handle large data sets to avoid excessive memory usage:

# Check whether all lines in the file contain specific keywordswith open("") as f:
    all_contain_keyword = all("ERROR" in line for line in f)

6. Practical application examples

(1) Permission verification

permissions = ["read", "write", "execute"]
user_permissions = ["read", "write"]
print(all(perm in user_permissions for perm in permissions)) # False

(2) Configuration check

config = {"debug": False, "logging": True, "test_mode": False}
print(all(())) # False（existFalsevalue）

Summarize

all() is a concise and efficient logical judgment tool in Python, suitable for batch verification conditions, data integrity check and other scenarios. Its short-circuit evaluation feature optimizes performance, but it should be noted that the parameters must be iterable objects, and the empty container's feature returning True may cause logical errors. Combining generators and conditional expressions can further improve the flexibility and efficiency of the code.

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