Python Built-in Methods – Python all() Function

Python all() Function

Determines whether all items in an iterable are True


The all() function returns True if all items in an iterable are True. Otherwise, it returns False.

If the iterable is empty, the function returns True.



Parameter Condition Description
iterable Required An iterable of type (list, string, tuple, set, dictionary etc.)

Falsy Values

In Python, all the following values are considered False.

  • Constants defined to be false: None and False.
  • Zero of any numeric type: 00.00jDecimal(0)Fraction(0, 1)
  • Empty sequences and collections: ''()[]{}set()range(0)

Basic Examples

# Check if all items in a list are True

L = [1, 1, 1]
print(all(L))   # Prints True

L = [0, 1, 1]
print(all(L))   # Prints False

Here are some scenarios where all() returns False.

L = [True, 0, 1]
print(all(L))   # Prints False

T = ('', 'red', 'green')
print(all(T))   # Prints False

S = {0j, 3+4j}
print(all(S))   # Prints False

all() on a Dictionary

When you use all() function on a dictionary, it checks if all the keys are true, not the values.

D1 = {0: 'Zero', 1: 'One', 2: 'Two'}
print(all(D1))   # Prints False

D2 = {'Zero': 0, 'One': 1, 'Two': 2}
print(all(D2))   # Prints True

all() on Empty Iterable

If the iterable is empty, the function returns True.

# empty iterable
L = []
print(all(L))   # Prints True

# iterable with empty items
L = [[], []]
print(all(L))   # Prints False


Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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