Comprehensions

Comprehensions condense for loops into a one-line expression.

List Comprehensions

A list comprehension creates a list:

squares = [x ** 2 for x in range(10)]

Produces the same result as:

squares = []
for x in range(10):
    squares.append(x ** 2)

Conditionals

The comprehension may contain an if clause to filter the list:

squares = [x ** 2 for x in range(10) if x % 3 == 0]

Note that you can also place an if on the other side of the for as a ternary expression. This leads to a different result:

squares = [x ** 2 if x % 3 == 0 else -1 for x in range(10)]

Nested Comprehensions

It is perfectly fine to place one comprehension inside another. The following code creates a 5x5 matrix:

[[x * y for x in range(5)] for y in range(5)]

Note that you can concatenate the for statements without the extra brackets. In that case the result is a flat list:

[x * y for x in range(5) for y in range(5)]

The join pattern

Comprehensions that produce a list of strings are often found together with join(), e.g. to format text output:

';'.join([char.upper() for char in 'abcde'])

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Dict comprehensions

This variant produces dictionaries:

ascii_table = {x: chr(x) for x in range(65, 91)}

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Set comprehensions

The same with a set:

unique = {x.upper() for x in 'Hello World'}

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Generator expressions

Finally, you can use a comprehension to define generators:

squares = (x ** 2 for x in range(1_000_000_000_000_000))
print(next(squares))

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Recap: Number Comprehensions

Create the following data with one-liners:

# 1. integers
a = ...
assert a == [1, 2, 3, 4, 5, 6]

# 2. squares
a = ...
assert a == [1, 4, 9, 16, 25, 36]

# 3. fractions
a = ...
assert a == [1.0, 0.5, 0.33, 0.25, 0.2, 0.17, 0.14]

# 4. filtering
a = range(100)
b = ... a ...
assert b == [11, 22, 33, 44, 55, 66, 77, 88, 99]

# 5. dictionary of squares
a = ...
assert a == {1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36}

# 6. split
b = ...
assert b == [[1, 2], [3, 4], [5, 6]]

# 7. concatenate
c = ... b ...
assert c == [1, 2, 3, 4, 5, 6]