Python Comprehensions: Difference between revisions

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===List Comprehensions===
===List Comprehensions===


A list comprehension produces a list from an [[Python_Language#Iterable_Types|iterable type]] by applying an expression to each of the elements, and optionally a condition.
A list comprehension produces a list from an [[Python_Language#Iterable_Types|iterable type]] by applying an expression to each of the elements, and optionally a condition. The iterable instance must be indeed iterable, otherwise we get an error message, shown below. So <code>None</code> does not work, it produces a runtime error.
 
<font size=-1>
TypeError: 'NoneType' object is not iterable
</font>


====Simple List Comprehension====
====Simple List Comprehension====
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===Dictionary Comprehensions===
===Dictionary Comprehensions===
<syntaxhighlight lang='py'>
{<key-expression>: <value-expression> for <var> in <iterable>}
</syntaxhighlight>
<syntaxhighlight lang='py'>
{<key-expression>: <value-expression> for <var> in <iterable> if <condition>}
</syntaxhighlight>
<font color=darkkhaki>
<font color=darkkhaki>
* PROCESS [[IPy]] Comprehensions Page 87.
* PROCESS [[IPy]] Comprehensions Page 87.
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===Set Comprehensions===
===Set Comprehensions===
<syntaxhighlight lang='py'>
{<expression> for <var> in <iterable>}
</syntaxhighlight>
<syntaxhighlight lang='py'>
{<expression> for <var> in <iterable>} if <condition>
</syntaxhighlight>
<font color=darkkhaki>
<font color=darkkhaki>
* PROCESS [[IPy]] Comprehensions Page 87.
* PROCESS [[IPy]] Comprehensions Page 87.
</font>
</font>
===Generator Comprehensions===
===Generator Comprehensions===
<font color=darkkhaki>
{{Internal|Python_Generators#Generator_Comprehensions|Generator Comprehensions}}
* PROCESS [[IPy]] Comprehensions Page 88.
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Latest revision as of 19:08, 17 May 2024

Internal

TODO

  • PROCESS PyOOP "Comprehensions" + "List comprehensions" + "Set and dictionary comprehensions"

Overview

A comprehension is a compact way of creating a data structure from one or more iterators. They are essentially loops with a more compact syntax.

List Comprehensions

A list comprehension produces a list from an iterable type by applying an expression to each of the elements, and optionally a condition. The iterable instance must be indeed iterable, otherwise we get an error message, shown below. So None does not work, it produces a runtime error.

TypeError: 'NoneType' object is not iterable

Simple List Comprehension

[<expression> for <var> in <iterable>]

The list comprehension moves the loop inside square brackets. This example iterates over a list of custom object instances and produces a comma-separated list of their __str__() representations, which printing the list with print() does not do:

class C:
  def __init__(self, i):
    self.i = i 
  def __str__(self):
    return str(self.i)

l = [C(1), C(2), C(3)]
print(l)

displays:

[<__main__.C object at 0x102f06b20>, <__main__.C object at 0x1030159d0>, <__main__.C object at 0x10302c9a0>]

The following list comprehension:

print(', '.join([str(i) for i in l]))

displays:

1, 2, 3

Conditional List Comprehension

[<expression> for <var> in <iterable> if <condition>]

An example of a conditional list comprehension is to generate a list of even numbers from a list of numbers:

l = list(range(10))
print(l)

displays:

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

To get the even numbers:

l2 = [i for i in l if i % 2 == 0]
print(l2)

displays:

[0, 2, 4, 6, 8]

Nested List Comprehensions

Just as there can be nested loops, there can be more than on set of for ... clauses.

rows = range(1, 4)
cols = range(1, 3)
cells = [(row, col) for row in rows for col in cols]

Use Comprehensions for Side Effects

The purpose of a comprehension is to generate a list from another list. However, it can be also used to exercise side effects on the elements. The result list is discarded.

class Box:
    def __init__(self):
        self.color = None

    def paint(self, value):
        self.color = value

boxes = [Box(), Box(), Box()]

[b.paint('blue') for b in boxes]

for b in boxes:
    assert b.color == 'blue'

Dictionary Comprehensions

{<key-expression>: <value-expression> for <var> in <iterable>}
{<key-expression>: <value-expression> for <var> in <iterable> if <condition>}

  • PROCESS IPy Comprehensions Page 87.

A dictionary comprehension starts from a list, and for each element of the list, generates a key and a value, which are then inserted in the dictionary. The key and the value are introduced with the (k, v) syntax:

l = ['a', 'b', 'c']
d = dict([(k, k.upper()) for k in l])
print(d)

will print:

{'a': 'A', 'b': 'B', 'c': 'C'}

Set Comprehensions

{<expression> for <var> in <iterable>}
{<expression> for <var> in <iterable>} if <condition>

  • PROCESS IPy Comprehensions Page 87.

Generator Comprehensions

Generator Comprehensions