Python Generators: Difference between revisions
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* [[Python_Language#Generators|Python Language]] | * [[Python_Language#Generators|Python Language]] | ||
* <tt>[[Python_Language_Functions#reversed|reversed()]]</tt> | |||
* [[Python_Iterators#Overview|Python Iterators]] | |||
=TODO= | =TODO= | ||
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=Overview= | =Overview= | ||
A generator is a way to construct a new [[Python_Iterators#Overview|iterable]] object. Whereas normal functions execute and return a single result at a time, generators can return a sequence of multiple values by pausing and resuming execution each time the generator is used. To create a generator, use the <code>[[Python_Language#yield|yield]]</code> keyword instead of <code>return</code> in a function. | |||
<syntaxhighlight lang='py'> | |||
def squares(n=10): | |||
for i in range(1, n+1): | |||
yield i ** 2 | |||
</syntaxhighlight> | |||
When the generator is called no code is immediately executed: | |||
<syntaxhighlight lang='py'> | |||
gen = squares() | |||
</syntaxhighlight> | |||
It is not until you request elements from the generator that it begins executing code: | |||
<syntaxhighlight lang='py'> | |||
for x in gen: | |||
print(x) | |||
</syntaxhighlight> | |||
=Generator Comprehensions= | |||
A generator expression is the generator analogues to [[Python_Comprehensions#List_Comprehensions|list]], [[Python_Comprehensions#Dictionary_Comprehensions|dictionary]] and [[Python_Comprehensions#Set_Comprehensions|set]] comprehensions. To create one, enclose what would be otherwise a list comprehension within '''parentheses''' instead of brackets. Depending on the number of elements produced by the comprehension expression, the generator expression can sometimes be meaningfully faster. | |||
<font color=darkkhaki> | |||
* PROCESS [[IPy]] Comprehensions Page 88. | |||
</font> | |||
=Use Cases= | |||
Create a [[Python_Language_List#Pass_a_Generator_Expression|list with a generator]]. | |||
Latest revision as of 19:11, 17 May 2024
Internal
TODO
- PROCESS IPy Generators Page 101.
- PROCESS PyOOP "Generator expressions"
- PROCESS PyOOP "Generators" + "Yield items from another iterable"
Overview
A generator is a way to construct a new iterable object. Whereas normal functions execute and return a single result at a time, generators can return a sequence of multiple values by pausing and resuming execution each time the generator is used. To create a generator, use the yield keyword instead of return in a function.
def squares(n=10):
for i in range(1, n+1):
yield i ** 2
When the generator is called no code is immediately executed:
gen = squares()
It is not until you request elements from the generator that it begins executing code:
for x in gen:
print(x)
Generator Comprehensions
A generator expression is the generator analogues to list, dictionary and set comprehensions. To create one, enclose what would be otherwise a list comprehension within parentheses instead of brackets. Depending on the number of elements produced by the comprehension expression, the generator expression can sometimes be meaningfully faster.
- PROCESS IPy Comprehensions Page 88.
Use Cases
Create a list with a generator.