I'm familiar with the for
loop in a block-code context. eg:
for c in "word": print c
I just came across some examples that use for
differently. Rather than beginning with the for
statement, they tag it at the end of an expression (and don't involve an indented code-block). eg:
sum(x*x for x in range(10))
Can anyone point me to some documentation that outlines this use of for
? I've been able to find examples, but not explanations. All the for
documentation I've been able to find describes the previous use (block-code example). I'm not even sure what to call this use, so I apologize if my question's title is unclear.
This generator uses an iterator, because the "for" loop is implemented using an iterator. If you time these, the generator is consistently faster. Why is this, when the generator uses an iterator? Thanks.
A generator expression is an expression that returns a generator object. Basically, a generator function is a function that contains a yield statement and returns a generator object.
In Python, to create iterators, we can use both regular functions and generators. Generators are written just like a normal function but we use yield() instead of return() for returning a result. It is more powerful as a tool to implement iterators.
So what's the difference between Generator Expressions and List Comprehensions? The generator yields one item at a time and generates item only when in demand. Whereas, in a list comprehension, Python reserves memory for the whole list.
What you are pointing to is Generator
in Python. Take a look at: -
See the documentation: - Generator Expression
which contains exactly the same example you have posted
From the documentation: -
Generators are a simple and powerful tool for creating iterators. They are written like regular functions but use the yield statement whenever they want to return data. Each time next() is called, the generator resumes where it left-off (it remembers all the data values and which statement was last executed)
Generators are similar to List Comprehension
that you use with square brackets
instead of brackets
, but they are more memory efficient. They don't return the complete list
of result at the same time, but they return generator object. Whenever you invoke next()
on the generator
object, the generator uses yield
to return the next value.
List Comprehension
for the above code would look like: -
[x * x for x in range(10)]
You can also add conditions to filter out results at the end of the for.
[x * x for x in range(10) if x % 2 != 0]
This will return a list of numbers
multiplied by 2 in the range 1 to 5, if the number is not divisible by 2.
An example of Generators
depicting the use of yield
can be: -
def city_generator(): yield("Konstanz") yield("Zurich") yield("Schaffhausen") yield("Stuttgart") >>> x = city_generator() >>> x.next() Konstanz >>> x.next() Zurich >>> x.next() Schaffhausen >>> x.next() Stuttgart >>> x.next() Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration
So, you see that, every call to next()
executes the next yield()
in generator
. and at the end it throws StopIteration
.
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With