Python generator object to list

Python generator object to list code example

Then call the generator function and collect only the values that you want (here not strings but numbers divisible by 100): The loop can be also replaced by so called list comprehension: . which is more understandable (i.e. resembles the above loop) when you write the same like that: Solution: If the goal is to convert to a of s (instead of generator of s), can do this easily: If a of s is all you need, it’s even easier: Just don’t do it for permutations on a larger set of inputs, or you’ll exhaust memory pretty quickly.

Convert generator object to list

If the goal is to convert to a list of list s (instead of generator of tuple s), map can do this easily:

perms = map(list, itertools.permutations(range(1,4))) 

If a list of tuple s is all you need, it’s even easier:

perms = list(itertools.permutations(range(1,4))) 

Just don’t do it for permutations on a larger set of inputs, or you’ll exhaust memory pretty quickly.

String — Python generator objects and .join, None of the objects is a «memory address». The string representation of a generator object (like that of many other objects) includes a memory address, so if you print it (as above) or write it to a file, you’ll see that address. But that doesn’t mean that object «is» that memory address, and the …

Mapping a generator to a list

In this case you can use lambda :

map(lambda e: list(foo(e)), your_list) 

This applies list(foo(e)) for each element e in your_list .

Python — Converting generator object to a list, The problem is that your generator has no end condition (while True and no break)Even if it could make sense to write such a generator (ex: yield all the decimals of Pi or the Fibonacci sequence I just recognized in your code :)), when you convert it to a list, it loops forever (and eats all the memory too). BTW: list() …

Explaining python generator for a long list

Your example is over simplified, so let’s pretend you wanted 10000 random numbers instead.

There are two ways to create a generator. One is with the yield statement:

def example(): for i in xrange(10000): yield random.random() 

The other way is with a generator expression:

(random.random() for i in xrange(10000)) 

Which one you choose will depend on your code complexity.

I am getting some string after crawling a web page and appending those string to a list,as the crawler geting large data,the list is getting bigger,finally when I want to iterate the list,its time and memory consuming

# generator function to crawl web pages def crawler(): while iStillHaveWebPagesToCrawl(): someStrings = getSomeStringsFromAWebPage() for aString in someStrings: yield aString def oneStringAtATime(): for aString in crawler(): doSomethingWith( aString ) 

When you call oneStringAtATime() , it sets up the generator function called crawler() ; each time crawler() executes yield , the loop in oneStringAtATime() iterates once with that string. When crawler() runs out of web pages and exits the function, the oneStringAtATime() loop exits.

If I understand you well, then you use the range(0, 10000) only to simulate the sequence. Whenever you use the yield in a function definition, it becomes a generator function . When generator function is used (called), it returns iterator — you do not see it. Try the following gen() instead:

def gen(n): while n > 0: yield n n -= 1 # decrement the value 

Then you typically use it in a loop:

for x in gen(10000): print x, # comma to suppress new line 

If you have a function that returns your strings, just yield s instead of building the list. Then call the generator function and collect only the values that you want (here not strings but numbers divisible by 100):

lst = [] # init for x in gen(10000): if x % 100 == 0: lst.append(x) print lst 

The loop can be also replaced by so called list comprehension:

lst = [ x for x in gen(10000) if x % 100 == 0 ] print lst 

. which is more understandable (i.e. resembles the above loop) when you write the same like that:

lst = [ x for x in gen(10000) if x % 100 == 0 ] print lst 

But you can use a generator function wherever a sequence is expected. If your generator can implement the decision whether your element is to be collected, then you can simply make a list of the produced elements like this:

lst = list(gen(100)) print lst 

Generator object python Code Example, Get code examples like «generator object python» instantly right from your google search results with the Grepper Chrome Extension.

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Convert Generator To List In Python

Last updated June 5, 2023 by Jarvis Silva Looking for a tutorial on how to convert generator to list in python then you are at the right place, a generator is like a normal function but instead of using the return keyword a generator function uses a yield keyword, when a function uses yield keyword it is called as a generator below is an example.

 # Normal Function def normal_func(): return 1 # Generator def gen_func(): yield 1 

When we print the generator function it returns a generator object, so our goal is to convert the generator function into a list, The list will contain all the yield values of the generator.

Convert Generator To List In Python Code

 def nums_gen(): yield 1 yield 2 yield 3 yield 4 yield 5 # prints a generator object print(nums_gen()) # converting generator to list using list() nums_gen = list(nums_gen()) # prints a list print(nums_gen) 

Above is the python program to convert a generator to list, the code defines a generator function that produces a sequence of numbers, we use the list() function to converted result of generator function to list the code gives below output.

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Convert Generator to List in Python

In Python, a list is iterable where every element gets stored at some specific index in a contiguous memory location. We can access the elements from their indexes.

Generator objects resemble iterators in Python but do not store elements in the memory. Both lists and generators represent a sequence of elements in Python. Let us understand more about generator objects in Python.

The return keyword is used to return some value from a function. The yield keyword in Python works similarly. It sends a value from the function to the caller and then resumes the function execution. Any function that has a yield keyword, is termed a generator function.

Using generators functions in Python, we can send a sequence of elements to the caller. These functions return a generator object which can be iterated over. We can iterate over the generator object using a for loop.

We can also use a generator expression to initiate a generator object. A generator expression is similar to list comprehension where we run a for loop in a single line of code to create a sequence.

The values from a generator can be stored in an actual data object. We can convert generator to list in Python.

Ways to convert generator to list in Python

We will now discuss how to convert generator to list in Python.

Using the list() function to convert generator to list in Python

The list() constructor is used to initiate list objects. We can pass a generator object to this function to convert generator to list in Python.

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Convert Generator Object to List in Python (3 Examples)

Hi! This tutorial will show you 3 ways to transform a generator object to a list in the Python programming language.

The table of content is structured as follows:

Let’s jump into the Python code!

Create Sample Generator Object

Here, we will create the sample generator object that will be changed into a Python list using tuple comprehension. Therefore, in your preferred Python IDE, run the line of code below to create the sample generator object:

gen_obj = (x for x in range(5)) print(type(gen_obj)) #

However, in each of the examples that follow, the generator object will need to be run first in order for it to be turned into a list of integers; otherwise, it will print out an empty list.

Example 1: Change Generator Object to List Using list() Constructor

In this first example, we will turn the generator object to a list using the list() constructor:

gen_obj = (x for x in range(5)) list_from_gen = list(gen_obj) print(list_from_gen) # [0, 1, 2, 3, 4] print(type(list_from_gen)) #

In the above code, the list() constructor changes the generator object into a list of integers, starting at 0.

Example 2: Change Generator Object to List Using extend() Method

In this next example, we will transform the generator object to a list using the extend() method:

gen_obj = (x for x in range(5)) list_from_gen = [] list_from_gen.extend(gen_obj) print(list_from_gen) # [0, 1, 2, 3, 4] print(type(list_from_gen)) #

In the above example, we first created an empty list with the square brackets [], and then we populated that empty list with the elements of the generator object using the extend() method.

Example 3: Change Generator Object to List Using List Comprehension

In this final example, we will change the generator object to a list using list comprehension like so:

gen_obj = (x for x in range(5)) list_from_gen = [x for x in gen_obj] print(list_from_gen) # [0, 1, 2, 3, 4] print(type(list_from_gen)) #

The list comprehension in the above example enabled us to iterate through the generator object and store its elements inside in a list.

With that, I hope you have learned how to convert a generator object to a list in Python. I hope you found this tutorial helpful!

Video, Further Resources & Summary

Do you need more explanations on how to convert a generator object to a list in Python? Then you should have a look at the following YouTube video of the Statistics Globe YouTube channel.

In the video, we explain in some more detail how to convert a generator object to a list in Python.

The YouTube video will be added soon.

Furthermore, I encourage you to check out other interesting Python list tutorials on Statistics Globe, starting with these ones:

This post has shown how to convert a generator object to a list in Python. In case you have further questions, you may leave a comment below.

This page was created in collaboration with Ifeanyi Idiaye. You might check out Ifeanyi’s personal author page to read more about his academic background and the other articles he has written for the Statistics Globe website.

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