I have such code: With the next result: The question is: Why does a list created with list([]) weigh less than a list created with just [] or for _ in condition? Answer (The details in this answer depend on the implementation, they are written to match CPython 3.10.0. Other versions or other implementations of Python work differently.) Lists in
Tag: generator
Speed comparision for iterating over List and Generator in Python
When comparing usage of Python Generators vs List for better performance/ optimisation, i read that Generators are faster to create than list but iterating over list is faster than generator. But I coded an example to test it with small and big sample of data and it contradicts with one another. When I test speed for iterating over generator and
Can you yield from a lambda function?
I have a generator function in a class: In another function I initalize it as a variable: And it’s yielded as necessary: Can defining the generator be done in one line, however? I’ve considered the below: Here’s a minimal code I’m working with: Output is below: I just wanted to see if I could get rid of Foo.generator and instead
Is there any way to call base python function in r using reticulate?
I got a “generator object” from a python function. However, I tried many ways but failed to read the “generator object” in r using reticulate. I know python base function list() can convert “generator object” to “json”, which I can then read in r. I wonder how to use base python function in r? (I would not prefer to use
Cloning Babeltrace events from generator for random-access traversal
I’m trying to check for a certain chain of events in an LTTNG event log using Babeltrace 1. The LTTNG log is loaded using a Babeltrace collection: The special events I’m looking for are almost indistinguishable from the normal events happening, except there is a few extra events once the chain have already started. So I need to look for
Use Python generator’s .send() when generator is wrapped to act as a context manager
Python’s contextlib provides wrappers to turn generators into context managers: And generators provide the ability to send values into generators that just yielded: Is there any way to get both behaviors at the same time? I would like to send a value into my context manager so that it can be used while handling __exit__. So something like this: I’m
Iterables / generators with specified length
Iterable objects are those that implement __iter__ function, which returns an iterator object, i.e. and object providing the functions __iter__ and __next__ and behaving correctly. Usually the size of the iterable object is not known beforehand, and iterable object is not expected to know how long the iteration will last; however, there are some cases in which knowing the length
python – different behavior of print(*generator, )
Question Please help understand why the two cases act differently although both use a generator (i for i in range(5)). Answer When you use the * operator on a generator expression (or any iterator for that matter), it consumes it: You’d need to recreate it to re-use it, which is what you’re doing in your first example: Note on range
Define a generator which updates global variables before the first __next__() call
Given the following function in python I would like to update the global variable before calling “next()”. Let me show it to you with an example. Then you can run: Which outputs the following: Finally: please note that there is a way of doing this via a class definition that has a class variable, but I’m currently wondering for a
What type of iterable is “x for x in y”?
I’ve been confused of list comprehension in Python, although its shorthand doing for loop is very convenient. I’m not sure if what’s in the join() function’s parameter below is list comprehension since usually you put [] around to show it’s a list comprehension. My question: What’s the type of iterable produced by str(x) for x in res[::-1] in join() below?