Constant
Description
The function will return when any future finishes or is cancelled.
The function will return when any future finishes by raising an exception. If no future raises an exception then it is equivalent to .
The function will return when all futures finish or are cancelled.
Returns an iterator over the Future instances (possibly created by different Executor instances) given by fs that yields futures as they complete (finished or cancelled futures). Any futures given by fs that are duplicated will be returned once. Any futures that completed before as_completed() is called will be yielded first. The returned iterator raises a concurrent.futures.TimeoutError if __next__() is called and the result isn’t available after timeout seconds from the original call to as_completed() . timeout can be an int or float. If timeout is not specified or None , there is no limit to the wait time.
The proposal which described this feature for inclusion in the Python standard library.
Raised when a future is cancelled.
Raised when a future operation exceeds the given timeout.
Derived from RuntimeError , this exception class is raised when an executor is broken for some reason, and cannot be used to submit or execute new tasks.
New in version 3.7.
Raised when an operation is performed on a future that is not allowed in the current state.
New in version 3.8.
Derived from BrokenExecutor , this exception class is raised when one of the workers of a ThreadPoolExecutor has failed initializing.
Derived from BrokenExecutor (formerly RuntimeError ), this exception class is raised when one of the workers of a ProcessPoolExecutor has terminated in a non-clean fashion (for example, if it was killed from the outside).
New in version 3.3.
The concurrent package
subprocess — Subprocess management
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This tutorial looks at how to speed up CPU-bound and IO-bound operations with multiprocessing, threading, and AsyncIO.
Sync example, threading example, concurrent.futures example, asyncio example, multiprocessing example.
Concurrency and parallelism are similar terms, but they are not the same thing.
Concurrency is the ability to run multiple tasks on the CPU at the same time. Tasks can start, run, and complete in overlapping time periods. In the case of a single CPU, multiple tasks are run with the help of context switching , where the state of a process is stored so that it can be called and executed later.
Parallelism, meanwhile, is the ability to run multiple tasks at the same time across multiple CPU cores.
Though they can increase the speed of your application, concurrency and parallelism should not be used everywhere. The use case depends on whether the task is CPU-bound or IO-bound.
Tasks that are limited by the CPU are CPU-bound. For example, mathematical computations are CPU-bound since computational power increases as the number of computer processors increases. Parallelism is for CPU-bound tasks. In theory, If a task is divided into n-subtasks, each of these n-tasks can run in parallel to effectively reduce the time to 1/n of the original non-parallel task. Concurrency is preferred for IO-bound tasks, as you can do something else while the IO resources are being fetched.
The best example of CPU-bound tasks is in data science. Data scientists deal with huge chunks of data. For data preprocessing, they can split the data into multiple batches and run them in parallel, effectively decreasing the total time to process. Increasing the number of cores results in faster processing.
Web scraping is IO-bound. Because the task has little effect on the CPU since most of the time is spent on reading from and writing to the network. Other common IO-bound tasks include database calls and reading and writing files to disk. Web applications, like Django and Flask, are IO-bound applications.
If you're interested in learning more about the differences between threads, multiprocessing, and async in Python, check out the Speeding Up Python with Concurrency, Parallelism, and asyncio article.
With that, let's take a look at how to speed up the following tasks:
All of the code examples in this tutorial can be found in the parallel-concurrent-examples-python repo.
We'll be using the following libraries from the standard library to speed up the above tasks:
Library | Class/Method | Processing Type |
---|---|---|
threading | concurrent | |
concurrent.futures | concurrent | |
asyncio | concurrent (via coroutines) | |
multiprocessing | parallel | |
concurrent.futures | parallel |
Again, IO-bound tasks spend more time on IO than on the CPU.
Since web scraping is IO bound, we should use threading to speed up the processing as the retrieving of the HTML (IO) is slower than parsing it (CPU).
Scenario: How to speed up a Python-based web scraping and crawling script?
Let's start with a benchmark.
Here, we made 100 HTTP requests using the make_request function. Since requests happen synchronously, each task is executed sequentially.
So, that's roughly 0.16 seconds per request.
Here, the same make_request function is called 100 times. This time the threading library is used to create a thread for each request.
The total time decreases from ~16s to ~1s.
Since we're using separate threads for each request, you might be wondering why the whole thing didn't take ~0.16s to finish. This extra time is the overhead for managing threads. The Global Interpreter Lock (GIL) in Python makes sure that only one thread uses the Python bytecode at a time.
Here we used concurrent.futures.ThreadPoolExecutor to achieve multithreading. After all the futures/promises are created, we used wait to wait for all of them to complete.
concurrent.futures.ThreadPoolExecutor is actually an abstraction around the multithreading library, which makes it easier to use. In the previous example, we assigned each request to a thread and in total 100 threads were used. But ThreadPoolExecutor defaults the number of worker threads to min(32, os.cpu_count() + 4) . ThreadPoolExecutor exists to ease the process of achieving multithreading. If you want more control over multithreading, use the multithreading library instead.
httpx is used here since requests does not support async operations.
Here, we used asyncio to achieve concurrency.
asyncio is faster than the other methods, because threading makes use of OS (Operating System) threads. So the threads are managed by the OS, where thread switching is preempted by the OS. asyncio uses coroutines, which are defined by the Python interpreter. With coroutines, the program decides when to switch tasks in an optimal way. This is handled by the even_loop in asyncio.
Scenario: How to speed up a simple data processing script?
Again, let's start with a benchmark.
Here, we executed the get_prime_numbers function for numbers from 1000 to 16000.
Here, we used multiprocessing to calculate the prime numbers.
Here, we achieved multiprocessing using concurrent.futures.ProcessPoolExecutor . Once the jobs are added to futures, wait(futures) waits for them to finish.
concurrent.futures.ProcessPoolExecutor is a wrapper around multiprocessing.Pool . It has the same limitations as the ThreadPoolExecutor . If you want more control over multiprocessing, use multiprocessing.Pool . concurrent.futures provides an abstraction over both multiprocessing and threading, making it easy to switch between the two.
It's worth noting that using multiprocessing to execute the make_request function will be much slower than the threading flavor since the processes will be need to wait for the IO. The multiprocessing approach will be faster then the sync approach, though.
Similarly, using concurrency for CPU-bound tasks is not worth the effort when compared to parallelism.
That being said, using concurrency or parallelism to execute your scripts adds complexity. Your code will generally be harder to read, test, and debug, so only use them when absolutely necessary for long-running scripts.
concurrent.futures is where I generally start since-
Grab the code from the parallel-concurrent-examples-python repo on GitHub.
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I am a newbie at python programming.I am referring to a book "Python Programming: An Introduction to Computer Science." Which is oriented to python 2.Since I cannot lay my hands upon a basic book oriented to python 3, I am facing a syntax problem depicted below
The result i got was
I tried to find the solution in some books like dive into python 3 and core python programming, but i think that they are way too high level for me right now.Please help.
"input" returns only one value at the time, so you can't really assign it for two variables. If you expect two values you may want to split string by space or any other convenient separator.
split(var, 1) - make sure you split string only once into two pieces. map(int...) - convert each string piece value into integer.
The input function returns one str (in Python 3), which won't be automatically split into two variables. You need to do something like:
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And now consider the following two versions of a simultaneous assignment over both a and i: Assignment version 1: a[i],i = i,i+1. Assignment version 2: i,a[i] = i+1,i. I would expect these two versions of simultaneous assignments to be semantically equivalent. However, if you check the values of a and i after each one of the simultaneous ...
See also Multiple assignment semantics regarding the effect and purpose of parentheses on the left-hand side of a multiple assignment. See also Understand Python swapping: why is a, b = b, a not always equivalent to b, a = a, b? for more complex cases, where the order of assignment matters.
This is called simultaneous assignment. Semantically, this tells Python to evaluate all the expressions on the right-hand side and then assign these values to the corresponding variables named on the left-hand side. Here's an example. Here sum would get the sum of x and y and diff would get the difference. This form of assignment seems strange ...
None in Python; Create calendar as text, HTML, list in Python; NumPy: Insert elements, rows, and columns into an array with np.insert() Shuffle a list, string, tuple in Python (random.shuffle, sample) Add and update an item in a dictionary in Python; Cartesian product of lists in Python (itertools.product) Remove a substring from a string in Python
Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal—or an expression that evaluates to a value. To execute an assignment statement like the above, Python runs the following steps: Evaluate the right-hand expression to produce a concrete value or object.
In Python, there are two common techniques for assigning multiple variables - unpacking and simultaneous assignment. Using the unpacking technique The unpacking technique involves assigning the values of an iterable object, such as a tuple or a list, to individual variables.
Looks at the nature of simultaneous assignments in Python while at the same time covering trace tables as a useful mechanism for describing program execution.
Recurrence relations and Python. A video by Raymond Hettinger points out that simultaneous assignment makes it much easier to understand code that evaluates a recurrence relation. His examples were in Python, but the same principle applies to any language supporting simultaneous evaluation. The simplest example of simultaneous evaluation is ...
Chapter 2: Shortcut assignment statements Simultaneous variable assignments in Python. face Josiah Wang. ... There is actually a different, recommended way to swap variables in Python. In Python, you can actually assign values to multiple variable simultaneously. Of course, whether this is easy to read is a different question! ...
Python's multiple assignment looks like this: >>> x, y = 10, 20. Here we're setting x to 10 and y to 20. What's happening at a lower level is that we're creating a tuple of 10, 20 and then looping over that tuple and taking each of the two items we get from looping and assigning them to x and y in order.
The multiple assignment syntax, often referred to as tuple unpacking or extended unpacking, is a powerful feature in Python. There are several ways to assign multiple values to variables at once. Let's start with a first example that uses extended unpacking. This syntax is used to assign values from an iterable (in this case, a string) to ...
This video is about simultaneous assignment in Python. It displays how to assign values to multiple variables at once. ------
Python Assignment Operators. Assignment operators are used to assign values to variables: Operator. Example. Same As. Try it. =. x = 5. x = 5.
In this tutorial, we'll learn about how to assign values to multiple variables in a single statement.This concept is very handy when it comes to writing comp...
2.3 Statements and the Assignment Operator 2.4 Cascaded and Simultaneous Assignment 2.5 Multi-Line Statements and Multi-Line Strings 2.6 Identifiers and Keywords 2.7 Names and Namespaces 2.8 Additional Arithmetic Operators 2.8.1 Exponentiation 2.8.2 Floor Division 2.8.3 Modulo and divmod() 2.8.4 Augmented Assignment 2.9 Chapter Summary
Simultaneous assignment: We can also do simultaneous assignments in Python which is not commonly available to other languages. To do that we have to use a comma between expressions and an equal (assignment) operator at the right side. The following variable swapping problem is a perfect example of a simultaneous assignment.
The concurrent.futures module provides a high-level interface for asynchronously executing callables. The asynchronous execution can be performed with threads, using ThreadPoolExecutor, or separate processes, using ProcessPoolExecutor. Both implement the same interface, which is defined by the abstract Executor class.
In your assignment with x = 3 and y = 5. x, y = y, (x+y) the right side is evaluated to a tuple (5, 8) first and then assigned to the values on the left side. Therefore y is 8. You could also think of it as. x, y = 3, 5. temp = y, x + y. x, y = temp. To see what really happens internal, you can disassemble your code:
Grab the code from the parallel-concurrent-examples-python repo on GitHub. Python Amal Shaji. Amal is a full-stack developer interested in deep learning for computer vision and autonomous vehicles. He enjoys working with Python, PyTorch, Go, FastAPI, and Docker. He writes to learn and is a professional introvert.
2. Slicing supports a step parameter. a = "Jack and Jill went up the hill". (user1 , user2) = a.split()[0:4:2] #picks 1 and 3 element in the list. but while it's possible to write funky oneliners in Python for sure it's not the best language for that kind of exercise. answered Nov 30, 2011 at 19:16. 6502.
Simultaneous assignment in python 3.3. Ask Question Asked 8 years, 7 months ago. Modified 8 years, 7 months ago. Viewed 926 times 0 I am a newbie at python programming.I am referring to a book "Python Programming: An Introduction to Computer Science." Which is oriented to python 2.Since I cannot lay my hands upon a basic book oriented to python ...