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This section outlines high-level asyncio APIs to work with coroutinesand Tasks.

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In newest VSCode for MAC need to modify the 'code-runner.executorMap' setting set python: 'python3' – d1820. Feb 15 '17 at 1:59. Add a comment 2. Windows 10 1803 (17134.407) Python 3.7.1 pipenv 2018.10.13 VSCode 1.29.1 Code Runner 0.9.5. When running a Python script using Code Runner the virtualenv is not activated before the code is run in the terminal for the first time, causing my code fail.

Coroutines declared with the async/await syntax is thepreferred way of writing asyncio applications. For example, the followingsnippet of code (requires Python 3.7+) prints “hello”, waits 1 second,and then prints “world”:

Note that simply calling a coroutine will not schedule it tobe executed:

To actually run a coroutine, asyncio provides three main mechanisms:

• The asyncio.run() function to run the top-levelentry point “main()” function (see the above example.)

• Awaiting on a coroutine. The following snippet of code willprint “hello” after waiting for 1 second, and then print “world”after waiting for another 2 seconds:

  Expected output:

• The asyncio.create_task() function to run coroutinesconcurrently as asyncio Tasks.

  Let’s modify the above example and run two say_after coroutinesconcurrently:

  Note that expected output now shows that the snippet runs1 second faster than before:

We say that an object is an awaitable object if it can be usedin an await expression. Many asyncio APIs are designed toaccept awaitables.

There are three main types of awaitable objects:coroutines, Tasks, and Futures.

Coroutines

Python coroutines are awaitables and therefore can be awaited fromother coroutines:

Important

In this documentation the term “coroutine” can be used fortwo closely related concepts:

• a coroutine function: an asyncdef function;

• a coroutine object: an object returned by calling acoroutine function.

asyncio also supports legacy generator-based coroutines.

Tasks

Tasks are used to schedule coroutines concurrently.

When a coroutine is wrapped into a Task with functions likeasyncio.create_task() the coroutine is automaticallyscheduled to run soon:

Futures

A Future is a special low-level awaitable object thatrepresents an eventual result of an asynchronous operation.

When a Future object is awaited it means that the coroutine willwait until the Future is resolved in some other place.

Future objects in asyncio are needed to allow callback-based codeto be used with async/await.

Normally there is no need to create Future objects at theapplication level code.

Future objects, sometimes exposed by libraries and some asyncioAPIs, can be awaited:

Serial killer monologue. A good example of a low-level function that returns a Future objectis loop.run_in_executor().

asyncio.run(coro, *, debug=False)¶

Execute the coroutinecoro and return the result.

This function runs the passed coroutine, taking care ofmanaging the asyncio event loop, finalizing asynchronousgenerators, and closing the threadpool.

This function cannot be called when another asyncio event loop isrunning in the same thread.

If debug is True, the event loop will be run in debug mode.

This function always creates a new event loop and closes it atthe end. It should be used as a main entry point for asyncioprograms, and should ideally only be called once.

Example:

New in version 3.7.

Changed in version 3.9: Updated to use loop.shutdown_default_executor().

Note

The source code for asyncio.run() can be found inLib/asyncio/runners.py.

asyncio.create_task(coro, *, name=None)¶

Wrap the corocoroutine into a Taskand schedule its execution. Return the Task object.

If name is not None, it is set as the name of the task usingTask.set_name().

The task is executed in the loop returned by get_running_loop(),RuntimeError is raised if there is no running loop incurrent thread.

This function has been added in Python 3.7. Prior toPython 3.7, the low-level asyncio.ensure_future() functioncan be used instead:

New in version 3.7.

coroutine asyncio.sleep(delay, result=None, *, loop=None)¶

Block for delay seconds.

If result is provided, it is returned to the callerwhen the coroutine completes.

sleep() always suspends the current task, allowing other tasksto run.

Setting the delay to 0 provides an optimized path to allow othertasks to run. This can be used by long-running functions to avoidblocking the event loop for the full duration of the function call.

Deprecated since version 3.8, will be removed in version 3.10: The loop parameter.

Example of coroutine displaying the current date every secondfor 5 seconds:

awaitable asyncio.gather(*aws, loop=None, return_exceptions=False)¶

Run awaitable objects in the awssequence concurrently.

If any awaitable in aws is a coroutine, it is automaticallyscheduled as a Task.

If all awaitables are completed successfully, the result is anaggregate list of returned values. The order of result valuescorresponds to the order of awaitables in aws.

If return_exceptions is False (default), the firstraised exception is immediately propagated to the task thatawaits on gather(). Other awaitables in the aws sequencewon’t be cancelled and will continue to run.

If return_exceptions is True, exceptions are treated thesame as successful results, and aggregated in the result list.

If gather() is cancelled, all submitted awaitables(that have not completed yet) are also cancelled.

If any Task or Future from the aws sequence is cancelled, it istreated as if it raised CancelledError – the gather()call is not cancelled in this case. This is to prevent thecancellation of one submitted Task/Future to cause otherTasks/Futures to be cancelled.

Deprecated since version 3.8, will be removed in version 3.10: The loop parameter.

Example:

Visionary serial killer examples. Note

If return_exceptions is False, cancelling gather() after ithas been marked done won’t cancel any submitted awaitables.For instance, gather can be marked done after propagating anexception to the caller, therefore, calling gather.cancel()after catching an exception (raised by one of the awaitables) fromgather won’t cancel any other awaitables.

Changed in version 3.7: If the gather itself is cancelled, the cancellation ispropagated regardless of return_exceptions.

awaitable asyncio.shield(aw, *, loop=None)¶

Protect an awaitable objectfrom being cancelled.

If aw is a coroutine it is automatically scheduled as a Task.

The statement:

is equivalent to:

except that if the coroutine containing it is cancelled, theTask running in something() is not cancelled. From the pointof view of something(), the cancellation did not happen.Although its caller is still cancelled, so the “await” expressionstill raises a CancelledError.

If something() is cancelled by other means (i.e. from withinitself) that would also cancel shield().

If it is desired to completely ignore cancellation (not recommended)the shield() function should be combined with a try/exceptclause, as follows:

Deprecated since version 3.8, will be removed in version 3.10: The loop parameter.

coroutine asyncio.wait_for(aw, timeout, *, loop=None)¶

Wait for the awawaitableto complete with a timeout.

If aw is a coroutine it is automatically scheduled as a Task.

timeout can either be None or a float or int number of secondsto wait for. If timeout is None, block until the futurecompletes.

If a timeout occurs, it cancels the task and raisesasyncio.TimeoutError.

To avoid the task cancellation,wrap it in shield().

The function will wait until the future is actually cancelled,so the total wait time may exceed the timeout. If an exceptionhappens during cancellation, it is propagated.

If the wait is cancelled, the future aw is also cancelled.

Deprecated since version 3.8, will be removed in version 3.10: The loop parameter.

Example:

Changed in version 3.7: When aw is cancelled due to a timeout, wait_for waitsfor aw to be cancelled. Previously, it raisedasyncio.TimeoutError immediately.

coroutine asyncio.wait(aws, *, loop=None, timeout=None, return_when=ALL_COMPLETED)¶

Run awaitable objects in the awsiterable concurrently and block until the condition specifiedby return_when.

The aws iterable must not be empty.

Returns two sets of Tasks/Futures: (done,pending).

Usage:

timeout (a float or int), if specified, can be used to controlthe maximum number of seconds to wait before returning.

Note that this function does not raise asyncio.TimeoutError.Futures or Tasks that aren’t done when the timeout occurs are simplyreturned in the second set.

return_when indicates when this function should return. It mustbe one of the following constants:

Tamil dubbed movie munnoru paruthi veerargal free download. Constant	Description
FIRST_COMPLETED	The function will return when anyfuture finishes or is cancelled.
FIRST_EXCEPTION	The function will return when anyfuture finishes by raising anexception. If no future raises anexception then it is equivalent toALL_COMPLETED.
ALL_COMPLETED	The function will return when allfutures finish or are cancelled.

Unlike wait_for(), wait() does not cancel thefutures when a timeout occurs.

Deprecated since version 3.8: If any awaitable in aws is a coroutine, it is automaticallyscheduled as a Task. Passing coroutines objects towait() directly is deprecated as it leads toconfusing behavior.

Deprecated since version 3.8, will be removed in version 3.10: The loop parameter.

Note

wait() schedules coroutines as Tasks automatically and laterreturns those implicitly created Task objects in (done,pending)sets. Therefore the following code won’t work as expected:

Here is how the above snippet can be fixed:

Deprecated since version 3.8, will be removed in version 3.11: Passing coroutine objects to wait() directly isdeprecated.

asyncio.as_completed(aws, *, loop=None, timeout=None)¶

Run awaitable objects in the awsiterable concurrently. Return an iterator of coroutines.Each coroutine returned can be awaited to get the earliest nextresult from the iterable of the remaining awaitables.

Raises asyncio.TimeoutError if the timeout occurs beforeall Futures are done.

Deprecated since version 3.8, will be removed in version 3.10: The loop parameter.

Example:

coroutine asyncio.to_thread(func, /, *args, **kwargs)¶

Asynchronously run function func in a separate thread.

Any *args and **kwargs supplied for this function are directly passedto func. Also, the current contextvars.Context is propagated,allowing context variables from the event loop thread to be accessed in theseparate thread.

Return a coroutine that can be awaited to get the eventual result of func.

This coroutine function is primarily intended to be used for executingIO-bound functions/methods that would otherwise block the event loop ifthey were ran in the main thread. For example:

Directly calling blocking_io() in any coroutine would block the event loopfor its duration, resulting in an additional 1 second of run time. Instead,by using asyncio.to_thread(), we can run it in a separate thread withoutblocking the event loop.

Note

Due to the GIL, asyncio.to_thread() can typically only be usedto make IO-bound functions non-blocking. However, for extension modulesthat release the GIL or alternative Python implementations that don’thave one, asyncio.to_thread() can also be used for CPU-bound functions.

asyncio.run_coroutine_threadsafe(coro, loop)¶

Submit a coroutine to the given event loop. Thread-safe.

Return a concurrent.futures.Future to wait for the resultfrom another OS thread.

This function is meant to be called from a different OS threadthan the one where the event loop is running. Example:

If an exception is raised in the coroutine, the returned Futurewill be notified. It can also be used to cancel the task inthe event loop:

See the concurrency and multithreadingsection of the documentation.

Unlike other asyncio functions this function requires the loopargument to be passed explicitly.

New in version 3.5.1.

asyncio.current_task(loop=None)¶

Return the currently running Task instance, or None ifno task is running.

If loop is Noneget_running_loop() is used to getthe current loop.

asyncio.all_tasks(loop=None)¶

Return a set of not yet finished Task objects run bythe loop.

If loop is None, get_running_loop() is used for gettingcurrent loop.

New in version 3.7.

class asyncio.Task(coro, *, loop=None, name=None)¶

A Future-like object that runs a Pythoncoroutine. Not thread-safe.

Tasks are used to run coroutines in event loops.If a coroutine awaits on a Future, the Task suspendsthe execution of the coroutine and waits for the completionof the Future. When the Future is done, the execution ofthe wrapped coroutine resumes.

Event loops use cooperative scheduling: an event loop runsone Task at a time. While a Task awaits for the completion of aFuture, the event loop runs other Tasks, callbacks, or performsIO operations.

Use the high-level asyncio.create_task() function to createTasks, or the low-level loop.create_task() orensure_future() functions. Manual instantiation of Tasksis discouraged.

To cancel a running Task use the cancel() method. Calling itwill cause the Task to throw a CancelledError exception intothe wrapped coroutine. If a coroutine is awaiting on a Futureobject during cancellation, the Future object will be cancelled.

cancelled() can be used to check if the Task was cancelled.The method returns True if the wrapped coroutine did notsuppress the CancelledError exception and was actuallycancelled.

asyncio.Task inherits from Future all of itsAPIs except Future.set_result() andFuture.set_exception().

Tasks support the contextvars module. When a Taskis created it copies the current context and later runs itscoroutine in the copied context.

Changed in version 3.7: Added support for the contextvars module.

Deprecated since version 3.8, will be removed in version 3.10: The loop parameter.

cancel(msg=None)¶

Request the Task to be cancelled.

This arranges for a CancelledError exception to be throwninto the wrapped coroutine on the next cycle of the event loop.

The coroutine then has a chance to clean up or even deny therequest by suppressing the exception with a try …… exceptCancelledError … finally block.Therefore, unlike Future.cancel(), Task.cancel() doesnot guarantee that the Task will be cancelled, althoughsuppressing cancellation completely is not common and is activelydiscouraged.

The following example illustrates how coroutines can interceptthe cancellation request:

cancelled()¶

Return True if the Task is cancelled.

The Task is cancelled when the cancellation was requested withcancel() and the wrapped coroutine propagated theCancelledError exception thrown into it.

done()¶

Return True if the Task is done.

A Task is done when the wrapped coroutine either returneda value, raised an exception, or the Task was cancelled.

result()¶

Return the result of the Task.

If the Task is done, the result of the wrapped coroutineis returned (or if the coroutine raised an exception, thatexception is re-raised.)

If the Task has been cancelled, this method raisesa CancelledError exception.

If the Task’s result isn’t yet available, this method raisesa InvalidStateError exception.

exception()¶

Return the exception of the Task.

If the wrapped coroutine raised an exception that exceptionis returned. If the wrapped coroutine returned normallythis method returns None.

If the Task has been cancelled, this method raises aCancelledError exception.

If the Task isn’t done yet, this method raises anInvalidStateError exception.

add_done_callback(callback, *, context=None)¶

Add a callback to be run when the Task is done.

This method should only be used in low-level callback-based code.

See the documentation of Future.add_done_callback()for more details.

remove_done_callback(callback)¶

Remove callback from the callbacks list.

This method should only be used in low-level callback-based code.

See the documentation of Future.remove_done_callback()for more details.

get_stack(*, limit=None)¶

Return the list of stack frames for this Task.

If the wrapped coroutine is not done, this returns the stackwhere it is suspended. If the coroutine has completedsuccessfully or was cancelled, this returns an empty list.If the coroutine was terminated by an exception, this returnsthe list of traceback frames.

The frames are always ordered from oldest to newest.

Only one stack frame is returned for a suspended coroutine.

The optional limit argument sets the maximum number of framesto return; by default all available frames are returned.The ordering of the returned list differs depending on whethera stack or a traceback is returned: the newest frames of astack are returned, but the oldest frames of a traceback arereturned. (This matches the behavior of the traceback module.)

print_stack(*, limit=None, file=None)¶

Print the stack or traceback for this Task.

This produces output similar to that of the traceback modulefor the frames retrieved by get_stack().

The limit argument is passed to get_stack() directly.

The file argument is an I/O stream to which the outputis written; by default output is written to sys.stderr.

get_coro()¶

Return the coroutine object wrapped by the Task.

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New in version 3.8.

get_name()¶

Return the name of the Task.

If no name has been explicitly assigned to the Task, the defaultasyncio Task implementation generates a default name duringinstantiation.

set_name(value)¶

Set the name of the Task.

The value argument can be any object, which is thenconverted to a string.

In the default Task implementation, the name will be visiblein the repr() output of a task object.

New in version 3.8.

Note

Support for generator-based coroutines is deprecated andis scheduled for removal in Python 3.10.

Generator-based coroutines predate async/await syntax. They arePython generators that use yieldfrom expressions to awaiton Futures and other coroutines.

Generator-based coroutines should be decorated with@asyncio.coroutine, although this is notenforced.

@asyncio.coroutine¶

Decorator to mark generator-based coroutines.

This decorator enables legacy generator-based coroutines to becompatible with async/await code:

This decorator should not be used for asyncdefcoroutines.

Deprecated since version 3.8, will be removed in version 3.10: Use asyncdef instead.

asyncio.iscoroutine(obj)¶

Return True if obj is a coroutine object.

This method is different from inspect.iscoroutine() becauseit returns True for generator-based coroutines.

asyncio.iscoroutinefunction(func)¶

Return True if func is a coroutine function.

This method is different from inspect.iscoroutinefunction()because it returns True for generator-based coroutine functionsdecorated with @coroutine.

Your Python code can be up on a code editor, IDE or a file. And, it won’t work unless you know how to execute your Python script.

In this blog post, we will take a look at 7 ways to execute Python code and scripts. No matter what your operating system is, your Python environment or the location of your code – we will show you how to execute that piece of code!

Table of Contents

1. Running Python Code Interactively
2. How are Python Script is Executed
3. How to Run Python Scripts
4. How to Run Python Scripts using Command Line
5. How to Run Python Code Interactively
6. Running Python Code from a Text Editor
7. Running Python Code from an IDE
8. How to Run Python Scripts from a File Manager
9. How to Run Python Scripts from Another Python Script

Where to run Python scripts and how?

You can run a Python script from:

1. OS Command line (also known as shell or Terminal)
2. Run Python scripts with a specific Python Version on Anaconda
3. Using a Crontab
4. Run a Python script using another Python script
5. Using FileManager
6. Using Python Interactive Mode
7. Using IDE or Code Editor

Running Python Code Interactively

To start an interactive session for Python code, simply open your Terminal or Command line and type in Python(or Python 3 depending on your Python version). And, as soon as you hit enter, you’ll be in the interactive mode.

Here’s how you enter interactive mode in Windows, Linux and MacOS.

Interactive Python Scripting Mode On Linux

Open up your Terminal.

It should look something like

Enter the Python script interactive mode after pressing “Enter”.

Interactive Python Scripting Mode On Mac OSX

Launching interactive Python script mode on Mac OS is pretty similar to Linux. The image below shows the interactive mode on Mac OS.

Interactive Python Scripting Mode On Windows

On Windows, go to your Command Prompt and write “python”. Once you hit enter you should see something like this:

Running Python Scripts Interactively

With interactive Python script mode, you can write code snippets and execute them to see if they give desired output or whether they fail.

Take an example of the for loop below.

Our code snippet was written to print everything including 0 and upto 5. So, what you see after print(i) is the output here.

To exit interactive Python script mode, write the following:

And, hit Enter. You should be back to the command line screen that you started with initially.

There are other ways to exit the interactive Python script mode too. With Linux you can simply to Ctrl + D and on Windows you need to press Ctrl + Z + Enter to exit.

Note that when you exit interactive mode, your Python scripts won’t be saved to a local file.

How are Python scripts executed?

A nice way to visualize what happens when you execute a Python script is by using the diagram below. The block represents a Python script (or function) we wrote, and each block within it, represents a line of code.

When you run this Python script, Python interpreter goes from top to bottom executing each line.

And, that’s how Python interpreter executes a Python script.

But that’s not it! There’s a lot more that happens.

Flow Chart of How Python Interpreter Runs Codes

Step 1: Your script or .py file is compiled and a binary format is generated. This new format is in either .pyc or .pyo.

Step 2: The binary file generated, is now read by the interpreter to execute instructions.

Think of them as a bunch of instructions that leads to the final outcome.

There are some benefits of inspecting bytecode. And, if you aim to turn yourself into a pro level Pythonista, you may want to learn and understand bytecode to write highly optimized Python scripts.

You can also use it to understand and guide your Python script’s design decisions. You can look at certain factors and understand why some functions/data structures are faster than others.

How to run Python scripts?

To run a Python script using command line, you need to first save your code as a local file.

Let’s take the case of our local Python file again. If you were to save it to a local .py file named python_script.py.

There are many ways to do that:

1. Create a Python script from command line and save it
2. Create a Python script using a text editor or IDE and save it

Saving a Python script from a code editor is pretty easy. Basically as simple as saving a text file.

But, to do it via Command line, there are a couple of steps involved.

First, head to your command line, and change your working directory to where you wish to save the Python script.

Once you are in the right directory, execute the following command in Terminal:

Once you hit enter, you’ll get into a command line interface that looks something like this:

Now, you can write a Python code here and easily run it using command line.

How to run Python scripts using command line?

Python scripts can be run using Python command over a command line interface. Make sure you specify the path to the script or have the same working directory. To execute your Python script(python_script.py) open command line and write python3 python_script.py

Replace python3 with python if your Python version is Python2.x.

Here’s what we saved in our python_script.py

And, the output on your command line looks something like this

Let’s say, we want to save the output of the Python code which is 0, 1, 2, 3, 4 – we use something called a pipe operator.

In our case, all we have to do is:

And, a file named “newfile.txt” would be created with our output saved in it.

How to run Python code interactively

There are more than 4 ways to run a Python script interactively. And, in the next few sections we will see all major ways to execute Python scripts.

Using Import to run your Python Scripts

We all use import module to load scripts and libraries extremely frequently. You can write your own Python script(let’s say code1.py) and import it into another code without writing the whole code in the new script again.

Here’s how you can import code1.py in your new Python script.

But, doing so would mean that you import everything that’s in code1.py to your Python code. That isn’t an issue till you start working in situations where your code has to be well optimized for performance, scalability and maintainability.

So, let’s say, we had a small function inside code1 that draws a beautiful chart e.g. chart_code1(). And, that function is the only reason why we wish to import the entire code1.py script. Rather than having to call the entire Python script, we can simply call the function instead.

Here’s how you would typically do it

And, you should be able to use chart_code1 in your new Python script as if it were present in your current Python code.

Next, let’s look at other ways to import Python code.

Using and importlib to run Python code

import_module() of importlib allows you to import and execute other Python scripts.

The way it works is pretty simple. For our Python script code1.py, all we have to do is:

There’s no need to add .py in import_module().

Let’s go through a case where we have complex directory structures and we wish to use importlib. Directory structure of the Python code we want to run is below:

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level1

|

+ – __init__.py

– level2

|

+ – __init__.py

– level3.py

In this case if you think you can do importlib.import_module(“level3”), you’ll get an error. This is called relative import, and the way you do it is by using a relative name with anchor explicit.

So, to run Python script level3.py, you can either do

or you can do

Run Python code using runpy

Runpy module locates and executes a Python script without importing it. Usage is pretty simple as you can easily call the module name inside of run_module().

To execute our code1.py module using runpy. Here’s what we will do.

Run Python Code Dynamically

We are going to take a look at exec() function to execute Python scripts dynamically. In Python 2, exec function was actually a statement.

Here’s how it helps you execute a Python code dynamically in case of a string.

Dynamic Code Was Executed

However, using exec() should be a last resort. As it is slow and unpredictable, try to see if there are any other better alternatives available.

Running Python Scripts from a Text Editor

To run Python script using a Python Text Editor you can use the default “run” command or use hot keys like Function + F5 or simply F5(depending on your OS).

Here’s an example of Python script being executed in IDLE.

Source: pitt.edu

However, note that you do not control the virtual environment like how you typically would from a command line interface execution.

That’s where IDEs and Advanced text editors are far better than Code Editors.

Running Python Scripts from an IDE

When it comes to executing scripts from an IDE, you can not only run your Python code, but also debug it and select the Python environment you would like to run it on.

While the IDE’s UI interface may vary, the process would be pretty much similar to save, run and edit a code.

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How to run Python scripts from a File Manager

What if there was a way to run a Python script just by double clicking on it? You can actually do that by creating executable files of your code. For example, in the case of Windows OS, you can simply create a .exe extension of your Python script and run it by double clicking on it.

How to run Python scripts from another Python script

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Although we haven’t already stated this, but, if you go back up and read, you’ll notice that you can:

1. Run a Python script via a command line that calls another Python script in it
2. Use a module like import to load a Python script

That’s it!

Key Takeaway

1. You can write a Python code in interactive and non interactive modes. Once you exit interactive mode, you lose the data. So, sudo nano your_python_filename.py it!
2. You can also run your Python Code via IDE, Code Editors or Command line
3. There are different ways to import a Python code and use it for another script. Pick wisely and look at the advantages and disadvantages.
4. Python reads the code you write, translates it into bytecodes, which are then used as instructions – all of that happen when you run a Python script. So, learn how to use bytecode to optimize your Python code.

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