Use the PythonOperator to execute Python callables.


def print_context(ds=None, **kwargs):
    """Print the Airflow context and ds variable from the context."""
    return 'Whatever you return gets printed in the logs'

run_this = print_context()

Passing in arguments

Use the op_args and op_kwargs arguments to pass additional arguments to the Python callable.


# Generate 5 sleeping tasks, sleeping from 0.0 to 0.4 seconds respectively
for i in range(5):

    def my_sleeping_function(random_base):
        """This is a function that will run within the DAG execution"""

    sleeping_task = my_sleeping_function(random_base=float(i) / 10)

    run_this >> sleeping_task


Airflow passes in an additional set of keyword arguments: one for each of the Jinja template variables and a templates_dict argument.

The templates_dict argument is templated, so each value in the dictionary is evaluated as a Jinja template.


Use the PythonVirtualenvOperator to execute Python callables inside a new Python virtual environment.


        task_id="virtualenv_python", requirements=["colorama==0.4.0"], system_site_packages=False
    def callable_virtualenv():
        Example function that will be performed in a virtual environment.

        Importing at the module level ensures that it will not attempt to import the
        library before it is installed.
        from time import sleep

        from colorama import Back, Fore, Style

        print(Fore.RED + 'some red text')
        print(Back.GREEN + 'and with a green background')
        print(Style.DIM + 'and in dim text')
        for _ in range(10):
            print(Style.DIM + 'Please wait...', flush=True)

    virtualenv_task = callable_virtualenv()

Passing in arguments

You can use the op_args and op_kwargs arguments the same way you use it in the PythonOperator. Unfortunately we currently do not support to serialize var and ti / task_instance due to incompatibilities with the underlying library. For Airflow context variables make sure that you either have access to Airflow through setting system_site_packages to True or add apache-airflow to the requirements argument. Otherwise you won’t have access to the most context variables of Airflow in op_kwargs. If you want the context related to datetime objects like data_interval_start you can add pendulum and lazy_object_proxy.

If additional parameters for package installation are needed pass them in requirements.txt as in the example below:

SomePackage==0.2.1 --pre --index-url
AnotherPackage==1.4.3 --no-index --find-links /my/local/archives

All supported options are listed in the requirements file format.


Use the ShortCircuitOperator to control whether a pipeline continues if a condition is satisfied or a truthy value is obtained. The evaluation of this condition and truthy value is done via the output of a python_callable. If the python_callable returns True or a truthy value, the pipeline is allowed to continue and an XCom of the output will be pushed. If the output is False or a falsy value, the pipeline will be short-circuited based on the configured short-circuiting (more on this later). In the example below, the tasks that follow the “condition_is_True” ShortCircuitOperator will execute while the tasks downstream of the “condition_is_False” ShortCircuitOperator will be skipped.


cond_true = ShortCircuitOperator(
    python_callable=lambda: True,

cond_false = ShortCircuitOperator(
    python_callable=lambda: False,

ds_true = [EmptyOperator(task_id='true_' + str(i)) for i in [1, 2]]
ds_false = [EmptyOperator(task_id='false_' + str(i)) for i in [1, 2]]

chain(cond_true, *ds_true)
chain(cond_false, *ds_false)

The “short-circuiting” can be configured to either respect or ignore the trigger rule defined for downstream tasks. If ignore_downstream_trigger_rules is set to True, the default configuration, all downstream tasks are skipped without considering the trigger_rule defined for tasks. If this parameter is set to False, the direct downstream tasks are skipped but the specified trigger_rule for other subsequent downstream tasks are respected. In this short-circuiting configuration, the operator assumes the direct downstream task(s) were purposely meant to be skipped but perhaps not other subsequent tasks. This configuration is especially useful if only part of a pipeline should be short-circuited rather than all tasks which follow the ShortCircuitOperator task.

In the example below, notice that the ShortCircuitOperator task is configured to respect downstream trigger rules. This means while the tasks that follow the “short_circuit” ShortCircuitOperator task will be skipped since the python_callable returns False, “task_7” will still execute as its set to execute when upstream tasks have completed running regardless of status (i.e. the TriggerRule.ALL_DONE trigger rule).


[task_1, task_2, task_3, task_4, task_5, task_6] = [
    EmptyOperator(task_id=f"task_{i}") for i in range(1, 7)

task_7 = EmptyOperator(task_id="task_7", trigger_rule=TriggerRule.ALL_DONE)

short_circuit = ShortCircuitOperator(
    task_id="short_circuit", ignore_downstream_trigger_rules=False, python_callable=lambda: False

chain(task_1, [task_2, short_circuit], [task_3, task_4], [task_5, task_6], task_7)

Passing in arguments

Both the op_args and op_kwargs arguments can be used in same way as described for the PythonOperator.


Jinja templating can be used in same way as described for the PythonOperator.

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