:mod:`airflow.operators.python_operator`
========================================

.. py:module:: airflow.operators.python_operator


Module Contents
---------------

.. py:class:: PythonOperator(python_callable, op_args=None, op_kwargs=None, provide_context=False, templates_dict=None, templates_exts=None, *args, **kwargs)

   Bases: :class:`airflow.models.BaseOperator`

   Executes a Python callable

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:PythonOperator`

   :param python_callable: A reference to an object that is callable
   :type python_callable: python callable
   :param op_kwargs: a dictionary of keyword arguments that will get unpacked
       in your function
   :type op_kwargs: dict (templated)
   :param op_args: a list of positional arguments that will get unpacked when
       calling your callable
   :type op_args: list (templated)
   :param provide_context: if set to true, Airflow will pass a set of
       keyword arguments that can be used in your function. This set of
       kwargs correspond exactly to what you can use in your jinja
       templates. For this to work, you need to define `**kwargs` in your
       function header.
   :type provide_context: bool
   :param templates_dict: a dictionary where the values are templates that
       will get templated by the Airflow engine sometime between
       ``__init__`` and ``execute`` takes place and are made available
       in your callable's context after the template has been applied. (templated)
   :type templates_dict: dict[str]
   :param templates_exts: a list of file extensions to resolve while
       processing templated fields, for examples ``['.sql', '.hql']``
   :type templates_exts: list[str]

   .. attribute:: template_fields
      :annotation: = ['templates_dict', 'op_args', 'op_kwargs']

      

   .. attribute:: ui_color
      :annotation: = #ffefeb

      

   .. attribute:: shallow_copy_attrs
      :annotation: = ['python_callable', 'op_kwargs']

      

   
   .. method:: execute(self, context)



   
   .. method:: execute_callable(self)




.. py:class:: BranchPythonOperator

   Bases: :class:`airflow.operators.python_operator.PythonOperator`, :class:`airflow.models.SkipMixin`

   Allows a workflow to "branch" or follow a path following the execution
   of this task.

   It derives the PythonOperator and expects a Python function that returns
   a single task_id or list of task_ids to follow. The task_id(s) returned
   should point to a task directly downstream from {self}. All other "branches"
   or directly downstream tasks are marked with a state of ``skipped`` so that
   these paths can't move forward. The ``skipped`` states are propagated
   downstream to allow for the DAG state to fill up and the DAG run's state
   to be inferred.

   Note that using tasks with ``depends_on_past=True`` downstream from
   ``BranchPythonOperator`` is logically unsound as ``skipped`` status
   will invariably lead to block tasks that depend on their past successes.
   ``skipped`` states propagates where all directly upstream tasks are
   ``skipped``.

   
   .. method:: execute(self, context)




.. py:class:: ShortCircuitOperator

   Bases: :class:`airflow.operators.python_operator.PythonOperator`, :class:`airflow.models.SkipMixin`

   Allows a workflow to continue only if a condition is met. Otherwise, the
   workflow "short-circuits" and downstream tasks are skipped.

   The ShortCircuitOperator is derived from the PythonOperator. It evaluates a
   condition and short-circuits the workflow if the condition is False. Any
   downstream tasks are marked with a state of "skipped". If the condition is
   True, downstream tasks proceed as normal.

   The condition is determined by the result of `python_callable`.

   
   .. method:: execute(self, context)




.. py:class:: PythonVirtualenvOperator(python_callable, requirements=None, python_version=None, use_dill=False, system_site_packages=True, op_args=None, op_kwargs=None, provide_context=False, string_args=None, templates_dict=None, templates_exts=None, *args, **kwargs)

   Bases: :class:`airflow.operators.python_operator.PythonOperator`

   Allows one to run a function in a virtualenv that is created and destroyed
   automatically (with certain caveats).

   The function must be defined using def, and not be
   part of a class. All imports must happen inside the function
   and no variables outside of the scope may be referenced. A global scope
   variable named virtualenv_string_args will be available (populated by
   string_args). In addition, one can pass stuff through op_args and op_kwargs, and one
   can use a return value.
   Note that if your virtualenv runs in a different Python major version than Airflow,
   you cannot use return values, op_args, or op_kwargs. You can use string_args though.

   :param python_callable: A python function with no references to outside variables,
       defined with def, which will be run in a virtualenv
   :type python_callable: function
   :param requirements: A list of requirements as specified in a pip install command
   :type requirements: list[str]
   :param python_version: The Python version to run the virtualenv with. Note that
       both 2 and 2.7 are acceptable forms.
   :type python_version: str
   :param use_dill: Whether to use dill to serialize
       the args and result (pickle is default). This allow more complex types
       but requires you to include dill in your requirements.
   :type use_dill: bool
   :param system_site_packages: Whether to include
       system_site_packages in your virtualenv.
       See virtualenv documentation for more information.
   :type system_site_packages: bool
   :param op_args: A list of positional arguments to pass to python_callable.
   :type op_kwargs: list
   :param op_kwargs: A dict of keyword arguments to pass to python_callable.
   :type op_kwargs: dict
   :param provide_context: if set to true, Airflow will pass a set of
       keyword arguments that can be used in your function. This set of
       kwargs correspond exactly to what you can use in your jinja
       templates. For this to work, you need to define `**kwargs` in your
       function header.
   :type provide_context: bool
   :param string_args: Strings that are present in the global var virtualenv_string_args,
       available to python_callable at runtime as a list[str]. Note that args are split
       by newline.
   :type string_args: list[str]
   :param templates_dict: a dictionary where the values are templates that
       will get templated by the Airflow engine sometime between
       ``__init__`` and ``execute`` takes place and are made available
       in your callable's context after the template has been applied
   :type templates_dict: dict of str
   :param templates_exts: a list of file extensions to resolve while
       processing templated fields, for examples ``['.sql', '.hql']``
   :type templates_exts: list[str]

   
   .. method:: execute_callable(self)



   
   .. method:: _pass_op_args(self)



   
   .. method:: _execute_in_subprocess(self, cmd)



   
   .. method:: _write_string_args(self, filename)



   
   .. method:: _write_args(self, input_filename)



   
   .. method:: _read_result(self, output_filename)



   
   .. method:: _write_script(self, script_filename)



   
   .. method:: _generate_virtualenv_cmd(self, tmp_dir)



   
   .. method:: _generate_pip_install_cmd(self, tmp_dir)



   
   .. staticmethod:: _generate_python_cmd(tmp_dir, script_filename, input_filename, output_filename, string_args_filename)



   
   .. method:: _generate_python_code(self)