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External Python DAG

If you need additional libraries for your DAG such as pandas, let us know so that we can configure them in your environment.


Below we make use of a python_scripts folder inside the orchestrate folder and develop as a best practice we locate our custom scripts in this location.



import pandas as pd

def print_sample_dataframe():

    # Creating a simple DataFrame
    data = {'Name': ['Alice', 'Bob', 'Charlie'],
            'Age': [25, 30, 35],
            'City': ['New York', 'San Francisco', 'Los Angeles']}

    df = pd.DataFrame(data)

    # Displaying the DataFrame
    print("DataFrame created using Pandas:")



Create a DAG in the dags folder.

To run the custom script from an Airflow DAG, you will use the DatacovesBashOperator as seen in the python_task below.


See Datacoves Operators documentation for more information on the Datacoves Airflow Operators.

from airflow.decorators import dag
from operators.datacoves.bash import DatacovesBashOperator
from pendulum import datetime

# Only here for reference, this is automatically activated by Datacoves Operator
DATACOVES_VIRTUAL_ENV = "/opt/datacoves/virtualenvs/main/bin/activate"

        "start_date": datetime(2022, 10, 10),
        "owner": "Noel Gomez",
        "email": "gomezn@example.com",
        "email_on_failure": True,
    description="Datacoves Sample dag",
    # This is a regular CRON schedule. Helpful resources
    # https://cron-ai.vercel.app/
    # https://crontab.guru/
    schedule_interval="0 0 1 */12 *",
def datacoves_sample_dag():

    # This is calling an external Python file after activating the venv
    # use this instead of the Airflow Python Operator
    python_task = DatacovesBashOperator(
        task_id = "run_python_script",
        # Virtual Environment is automatically activated. Can be set to False to access Airflow environment variables.
        # activate_venv=True,
        bash_command = "python orchestrate/python_scripts/sample_script.py"

# Invoke Dag
dag = datacoves_sample_dag()