v6d_etl_pandas

load(order_summary[“total_order_value”])


Providers:

Run this DAG

1. Install the Astronomer CLI:Skip if you already have the CLI

2. Initate the project in a local directory:

3. Copy and paste the code below into a file in the

dags
directory.

4. Add the following to your

requirements.txt
file:

5. Run the DAG from the local directory where the project was initiated:

#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
#
# Adapted from example dags in Airflow's documentation, see also
#
# https://airflow.apache.org/docs/apache-airflow/stable/tutorial_taskflow_api.html
#
import numpy as np
import pandas as pd
from airflow.decorators import dag, task
from airflow.utils.dates import days_ago
default_args = {
'owner': 'airflow',
}
@dag(default_args=default_args, schedule_interval=None, start_date=days_ago(2), tags=['example'])
def taskflow_etl_pandas():
@task()
def extract():
order_data_dict = pd.DataFrame({'a': np.random.rand(100000), 'b': np.random.rand(100000)})
return order_data_dict
@task(multiple_outputs=True)
def transform(order_data_dict: dict):
return {"total_order_value": order_data_dict["a"].sum()}
@task()
def load(total_order_value: float):
print(f"Total order value is: {total_order_value:.2f}")
order_data = extract()
order_summary = transform(order_data)
load(order_summary["total_order_value"])
taskflow_etl_pandas_dag = taskflow_etl_pandas()