(airflow) branch main updated: Move `duckdb` & `pandas` import in tutorial DAG into task (#35964)

[bolke]

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bolke pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/airflow.git


The following commit(s) were added to refs/heads/main by this push:
     new f0ba2dced9 Move `duckdb` & `pandas` import in tutorial DAG into task 
(#35964)
f0ba2dced9 is described below

commit f0ba2dced92c767367aaf0fa3147942b4a576f92
Author: Ephraim Anierobi <splendidzig...@gmail.com>
AuthorDate: Thu Nov 30 07:46:35 2023 +0100

    Move `duckdb` & `pandas` import in tutorial DAG into task (#35964)
    
    This improves the code as per best practices and avoids import
    error if duckdb is not installed
---
 airflow/example_dags/tutorial_objectstorage.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/airflow/example_dags/tutorial_objectstorage.py 
b/airflow/example_dags/tutorial_objectstorage.py
index 47db595c24..11d817400d 100644
--- a/airflow/example_dags/tutorial_objectstorage.py
+++ b/airflow/example_dags/tutorial_objectstorage.py
@@ -47,7 +47,6 @@ base = ObjectStoragePath("s3://airflow-tutorial-data/", 
conn_id="aws_default")
 # [END create_object_storage_path]
 
 
-# [START instantiate_dag]
 @dag(
     schedule=None,
     start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
@@ -62,9 +61,6 @@ def tutorial_objectstorage():
     located
     
[here](https://airflow.apache.org/docs/apache-airflow/stable/tutorial/objectstorage.html)
     """
-    # [END instantiate_dag]
-    import duckdb
-    import pandas as pd
 
     # [START get_air_quality_data]
     @task
@@ -74,6 +70,8 @@ def tutorial_objectstorage():
         This task gets air quality data from the Finnish Meteorological 
Institute's
         open data API. The data is saved as parquet.
         """
+        import pandas as pd
+
         execution_date = kwargs["logical_date"]
         start_time = kwargs["data_interval_start"]
 
@@ -113,6 +111,8 @@ def tutorial_objectstorage():
         #### Analyze
         This task analyzes the air quality data, prints the results
         """
+        import duckdb
+
         conn = duckdb.connect(database=":memory:")
         conn.register_filesystem(path.fs)
         conn.execute(f"CREATE OR REPLACE TABLE airquality_urban AS SELECT * 
FROM read_parquet('{path}')")