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PyStarburst DataFrame API allows you to query and transform data in Starburst products in a data pipeline without having to download the data locally.
PyStarburst DataFrame API allows you to query and transform data in Starburst products in a data pipeline without having to download the data locally.
See the PyStarburst API documentation and the examples repository.
Install pystarburst
pip install pystarburst
The parameters are the same connect parameters as in Trino Python Client.
from pystarburst import Session
connection_parameters = {
"host": "localhost",
"port": 8080,
"user": "admin",
"catalog": "tpch",
"schema": "tiny"
}
session = Session.builder.configs(connection_parameters).create()
from pystarburst import Session
session = Session.builder.configs({ ... }).create()
session.sql("SELECT 1 as a").show()
from pystarburst import Session
session = Session.builder.configs({ ... }).create()
df = session.table("nation")
print(df.schema)
df.show()
from pystarburst import Session
session = Session.builder.configs({ ... }).create()
df = session.table("nation")
df.filter(df.col("regionkey") == 0).show()
from pystarburst import Session
session = Session.builder.configs({ ... }).create()
df = session.table("nation")
df.filter(df.col("regionkey") == 0).show()
from pystarburst import Session
from pystarburst.functions import col
session = Session.builder.configs({ ... }).create()
df = session.table("nation")
df.agg((col("regionkey"), "max"), (col("regionkey"), "avg")).show()
FAQs
PyStarburst DataFrame API allows you to query and transform data in Starburst products in a data pipeline without having to download the data locally.
We found that pystarburst demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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