docs-v2/content/influxdb3/clustered/reference/client-libraries/flight/python-flight.md

title	description	menu	influxdb3/clustered/tags	aliases	weight	list_code_example
Python Flight client	The Python Flight client integrates with Python scripts and applications to query data stored in InfluxDB.	influxdb3_clustered name parent identifier Python Arrow Flight clients python-flight-client	Flight client Python gRPC SQL Flight SQL client libraries	/influxdb3/clustered/reference/client-libraries/flight-sql/python-flightsql/	201	```py from pyarrow.flight import FlightClient, Ticket, FlightCallOptions import json import pandas import tabulate # Downsampling query groups data into 2-hour bins sql=""" SELECT DATE_BIN(INTERVAL '2 hours', time, '1970-01-01T00:00:00Z') AS time, room, selector_max(temp, time)['value'] AS 'max temp', selector_min(temp, time)['value'] AS 'min temp', avg(temp) AS 'average temp' FROM home GROUP BY 1, room ORDER BY room, 1""" flight_ticket = Ticket(json.dumps({ "namespace_name": "DATABASE_NAME", "sql_query": sql, "query_type": "sql" })) token = (b"authorization", bytes(f"Bearer DATABASE_TOKEN".encode('utf-8'))) options = FlightCallOptions(headers=[token]) client = FlightClient(f"grpc+tls://{{< influxdb/host >}}:443") reader = client.do_get(flight_ticket, options) arrow_table = reader.read_all() ```

Apache Arrow Python bindings integrate with Python scripts and applications to query data stored in InfluxDB.

{{% note %}}

Use InfluxDB v3 client libraries

We recommend using the influxdb3-python Python client library for integrating InfluxDB v3 with your Python application code.

InfluxDB v3 client libraries wrap Apache Arrow Flight clients and provide convenient methods for writing, querying, and processing data stored in {{% product-name %}}. Client libraries can query using SQL or InfluxQL. {{% /note %}}

The following examples show how to use the pyarrow.flight and pandas Python modules to query and format data stored in an {{% product-name %}} database:

{{% code-tabs-wrapper %}} {{% code-tabs %}} SQL InfluxQL {{% /code-tabs %}}

{{% code-tab-content %}}

{{% code-placeholders "DATABASE_NAME|DATABASE_TOKEN" %}}

# Using pyarrow>=12.0.0 FlightClient
from pyarrow.flight import FlightClient, Ticket, FlightCallOptions 
import json
import pandas
import tabulate

# Downsampling query groups data into 2-hour bins
sql="""
  SELECT DATE_BIN(INTERVAL '2 hours', time) AS time,
    room,
    selector_max(temp, time)['value'] AS 'max temp',
    selector_min(temp, time)['value'] AS 'min temp',
    avg(temp) AS 'average temp'
  FROM home
  GROUP BY
    1,
    room
  ORDER BY room, 1"""
  
flight_ticket = Ticket(json.dumps({
  "namespace_name": "DATABASE_NAME",
  "sql_query": sql,
  "query_type": "sql"
}))

token = (b"authorization", bytes(f"Bearer DATABASE_TOKEN".encode('utf-8')))
options = FlightCallOptions(headers=[token])
client = FlightClient(f"grpc+tls://{{< influxdb/host >}}:443")

reader = client.do_get(flight_ticket, options)
arrow_table = reader.read_all()
# Use pyarrow and pandas to view and analyze data
data_frame = arrow_table.to_pandas()
print(data_frame.to_markdown())

{{% /code-placeholders %}}

{{% /code-tab-content %}} {{% code-tab-content %}}

{{% code-placeholders "DATABASE_NAME|DATABASE_TOKEN" %}}

# Using pyarrow>=12.0.0 FlightClient
from pyarrow.flight import FlightClient, Ticket, FlightCallOptions 
import json
import pandas
import tabulate

# Downsampling query groups data into 2-hour bins
influxql="""
  SELECT FIRST(temp)
  FROM home 
  WHERE room = 'kitchen'
    AND time >= now() - 100d
    AND time <= now() - 10d
  GROUP BY time(2h)"""
  
flight_ticket = Ticket(json.dumps({
  "namespace_name": "DATABASE_NAME",
  "sql_query": influxql,
  "query_type": "influxql"
}))

token = (b"authorization", bytes(f"Bearer DATABASE_TOKEN".encode('utf-8')))
options = FlightCallOptions(headers=[token])
client = FlightClient(f"grpc+tls://{{< influxdb/host >}}:443")

reader = client.do_get(flight_ticket, options)
arrow_table = reader.read_all()
# Use pyarrow and pandas to view and analyze data
data_frame = arrow_table.to_pandas()
print(data_frame.to_markdown())

{{% /code-placeholders %}}

{{% /code-tab-content %}}

Replace the following:

• {{% code-placeholder-key %}}DATABASE_NAME{{% /code-placeholder-key %}}: your {{% product-name %}} database
• {{% code-placeholder-key %}}DATABASE_TOKEN{{% /code-placeholder-key %}}: a database token with sufficient permissions to the specified database

{{% /code-tabs-wrapper %}}