---
title: Python Flight client
description: The Python Flight client integrates with Python scripts and applications to query data stored in InfluxDB.
url: https://docs.influxdata.com/influxdb3/clustered/reference/client-libraries/flight/python-flight/
estimated_tokens: 839
publisher: InfluxData
canonical: https://docs.influxdata.com/influxdb3/clustered/reference/client-libraries/flight/python-flight/
date: '2026-05-15T15:46:14-06:00'
lastmod: '2026-05-15T15:46:14-06:00'
---

[Apache Arrow Python bindings](https://arrow.apache.org/docs/python/index.html) integrate with Python scripts and applications to query data stored in InfluxDB.

#### Use InfluxDB 3 client libraries

We recommend using the [`influxdb3-python` Python client library](/influxdb3/clustered/reference/client-libraries/v3/python/) for integrating InfluxDB 3 with your Python application code.

[InfluxDB 3 client libraries](/influxdb3/clustered/reference/client-libraries/v3/) wrap Apache Arrow Flight clients
and provide convenient methods for [writing](/influxdb3/clustered/get-started/write/#write-line-protocol-to-influxdb), [querying](/influxdb3/clustered/get-started/query/#execute-an-sql-query), and processing data stored in InfluxDB Clustered.
Client libraries can query using SQL or InfluxQL.

The following examples show how to use the `pyarrow.flight` and `pandas` Python modules to query and format data stored in an InfluxDB Clustered database:

[SQL](#sql-python)[InfluxQL](#influxql-python)

```
# 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://cluster-host.com: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())
```

```
# 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://cluster-host.com: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())
```

Replace the following:

* `DATABASE_NAME`: your InfluxDB Clustered database
* `DATABASE_TOKEN`: a [database token](/influxdb3/clustered/admin/tokens/#database-tokens) with sufficient permissions to the specified database

[Flight client](/influxdb3/clustered/tags/flight-client/)[Python](/influxdb3/clustered/tags/python/)[gRPC](/influxdb3/clustered/tags/grpc/)[SQL](/influxdb3/clustered/tags/sql/)[Flight SQL](/influxdb3/clustered/tags/flight-sql/)[client libraries](/influxdb3/clustered/tags/client-libraries/)