query.py

from influxdb_client import InfluxDBClient, Point, Dialect
from influxdb_client.client.write_api import SYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()

"""
Prepare data
"""

_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)

write_api.write(bucket="my-bucket", record=[_point1, _point2])

"""
Query: using Table structure
"""
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

for table in tables:
    print(table)
    for record in table.records:
        print(record.values)

print()
print()

"""
Query: using Stream
"""
records = query_api.query_stream('from(bucket:"my-bucket") |> range(start: -10m)')

for record in records:
    print(f'Temperature in {record["location"]} is {record["_value"]}')

"""
Interrupt a stream after retrieve a required data
"""
large_stream = query_api.query_stream('from(bucket:"my-bucket") |> range(start: -100d)')
for record in large_stream:
    if record["location"] == "New York":
        print(f'New York temperature: {record["_value"]}')
        break

large_stream.close()

print()
print()

"""
Query: using csv library
"""
csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)',
                                 dialect=Dialect(header=False, delimiter=",", comment_prefix="#", annotations=[],
                                                 date_time_format="RFC3339"))
for csv_line in csv_result:
    if not len(csv_line) == 0:
        print(f'Temperature in {csv_line[9]} is {csv_line[6]}')

print()
print()

"""
Query: using Pandas DataFrame
"""
data_frame = query_api.query_data_frame('from(bucket:"my-bucket") '
                                        '|> range(start: -10m) '
                                        '|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") '
                                        '|> keep(columns: ["location", "temperature"])')
print(data_frame.to_string())

"""
Close client
"""
client.__del__()