python client
https://docs.influxdata.com/influxdb/v2/api-guide/tutorials/python/#authenticate-with-an-influxdb-api-token
Follow this step-by-step tutorial to build an Internet-of-Things (IoT) application with InfluxData client libraries and your favorite framework or language.
In this tutorial, you’ll use the InfluxDB API and client libraries to build a modern application as you learn the following:
InfluxDB core concepts.
How the application interacts with devices and InfluxDB.
How to authenticate apps and devices to the API.
How to install a client library.
How to write and query data in InfluxDB.
How to use the InfluxData UI libraries to format data and create visualizations.
Official Client Example
https://github.com/influxdata/influxdb-client-python/blob/master/examples/query.py
import datetime as datetime
from influxdb_client import InfluxDBClient, Point, Dialect
from influxdb_client.client.write_api import SYNCHRONOUS
with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=False) as client:
write_api = client.write_api(write_options=SYNCHRONOUS)
"""
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_api = client.query_api()
"""
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 Bind parameters
"""
p = {"_start": datetime.timedelta(hours=-1),
"_location": "Prague",
"_desc": True,
"_floatParam": 25.1,
"_every": datetime.timedelta(minutes=5)
}
tables = query_api.query('''
from(bucket:"my-bucket") |> range(start: _start)
|> filter(fn: (r) => r["_measurement"] == "my_measurement")
|> filter(fn: (r) => r["_field"] == "temperature")
|> filter(fn: (r) => r["location"] == _location and r["_value"] > _floatParam)
|> aggregateWindow(every: _every, fn: mean, createEmpty: true)
|> sort(columns: ["_time"], desc: _desc)
''', params=p)
for table in tables:
print(table)
for record in table.records:
print(str(record["_time"]) + " - " + record["location"] + ": " + str(record["_value"]))
print()
print()
"""
Query: using Stream
"""
records = query_api.query_stream('''
from(bucket:"my-bucket")
|> range(start: -10m)
|> filter(fn: (r) => r["_measurement"] == "my_measurement")
''')
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)
|> filter(fn: (r) => r["_measurement"] == "my_measurement")
''')
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:
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)
|> filter(fn: (r) => r["_measurement"] == "my_measurement")
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
|> keep(columns: ["_time","location", "temperature"])
''')
print(data_frame.to_string())
其它丰富的示例：
https://github.com/influxdata/influxdb-client-python/tree/master/examples
python client API
https://influxdb-client.readthedocs.io/en/stable/usage.html#query
from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS
bucket = "my-bucket"
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()
p = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
write_api.write(bucket=bucket, record=p)
## using Table structure
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')
for table in tables:
print(table)
for row in table.records:
print (row.values)
## using csv library
csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)')
val_count = 0
for row in csv_result:
for cell in row:
val_count += 1
pandas dataframe <--> InfluxDB
https://www.influxdata.com/blog/getting-started-with-influxdb-and-pandas/
Import the client and Pandas:
Copyfrom influxdb_client import InfluxDBClient import pandas as pdSupply auth parameters:
Copymy_token = my-token my_org = "my-org" bucket = "system"Write your Flux query:
Copyquery= ''' from(bucket: "system") |> range(start:-5m, stop: now()) |> filter(fn: (r) => r._measurement == "cpu") |> filter(fn: (r) => r._field == "usage_user") |> filter(fn: (r) => r.cpu == "cpu-total")'''Query InfluxDB and return a Dataframe:
client = InfluxDBClient(url="http://localhost:9999", token=my_token, org=my_org, debug=False) system_stats = client.query_api().query_data_frame(org=my_org, query=query) display(system_stats.head())
influxDB to dataframe with pivot API
https://blog.csdn.net/weixin_34890916/article/details/123251449
with InfluxDBClient(url=dbconf.HOST, token=dbconf.TOKEN, org=dbconf.ORG, debug=False) as client:
global stopstamp,startstamp
startstamp, stopstamp = dbconf.getlocaltime()
query_api = client.query_api()
query_str = 'import "timezone" option location = timezone.location(name: "Asia/Shanghai") '
query_str += 'from(bucket: "' + dbconf.BUCKET + '")'
query_str += ' |> range(start: ' + \
str(startstamp) + ', stop: ' + str(stopstamp) + ') '
query_str += '|> filter(fn: (r) => r["_measurement"] == "' + \
dbconf.MEASUREMENTONE + '")'
query_str += '|> filter(fn: (r) => r["_field"] == "RotateSpeed2_decimal" or r["_field"] == "RotateSpeed1_decimal" or r["_field"] == "Torque_decimal" or r["_field"] == "EnvironmentTemp_decimal" or r["_field"] == "Mot2_speed_rpm_gui_int" or r["_field"] == "AI_PhaseWCurrent2_gui_decimal" or r["_field"] == "AI_PhaseWCurrent2_RMS_gui_decimal" or r["_field"] == "AI_PhaseUCurrent2_RMS_gui_decimal" or r["_field"] == "AI_PhaseUCurrent2_gui_decimal" or r["_field"] == "AI_BusNegCurrent_gui_decimal" or r["_field"]== "AI_Motor2Temp1_gui_decimal")'
query_str += '|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")'
query_str += '|> yield()'
print(query_str)
tables = query_api.query(query_str, org=dbconf.ORG)
data = pd.DataFrame()
list_time = []
RotateSpeed2_decimal_list = []
RotateSpeed1_decimal_list = []
Torque_decimal_list = []
EnvironmentTemp_decimal_list = []
Mot2_speed_rpm_gui_int_list = []
AI_PhaseWCurrent2_gui_decimal_list = []
AI_PhaseWCurrent2_RMS_gui_decimal_list = []
AI_PhaseUCurrent2_RMS_gui_decimal_list = []
AI_PhaseUCurrent2_gui_decimal_list = []
AI_BusNegCurrent_gui_decimal_list = []
AI_Motor2Temp1_gui_decimal_list = []
for table in tables:
for row in table.records:
list_time.append(row.__getitem__(
'_time').strftime('%Y-%m-%dT%H:%M:%S.%fZ'))
RotateSpeed2_decimal_list.append(
row.__getitem__('RotateSpeed2_decimal'))
RotateSpeed1_decimal_list.append(
row.__getitem__('RotateSpeed1_decimal'))
Torque_decimal_list.append(row.__getitem__('Torque_decimal'))
EnvironmentTemp_decimal_list.append(
row.__getitem__('EnvironmentTemp_decimal'))
Mot2_speed_rpm_gui_int_list.append(
row.__getitem__('Mot2_speed_rpm_gui_int'))
AI_PhaseWCurrent2_gui_decimal_list.append(
row.__getitem__('AI_PhaseWCurrent2_gui_decimal'))
AI_PhaseWCurrent2_RMS_gui_decimal_list.append(
row.__getitem__('AI_PhaseWCurrent2_RMS_gui_decimal'))
AI_PhaseUCurrent2_RMS_gui_decimal_list.append(
row.__getitem__('AI_PhaseUCurrent2_RMS_gui_decimal'))
AI_PhaseUCurrent2_gui_decimal_list.append(
row.__getitem__('AI_PhaseUCurrent2_gui_decimal'))
AI_BusNegCurrent_gui_decimal_list.append(
row.__getitem__('AI_BusNegCurrent_gui_decimal'))
AI_Motor2Temp1_gui_decimal_list.append(
row.__getitem__('AI_Motor2Temp1_gui_decimal'))
data['time'] = list_time
data['RotateSpeed2_decimal'] = RotateSpeed2_decimal_list
data['RotateSpeed1_decimal'] = RotateSpeed1_decimal_list
data['Torque_decimal'] = Torque_decimal_list
data['EnvironmentTemp_decimal'] = EnvironmentTemp_decimal_list
data['Mot2_speed_rpm_gui_int'] = Mot2_speed_rpm_gui_int_list
data['AI_PhaseWCurrent2_gui_decimal'] = AI_PhaseWCurrent2_gui_decimal_list
data['AI_PhaseWCurrent2_RMS_gui_decimal'] = AI_PhaseWCurrent2_RMS_gui_decimal_list
data['AI_PhaseUCurrent2_RMS_gui_decimal'] = AI_PhaseUCurrent2_RMS_gui_decimal_list
data['AI_PhaseUCurrent2_gui_decimal'] = AI_PhaseUCurrent2_gui_decimal_list
data['AI_BusNegCurrent_gui_decimal'] = AI_BusNegCurrent_gui_decimal_list
data['AI_Motor2Temp1_gui_decimal'] = AI_Motor2Temp1_gui_decimal_list
标签：python,gui,list,AI,v2,client,query,my,decimal From： https://www.cnblogs.com/lightsong/p/17963943