influxdb-client-python

InfluxDB 2.0 python client library.

Note: Use this client library with InfluxDB 2.x and InfluxDB 1.8+. For connecting to InfluxDB 1.7 or earlier instances, use the influxdb-python client library.

InfluxDB 2.0 client features

Querying data
- using the Flux language
- into csv, raw data, flux_table structure, Pandas DataFrame
- How to queries
Writing data using
- Line Protocol
- Data Point
- RxPY Observable
- Pandas DataFrame
- How to writes
InfluxDB 2.0 API client for management
- the client is generated from the swagger by using the openapi-generator
- organizations & users management
- buckets management
- tasks management
- authorizations
- health check
- ...
InfluxDB 1.8 API compatibility
Examples

Installation

InfluxDB python library uses RxPY - The Reactive Extensions for Python (RxPY).

Python 3.6 or later is required.

Note

It is recommended to use ciso8601 with client for parsing dates. ciso8601 is much faster than built-in Python datetime. Since it's written as a C module the best way is build it from sources:

Windows:

You have to install Visual C++ Build Tools 2015 to build ciso8601 by pip.

conda:

Install from sources: conda install -c conda-forge/label/cf202003 ciso8601.

pip install

The python package is hosted on PyPI, you can install latest version directly:

pip install influxdb-client[ciso]

Then import the package:

import influxdb_client

Setuptools

Install via Setuptools.

python setup.py install --user

(or sudo python setup.py install to install the package for all users)

Getting Started

Please follow the Installation and then run the following:

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

Client configuration

Via File

A client can be configured via *.ini file in segment influx2.

The following options are supported:

url - the url to connect to InfluxDB
org - default destination organization for writes and queries
token - the token to use for the authorization
timeout - socket timeout in ms (default value is 10000)
verify_ssl - set this to false to skip verifying SSL certificate when calling API from https server
ssl_ca_cert - set this to customize the certificate file to verify the peer

self.client = InfluxDBClient.from_config_file("config.ini")

[influx2]
url=http://localhost:8086
org=my-org
token=my-token
timeout=6000
verify_ssl=False

Via Environment Properties

A client can be configured via environment properties.

Supported properties are:

INFLUXDB_V2_URL - the url to connect to InfluxDB
INFLUXDB_V2_ORG - default destination organization for writes and queries
INFLUXDB_V2_TOKEN - the token to use for the authorization
INFLUXDB_V2_TIMEOUT - socket timeout in ms (default value is 10000)
INFLUXDB_V2_VERIFY_SSL - set this to false to skip verifying SSL certificate when calling API from https server
INFLUXDB_V2_SSL_CA_CERT - set this to customize the certificate file to verify the peer

self.client = InfluxDBClient.from_env_properties()

How to use

Writes

The WriteApi supports synchronous, asynchronous and batching writes into InfluxDB 2.0. The data should be passed as a InfluxDB Line Protocol, Data Point or Observable stream.

The default instance of WriteApi use batching.

The data could be written as

string or bytes that is formatted as a InfluxDB's line protocol
Data Point structure
Dictionary style mapping with keys: measurement, tags, fields and time
List of above items
A batching type of write also supports an Observable that produce one of an above item
Pandas DataFrame

Batching

The batching is configurable by write_options:

Property	Description	Default Value
batch_size	the number of data pointx to collect in a batch	`1000`
flush_interval	the number of milliseconds before the batch is written	`1000`
jitter_interval	the number of milliseconds to increase the batch flush interval by a random amount	`0`
retry_interval	the number of milliseconds to retry unsuccessful write. The retry interval is used when the InfluxDB server does not specify "Retry-After" header.	`5000`
max_retries	the number of max retries when write fails	`3`
max_retry_delay	the maximum delay between each retry attempt in milliseconds	`180_000`
exponential_base	the base for the exponential retry delay, the next delay is computed as `retry_interval * exponential_base^(attempts-1) + random(jitter_interval)`	`5`

import rx
from rx import operators as ops

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

_client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
_write_client = _client.write_api(write_options=WriteOptions(batch_size=500,
                                                             flush_interval=10_000,
                                                             jitter_interval=2_000,
                                                             retry_interval=5_000,
                                                             max_retries=5,
                                                             max_retry_delay=30_000,
                                                             exponential_base=2))

"""
Write Line Protocol formatted as string
"""
_write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1")
_write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2",
                                            "h2o_feet,location=coyote_creek water_level=3.0 3"])

"""
Write Line Protocol formatted as byte array
"""
_write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1".encode())
_write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2".encode(),
                                            "h2o_feet,location=coyote_creek water_level=3.0 3".encode()])

"""
Write Dictionary-style object
"""
_write_client.write("my-bucket", "my-org", {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                            "fields": {"water_level": 1.0}, "time": 1})
_write_client.write("my-bucket", "my-org", [{"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                            "fields": {"water_level": 2.0}, "time": 2},
                                            {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                            "fields": {"water_level": 3.0}, "time": 3}])

"""
Write Data Point
"""
_write_client.write("my-bucket", "my-org", Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 4.0).time(4))
_write_client.write("my-bucket", "my-org", [Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 5.0).time(5),
                                            Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 6.0).time(6)])

"""
Write Observable stream
"""
_data = rx \
    .range(7, 11) \
    .pipe(ops.map(lambda i: "h2o_feet,location=coyote_creek water_level={0}.0 {0}".format(i)))

_write_client.write("my-bucket", "my-org", _data)

"""
Write Pandas DataFrame
"""
_now = pd.Timestamp().now('UTC')
_data_frame = pd.DataFrame(data=[["coyote_creek", 1.0], ["coyote_creek", 2.0]],
                           index=[now, now + timedelta(hours=1)],
                           columns=["location", "water_level"])

_write_client.write(bucket.name, record=data_frame, data_frame_measurement_name='h2o_feet',
                    data_frame_tag_columns=['location'])

"""
Close client
"""
_write_client.__del__()
_client.__del__()

Default Tags

Sometimes is useful to store same information in every measurement e.g. hostname, location, customer. The client is able to use static value or env property as a tag value.

The expressions:

California Miner - static value
${env.hostname} - environment property

Via API

point_settings = PointSettings()
point_settings.add_default_tag("id", "132-987-655")
point_settings.add_default_tag("customer", "California Miner")
point_settings.add_default_tag("data_center", "${env.data_center}")

self.write_client = self.client.write_api(write_options=SYNCHRONOUS, point_settings=point_settings)

self.write_client = self.client.write_api(write_options=SYNCHRONOUS,
                                              point_settings=PointSettings(**{"id": "132-987-655",
                                                                              "customer": "California Miner"}))

Via Configuration file

In a ini configuration file you are able to specify default tags by tags segment.

self.client = InfluxDBClient.from_config_file("config.ini")

[influx2]
url=http://localhost:8086
org=my-org
token=my-token
timeout=6000

[tags]
id = 132-987-655
customer = California Miner
data_center = ${env.data_center}

Via Environment Properties

You are able to specify default tags by environment properties with prefix INFLUXDB_V2_TAG_.

Examples:

INFLUXDB_V2_TAG_ID
INFLUXDB_V2_TAG_HOSTNAME

self.client = InfluxDBClient.from_env_properties()

Asynchronous client

Data are writes in an asynchronous HTTP request.

from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import ASYNCHRONOUS

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

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

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

client.__del__()

Synchronous client

Data are writes in a synchronous HTTP request.

from influxdb_client import InfluxDBClient, Point
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)

_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])

client.__del__()

Queries

The result retrieved by QueryApi could be formatted as a:

Flux data structure: FluxTable, FluxColumn and FluxRecord
csv.reader which will iterate over CSV lines
Raw unprocessed results as a str iterator
Pandas DataFrame

The API also support streaming FluxRecord via query_stream, see example below:

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]}')

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

Pandas DataFrame

Note

For DataFrame querying you should install Pandas dependency via pip install influxdb-client[extra].

Note

Note that if a query returns more then one table then the client generates a DataFrame for each of them.

The client is able to retrieve data in Pandas DataFrame format thought query_data_frame:

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 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__()

Output:

result table  location  temperature

0 _result 0 New York 24.3 1 _result 1 Prague 25.3

Examples

How to efficiently import large dataset

The following example shows how to import dataset with dozen megabytes. If you would like to import gigabytes of data then use our multiprocessing example: import_data_set_multiprocessing.py for use a full capability of your hardware.

sources - import_data_set.py

"""
Import VIX - CBOE Volatility Index - from "vix-daily.csv" file into InfluxDB 2.0

https://datahub.io/core/finance-vix#data
"""

from collections import OrderedDict
from csv import DictReader

import rx
from rx import operators as ops

from influxdb_client import InfluxDBClient, Point, WriteOptions

def parse_row(row: OrderedDict):
    """Parse row of CSV file into Point with structure:

        financial-analysis,type=ily close=18.47,high=19.82,low=18.28,open=19.82 1198195200000000000

    CSV format:
        Date,VIX Open,VIX High,VIX Low,VIX Close\n
        2004-01-02,17.96,18.68,17.54,18.22\n
        2004-01-05,18.45,18.49,17.44,17.49\n
        2004-01-06,17.66,17.67,16.19,16.73\n
        2004-01-07,16.72,16.75,15.5,15.5\n
        2004-01-08,15.42,15.68,15.32,15.61\n
        2004-01-09,16.15,16.88,15.57,16.75\n
        ...

    :param row: the row of CSV file
    :return: Parsed csv row to [Point]
    """

    """
     For better performance is sometimes useful directly create a LineProtocol to avoid unnecessary escaping overhead:
     """
     # from pytz import UTC
     # import ciso8601
     # from influxdb_client.client.write.point import EPOCH
     #
     # time = (UTC.localize(ciso8601.parse_datetime(row["Date"])) - EPOCH).total_seconds() * 1e9
     # return f"financial-analysis,type=vix-daily" \
     #        f" close={float(row['VIX Close'])},high={float(row['VIX High'])},low={float(row['VIX Low'])},open={float(row['VIX Open'])} " \
     #        f" {int(time)}"

    return Point("financial-analysis") \
        .tag("type", "vix-daily") \
        .field("open", float(row['VIX Open'])) \
        .field("high", float(row['VIX High'])) \
        .field("low", float(row['VIX Low'])) \
        .field("close", float(row['VIX Close'])) \
        .time(row['Date'])


"""
Converts vix-daily.csv into sequence of datad point
"""
data = rx \
    .from_iterable(DictReader(open('vix-daily.csv', 'r'))) \
    .pipe(ops.map(lambda row: parse_row(row)))

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

"""
Create client that writes data in batches with 50_000 items.
"""
write_api = client.write_api(write_options=WriteOptions(batch_size=50_000, flush_interval=10_000))

"""
Write data into InfluxDB
"""
write_api.write(bucket="my-bucket", record=data)
write_api.__del__()

"""
Querying max value of CBOE Volatility Index
"""
query = 'from(bucket:"my-bucket")' \
        ' |> range(start: 0, stop: now())' \
        ' |> filter(fn: (r) => r._measurement == "financial-analysis")' \
        ' |> max()'
result = client.query_api().query(query=query)

"""
Processing results
"""
print()
print("=== results ===")
print()
for table in result:
    for record in table.records:
        print('max {0:5} = {1}'.format(record.get_field(), record.get_value()))

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

Efficiency write data from IOT sensor

sources - iot_sensor.py

"""
Efficiency write data from IOT sensor - write changed temperature every minute
"""
import atexit
import platform
from datetime import timedelta

import psutil as psutil
import rx
from rx import operators as ops

from influxdb_client import InfluxDBClient, WriteApi, WriteOptions

def on_exit(db_client: InfluxDBClient, write_api: WriteApi):
    """Close clients after terminate a script.

    :param db_client: InfluxDB client
    :param write_api: WriteApi
    :return: nothing
    """
    write_api.__del__()
    db_client.__del__()


def sensor_temperature():
    """Read a CPU temperature. The [psutil] doesn't support MacOS so we use [sysctl].

    :return: actual CPU temperature
    """
    os_name = platform.system()
    if os_name == 'Darwin':
        from subprocess import check_output
        output = check_output(["sysctl", "machdep.xcpm.cpu_thermal_level"])
        import re
        return re.findall(r'\d+', str(output))[0]
    else:
        return psutil.sensors_temperatures()["coretemp"][0]


def line_protocol(temperature):
    """Create a InfluxDB line protocol with structure:

        iot_sensor,hostname=mine_sensor_12,type=temperature value=68

    :param temperature: the sensor temperature
    :return: Line protocol to write into InfluxDB
    """

    import socket
    return 'iot_sensor,hostname={},type=temperature value={}'.format(socket.gethostname(), temperature)


"""
Read temperature every minute; distinct_until_changed - produce only if temperature change
"""
data = rx\
    .interval(period=timedelta(seconds=60))\
    .pipe(ops.map(lambda t: sensor_temperature()),
          ops.distinct_until_changed(),
          ops.map(lambda temperature: line_protocol(temperature)))

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

"""
Create client that writes data into InfluxDB
"""
_write_api = _db_client.write_api(write_options=WriteOptions(batch_size=1))
_write_api.write(bucket="my-bucket", record=data)


"""
Call after terminate a script
"""
atexit.register(on_exit, _db_client, _write_api)

input()

Connect to InfluxDB Cloud

The following example demonstrate a simplest way how to write and query date with the InfluxDB Cloud.

At first point you should create an authentication token as is described here.

After that you should configure properties: influx_cloud_url, influx_cloud_token, bucket and org in a influx_cloud.py example.

The last step is run a python script via: python3 influx_cloud.py.

sources - influx_cloud.py

"""
Connect to InfluxDB 2.0 - write data and query them
"""

from datetime import datetime

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

"""
Configure credentials
"""
influx_cloud_url = 'https://us-west-2-1.aws.cloud2.influxdata.com'
influx_cloud_token = '...'
bucket = '...'
org = '...'

client = InfluxDBClient(url=influx_cloud_url, token=influx_cloud_token)
try:
    kind = 'temperature'
    host = 'host1'
    device = 'opt-123'

    """
    Write data by Point structure
    """
    point = Point(kind).tag('host', host).tag('device', device).field('value', 25.3).time(time=datetime.utcnow())

    print(f'Writing to InfluxDB cloud: {point.to_line_protocol()} ...')

    write_api = client.write_api(write_options=SYNCHRONOUS)
    write_api.write(bucket=bucket, org=org, record=point)

    print()
    print('success')
    print()
    print()

    """
    Query written data
    """
    query = f'from(bucket: "{bucket}") |> range(start: -1d) |> filter(fn: (r) => r._measurement == "{kind}")'
    print(f'Querying from InfluxDB cloud: "{query}" ...')
    print()

    query_api = client.query_api()
    tables = query_api.query(query=query, org=org)

    for table in tables:
        for row in table.records:
            print(f'{row.values["_time"]}: host={row.values["host"]},device={row.values["device"]} '
                  f'{row.values["_value"]} °C')

    print()
    print('success')

except Exception as e:
    print(e)
finally:
    client.close()

How to use Jupyter + Pandas + InfluxDB 2

The first example shows how to use client capabilities to predict stock price via Keras, TensorFlow, sklearn:

The example is taken from Kaggle.

sources - stock-predictions.ipynb

Result:

The second example shows how to use client capabilities to realtime visualization via hvPlot, Streamz, RxPY:

sources - realtime-stream.ipynb

Advanced Usage

Gzip support

InfluxDBClient does not enable gzip compression for http requests by default. If you want to enable gzip to reduce transfer data's size, you can call:

from influxdb_client import InfluxDBClient

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

InfluxDB 1.8 API compatibility

InfluxDB 1.8.0 introduced forward compatibility APIs for InfluxDB 2.0. This allow you to easily move from InfluxDB 1.x to InfluxDB 2.0 Cloud or open source.

The following forward compatible APIs are available:

API	Endpoint	Description
query_api.py	/api/v2/query	Query data in InfluxDB 1.8.0+ using the InfluxDB 2.0 API and Flux (endpoint should be enabled by flux-enabled option)
write_api.py	/api/v2/write	Write data to InfluxDB 1.8.0+ using the InfluxDB 2.0 API
health()	/health	Check the health of your InfluxDB instance

For detail info see InfluxDB 1.8 example.

HTTP Retry Strategy

By default the client uses a retry strategy only for batching writes (for more info see Batching). For other HTTP requests there is no one retry strategy, but it could be configured by retries parameter of InfluxDBClient.

For more info about how configure HTTP retry see details in urllib3 documentation.

from urllib3 import Retry

from influxdb_client import InfluxDBClient

retries = Retry(connect=5, read=2, redirect=5)
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", retries=retries)

Nanosecond precision

The Python's datetime doesn't support precision with nanoseconds so the library during writes and queries ignores everything after microseconds.

If you would like to use datetime with nanosecond precision you should use pandas.Timestamp that is replacement for python datetime.datetime object and also you should set a proper DateTimeHelper to the client.

sources - nanosecond_precision.py

from influxdb_client import Point, InfluxDBClient
from influxdb_client.client.util.date_utils_pandas import PandasDateTimeHelper
from influxdb_client.client.write_api import SYNCHRONOUS

"""
Set PandasDate helper which supports nanoseconds.
"""
import influxdb_client.client.util.date_utils as date_utils

date_utils.date_helper = PandasDateTimeHelper()

"""
Prepare client.
"""
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
"""

point = Point("h2o_feet") \
    .field("water_level", 10) \
    .tag("location", "pacific") \
    .time('1996-02-25T21:20:00.001001231Z')

print(f'Time serialized with nanosecond precision: {point.to_line_protocol()}')
print()

write_api.write(bucket="my-bucket", record=point)

"""
Query: using Stream
"""
query = '''
from(bucket:"my-bucket")
        |> range(start: 0, stop: now())
        |> filter(fn: (r) => r._measurement == "h2o_feet")
'''
records = query_api.query_stream(query)

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

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

Local tests

# start/restart InfluxDB2 on local machine using docker
./scripts/influxdb-restart.sh

# install requirements
pip install -r requirements.txt --user
pip install -r extra-requirements.txt --user
pip install -r test-requirements.txt --user

# run unit & integration tests
pytest tests

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/influxdata/influxdb-client-python.

License

The gem is available as open source under the terms of the MIT License.

Name		Name	Last commit message	Last commit date
Latest commit History 188 Commits
.circleci		.circleci
.github		.github
docs		docs
examples		examples
influxdb_client		influxdb_client
notebooks		notebooks
scripts		scripts
tests		tests
.codecov.yml		.codecov.yml
.gitignore		.gitignore
.readthedocs.yml		.readthedocs.yml
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.rst		README.rst
ciso-requirements.txt		ciso-requirements.txt
extra-requirements.txt		extra-requirements.txt
requirements.txt		requirements.txt
setup.py		setup.py
test-requirements.txt		test-requirements.txt
tox.ini		tox.ini

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prismdata-dc/influxdb-client-python

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influxdb-client-python

InfluxDB 2.0 client features

Installation

pip install

Setuptools

Getting Started

Client configuration

Via File

Via Environment Properties

How to use

Writes

The data could be written as

Batching

Default Tags

Via API

Via Configuration file

Via Environment Properties

Asynchronous client

Synchronous client

Queries

Pandas DataFrame

Examples

How to efficiently import large dataset

Efficiency write data from IOT sensor

Connect to InfluxDB Cloud

How to use Jupyter + Pandas + InfluxDB 2

Advanced Usage

Gzip support

InfluxDB 1.8 API compatibility

HTTP Retry Strategy

Nanosecond precision

Local tests

Contributing

License

About

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