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TDengine Python Connector

taospy is the official Python connector for TDengine. taospy provides a rich API that makes it easy for Python applications to use TDengine. taospy wraps both the native interface and REST interface of TDengine, which correspond to the taos and taosrest modules of the taospy package, respectively. In addition to wrapping the native and REST interfaces, taospy also provides a set of programming interfaces that conforms to the Python Data Access Specification (PEP 249). It is easy to integrate taospy with many third-party tools, such as SQLAlchemy and pandas.

The direct connection to the server using the native interface provided by the client driver is referred to hereinafter as a "native connection"; the connection to the server using the REST interface provided by taosAdapter is referred to hereinafter as a "REST connection".

The source code for the Python connector is hosted on GitHub.

Supported platforms

  • The supported platforms for the native connection are the same as the ones supported by the TDengine client.
  • REST connections are supported on all platforms that can run Python.

Version selection

We recommend using the latest version of taospy, regardless of the version of TDengine.

Supported features

  • Native connections support all the core features of TDengine, including connection management, SQL execution, bind interface, subscriptions, and schemaless writing.
  • REST connections support features such as connection management and SQL execution. (SQL execution allows you to: manage databases, tables, and supertables, write data, query data, create continuous queries, etc.).



  1. Install Python. The recent taospy package requires Python 3.6+. The earlier versions of taospy require Python 3.7+. The taos-ws-py package requires Python 3.7+. If Python is not available on your system, refer to the Python BeginnersGuide to install it.
  2. Install pip. In most cases, the Python installer comes with the pip utility. If not, please refer to pip documentation to install it. If you use a native connection, you will also need to Install Client Driver. The client install package includes the TDengine client dynamic link library ( or taos.dll) and the TDengine CLI.

Install via pip

Uninstalling an older version

If you have installed an older version of the Python Connector, please uninstall it beforehand.

pip3 uninstall taos taospy

Earlier TDengine client software includes the Python connector. If the Python connector is installed from the client package's installation directory, the corresponding Python package name is taos. So the above uninstall command includes taos, and it doesn't matter if it doesn't exist.

To install taospy

Install the latest version of:

pip3 install taospy

You can also specify a specific version to install:

pip3 install taospy==2.3.0

Install taos-ws-py (Optional)

The taos-ws-py package provides the way to access TDengine via WebSocket.

Install taos-ws-py with taospy
pip3 install taospy[ws]
Install taos-ws-py only
pip3 install taos-ws-py


For REST connections, verifying that the taosrest module can be imported successfully can be done in the Python Interactive Shell by typing.

import taosrest

If you have multiple versions of Python on your system, you may have various pip commands. Be sure to use the correct path for the pip command. Above, we installed the pip3 command, which rules out the possibility of using the pip corresponding to Python 2.x versions. However, if you have more than one version of Python 3.x on your system, you still need to check that the installation path is correct. The easiest way to verify this is to type pip3 install taospy again in the command, and it will print out the exact location of taospy, for example, on Windows.

C:\> pip3 install taospy
Looking in indexes:
Requirement already satisfied: taospy in c:\users\username\appdata\local\programs\python\python310\lib\site-packages (2.3.0)

Establishing a connection

Connectivity testing

Before establishing a connection with the connector, we recommend testing the connectivity of the local TDengine CLI to the TDengine cluster.

For REST connections, make sure the cluster and taosAdapter component, are running. This can be tested using the following curl command.

curl -u root:taosdata http://<FQDN>:<PORT>/rest/sql -d "select server_version()"

The FQDN above is the FQDN of the machine running taosAdapter, PORT is the port taosAdapter listening, default is 6041. If the test is successful, it will output the server version information, e.g.

"code": 0,
"column_meta": [
"data": [
"rows": 1

Using connectors to establish connections

The following example code assumes that TDengine is installed locally and that the default configuration is used for both FQDN and serverPort.

from taosrest import connect, TaosRestConnection, TaosRestCursor

conn = connect(url="http://localhost:6041",

view source code

All arguments to the connect() function are optional keyword arguments. The following are the connection parameters specified.

  • url: The URL of taosAdapter REST service. The default is http://localhost:6041.
  • user: TDengine user name. The default is root.
  • password: TDengine user password. The default is taosdata.
  • timeout: HTTP request timeout. Enter a value in seconds. The default is socket._GLOBAL_DEFAULT_TIMEOUT. Usually, no configuration is needed.

Example program

Basic Usage

Use of TaosRestCursor class

The TaosRestCursor class is an implementation of the PEP249 Cursor interface.

Use of TaosRestCursor
# create STable
cursor = conn.cursor()
cursor.execute("DROP DATABASE IF EXISTS power")
cursor.execute("CREATE DATABASE power")
"CREATE STABLE power.meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT)")

# insert data
cursor.execute("""INSERT INTO power.d1001 USING power.meters TAGS('California.SanFrancisco', 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000)
power.d1002 USING power.meters TAGS('California.SanFrancisco', 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000)
power.d1003 USING power.meters TAGS('California.LosAngeles', 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000)
power.d1004 USING power.meters TAGS('California.LosAngeles', 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)""")
print("inserted row count:", cursor.rowcount)

# query data
cursor.execute("SELECT * FROM power.meters LIMIT 3")
# get total rows
print("queried row count:", cursor.rowcount)
# get column names from cursor
column_names = [meta[0] for meta in cursor.description]
# get rows
data = cursor.fetchall()
for row in data:

# output:
# inserted row count: 8
# queried row count: 3
# ['ts', 'current', 'voltage', 'phase', 'location', 'groupid']
# [datetime.datetime(2018, 10, 3, 14, 38, 5, 500000, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 11.8, 221, 0.28, 'california.losangeles', 2]
# [datetime.datetime(2018, 10, 3, 14, 38, 16, 600000, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 13.4, 223, 0.29, 'california.losangeles', 2]
# [datetime.datetime(2018, 10, 3, 14, 38, 5, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 10.8, 223, 0.29, 'california.losangeles', 3]

view source code

  • cursor.execute: Used to execute arbitrary SQL statements.
  • cursor.rowcount : For write operations, returns the number of successful rows written. For query operations, returns the number of rows in the result set.
  • cursor.description : Returns the description of the field. Please refer to TaosRestCursor for the specific format of the description information.
Use of the RestClient class

The RestClient class is a direct wrapper for the REST API. It contains only a sql() method for executing arbitrary SQL statements and returning the result.

Use of RestClient
from taosrest import RestClient

client = RestClient("http://localhost:6041", user="root", password="taosdata")
res: dict = client.sql("SELECT ts, current FROM power.meters LIMIT 1")

# output:
# {'status': 'succ', 'head': ['ts', 'current'], 'column_meta': [['ts', 9, 8], ['current', 6, 4]], 'data': [[datetime.datetime(2018, 10, 3, 14, 38, 5, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 10.3]], 'rows': 1}

view source code

For a more detailed description of the sql() method, please refer to RestClient.

Used with pandas

import pandas
from sqlalchemy import create_engine, text

engine = create_engine("taosrest://root:taosdata@localhost:6041")
conn = engine.connect()
df: pandas.DataFrame = pandas.read_sql(text("SELECT * FROM power.meters"), conn)

# print index
# print data type of element in ts column

# output:
# RangeIndex(start=0, stop=8, step=1)
# <class 'pandas._libs.tslibs.timestamps.Timestamp'>
# ts current ... location groupid
# 0 2018-10-03 06:38:05.500000+00:00 11.8 ... california.losangeles 2
# 1 2018-10-03 06:38:16.600000+00:00 13.4 ... california.losangeles 2
# 2 2018-10-03 06:38:05+00:00 10.8 ... california.losangeles 3

view source code

Other sample programs

Example program linksExample program content
bind_multi.pyparameter binding, bind multiple rows at once
insert_lines.pyInfluxDB line protocol writing
json_tag.pyUse JSON type tags
tmq.pyTMQ subscription

Other notes

Exception handling

All errors from database operations are thrown directly as exceptions and the error message from the database is passed up the exception stack. The application is responsible for exception handling. For example:

import taos

conn = taos.connect()
conn.execute("CREATE TABLE 123") # wrong sql
except taos.Error as e:
print("exception class: ", e.__class__.__name__)
print("error number:", e.errno)
print("error message:", e.msg)
except BaseException as other:
print("exception occur")

# output:
# [0x0216]: syntax error near 'Incomplete SQL statement'
# exception class: ProgrammingError
# error number: -2147483114
# error message: syntax error near 'Incomplete SQL statement'

view source code

About nanoseconds

Due to the current imperfection of Python's nanosecond support (see link below), the current implementation returns integers at nanosecond precision instead of the datetime type produced by ms and us, which application developers will need to handle on their own. And it is recommended to use pandas' to_datetime(). The Python Connector may modify the interface in the future if Python officially supports nanoseconds in full.


Important Update

[Release Notes] (

API Reference

Frequently Asked Questions

Welcome to ask questions or report questions.