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NumPy based Python module for reading TDMS files produced by LabView
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Latest commit bbef520 Feb 16, 2020



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npTDMS is a cross-platform Python package for reading and writing TDMS files as produced by LabVIEW, and is built on top of the numpy package. Data read from a TDMS file is stored in numpy arrays, and numpy arrays are also used when writing TDMS file.

Typical usage when reading a TDMS file might look like:

from nptdms import TdmsFile

tdms_file = TdmsFile("path_to_file.tdms")
channel = tdms_file.object('Group', 'Channel1')
data =
time = channel.time_track()
# do stuff with data

And to write a file:

from nptdms import TdmsWriter, ChannelObject
import numpy

with TdmsWriter("path_to_file.tdms") as tdms_writer:
    data_array = numpy.linspace(0, 1, 10)
    channel = ChannelObject('Group', 'Channel1', data_array)

For more information, see the npTDMS documentation.


npTDMS is available from the Python Package Index, so the easiest way to install it is by running:

pip install npTDMS

There are optional features available that require additional dependencies. These are hdf for hdf export, pandas for pandas DataFrame export, and thermocouple_scaling for using thermocouple scalings. You can specify these extra features when installing npTDMS to also install the dependencies they require:

pip install npTDMS[hdf,pandas,thermocouple_scaling]

Alternatively, after downloading the source code you can extract it and change into the new directory, then run:

python install


Source code lives at and any issues can be reported at Documentation is available at


This module doesn't support TDMS files with XML headers or with extended floating point data.

TDMS files support timestamps with a resolution of 2^-64 seconds but these are read as numpy datetime64 values with microsecond resolution.


Thanks to Floris van Vugt who wrote the pyTDMS module, which helped when writing this module.

Thanks to Tony Perkins, Ruben De Smet, Martin Hochwallner and Peter Duncan for contributing support for converting to Pandas DataFrames.

Thanks to nmgeek and jshridha for implementing support for DAQmx raw data files.

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