Core elements in pydicom

pydicom object model, description of classes, examples


Dataset is the main object you will work with directly. Dataset wraps a dictionary, where the key is the DICOM tag (as a BaseTag object, described below), and the value is the corresponding DataElement instance (also described below). It implements most of the methods of dict, so that it mostly behaves like a wrapped dict. This allows direct access to the data elements via their tags, as shown below.


The iterator of a Dataset yields DataElement instances, e.g. the values of the dictionary instead of the keys normally yielded by iterating a dict.

A Dataset could be created directly, but you will usually get one by reading an existing DICOM file:

>>> import pydicom
>>> from import get_testdata_file
>>> # get some test data
>>> filename = get_testdata_file("rtplan.dcm")
>>> ds = pydicom.dcmread(filename)

You can display the entire dataset by simply printing its string (str() or repr()) value:

>>> ds 
Dataset.file_meta -------------------------------
(0002, 0000) File Meta Information Group Length  UL: 156
(0002, 0001) File Meta Information Version       OB: b'\x00\x01'
(0002, 0002) Media Storage SOP Class UID         UI: RT Plan Storage
(0002, 0003) Media Storage SOP Instance UID      UI: 1.2.999.999.99.9.9999.9999.20030903150023
(0002, 0010) Transfer Syntax UID                 UI: Implicit VR Little Endian
(0002, 0012) Implementation Class UID            UI: 1.2.888.888.
(0008, 0012) Instance Creation Date              DA: '20030903'
(0008, 0013) Instance Creation Time              TM: '150031'
(0008, 0016) SOP Class UID                       UI: RT Plan Storage
(0008, 0018) SOP Instance UID                    UI: 1.2.777.777.77.7.7777.7777.20030903150023
(0008, 0020) Study Date                          DA: '20030716'
(0008, 0030) Study Time                          TM: '153557'
(0008, 0050) Accession Number                    SH: ''
(0008, 0060) Modality                            CS: 'RTPLAN'


You can also view DICOM files in a collapsible tree using the example program

You can access specific elements by their DICOM keyword or tag number:

>>> ds.PatientName
>>> ds[0x10,0x10].value

When using the tag number directly a DataElement instance is returned, so DataElement.value must be used to get the value.


In pydicom, private data elements are displayed with square brackets around the name (if the name is known to pydicom). These are shown for convenience only; the descriptive name in brackets cannot be used to retrieve data elements. See details in Private Data Elements.

You can also set an element’s value by using the element’s keyword or tag number:

>>> ds.PatientID = "12345"
>>> ds.SeriesNumber = 5
>>> ds[0x10,0x10].value = 'Test'

The use of names is possible because pydicom intercepts requests for member variables, and checks if they are in the DICOM dictionary. It translates the keyword to a (group,element) tag and returns the corresponding value for that tag if it exists in the dataset.

See Anonymize DICOM data for a usage example of data elements removal and assignation.


To understand using Sequence in pydicom, please refer to this object model:

The value of sequence elements is a Sequence instance, which wraps a Python list. Items in the sequence are referenced by number, beginning at index 0 as per Python convention:

>>> ds.BeamSequence[0].BeamName
'Field 1'
>>> # Or, set an intermediate variable to a dataset in the list
>>> beam1 = ds.BeamSequence[0]  # First dataset in the sequence
>>> beam1.BeamName
'Field 1'

See Working with sequences.

Using DICOM keywords is the recommended way to access data elements, but you can also use the tag numbers directly, such as:

>>> # Same thing with tag numbers - much harder to read:
>>> # Really should only be used if DICOM keyword not in pydicom dictionary
>>> ds[0x300a,0xb0][0][0x300a,0xc2].value
'Field 1'

If you don’t remember or know the exact element tag or keyword, Dataset provides a handy Dataset.dir() method, useful during interactive sessions at the Python prompt:

>>> ds.dir("pat")
['PatientBirthDate', 'PatientID', 'PatientName', 'PatientSetupSequence', 'PatientSex']

Dataset.dir() will return any non-private element keywords in the dataset that have the specified string anywhere in the keyword (case insensitive).


Calling Dataset.dir() without passing it an argument will return a list of all non-private element keywords in the dataset.

You can also see all the names that pydicom knows about by viewing the file. It should not normally be necessary, but you can add your own entries to the DICOM dictionary at run time using add_dict_entry() or add_dict_entries(). Similarly, you can add private data elements to the private dictionary using add_private_dict_entry() or add_private_dict_entries().

Under the hood, Dataset stores a DataElement object for each item, but when accessed by keyword (e.g. ds.PatientName) only the value of that DataElement is returned. If you need the object itself, you can use the access the item using either the keyword (for official DICOM elements) or tag number:

>>> # reload the data
>>> ds = pydicom.dcmread(filename)
>>> elem = ds['PatientName']
>>> elem.VR, elem.value
('PN', 'Last^First^mid^pre')
>>> # an alternative is to use:
>>> elem = ds[0x0010,0x0010]
>>> elem.VR, elem.value
('PN', 'Last^First^mid^pre')

To see whether the Dataset contains a particular element use the in operator with the element’s keyword or tag:

>>> "PatientName" in ds  # or (0x0010,0x0010) in ds

To remove an element from the Dataset use the del operator:

>>> del ds.SoftwareVersions  # or del ds[0x0018, 0x1020]

To work with (7FE0,0010) Pixel Data, the raw bytes are available through the PixelData keyword:

>>> # read data with actual pixel data
>>> filename = get_testdata_file("CT_small.dcm")
>>> ds = pydicom.dcmread(filename)
>>> pixel_bytes = ds.PixelData

However its much more convenient to use Dataset.pixel_array to return a numpy.ndarray (requires the NumPy library):

>>> arr = ds.pixel_array
>>> arr 
array([[175, 180, 166, ..., 203, 207, 216],
       [186, 183, 157, ..., 181, 190, 239],
       [184, 180, 171, ..., 152, 164, 235],
       [906, 910, 923, ..., 922, 929, 927],
       [914, 954, 938, ..., 942, 925, 905],
       [959, 955, 916, ..., 911, 904, 909]], dtype=int16)

For more details, see Working with Pixel Data.


The DataElement class is not usually used directly in user code, but is used extensively by Dataset. DataElement is a simple object which stores the following things:

  • tag – the element’s tag (as a BaseTag object)

  • VR – the element’s Value Representation – a two letter str that describes to the format of the stored value

  • VM – the element’s Value Multiplicity as an int. This is automatically determined from the contents of the value.

  • value – the element’s actual value. A regular value like a number or string (or list of them if the VM > 1), or a Sequence.


Tag() is not generally used directly in user code, as BaseTags are automatically created when you assign or read elements using their keywords as illustrated in sections above.

The BaseTag class is derived from int, so in effect, it is just a number with some extra behavior:

  • Tag() is used to create instances of BaseTag and enforces that the DICOM tag fits in the expected 4-byte (group,element)

  • A BaseTag instance can be created from an int or a tuple containing the (group,element), or from the DICOM keyword:

    >>> from pydicom.tag import Tag
    >>> t1 = Tag(0x00100010) # all of these are equivalent
    >>> t2 = Tag(0x10,0x10)
    >>> t3 = Tag((0x10, 0x10))
    >>> t4 = Tag("PatientName")
    >>> t1
    (0010, 0010)
    >>> type(t1)
    <class `pydicom.tag.BaseTag`>
    >>> t1==t2, t1==t3, t1==t4
    (True, True, True)
  • and BaseTag.elem to return the group and element portions of the tag.

  • The BaseTag.is_private property checks whether the tag represents a private tag (i.e. if group number is odd).


Sequence is derived from Python’s list. The only added functionality is to make string representations prettier. Otherwise all the usual methods of list like item selection, append, etc. are available.

For examples of accessing data nested in sequences, see Working with sequences.