Mixin Columns#

astropy tables support the concept of “mixin columns”, which allows integration of appropriate non-Column based class objects within a Table object. These mixin column objects are not converted in any way but are used natively.

The available built-in mixin column classes are:

Basic Example#

As an example we can create a table and add a time column:

>>> from astropy.table import Table
>>> from astropy.time import Time
>>> t = Table()
>>> t['index'] = [1, 2]
>>> t['time'] = Time(['2001-01-02T12:34:56', '2001-02-03T00:01:02'])
>>> print(t)
index           time
----- -----------------------
    1 2001-01-02T12:34:56.000
    2 2001-02-03T00:01:02.000

The important point here is that the time column is a bona fide Time object:

>>> t['time']
<Time object: scale='utc' format='isot' value=['2001-01-02T12:34:56.000' '2001-02-03T00:01:02.000']>
>>> t['time'].mjd  
array([51911.52425926, 51943.00071759])

Quantity and QTable#

The ability to natively handle Quantity objects within a table makes it more convenient to manipulate tabular data with units in a natural and robust way. However, this feature introduces an ambiguity because data with a unit (e.g., from a FITS binary table) can be represented as either a Column with a unit attribute or as a Quantity object. In order to cleanly resolve this ambiguity, astropy defines a minor variant of the Table class called QTable. The QTable class is exactly the same as Table except that Quantity is the default for any data column with a defined unit.

If you take advantage of the Quantity infrastructure in your analysis, then QTable is the preferred way to create tables with units. If instead you use table column units more as a descriptive label, then the plain Table class is probably the best class to use.


To illustrate these concepts we first create a standard Table where we supply as input a Time object and a Quantity object with units of m / s. In this case the quantity is converted to a Column (which has a unit attribute but does not have all of the features of a Quantity):

>>> import astropy.units as u
>>> t = Table()
>>> t['index'] = [1, 2]
>>> t['time'] = Time(['2001-01-02T12:34:56', '2001-02-03T00:01:02'])
>>> t['velocity'] = [3, 4] * u.m / u.s

>>> print(t)
index           time          velocity
                               m / s
----- ----------------------- --------
    1 2001-01-02T12:34:56.000      3.0
    2 2001-02-03T00:01:02.000      4.0

>>> type(t['velocity'])
<class 'astropy.table.column.Column'>

>>> t['velocity'].unit
Unit("m / s")

>>> (t['velocity'] ** 2).unit  # WRONG because Column is not smart about unit
Unit("m / s")

So instead let’s do the same thing using a QTable:

>>> from astropy.table import QTable

>>> qt = QTable()
>>> qt['index'] = [1, 2]
>>> qt['time'] = Time(['2001-01-02T12:34:56', '2001-02-03T00:01:02'])
>>> qt['velocity'] = [3, 4] * u.m / u.s

The velocity column is now a Quantity and behaves accordingly:

>>> type(qt['velocity'])
<class 'astropy.units.quantity.Quantity'>

>>> qt['velocity'].unit
Unit("m / s")

>>> (qt['velocity'] ** 2).unit  # GOOD!
Unit("m2 / s2")

You can conveniently convert Table to QTable and vice-versa:

>>> qt2 = QTable(t)
>>> type(qt2['velocity'])
<class 'astropy.units.quantity.Quantity'>

>>> t2 = Table(qt2)
>>> type(t2['velocity'])
<class 'astropy.table.column.Column'>


To summarize: the only difference between QTable and Table is the behavior when adding a column that has a specified unit. With QTable such a column is always converted to a Quantity object before being added to the table. Likewise if a unit is specified for an existing unit-less Column in a QTable, then the column is converted to Quantity.

The converse is that if you add a Quantity column to an ordinary Table then it gets converted to an ordinary Column with the corresponding unit attribute.


When a column of int dtype is converted to Quantity, its dtype is converted to float.

For example, for a quality flag column of int, if it is assigned with the dimensionless unit, it will still be converted to float. Therefore such columns typically should not be assigned with any unit.

Mixin Attributes#

The usual column attributes name, dtype, unit, format, and description are available in any mixin column via the info property:

>>> qt['velocity'].info.name

This info property is a key bit of glue that allows a non-Column object to behave much like a Column.

The same info property is also available in standard Column objects. These info attributes like t['a'].info.name refer to the direct Column attribute (e.g., t['a'].name) and can be used interchangeably. Likewise in a Quantity object, info.dtype attribute refers to the native dtype attribute of the object.


When writing generalized code that handles column objects which might be mixin columns, you must always use the info property to access column attributes.

Details and Caveats#

Most common table operations behave as expected when mixin columns are part of the table. However, there are limitations in the current implementation.

Adding or inserting a row

Adding or inserting a row works as expected only for mixin classes that are mutable (data can be changed internally) and that have an insert() method. Adding rows to a Table with Quantity, Time or SkyCoord columns does work.


Masking of mixin columns is enabled by the Masked class. See Masked Values (astropy.utils.masked) for details.

ASCII table writing

Tables with mixin columns can be written out to file using the astropy.io.ascii module, but the fast C-based writers are not available. Instead, the pure-Python writers will be used. For writing tables with mixin columns it is recommended to use the ECSV Format. This will fully serialize the table data and metadata, allowing full “round-trip” of the table when it is read back.

Binary table writing

Tables with mixin columns can be written to binary files using FITS, HDF5 and Parquet formats. These can be read back to recover exactly the original Table including mixin columns and metadata. See Unified File Read/Write Interface for details.

Mixin Protocol#

A key idea behind mixin columns is that any class which satisfies a specified protocol can be used. That means many user-defined class objects which handle array-like data can be used natively within a Table. The protocol is relatively concise and requires that a class behave like a minimal numpy array with the following properties:

  • Contains array-like data.

  • Implements __getitem__() to support getting data as a single item, slicing, or index array access.

  • Has a shape attribute.

  • Has a __len__() method for length.

  • Has an info class descriptor which is a subclass of the astropy.utils.data_info.MixinInfo class.

The Example: ArrayWrapper section shows a minimal working example of a class which can be used as a mixin column. A pandas.Series object can function as a mixin column as well.

Other interesting possibilities for mixin columns include:

  • Columns which are dynamically computed as a function of other columns (AKA spreadsheet).

  • Columns which are themselves a Table (i.e., nested tables). A proof of concept is available.

new_like() method#

In order to support high-level operations like join() and vstack(), a mixin class must provide a new_like() method in the info class descriptor. A key part of the functionality is to ensure that the input column metadata are merged appropriately and that the columns have consistent properties such as the shape.

A mixin class that provides new_like() must also implement __setitem__() to support setting via a single item, slicing, or index array.

The new_like() method has the following signature:

def new_like(self, cols, length, metadata_conflicts='warn', name=None):
    Return a new instance of this class which is consistent with the
    input ``cols`` and has ``length`` rows.

    This is intended for creating an empty column object whose elements can
    be set in-place for table operations like join or vstack.

    cols : list
        List of input columns
    length : int
        Length of the output column object
    metadata_conflicts : {'warn', 'error', 'silent'}
        How to handle metadata conflicts
    name : str
        Output column name

    col : object
        New instance of this class consistent with ``cols``

Examples of this are found in the ColumnInfo and QuantityInfo classes.

Example: ArrayWrapper#

The code listing below shows an example of a data container class which acts as a mixin column class. This class is a wrapper around a numpy.ndarray. It is used in the astropy mixin test suite and is fully compliant as a mixin column.

from astropy.utils.data_info import ParentDtypeInfo

class ArrayWrapper(object):
    Minimal mixin using a simple wrapper around a numpy array
    info = ParentDtypeInfo()

    def __init__(self, data):
        self.data = np.array(data)
        if 'info' in getattr(data, '__dict__', ()):
            self.info = data.info

    def __getitem__(self, item):
        if isinstance(item, (int, np.integer)):
            out = self.data[item]
            out = self.__class__(self.data[item])
            if 'info' in self.__dict__:
                out.info = self.info
        return out

    def __setitem__(self, item, value):
        self.data[item] = value

    def __len__(self):
        return len(self.data)

    def dtype(self):
        return self.data.dtype

    def shape(self):
        return self.data.shape

    def __repr__(self):
        return f"<{self.__class__.__name__} name='{self.info.name}' data={self.data}>"

Registering array-like objects as mixin columns#

In some cases, you may want to directly add an array-like object as a table column while maintaining the original object properties (instead of the default conversion of the object to a Column). This is done by registering the object class as a mixin column and defining a handler which allows Table to treat that object class as a mixin similar to the built-in mixin columns such as Time or Quantity.

This can be done for data classes that are defined in third-party packages and which you have no control over. As an example, we define a class that is not numpy-like and stores the data in a private attribute:

>>> import numpy as np
>>> class ExampleDataClass:
...     def __init__(self):
...         self._data = np.array([0, 1, 3, 4], dtype=float)

By default, this cannot be used as a table column:

>>> t = Table()
>>> t['data'] = ExampleDataClass()
Traceback (most recent call last):
TypeError: Empty table cannot have column set to scalar value

However, you can create a function (or ‘handler’) which takes an instance of the data class you want to have automatically handled and returns a mixin column:

>>> from astropy.table.table_helpers import ArrayWrapper
>>> def handle_example_data_class(obj):
...     return ArrayWrapper(obj._data)

You can then register this by providing the fully qualified name of the class and the handler function:

>>> from astropy.table.mixins.registry import register_mixin_handler
>>> register_mixin_handler('__main__.ExampleDataClass', handle_example_data_class)
>>> t['data'] = ExampleDataClass()
>>> t
<Table length=4>

Because we defined the data class as part of the example above, the fully qualified name starts with __main__, but for a class in a third-party package, this might look like package.Class for example.