Constructing a table

There is great deal of flexibility in the way that a table can be initially constructed. Details on the inputs to the Table constructor are in the Initialization Details section. However, the easiest way to understand how to make a table is by example.


Much of the flexibility lies in the types of data structures which can be used to initialize the table data. The examples below show how to create a table from scratch with no initial data, create a table with a list of columns, a dictionary of columns, or from numpy arrays (either structured or homogeneous).


For the following examples you need to import the Table and Column classes along with the numpy package:

>>> from astropy.table import Table, Column
>>> import numpy as np

Creating from scratch

A Table can be created without any initial input data or even without any initial columns. This is useful for building tables dynamically if the initial size, columns, or data are not known.


Adding rows requires making a new copy of the entire table each time, so in the case of large tables this may be slow. On the other hand, adding columns is reasonably fast.

>>> t = Table()
>>> t['a'] = [1, 4]
>>> t['b'] = Column([2.0, 5.0], unit='cm', description='Velocity')
>>> t['c'] = ['x', 'y']

>>> t = Table(names=('a', 'b', 'c'), dtype=('f4', 'i4', 'S2'))
>>> t.add_row((1, 2.0, 'x'))
>>> t.add_row((4, 5.0, 'y'))

>>> t = Table(dtype=[('a', 'f4'), ('b', 'i4'), ('c', 'S2')])

Another option for creating a table is using the QTable class. In this case any Quantity column objects will be stored natively within the table via the “mixin” column protocol (see Columns and Quantities for details):

>>> from astropy.table import QTable
>>> from astropy import units as u
>>> t = QTable()
>>> t['velocity'] = [3, 4] * u.m / u.s
>>> type(t['velocity'])  

List of columns

A typical case is where you have a number of data columns with the same length defined in different variables. These might be Python lists or numpy arrays or a mix of the two. These can be used to create a Table by putting the column data variables into a Python list. In this case the column names are not defined by the input data, so they must either be set using the names keyword or they will be auto-generated as col<N>.

>>> a = np.array([1, 4], dtype=np.int32)
>>> b = [2.0, 5.0]
>>> c = ['x', 'y']
>>> t = Table([a, b, c], names=('a', 'b', 'c'))
>>> t
<Table length=2>
  a      b     c
int32 float64 str1
----- ------- ----
    1     2.0    x
    4     5.0    y

Make a new table using columns from the first table

Once you have a Table then you can make new table by selecting columns and putting this into a Python list, e.g. [ t['c'], t['a'] ]:

>>> Table([t['c'], t['a']])
<Table length=2>
 c     a
str1 int32
---- -----
   x     1
   y     4

Make a new table using expressions involving columns

The Column object is derived from the standard numpy array and can be used directly in arithmetic expressions. This allows for a compact way of making a new table with modified column values:

>>> Table([t['a']**2, t['b'] + 10])
<Table length=2>
  a      b
int32 float64
----- -------
    1    12.0
   16    15.0

Different types of column data

The list input method for Table is very flexible since you can use a mix of different data types to initialize a table:

>>> a = (1, 4)
>>> b = np.array([[2, 3], [5, 6]])  # vector column
>>> c = Column(['x', 'y'], name='axis')
>>> arr = (a, b, c)
>>> Table(arr)  
<Table length=2>
 col0 col1 [2] axis
int64  int64   str1
----- -------- ----
    1   2 .. 3    x
    4   5 .. 6    y

Notice that in the third column the existing column name 'axis' is used.

Dict of columns

A dictionary of column data can be used to initialize a Table.

>>> arr = {'a': np.array([1, 4], dtype=np.int32),
...        'b': [2.0, 5.0],
...        'c': ['x', 'y']}
>>> Table(arr)  
<Table length=2>
  a    c      b
int32 str1 float64
----- ---- -------
    1    x     2.0
    4    y     5.0

Specify the column order and optionally the data types

>>> Table(arr, names=('a', 'b', 'c'), dtype=('f8', 'i4', 'S2'))  
<Table length=2>
   a      b    c
float64 int32 str2
------- ----- ----
    1.0     2    x
    4.0     5    y

Different types of column data

The input column data can be any data type that can initialize a Column object:

>>> arr = {'a': (1, 4),
...        'b': np.array([[2, 3], [5, 6]]),
...        'c': Column(['x', 'y'], name='axis')}
>>> Table(arr, names=('a', 'b', 'c'))  
<Table length=2>
  a   b [2]   c
int64 int64  str1
----- ------ ----
    1 2 .. 3    x
    4 5 .. 6    y

Notice that the key 'c' takes precedence over the existing column name 'axis' in the third column. Also see that the 'b' column is a vector column where each row element is itself a 2-element array.

Renaming columns is not possible

>>> Table(arr, names=('a_new', 'b_new', 'c_new'))
Traceback (most recent call last):
KeyError: 'a_new'

Row data

Row-oriented data can be used to create a table using the rows keyword argument.

List of data records as list or tuple

If you have row-oriented input data such as a list of records, you need to use the rows keyword to create a table:

>>> data_rows = [(1, 2.0, 'x'),
...              (4, 5.0, 'y'),
...              (5, 8.2, 'z')]
>>> t = Table(rows=data_rows, names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 2.0   x
  4 5.0   y
  5 8.2   z

The data object passed as the rows argument can be any form which is parsable by the np.rec.fromrecords() function.

List of dict objects

You can also initialize a table with row values. This is constructed as a list of dict objects. The keys determine the column names:

>>> data = [{'a': 5, 'b': 10},
...         {'a': 15, 'b': 20}]
>>> Table(rows=data)  
<Table length=2>
  a     b
int64 int64
----- -----
    5    10
   15    20

Every row must have the same set of keys or a ValueError will be thrown:

>>> t = Table(rows=[{'a': 5, 'b': 10}, {'a': 15, 'b': 30, 'c': 50}])
Traceback (most recent call last):
ValueError: Row 0 has no value for column c

Single row

You can also make a new table from a single row of an existing table:

>>> a = [1, 4]
>>> b = [2.0, 5.0]
>>> t = Table([a, b], names=('a', 'b'))
>>> t2 = Table(rows=t[1])

Remember that a Row has effectively a zero length compared to the newly created Table which has a length of one. This is similar to the difference between a scalar 1 (length 0) and an array like np.array([1]) with length 1.


In the case of input data as a list of dicts or a single Table row, it is allowed to supply the data as the data argument since these forms are always unambiguous. For example Table([{'a': 1}, {'a': 2}]) is accepted. However, a list of records must always be provided using the rows keyword, otherwise it will be interpreted as a list of columns.

NumPy structured array

The structured array is the standard mechanism in numpy for storing heterogeneous table data. Most scientific I/O packages that read table files (e.g. PyFITS, vo.table, asciitable) will return the table in an object that is based on the structured array. A structured array can be created using:

>>> arr = np.array([(1, 2.0, 'x'),
...                 (4, 5.0, 'y')],
...                dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'S2')])

From arr it is simple to create the corresponding Table object:

>>> Table(arr)  
<Table length=2>
  a      b     c
int32 float64 str2
----- ------- ----
    1     2.0    x
    4     5.0    y

Note that in the above example and most the following ones we are creating a table and immediately asking the interactive Python interpreter to print the table to see what we made. In real code you might do something like:

>>> table = Table(arr)
>>> print(table)
 a   b   c
--- --- ---
  1 2.0   x
  4 5.0   y

New column names

The column names can be changed from the original values by providing the names argument:

>>> Table(arr, names=('a_new', 'b_new', 'c_new'))  
<Table length=2>
a_new  b_new  c_new
int32 float64  str2
----- ------- -----
    1     2.0     x
    4     5.0     y

New data types

Likewise the data type for each column can by changed with dtype:

>>> Table(arr, dtype=('f4', 'i4', 'S4'))  
<Table length=2>
   a      b    c
float32 int32 str4
------- ----- ----
    1.0     2    x
    4.0     5    y

>>> Table(arr, names=('a_new', 'b_new', 'c_new'), dtype=('f4', 'i4', 'S4'))  
<Table length=2>
 a_new  b_new c_new
float32 int32  str4
------- ----- -----
    1.0     2     x
    4.0     5     y

NumPy homogeneous array

A numpy 1-d array is treated as a single row table where each element of the array corresponds to a column:

>>> Table(np.array([1, 2, 3]), names=['a', 'b', 'c'], dtype=('i8', 'i8', 'i8'))
<Table length=1>
  a     b     c
int64 int64 int64
----- ----- -----
    1     2     3

A numpy 2-d array (where all elements have the same type) can also be converted into a Table. In this case the column names are not specified by the data and must either be provided by the user or will be automatically generated as col<N> where <N> is the column number.

Basic example with automatic column names

>>> arr = np.array([[1, 2, 3],
...                 [4, 5, 6]], dtype=np.int32)
>>> Table(arr)
<Table length=2>
 col0  col1  col2
int32 int32 int32
----- ----- -----
    1     2     3
    4     5     6

Column names and types specified

>>> Table(arr, names=('a_new', 'b_new', 'c_new'), dtype=('f4', 'i4', 'S4'))  
<Table length=2>
 a_new  b_new c_new
float32 int32  str4
------- ----- -----
    1.0     2     3
    4.0     5     6

Referencing the original data

It is possible to reference the original data for an homogeneous array as long as the data types are not changed:

>>> t = Table(arr, copy=False)

Python arrays versus `numpy` arrays as input

There is a slightly subtle issue that is important to understand in the way that Table objects are created. Any data input that looks like a Python list (including a tuple) is considered to be a list of columns. In contrast an homogeneous numpy array input is interpreted as a list of rows:

>>> arr = [[1, 2, 3],
...        [4, 5, 6]]
>>> np_arr = np.array(arr)

>>> print(Table(arr))    # Two columns, three rows
col0 col1
---- ----
   1    4
   2    5
   3    6

>>> print(Table(np_arr))  # Three columns, two rows
col0 col1 col2
---- ---- ----
   1    2    3
   4    5    6

This dichotomy is needed to support flexible list input while retaining the natural interpretation of 2-d numpy arrays where the first index corresponds to data “rows” and the second index corresponds to data “columns”.

From existing table

A new table can be created by selecting a subset of columns in an existing table:

>>> t = Table(names=('a', 'b', 'c'))
>>> t['c', 'b', 'a']  # Makes a copy of the data
<Table length=0>
   c       b       a
float64 float64 float64
------- ------- -------

An alternate way to use the columns attribute (explained in the TableColumns section) to initialize a new table. This let’s you choose columns by their numerical index or name and supports slicing syntax:

>>> Table(t.columns[0:2])
<Table length=0>
   a       b
float64 float64
------- -------

>>> Table([t.columns[0], t.columns['c']])
<Table length=0>
   a       c
float64 float64
------- -------

To create a copy of an existing table that is empty (has no rows):

>>> t = Table([[1.0, 2.3], [2.1, 3]], names=['x', 'y'])
>>> t
<Table length=2>
   x       y
float64 float64
------- -------
    1.0     2.1
    2.3     3.0

>>> tcopy = t[:0].copy()
>>> tcopy
<Table length=0>
   x       y
float64 float64
------- -------

Initialization Details

A table object is created by initializing a Table class object with the following arguments, all of which are optional:

data : numpy ndarray, dict, list, or Table
Data to initialize table.
names : list
Specify column names
dtype : list
Specify column data types
meta : dict-like
Meta-Data associated with the table
copy : boolean
Copy the input data (default=True).

The following subsections provide further detail on the values and options for each of the keyword arguments that can be used to create a new Table object.


The Table object can be initialized with several different forms for the data argument.

numpy ndarray (structured array)
The base column names are the field names of the data structured array. The names list (optional) can be used to select particular fields and/or reorder the base names. The dtype list (optional) must match the length of names and is used to override the existing data types.
numpy ndarray (homogeneous)

If the data ndarray is 1-dimensional then it is treated as a single row table where each element of the array corresponds to a column.

If the data ndarray is at least 2-dimensional then the first (left-most) index corresponds to row number (table length) and the second index corresponds to column number (table width). Higher dimensions get absorbed in the shape of each table cell.

If provided the names list must match the “width” of the data argument. The default for names is to auto-generate column names in the form “col<N>”. If provided the dtype list overrides the base column types and must match the length of names.

The keys of the data object define the base column names. The corresponding values can be Column objects, numpy arrays, or list-like objects. The names list (optional) can be used to select particular fields and/or reorder the base names. The dtype list (optional) must match the length of names and is used to override the existing or default data types.
Each item in the data list provides a column of data values and can be a Column object, numpy array, or list-like object. The names list defines the name of each column. The names will be auto-generated if not provided (either from the names argument or by Column objects). If provided the names argument must match the number of items in the data list. The optional dtype list will override the existing or default data types and must match names in length.
Similar to Python’s builtin csv.DictReader, each item in the data list provides a row of data values and must be a dict. The key values in each dict define the column names and each row must have identical column names. The names argument may be supplied to specify column ordering. If it is not provided, the column order will default to alphabetical. The dtype list may be specified, and must correspond to the order of output columns. If any row’s keys do no match the rest of the rows, a ValueError will be thrown.
table-like object
If another table-like object has a __astropy_table__ method then that object can be used to directly create a Table object. See the Table-like objects section for details.
Initialize a zero-length table. If names and optionally dtype are provided then the corresponding columns are created.


The names argument provides a way to specify the table column names or override the existing ones. By default the column names are either taken from existing names (for ndarray or Table input) or auto-generated as col<N>. If names is provided then it must be a list with the same length as the number of columns. Any list elements with value None fall back to the default name.

In the case where data is provided as dict of columns, the names argument can be supplied to specify the order of columns. The names list must then contain each of the keys in the data dict. If names is not supplied then the order of columns in the output table is not determinate.


The dtype argument provides a way to specify the table column data types or override the existing types. By default the types are either taken from existing types (for ndarray or Table input) or auto-generated by the numpy.array() routine. If dtype is provided then it must be a list with the same length as the number of columns. The values must be valid numpy.dtype initializers or None. Any list elements with value None fall back to the default type.

In the case where data is provided as dict of columns, the dtype argument must be accompanied by a corresponding names argument in order to uniquely specify the column ordering.


The meta argument is simply an object that contains meta-data associated with the table. It is recommended that this object be a dict or OrderedDict, but the only firm requirement is that it can be copied with the standard library copy.deepcopy() routine. By default meta is an empty OrderedDict.


By default the input data are copied into a new internal np.ndarray object in the Table object. In the case where data is either an np.ndarray object or an existing Table, it is possible to use a reference to the existing data by setting copy=False. This has the advantage of reducing memory use and being faster. However one should take care because any modifications to the new Table data will also be seen in the original input data. See the Copy versus Reference section for more information.

Copy versus Reference

Normally when a new Table object is created, the input data are copied into a new internal array object. This ensures that if the new table elements are modified then the original data will not be affected. However, when creating a table from a numpy ndarray object (structured or homogeneous), it is possible to disable copying so that instead a memory reference to the original data is used. This has the advantage of being faster and using less memory. However, caution must be exercised because the new table data and original data will be linked, as shown below:

>>> arr = np.array([(1, 2.0, 'x'),
...                 (4, 5.0, 'y')],
...                dtype=[('a', 'i8'), ('b', 'f8'), ('c', 'S2')])
>>> print(arr['a'])  # column "a" of the input array
[1 4]
>>> t = Table(arr, copy=False)
>>> t['a'][1] = 99
>>> print(arr['a'])  # arr['a'] got changed when we modified t['a']
[ 1 99]

Note that when referencing the data it is not possible to change the data types since that operation requires making a copy of the data. In this case an error occurs:

>>> t = Table(arr, copy=False, dtype=('f4', 'i4', 'S4'))
Traceback (most recent call last):
ValueError: Cannot specify dtype when copy=False

Another caveat in using referenced data is that you if add a new row to the table then the reference to the original data array is lost and instead the table will now hold a copy of the original values (in addition to the new row).

Column and TableColumns classes

There are two classes, Column and TableColumns, that are useful when constructing new tables.


A Column object can be created as follows, where in all cases the column name should be provided as a keyword argument and one can optionally provide these values:

data : list, ndarray or None
Column data values
dtype : numpy.dtype compatible value
Data type for column
description : str
Full description of column
unit : str
Physical unit
format : str or function
Format specifier for outputting column values
meta : dict
Meta-data associated with the column

Initialization options

The column data values, shape, and data type are specified in one of two ways:

Provide a ``data`` value but not a ``length`` or ``shape``


col = Column([1, 2], name='a')  # shape=(2,)
col = Column([[1, 2], [3, 4]], name='a')  # shape=(2, 2)
col = Column([1, 2], name='a', dtype=float)
col = Column(np.array([1, 2]), name='a')
col = Column(['hello', 'world'], name='a')

The dtype argument can be any value which is an acceptable fixed-size data-type initializer for the numpy.dtype() method. See Examples include:

  • Python non-string type (float, int, bool)
  • Numpy non-string type (e.g. np.float32, np.int64, np.bool)
  • Numpy.dtype array-protocol type strings (e.g. ‘i4’, ‘f8’, ‘S15’)

If no dtype value is provided then the type is inferred using np.array(data). When data is provided then the shape and length arguments are ignored.

Provide ``length`` and optionally ``shape``, but not ``data``


col = Column(name='a', length=5)
col = Column(name='a', dtype=int, length=10, shape=(3,4))

The default dtype is np.float64. The shape argument is the array shape of a single cell in the column. The default shape is () which means a single value in each element.


After setting the type for a column, that type cannot be changed. If data values of a different type are assigned to the column then they will be cast to the existing column type.

Format specifier

The format specifier controls the output of column values when a table or column is printed or written to an ASCII table. In the simplest case, it is a string that can be passed to python’s built-in format function. For more complicated formatting, one can also give “old-style” or “new-style” format strings, or even a function:

Plain format specification

This type of string specifies directly how the value should be formatted, using a format specification mini-language that is quite similar to C.

".4f" will give four digits after the decimal in float format, or

"6d" will give integers in 6-character fields.

Old-style format string

This corresponds to syntax like "%.4f" % value as documented in String formatting operations.

"%.4f" to print four digits after the decimal in float format, or

"%6d" to print an integer in a 6-character wide field.

New-style format string

This corresponds to syntax like "{:.4f}".format(value) as documented in format string syntax.

"{:.4f}" to print four digits after the decimal in float format, or

"{:6d}" to print an integer in a 6-character wide field.

Note that in either format string case any Python string that formats exactly one value is valid, so {:.4f} angstroms or Value: %12.2f would both work.


The greatest flexibility can be achieved by setting a formatting function. This function must accept a single argument (the value) and return a string. In the following example this is used to make a LaTeX ready output:

>>> t = Table([[1,2],[1.234e9,2.34e-12]], names = ('a','b'))
>>> def latex_exp(value):
...     val = '{0:8.2}'.format(value)
...     mant, exp = val.split('e')
...     # remove leading zeros
...     exp = exp[0] + exp[1:].lstrip('0')
...     return '$ {0} \\times 10^{{ {1} }}$' .format(mant, exp)
>>> t['b'].format = latex_exp
>>> t['a'].format = '.4f'
>>> import sys
>>> t.write(sys.stdout, format='latex')
a & b \\
1.0000 & $  1.2 \times 10^{ +9 }$ \\
2.0000 & $  2.3 \times 10^{ -12 }$ \\


Each Table object has an attribute columns which is an ordered dictionary that stores all of the Column objects in the table (see also the Column section). Technically the columns attribute is a TableColumns object, which is an enhanced ordered dictionary that provides easier ways to select multiple columns. There are a few key points to remember:

So now look at the ways to select columns from a TableColumns object:

Select columns by name

>>> t = Table(names=('a', 'b', 'c', 'd'))

>>> t.columns['d', 'c', 'b']
<TableColumns names=('d','c','b')>

Select columns by index slicing

>>> t.columns[0:2]  # Select first two columns
<TableColumns names=('a','b')>

>>> t.columns[::-1]  # Reverse column order
<TableColumns names=('d','c','b','a')>

Select column by index or name

>>> t.columns[1]  # Choose columns by index
<Column name='b' dtype='float64' length=0>

>>> t.columns['b']  # Choose column by name
<Column name='b' dtype='float64' length=0>

Subclassing Table

For some applications it can be useful to subclass the Table class in order to introduce specialized behavior. In addition to subclassing Table it is frequently desirable to change the behavior of the internal class objects which are contained or created by a Table. This includes rows, columns, formatting, and the columns container. In order to do this the subclass needs to declare what class to use (if it is different from the built-in version). This is done by specifying one or more of the class attributes Row, Column, MaskedColumn, TableColumns, or TableFormatter.

The following trivial example overrides all of these with do-nothing subclasses, but in practice you would override only the necessary subcomponents:

>>> from astropy.table import Table, Row, Column, MaskedColumn, TableColumns, TableFormatter

>>> class MyRow(Row): pass
>>> class MyColumn(Column): pass
>>> class MyMaskedColumn(MaskedColumn): pass
>>> class MyTableColumns(TableColumns): pass
>>> class MyTableFormatter(TableFormatter): pass

>>> class MyTable(Table):
...     """
...     Custom subclass of astropy.table.Table
...     """
...     Row = MyRow  # Use MyRow to create a row object
...     Column = MyColumn  # Column
...     MaskedColumn = MyMaskedColumn  # Masked Column
...     TableColumns = MyTableColumns  # Ordered dict holding Column objects
...     TableFormatter = MyTableFormatter  # Controls table output


As a more practical example, suppose you have a table of data with a certain set of fixed columns, but you also want to carry an arbitrary dictionary of keyword=value parameters for each row and then access those values using the same item access syntax as if they were columns. It is assumed here that the extra parameters are contained in a numpy object-dtype column named params:

>>> from astropy.table import Table, Row
>>> class ParamsRow(Row):
...    """
...    Row class that allows access to an arbitrary dict of parameters
...    stored as a dict object in the ``params`` column.
...    """
...    def __getitem__(self, item):
...        if item not in self.colnames:
...            return super(ParamsRow, self).__getitem__('params')[item]
...        else:
...            return super(ParamsRow, self).__getitem__(item)
...    def keys(self):
...        out = [name for name in self.colnames if name != 'params']
...        params = [key.lower() for key in sorted(self['params'])]
...        return out + params
...    def values(self):
...        return [self[key] for key in self.keys()]

Now we put this into action with a trivial Table subclass:

>>> class ParamsTable(Table):
...     Row = ParamsRow

First make a table and add a couple of rows:

>>> t = ParamsTable(names=['a', 'b', 'params'], dtype=['i', 'f', 'O'])
>>> t.add_row((1, 2.0, {'x': 1.5, 'y': 2.5}))
>>> t.add_row((2, 3.0, {'z': 'hello', 'id': 123123}))
>>> print(t)  
 a   b             params
--- --- ----------------------------
  1 2.0         {'y': 2.5, 'x': 1.5}
  2 3.0 {'z': 'hello', 'id': 123123}

Now see what we have from our specialized ParamsRow object:

>>> t[0]['y']
>>> t[1]['id']
>>> t[1].keys()
['a', 'b', 'id', 'z']
>>> t[1].values()
[2, 3.0, 123123, 'hello']

To make this example really useful you might want to override Table.__getitem__ in order to allow table-level access to the parameter fields. This might look something like:

class ParamsTable(table.Table):
    Row = ParamsRow

    def __getitem__(self, item):
        if isinstance(item, six.string_types):
            if item in self.colnames:
                return self.columns[item]
                # If item is not a column name then create a new MaskedArray
                # corresponding to self['params'][item] for each row.  This
                # might not exist in some rows so mark as masked (missing) in
                # those cases.
                mask = np.zeros(len(self), dtype=np.bool)
                item = item.upper()
                values = [params.get(item) for params in self['params']]
                for ii, value in enumerate(values):
                    if value is None:
                        mask[ii] = True
                        values[ii] = ''
                return self.MaskedColumn(name=item, data=values, mask=mask)

        # ... and then the rest of the original __getitem__ ...

Columns and Quantities

Astropy Quantity objects can be handled within tables in two complementary ways. The first method stores the Quantity object natively within the table via the “mixin” column protocol. See the sections on Mixin columns and Quantity and QTable for details, but in brief the key difference is using the QTable class to indicate that a Quantity should be stored natively within the table:

>>> from astropy.table import QTable
>>> from astropy import units as u
>>> t = QTable()
>>> t['velocity'] = [3, 4] * u.m / u.s
>>> type(t['velocity'])  

For new code that is quantity-aware we recommend using QTable, but this may not be possible in all situations (particularly when interfacing with legacy code that does not handle quantities) and there are Details and caveats that apply. In this case use the Table class, which will convert a Quantity to a Column object with a unit attribute:

>>> from astropy.table import Table
>>> t = Table()
>>> t['velocity'] = [3, 4] * u.m / u.s
>>> type(t['velocity'])  
>>> t['velocity'].unit
Unit("m / s")

To learn more about using standard Column objects with defined units, see the Columns with Units section.

Table-like objects

In order to improve interoperability between different table classes, an astropy Table object can be created directly from any other table-like object that provides an __astropy_table__ method. In this case the __astropy_table__ method will be called as follows:

>>> data = SomeOtherTableClass({'a': [1, 2], 'b': [3, 4]})  
>>> t = QTable(data, copy=False, strict_copy=True)  

Internally the following call will be made to ask the data object to return a representation of itself as an astropy Table, respecting the copy preference of the original call to QTable():

data.__astropy_table__(cls, copy, **kwargs)

Here cls is the Table class or subclass that is being instantiated (QTable in this example), copy indicates whether a copy of the values in data should be provided, and **kwargs are any extra keyword arguments which are not valid Table init keyword arguments. In the example above, strict_copy=True would end up in **kwargs and get passed to __astropy_table__().

If copy is True then the __astropy_table__ method must ensure that a copy of the original data is returned. If copy is False then a reference to the table data should returned if possible. If it is not possible (e.g. the original data are in a Python list or must be otherwise transformed in memory) then __astropy_table__ method is free to either return a copy or else raise an exception. This choice depends on the preference of the implementation. The implementation might choose to allow an additional keyword argument (e.g. strict_copy which gets passed via **kwargs) to control the behavior in this case.

As a simple example, imagine a dict-based table class. (Note that Table already can be initialized from a dict-like object, so this is a bit contrived but does illustrate the principles involved.) Please pay attention to the method signature:

def __astropy_table__(self, cls, copy, **kwargs):

Your class implementation of this must use the **kwargs technique for catching keyword arguments at the end. This is to ensure future compatibility in case additional keywords are added to the internal table = data.__astropy_table__(cls, copy) call. Including **kwargs will prevent breakage in this case.

class DictTable(dict):
    Trivial "table" class that just uses a dict to hold columns.
    This does not actually implement anything useful that makes
    this a table.

    The non-standard ``strict_copy=False`` keyword arg here will be passed
    via the **kwargs of Table __init__().

    def __astropy_table__(self, cls, copy, strict_copy=False, **kwargs):
        Return an astropy Table of type ``cls``.

        cls : type
             Astropy ``Table`` class or subclass
        copy : bool
             Copy input data (True) or return a reference (False)
        strict_copy : bool, optional
             Raise an exception if copy is False but reference is not
        **kwargs : dict, optional
             Additional keyword args (ignored currently)
        if kwargs:
            warnings.warn('unexpected keyword args {}'.format(kwargs))

        cols = list(self.values())
        names = list(self.keys())

        # If returning a reference to existing data (copy=False) and
        # strict_copy=True, make sure that each column is a numpy ndarray.
        # If a column is a Python list or tuple then it must be copied for
        # representation in an astropy Table.

        if not copy and strict_copy:
            for name, col in zip(names, cols):
                if not isinstance(col, np.ndarray):
                    raise ValueError('cannot have copy=False because column {} is '
                                     'not an ndarray'.format(name))

        return cls(cols, names=names, copy=copy)