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 and QTable constructors are in the Initialization Details section. However, the best way to understand how to make a table is by example.



For the following examples you need to import the QTable, Table, and Column classes along with the Units and Quantities (astropy.units) package and the numpy package:

>>> from astropy.table import QTable, Table, Column
>>> from astropy import units as u
>>> 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 fast.

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

If your data columns have physical units associated with them then we recommend using the QTable class. This will allow the column to be stored in the table as a native Quantity and bring the full power of Units and Quantities (astropy.units) to the table. See Quantity and QTable for details.

>>> t = QTable()
>>> t['a'] = [1, 4]
>>> t['b'] = [2.0, 5.0] * / u.s
>>> t['c'] = ['x', 'y']
>>> type(t['b'])
<class 'astropy.units.quantity.Quantity'>

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 automatically 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 a new table by selecting columns and putting them 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.ndarray 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]], dtype=np.int64)  # vector column
>>> c = Column(['x', 'y'], name='axis')
>>> d = u.Quantity([([1., 2., 3.], [.1, .2, .3]),
...                 ([4., 5., 6.], [.4, .5, .6])], 'm,m/s')
>>> QTable([a, b, c, d])
<QTable length=2>
  col0    col1   axis          col3 [f0, f1]
                                  (m, m / s)
float64 int64[2] str1     (float64[3], float64[3])
------- -------- ---- -------------------------------
    1.0   2 .. 3    x ([1., 2., 3.], [0.1, 0.2, 0.3])
    4.0   5 .. 6    y ([4., 5., 6.], [0.4, 0.5, 0.6])

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

Dict of Columns#

A dict 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      b     c
int32 float64 str1
----- ------- ----
    1     2.0    x
    4     5.0    y

Specify the column order and optionally the data types

>>> Table(arr, names=('a', 'c', 'b'), dtype=('f8', 'U2', 'i4'))
<Table length=2>
   a     c     b
float64 str2 int32
------- ---- -----
    1.0    x     2
    4.0    y     5

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]], dtype=np.int64),
...        'c': Column(['x', 'y'], name='axis')}
>>> Table(arr, names=('a', 'b', 'c'))
<Table length=2>
   a       b      c
float64 int64[2] str1
------- -------- ----
    1.0   2 .. 3    x
    4.0   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 two-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 or tuple of data records

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

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}]
>>> t = Table(rows=data)
>>> print(t)
 a   b
--- ---
  5  10
 15  20

If there are missing keys in one or more rows then the corresponding values will be marked as missing (masked):

>>> t = Table(rows=[{'a': 5, 'b': 10}, {'a': 15, 'c': 50}])
>>> print(t)
 a   b   c
--- --- ---
  5  10  --
 15  --  50

You can specify the column order with the names argument:

>>> data = [{'a': 5, 'b': 10},
...         {'a': 15, 'b': 20}]
>>> t = Table(rows=data, names=('b', 'a'))
>>> print(t)
 b   a
--- ---
 10   5
 20  15

If names are not provided then column ordering will be determined by the first dict if it contains values for all the columns, or by sorting the column names alphabetically if it doesn’t:

>>> data = [{'b': 10, 'c': 7, 'a': 5},
...         {'a': 15, 'c': 35, 'b': 20}]
>>> t = Table(rows=data)
>>> print(t)
 b   c   a
--- --- ---
 10   7   5
 20  35  15
>>> data = [{'b': 10, 'c': 7, },
...         {'a': 15, 'c': 35, 'b': 20}]
>>> t = Table(rows=data)
>>> print(t)
 a   b   c
--- --- ---
 --  10   7
 15  20  35

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 such as np.array([1]) with length 1.


In the case of input data as a list of dicts or a single Table row, you can 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.,,, and 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', 'U2')])

From arr it is possible 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 of the following examples 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

Structured Array as a Column#

In some cases it is convenient to include a structured array as a single column in a table. The EarthLocation class is one case in astropy where this is done, where the structured column has three elements x, y and z. Another example would be a modeling parameter that has a value, a minimum allowed value and a maximum allowed value. Here we demonstrate including the simple structured array defined previously as a column:

>>> table = Table()
>>> table['name'] = ['Micah', 'Mazzy']
>>> table['arr'] = arr
>>> print(table)
 name arr [a, b, c]
----- -------------
Micah  (1, 2., 'x')
Mazzy  (4, 5., 'y')

You can access or print a single field in the structured column as follows:

>>> print(table['arr']['b'])
[2. 5.]

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

The data type for each column can likewise be changed with dtype:

>>> Table(arr, dtype=('f4', 'i4', 'U4'))
<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', 'U4'))
<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 1D 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 2D 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', 'U4'))
<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 as long as the data types are not changed:

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

See the Copy versus Reference section for more information.

Python arrays versus NumPy arrays as input

There is a slightly subtle issue that is important to understand about 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, a homogeneous numpy.ndarray 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 2D numpy arrays where the first index corresponds to data “rows” and the second index corresponds to data “columns.”

From an 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 is to use the columns attribute (explained in the TableColumns section) to initialize a new table. This lets 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
------- -------

Empty Array of a Known Size#

If you do know the size that your table will be, but do not know the values in advance, you can create a zeroed numpy.ndarray and build the Table from it:

>>> N = 3
>>> dtype = [('a', 'i4'), ('b', 'f8'), ('c', 'bool')]
>>> t = Table(data=np.zeros(N, dtype=dtype))
>>> t
<Table length=3>
  a      b      c
int32 float64  bool
----- ------- -----
    0     0.0 False
    0     0.0 False
    0     0.0 False

For example, you can then fill in this table row by row with values extracted from another table, or generated on the fly:

>>> for i in range(len(t)):
...     t[i] = (i, 2.5*i, i % 2)
>>> t
<Table length=3>
  a      b      c
int32 float64  bool
----- ------- -----
    0     0.0 False
    1     2.5  True
    2     5.0 False


A SkyCoord object can be converted to a QTable using its to_table() method. For details and examples see Converting a SkyCoord to a Table.

Pandas DataFrame#

The section on Interfacing with the Pandas Package gives details on how to initialize a Table using a pandas.DataFrame via the from_pandas() class method. This provides a convenient way to take advantage of the many I/O and table manipulation methods in pandas.

Comment Lines#

Comment lines in an ASCII file can be added via the 'comments' key in the table’s metadata. The following will insert two comment lines in the output ASCII file unless comment=False is explicitly set in write():

>>> import sys
>>> from astropy.table import Table
>>> t = Table(names=('a', 'b', 'c'), dtype=('f4', 'i4', 'S2'))
>>> t.add_row((1, 2.0, 'x'))
>>> t.meta['comments'] = ['Here is my explanatory text. This is awesome.',
...                       'Second comment line.']
>>> t.write(sys.stdout, format='ascii')
# Here is my explanatory text. This is awesome.
# Second comment line.
a b c
1.0 2 x

Initialization Details#

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

datanumpy.ndarray, dict, list, Table, or table-like object, optional

Data to initialize table.

maskedbool, optional

Specify whether the table is masked.

nameslist, optional

Specify column names.

dtypelist, optional

Specify column data types.

metadict, optional

Metadata associated with the table.

copybool, optional

Copy the input data. If the input is a Table the meta is always copied regardless of the copy parameter. Default is True.

rowsnumpy.ndarray, list of lists, optional

Row-oriented data for table instead of data argument.

copy_indicesbool, optional

Copy any indices in the input data. Default is True.

unitslist, dict, optional

List or dict of units to apply to columns.

descriptionslist, dict, optional

List or dict of descriptions to apply to columns.

**kwargsdict, optional

Additional keyword args when converting table-like object.

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

If the data is an at least two-dimensional numpy.ndarray, 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.ndarray, or list-like object. The names list defines the name of each column. The names will be auto-generated if not provided (either with 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 built-in 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. The names argument may be supplied to specify column ordering. If names are not provided then column ordering will be determined by the first dict if it contains values for all the columns, or by sorting the column names alphabetically if it does not. The dtype list may be specified, and must correspond to the order of output columns.

Table-like object

If another table-like object has a __astropy_table__() method then that object can be used to directly create a Table. 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 numpy.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 a 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.


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 numpy.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.


The meta argument is an object that contains metadata 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.


In the case where data is either an numpy.ndarray object, a dict, 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, you 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.


This argument allows for providing data as a sequence of rows, in contrast to the data keyword, which generally assumes data are a sequence of columns. The Row data section provides details.


If you are initializing a Table from another Table that makes use of Table Indexing, then this option allows copying that table without copying the indices by setting copy_indices=False. By default, the indices are copied.


This allows for setting the unit for one or more columns at the time of creating the table. The input can be either a list of unit values corresponding to each of the columns in the table (using None or '' for no unit), or a dict that provides the unit for specified column names. For example:

>>> dat = [[1, 2], ['hello', 'world']]
>>> qt = QTable(dat, names=['a', 'b'], units=(u.m, None))
>>> qt = QTable(dat, names=['a', 'b'], units={'a': u.m})

See Quantity and QTable for why we used a QTable here instead of a Table.


This allows for setting the description for one or more columns at the time of creating the table. The input can be either a list of description values corresponding to each of the columns in the table (using None for no description), or a dict that provides the description for specified column names. This works in the same way as the units example above.

Copy versus Reference#

Normally when a new Table object is created, the input data are copied. This ensures that if the new table elements are modified then the original data will not be affected. However, when creating a table from an existing Table, a numpy.ndarray object (structured or homogeneous) or a dict, it is possible to disable copying so that a memory reference to the original data is used instead. 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 to using referenced data is that if you add a new row to the table, the reference to the original data array is lost and the table will now instead 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 you can optionally provide these values:

datalist, numpy.ndarray or None

Column data values.

dtypenumpy.dtype compatible value

Data type for column.


Full description of column.


Physical unit.

formatstr or function

Format specifier for outputting column values.


Metadata associated with the column.

Initialization Options#

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

Provide data but not 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 a numpy.dtype. See the reference for data type objects. Examples include:

  • Python non-string type (float, int, bool).

  • numpy non-string type (e.g., np.float32, np.int64).

  • numpy.dtype array-protocol type strings (e.g., 'i4', 'f8', 'U15').

If no dtype value is provided, then the type is inferred using numpy.array(). 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 six-character fields.

Old style format string

This corresponds to syntax like "%.4f" % value as documented in printf-style String Formatting.

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

"%6d" to print an integer in a six-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 six-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. One caveat is that such a format function cannot be saved to file and you will get an exception if you attempt to do so. 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 = f'{value:8.2}'
...     mant, exp = val.split('e')
...     # remove leading zeros
...     exp = exp[0] + exp[1:].lstrip('0')
...     return f'$ {mant} \\times 10^{{ {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 }$ \\

Format string for structured array column

For columns which are structured arrays, the format string must be a a string that uses “new style” format strings with parameter substitutions corresponding to the field names in the structured array. See Structured array columns for an example.


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:

There are a few different 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 single columns by index or name

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

>>> t.columns['b']  # Choose a 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. Here we address two particular use cases for subclassing: adding custom table attributes and changing the behavior of internal class objects.

Adding Custom Table Attributes#

One simple customization that can be useful is adding new attributes to the table object. There is nothing preventing setting an attribute on an existing table object, for example = 'hello'. However, this attribute would be ephemeral because it will be lost if the table is sliced, copied, or pickled. Instead, you can add persistent attributes as shown in this example:

from astropy.table import Table, TableAttribute

class MyTable(Table):
    foo = TableAttribute()
    bar = TableAttribute(default=[])
    baz = TableAttribute(default=1)

t = MyTable([[1, 2]], foo='foo')
t.baz = 'baz'

Some key points:

  • A custom attribute can be set when the table is created or using the usual syntax for setting an object attribute.

  • A custom attribute always has a default value, either explicitly set in the class definition or None.

  • The attribute values are stored in the table meta dictionary. This is the mechanism by which they are persistent through copy, slice, and serialization such as pickling or writing to an ECSV Format file.

Changing Behavior of Internal Class Objects#

It is also possible 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 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().__getitem__('params')[item]
...        else:
...            return super().__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         {'x': 1.5, 'y': 2.5}
  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, str):
            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'])
<class 'astropy.units.quantity.Quantity'>

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'])
<class 'astropy.table.column.Column'>
>>> 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, mask_invalid=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, mask_invalid=True would end up in **kwargs and get passed to __astropy_table__().

The implementation might choose to allow additional keyword arguments (e.g., mask_invalid which gets passed via **kwargs).

As a concise 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 ``mask_invalid=False`` keyword arg here will be passed
    via the **kwargs of Table __init__().

    def __astropy_table__(self, cls, copy, mask_invalid=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).
        mask_invalid : bool, optional
             Controls whether invalid values (NaNs) should be masked.
             Default is False.
        **kwargs : dict, optional
             Additional keyword args (ignored currently).
        if kwargs:
            warnings.warn(f'unexpected keyword args {kwargs}')

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

        if mask_invalid:
            cols = [
                Masked(col, mask=mask) if np.any(mask := np.isnan(col)) else col
                for col in cols

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