astro py:docs

- Index
- Modules

### Navigation

- next »
- « previous |
- Astropy v1.1.1 »
- Data Tables (`astropy.table`) »
- Constructing a table

# 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.

## Examples

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).

### Setup

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.

Note

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'])
    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 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.

Note

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”.

### Table columns

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
    ------- -------

## 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.

### data

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`.

dict-like
  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.
list-like
  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.
list-of-dicts
  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.
None
  Initialize a zero-length table. If `names` and optionally `dtype` are provided then the corresponding columns are created.

### names

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.

### dtype

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.

### meta

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.

### copy

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.

### Column

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``

> Examples:

>     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 http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html. 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``

> Examples:

>     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.

Note

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.

Function

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')
    \begin{table}
    \begin{tabular}{cc}
    a & b \\
    1.0000 & $  1.2 \times 10^{ +9 }$ \\
    2.0000 & $  2.3 \times 10^{ -12 }$ \\
    \end{tabular}
    \end{table}

### TableColumns

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:

- A `Table` can be initialized from a `TableColumns` object (copy is always True).
- Selecting multiple columns from a `TableColumns` object returns another `TableColumns` object.
- Select one column from a `TableColumns` object returns a `Column`.

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

### Example

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']
    2.5
    >>> t[1]['id']
    123123
    >>> 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]
                else:
                    # 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'])
    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'])
    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.