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.. _astropy-table:
*****************************
Data Tables (`astropy.table`)
*****************************
Introduction
============
`astropy.table` provides functionality for storing and manipulating
heterogeneous tables of data in a way that is familiar to ``numpy`` users. A few
notable capabilities of this package are:
* Initialize a table from a wide variety of input data structures and types.
* Modify a table by adding or removing columns, changing column names,
or adding new rows of data.
* Handle tables containing missing values.
* Include table and column metadata as flexible data structures.
* Specify a description, units, and output formatting for columns.
* Interactively scroll through long tables similar to using ``more``.
* Create a new table by selecting rows or columns from a table.
* Perform :ref:`table_operations` like database joins, concatenation, and binning.
* Maintain a table index for fast retrieval of table items or ranges.
* Manipulate multidimensional and :ref:`structured array columns <structured-array-as-a-column>`.
* Handle non-native (mixin) column types within table.
* Methods for :ref:`read_write_tables` to files.
* Hooks for :ref:`subclassing_table` and its component classes.
Getting Started
===============
The basic workflow for creating a table, accessing table elements,
and modifying the table is shown below. These examples demonstrate a concise
case, while the full `astropy.table` documentation is available from the
:ref:`using_astropy_table` section.
First create a simple table with columns of data named ``a``, ``b``, ``c``, and
``d``. These columns have integer, float, string, and |Quantity| values
respectively::
>>> from astropy.table import QTable
>>> import astropy.units as u
>>> import numpy as np
>>> a = np.array([1, 4, 5], dtype=np.int32)
>>> b = [2.0, 5.0, 8.5]
>>> c = ['x', 'y', 'z']
>>> d = [10, 20, 30] * u.m / u.s
>>> t = QTable([a, b, c, d],
... names=('a', 'b', 'c', 'd'),
... meta={'name': 'first table'})
Comments:
- Column ``a`` is a |ndarray| with a specified ``dtype`` of ``int32``. If the
data type is not provided, the default type for integers is ``int64`` on Mac
and Linux and ``int32`` on Windows.
- Column ``b`` is a list of ``float`` values, represented as ``float64``.
- Column ``c`` is a list of ``str`` values, represented as unicode.
See :ref:`bytestring-columns-python-3` for more information.
- Column ``d`` is a |Quantity| array. Since we used |QTable|, this stores a
native |Quantity| within the table and brings the full power of
:ref:`astropy-units` to this column in the table.
.. Note::
If the table data have no units or you prefer to not use |Quantity|, then you
can use the |Table| class to create tables. The **only** difference between
|QTable| and |Table| is the behavior when adding a column that has units.
See :ref:`quantity_and_qtable` and :ref:`columns_with_units` for details on
the differences and use cases.
There are many other ways of :ref:`construct_table`, including from a list of
rows (either tuples or dicts), from a ``numpy`` structured or 2D array, by
adding columns or rows incrementally, or even converting from a |SkyCoord| or a
:class:`pandas.DataFrame`.
There are a few ways of :ref:`access_table`. You can get detailed information
about the table values and column definitions as follows::
>>> t
<QTable length=3>
a b c d
m / s
int32 float64 str1 float64
----- ------- ---- -------
1 2.0 x 10.0
4 5.0 y 20.0
5 8.5 z 30.0
You can get summary information about the table as follows::
>>> t.info
<QTable length=3>
name dtype unit class
---- ------- ----- --------
a int32 Column
b float64 Column
c str1 Column
d float64 m / s Quantity
From within a `Jupyter notebook <https://jupyter.org/>`_, the table is
displayed as a formatted HTML table (details of how it appears can be changed
by altering the `astropy.table.conf.default_notebook_table_class
<astropy.table.Conf.default_notebook_table_class>` item in the
:ref:`astropy_config`:
.. image:: table_repr_html.png
:width: 450px
Or you can get a fancier notebook interface with in-browser search, and sort
using :meth:`~astropy.table.Table.show_in_notebook`:
.. image:: table_show_in_nb.png
:width: 450px
If you print the table (either from the notebook or in a text console session)
then a formatted version appears::
>>> print(t)
a b c d
m / s
--- --- --- -----
1 2.0 x 10.0
4 5.0 y 20.0
5 8.5 z 30.0
If you do not like the format of a particular column, you can change it through
:ref:`the 'info' property <mixin_attributes>`::
>>> t['b'].info.format = '7.3f'
>>> print(t)
a b c d
m / s
--- ------- --- -----
1 2.000 x 10.0
4 5.000 y 20.0
5 8.500 z 30.0
For a long table you can scroll up and down through the table one page at
time::
>>> t.more() # doctest: +SKIP
You can also display it as an HTML-formatted table in the browser::
>>> t.show_in_browser() # doctest: +SKIP
Or as an interactive (searchable and sortable) javascript table::
>>> t.show_in_browser(jsviewer=True) # doctest: +SKIP
Now examine some high-level information about the table::
>>> t.colnames
['a', 'b', 'c', 'd']
>>> len(t)
3
>>> t.meta
{'name': 'first table'}
Access the data by column or row using familiar ``numpy`` structured array
syntax::
>>> t['a'] # Column 'a'
<Column name='a' dtype='int32' length=3>
1
4
5
>>> t['a'][1] # Row 1 of column 'a'
4
>>> t[1] # Row 1 of the table
<Row index=1>
a b c d
m / s
int32 float64 str1 float64
----- ------- ---- -------
4 5.000 y 20.0
>>> t[1]['a'] # Column 'a' of row 1
4
You can retrieve a subset of a table by rows (using a :class:`slice`) or by
columns (using column names), where the subset is returned as a new table::
>>> print(t[0:2]) # Table object with rows 0 and 1
a b c d
m / s
--- ------- --- -----
1 2.000 x 10.0
4 5.000 y 20.0
>>> print(t['a', 'c']) # Table with cols 'a' and 'c'
a c
--- ---
1 x
4 y
5 z
:ref:`modify_table` in place is flexible and works as you would expect::
>>> t['a'][:] = [-1, -2, -3] # Set all column values in place
>>> t['a'][2] = 30 # Set row 2 of column 'a'
>>> t[1] = (8, 9.0, "W", 4 * u.m / u.s) # Set all values of row 1
>>> t[1]['b'] = -9 # Set column 'b' of row 1
>>> t[0:2]['b'] = 100.0 # Set column 'b' of rows 0 and 1
>>> print(t)
a b c d
m / s
--- ------- --- -----
-1 100.000 x 10.0
8 100.000 W 4.0
30 8.500 z 30.0
Replace, add, remove, and rename columns with the following::
>>> t['b'] = ['a', 'new', 'dtype'] # Replace column 'b' (different from in-place)
>>> t['e'] = [1, 2, 3] # Add column 'e'
>>> del t['c'] # Delete column 'c'
>>> t.rename_column('a', 'A') # Rename column 'a' to 'A'
>>> t.colnames
['A', 'b', 'd', 'e']
Adding a new row of data to the table is as follows. Note that the unit
value is given in ``cm / s`` but will be added to the table as ``0.1 m / s`` in
accord with the existing unit.
>>> t.add_row([-8, 'string', 10 * u.cm / u.s, 10])
>>> t['d']
<Quantity [10. , 4. , 30. , 0.1] m / s>
Tables can be used for data with missing values::
>>> from astropy.table import MaskedColumn
>>> a_masked = MaskedColumn(a, mask=[True, True, False])
>>> t = QTable([a_masked, b, c], names=('a', 'b', 'c'),
... dtype=('i4', 'f8', 'U1'))
>>> t
<QTable length=3>
a b c
int32 float64 str1
----- ------- ----
-- 2.0 x
-- 5.0 y
5 8.5 z
In addition to |Quantity|, you can include certain object types like
`~astropy.time.Time`, `~astropy.coordinates.SkyCoord`, and
`~astropy.table.NdarrayMixin` in your table. These "mixin" columns behave like
a hybrid of a regular `~astropy.table.Column` and the native object type (see
:ref:`mixin_columns`). For example::
>>> from astropy.time import Time
>>> from astropy.coordinates import SkyCoord
>>> tm = Time(['2000:002', '2002:345'])
>>> sc = SkyCoord([10, 20], [-45, +40], unit='deg')
>>> t = QTable([tm, sc], names=['time', 'skycoord'])
>>> t
<QTable length=2>
time skycoord
deg,deg
Time SkyCoord
--------------------- ----------
2000:002:00:00:00.000 10.0,-45.0
2002:345:00:00:00.000 20.0,40.0
Now let us compute the interval since the launch of the `Chandra X-ray Observatory
<https://en.wikipedia.org/wiki/Chandra_X-ray_Observatory>`_ aboard `STS-93
<https://en.wikipedia.org/wiki/STS-93>`_ and store this in our table as a
|Quantity| in days::
>>> dt = t['time'] - Time('1999-07-23 04:30:59.984')
>>> t['dt_cxo'] = dt.to(u.d)
>>> t['dt_cxo'].info.format = '.3f'
>>> print(t)
time skycoord dt_cxo
deg,deg d
--------------------- ---------- --------
2000:002:00:00:00.000 10.0,-45.0 162.812
2002:345:00:00:00.000 20.0,40.0 1236.812
.. _using_astropy_table:
Using ``table``
===============
The details of using `astropy.table` are provided in the following sections:
Construct Table
---------------
.. toctree::
:maxdepth: 2
construct_table.rst
Access Table
------------
.. toctree::
:maxdepth: 2
access_table.rst
Modify Table
------------
.. toctree::
:maxdepth: 2
modify_table.rst
Table Operations
----------------
.. toctree::
:maxdepth: 2
operations.rst
Indexing
--------
.. toctree::
:maxdepth: 2
indexing.rst
Masking
-------
.. toctree::
:maxdepth: 2
masking.rst
I/O with Tables
---------------
.. toctree::
:maxdepth: 2
io.rst
pandas.rst
Mixin Columns
-------------
.. toctree::
:maxdepth: 2
mixin_columns.rst
Implementation
--------------
.. toctree::
:maxdepth: 2
implementation_details.rst
.. note that if this section gets too long, it should be moved to a separate
doc page - see the top of performance.inc.rst for the instructions on how to do
that
.. include:: performance.inc.rst
Reference/API
=============
.. automodapi:: astropy.table
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