Data Tables (astropy.table)

Introduction

astropy.table provides functionality for storing and manipulating heterogenous tables of data in a way that is familiar to numpy users. A few notable features 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.
• Full support for multidimensional columns.
• Create a table by referencing (not copying) an existing numpy table.

Currently astropy.table is used when reading an ASCII table using astropy.io.ascii. Future releases of AstroPy are expected to use the Table class for other subpackages such as astropy.io.votable and astropy.io.fits.

Getting Started

The basic workflow for creating a table, accessing table elements, and modifying the table is shown below. These examples show a very simple case, while the full astropy.table documentation is available from the Using table section.

First create a simple table with three columns of data named a, b, and c. These columns have integer, float, and string values respectively:

>>> from astropy.table import Table, Column
>>> a = [1, 4, 5]
>>> b = [2.0, 5.0, 8.2]
>>> c = ['x', 'y', 'z']
>>> t = Table([a, b, c], names=('a', 'b', 'c'), meta={'name': 'first table'})

There are a few ways to examine the table. You can get detailed information about the table values and column definitions as follows:

>>> t
<Table rows=3 names=('a','b','c')>
array([(1, 2.0, 'x'), (4, 5.0, 'y'), (5, 8.2, 'z')],
      dtype=[('a', '<i8'), ('b', '<f8'), ('c', '|S1')])

From within the IPython notebook, the table is displayed as a formatted HTML table:

If you print the table (either from the noteboook or in a text console session) then a formatted version appears:

>>> print(t)
  a   b   c
--- --- ---
  1 2.0   x
  4 5.0   y
  5 8.2   z

For a long table you can scroll up and down through the table one page at time:

>>> t.more()

Now examine some high-level information about the table:

>>> t.colnames
['a', 'b', 'c']
>>> 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' units=None format=None description=None>
array([1, 4, 5])

>>> t['a'][1]    # Row 1 of column 'a'
4

>>> t[1]         # Row obj for with row 1 values
<Row 1 of table
 values=(4, 5.0, 'y')
 dtype=[('a', '<i8'), ('b', '<f8'), ('c', '|S1')]>

>>> t[1]['a']    # Column 'a' of row 1
4

One can retreive a subset of a table by rows (using a slice) or 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
--- --- ---
  1 2.0   x
  4 5.0   y

>>> t['a', 'c']  # Table with cols 'a', 'c'
 a   c
--- ---
  1   x
  4   y
  5   z

Modifying table values in place is flexible and works as one would expect:

>>> t['a'] = [-1, -2, -3]       # Set all column values
>>> t['a'][2] = 30              # Set row 2 of column 'a'
>>> t[1] = (8, 9.0, "W")        # Set all row values
>>> t[1]['b'] = -9              # Set column 'b' of row 1
>>> t[0:2]['b'] = 100.0         # Set column 'c' of rows 0 and 1
>>> print(t)
 a    b    c
--- ----- ---
 -1 100.0   x
  8 100.0   W
 30   8.2   z

Add, remove, and rename columns with the following:

>>> t.add_column(Column('d', [1, 2, 3])))
>>> t.remove_column('c')
>>> t.rename_column('a', 'A')
>>> t.colnames
['A', 'b', 'd']

Adding a new row of data to the table is as follows:

>>> t.add_row([-8, -9, 10])
>>> len(t)
4

Lastly, one can create a table with support for missing values, for example by setting masked=True:

>>> t = Table([a, b, c], names=('a', 'b', 'c'), masked=True)
>>> t['a'].mask = [True, True, False]
>>> t
<Table rows=3 names=('a','b','c')>
masked_array(data = [(--, 2.0, 'x') (--, 5.0, 'y') (5, 8.2, 'z')],
             mask = [(True, False, False) (True, False, False) (False, False, False)],
       fill_value = (999999, 1e+20, 'N'),
            dtype = [('a', '<i8'), ('b', '<f8'), ('c', '|S1')])

>>> print(t)
 a   b   c
--- --- ---
 -- 2.0   x
 -- 5.0   y
  5 8.2   z

Using table

The details of using astropy.table are provided in the following sections:

• Constructing a table
  • Examples
  • Initialization Details
  • Copy versus Reference
  • Column and TableColumns classes
• Accessing a table
  • Quick overview
  • Details
• Modifying a table
  • Quick overview
  • Caveats
• Masking and missing values
  • Table creation
  • Table access
  • Masking and filling
• Reading and writing Table objects
  • Introduction
  • Built-in readers/writers
  • Creating a custom reader/writer

Reference/API

astropy.table Module

Classes

Column	Define a data column for use in a Table object.
MaskedColumn
Row(table, index)	A class to represent one row of a Table object.
Table([data, masked, names, dtypes, meta, copy])	A class to represent tables of heterogeneous data.
TableColumns([cols])	OrderedDict subclass for a set of columns.

Class Inheritance Diagram

astropy.table.io_registry Module

Functions

register_reader(table_format, function[, force])	Register a table reader function.
register_writer(table_format, function[, force])	Register a table writer function.
register_identifier(table_format, identifier)	Associate an identifier function with a specific table type.
identify_format(origin, args, kwargs)
get_reader(table_format)
get_writer(table_format)

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