Table Data#

The QTable and Table classes includes two methods, read() and write(), that make it possible to read from and write to files. A number of formats are supported (see Built-in table readers/writers) and new file formats and extensions can be registered with the Table class (see I/O Registry).

Table Formats

To use this interface, first import the Table class, then call the Table read() method with the name of the file and the file format, for instance 'ascii.daophot':

>>> from astropy.table import Table
>>> t = Table.read('photometry.dat', format='ascii.daophot')

It is possible to load tables directly from the Internet using URLs. For example, download tables from Vizier catalogues in CDS format ('ascii.cds'):

>>> from astropy.table import Table
>>> t = Table.read("ftp://cdsarc.unistra.fr/pub/cats/VII/253/snrs.dat",
...         readme="ftp://cdsarc.unistra.fr/pub/cats/VII/253/ReadMe",
...         format="ascii.cds")

For certain file formats the format can be automatically detected, for example, from the filename extension:

>>> t = Table.read('table.tex')

For writing a table, the format can be explicitly specified:

>>> t.write(filename, format='latex')

As for the read() method, the format may be automatically identified in some cases.

The underlying file handler will also automatically detect various compressed data formats and uncompress them as far as supported by the Python installation (see get_readable_fileobj()).

For writing, you can also specify details about the Table serialization methods via the serialize_method keyword argument. This allows fine control of the way to write out certain columns, for instance writing an ISO format Time column as a pair of JD1/JD2 floating point values (for full resolution) or as a formatted ISO date string.

Built-In Table Readers/Writers#

The Table class has built-in support for various input and output formats including astropy.io.ascii, FITS, HDF5, Pandas, Parquet, and VO Tables.

A full list of the supported formats and corresponding classes is shown in the table below. The Write column indicates those formats that support write functionality, and the Suffix column indicates the filename suffix indicating a particular format. If the value of Suffix is auto, the format is auto-detected from the file itself. Not all formats support auto- detection.

Format

Write

Suffix

Description

ascii

Yes

Text table in most supported formats (uses guessing)

ascii.aastex

Yes

AASTex: AASTeX deluxetable used for AAS journals

ascii.basic

Yes

Basic: Basic table with custom delimiters

ascii.cds

No

Cds: CDS format table

ascii.commented_header

Yes

CommentedHeader: Column names in a commented line

ascii.csv

Yes

.csv

Csv: Basic table with comma-separated values

ascii.daophot

No

Daophot: IRAF DAOphot format table

ascii.ecsv

Yes

.ecsv

Ecsv: Basic table with Enhanced CSV (supporting metadata)

ascii.fixed_width

Yes

FixedWidth: Fixed width

ascii.fixed_width_no_header

Yes

FixedWidthNoHeader: Fixed width with no header

ascii.fixed_width_two_line

Yes

FixedWidthTwoLine: Fixed width with second header line

ascii.html

Yes

.html

HTML: HTML table

ascii.ipac

Yes

Ipac: IPAC format table

ascii.latex

Yes

.tex

Latex: LaTeX table

ascii.mrt

Yes

Mrt: AAS Machine-Readable Table format

ascii.no_header

Yes

NoHeader: Basic table with no headers

ascii.qdp

Yes

.qdp

QDP: Quick and Dandy Plotter files

ascii.rdb

Yes

.rdb

Rdb: Tab-separated with a type definition header line

ascii.rst

Yes

.rst

RST: reStructuredText simple format table

ascii.sextractor

No

SExtractor: SExtractor format table

ascii.tab

Yes

Tab: Basic table with tab-separated values

ascii.tdat

Yes

.tdat

Tdat: Transportable Database Aggregate Table format

fits

Yes

auto

fits: Flexible Image Transport System file

hdf5

Yes

auto

HDF5: Hierarchical Data Format binary file

jsviewer

Yes

JavaScript viewer format (write-only)

pandas.csv

Yes

pandas.read_csv() and pandas.DataFrame.to_csv()

pandas.fwf

No

pandas.read_fwf() (fixed width format)

pandas.html

Yes

pandas.read_html() and pandas.DataFrame.to_html()

pandas.json

Yes

pandas.read_json() and pandas.DataFrame.to_json()

parquet

Yes

auto

Parquet: Apache Parquet binary file

votable

Yes

auto

votable: Table format used by Virtual Observatory (VO) initiative

Details#

Table Serialization Methods#

astropy supports fine-grained control of the way to write out (serialize) the columns in a Table. For instance, if you are writing an ISO format Time column to an ECSV text table file, you may want to write this as a pair of JD1/JD2 floating point values for full resolution (perfect “round-trip”), or as a formatted ISO date string so that the values are easily readable by your other applications.

The default method for serialization depends on the format (FITS, ECSV, HDF5). For instance HDF5 is a binary format and so it would make sense to store a Time object as JD1/JD2, while ECSV is a flat text format and commonly you would want to see the date in the same format as the Time object. The defaults also reflect an attempt to minimize compatibility issues between astropy versions. For instance, it was possible to write Time columns to ECSV as formatted strings in a version prior to the ability to write as JD1/JD2 pairs, so the current default for ECSV is to write as formatted strings.

The two classes which have configurable serialization methods are Time and MaskedColumn. The defaults for each format are listed below:

Format

Time

MaskedColumn

FITS

jd1_jd2

null_value

ECSV

formatted_value

null_value

HDF5

jd1_jd2

data_mask

YAML

jd2_jd2

—

Examples#

Start by making a table with a Time column and masked column:

>>> import sys
>>> from astropy.time import Time
>>> from astropy.table import Table, MaskedColumn

>>> t = Table(masked=True)
>>> t['tm'] = Time(['2000-01-01', '2000-01-02'])
>>> t['mc1'] = MaskedColumn([1.0, 2.0], mask=[True, False])
>>> t['mc2'] = MaskedColumn([3.0, 4.0], mask=[False, True])
>>> t
<Table masked=True length=2>
           tm             mc1     mc2
          Time          float64 float64
----------------------- ------- -------
2000-01-01 00:00:00.000      --     3.0
2000-01-02 00:00:00.000     2.0      --

Now specify that you want all Time columns written as JD1/JD2 and the mc1 column written as a data/mask pair and write to ECSV:

>>> serialize_method = {Time: 'jd1_jd2', 'mc1': 'data_mask'}
>>> t.write(sys.stdout, format='ascii.ecsv', serialize_method=serialize_method)
# %ECSV 1.0
 ...
# schema: astropy-2.0
 tm.jd1    tm.jd2  mc1  mc1.mask  mc2
2451544.0    0.5   1.0   True     3.0
2451546.0   -0.5   2.0   False     ""

(Spaces added for clarity)

Notice that the tm column has been replaced by the tm.jd1 and tm.jd2 columns, and likewise a new column mc1.mask has appeared and it explicitly contains the mask values. When this table is read back with the ascii.ecsv reader then the original columns are reconstructed.

The serialize_method argument can be set in two different ways:

  • As a single string like data_mask. This value then applies to every column, and is a convenient strategy for a masked table with no Time columns.

  • As a dict, where the key can be either a single column name or a class (as shown in the example above), and the value is the corresponding serialization method.

Reading and Writing Column Objects#

Individual table columns do not have their own functions for reading and writing but it is easy to select just a single column (here “obstime”) from a table for writing:

>>> from astropy.time import Time
>>> tab = Table({'name': ['AB Aur', 'SU Aur'],
...              'obstime': Time(['2013-05-23T14:23:12', '2011-11-11T11:11:11'])})
>>> tab[['obstime']].write('obstime.fits')

Note the notation [['obstime']] in the last line - indexing a table with a list of strings gives us a new table with the columns given by the strings. Since the inner list has only one element, the resulting table has only one column.

Then, we can read back that single-column table and extract the column from it:

>>> col = Table.read('obstime.fits').columns[0]
>>> type(col)
<class 'astropy.table.column.Column'>

Note on Filenames#

Both the read() and write() methods can accept file paths of the form ~/data/file.csv or ~username/data/file.csv. These tilde-prefixed paths are expanded in the same way as is done by many command-line utilities, to represent the home directory of the current or specified user, respectively.

Command-Line Utility#

For convenience, the command-line tool showtable can be used to print the content of tables for the formats supported by the unified I/O interface.

Example#

To view the contents of a table on the command line:

$ showtable astropy/io/fits/tests/data/table.fits

 target V_mag
------- -----
NGC1001  11.1
NGC1002  12.3
NGC1003  15.2

To get full documentation on the usage and available options, do showtable --help.