Unified file read/write interface

Astropy provides a unified interface for reading and writing data in different formats. For many common cases this will simplify the process of file I/O and reduce the need to master the separate details of all the I/O packages within Astropy. This functionality is still in active development and the number of supported formats will be increasing. For details on the implementation see I/O Registry (astropy.io.registry).

Getting started with Table I/O

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

To use this interface, first import the Table class, then simply 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'):

>>> t = Table.read("ftp://cdsarc.u-strasbg.fr/pub/cats/VII/253/snrs.dat",
...         readme="ftp://cdsarc.u-strasbg.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')

Similarly, for writing, 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 transparently uncompress them as far as supported by the Python installation (see get_readable_fileobj()).

Any additional arguments specified will depend on the format. For examples of this see the section Built-in table readers/writers. This section also provides the full list of choices for the format argument.

Built-in table readers/writers

The Table class has built-in support for various input and output formats including ASCII formats, -FITS, HDF5, 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   ASCII table in any supported format (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.no_header Yes   NoHeader: Basic table with no headers
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
fits Yes auto fits: Flexible Image Transport System file
hdf5 Yes auto HDF5: Hierarchical Data Format binary file
votable Yes auto votable: Table format used by Virtual Observatory (VO) initiative

ASCII formats

The read() and write() methods can be used to read and write formats supported by astropy.io.ascii.

Use format='ascii' in order to interface to the generic read() and write() functions from astropy.io.ascii. When reading a table this means that all supported ASCII table formats will be tried in order to successfully parse the input. For example:

>>> t = Table.read('astropy/io/ascii/tests/t/latex1.tex', format='ascii')
>>> print(t)
cola colb colc
---- ---- ----
   a    1    2
   b    3    4

When writing a table with format='ascii' the output is a basic character-delimited file with a single header line containing the column names.

All additional arguments are passed to the astropy.io.ascii read() and write() functions. Further details are available in the sections on Parameters for read() and Parameters for write(). For example, to change column delimiter and the output format for the colc column use:

>>> t.write(sys.stdout, format='ascii', delimiter='|', formats={'colc': '%0.2f'})


When specifying a specific ASCII table format using the unified interface, the format name is prefixed with ascii in order to identify the format as ASCII-based. Compare the table above to the astropy.io.ascii list of supported formats where the prefix is not needed. Therefore the following are equivalent:

>>> dat = ascii.read('file.dat', format='daophot')
>>> dat = Table.read('file.dat', format='ascii.daophot')

For compatibility with astropy version 0.2 and earlier, the following format values are also allowed in Table.read(): daophot, ipac, html, latex, and rdb.


Reading and writing tables in FITS format is supported with format='fits'. In most cases, existing FITS files should be automatically identified as such based on the header of the file, but if not, or if writing to disk, then the format should be explicitly specified.


If a FITS table file contains only a single table, then it can be read in with:

>>> from astropy.table import Table
>>> t = Table.read('data.fits')

If more than one table is present in the file, you can select the HDU as follows:

>>> t = Table.read('data.fits', hdu=3)

In this case if the hdu argument is omitted then the first table found will be read in and a warning will be emitted:

>>> t = Table.read('data.fits')
WARNING: hdu= was not specified but multiple tables are present, reading in first available table (hdu=1) [astropy.io.fits.connect]


To write a table t to a new file:

>>> t.write('new_table.fits')

If the file already exists and you want to overwrite it, then set the overwrite keyword:

>>> t.write('existing_table.fits', overwrite=True)

At this time there is no support for appending an HDU to an existing file or writing multi-HDU files using the Table interface. Instead one can use the convenience function table_to_hdu() to create a single binary table HDU and insert or append that to an existing HDUList.


The FITS keywords associated with an HDU table are represented in the meta ordered dictionary attribute of a Table. After reading a table one can view the available keywords in a readable format using:

>>> for key, value in t.meta.items():
...     print('{0} = {1}'.format(key, value))

This does not include the “internal” FITS keywords that are required to specify the FITS table properties (e.g. NAXIS, TTYPE1). HISTORY and COMMENT keywords are treated specially and are returned as a list of values.

Conversely, the following shows examples of setting user keyword values for a table t:

>>> t.meta['MY_KEYWD'] = 'my value'
>>> t.meta['COMMENT'] = ['First comment', 'Second comment', 'etc']
>>> t.write('my_table.fits', overwrite=True)

The keyword names (e.g. MY_KEYWD) will be automatically capitalized prior to writing.

At this time, the meta attribute of the Table class is simply an ordered dictionary and does not fully represent the structure of a FITS header (for example, keyword comments are dropped).


Reading/writing from/to HDF5 files is supported with format='hdf5' (this requires h5py to be installed). However, the .hdf5 file extension is automatically recognized when writing files, and HDF5 files are automatically identified (even with a different extension) when reading in (using the first few bytes of the file to identify the format), so in most cases you will not need to explicitly specify format='hdf5'.

Since HDF5 files can contain multiple tables, the full path to the table should be specified via the path= argument when reading and writing. For example, to read a table called data from an HDF5 file named observations.hdf5, you can do:

>>> t = Table.read('observations.hdf5', path='data')

To read a table nested in a group in the HDF5 file, you can do:

>>> t = Table.read('observations.hdf5', path='group/data')

To write a table to a new file, the path should also be specified:

>>> t.write('new_file.hdf5', path='updated_data')

It is also possible to write a table to an existing file using append=True:

>>> t.write('observations.hdf5', path='updated_data', append=True)

As with other formats, the overwrite=True argument is supported for overwriting existing files. To overwrite only a single table within an HDF5 file that has multiple datasets, use both the overwrite=True and append=True arguments.

If the metadata of the table cannot be written directly to the HDF5 file (e.g. dictionaries), or if you want to preserve the units and description of tables and columns, use using serialize_meta=True:

>>> t.write('observations.hdf5', path='updated_data', serialize_meta=True)

Finally, when writing to HDF5 files, the compression= argument can be used to ensure that the data is compressed on disk:

>>> t.write('new_file.hdf5', path='updated_data', compression=True)

VO Tables

Reading/writing from/to VO table files is supported with format='votable'. In most cases, existing VO tables should be automatically identified as such based on the header of the file, but if not, or if writing to disk, then the format should be explicitly specified.

If a VO table file contains only a single table, then it can be read in with:

>>> t = Table.read('aj285677t3_votable.xml')

If more than one table is present in the file, an error will be raised, unless the table ID is specified via the table_id= argument:

>>> t = Table.read('catalog.xml')
Traceback (most recent call last):
ValueError: Multiple tables found: table id should be set via the table_id= argument. The available tables are twomass, spitzer

>>> t = Table.read('catalog.xml', table_id='twomass')

To write to a new file, the ID of the table should also be specified (unless t.meta['ID'] is defined):

>>> t.write('new_catalog.xml', table_id='updated_table', format='votable')

When writing, the compression=True argument can be used to force compression of the data on disk, and the overwrite=True argument can be used to overwrite an existing file.