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.

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:

$ 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.

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.

As of astropy version 3.0 there is support for writing a table which contains Mixin columns such as Time or SkyCoord. This uses FITS COMMENT cards to capture additional information needed order to fully reconstruct the mixin columns when reading back from FITS. The information is a Python dict structure which is serialized using YAML.


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).

Astropy native objects (mixin columns)

It is possible to store not only standard Column objects to a FITS table HDU, but also any Astropy native objects (Mixin columns) within a Table or QTable. This includes Time, Quantity, SkyCoord, and many others.

In general a mixin column may contain multiple data components as well as object attributes beyond the standard Column attributes like format or description. Abiding by the rules set by the FITS standard requires mapping of these data components and object attributes to the appropriate FITS table columns and keywords. Thus, a well defined protocol has been developed to allow the storage of these mixin columns in FITS while allowing the object to “round-trip” through the file with no loss of data or attributes.


A Quantity mixin column in a QTable is represented in a FITS table using the TUNITn FITS column keyword to incorporate the unit attribute of Quantity. For example:

>>> from astropy.table import QTable
>>> import astropy.units as u
>>> t = QTable([[1, 2] * u.angstrom)])
>>> t.write('my_table.fits', overwrite=True)
>>> qt = QTable.read('my_table.fits')
>>> qt
<QTable length=2>

Astropy provides the following features for reading and writing Time:

  • Writing and reading Time Table columns to and from FITS tables
  • Reading time coordinate columns in FITS tables (compliant with the time standard) as Time Table columns
Writing and reading Astropy Time columns

By default, a Time mixin column within a Table or QTable will be written to FITS in full precision. This will be done using the FITS time standard by setting the necessary FITS header keywords.

The default behaviour for reading a FITS table into an Table has historically been to convert all FITS columns to Column objects, which have closely matching properties. For some columns, however, closer native astropy representations are possible, and one can indicate these should be used by passing astropy_native=True (for backwards compatibility, this is not done by default). This will convert columns conforming to the FITS time standard to Time instances, avoiding any loss of precision. For example:

>>> from astropy.time import Time
>>> from astropy.table import Table
>>> from astropy.coordinates import EarthLocation
>>> t = Table()
>>> t['a'] = Time([100.0, 200.0], scale='tt', format='mjd',
...               location=EarthLocation(-2446354, 4237210, 4077985, unit='m'))
>>> t.write('my_table.fits', overwrite=True)
>>> tm = Table.read('my_table.fits', astropy_native=True)
>>> tm['a']
<Time object: scale='tt' format='jd' value=[ 2400100.5  2400200.5]>
>>> tm['a'].location
<EarthLocation (-2446354.,  4237210.,  4077985.) m>
>>> all(tm['a'] == t['a'])

The same will work with QTable.

In addition to binary table columns, various global time informational FITS keywords are treated specially with astropy_native=True. In particular the keywords DATE, DATE-* (ISO-8601 datetime strings) and the MJD-* (MJD date values) will be returned as Time objects in the Table meta. For more details regarding the FITS time paper and the implementation, refer to FITS Table with Time Columns.

Since not all FITS readers are able to use the FITS time standard, it is also possible to store Time instances using the _time_format. For this case, none of the special header keywords associated with the FITS time standard will be set. When reading this back into Astropy, the column will be an ordinary Column instead of a Time object. See the Details section below for an example.

Reading FITS standard compliant time coordinate columns in binary tables

Reading FITS files which are compliant with the FITS time standard is supported by Astropy by following the multifarious rules and conventions set by the standard. The standard was devised in order to describe time coordinates in an unambiguous and comprehensive manner and also to provide flexibility for its multiple use-cases. Thus, while reading time coordinate columns in FITS compliant files, multiple aspects of the standard are taken into consideration.

Time coordinate columns strictly compliant with the two-vector JD subset of the standard (described in the Details section below) can be read as native Time objects. The other subsets of the standard are also supported by Astropy; a thorough examination of the FITS standard time-related keywords is done and the time data is interpreted accordingly.

The standard describes the various components in the specification of time:

  • Time coordinate frame
  • Time unit
  • Corrections, errors, etc.
  • Durations

The keywords used to specify times define these components. Using these keywords, time coordinate columns are identified and read as Time objects. Refer to FITS Table with Time Columns for the specification of these keywords and their description.

There are two aspects of the standard that require special attention due to the subtleties involved while handling them. These are:

  • Column named TIME with time unit

A common convention found in existing FITS files is that a FITS binary table column with TTYPEn = ‘TIME’ represents a time coordinate column. Many astronomical data files, including official data products from major observatories, follow this convention that pre-dates the FITS standard. The FITS time standard states that such a column will be controlled by the global time reference frame keywords, and this will still be compliant with the present standard.

Using this convention which has been incorporated into the standard, Astropy can read time coordinate columns from all such FITS tables as native Time objects. Common examples of FITS files following this convention are Chandra, XMM, and HST files.

The following is an example of a Header extract of a Chandra event list:

COMMENT      ---------- Globally valid key words ----------------
DATE    = '2016-01-27T12:34:24' / Date and time of file creation
TIMESYS = 'TT      '           / Time system
MJDREF  =  5.0814000000000E+04 / [d] MJD zero point for times
TIMEUNIT= 's       '           / Time unit
TIMEREF = 'LOCAL   '           / Time reference (barycenter/local)

COMMENT      ---------- Time Column -----------------------
TTYPE1  = 'time    '           / S/C TT corresponding to mid-exposure
TFORM1  = '1D      '           / format of field
TUNIT1  = 's       '

When reading such a FITS table with astropy_native=True, Astropy checks whether the name of a column is “TIME”/ “time” (TTYPEn = ‘TIME’) and whether its unit is a FITS recognized time unit (TUNITn is a time unit).

For example, reading a Chandra event list which has the above mentioned header and the time coordinate column time as [1, 2] will give:

>>> from astropy.table import Table
>>> from astropy.time import Time, TimeDelta
>>> from astropy.utils.data import get_pkg_data_filename
>>> chandra_events = get_pkg_data_filename('data/chandra_time.fits',
...                                        package='astropy.io.fits.tests')
>>> native = Table.read(chandra_events, astropy_native=True)
>>> native['time']  
<Time object: scale='tt' format='mjd' value=[57413.76033393 57413.76033393]>
>>> non_native = Table.read(chandra_events)
>>> # MJDREF  =  5.0814000000000E+04, TIMESYS = 'TT'
>>> ref_time = Time(non_native.meta['MJDREF'], format='mjd',
...                 scale=non_native.meta['TIMESYS'].lower())
>>> # TTYPE1  = 'time', TUNIT1 = 's'
>>> delta_time = TimeDelta(non_native['time'])
>>> all(ref_time + delta_time == native['time'])

By default, FITS table columns will be read as standard Column objects without taking the FITS time standard into consideration.

  • String time column in ISO-8601 Datetime format

FITS uses a subset of ISO-8601 (which in itself does not imply a particular time scale) for several time-related keywords, such as DATE-xxx. Following the FITS standard its values must be written as a character string in the following datetime format:


A time coordinate column can be constructed using this representation of time. The following is an example of an ISO-8601 datetime format time column:


The criteria for identifying a time coordinate column in ISO-8601 format is as follows:

A time column is identified using the time coordinate frame keywords as described in FITS Table with Time Columns. Once it has been identified, its datatype is checked in order to determine its representation format. Since ISO-8601 datetime format is the only string representation of time, a time coordinate column having string datatype will be automatically read as a Time object with format='fits' (‘fits’ represents the FITS ISO-8601 format).

As this format does not imply a particular time scale, it is determined using the time scale keywords in the header (TCTYP or TIMESYS) or their defaults. The other time coordinate information is also determined in the same way, using the time coordinate frame keywords. All ISO-8601 times are relative to a globally accepted zero point (year 0 corresponds to 1 BCE) and are thus are not relative to the reference time keywords (MJDREF, JDREF or DATEREF). Hence, these keywords will be ignored while dealing with ISO-8601 time columns.


Reading FITS files with time coordinate columns may fail. Astropy supports a large subset of these files, but there are still some FITS files which are not compliant with any aspect of the standard. If you have such a file, please don’t hesitate to let us know, e.g., by opening an issue in the issue tracker.

Also, reading a column having TTYPEn = ‘TIME’ as Time will fail if TUNITn for the column is not a FITS recognized time unit.


Time as a dimension in astronomical data presents challenges in its representation in FITS files. The standard has therefore been extended to describe rigorously the time coordinate in the World Coordinate System framework. Refer to FITS WCS paper IV for details.

Allowing Time columns to be written as time coordinate columns in FITS tables thus involves storing time values in a way that ensures retention of precision and mapping the associated metadata to the relevant FITS keywords.

In accordance with the standard which states that in binary tables one may use pairs of doubles, the Astropy Time column is written in such a table as a vector of two doubles (TFORMn = ‘2D’) (jd1, jd2) where JD = jd1 + jd2. This reproduces the time values to double-double precision and is the “lossless” version, exploiting the higher precision provided in binary tables. Note that jd1 is always a half-integer or integer, while abs(jd2) < 1. Round-tripping of Astropy written FITS binary tables containing time coordinate columns has been partially achieved by mapping selected metadata, scale and singular location of Time, to corresponding keywords. Note that the arbitrary metadata allowed in Table objects within the meta dict is not written and will be lost.

Consider the following Time column:

>>> t['a'] = Time([100.0, 200.0], scale='tt', format='mjd')  

The FITS standard requires an additional translation layer back into the desired format. The Time column t['a'] will undergo the translation Astropy Time --> FITS --> Astropy Time which corresponds to the format conversion mjd --> (jd1, jd2) --> jd. Thus, the final conversion from (jd1, jd2) will require a software implementation which is fully compliant with the FITS time standard.

Taking this into consideration, the functionality to read/write Time from/to FITS can be explicitly turned off, by opting to store the time representation values in the format specified by the format attribute of the Time column, instead of the (jd1, jd2) format, with no extra metadata in the header. This is the “lossy” version, but can help portability. For the above example, the FITS column corresponding to t['a'] will then store [100.0 200.0] instead of [[ 2400100.5, 0. ], [ 2400200.5, 0. ]]. This is done by using a special info.serialize_method attribute, as in the following example:

>>> from astropy.time import Time
>>> from astropy.table import Table
>>> from astropy.coordinates import EarthLocation
>>> t = Table()
>>> t['a'] = Time([100.0, 200.0], scale='tt', format='mjd')
>>> t['a'].info.serialize_method['fits'] = 'formatted_value'
>>> t.write('my_table.fits', overwrite=True)
>>> tm = Table.read('my_table.fits')
>>> tm['a']
<Column name='a' dtype='float64' length=2>
>>> all(tm['a'] == t['a'].value)

By default, serialize_method['fits'] in a Time column info is equal to 'jd1_jd2', that is, Time column will be written in full precision.


The Astropy Time object does not precisely map to the FITS time standard.


    The FITS format considers only three formats, ISO-8601, JD and MJD. Astropy Time allows for many other formats like unix or cxcsec for representing the values.


    In Astropy Time, location can be an array which is broadcastable to the Time values. In the FITS standard, location is a scalar expressed via keywords.

Hence the format attribute and a vector location attribute are not stored. After reading from FITS the user must set the format as desired.


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.

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)

Metadata and mixin columns

Astropy tables can contain metadata, both in the table meta attribute (which is an ordered dictionary of arbitrary key/value pairs), and within the columns, which each have attributes unit, format, description, and meta.

By default, when writing a table to HDF5 the code will attempt to store each key/value pair within the table meta as HDF5 attributes of the table dataset. This will fail of the values within meta are not objects that can be stored as HDF5 attributes. In addition, if the table columns being stored have defined values for any of the above-listed column attributes, these metadata will not be stored and a warning will be issued.


To enable storing all table and column metadata to the HDF5 file, call the write() method with serialize_meta=True. This will store metadata in a separate HDF5 dataset, contained in the same file, which is named <path>.__table_column_meta__. Here path is the argument provided in the call to write():

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

As of astropy 3.0, by specifying serialize_meta=True one can also store to HDF5 tables that contain Mixin columns such as Time or SkyCoord columns.


The way metadata are saved in the HDF5 dataset has changed in astropy 3.0. Previously the metadata were serialized with YAML and this was stored as an HDF5 attribute. This process was subject to a fixed limit on the size of an attribute. Starting with 3.0 the YAML-serialized metadata are stored as a separate dataset as described above, with no size limit.

Files using the old convention are automatically recognized and will always be read correctly.

If for some reason the user needs to write in the old format, they should specify the deprecated compatibility_mode keyword:

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


The compatibility_mode keyword will be removed in a future version of astropy so your code should be changed.


Provides an interactive HTML export of a Table, like the HTML writer but using the DataTables library, which allow to visualize interactively an HTML table (with columns sorting, search, pagination).

To write a table t to a new file:

>>> t.write('new_table.html', format='jsviewer')

Several additional parameters can be used:

  • table_id: the HTML id of the <table> tag, defaults to 'table{id}' where id is the id of the Table object.
  • max_lines: maximum number of lines.
  • table_class: HTML classes added to the <table> tag, can be useful to customize the style of the table.
  • jskwargs: additional arguments passed to JSViewer.
  • css: CSS style, default to astropy.table.jsviewer.DEFAULT_CSS.
  • htmldict: additional arguments passed to HTML.

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.