Unified File Read/Write Interface#

astropy provides a unified interface for reading and writing data in different formats. For many common cases this will streamline the process of file I/O and reduce the need to learn the separate details of all of the I/O packages within astropy. For details on the implementation see I/O Registry (astropy.io.registry).

Getting Started with Image I/O#

Reading and writing image data in the unified I/O interface is supported though the CCDData class using FITS file format:

>>> # Read CCD image
>>> ccd = CCDData.read('image.fits')
>>> # Write back CCD image
>>> ccd.write('new_image.fits')

Note that the unit is stored in the BUNIT keyword in the header on saving, and is read from the header if it is present.

Detailed help on the available keyword arguments for reading and writing can be obtained via the help() method as follows:

>>> CCDData.read.help('fits')  # Get help on the CCDData FITS reader
>>> CCDData.writer.help('fits')  # Get help on the CCDData FITS writer

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 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')  

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 transparently 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.

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.

Getting Help on Readers and Writers#

Each file format is handled by a specific reader or writer, and each of those functions will have its own set of arguments. 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.

To get help on the available arguments for each format, use the help() method of the read or write methods. Each of these calls prints a long help document which is divided into two sections, the generic read/write documentation (common to any call) and the format-specific documentation. For ASCII tables, the format-specific documentation includes the generic astropy.io.ascii package interface and then a description of the particular ASCII sub-format.

In the examples below we do not show the long output:

>>> Table.read.help('fits')
>>> Table.read.help('ascii')
>>> Table.read.help('ascii.latex')
>>> Table.write.help('hdf5')
>>> Table.write.help('csv')

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.


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.

Built-In Table Readers/Writers#

The Table class has built-in support for various input and output formats including ASCII Formats, -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.







ASCII table in any supported format (uses guessing)



AASTex: AASTeX deluxetable used for AAS journals



Basic: Basic table with custom delimiters



Cds: CDS format table



CommentedHeader: Column names in a commented line




Csv: Basic table with comma-separated values



Daophot: IRAF DAOphot format table




Ecsv: Basic table with Enhanced CSV (supporting metadata)



FixedWidth: Fixed width



FixedWidthNoHeader: Fixed width with no header



FixedWidthTwoLine: Fixed width with second header line




HTML: HTML table



Ipac: IPAC format table




Latex: LaTeX table



Mrt: AAS Machine-Readable Table format



NoHeader: Basic table with no headers




QDP: Quick and Dandy Plotter files




Rdb: Tab-separated with a type definition header line




RST: reStructuredText simple format table



SExtractor: SExtractor format table



Tab: Basic table with tab-separated values




fits: Flexible Image Transport System file




HDF5: Hierarchical Data Format binary file




Parquet: Apache Parquet binary file



Wrapper around pandas.read_csv() and pandas.to_csv()



Wrapper around pandas.read_fwf() (fixed width format)



Wrapper around pandas.read_html() and pandas.to_html()



Wrapper around pandas.read_json() and pandas.to_json()




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.


To read and write formats supported by astropy.io.ascii:

>>> 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 the column delimiter and the output format for the colc column use:

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


When specifying an 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')


ECSV is recommended

For writing and reading tables to ASCII in a way that fully reproduces the table data, types, and metadata (i.e., the table will “round-trip”), we highly recommend using the ECSV Format. This writes the actual data in a space-delimited format (the basic format) that any ASCII table reader can parse, but also includes metadata encoded in a comment block that allows full reconstruction of the original columns. This includes support for Mixin Columns (such as SkyCoord or Time) and Multidimensional Columns.


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
>>> from astropy.utils.data import get_pkg_data_filename
>>> chandra_events = get_pkg_data_filename('data/chandra_time.fits',
...                                        package='astropy.io.fits.tests')
>>> t = Table.read(chandra_events)

If more than one table is present in the file, you can select the HDU by index or by name:

>>> t = Table.read(chandra_events, hdu="EVENTS")

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

You can also read a table from the HDUs of an in-memory FITS file. This will round-trip any Mixin Columns that were written to that HDU, using the header information to reconstruct them:

>>> from astropy.io import fits
>>> with fits.open(chandra_events) as hdul:
...     t = Table.read(hdul["EVENTS"])

If a column contains unit information, it will have an associated astropy.units object:

>>> t["energy"].unit

It is also possible to get directly a table with columns as Quantity objects by using the QTable class:

>>> from astropy.table import QTable
>>> t2 = QTable.read(chandra_events, hdu=1)
>>> t2['energy']
<Quantity [7782.7305, 5926.725 ] eV>


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)  

If you want to append a table to an existing file, set the append keyword:

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

Alternatively, you can use the convenience function table_to_hdu() to create a single binary table HDU and insert or append that to an existing HDUList.

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 you can view the available keywords in a readable format using:

>>> for key, value in t.meta.items():
...     print(f'{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 an ordered dictionary and does not fully represent the structure of a FITS header (for example, keyword comments are dropped).

TDISPn Keyword#

TDISPn FITS keywords will map to and from the Column format attribute if the display format is convertible to and from a Python display format. Below are the rules used for both conversion directions.

TDISPn to Python format string#

TDISPn format characters are defined in the table below.










Binary, integers only


Octal, integers only


Hexadecimal, integers only


Floating-point, fixed decimal notation


Floating-point, exponential notation


Engineering; E format with exponent multiple of three


Scientific; same as EN but non-zero leading digit if not zero


General; appears as F if significance not lost, also E


Floating-point, exponential notation, double precision

Where w is the width in characters of displayed values, m is the minimum number of digits displayed, d is the number of digits to the right of decimal, and e is the number of digits in the exponent. The .m and Ee fields are optional.

The A (character), L (logical), F (floating point), and G (general) display formats can be directly translated to Python format strings. The other formats need to be modified to match Python display formats.

For the integer formats (I, B, O, and Z), the width (w) value is used to add space padding to the left of the column value. The minimum number (m) value is not used. For the E, G, D, EN, and ES formats (floating point exponential) the width (w) and precision (d) are both used, but the exponential (e) is not used.

Python format string to TDISPn#

The conversion from Python format strings back to TDISPn is slightly more complicated.

Python strings map to the TDISP format A if the Python formatting string does not contain right space padding. It will accept left space padding. The same applies to the logical format L.

The integer formats (decimal integer, binary, octal, hexadecimal) map to the I, B, O, and Z TDISP formats respectively. Integer formats do not accept a zero padded format string or a format string with no left padding defined (a width is required in the TDISP format standard for the Integer formats).

For all float and exponential values, zero padding is not accepted. There must be at least a width or precision defined. If only a width is defined, there is no precision set for the TDISPn format. If only a precision is defined, the width is set to the precision plus an extra padding value depending on format type, and both are set in the TDISPn format. Otherwise, if both a width and precision are present they are both set in the TDISPn format. A Python f or F map to TDISP F format. The Python g or G map to TDISP G format. The Python e and E map to TDISP E format.

Masked Columns#

Tables that contain MaskedColumn columns can be written to FITS. By default this will replace the masked data elements with certain sentinel values according to the FITS standard:

  • NaN for float columns.

  • Value of TNULLn for integer columns, as defined by the column fill_value attribute.

  • Null string for string columns (not currently implemented).

When the file is read back those elements are marked as masked in the returned table, but see issue #4708 for problems in all three cases. It is possible to deactivate the masking with mask_invalid=False.

The FITS standard has a few limitations:

  • Not all data types are supported (e.g., logical / boolean).

  • Integer columns require picking one value as the NULL indicator. If all possible values are represented in valid data (e.g., an unsigned int columns with all 256 possible values in valid data), then there is no way to represent missing data.

  • The masked data values are permanently lost, precluding the possibility of later unmasking the values.

astropy provides a work-around for this limitation that users can choose to use. The key part is to use the serialize_method='data_mask' keyword argument when writing the table. This tells the FITS writer to split each masked column into two separate columns, one for the data and one for the mask. When it gets read back that process is reversed and the two columns are merged back into one masked column.

>>> from astropy.table.table_helpers import simple_table
>>> t = simple_table(masked=True)
>>> t['d'] = [False, False, True]
>>> t['d'].mask = [True, False, False]
>>> t
<Table masked=True length=3>
  a      b     c     d
int64 float64 str1  bool
----- ------- ---- -----
   --     1.0    c    --
    2     2.0   -- False
    3      --    e  True
>>> t.write('data.fits', serialize_method='data_mask', overwrite=True)
>>> Table.read('data.fits')
<Table masked=True length=3>
  a      b      c      d
int64 float64 bytes1  bool
----- ------- ------ -----
   --     1.0      c    --
    2     2.0     -- False
    3      --      e  True


This option goes outside of the established FITS standard for representing missing data, so users should be careful about choosing this option, especially if other (non-astropy) users will be reading the file(s). Behind the scenes, astropy is converting the masked columns into two distinct data and mask columns, then writing metadata into COMMENT cards to allow reconstruction of the original data.

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 the 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 behavior for reading a FITS table into a 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 you 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.


To read a FITS table into Table:

>>> 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 Tables 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 Tables 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 predates 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 timescale) 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 Tables 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 timescale, it is determined using the timescale 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 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 do not hesitate to let us know (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 with 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 setting the Table serialization methods for Time columns when writing, 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.write('my_table.fits', overwrite=True,
...         serialize_method={Time: 'formatted_value'})
>>> 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 for Time columns is equal to 'jd1_jd2', that is, Time columns 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.

    Hence, the format attribute of Time is not stored. After reading from FITS the user must set the format as desired.


    In the FITS standard, the reference position for a time coordinate is a scalar expressed via keywords. However, vectorized reference position or location can be supported by the Green Bank Keyword Convention which is a Registered FITS Convention. In astropy Time, location can be an array which is broadcastable to the Time values.

    Hence, vectorized location attribute of Time is stored and read following this convention.


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.


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 if 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)

The table metadata are stored as a dataset of strings by serializing the metadata in YAML following the ECSV header format definition. Since there are YAML parsers for most common languages, one can easily access and use the table metadata if reading the HDF5 in a non-astropy application.

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.


Certain kind of metadata (e.g., numpy object arrays) cannot be serialized correctly using YAML.


Reading and writing Parquet files is supported with format='parquet' if the pyarrow package is installed. For writing, the file extensions .parquet or .parq will automatically imply the 'parquet' format. For reading, Parquet files are automatically identified regardless of the extension if the first four bytes of the file are b'PAR1'. In many cases you do not need to explicitly specify format='parquet', but it may be a good idea anyway if there is any ambiguity about the file format.

Multiple-file Parquet datasets are not supported for reading and writing.


To read a table from a Parquet file named observations.parquet, you can do:

>>> t = Table.read('observations.parquet')

To write a table to a new file, simply do:

>>> t.write('new_file.parquet')

As with other formats, the overwrite=True argument is supported for overwriting existing files.

One big advantage of the Parquet files is that each column is stored independently, and thus reading a subset of columns is fast and efficient. To find out which columns are stored in a table without reading the data, use the schema_only=True as shown below. This returns a zero-length table with the appropriate columns:

>>> schema = Table.read('observations.parquet', schema_only=True)

To read only a subset of the columns, use the include_names and/or exclude_names keywords:

>>> t_sub = Table.read('observations.parquet', include_names=['mjd', 'airmass'])


astropy Table supports the ability to read or write tables using some of the I/O methods available within pandas. This interface thus provides convenient wrappers to the following functions / methods:

Format name

Data Description
















Fixed Width



When reading or writing a table, any keyword arguments apart from the format and file name are passed through to pandas, for instance:

>>> t.write('data.csv', format='pandas.csv', sep=' ', header=False)
>>> t2 = Table.read('data.csv', format='pandas.csv', sep=' ', names=['a', 'b', 'c'])


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, and 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.

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 ASCII 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 ASCII 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. See the sections on Time Details and Masked columns, respectively, for additional information. The defaults for each format are listed below:
















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
         object         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 0.9
# 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.