# Writing Tables¶

astropy.io.ascii is able to write ASCII tables out to a file or file-like object using the same class structure and basic user interface as for reading tables.

The write() function provides a way to write a data table as a formatted ASCII table.

## Examples¶

To write a formatted ASCII table using the write() function:

>>> import numpy as np
>>> from astropy.io import ascii
>>> from astropy.table import Table
>>> data = Table()
>>> data['x'] = np.array([1, 2, 3], dtype=np.int32)
>>> data['y'] = data['x'] ** 2
>>> ascii.write(data, 'values.dat', overwrite=True)


The values.dat file will then contain:

x y
1 1
2 4
3 9


It is also possible and encouraged to use the write functionality from astropy.io.ascii through a higher level interface in the Data Tables package (see Unified File Read/Write Interface for more details). For example:

>>> data.write('values.dat', format='ascii', overwrite=True)


For a more reproducible ASCII version of your table, we recommend using the ECSV Format. This stores all the table meta-data (in particular the column types and units) to a comment section at the beginning while still maintaining compatibility with most plain CSV readers. It also allows storing richer data like SkyCoord or multidimensional or variable-length columns. For our simple example:

>>> data.write('values.ecsv', overwrite=True)


The .ecsv extension is recognized and implies using ECSV (equivalent to format='ascii.ecsv'). The values.ecsv file will then contain:

# %ECSV 1.0
# ---
# datatype:
# - {name: x, datatype: int32}
# - {name: y, datatype: int32}
# schema: astropy-2.0
x y
1 1
2 4
3 9


Most of the input table Supported Formats for reading are also available for writing. This provides a great deal of flexibility in the format for writing. The example below writes the data as a LaTeX table, using the option to send the output to sys.stdout instead of a file:

>>> ascii.write(data, format='latex')
\begin{table}
\begin{tabular}{cc}
x & y \\
1 & 1 \\
2 & 4 \\
3 & 9 \\
\end{tabular}
\end{table}


There is also a faster Cython engine for writing simple formats, which is enabled by default for these formats (see Fast ASCII I/O). To disable this engine, use the parameter fast_writer:

>>> ascii.write(data, 'values.csv', format='csv', fast_writer=False)


Note

For most supported formats one can write a masked table and then read it back without losing information about the masked table entries. This is accomplished by using a blank string entry to indicate a masked (missing) value. See the Bad or Missing Values section for more information.

## Parameters for write()¶

The write() function accepts a number of parameters that specify the detailed output table format. Each of the Supported Formats is handled by a corresponding Writer class that can define different defaults, so the descriptions below sometimes mention “typical” default values. This refers to the Basic writer and other similar Writer classes.

Some output format Writer classes (e.g., Latex or AASTex) accept additional keywords that can customize the output further. See the documentation of these classes for details.

output: output specifier

There are two ways to specify the output for the write operation:

• Name of a file (string)

• File-like object (from open(), StringIO, etc.)

table: input table

Any value that is supported for initializing a Table object (see Constructing a Table). This includes a table with a list of columns, a dictionary of columns, or from numpy arrays (either structured or homogeneous).

format: output format (default=’basic’)

This specifies the format of the ASCII table to be written, such as a basic character delimited table, fixed-format table, or a CDS-compatible table, etc. The value of this parameter must be one of the Supported Formats.

delimiter: column delimiter string

A one-character string used to separate fields which typically defaults to the space character. Other common values might be “,” or “|” or “\t”.

comment: string defining start of a comment line in output table

For the Basic Writer this defaults to “# “. Which comments are written and how depends on the format chosen. The comments are defined as a list of strings in the input table meta['comments'] element. Comments in the metadata of the given Table will normally be written before the header, although CommentedHeader writes table comments after the commented header. To disable writing comments, set comment=False.

formats: dict of data type converters

For each key (column name) use the given value to convert the column data to a string. If the format value is string-like, then it is used as a Python format statement (e.g., ‘%0.2f’ % value). If it is a callable function, then that function is called with a single argument containing the column value to be converted. Example:

astropy.io.ascii.write(table, sys.stdout, formats={'XCENTER': '%12.1f',
'YCENTER': lambda x: round(x, 1)},

names: list of output column names

Define the complete list of output column names to write for the data table, overriding the existing column names.

include_names: list of names to include in output

From the list of column names found from the data table or the names parameter, select for output only columns within this list. If not supplied then include all names.

exclude_names: list of names to exclude from output

Exclude these names from the list of output columns. This is applied after the include_names filtering. If not specified then no columns are excluded.

fill_values: list of fill value specifiers

This can be used to fill missing values in the table or replace values with special meaning.

See the Bad or Missing Values section for more information on the syntax. The syntax is almost the same as when reading a table. There is a special value astropy.io.ascii.masked that is used to say “output this string for all masked values in a masked table” (the default is to use an empty string ""):

>>> import sys
>>> from astropy.table import Table, Column, MaskedColumn
>>> from astropy.io import ascii
>>> t = Table([(1, 2), (3, 4)], names=('a', 'b'), masked=True)
>>> ascii.write(t, sys.stdout)
a b
"" 3
2 4
a b
N/A 3
2 4


Note that when writing a table, all values are converted to strings before any value is replaced. Because fill_values only replaces cells that are an exact match to the specification, you need to provide the string representation (stripped of whitespace) for each value. For example, in the following commands -99 is formatted with two digits after the comma, so we need to replace -99.00 and not -99:

>>> t = Table([(-99, 2), (3, 4)], names=('a', 'b'))
>>> ascii.write(t, sys.stdout, fill_values = [('-99.00', 'no data')],
...             formats={'a': '%4.2f'})
a b
"no data" 3
2.00 4


Similarly, if you replace a value in a column that has a fixed length format (e.g., 'f4.2'), then the string you want to replace must have the same number of characters. In the example above, fill_values=[(' nan',' N/A')] would work.

fill_include_names: list of column names, which are affected by fill_values

If not supplied, then fill_values can affect all columns.

fill_exclude_names: list of column names, which are not affected by fill_values

If not supplied, then fill_values can affect all columns.

fast_writer: whether to use the fast Cython writer

If this parameter is None (which it is by default), write() will attempt to use the faster writer (described in Fast ASCII I/O) if possible. Specifying fast_writer=False disables this behavior.

WriterWriter class (deprecated in favor of format)

This specifies the top-level format of the ASCII table to be written, such as a basic character delimited table, fixed-format table, or a CDS-compatible table, etc. The value of this parameter must be a Writer class. For basic usage this means one of the built-in Extension Reader Classes. Note that Reader classes and Writer classes are synonymous; in other words, Reader classes can also write, but for historical reasons they are often called Reader classes.

The American Astronomical Society Journals’ Machine-Readable Table (MRT) format consists of single file with the table description header and the table data itself. MRT is similar to the CDS format standard, but differs in the table description sections and the lack of a separate ReadMe file. Astropy does not support writing in the CDS format.

The Mrt writer supports writing tables to MRT format.

Note

The metadata of the table, apart from column unit, name and description, are not written in the output file. Placeholders for the title, authors, and table name fields are put into the output file and can be edited after writing.

### Examples¶

The command ascii.write(format='mrt') writes an astropy Table to the MRT format. Section dividers --- and === are used to divide the table into different sections, with the last section always been the actual data.

As the MRT standard requires, for columns that have a unit attribute not set to None, the unit names are tabulated in the Byte-By-Byte description of the column. When columns do not contain any units, --- is put instead. A ? is prefixed to the column description in the Byte-By-Byte for Masked columns or columns that have null values, indicating them as such.

The example below initializes a table with columns that have a unit attribute and has masked values.

>>> from astropy.io import ascii
>>> from astropy.table import Table, Column, MaskedColumn
>>> from astropy import units as u
>>> table = Table()
>>> table['Name'] = ['ASASSN-15lh', 'ASASSN-14li']
>>> # MRT Standard requires all quantities in SI units.
>>> temperature = [0.0334, 0.297] * u.K
>>> table['Temperature'] = temperature.to(u.keV, equivalencies=u.temperature_energy())
>>> table['nH'] = Column([0.025, 0.0188], unit=u.Unit(10**22))
>>> table['Flux'] = ([2.044 * 10**-11] * u.erg * u.cm**-2).to(u.Jy * u.Unit(10**12))
>>> table['magnitude'] = [u.Magnitude(25), u.Magnitude(-9)]


Note that for columns with Time, TimeDelta and related values, the writer does not do any internal conversion or modification. These columns should be converted to regular columns with proper unit and name attribute before writing the table. Thus:

>>> from astropy.time import Time, TimeDelta
>>> from astropy.timeseries import TimeSeries
>>> ts = TimeSeries(time_start=Time('2019-01-01'), time_delta=2*u.day, n_samples=1)
>>> table['Obs'] = Column(ts.time.decimalyear, description='Time of Observation')
...                           unit=u.s)


Columns that are SkyCoord objects or columns with values that are such objects are recognized as such, and some predefined labels and description is used for them. Coordinate columns that have SphericalRepresentation are additionally sub-divided into their coordinate component columns. Representations that have ra and dec components are divided into their hour-min-sec and deg-arcmin-arcsec components respectively. Whereas columns with SkyCoord objects in the Galactic or any of the Ecliptic frames are divided into their latitude(ELAT/GLAT) and longitude components (ELON/GLAT) only. The original table remains accessible as such, while the file is written from a modified copy of the table. The new coordinate component columns are appended to the end of the table.

It should be noted that the default precision of the latitude, longitude and seconds (of arc) columns is set at a default number of 12, 10 and 9 digits after the decimal for deg, sec and arcsec values, respectively. This default is set to match a machine precision of 1e-15 relative to the original SkyCoord those columns were extracted from. As all other columns, the format can be expliclty set by passing the formats keyword to the write function or by setting the format attribute of individual columns (the latter will only work for columns that are not decomposed). To customize the number of significant digits, presicions should therefore be specified in the formats dictionary for the output column names, such as formats={'RAs': '07.4f', 'DEs': '06.3f'} or formats={'GLAT': '+10.6f', 'GLON': '9.6f'} for milliarcsecond accuracy. Note that the forms with leading zeros for the seconds and including the sign for latitudes are recommended for better consistency and readability.

The following code illustrates the above.

>>> from astropy.coordinates import SkyCoord
>>> table['coord'] = [SkyCoord.from_name('ASASSN-15lh'),
...                   SkyCoord.from_name('ASASSN-14li')]
>>> table.write('coord_cols.dat', format='ascii.mrt')
>>> table['coord'] = table['coord'].geocentrictrueecliptic
>>> table['Temperature'].format = '.5E' # Set default column format.
>>> table.write('ecliptic_cols.dat', format='ascii.mrt')


After execution, the contents of coords_cols.dat will be:

Title:
Authors:
Table:
================================================================================
Byte-by-byte Description of file: table.dat
--------------------------------------------------------------------------------
Bytes Format Units  Label     Explanations
--------------------------------------------------------------------------------
1-11  A11     ---    Name        Description of Name
13-23  E11.6   keV    Temperature [0.0/0.01] Description of Temperature
25-30  F6.4    10+22  nH          [0.01/0.03] Description of nH
32-36  F5.3   10+12Jy Flux        ? Description of Flux
38-42  E5.1    mag    magnitude   [0.0/3981.08] Description of magnitude
44-49  F6.1    ---    Obs         [2019.0/2019.0] Time of Observation
55-56  I2     h      RAh           Right Ascension (hour)
58-59  I2     min    RAm           Right Ascension (minute)
61-73  F13.10 s      RAs           Right Ascension (second)
75  A1     ---    DE-           Sign of Declination
76-77  I2     deg    DEd           Declination (degree)
79-80  I2     arcmin DEm           Declination (arcmin)
82-93  F12.9  arcsec DEs           Declination (arcsec)
--------------------------------------------------------------------------------
Notes:
--------------------------------------------------------------------------------
ASASSN-15lh 2.87819e-09 0.0250       1e-10 2019.0 100 22 02 15.4500000000 -61 39 34.599996000
ASASSN-14li 2.55935e-08 0.0188 2.044 4e+03 2019.0 100 12 48 15.2244072000 +17 46 26.496624000


And the file ecliptic_cols.dat will look like:

Title:
Authors:
Table:
================================================================================
Byte-by-byte Description of file: table.dat
--------------------------------------------------------------------------------
Bytes Format Units  Label     Explanations
--------------------------------------------------------------------------------
1- 11  A11     ---    Name        Description of Name
13- 23  E11.6   keV    Temperature [0.0/0.01] Description of Temperature
25- 30  F6.4    10+22  nH          [0.01/0.03] Description of nH
32- 36  F5.3   10+12Jy Flux        ? Description of Flux
38- 42  E5.1    mag    magnitude   [0.0/3981.08] Description of magnitude
44- 49  F6.1    ---    Obs         [2019.0/2019.0] Time of Observation
55- 70  F16.12  deg    ELON        Ecliptic Longitude (geocentrictrueecliptic)
72- 87  F16.12  deg    ELAT        Ecliptic Latitude (geocentrictrueecliptic)
--------------------------------------------------------------------------------
Notes:
--------------------------------------------------------------------------------
ASASSN-15lh 2.87819e-09 0.0250       1e-10 2019.0 100 306.224208650096 -45.621789850825
ASASSN-14li 2.55935e-08 0.0188 2.044 4e+03 2019.0 100 183.754980099243  21.051410763027


Finally, MRT has some specific naming conventions for columns (https://journals.aas.org/mrt-labels/#reflab). For example, if a column contains the mean error for the data in a column named label, then this column should be named e_label. These kinds of relative column naming cannot be enforced by the MRT writer because it does not know what the column data means and thus, the relation between the columns cannot be figured out. Therefore, it is up to the user to use Table.rename_columns to appropriately rename any columns before writing the table to MRT format. The following example shows a similar situation, using the option to send the output to sys.stdout instead of a file:

>>> table['error'] = [1e4, 450] * u.Jy  # Error in the Flux values.
>>> outtab = table.copy()  # So that changes don't affect the original table.
>>> outtab.rename_column('error', 'e_Flux')
>>> # re-order so that related columns are placed next to eachother.
>>> outtab = outtab['Name', 'Obs', 'coord', 'Cadence', 'nH', 'magnitude',
...                 'Temperature', 'Flux', 'e_Flux']

>>> ascii.write(outtab, format='mrt')
Title:
Authors:
Table:
================================================================================
Byte-by-byte Description of file: table.dat
--------------------------------------------------------------------------------
Bytes Format Units  Label     Explanations
--------------------------------------------------------------------------------
1- 11  A11     ---    Name        Description of Name
13- 18  F6.1    ---    Obs         [2019.0/2019.0] Time of Observation
24- 29  F6.4    10+22  nH          [0.01/0.03] Description of nH
31- 35  E5.1    mag    magnitude   [0.0/3981.08] Description of magnitude
37- 47  E11.6   keV    Temperature [0.0/0.01] Description of Temperature
49- 53  F5.3   10+12Jy Flux        ? Description of Flux
55- 61  F7.1    Jy     e_Flux      [450.0/10000.0] Description of e_Flux
63- 78  F16.12  deg    ELON        Ecliptic Longitude (geocentrictrueecliptic)
80- 95  F16.12  deg    ELAT        Ecliptic Latitude (geocentrictrueecliptic)
--------------------------------------------------------------------------------
Notes:
--------------------------------------------------------------------------------
ASASSN-15lh 2019.0 100 0.0250 1e-10 2.87819e-09       10000.0 306.224208650096 -45.621789850825
ASASSN-14li 2019.0 100 0.0188 4e+03 2.55935e-08 2.044   450.0 183.754980099243  21.051410763027


Attention

The MRT writer currently supports automatic writing of a single coordinate column in Tables. For tables with more than one coordinate column of a given kind (e.g. equatorial, galactic or ecliptic), only the first found coordinate column will be decomposed into its component columns, and the rest of the coordinate columns of the same type will be converted to string columns. Thus users should take care that the additional coordinate columns are dealt with (e.g. by converting them to unique float-valued columns) before using SkyCoord methods.