Table¶

class gwpy.table.Table(data=None, masked=None, names=None, dtype=None, meta=None, copy=True, rows=None, copy_indices=True, **kwargs)¶

Bases: object

A class to represent tables of heterogeneous data.

Table provides a class for heterogeneous tabular data, making use of a numpy structured array internally to store the data values. A key enhancement provided by the Table class is the ability to easily modify the structure of the table by adding or removing columns, or adding new rows of data. In addition table and column metadata are fully supported.

Table differs from NDData by the assumption that the input data consists of columns of homogeneous data, where each column has a unique identifier and may contain additional metadata such as the data unit, format, and description.

Parameters:

data : numpy ndarray, dict, list, Table, or table-like object, optional Data to initialize table. masked : bool, optional Specify whether the table is masked. names : list, optional Specify column names. dtype : list, optional Specify column data types. meta : dict, optional Metadata associated with the table. copy : bool, optional Copy the input data. Default is True. rows : numpy ndarray, list of lists, optional Row-oriented data for table instead of data argument. copy_indices : bool, optional Copy any indices in the input data. Default is True. **kwargs : dict, optional Additional keyword args when converting table-like object. .. note:: If the input is a Table the meta is always copied regardless of the copy parameter.

Methods Summary

add_column(col[, index, name, rename_duplicate])	Add a new Column object col to the table.
add_columns(cols[, indexes, names, copy, …])	Add a list of new Column objects cols to the table.
add_index(colnames[, engine, unique])	Insert a new index among one or more columns.
add_row([vals, mask])	Add a new row to the end of the table.
argsort([keys, kind])	Return the indices which would sort the table according to one or more key columns.
as_array([keep_byteorder])	Return a new copy of the table in the form of a structured np.ndarray or np.ma.MaskedArray object (as appropriate).
convert_bytestring_to_unicode([python3_only])	Convert bytestring columns (dtype.kind=’S’) to unicode (dtype.kind=’U’) assuming ASCII encoding.
convert_unicode_to_bytestring([python3_only])	Convert ASCII-only unicode columns (dtype.kind=’U’) to bytestring (dtype.kind=’S’).
copy([copy_data])	Return a copy of the table.
field(item)	Return column[item] for recarray compatibility.
filled([fill_value])	Return a copy of self, with masked values filled.
from_pandas(dataframe)	Create a Table from a pandas.DataFrame instance
group_by(keys)	Group this table by the specified keys
index_column(name)	Return the positional index of column name.
index_mode(mode)	Return a context manager for an indexing mode.
insert_row(index[, vals, mask])	Add a new row before the given index position in the table.
itercols()	Iterate over the columns of this table.
keep_columns(names)	Keep only the columns specified (remove the others).
keys()
more([max_lines, max_width, show_name, …])	Interactively browse table with a paging interface.
pformat([max_lines, max_width, show_name, …])	Return a list of lines for the formatted string representation of the table.
pprint([max_lines, max_width, show_name, …])	Print a formatted string representation of the table.
read(*args, **kwargs)	Read and parse a data table and return as a Table.
remove_column(name)	Remove a column from the table.
remove_columns(names)	Remove several columns from the table.
remove_indices(colname)	Remove all indices involving the given column.
remove_row(index)	Remove a row from the table.
remove_rows(row_specifier)	Remove rows from the table.
rename_column(name, new_name)	Rename a column.
replace_column(name, col)	Replace column name with the new col object.
reverse()	Reverse the row order of table rows.
show_in_browser([max_lines, jsviewer, …])	Render the table in HTML and show it in a web browser.
show_in_notebook([tableid, css, …])	Render the table in HTML and show it in the IPython notebook.
sort([keys])	Sort the table according to one or more keys.
to_pandas()	Return a pandas.DataFrame instance
write(*args, **kwargs)	Write this Table object out in the specified format.

Methods Documentation

add_column(col, index=None, name=None, rename_duplicate=False)¶

Add a new Column object col to the table. If index is supplied then insert column before index position in the list of columns, otherwise append column to the end of the list.

Parameters:	col : Column Column object to add. index : int or None Insert column before this position or at end (default). name : str Column name rename_duplicate : bool Uniquify column name if it already exist. Default is False.

Examples

Create a table with two columns ‘a’ and ‘b’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> print(t)
 a   b
--- ---
  1 0.1
  2 0.2
  3 0.3

Create a third column ‘c’ and append it to the end of the table:

>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> t.add_column(col_c)
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Add column ‘d’ at position 1. Note that the column is inserted before the given index:

>>> col_d = Column(name='d', data=['a', 'b', 'c'])
>>> t.add_column(col_d, 1)
>>> print(t)
 a   d   b   c
--- --- --- ---
  1   a 0.1   x
  2   b 0.2   y
  3   c 0.3   z

Add second column named ‘b’ with rename_duplicate:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> col_b = Column(name='b', data=[1.1, 1.2, 1.3])
>>> t.add_column(col_b, rename_duplicate=True)
>>> print(t)
 a   b  b_1
--- --- ---
  1 0.1 1.1
  2 0.2 1.2
  3 0.3 1.3

Add an unnamed column or mixin object in the table using a default name or by specifying an explicit name with name. Name can also be overridden:

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> col_c = Column(data=['x', 'y'])
>>> t.add_column(col_c)
>>> t.add_column(col_c, name='c')
>>> col_b = Column(name='b', data=[1.1, 1.2])
>>> t.add_column(col_b, name='d')
>>> print(t)
 a   b  col2  c   d
--- --- ---- --- ---
  1 0.1    x   x 1.1
  2 0.2    y   y 1.2

To add several columns use add_columns.

add_columns(cols, indexes=None, names=None, copy=True, rename_duplicate=False)¶

Add a list of new Column objects cols to the table. If a corresponding list of indexes is supplied then insert column before each index position in the original list of columns, otherwise append columns to the end of the list.

Parameters:	cols : list of Columns Column objects to add. indexes : list of ints or None Insert column before this position or at end (default). names : list of str Column names copy : bool Make a copy of the new columns. Default is True. rename_duplicate : bool Uniquify new column names if they duplicate the existing ones. Default is False.

Examples

Create a table with two columns ‘a’ and ‘b’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> print(t)
 a   b
--- ---
  1 0.1
  2 0.2
  3 0.3

Create column ‘c’ and ‘d’ and append them to the end of the table:

>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> col_d = Column(name='d', data=['u', 'v', 'w'])
>>> t.add_columns([col_c, col_d])
>>> print(t)
 a   b   c   d
--- --- --- ---
  1 0.1   x   u
  2 0.2   y   v
  3 0.3   z   w

Add column ‘c’ at position 0 and column ‘d’ at position 1. Note that the columns are inserted before the given position:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> col_d = Column(name='d', data=['u', 'v', 'w'])
>>> t.add_columns([col_c, col_d], [0, 1])
>>> print(t)
 c   a   d   b
--- --- --- ---
  x   1   u 0.1
  y   2   v 0.2
  z   3   w 0.3

Add second column ‘b’ and column ‘c’ with rename_duplicate:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> col_b = Column(name='b', data=[1.1, 1.2, 1.3])
>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> t.add_columns([col_b, col_c], rename_duplicate=True)
>>> print(t)
 a   b  b_1  c
--- --- --- ---
  1 0.1 1.1  x
  2 0.2 1.2  y
  3 0.3 1.3  z

Add unnamed columns or mixin objects in the table using default names or by specifying explicit names with names. Names can also be overridden:

>>> t = Table()
>>> col_a = Column(data=['x', 'y'])
>>> col_b = Column(name='b', data=['u', 'v'])
>>> t.add_columns([col_a, col_b])
>>> t.add_columns([col_a, col_b], names=['c', 'd'])
>>> print(t)
col0  b   c   d
---- --- --- ---
   x   u   x   u
   y   v   y   v

add_index(colnames, engine=None, unique=False)¶

Insert a new index among one or more columns. If there are no indices, make this index the primary table index.

Parameters:	colnames : str or list List of column names (or a single column name) to index engine : type or None Indexing engine class to use, from among SortedArray, BST, FastBST, and FastRBT. If the supplied argument is None (by default), use SortedArray. unique : bool Whether the values of the index must be unique. Default is False.

add_row(vals=None, mask=None)¶

Add a new row to the end of the table.

The vals argument can be:

sequence (e.g. tuple or list)

Column values in the same order as table columns.

mapping (e.g. dict)

Keys corresponding to column names. Missing values will be filled with np.zeros for the column dtype.

None

All values filled with np.zeros for the column dtype.

This method requires that the Table object “owns” the underlying array data. In particular one cannot add a row to a Table that was initialized with copy=False from an existing array.

The mask attribute should give (if desired) the mask for the values. The type of the mask should match that of the values, i.e. if vals is an iterable, then mask should also be an iterable with the same length, and if vals is a mapping, then mask should be a dictionary.

Parameters:	vals : tuple, list, dict or None Use the specified values in the new row mask : tuple, list, dict or None Use the specified mask values in the new row

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1,2],[4,5],[7,8]], names=('a','b','c'))
>>> print(t)
 a   b   c
--- --- ---
  1   4   7
  2   5   8

Adding a new row with entries ‘3’ in ‘a’, ‘6’ in ‘b’ and ‘9’ in ‘c’:

>>> t.add_row([3,6,9])
>>> print(t)
  a   b   c
  --- --- ---
  1   4   7
  2   5   8
  3   6   9

argsort(keys=None, kind=None)¶

Return the indices which would sort the table according to one or more key columns. This simply calls the numpy.argsort function on the table with the order parameter set to keys.

Parameters:	keys : str or list of str The column name(s) to order the table by kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, optional Sorting algorithm.
Returns:	index_array : ndarray, int Array of indices that sorts the table by the specified key column(s).

as_array(keep_byteorder=False)¶

Return a new copy of the table in the form of a structured np.ndarray or np.ma.MaskedArray object (as appropriate).

Parameters:	keep_byteorder : bool, optional By default the returned array has all columns in native byte order. However, if this option is True this preserves the byte order of all columns (if any are non-native).
Returns:	table_array : np.ndarray (unmasked) or np.ma.MaskedArray (masked) Copy of table as a numpy structured array

convert_bytestring_to_unicode(python3_only=False)¶

Convert bytestring columns (dtype.kind=’S’) to unicode (dtype.kind=’U’) assuming ASCII encoding.

Internally this changes string columns to represent each character in the string with a 4-byte UCS-4 equivalent, so it is inefficient for memory but allows Python 3 scripts to manipulate string arrays with natural syntax.

The python3_only parameter is provided as a convenience so that code can be written in a Python 2 / 3 compatible way:

>>> t = Table.read('my_data.fits')
>>> t.convert_bytestring_to_unicode(python3_only=True)

Parameters:	python3_only : bool Only do this operation for Python 3

convert_unicode_to_bytestring(python3_only=False)¶

Convert ASCII-only unicode columns (dtype.kind=’U’) to bytestring (dtype.kind=’S’).

When exporting a unicode string array to a file in Python 3, it may be desirable to encode unicode columns as bytestrings. This routine takes advantage of numpy automated conversion which works for strings that are pure ASCII.

The python3_only parameter is provided as a convenience so that code can be written in a Python 2 / 3 compatible way:

>>> t.convert_unicode_to_bytestring(python3_only=True)
>>> t.write('my_data.fits')

Parameters:	python3_only : bool Only do this operation for Python 3

copy(copy_data=True)¶

Return a copy of the table.

Parameters:	copy_data : bool If True (the default), copy the underlying data array. Otherwise, use the same data array .. note:: The meta is always deepcopied regardless of the value for copy_data.

field(item)¶

Return column[item] for recarray compatibility.

filled(fill_value=None)¶

Return a copy of self, with masked values filled.

If input fill_value supplied then that value is used for all masked entries in the table. Otherwise the individual fill_value defined for each table column is used.

Parameters:	fill_value : str If supplied, this fill_value is used for all masked entries in the entire table.
Returns:	filled_table : Table New table with masked values filled

classmethod from_pandas(dataframe)¶

Create a Table from a pandas.DataFrame instance

Parameters:	dataframe : pandas.DataFrame The pandas pandas.DataFrame instance
Returns:	table : Table A Table (or subclass) instance

group_by(keys)¶

Group this table by the specified keys

This effectively splits the table into groups which correspond to unique values of the keys grouping object. The output is a new TableGroups which contains a copy of this table but sorted by row according to keys.

The keys input to group_by can be specified in different ways:

• String or list of strings corresponding to table column name(s)
• Numpy array (homogeneous or structured) with same length as this table
• Table with same length as this table

Parameters:	keys : str, list of str, numpy array, or Table Key grouping object
Returns:	out : Table New table with groups set

index_column(name)¶

Return the positional index of column name.

Parameters:	name : str column name
Returns:	index : int Positional index of column name.

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Get index of column ‘b’ of the table:

>>> t.index_column('b')
1

index_mode(mode)¶

Return a context manager for an indexing mode.

Parameters:

mode : str Either ‘freeze’, ‘copy_on_getitem’, or ‘discard_on_copy’. In ‘discard_on_copy’ mode, indices are not copied whenever columns or tables are copied. In ‘freeze’ mode, indices are not modified whenever columns are modified; at the exit of the context, indices refresh themselves based on column values. This mode is intended for scenarios in which one intends to make many additions or modifications in an indexed column. In ‘copy_on_getitem’ mode, indices are copied when taking column slices as well as table slices, so col[i0:i1] will preserve indices.

insert_row(index, vals=None, mask=None)¶

Add a new row before the given index position in the table.

The vals argument can be:

sequence (e.g. tuple or list)

Column values in the same order as table columns.

mapping (e.g. dict)

Keys corresponding to column names. Missing values will be filled with np.zeros for the column dtype.

None

All values filled with np.zeros for the column dtype.

The mask attribute should give (if desired) the mask for the values. The type of the mask should match that of the values, i.e. if vals is an iterable, then mask should also be an iterable with the same length, and if vals is a mapping, then mask should be a dictionary.

Parameters:	vals : tuple, list, dict or None Use the specified values in the new row mask : tuple, list, dict or None Use the specified mask values in the new row

itercols()¶

Iterate over the columns of this table.

Examples

To iterate over the columns of a table:

>>> t = Table([[1], [2]])
>>> for col in t.itercols():
...     print(col)
col0
----
   1
col1
----
   2

Using itercols() is similar to for col in t.columns.values() but is syntactically preferred.

keep_columns(names)¶

Keep only the columns specified (remove the others).

Parameters:	names : list A list containing the names of the columns to keep. All other columns will be removed.

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Specifying only a single column name keeps only this column. Keep only column ‘a’ of the table:

>>> t.keep_columns('a')
>>> print(t)
 a
---
  1
  2
  3

Specifying a list of column names is keeps is also possible. Keep columns ‘a’ and ‘c’ of the table:

>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> t.keep_columns(['a', 'c'])
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

keys()¶

more(max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False)¶

Interactively browse table with a paging interface.

Supported keys:

f, <space> : forward one page
b : back one page
r : refresh same page
n : next row
p : previous row
< : go to beginning
> : go to end
q : quit browsing
h : print this help

Parameters:

max_lines : int Maximum number of lines in table output max_width : int or None Maximum character width of output show_name : bool Include a header row for column names. Default is True. show_unit : bool Include a header row for unit. Default is to show a row for units only if one or more columns has a defined value for the unit. show_dtype : bool Include a header row for column dtypes. Default is True.

pformat(max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False, html=False, tableid=None, align=None, tableclass=None)¶

Return a list of lines for the formatted string representation of the table.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default is taken from the configuration item astropy.conf.max_lines. If a negative value of max_lines is supplied then there is no line limit applied.

The same applies for max_width except the configuration item is astropy.conf.max_width.

Parameters:

max_lines : int or None Maximum number of rows to output max_width : int or None Maximum character width of output show_name : bool Include a header row for column names. Default is True. show_unit : bool Include a header row for unit. Default is to show a row for units only if one or more columns has a defined value for the unit. show_dtype : bool Include a header row for column dtypes. Default is True. html : bool Format the output as an HTML table. Default is False. tableid : str or None An ID tag for the table; only used if html is set. Default is “table{id}”, where id is the unique integer id of the table object, id(self) align : str or list or tuple or None Left/right alignment of columns. Default is right (None) for all columns. Other allowed values are ‘>’, ‘<’, ‘^’, and ‘0=’ for right, left, centered, and 0-padded, respectively. A list of strings can be provided for alignment of tables with multiple columns. tableclass : str or list of str or None CSS classes for the table; only used if html is set. Default is None.

Returns:

lines : list Formatted table as a list of strings.

pprint(max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False, align=None)¶

Print a formatted string representation of the table.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default is taken from the configuration item astropy.conf.max_lines. If a negative value of max_lines is supplied then there is no line limit applied.

The same applies for max_width except the configuration item is astropy.conf.max_width.

Parameters:

max_lines : int Maximum number of lines in table output. max_width : int or None Maximum character width of output. show_name : bool Include a header row for column names. Default is True. show_unit : bool Include a header row for unit. Default is to show a row for units only if one or more columns has a defined value for the unit. show_dtype : bool Include a header row for column dtypes. Default is True. align : str or list or tuple or None Left/right alignment of columns. Default is right (None) for all columns. Other allowed values are ‘>’, ‘<’, ‘^’, and ‘0=’ for right, left, centered, and 0-padded, respectively. A list of strings can be provided for alignment of tables with multiple columns.

classmethod read(*args, **kwargs)¶

Read and parse a data table and return as a Table.

This function provides the Table interface to the astropy unified I/O layer. This allows easily reading a file in many supported data formats using syntax such as:

>>> from astropy.table import Table
>>> dat = Table.read('table.dat', format='ascii')
>>> events = Table.read('events.fits', format='fits')

The arguments and keywords (other than format) provided to this function are passed through to the underlying data reader (e.g. read).

The available built-in formats are:

Format	Read	Write	Auto-identify	Deprecated
ascii	Yes	Yes	No
ascii.aastex	Yes	Yes	No
ascii.basic	Yes	Yes	No
ascii.cds	Yes	No	No
ascii.commented_header	Yes	Yes	No
ascii.csv	Yes	Yes	No
ascii.cwb	Yes	Yes	No
ascii.daophot	Yes	No	No
ascii.ecsv	Yes	Yes	No
ascii.fast_basic	Yes	Yes	No
ascii.fast_commented_header	Yes	Yes	No
ascii.fast_csv	Yes	Yes	No
ascii.fast_no_header	Yes	Yes	No
ascii.fast_rdb	Yes	Yes	No
ascii.fast_tab	Yes	Yes	No
ascii.fixed_width	Yes	Yes	No
ascii.fixed_width_no_header	Yes	Yes	No
ascii.fixed_width_two_line	Yes	Yes	No
ascii.html	Yes	Yes	Yes
ascii.ipac	Yes	Yes	No
ascii.latex	Yes	Yes	Yes
ascii.no_header	Yes	Yes	No
ascii.omega	Yes	Yes	No
ascii.rdb	Yes	Yes	Yes
ascii.rst	Yes	Yes	No
ascii.sextractor	Yes	No	No
ascii.tab	Yes	Yes	No
fits	Yes	Yes	Yes
gwf	Yes	No	No
hdf5	Yes	Yes	Yes
ligolw	Yes	Yes	Yes
ligolw.coinc_definer	Yes	Yes	No
ligolw.coinc_event	Yes	Yes	No
ligolw.coinc_event_map	Yes	Yes	No
ligolw.coinc_inspiral	Yes	Yes	No
ligolw.coinc_ringdown	Yes	Yes	No
ligolw.dq_list	Yes	Yes	No
ligolw.experiment	Yes	Yes	No
ligolw.experiment_map	Yes	Yes	No
ligolw.experiment_summary	Yes	Yes	No
ligolw.external_trigger	Yes	Yes	No
ligolw.filter	Yes	Yes	No
ligolw.gds_trigger	Yes	Yes	No
ligolw.lfn	Yes	Yes	No
ligolw.ligolw_mon	Yes	Yes	No
ligolw.multi_burst	Yes	Yes	No
ligolw.multi_inspiral	Yes	Yes	No
ligolw.process	Yes	Yes	No
ligolw.process_params	Yes	Yes	No
ligolw.search_summary	Yes	Yes	No
ligolw.search_summvars	Yes	Yes	No
ligolw.segment	Yes	Yes	No
ligolw.segment_definer	Yes	Yes	No
ligolw.segment_summary	Yes	Yes	No
ligolw.sim_burst	Yes	Yes	No
ligolw.sim_inspiral	Yes	Yes	No
ligolw.sim_inst_params	Yes	Yes	No
ligolw.sim_ringdown	Yes	Yes	No
ligolw.sngl_burst	Yes	Yes	No
ligolw.sngl_inspiral	Yes	Yes	No
ligolw.sngl_ringdown	Yes	Yes	No
ligolw.stochastic	Yes	Yes	No
ligolw.stochsumm	Yes	Yes	No
ligolw.summ_mime	Yes	Yes	No
ligolw.summ_value	Yes	Yes	No
ligolw.time_slide	Yes	Yes	No
ligolw.time_slide_segment_map	Yes	Yes	No
ligolw.veto_definer	Yes	Yes	No
root	Yes	Yes	Yes
votable	Yes	Yes	Yes
aastex	Yes	Yes	No	Yes
cds	Yes	No	No	Yes
csv	Yes	Yes	Yes	Yes
daophot	Yes	No	No	Yes
html	Yes	Yes	No	Yes
ipac	Yes	Yes	No	Yes
latex	Yes	Yes	No	Yes
rdb	Yes	Yes	No	Yes

Deprecated format names like aastex will be removed in a future version. Use the full name (e.g. ascii.aastex) instead.

remove_column(name)¶

Remove a column from the table.

This can also be done with:

del table[name]

Parameters:	name : str Name of column to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove column ‘b’ from the table:

>>> t.remove_column('b')
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

To remove several columns at the same time use remove_columns.

remove_columns(names)¶

Remove several columns from the table.

Parameters:	names : list A list containing the names of the columns to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...     names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove columns ‘b’ and ‘c’ from the table:

>>> t.remove_columns(['b', 'c'])
>>> print(t)
 a
---
  1
  2
  3

Specifying only a single column also works. Remove column ‘b’ from the table:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...     names=('a', 'b', 'c'))
>>> t.remove_columns('b')
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

This gives the same as using remove_column.

remove_indices(colname)¶

Remove all indices involving the given column. If the primary index is removed, the new primary index will be the most recently added remaining index.

Parameters:	colname : str Name of column

remove_row(index)¶

Remove a row from the table.

Parameters:	index : int Index of row to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove row 1 from the table:

>>> t.remove_row(1)
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  3 0.3   z

To remove several rows at the same time use remove_rows.

remove_rows(row_specifier)¶

Remove rows from the table.

Parameters:	row_specifier : slice, int, or array of ints Specification for rows to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove rows 0 and 2 from the table:

>>> t.remove_rows([0, 2])
>>> print(t)
 a   b   c
--- --- ---
  2 0.2   y

Note that there are no warnings if the slice operator extends outside the data:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> t.remove_rows(slice(10, 20, 1))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

rename_column(name, new_name)¶

Rename a column.

This can also be done directly with by setting the name attribute for a column:

table[name].name = new_name

TODO: this won’t work for mixins

Parameters:	name : str The current name of the column. new_name : str The new name for the column

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1,2],[3,4],[5,6]], names=('a','b','c'))
>>> print(t)
 a   b   c
--- --- ---
  1   3   5
  2   4   6

Renaming column ‘a’ to ‘aa’:

>>> t.rename_column('a' , 'aa')
>>> print(t)
 aa  b   c
--- --- ---
  1   3   5
  2   4   6

replace_column(name, col)¶

Replace column name with the new col object.

Parameters:	name : str Name of column to replace col : column object (list, ndarray, Column, etc) New column object to replace the existing column

Examples

Replace column ‘a’ with a float version of itself:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> float_a = t['a'].astype(float)
>>> t.replace_column('a', float_a)

reverse()¶

Reverse the row order of table rows. The table is reversed in place and there are no function arguments.

Examples

Create a table with three columns:

>>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'],
...         [12,15,18]], names=('firstname','name','tel'))
>>> print(t)
firstname   name  tel
--------- ------- ---
      Max  Miller  12
       Jo  Miller  15
     John Jackson  18

Reversing order:

>>> t.reverse()
>>> print(t)
firstname   name  tel
--------- ------- ---
     John Jackson  18
       Jo  Miller  15
      Max  Miller  12

show_in_browser(max_lines=5000, jsviewer=False, browser=u'default', jskwargs={u'use_local_files': True}, tableid=None, table_class=u'display compact', css=None, show_row_index=u'idx')¶

Render the table in HTML and show it in a web browser.

Parameters:

max_lines : int Maximum number of rows to export to the table (set low by default to avoid memory issues, since the browser view requires duplicating the table in memory). A negative value of max_lines indicates no row limit. jsviewer : bool If True, prepends some javascript headers so that the table is rendered as a DataTables data table. This allows in-browser searching & sorting. browser : str Any legal browser name, e.g. 'firefox', 'chrome', 'safari' (for mac, you may need to use 'open -a "/Applications/Google Chrome.app" {}' for Chrome). If 'default', will use the system default browser. jskwargs : dict Passed to the astropy.table.JSViewer init. Defaults to {'use_local_files': True} which means that the JavaScript libraries will be served from local copies. tableid : str or None An html ID tag for the table. Default is table{id}, where id is the unique integer id of the table object, id(self). table_class : str or None A string with a list of HTML classes used to style the table. Default is “display compact”, and other possible values can be found in http://www.datatables.net/manual/styling/classes css : string A valid CSS string declaring the formatting for the table. Defaults to astropy.table.jsviewer.DEFAULT_CSS. show_row_index : str or False If this does not evaluate to False, a column with the given name will be added to the version of the table that gets displayed. This new column shows the index of the row in the table itself, even when the displayed table is re-sorted by another column. Note that if a column with this name already exists, this option will be ignored. Defaults to “idx”.

show_in_notebook(tableid=None, css=None, display_length=50, table_class=u'astropy-default', show_row_index=u'idx')¶

Render the table in HTML and show it in the IPython notebook.

Parameters:

tableid : str or None An html ID tag for the table. Default is table{id}-XXX, where id is the unique integer id of the table object, id(self), and XXX is a random number to avoid conflicts when printing the same table multiple times. table_class : str or None A string with a list of HTML classes used to style the table. The special default string (‘astropy-default’) means that the string will be retrieved from the configuration item astropy.table.default_notebook_table_class. Note that these table classes may make use of bootstrap, as this is loaded with the notebook. See this page for the list of classes. css : string A valid CSS string declaring the formatting for the table. Defaults to astropy.table.jsviewer.DEFAULT_CSS_NB. display_length : int, optional Number or rows to show. Defaults to 50. show_row_index : str or False If this does not evaluate to False, a column with the given name will be added to the version of the table that gets displayed. This new column shows the index of the row in the table itself, even when the displayed table is re-sorted by another column. Note that if a column with this name already exists, this option will be ignored. Defaults to “idx”.

Notes

Currently, unlike show_in_browser (with jsviewer=True), this method needs to access online javascript code repositories. This is due to modern browsers’ limitations on accessing local files. Hence, if you call this method while offline (and don’t have a cached version of jquery and jquery.dataTables), you will not get the jsviewer features.

sort(keys=None)¶

Sort the table according to one or more keys. This operates on the existing table and does not return a new table.

Parameters:	keys : str or list of str The key(s) to order the table by. If None, use the primary index of the Table.

Examples

Create a table with 3 columns:

>>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'],
...         [12,15,18]], names=('firstname','name','tel'))
>>> print(t)
firstname   name  tel
--------- ------- ---
      Max  Miller  12
       Jo  Miller  15
     John Jackson  18

Sorting according to standard sorting rules, first ‘name’ then ‘firstname’:

>>> t.sort(['name','firstname'])
>>> print(t)
firstname   name  tel
--------- ------- ---
     John Jackson  18
       Jo  Miller  15
      Max  Miller  12

to_pandas()¶

Return a pandas.DataFrame instance

Returns:	dataframe : pandas.DataFrame A pandas pandas.DataFrame instance
Raises:	ImportError If pandas is not installed ValueError If the Table contains mixin or multi-dimensional columns

write(*args, **kwargs)¶

Write this Table object out in the specified format.

This function provides the Table interface to the astropy unified I/O layer. This allows easily writing a file in many supported data formats using syntax such as:

>>> from astropy.table import Table
>>> dat = Table([[1, 2], [3, 4]], names=('a', 'b'))
>>> dat.write('table.dat', format='ascii')

The arguments and keywords (other than format) provided to this function are passed through to the underlying data reader (e.g. write).

The available built-in formats are:

Format	Read	Write	Auto-identify	Deprecated
ascii	Yes	Yes	No
ascii.aastex	Yes	Yes	No
ascii.basic	Yes	Yes	No
ascii.commented_header	Yes	Yes	No
ascii.csv	Yes	Yes	No
ascii.cwb	Yes	Yes	No
ascii.ecsv	Yes	Yes	No
ascii.fast_basic	Yes	Yes	No
ascii.fast_commented_header	Yes	Yes	No
ascii.fast_csv	Yes	Yes	No
ascii.fast_no_header	Yes	Yes	No
ascii.fast_rdb	Yes	Yes	No
ascii.fast_tab	Yes	Yes	No
ascii.fixed_width	Yes	Yes	No
ascii.fixed_width_no_header	Yes	Yes	No
ascii.fixed_width_two_line	Yes	Yes	No
ascii.html	Yes	Yes	Yes
ascii.ipac	Yes	Yes	No
ascii.latex	Yes	Yes	Yes
ascii.no_header	Yes	Yes	No
ascii.omega	Yes	Yes	No
ascii.rdb	Yes	Yes	Yes
ascii.rst	Yes	Yes	No
ascii.tab	Yes	Yes	No
fits	Yes	Yes	Yes
gwf	Yes	Yes	No
hdf5	Yes	Yes	Yes
jsviewer	No	Yes	No
ligolw	Yes	Yes	Yes
ligolw.coinc_definer	Yes	Yes	No
ligolw.coinc_event	Yes	Yes	No
ligolw.coinc_event_map	Yes	Yes	No
ligolw.coinc_inspiral	Yes	Yes	No
ligolw.coinc_ringdown	Yes	Yes	No
ligolw.dq_list	Yes	Yes	No
ligolw.experiment	Yes	Yes	No
ligolw.experiment_map	Yes	Yes	No
ligolw.experiment_summary	Yes	Yes	No
ligolw.external_trigger	Yes	Yes	No
ligolw.filter	Yes	Yes	No
ligolw.gds_trigger	Yes	Yes	No
ligolw.lfn	Yes	Yes	No
ligolw.ligolw_mon	Yes	Yes	No
ligolw.multi_burst	Yes	Yes	No
ligolw.multi_inspiral	Yes	Yes	No
ligolw.process	Yes	Yes	No
ligolw.process_params	Yes	Yes	No
ligolw.search_summary	Yes	Yes	No
ligolw.search_summvars	Yes	Yes	No
ligolw.segment	Yes	Yes	No
ligolw.segment_definer	Yes	Yes	No
ligolw.segment_summary	Yes	Yes	No
ligolw.sim_burst	Yes	Yes	No
ligolw.sim_inspiral	Yes	Yes	No
ligolw.sim_inst_params	Yes	Yes	No
ligolw.sim_ringdown	Yes	Yes	No
ligolw.sngl_burst	Yes	Yes	No
ligolw.sngl_inspiral	Yes	Yes	No
ligolw.sngl_ringdown	Yes	Yes	No
ligolw.stochastic	Yes	Yes	No
ligolw.stochsumm	Yes	Yes	No
ligolw.summ_mime	Yes	Yes	No
ligolw.summ_value	Yes	Yes	No
ligolw.time_slide	Yes	Yes	No
ligolw.time_slide_segment_map	Yes	Yes	No
ligolw.veto_definer	Yes	Yes	No
root	Yes	Yes	Yes
votable	Yes	Yes	Yes
aastex	Yes	Yes	No	Yes
csv	Yes	Yes	Yes	Yes
html	Yes	Yes	No	Yes
ipac	Yes	Yes	No	Yes
latex	Yes	Yes	No	Yes
rdb	Yes	Yes	No	Yes

Deprecated format names like aastex will be removed in a future version. Use the full name (e.g. ascii.aastex) instead.

class Column¶

Bases: astropy.table.column.BaseColumn

Define a data column for use in a Table object.

Parameters:

data : list, ndarray or None Column data values name : str Column name and key for reference within Table dtype : numpy.dtype compatible value Data type for column shape : tuple or () Dimensions of a single row element in the column data length : int or 0 Number of row elements in column data description : str or None Full description of column unit : str or None Physical unit format : str or None or function or callable Format string for outputting column values. This can be an “old-style” (format % value) or “new-style” (str.format) format specification string or a function or any callable object that accepts a single value and returns a string. meta : dict-like or None Meta-data associated with the column

Examples

A Column can be created in two different ways:

• Provide a data value but not shape or length (which are inferred from the data).

  Examples:

  col = Column(data=[1, 2], name='name')  # shape=(2,)
  col = Column(data=[[1, 2], [3, 4]], name='name')  # shape=(2, 2)
  col = Column(data=[1, 2], name='name', dtype=float)
  col = Column(data=np.array([1, 2]), name='name')
  col = Column(data=['hello', 'world'], name='name')
  

  The dtype argument can be any value which is an acceptable fixed-size data-type initializer for the numpy.dtype() method. See http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html. Examples include:

  • Python non-string type (float, int, bool)
  • Numpy non-string type (e.g. np.float32, np.int64, np.bool)
  • Numpy.dtype array-protocol type strings (e.g. ‘i4’, ‘f8’, ‘S15’)

  If no dtype value is provide then the type is inferred using np.array(data).

• Provide length and optionally shape, but not data

  Examples:

  col = Column(name='name', length=5)
  col = Column(name='name', dtype=int, length=10, shape=(3,4))
  

  The default dtype is np.float64. The shape argument is the array shape of a single cell in the column.

convert_unit_to(new_unit, equivalencies=[])¶

Converts the values of the column in-place from the current unit to the given unit.

To change the unit associated with this column without actually changing the data values, simply set the unit property.

Parameters:	new_unit : str or astropy.units.UnitBase instance The unit to convert to. equivalencies : list of equivalence pairs, optional A list of equivalence pairs to try if the unit are not directly convertible. See Equivalencies.
Raises:	astropy.units.UnitsError If units are inconsistent

copy(order=u'C', data=None, copy_data=True)¶

Return a copy of the current instance.

If data is supplied then a view (reference) of data is used, and copy_data is ignored.

Parameters:

order : {‘C’, ‘F’, ‘A’, ‘K’}, optional Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and :func:numpy.copy are very similar, but have different default values for their order= arguments.) Default is ‘C’. data : array, optional If supplied then use a view of data instead of the instance data. This allows copying the instance attributes and meta. copy_data : bool, optional Make a copy of the internal numpy array instead of using a reference. Default is True.

Returns:

col : Column or MaskedColumn Copy of the current column (same type as original)

insert(obj, values, axis=0)¶

Insert values before the given indices in the column and return a new Column object.

Parameters:

obj : int, slice or sequence of ints Object that defines the index or indices before which values is inserted. values : array_like Value(s) to insert. If the type of values is different from that of quantity, values is converted to the matching type. values should be shaped so that it can be broadcast appropriately axis : int, optional Axis along which to insert values. If axis is None then the column array is flattened before insertion. Default is 0, which will insert a row.

Returns:

out : Column A copy of column with values and mask inserted. Note that the insertion does not occur in-place: a new column is returned.

more(max_lines=None, show_name=True, show_unit=False)¶

Interactively browse column with a paging interface.

Supported keys:

f, <space> : forward one page
b : back one page
r : refresh same page
n : next row
p : previous row
< : go to beginning
> : go to end
q : quit browsing
h : print this help

Parameters:	max_lines : int Maximum number of lines in table output. show_name : bool Include a header row for column names. Default is True. show_unit : bool Include a header row for unit. Default is False.

name¶

The name of this column.

pformat(max_lines=None, show_name=True, show_unit=False, show_dtype=False, html=False)¶

Return a list of formatted string representation of column values.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default will be determined using the astropy.conf.max_lines configuration item. If a negative value of max_lines is supplied then there is no line limit applied.

Parameters:	max_lines : int Maximum lines of output (header + data rows) show_name : bool Include column name. Default is True. show_unit : bool Include a header row for unit. Default is False. show_dtype : bool Include column dtype. Default is False. html : bool Format the output as an HTML table. Default is False.
Returns:	lines : list List of lines with header and formatted column values

pprint(max_lines=None, show_name=True, show_unit=False, show_dtype=False)¶

Print a formatted string representation of column values.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default will be determined using the astropy.conf.max_lines configuration item. If a negative value of max_lines is supplied then there is no line limit applied.

Parameters:	max_lines : int Maximum number of values in output show_name : bool Include column name. Default is True. show_unit : bool Include a header row for unit. Default is False. show_dtype : bool Include column dtype. Default is True.

quantity¶

A view of this table column as a Quantity object with units given by the Column’s unit parameter.

to(unit, equivalencies=[], **kwargs)¶

Converts this table column to a Quantity object with the requested units.

Parameters:	unit : Unit or str The unit to convert to (i.e., a valid argument to the astropy.units.Quantity.to() method). equivalencies : list of equivalence pairs, optional Equivalencies to use for this conversion. See astropy.units.Quantity.to() for more details.
Returns:	quantity : Quantity A quantity object with the contents of this column in the units unit.

unit¶

The unit associated with this column. May be a string or a astropy.units.UnitBase instance.

Setting the unit property does not change the values of the data. To perform a unit conversion, use convert_unit_to.

class MaskedColumn¶

Bases: astropy.table.column.Column, astropy.table._column_mixins._MaskedColumnGetitemShim, numpy.ma.core.MaskedArray

Define a masked data column for use in a Table object.

Parameters:

data : list, ndarray or None Column data values name : str Column name and key for reference within Table mask : list, ndarray or None Boolean mask for which True indicates missing or invalid data fill_value : float, int, str or None Value used when filling masked column elements dtype : numpy.dtype compatible value Data type for column shape : tuple or () Dimensions of a single row element in the column data length : int or 0 Number of row elements in column data description : str or None Full description of column unit : str or None Physical unit format : str or None or function or callable Format string for outputting column values. This can be an “old-style” (format % value) or “new-style” (str.format) format specification string or a function or any callable object that accepts a single value and returns a string. meta : dict-like or None Meta-data associated with the column

Examples

A MaskedColumn is similar to a Column except that it includes mask and fill_value attributes. It can be created in two different ways:

• Provide a data value but not shape or length (which are inferred from the data).

  Examples:

  col = MaskedColumn(data=[1, 2], name='name')
  col = MaskedColumn(data=[1, 2], name='name', mask=[True, False])
  col = MaskedColumn(data=[1, 2], name='name', dtype=float, fill_value=99)
  

  The mask argument will be cast as a boolean array and specifies which elements are considered to be missing or invalid.

  The dtype argument can be any value which is an acceptable fixed-size data-type initializer for the numpy.dtype() method. See http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html. Examples include:

  • Python non-string type (float, int, bool)
  • Numpy non-string type (e.g. np.float32, np.int64, np.bool)
  • Numpy.dtype array-protocol type strings (e.g. ‘i4’, ‘f8’, ‘S15’)

  If no dtype value is provide then the type is inferred using np.array(data). When data is provided then the shape and length arguments are ignored.

• Provide length and optionally shape, but not data

  Examples:

  col = MaskedColumn(name='name', length=5)
  col = MaskedColumn(name='name', dtype=int, length=10, shape=(3,4))
  

  The default dtype is np.float64. The shape argument is the array shape of a single cell in the column.

convert_unit_to(new_unit, equivalencies=[])¶

Converts the values of the column in-place from the current unit to the given unit.

To change the unit associated with this column without actually changing the data values, simply set the unit property.

Parameters:	new_unit : str or astropy.units.UnitBase instance The unit to convert to. equivalencies : list of equivalence pairs, optional A list of equivalence pairs to try if the unit are not directly convertible. See Equivalencies.
Raises:	astropy.units.UnitsError If units are inconsistent

copy(order=u'C', data=None, copy_data=True)¶

Return a copy of the current instance.

If data is supplied then a view (reference) of data is used, and copy_data is ignored.

Parameters:

order : {‘C’, ‘F’, ‘A’, ‘K’}, optional Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and :func:numpy.copy are very similar, but have different default values for their order= arguments.) Default is ‘C’. data : array, optional If supplied then use a view of data instead of the instance data. This allows copying the instance attributes and meta. copy_data : bool, optional Make a copy of the internal numpy array instead of using a reference. Default is True.

Returns:

col : Column or MaskedColumn Copy of the current column (same type as original)

filled(fill_value=None)¶

Return a copy of self, with masked values filled with a given value.

Parameters:	fill_value : scalar; optional The value to use for invalid entries (None by default). If None, the fill_value attribute of the array is used instead.
Returns:	filled_column : Column A copy of self with masked entries replaced by fill_value (be it the function argument or the attribute of self).

insert(obj, values, mask=None, axis=0)¶

Insert values along the given axis before the given indices and return a new MaskedColumn object.

Parameters:

obj : int, slice or sequence of ints Object that defines the index or indices before which values is inserted. values : array_like Value(s) to insert. If the type of values is different from that of quantity, values is converted to the matching type. values should be shaped so that it can be broadcast appropriately mask : boolean array_like Mask value(s) to insert. If not supplied then False is used. axis : int, optional Axis along which to insert values. If axis is None then the column array is flattened before insertion. Default is 0, which will insert a row.

Returns:

out : MaskedColumn A copy of column with values and mask inserted. Note that the insertion does not occur in-place: a new masked column is returned.

more(max_lines=None, show_name=True, show_unit=False)¶

Interactively browse column with a paging interface.

Supported keys:

f, <space> : forward one page
b : back one page
r : refresh same page
n : next row
p : previous row
< : go to beginning
> : go to end
q : quit browsing
h : print this help

Parameters:	max_lines : int Maximum number of lines in table output. show_name : bool Include a header row for column names. Default is True. show_unit : bool Include a header row for unit. Default is False.

name¶

The name of this column.

pformat(max_lines=None, show_name=True, show_unit=False, show_dtype=False, html=False)¶

Return a list of formatted string representation of column values.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default will be determined using the astropy.conf.max_lines configuration item. If a negative value of max_lines is supplied then there is no line limit applied.

Parameters:	max_lines : int Maximum lines of output (header + data rows) show_name : bool Include column name. Default is True. show_unit : bool Include a header row for unit. Default is False. show_dtype : bool Include column dtype. Default is False. html : bool Format the output as an HTML table. Default is False.
Returns:	lines : list List of lines with header and formatted column values

pprint(max_lines=None, show_name=True, show_unit=False, show_dtype=False)¶

Print a formatted string representation of column values.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default will be determined using the astropy.conf.max_lines configuration item. If a negative value of max_lines is supplied then there is no line limit applied.

Parameters:	max_lines : int Maximum number of values in output show_name : bool Include column name. Default is True. show_unit : bool Include a header row for unit. Default is False. show_dtype : bool Include column dtype. Default is True.

class Row(table, index)¶

Bases: object

A class to represent one row of a Table object.

A Row object is returned when a Table object is indexed with an integer or when iterating over a table:

>>> from astropy.table import Table
>>> table = Table([(1, 2), (3, 4)], names=('a', 'b'),
...               dtype=('int32', 'int32'))
>>> row = table[1]
>>> row
<Row index=1>
  a     b
int32 int32
----- -----
    2     4
>>> row['a']
2
>>> row[1]
4

as_void()¶

Returns a read-only copy of the row values in the form of np.void or np.ma.mvoid objects. This corresponds to the object types returned for row indexing of a pure numpy structured array or masked array. This method is slow and its use is discouraged when possible.

Returns:	void_row : np.void (unmasked) or np.ma.mvoid (masked) Copy of row values

class TableColumns(cols={})¶

Bases: collections.OrderedDict

OrderedDict subclass for a set of columns.

This class enhances item access to provide convenient access to columns by name or index, including slice access. It also handles renaming of columns.

The initialization argument cols can be a list of Column objects or any structure that is valid for initializing a Python dict. This includes a dict, list of (key, val) tuples or [key, val] lists, etc.

Parameters:	cols : dict, list, tuple; optional Column objects as data structure that can init dict (see above)

isinstance(cls)¶

Return a list of columns which are instances of the specified classes.

Parameters:	cls : class or tuple of classes Column class (including mixin) or tuple of Column classes.
Returns:	col_list : list of Columns List of Column objects which are instances of given classes.

not_isinstance(cls)¶

Return a list of columns which are not instances of the specified classes.

Parameters:	cls : class or tuple of classes Column class (including mixin) or tuple of Column classes.
Returns:	col_list : list of Columns List of Column objects which are not instances of given classes.

add_column(col, index=None, name=None, rename_duplicate=False)

Add a new Column object col to the table. If index is supplied then insert column before index position in the list of columns, otherwise append column to the end of the list.

Parameters:	col : Column Column object to add. index : int or None Insert column before this position or at end (default). name : str Column name rename_duplicate : bool Uniquify column name if it already exist. Default is False.

Examples

Create a table with two columns ‘a’ and ‘b’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> print(t)
 a   b
--- ---
  1 0.1
  2 0.2
  3 0.3

Create a third column ‘c’ and append it to the end of the table:

>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> t.add_column(col_c)
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Add column ‘d’ at position 1. Note that the column is inserted before the given index:

>>> col_d = Column(name='d', data=['a', 'b', 'c'])
>>> t.add_column(col_d, 1)
>>> print(t)
 a   d   b   c
--- --- --- ---
  1   a 0.1   x
  2   b 0.2   y
  3   c 0.3   z

Add second column named ‘b’ with rename_duplicate:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> col_b = Column(name='b', data=[1.1, 1.2, 1.3])
>>> t.add_column(col_b, rename_duplicate=True)
>>> print(t)
 a   b  b_1
--- --- ---
  1 0.1 1.1
  2 0.2 1.2
  3 0.3 1.3

Add an unnamed column or mixin object in the table using a default name or by specifying an explicit name with name. Name can also be overridden:

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> col_c = Column(data=['x', 'y'])
>>> t.add_column(col_c)
>>> t.add_column(col_c, name='c')
>>> col_b = Column(name='b', data=[1.1, 1.2])
>>> t.add_column(col_b, name='d')
>>> print(t)
 a   b  col2  c   d
--- --- ---- --- ---
  1 0.1    x   x 1.1
  2 0.2    y   y 1.2

To add several columns use add_columns.

add_columns(cols, indexes=None, names=None, copy=True, rename_duplicate=False)

Add a list of new Column objects cols to the table. If a corresponding list of indexes is supplied then insert column before each index position in the original list of columns, otherwise append columns to the end of the list.

Parameters:	cols : list of Columns Column objects to add. indexes : list of ints or None Insert column before this position or at end (default). names : list of str Column names copy : bool Make a copy of the new columns. Default is True. rename_duplicate : bool Uniquify new column names if they duplicate the existing ones. Default is False.

Examples

Create a table with two columns ‘a’ and ‘b’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> print(t)
 a   b
--- ---
  1 0.1
  2 0.2
  3 0.3

Create column ‘c’ and ‘d’ and append them to the end of the table:

>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> col_d = Column(name='d', data=['u', 'v', 'w'])
>>> t.add_columns([col_c, col_d])
>>> print(t)
 a   b   c   d
--- --- --- ---
  1 0.1   x   u
  2 0.2   y   v
  3 0.3   z   w

Add column ‘c’ at position 0 and column ‘d’ at position 1. Note that the columns are inserted before the given position:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> col_d = Column(name='d', data=['u', 'v', 'w'])
>>> t.add_columns([col_c, col_d], [0, 1])
>>> print(t)
 c   a   d   b
--- --- --- ---
  x   1   u 0.1
  y   2   v 0.2
  z   3   w 0.3

Add second column ‘b’ and column ‘c’ with rename_duplicate:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> col_b = Column(name='b', data=[1.1, 1.2, 1.3])
>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> t.add_columns([col_b, col_c], rename_duplicate=True)
>>> print(t)
 a   b  b_1  c
--- --- --- ---
  1 0.1 1.1  x
  2 0.2 1.2  y
  3 0.3 1.3  z

Add unnamed columns or mixin objects in the table using default names or by specifying explicit names with names. Names can also be overridden:

>>> t = Table()
>>> col_a = Column(data=['x', 'y'])
>>> col_b = Column(name='b', data=['u', 'v'])
>>> t.add_columns([col_a, col_b])
>>> t.add_columns([col_a, col_b], names=['c', 'd'])
>>> print(t)
col0  b   c   d
---- --- --- ---
   x   u   x   u
   y   v   y   v

add_index(colnames, engine=None, unique=False)

Insert a new index among one or more columns. If there are no indices, make this index the primary table index.

Parameters:	colnames : str or list List of column names (or a single column name) to index engine : type or None Indexing engine class to use, from among SortedArray, BST, FastBST, and FastRBT. If the supplied argument is None (by default), use SortedArray. unique : bool Whether the values of the index must be unique. Default is False.

add_row(vals=None, mask=None)

Add a new row to the end of the table.

The vals argument can be:

sequence (e.g. tuple or list)

Column values in the same order as table columns.

mapping (e.g. dict)

Keys corresponding to column names. Missing values will be filled with np.zeros for the column dtype.

None

All values filled with np.zeros for the column dtype.

This method requires that the Table object “owns” the underlying array data. In particular one cannot add a row to a Table that was initialized with copy=False from an existing array.

The mask attribute should give (if desired) the mask for the values. The type of the mask should match that of the values, i.e. if vals is an iterable, then mask should also be an iterable with the same length, and if vals is a mapping, then mask should be a dictionary.

Parameters:	vals : tuple, list, dict or None Use the specified values in the new row mask : tuple, list, dict or None Use the specified mask values in the new row

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1,2],[4,5],[7,8]], names=('a','b','c'))
>>> print(t)
 a   b   c
--- --- ---
  1   4   7
  2   5   8

Adding a new row with entries ‘3’ in ‘a’, ‘6’ in ‘b’ and ‘9’ in ‘c’:

>>> t.add_row([3,6,9])
>>> print(t)
  a   b   c
  --- --- ---
  1   4   7
  2   5   8
  3   6   9

argsort(keys=None, kind=None)

Return the indices which would sort the table according to one or more key columns. This simply calls the numpy.argsort function on the table with the order parameter set to keys.

Parameters:	keys : str or list of str The column name(s) to order the table by kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, optional Sorting algorithm.
Returns:	index_array : ndarray, int Array of indices that sorts the table by the specified key column(s).

as_array(keep_byteorder=False)

Return a new copy of the table in the form of a structured np.ndarray or np.ma.MaskedArray object (as appropriate).

Parameters:	keep_byteorder : bool, optional By default the returned array has all columns in native byte order. However, if this option is True this preserves the byte order of all columns (if any are non-native).
Returns:	table_array : np.ndarray (unmasked) or np.ma.MaskedArray (masked) Copy of table as a numpy structured array

convert_bytestring_to_unicode(python3_only=False)

Convert bytestring columns (dtype.kind=’S’) to unicode (dtype.kind=’U’) assuming ASCII encoding.

Internally this changes string columns to represent each character in the string with a 4-byte UCS-4 equivalent, so it is inefficient for memory but allows Python 3 scripts to manipulate string arrays with natural syntax.

The python3_only parameter is provided as a convenience so that code can be written in a Python 2 / 3 compatible way:

>>> t = Table.read('my_data.fits')
>>> t.convert_bytestring_to_unicode(python3_only=True)

Parameters:	python3_only : bool Only do this operation for Python 3

convert_unicode_to_bytestring(python3_only=False)

Convert ASCII-only unicode columns (dtype.kind=’U’) to bytestring (dtype.kind=’S’).

When exporting a unicode string array to a file in Python 3, it may be desirable to encode unicode columns as bytestrings. This routine takes advantage of numpy automated conversion which works for strings that are pure ASCII.

The python3_only parameter is provided as a convenience so that code can be written in a Python 2 / 3 compatible way:

>>> t.convert_unicode_to_bytestring(python3_only=True)
>>> t.write('my_data.fits')

Parameters:	python3_only : bool Only do this operation for Python 3

copy(copy_data=True)

Return a copy of the table.

Parameters:	copy_data : bool If True (the default), copy the underlying data array. Otherwise, use the same data array .. note:: The meta is always deepcopied regardless of the value for copy_data.

field(item)

Return column[item] for recarray compatibility.

filled(fill_value=None)

Return a copy of self, with masked values filled.

If input fill_value supplied then that value is used for all masked entries in the table. Otherwise the individual fill_value defined for each table column is used.

Parameters:	fill_value : str If supplied, this fill_value is used for all masked entries in the entire table.
Returns:	filled_table : Table New table with masked values filled

classmethod from_pandas(dataframe)

Create a Table from a pandas.DataFrame instance

Parameters:	dataframe : pandas.DataFrame The pandas pandas.DataFrame instance
Returns:	table : Table A Table (or subclass) instance

group_by(keys)

Group this table by the specified keys

This effectively splits the table into groups which correspond to unique values of the keys grouping object. The output is a new TableGroups which contains a copy of this table but sorted by row according to keys.

The keys input to group_by can be specified in different ways:

• String or list of strings corresponding to table column name(s)
• Numpy array (homogeneous or structured) with same length as this table
• Table with same length as this table

Parameters:	keys : str, list of str, numpy array, or Table Key grouping object
Returns:	out : Table New table with groups set

has_mixin_columns¶

True if table has any mixin columns (defined as columns that are not Column subclasses).

iloc¶

Return a TableILoc object that can be used for retrieving indexed rows in the order they appear in the index.

index_column(name)

Return the positional index of column name.

Parameters:	name : str column name
Returns:	index : int Positional index of column name.

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Get index of column ‘b’ of the table:

>>> t.index_column('b')
1

index_mode(mode)

Return a context manager for an indexing mode.

Parameters:

mode : str Either ‘freeze’, ‘copy_on_getitem’, or ‘discard_on_copy’. In ‘discard_on_copy’ mode, indices are not copied whenever columns or tables are copied. In ‘freeze’ mode, indices are not modified whenever columns are modified; at the exit of the context, indices refresh themselves based on column values. This mode is intended for scenarios in which one intends to make many additions or modifications in an indexed column. In ‘copy_on_getitem’ mode, indices are copied when taking column slices as well as table slices, so col[i0:i1] will preserve indices.

indices¶

Return the indices associated with columns of the table as a TableIndices object.

insert_row(index, vals=None, mask=None)

Add a new row before the given index position in the table.

The vals argument can be:

sequence (e.g. tuple or list)

Column values in the same order as table columns.

mapping (e.g. dict)

Keys corresponding to column names. Missing values will be filled with np.zeros for the column dtype.

None

All values filled with np.zeros for the column dtype.

The mask attribute should give (if desired) the mask for the values. The type of the mask should match that of the values, i.e. if vals is an iterable, then mask should also be an iterable with the same length, and if vals is a mapping, then mask should be a dictionary.

Parameters:	vals : tuple, list, dict or None Use the specified values in the new row mask : tuple, list, dict or None Use the specified mask values in the new row

itercols()

Iterate over the columns of this table.

Examples

To iterate over the columns of a table:

>>> t = Table([[1], [2]])
>>> for col in t.itercols():
...     print(col)
col0
----
   1
col1
----
   2

Using itercols() is similar to for col in t.columns.values() but is syntactically preferred.

keep_columns(names)

Keep only the columns specified (remove the others).

Parameters:	names : list A list containing the names of the columns to keep. All other columns will be removed.

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Specifying only a single column name keeps only this column. Keep only column ‘a’ of the table:

>>> t.keep_columns('a')
>>> print(t)
 a
---
  1
  2
  3

Specifying a list of column names is keeps is also possible. Keep columns ‘a’ and ‘c’ of the table:

>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> t.keep_columns(['a', 'c'])
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

loc¶

Return a TableLoc object that can be used for retrieving rows by index in a given data range. Note that both loc and iloc work only with single-column indices.

more(max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False)

Interactively browse table with a paging interface.

Supported keys:

f, <space> : forward one page
b : back one page
r : refresh same page
n : next row
p : previous row
< : go to beginning
> : go to end
q : quit browsing
h : print this help

Parameters:

max_lines : int Maximum number of lines in table output max_width : int or None Maximum character width of output show_name : bool Include a header row for column names. Default is True. show_unit : bool Include a header row for unit. Default is to show a row for units only if one or more columns has a defined value for the unit. show_dtype : bool Include a header row for column dtypes. Default is True.

pformat(max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False, html=False, tableid=None, align=None, tableclass=None)

Return a list of lines for the formatted string representation of the table.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default is taken from the configuration item astropy.conf.max_lines. If a negative value of max_lines is supplied then there is no line limit applied.

The same applies for max_width except the configuration item is astropy.conf.max_width.

Parameters:

max_lines : int or None Maximum number of rows to output max_width : int or None Maximum character width of output show_name : bool Include a header row for column names. Default is True. show_unit : bool Include a header row for unit. Default is to show a row for units only if one or more columns has a defined value for the unit. show_dtype : bool Include a header row for column dtypes. Default is True. html : bool Format the output as an HTML table. Default is False. tableid : str or None An ID tag for the table; only used if html is set. Default is “table{id}”, where id is the unique integer id of the table object, id(self) align : str or list or tuple or None Left/right alignment of columns. Default is right (None) for all columns. Other allowed values are ‘>’, ‘<’, ‘^’, and ‘0=’ for right, left, centered, and 0-padded, respectively. A list of strings can be provided for alignment of tables with multiple columns. tableclass : str or list of str or None CSS classes for the table; only used if html is set. Default is None.

Returns:

lines : list Formatted table as a list of strings.

pprint(max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False, align=None)

Print a formatted string representation of the table.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default is taken from the configuration item astropy.conf.max_lines. If a negative value of max_lines is supplied then there is no line limit applied.

The same applies for max_width except the configuration item is astropy.conf.max_width.

Parameters:

max_lines : int Maximum number of lines in table output. max_width : int or None Maximum character width of output. show_name : bool Include a header row for column names. Default is True. show_unit : bool Include a header row for unit. Default is to show a row for units only if one or more columns has a defined value for the unit. show_dtype : bool Include a header row for column dtypes. Default is True. align : str or list or tuple or None Left/right alignment of columns. Default is right (None) for all columns. Other allowed values are ‘>’, ‘<’, ‘^’, and ‘0=’ for right, left, centered, and 0-padded, respectively. A list of strings can be provided for alignment of tables with multiple columns.

classmethod read(*args, **kwargs)

Read and parse a data table and return as a Table.

This function provides the Table interface to the astropy unified I/O layer. This allows easily reading a file in many supported data formats using syntax such as:

>>> from astropy.table import Table
>>> dat = Table.read('table.dat', format='ascii')
>>> events = Table.read('events.fits', format='fits')

The arguments and keywords (other than format) provided to this function are passed through to the underlying data reader (e.g. read).

The available built-in formats are:

Format	Read	Write	Auto-identify	Deprecated
ascii	Yes	Yes	No
ascii.aastex	Yes	Yes	No
ascii.basic	Yes	Yes	No
ascii.cds	Yes	No	No
ascii.commented_header	Yes	Yes	No
ascii.csv	Yes	Yes	No
ascii.cwb	Yes	Yes	No
ascii.daophot	Yes	No	No
ascii.ecsv	Yes	Yes	No
ascii.fast_basic	Yes	Yes	No
ascii.fast_commented_header	Yes	Yes	No
ascii.fast_csv	Yes	Yes	No
ascii.fast_no_header	Yes	Yes	No
ascii.fast_rdb	Yes	Yes	No
ascii.fast_tab	Yes	Yes	No
ascii.fixed_width	Yes	Yes	No
ascii.fixed_width_no_header	Yes	Yes	No
ascii.fixed_width_two_line	Yes	Yes	No
ascii.html	Yes	Yes	Yes
ascii.ipac	Yes	Yes	No
ascii.latex	Yes	Yes	Yes
ascii.no_header	Yes	Yes	No
ascii.omega	Yes	Yes	No
ascii.rdb	Yes	Yes	Yes
ascii.rst	Yes	Yes	No
ascii.sextractor	Yes	No	No
ascii.tab	Yes	Yes	No
fits	Yes	Yes	Yes
gwf	Yes	No	No
hdf5	Yes	Yes	Yes
ligolw	Yes	Yes	Yes
ligolw.coinc_definer	Yes	Yes	No
ligolw.coinc_event	Yes	Yes	No
ligolw.coinc_event_map	Yes	Yes	No
ligolw.coinc_inspiral	Yes	Yes	No
ligolw.coinc_ringdown	Yes	Yes	No
ligolw.dq_list	Yes	Yes	No
ligolw.experiment	Yes	Yes	No
ligolw.experiment_map	Yes	Yes	No
ligolw.experiment_summary	Yes	Yes	No
ligolw.external_trigger	Yes	Yes	No
ligolw.filter	Yes	Yes	No
ligolw.gds_trigger	Yes	Yes	No
ligolw.lfn	Yes	Yes	No
ligolw.ligolw_mon	Yes	Yes	No
ligolw.multi_burst	Yes	Yes	No
ligolw.multi_inspiral	Yes	Yes	No
ligolw.process	Yes	Yes	No
ligolw.process_params	Yes	Yes	No
ligolw.search_summary	Yes	Yes	No
ligolw.search_summvars	Yes	Yes	No
ligolw.segment	Yes	Yes	No
ligolw.segment_definer	Yes	Yes	No
ligolw.segment_summary	Yes	Yes	No
ligolw.sim_burst	Yes	Yes	No
ligolw.sim_inspiral	Yes	Yes	No
ligolw.sim_inst_params	Yes	Yes	No
ligolw.sim_ringdown	Yes	Yes	No
ligolw.sngl_burst	Yes	Yes	No
ligolw.sngl_inspiral	Yes	Yes	No
ligolw.sngl_ringdown	Yes	Yes	No
ligolw.stochastic	Yes	Yes	No
ligolw.stochsumm	Yes	Yes	No
ligolw.summ_mime	Yes	Yes	No
ligolw.summ_value	Yes	Yes	No
ligolw.time_slide	Yes	Yes	No
ligolw.time_slide_segment_map	Yes	Yes	No
ligolw.veto_definer	Yes	Yes	No
root	Yes	Yes	Yes
votable	Yes	Yes	Yes
aastex	Yes	Yes	No	Yes
cds	Yes	No	No	Yes
csv	Yes	Yes	Yes	Yes
daophot	Yes	No	No	Yes
html	Yes	Yes	No	Yes
ipac	Yes	Yes	No	Yes
latex	Yes	Yes	No	Yes
rdb	Yes	Yes	No	Yes

Deprecated format names like aastex will be removed in a future version. Use the full name (e.g. ascii.aastex) instead.

remove_column(name)

Remove a column from the table.

This can also be done with:

del table[name]

Parameters:	name : str Name of column to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove column ‘b’ from the table:

>>> t.remove_column('b')
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

To remove several columns at the same time use remove_columns.

remove_columns(names)

Remove several columns from the table.

Parameters:	names : list A list containing the names of the columns to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...     names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove columns ‘b’ and ‘c’ from the table:

>>> t.remove_columns(['b', 'c'])
>>> print(t)
 a
---
  1
  2
  3

Specifying only a single column also works. Remove column ‘b’ from the table:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...     names=('a', 'b', 'c'))
>>> t.remove_columns('b')
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

This gives the same as using remove_column.

remove_indices(colname)

Remove all indices involving the given column. If the primary index is removed, the new primary index will be the most recently added remaining index.

Parameters:	colname : str Name of column

remove_row(index)

Remove a row from the table.

Parameters:	index : int Index of row to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove row 1 from the table:

>>> t.remove_row(1)
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  3 0.3   z

To remove several rows at the same time use remove_rows.

remove_rows(row_specifier)

Remove rows from the table.

Parameters:	row_specifier : slice, int, or array of ints Specification for rows to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove rows 0 and 2 from the table:

>>> t.remove_rows([0, 2])
>>> print(t)
 a   b   c
--- --- ---
  2 0.2   y

Note that there are no warnings if the slice operator extends outside the data:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> t.remove_rows(slice(10, 20, 1))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

rename_column(name, new_name)

Rename a column.

This can also be done directly with by setting the name attribute for a column:

table[name].name = new_name

TODO: this won’t work for mixins

Parameters:	name : str The current name of the column. new_name : str The new name for the column

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1,2],[3,4],[5,6]], names=('a','b','c'))
>>> print(t)
 a   b   c
--- --- ---
  1   3   5
  2   4   6

Renaming column ‘a’ to ‘aa’:

>>> t.rename_column('a' , 'aa')
>>> print(t)
 aa  b   c
--- --- ---
  1   3   5
  2   4   6

replace_column(name, col)

Replace column name with the new col object.

Parameters:	name : str Name of column to replace col : column object (list, ndarray, Column, etc) New column object to replace the existing column

Examples

Replace column ‘a’ with a float version of itself:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> float_a = t['a'].astype(float)
>>> t.replace_column('a', float_a)

reverse()

Reverse the row order of table rows. The table is reversed in place and there are no function arguments.

Examples

Create a table with three columns:

>>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'],
...         [12,15,18]], names=('firstname','name','tel'))
>>> print(t)
firstname   name  tel
--------- ------- ---
      Max  Miller  12
       Jo  Miller  15
     John Jackson  18

Reversing order:

>>> t.reverse()
>>> print(t)
firstname   name  tel
--------- ------- ---
     John Jackson  18
       Jo  Miller  15
      Max  Miller  12

show_in_browser(max_lines=5000, jsviewer=False, browser=u'default', jskwargs={u'use_local_files': True}, tableid=None, table_class=u'display compact', css=None, show_row_index=u'idx')

Render the table in HTML and show it in a web browser.

Parameters:

max_lines : int Maximum number of rows to export to the table (set low by default to avoid memory issues, since the browser view requires duplicating the table in memory). A negative value of max_lines indicates no row limit. jsviewer : bool If True, prepends some javascript headers so that the table is rendered as a DataTables data table. This allows in-browser searching & sorting. browser : str Any legal browser name, e.g. 'firefox', 'chrome', 'safari' (for mac, you may need to use 'open -a "/Applications/Google Chrome.app" {}' for Chrome). If 'default', will use the system default browser. jskwargs : dict Passed to the astropy.table.JSViewer init. Defaults to {'use_local_files': True} which means that the JavaScript libraries will be served from local copies. tableid : str or None An html ID tag for the table. Default is table{id}, where id is the unique integer id of the table object, id(self). table_class : str or None A string with a list of HTML classes used to style the table. Default is “display compact”, and other possible values can be found in http://www.datatables.net/manual/styling/classes css : string A valid CSS string declaring the formatting for the table. Defaults to astropy.table.jsviewer.DEFAULT_CSS. show_row_index : str or False If this does not evaluate to False, a column with the given name will be added to the version of the table that gets displayed. This new column shows the index of the row in the table itself, even when the displayed table is re-sorted by another column. Note that if a column with this name already exists, this option will be ignored. Defaults to “idx”.

show_in_notebook(tableid=None, css=None, display_length=50, table_class=u'astropy-default', show_row_index=u'idx')

Render the table in HTML and show it in the IPython notebook.

Parameters:

tableid : str or None An html ID tag for the table. Default is table{id}-XXX, where id is the unique integer id of the table object, id(self), and XXX is a random number to avoid conflicts when printing the same table multiple times. table_class : str or None A string with a list of HTML classes used to style the table. The special default string (‘astropy-default’) means that the string will be retrieved from the configuration item astropy.table.default_notebook_table_class. Note that these table classes may make use of bootstrap, as this is loaded with the notebook. See this page for the list of classes. css : string A valid CSS string declaring the formatting for the table. Defaults to astropy.table.jsviewer.DEFAULT_CSS_NB. display_length : int, optional Number or rows to show. Defaults to 50. show_row_index : str or False If this does not evaluate to False, a column with the given name will be added to the version of the table that gets displayed. This new column shows the index of the row in the table itself, even when the displayed table is re-sorted by another column. Note that if a column with this name already exists, this option will be ignored. Defaults to “idx”.

Notes

Currently, unlike show_in_browser (with jsviewer=True), this method needs to access online javascript code repositories. This is due to modern browsers’ limitations on accessing local files. Hence, if you call this method while offline (and don’t have a cached version of jquery and jquery.dataTables), you will not get the jsviewer features.

sort(keys=None)

Sort the table according to one or more keys. This operates on the existing table and does not return a new table.

Parameters:	keys : str or list of str The key(s) to order the table by. If None, use the primary index of the Table.

Examples

Create a table with 3 columns:

>>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'],
...         [12,15,18]], names=('firstname','name','tel'))
>>> print(t)
firstname   name  tel
--------- ------- ---
      Max  Miller  12
       Jo  Miller  15
     John Jackson  18

Sorting according to standard sorting rules, first ‘name’ then ‘firstname’:

>>> t.sort(['name','firstname'])
>>> print(t)
firstname   name  tel
--------- ------- ---
     John Jackson  18
       Jo  Miller  15
      Max  Miller  12

to_pandas()

Return a pandas.DataFrame instance

Returns:	dataframe : pandas.DataFrame A pandas pandas.DataFrame instance
Raises:	ImportError If pandas is not installed ValueError If the Table contains mixin or multi-dimensional columns

write(*args, **kwargs)

Write this Table object out in the specified format.

This function provides the Table interface to the astropy unified I/O layer. This allows easily writing a file in many supported data formats using syntax such as:

>>> from astropy.table import Table
>>> dat = Table([[1, 2], [3, 4]], names=('a', 'b'))
>>> dat.write('table.dat', format='ascii')

The arguments and keywords (other than format) provided to this function are passed through to the underlying data reader (e.g. write).

The available built-in formats are:

Format	Read	Write	Auto-identify	Deprecated
ascii	Yes	Yes	No
ascii.aastex	Yes	Yes	No
ascii.basic	Yes	Yes	No
ascii.commented_header	Yes	Yes	No
ascii.csv	Yes	Yes	No
ascii.cwb	Yes	Yes	No
ascii.ecsv	Yes	Yes	No
ascii.fast_basic	Yes	Yes	No
ascii.fast_commented_header	Yes	Yes	No
ascii.fast_csv	Yes	Yes	No
ascii.fast_no_header	Yes	Yes	No
ascii.fast_rdb	Yes	Yes	No
ascii.fast_tab	Yes	Yes	No
ascii.fixed_width	Yes	Yes	No
ascii.fixed_width_no_header	Yes	Yes	No
ascii.fixed_width_two_line	Yes	Yes	No
ascii.html	Yes	Yes	Yes
ascii.ipac	Yes	Yes	No
ascii.latex	Yes	Yes	Yes
ascii.no_header	Yes	Yes	No
ascii.omega	Yes	Yes	No
ascii.rdb	Yes	Yes	Yes
ascii.rst	Yes	Yes	No
ascii.tab	Yes	Yes	No
fits	Yes	Yes	Yes
gwf	Yes	Yes	No
hdf5	Yes	Yes	Yes
jsviewer	No	Yes	No
ligolw	Yes	Yes	Yes
ligolw.coinc_definer	Yes	Yes	No
ligolw.coinc_event	Yes	Yes	No
ligolw.coinc_event_map	Yes	Yes	No
ligolw.coinc_inspiral	Yes	Yes	No
ligolw.coinc_ringdown	Yes	Yes	No
ligolw.dq_list	Yes	Yes	No
ligolw.experiment	Yes	Yes	No
ligolw.experiment_map	Yes	Yes	No
ligolw.experiment_summary	Yes	Yes	No
ligolw.external_trigger	Yes	Yes	No
ligolw.filter	Yes	Yes	No
ligolw.gds_trigger	Yes	Yes	No
ligolw.lfn	Yes	Yes	No
ligolw.ligolw_mon	Yes	Yes	No
ligolw.multi_burst	Yes	Yes	No
ligolw.multi_inspiral	Yes	Yes	No
ligolw.process	Yes	Yes	No
ligolw.process_params	Yes	Yes	No
ligolw.search_summary	Yes	Yes	No
ligolw.search_summvars	Yes	Yes	No
ligolw.segment	Yes	Yes	No
ligolw.segment_definer	Yes	Yes	No
ligolw.segment_summary	Yes	Yes	No
ligolw.sim_burst	Yes	Yes	No
ligolw.sim_inspiral	Yes	Yes	No
ligolw.sim_inst_params	Yes	Yes	No
ligolw.sim_ringdown	Yes	Yes	No
ligolw.sngl_burst	Yes	Yes	No
ligolw.sngl_inspiral	Yes	Yes	No
ligolw.sngl_ringdown	Yes	Yes	No
ligolw.stochastic	Yes	Yes	No
ligolw.stochsumm	Yes	Yes	No
ligolw.summ_mime	Yes	Yes	No
ligolw.summ_value	Yes	Yes	No
ligolw.time_slide	Yes	Yes	No
ligolw.time_slide_segment_map	Yes	Yes	No
ligolw.veto_definer	Yes	Yes	No
root	Yes	Yes	Yes
votable	Yes	Yes	Yes
aastex	Yes	Yes	No	Yes
csv	Yes	Yes	Yes	Yes
html	Yes	Yes	No	Yes
ipac	Yes	Yes	No	Yes
latex	Yes	Yes	No	Yes
rdb	Yes	Yes	No	Yes

Deprecated format names like aastex will be removed in a future version. Use the full name (e.g. ascii.aastex) instead.