<no title>

InitialCMCTable

class cosmic.sample.initialcmctable.InitialCMCTable(data=None, index: Axes | None = None, columns: Axes | None = None, dtype: Dtype | None = None, copy: bool | None = None)

Bases: DataFrame

classmethod InitialCMCBinaries(index, id1, k1, m1, Reff1, id2, k2, m2, Reff2, a, e)

Create A Table of CMC Binaries

Parameters:

m1float

Primary mass [Msun]

m2float

Secondary mass [Msun]

porbfloat

Orbital period [days]

eccfloat

Eccentricity

kstar1array

0-14 Initial stellar type of the larger object; main sequence stars are 0 if m < 0.7 Msun and 1 otherwise

kstar2array

0-14 Initial stellar type of the smaller object; main sequence stars are 0 if m < 0.7 Msun and 1 otherwise

metallicityfloat

Metallicity of the binaries; Z_sun = 0.02

**kwargs

binfracfloat: System-specific probability of the primary star being in a binary

Returns:

InitialBinariesDataFrame: Single binary initial conditions

classmethod InitialCMCSingles(id_idx, k, m, Reff, r, vr, vt, binind)

Create A Table of CMC Singles

Parameters:

m1float

Primary mass [Msun]

m2float

Secondary mass [Msun]

porbfloat

Orbital period [days]

eccfloat

Eccentricity

kstar1array

0-14 Initial stellar type of the larger object; main sequence stars are 0 if m < 0.7 Msun and 1 otherwise

kstar2array

0-14 Initial stellar type of the smaller object; main sequence stars are 0 if m < 0.7 Msun and 1 otherwise

metallicityfloat

Metallicity of the binaries; Z_sun = 0.02

**kwargs

binfracfloat: System-specific probability of the primary star being in a binary

Returns:

InitialBinariesDataFrame: Single binary initial conditions

ScaleCentralBHMass(Mtotal)

Rescale the central BH mass; needed since this is a class attribute Parameters ———-

Mtotalfloat: total mass of the cluster

classmethod ScaleToNBodyUnits(Singles, Binaries, virial_radius=1, central_bh=0, scale_with_central_bh=False)

Rescale the single masses, radii, and velocities into N-body units

i.e. sum m = M = 1: Kinetic Energy = 0.25 Potential Energy = -0.5

Note that this is already done for r, vr, and vt from the profile generators. However, after the stellar masses are assigned we need to redo it, and the stellar radii and seperations need to be converted from RSUN to code units

Parameters:

SinglesDataFrame: Pandas DataFrame from the InitialCMCSingles function
BinariesDataFrame: Pandas DataFrame from the InitialCMCSingles function
virial_radiusfloat: Virial radius of the cluster in parsec (default 1pc)

Returns:

None: Pandas dataframes are modified in place

__annotations__ = {}

__doc__ = None

__module__ = 'cosmic.sample.initialcmctable'

central_bh = 0.0

mass_of_cluster = None

metallicity = None

classmethod read(filename)

Read Singles and Binaries to HDF5 or FITS file

Parameters:

filename(str): Must end in “.fits” or “.hdf5/h5”

Returns:

SinglesDataFrame: Pandas DataFrame from the InitialCMCSingles function
BinariesDataFrame: Pandas DataFrame from the InitialCMCBinaries function

classmethod sampler(format_, *args, **kwargs)

Fetch a method to generate an initial binary sample

Parameters:

formatstr: the method name; Choose from ‘independent’ or ‘multidim’
*args: the arguments necessary for the registered sample method; see help(InitialCMCTable.sampler(‘independent’) to see the arguments necessary for the independent sample
The available named formats are:
============== …: Format …
============== …: cmc …
cmc_point_mass …
============== …

scale_with_central_bh = False

scaled_to_nbody_units = False

tidal_radius = None

virial_radius = None

classmethod write(Singles, Binaries, filename='input.hdf5', **kwargs)

Save Singles and Binaries to HDF5 or FITS file

Parameters:

SinglesDataFrame: Pandas DataFrame from the InitialCMCSingles function
BinariesDataFrame: Pandas DataFrame from the InitialCMCBinaries function
filename(str): Must end in “.fits” or “.hdf5/h5”

Returns:

None:

class cosmic.sample.initialcmctable.Table(data=None, masked=False, names=None, dtype=None, meta=None, copy=True, rows=None, copy_indices=True, units=None, descriptions=None, **kwargs)

Bases: object

A class to represent tables of heterogeneous data.

~astropy.table.Table provides a class for heterogeneous tabular data. A key enhancement provided by the ~astropy.table.Table class over e.g. a numpy structured array 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.

~astropy.table.Table differs from ~astropy.nddata.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:

datanumpy ndarray, dict, list, table-like object, optional: Data to initialize table.
maskedbool, optional: Specify whether the table is masked.
nameslist, optional: Specify column names.
dtypelist, optional: Specify column data types.
metadict, optional: Metadata associated with the table.
copybool, optional: Copy the input column data and make a deep copy of the input meta. Default is True.
rowsnumpy ndarray, list of list, optional: Row-oriented data for table instead of data argument.
copy_indicesbool, optional: Copy any indices in the input data. Default is True.
unitslist, dict, optional: List or dict of units to apply to columns.
descriptionslist, dict, optional: List or dict of descriptions to apply to columns.
**kwargsdict, optional: Additional keyword args when converting table-like object.

class Column(data=None, name=None, dtype=None, shape=(), length=0, description=None, unit=None, format=None, meta=None, copy=False, copy_indices=True)

Bases: BaseColumn

Define a data column for use in a Table object.

Parameters:

datalist, ndarray, or None: Column data values
namestr: Column name and key for reference within Table
dtype~numpy.dtype-like: Data type for column
shapetuple or (): Dimensions of a single row element in the column data
lengthint or 0: Number of row elements in column data
descriptionstr or None: Full description of column
unitstr or None: Physical unit
formatstr, None, 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.
metadict-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 https://numpy.org/doc/stable/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.

To access the Column data as a raw numpy.ndarray object, you can use one of the data or value attributes (which are equivalent):

col.data
col.value

__bytes__()

__doc__ = 'Define a data column for use in a Table object.\n\n Parameters\n ----------\n data : list, ndarray, or None\n Column data values\n name : str\n Column name and key for reference within Table\n dtype : `~numpy.dtype`-like\n Data type for column\n shape : tuple or ()\n Dimensions of a single row element in the column data\n length : int or 0\n Number of row elements in column data\n description : str or None\n Full description of column\n unit : str or None\n Physical unit\n format : str, None, or callable\n Format string for outputting column values. This can be an\n "old-style" (``format % value``) or "new-style" (`str.format`)\n format specification string or a function or any callable object that\n accepts a single value and returns a string.\n meta : dict-like or None\n Meta-data associated with the column\n\n Examples\n --------\n A Column can be created in two different ways:\n\n - Provide a ``data`` value but not ``shape`` or ``length`` (which are\n inferred from the data).\n\n Examples::\n\n col = Column(data=[1, 2], name=\'name\') # shape=(2,)\n col = Column(data=[[1, 2], [3, 4]], name=\'name\') # shape=(2, 2)\n col = Column(data=[1, 2], name=\'name\', dtype=float)\n col = Column(data=np.array([1, 2]), name=\'name\')\n col = Column(data=[\'hello\', \'world\'], name=\'name\')\n\n The ``dtype`` argument can be any value which is an acceptable\n fixed-size data-type initializer for the numpy.dtype() method. See\n `<https://numpy.org/doc/stable/reference/arrays.dtypes.html>`_.\n Examples include:\n\n - Python non-string type (float, int, bool)\n - Numpy non-string type (e.g. np.float32, np.int64, np.bool\\_)\n - Numpy.dtype array-protocol type strings (e.g. \'i4\', \'f8\', \'S15\')\n\n If no ``dtype`` value is provide then the type is inferred using\n ``np.array(data)``.\n\n - Provide ``length`` and optionally ``shape``, but not ``data``\n\n Examples::\n\n col = Column(name=\'name\', length=5)\n col = Column(name=\'name\', dtype=int, length=10, shape=(3,4))\n\n The default ``dtype`` is ``np.float64``. The ``shape`` argument is the\n array shape of a single cell in the column.\n\n To access the ``Column`` data as a raw `numpy.ndarray` object, you can use\n one of the ``data`` or ``value`` attributes (which are equivalent)::\n\n col.data\n col.value\n '

__eq__(other): Return self==value.

__ge__(other): Return self>=value.

__gt__(other): Return self>value.

__hash__ = None

__le__(other): Return self<=value.

__lt__(other): Return self<value.

__module__ = 'astropy.table.column'

__ne__(other): Return self!=value.

static __new__(cls, data=None, name=None, dtype=None, shape=(), length=0, description=None, unit=None, format=None, meta=None, copy=False, copy_indices=True)

__repr__(): Return repr(self).

__setattr__(item, value): Implement setattr(self, name, value).

__setitem__(index, value): Set self[key] to value.

__str__(): Return str(self).

_base_repr_(html=False)

_check_string_truncate(value): Emit a warning if any elements of value will be truncated when value is assigned to self.

_repr_html_()

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_unitstr or astropy.units.UnitBase instance: The unit to convert to.
equivalencieslist of tuple: 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='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’.
dataarray, optional: If supplied then use a view of data instead of the instance data. This allows copying the instance attributes and meta.
copy_databool, optional: Make a copy of the internal numpy array instead of using a reference. Default is True.

Returns:

colColumn 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 ~astropy.table.Column object.

Parameters:

objint, slice or sequence of int: Object that defines the index or indices before which values is inserted.
valuesarray-like: Value(s) to insert. If the type of values is different from that of the column, values is converted to the matching type. values should be shaped so that it can be broadcast appropriately.
axisint, 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~astropy.table.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_linesint: Maximum number of lines in table output.
show_namebool: Include a header row for column names. Default is True.
show_unitbool: Include a header row for unit. Default is False.

property 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_linesint: Maximum lines of output (header + data rows)
show_namebool: Include column name. Default is True.
show_unitbool: Include a header row for unit. Default is False.
show_dtypebool: Include column dtype. Default is False.
htmlbool: Format the output as an HTML table. Default is False.

Returns:

lineslist: 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_linesint: Maximum number of values in output
show_namebool: Include column name. Default is True.
show_unitbool: Include a header row for unit. Default is False.
show_dtypebool: Include column dtype. Default is True.

property quantity: A view of this table column as a ~astropy.units.Quantity object with units given by the Column’s unit parameter.

to(unit, equivalencies=[], **kwargs)

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

Parameters:

unitunit-like: The unit to convert to (i.e., a valid argument to the astropy.units.Quantity.to() method).
equivalencieslist of tuple: Equivalencies to use for this conversion. See astropy.units.Quantity.to() for more details.

Returns:

quantity~astropy.units.Quantity: A quantity object with the contents of this column in the units unit.

property 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.

property ColumnClass

class MaskedColumn(data=None, name=None, mask=None, fill_value=None, dtype=None, shape=(), length=0, description=None, unit=None, format=None, meta=None, copy=False, copy_indices=True)

Bases: Column, _MaskedColumnGetitemShim, MaskedArray

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

Parameters:

datalist, ndarray, or None: Column data values
namestr: Column name and key for reference within Table
masklist, ndarray or None: Boolean mask for which True indicates missing or invalid data
fill_valuefloat, int, str, or None: Value used when filling masked column elements
dtype~numpy.dtype-like: Data type for column
shapetuple or (): Dimensions of a single row element in the column data
lengthint or 0: Number of row elements in column data
descriptionstr or None: Full description of column
unitstr or None: Physical unit
formatstr, None, 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.
metadict-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 https://numpy.org/doc/stable/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.

To access the Column data as a raw numpy.ma.MaskedArray object, you can use one of the data or value attributes (which are equivalent):

col.data
col.value

__annotations__ = {}

__doc__ = 'Define a masked data column for use in a Table object.\n\n Parameters\n ----------\n data : list, ndarray, or None\n Column data values\n name : str\n Column name and key for reference within Table\n mask : list, ndarray or None\n Boolean mask for which True indicates missing or invalid data\n fill_value : float, int, str, or None\n Value used when filling masked column elements\n dtype : `~numpy.dtype`-like\n Data type for column\n shape : tuple or ()\n Dimensions of a single row element in the column data\n length : int or 0\n Number of row elements in column data\n description : str or None\n Full description of column\n unit : str or None\n Physical unit\n format : str, None, or callable\n Format string for outputting column values. This can be an\n "old-style" (``format % value``) or "new-style" (`str.format`)\n format specification string or a function or any callable object that\n accepts a single value and returns a string.\n meta : dict-like or None\n Meta-data associated with the column\n\n Examples\n --------\n A MaskedColumn is similar to a Column except that it includes ``mask`` and\n ``fill_value`` attributes. It can be created in two different ways:\n\n - Provide a ``data`` value but not ``shape`` or ``length`` (which are\n inferred from the data).\n\n Examples::\n\n col = MaskedColumn(data=[1, 2], name=\'name\')\n col = MaskedColumn(data=[1, 2], name=\'name\', mask=[True, False])\n col = MaskedColumn(data=[1, 2], name=\'name\', dtype=float, fill_value=99)\n\n The ``mask`` argument will be cast as a boolean array and specifies\n which elements are considered to be missing or invalid.\n\n The ``dtype`` argument can be any value which is an acceptable\n fixed-size data-type initializer for the numpy.dtype() method. See\n `<https://numpy.org/doc/stable/reference/arrays.dtypes.html>`_.\n Examples include:\n\n - Python non-string type (float, int, bool)\n - Numpy non-string type (e.g. np.float32, np.int64, np.bool\\_)\n - Numpy.dtype array-protocol type strings (e.g. \'i4\', \'f8\', \'S15\')\n\n If no ``dtype`` value is provide then the type is inferred using\n ``np.array(data)``. When ``data`` is provided then the ``shape``\n and ``length`` arguments are ignored.\n\n - Provide ``length`` and optionally ``shape``, but not ``data``\n\n Examples::\n\n col = MaskedColumn(name=\'name\', length=5)\n col = MaskedColumn(name=\'name\', dtype=int, length=10, shape=(3,4))\n\n The default ``dtype`` is ``np.float64``. The ``shape`` argument is the\n array shape of a single cell in the column.\n\n To access the ``Column`` data as a raw `numpy.ma.MaskedArray` object, you can\n use one of the ``data`` or ``value`` attributes (which are equivalent)::\n\n col.data\n col.value\n '

__module__ = 'astropy.table.column'

static __new__(cls, data=None, name=None, mask=None, fill_value=None, dtype=None, shape=(), length=0, description=None, unit=None, format=None, meta=None, copy=False, copy_indices=True)

__setitem__(index, value): Set self[key] to value.

_copy_attrs_slice(out)

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_unitstr or astropy.units.UnitBase instance: The unit to convert to.
equivalencieslist of tuple: 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='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’.
dataarray, optional: If supplied then use a view of data instead of the instance data. This allows copying the instance attributes and meta.
copy_databool, optional: Make a copy of the internal numpy array instead of using a reference. Default is True.

Returns:

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

property data: The plain MaskedArray data held by this column.

property fill_value

The filling value of the masked array is a scalar. When setting, None will set to a default based on the data type.

Examples

>>> for dt in [np.int32, np.int64, np.float64, np.complex128]:
...     np.ma.array([0, 1], dtype=dt).get_fill_value()
...
999999
999999
1e+20
(1e+20+0j)

>>> x = np.ma.array([0, 1.], fill_value=-np.inf)
>>> x.fill_value
-inf
>>> x.fill_value = np.pi
>>> x.fill_value
3.1415926535897931 # may vary

Reset to default:

>>> x.fill_value = None
>>> x.fill_value
1e+20

filled(fill_value=None)

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

Parameters:

fill_valuescalar; 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_columnColumn: A copy of self with masked entries replaced by fill_value (be it the function argument or the attribute of self).

info

Container for meta information like name, description, format.

This is required when the object is used as a mixin column within a table, but can be used as a general way to store meta information. In this case it just adds the mask_val attribute.

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

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

Parameters:

objint, slice or sequence of int: Object that defines the index or indices before which values is inserted.
valuesarray-like: Value(s) to insert. If the type of values is different from that of the column, values is converted to the matching type. values should be shaped so that it can be broadcast appropriately.
maskbool or array-like: Mask value(s) to insert. If not supplied, and values does not have a mask either, then False is used.
axisint, 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~astropy.table.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_linesint: Maximum number of lines in table output.
show_namebool: Include a header row for column names. Default is True.
show_unitbool: Include a header row for unit. Default is False.

property 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_linesint: Maximum lines of output (header + data rows)
show_namebool: Include column name. Default is True.
show_unitbool: Include a header row for unit. Default is False.
show_dtypebool: Include column dtype. Default is False.
htmlbool: Format the output as an HTML table. Default is False.

Returns:

lineslist: 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_linesint: Maximum number of values in output
show_namebool: Include column name. Default is True.
show_unitbool: Include a header row for unit. Default is False.
show_dtypebool: 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

__array__(dtype=None, copy=False)

Support converting Row to np.array via np.array(table).

Coercion to a different dtype via np.array(table, dtype) is not supported and will raise a ValueError.

If the parent table is masked then the mask information is dropped.

__bytes__()

__dict__ = mappingproxy({'__module__': 'astropy.table.row', '__doc__': "A class to represent one row of a Table object.\n\n A Row object is returned when a Table object is indexed with an integer\n or when iterating over a table::\n\n >>> from astropy.table import Table\n >>> table = Table([(1, 2), (3, 4)], names=('a', 'b'),\n ... dtype=('int32', 'int32'))\n >>> row = table[1]\n >>> row\n <Row index=1>\n a b\n int32 int32\n ----- -----\n 2 4\n >>> row['a']\n 2\n >>> row[1]\n 4\n ", '__init__': <function Row.__init__>, '__getitem__': <function Row.__getitem__>, '__setitem__': <function Row.__setitem__>, '_ipython_key_completions_': <function Row._ipython_key_completions_>, '__eq__': <function Row.__eq__>, '__ne__': <function Row.__ne__>, '__array__': <function Row.__array__>, '__len__': <function Row.__len__>, '__iter__': <function Row.__iter__>, 'get': <function Row.get>, 'keys': <function Row.keys>, 'values': <function Row.values>, 'table': <property object>, 'index': <property object>, 'as_void': <function Row.as_void>, 'meta': <property object>, 'columns': <property object>, 'colnames': <property object>, 'dtype': <property object>, '_base_repr_': <function Row._base_repr_>, '_repr_html_': <function Row._repr_html_>, '__repr__': <function Row.__repr__>, '__str__': <function Row.__str__>, '__bytes__': <function Row.__bytes__>, '__dict__': <attribute '__dict__' of 'Row' objects>, '__weakref__': <attribute '__weakref__' of 'Row' objects>, '__hash__': None, '__annotations__': {}})

__doc__ = "A class to represent one row of a Table object.\n\n A Row object is returned when a Table object is indexed with an integer\n or when iterating over a table::\n\n >>> from astropy.table import Table\n >>> table = Table([(1, 2), (3, 4)], names=('a', 'b'),\n ... dtype=('int32', 'int32'))\n >>> row = table[1]\n >>> row\n <Row index=1>\n a b\n int32 int32\n ----- -----\n 2 4\n >>> row['a']\n 2\n >>> row[1]\n 4\n "

__eq__(other): Return self==value.

__getitem__(item)

__hash__ = None

__init__(table, index)

__iter__()

__len__()

__module__ = 'astropy.table.row'

__ne__(other): Return self!=value.

__repr__(): Return repr(self).

__setitem__(item, val)

__str__(): Return str(self).

__weakref__: list of weak references to the object (if defined)

_base_repr_(html=False): Display row as a single-line table but with appropriate header line.

_ipython_key_completions_()

_repr_html_()

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_rownumpy.void or numpy.ma.mvoid: Copy of row values. numpy.void if unmasked, numpy.ma.mvoid else.

property colnames

property columns

property dtype

get(key, default=None, /)

Return the value for key if key is in the columns, else default.

Parameters:

keystr, positional-only: The name of the column to look for.
defaultobject, optional, positional-only: The value to return if the key is not among the columns.

Returns:

object: The value in the key column of the row if present, default otherwise.

Examples

>>> from astropy.table import Table
>>> t = Table({"a": [2, 3, 5], "b": [7, 11, 13]})
>>> t[0].get("a")
2
>>> t[1].get("b", 0)
11
>>> t[2].get("c", 0)
0

property index

keys()

property meta

property table

values()

class TableColumns(cols={})

Bases: 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:

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

__delitem__(name): Delete self[key].

__doc__ = 'OrderedDict subclass for a set of columns.\n\n This class enhances item access to provide convenient access to columns\n by name or index, including slice access. It also handles renaming\n of columns.\n\n The initialization argument ``cols`` can be a list of ``Column`` objects\n or any structure that is valid for initializing a Python dict. This\n includes a dict, list of (key, val) tuples or [key, val] lists, etc.\n\n Parameters\n ----------\n cols : dict, list, tuple; optional\n Column objects as data structure that can init dict (see above)\n '

__getitem__(item)

Get items from a TableColumns object.

tc = TableColumns(cols=[Column(name='a'), Column(name='b'), Column(name='c')])
tc['a']  # Column('a')
tc[1] # Column('b')
tc['a', 'b'] # <TableColumns names=('a', 'b')>
tc[1:3] # <TableColumns names=('b', 'c')>

__init__(cols={})

__module__ = 'astropy.table.table'

__repr__(): Return repr(self).

__setitem__(item, value, validated=False)

Set item in this dict instance, but do not allow directly replacing an existing column unless it is already validated (and thus is certain to not corrupt the table).

NOTE: it is easily possible to corrupt a table by directly adding a new key to the TableColumns attribute of a Table, e.g. t.columns['jane'] = 'doe'.

_rename_column(name, new_name)

isinstance(cls)

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

Parameters:

clsclass or tuple thereof: Column class (including mixin) or tuple of Column classes.

Returns:

col_listlist of Column: 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:

clsclass or tuple thereof: Column class (including mixin) or tuple of Column classes.

Returns:

col_listlist of Column: List of Column objects which are not instances of given classes.

setdefault(key, default): Deprecated since version 6.1: The t.columns.setdefault() function is deprecated and may be removed in a future version. Use t.setdefault() instead.

update(*args, **kwargs): Deprecated since version 6.1: The t.columns.update() function is deprecated and may be removed in a future version. Use t.update() instead.

class TableFormatter

Bases: object

__dict__ = mappingproxy({'__module__': 'astropy.table.pprint', '_get_pprint_size': <staticmethod(<function TableFormatter._get_pprint_size>)>, '_pformat_col': <function TableFormatter._pformat_col>, '_name_and_structure': <function TableFormatter._name_and_structure>, '_pformat_col_iter': <function TableFormatter._pformat_col_iter>, '_pformat_table': <function TableFormatter._pformat_table>, '_more_tabcol': <function TableFormatter._more_tabcol>, '__dict__': <attribute '__dict__' of 'TableFormatter' objects>, '__weakref__': <attribute '__weakref__' of 'TableFormatter' objects>, '__doc__': None, '__annotations__': {}})

__doc__ = None

__module__ = 'astropy.table.pprint'

__weakref__: list of weak references to the object (if defined)

static _get_pprint_size(max_lines=None, max_width=None)

Get the output size (number of lines and character width) for Column and Table pformat/pprint methods.

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.table.conf.max_lines configuration item. 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.table.conf.max_width.

Parameters:

max_linesint or None: Maximum lines of output (header + data rows)
max_widthint or None: Maximum width (characters) output

Returns:

max_lines, max_widthint

_more_tabcol(tabcol, max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False)

Interactive “more” of a table or column.

Parameters:

max_linesint or None: Maximum number of rows to output
max_widthint or None: Maximum character width of output
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is False.

_name_and_structure(name, dtype, sep=' ')

Format a column name, including a possible structure.

Normally, just returns the name, but if it has a structured dtype, will add the parts in between square brackets. E.g., “name [f0, f1]” or “name [f0[sf0, sf1], f1]”.

_pformat_col(col, max_lines=None, show_name=True, show_unit=None, show_dtype=False, show_length=None, html=False, align=None)

Return a list of formatted string representation of column values.

Parameters:

max_linesint: Maximum lines of output (header + data rows)
show_namebool: Include column name. Default is True.
show_unitbool: 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_dtypebool: Include column dtype. Default is False.
show_lengthbool: Include column length at end. Default is to show this only if the column is not shown completely.
htmlbool: Output column as HTML
alignstr: Left/right alignment of columns. Default is ‘>’ (right) for all columns. Other allowed values are ‘<’, ‘^’, and ‘0=’ for left, centered, and 0-padded, respectively.

Returns:

lineslist: List of lines with formatted column values
outsdict: Dict which is used to pass back additional values defined within the iterator.

_pformat_col_iter(col, max_lines, show_name, show_unit, outs, show_dtype=False, show_length=None)

Iterator which yields formatted string representation of column values.

Parameters:

max_linesint: Maximum lines of output (header + data rows)
show_namebool: Include column name. Default is True.
show_unitbool: 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.
outsdict: Must be a dict which is used to pass back additional values defined within the iterator.
show_dtypebool: Include column dtype. Default is False.
show_lengthbool: Include column length at end. Default is to show this only if the column is not shown completely.

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

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

Parameters:

max_linesint or None: Maximum number of rows to output
max_widthint or None: Maximum character width of output
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is to False.
htmlbool: Format the output as an HTML table. Default is False.
tableidstr 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(table)
tableclassstr or list of str or None: CSS classes for the table; only used if html is set. Default is none
alignstr or list or tuple: Left/right alignment of columns. Default is ‘>’ (right) for all columns. Other allowed values are ‘<’, ‘^’, and ‘0=’ for left, centered, and 0-padded, respectively. A list of strings can be provided for alignment of tables with multiple columns.

Returns:

rowslist: Formatted table as a list of strings
outsdict: Dict which is used to pass back additional values defined within the iterator.

__array__(dtype=None, copy=False)

Support converting Table to np.array via np.array(table).

Coercion to a different dtype via np.array(table, dtype) is not supported and will raise a ValueError.

__bytes__()

__copy__()

__deepcopy__(memo=None)

__delitem__(item)

__dict__ = mappingproxy({'__module__': 'astropy.table.table', '__doc__': 'A class to represent tables of heterogeneous data.\n\n `~astropy.table.Table` provides a class for heterogeneous tabular data.\n A key enhancement provided by the `~astropy.table.Table` class over\n e.g. a `numpy` structured array is the ability to easily modify the\n structure of the table by adding or removing columns, or adding new\n rows of data. In addition table and column metadata are fully supported.\n\n `~astropy.table.Table` differs from `~astropy.nddata.NDData` by the\n assumption that the input data consists of columns of homogeneous data,\n where each column has a unique identifier and may contain additional\n metadata such as the data unit, format, and description.\n\n See also: https://docs.astropy.org/en/stable/table/\n\n Parameters\n ----------\n data : numpy ndarray, dict, list, table-like object, optional\n Data to initialize table.\n masked : bool, optional\n Specify whether the table is masked.\n names : list, optional\n Specify column names.\n dtype : list, optional\n Specify column data types.\n meta : dict, optional\n Metadata associated with the table.\n copy : bool, optional\n Copy the input column data and make a deep copy of the input meta.\n Default is True.\n rows : numpy ndarray, list of list, optional\n Row-oriented data for table instead of ``data`` argument.\n copy_indices : bool, optional\n Copy any indices in the input data. Default is True.\n units : list, dict, optional\n List or dict of units to apply to columns.\n descriptions : list, dict, optional\n List or dict of descriptions to apply to columns.\n **kwargs : dict, optional\n Additional keyword args when converting table-like object.\n ', 'meta': <astropy.utils.metadata.core.MetaData object>, 'Row': <class 'astropy.table.row.Row'>, 'Column': <class 'astropy.table.column.Column'>, 'MaskedColumn': <class 'astropy.table.column.MaskedColumn'>, 'TableColumns': <class 'astropy.table.table.TableColumns'>, 'TableFormatter': <class 'astropy.table.pprint.TableFormatter'>, 'read': <astropy.io.registry.interface.UnifiedReadWriteMethod object>, 'write': <astropy.io.registry.interface.UnifiedReadWriteMethod object>, 'pprint_exclude_names': <PprintIncludeExclude name=pprint_exclude_names default=None>, 'pprint_include_names': <PprintIncludeExclude name=pprint_include_names default=None>, 'as_array': <function Table.as_array>, '__init__': <function Table.__init__>, '_set_column_attribute': <function Table._set_column_attribute>, '__getstate__': <function Table.__getstate__>, '__setstate__': <function Table.__setstate__>, 'mask': <property object>, '_mask': <property object>, 'filled': <function Table.filled>, 'indices': <property object>, 'loc': <property object>, 'loc_indices': <property object>, 'iloc': <property object>, 'add_index': <function Table.add_index>, 'remove_indices': <function Table.remove_indices>, 'index_mode': <function Table.index_mode>, '__array__': <function Table.__array__>, '_check_names_dtype': <function Table._check_names_dtype>, '_init_from_list_of_dicts': <function Table._init_from_list_of_dicts>, '_init_from_list': <function Table._init_from_list>, '_convert_data_to_col': <function Table._convert_data_to_col>, '_init_from_ndarray': <function Table._init_from_ndarray>, '_init_from_dict': <function Table._init_from_dict>, '_get_col_cls_for_table': <function Table._get_col_cls_for_table>, '_convert_col_for_table': <function Table._convert_col_for_table>, '_init_from_cols': <function Table._init_from_cols>, '_new_from_slice': <function Table._new_from_slice>, '_make_table_from_cols': <staticmethod(<function Table._make_table_from_cols>)>, '_set_col_parent_table_and_mask': <function Table._set_col_parent_table_and_mask>, 'itercols': <function Table.itercols>, '_base_repr_': <function Table._base_repr_>, '_repr_html_': <function Table._repr_html_>, '__repr__': <function Table.__repr__>, '__str__': <function Table.__str__>, '__bytes__': <function Table.__bytes__>, 'has_mixin_columns': <property object>, 'has_masked_columns': <property object>, 'has_masked_values': <property object>, '_is_mixin_for_table': <function Table._is_mixin_for_table>, 'pprint': <function Table.pprint>, 'pprint_all': <function Table.pprint_all>, '_make_index_row_display_table': <function Table._make_index_row_display_table>, 'show_in_notebook': <function Table.show_in_notebook>, 'show_in_browser': <function Table.show_in_browser>, 'pformat': <function Table.pformat>, 'pformat_all': <function Table.pformat_all>, 'more': <function Table.more>, '__getitem__': <function Table.__getitem__>, '__setitem__': <function Table.__setitem__>, '__delitem__': <function Table.__delitem__>, '_ipython_key_completions_': <function Table._ipython_key_completions_>, 'field': <function Table.field>, 'masked': <property object>, '_set_masked': <function Table._set_masked>, 'ColumnClass': <property object>, 'dtype': <property object>, 'colnames': <property object>, '_is_list_or_tuple_of_str': <staticmethod(<function Table._is_list_or_tuple_of_str>)>, 'keys': <function Table.keys>, 'values': <function Table.values>, 'items': <function Table.items>, '__len__': <function Table.__len__>, '__or__': <function Table.__or__>, '__ior__': <function Table.__ior__>, 'index_column': <function Table.index_column>, 'add_column': <function Table.add_column>, 'add_columns': <function Table.add_columns>, '_replace_column_warnings': <function Table._replace_column_warnings>, 'replace_column': <function Table.replace_column>, 'remove_row': <function Table.remove_row>, 'remove_rows': <function Table.remove_rows>, 'iterrows': <function Table.iterrows>, '_set_of_names_in_colnames': <function Table._set_of_names_in_colnames>, 'remove_column': <function Table.remove_column>, 'remove_columns': <function Table.remove_columns>, '_convert_string_dtype': <function Table._convert_string_dtype>, 'convert_bytestring_to_unicode': <function Table.convert_bytestring_to_unicode>, 'convert_unicode_to_bytestring': <function Table.convert_unicode_to_bytestring>, 'keep_columns': <function Table.keep_columns>, 'rename_column': <function Table.rename_column>, 'rename_columns': <function Table.rename_columns>, '_set_row': <function Table._set_row>, 'add_row': <function Table.add_row>, 'insert_row': <function Table.insert_row>, '_replace_cols': <function Table._replace_cols>, 'setdefault': <function Table.setdefault>, 'update': <function Table.update>, 'argsort': <function Table.argsort>, 'sort': <function Table.sort>, 'reverse': <function Table.reverse>, 'round': <function Table.round>, 'copy': <function Table.copy>, '__deepcopy__': <function Table.__deepcopy__>, '__copy__': <function Table.__copy__>, '__eq__': <function Table.__eq__>, '__ne__': <function Table.__ne__>, '_rows_equal': <function Table._rows_equal>, 'values_equal': <function Table.values_equal>, 'groups': <property object>, 'group_by': <function Table.group_by>, 'to_pandas': <function Table.to_pandas>, 'from_pandas': <classmethod(<function Table.from_pandas>)>, 'info': <astropy.table.info.TableInfo object>, '__dict__': <attribute '__dict__' of 'Table' objects>, '__weakref__': <attribute '__weakref__' of 'Table' objects>, '__hash__': None, '__annotations__': {}})

__doc__ = 'A class to represent tables of heterogeneous data.\n\n `~astropy.table.Table` provides a class for heterogeneous tabular data.\n A key enhancement provided by the `~astropy.table.Table` class over\n e.g. a `numpy` structured array is the ability to easily modify the\n structure of the table by adding or removing columns, or adding new\n rows of data. In addition table and column metadata are fully supported.\n\n `~astropy.table.Table` differs from `~astropy.nddata.NDData` by the\n assumption that the input data consists of columns of homogeneous data,\n where each column has a unique identifier and may contain additional\n metadata such as the data unit, format, and description.\n\n See also: https://docs.astropy.org/en/stable/table/\n\n Parameters\n ----------\n data : numpy ndarray, dict, list, table-like object, optional\n Data to initialize table.\n masked : bool, optional\n Specify whether the table is masked.\n names : list, optional\n Specify column names.\n dtype : list, optional\n Specify column data types.\n meta : dict, optional\n Metadata associated with the table.\n copy : bool, optional\n Copy the input column data and make a deep copy of the input meta.\n Default is True.\n rows : numpy ndarray, list of list, optional\n Row-oriented data for table instead of ``data`` argument.\n copy_indices : bool, optional\n Copy any indices in the input data. Default is True.\n units : list, dict, optional\n List or dict of units to apply to columns.\n descriptions : list, dict, optional\n List or dict of descriptions to apply to columns.\n **kwargs : dict, optional\n Additional keyword args when converting table-like object.\n '

__eq__(other): Return self==value.

__getitem__(item)

__getstate__()

__hash__ = None

__init__(data=None, masked=False, names=None, dtype=None, meta=None, copy=True, rows=None, copy_indices=True, units=None, descriptions=None, **kwargs)

__ior__(other)

__len__()

__module__ = 'astropy.table.table'

__ne__(other): Return self!=value.

__or__(other): Return self|value.

__repr__(): Return repr(self).

__setitem__(item, value)

__setstate__(state)

__str__(): Return str(self).

__weakref__: list of weak references to the object (if defined)

_base_repr_(html=False, descr_vals=None, max_width=None, tableid=None, show_dtype=True, max_lines=None, tableclass=None)

_check_names_dtype(names, dtype, n_cols): Make sure that names and dtype are both iterable and have the same length as data.

_convert_col_for_table(col): Make sure that all Column objects have correct base class for this type of Table. For a base Table this most commonly means setting to MaskedColumn if the table is masked. Table subclasses like QTable override this method.

_convert_data_to_col(data, copy=True, default_name=None, dtype=None, name=None)

Convert any allowed sequence data col to a column object that can be used directly in the self.columns dict. This could be a Column, MaskedColumn, or mixin column.

The final column name is determined by:

name or data.info.name or def_name

If data has no info then name = name or def_name.

The behavior of copy for Column objects is: - copy=True: new class instance with a copy of data and deep copy of meta - copy=False: new class instance with same data and a key-only copy of meta

For mixin columns: - copy=True: new class instance with copy of data and deep copy of meta - copy=False: original instance (no copy at all)

Parameters:

dataobject (column-like sequence): Input column data
copybool: Make a copy
default_namestr: Default name
dtypenp.dtype or None: Data dtype
namestr or None: Column name

Returns:

colColumn, MaskedColumn, mixin-column type: Object that can be used as a column in self

_convert_string_dtype(in_kind, out_kind, encode_decode_func)

Convert string-like columns to/from bytestring and unicode (internal only).

Parameters:

in_kindstr: Input dtype.kind
out_kindstr: Output dtype.kind

_get_col_cls_for_table(col)

Get the correct column class to use for upgrading any Column-like object.

For a masked table, ensure any Column-like object is a subclass of the table MaskedColumn.

For unmasked table, ensure any MaskedColumn-like object is a subclass of the table MaskedColumn. If not a MaskedColumn, then ensure that any Column-like object is a subclass of the table Column.

_init_from_cols(cols): Initialize table from a list of Column or mixin objects.

_init_from_dict(data, names, dtype, n_cols, copy): Initialize table from a dictionary of columns.

_init_from_list(data, names, dtype, n_cols, copy): Initialize table from a list of column data. A column can be a Column object, np.ndarray, mixin, or any other iterable object.

_init_from_list_of_dicts(data, names, dtype, n_cols, copy): Initialize table from a list of dictionaries representing rows.

_init_from_ndarray(data, names, dtype, n_cols, copy): Initialize table from an ndarray structured array.

_ipython_key_completions_()

static _is_list_or_tuple_of_str(names): Check that names is a tuple or list of strings.

_is_mixin_for_table(col): Determine if col should be added to the table directly as a mixin column.

_make_index_row_display_table(index_row_name)

static _make_table_from_cols(table, cols, verify=True, names=None): Make table in-place so that it represents the given list of cols.

property _mask: This is needed so that comparison of a masked Table and a MaskedArray works. The requirement comes from numpy.ma.core so don’t remove this property.

_new_from_slice(slice_): Create a new table as a referenced slice from self.

_replace_cols(columns)

_replace_column_warnings(name, col): Same as replace_column but issues warnings under various circumstances.

_repr_html_()

_rows_equal(other)

Row-wise comparison of table with any other object.

This is actual implementation for __eq__.

Returns a 1-D boolean numpy array showing result of row-wise comparison, or a bool (False) in cases where comparison isn’t possible (uncomparable dtypes or unbroadcastable shapes). Intended to follow legacy numpy’s elementwise comparison rules.

This is the same as the == comparison for tables.

Parameters:

otherTable or DataFrame or ndarray: An object to compare with table

Examples

Comparing one Table with other:

>>> t1 = Table([[1,2],[4,5],[7,8]], names=('a','b','c'))
>>> t2 = Table([[1,2],[4,5],[7,8]], names=('a','b','c'))
>>> t1._rows_equal(t2)
array([ True,  True])

_set_col_parent_table_and_mask(col): Set col.parent_table = self and force col to have mask attribute if the table is masked and col.mask does not exist.

_set_column_attribute(attr, values): Set attr for columns to values, which can be either a dict (keyed by column name) or a dict of name: value pairs. This is used for handling the units and descriptions kwargs to __init__.

_set_masked(masked)

Set the table masked property.

Parameters:

maskedbool: State of table masking (True or False)

_set_of_names_in_colnames(names)

Return names as a set if valid, or raise a KeyError.

names is valid if all elements in it are in self.colnames. If names is a string then it is interpreted as a single column name.

_set_row(idx, colnames, vals)

add_column(col, index=None, name=None, rename_duplicate=False, copy=True, default_name=None)

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

The col input can be any data object which is acceptable as a ~astropy.table.Table column object or can be converted. This includes mixin columns and scalar or length=1 objects which get broadcast to match the table length.

To add several columns at once use add_columns() or simply call add_column() for each one. There is very little performance difference in the two approaches.

Parameters:

colobject: Data object for the new column
indexint or None: Insert column before this position or at end (default).
namestr: Column name
rename_duplicatebool: Uniquify column name if it already exist. Default is False.
copybool: Make a copy of the new column. Default is True.
default_namestr or None: Name to use if both name and col.info.name are not available. Defaults to col{number_of_columns}.

Examples

Create a table with two columns ‘a’ and ‘b’, then create a third column ‘c’ and append it to the end of the table:

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

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

>>> t.add_column(['a', 'b'], name='d', index=1)
>>> print(t)
 a   d   b   c
--- --- --- ---
  1   a 0.1   x
  2   b 0.2   y

Add second column named ‘b’ with rename_duplicate:

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_column(1.1, name='b', rename_duplicate=True)
>>> print(t)
 a   b  b_1
--- --- ---
  1 0.1 1.1
  2 0.2 1.1

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'))
>>> t.add_column(['a', 'b'])
>>> t.add_column(col_c, name='d')
>>> print(t)
 a   b  col2  d
--- --- ---- ---
  1 0.1    a   x
  2 0.2    b   y

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

Add a list of new columns the table using cols data objects. 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.

The cols input can include any data objects which are acceptable as ~astropy.table.Table column objects or can be converted. This includes mixin columns and scalar or length=1 objects which get broadcast to match the table length.

From a performance perspective there is little difference between calling this method once or looping over the new columns and calling add_column() for each column.

Parameters:

colslist of object: List of data objects for the new columns
indexeslist of int or None: Insert column before this position or at end (default).
nameslist of str: Column names
copybool: Make a copy of the new columns. Default is True.
rename_duplicatebool: Uniquify new column names if they duplicate the existing ones. Default is False.

See also

astropy.table.hstack, update, replace_column

Examples

Create a table with two columns ‘a’ and ‘b’, then create columns ‘c’ and ‘d’ and append them to the end of the table:

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

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], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_columns([['x', 'y'], ['u', 'v']], names=['c', 'd'],
...               indexes=[0, 1])
>>> print(t)
 c   a   d   b
--- --- --- ---
  x   1   u 0.1
  y   2   v 0.2

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

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_columns([[1.1, 1.2], ['x', 'y']], names=('b', 'c'),
...               rename_duplicate=True)
>>> print(t)
 a   b  b_1  c
--- --- --- ---
  1 0.1 1.1  x
  2 0.2 1.2  y

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_b = Column(name='b', data=['u', 'v'])
>>> t.add_columns([[1, 2], col_b])
>>> t.add_columns([[3, 4], col_b], names=['c', 'd'])
>>> print(t)
col0  b   c   d
---- --- --- ---
   1   u   3   u
   2   v   4   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:

colnamesstr or list: List of column names (or a single column name) to index
enginetype or None: Indexing engine class to use, either ~astropy.table.SortedArray, ~astropy.table.BST, or ~astropy.table.SCEngine. If the supplied argument is None (by default), use ~astropy.table.SortedArray.
uniquebool: 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:

valstuple, list, dict or None: Use the specified values in the new row
masktuple, 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, reverse=False)

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:

keysstr or list of str: The column name(s) to order the table by
kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional: Sorting algorithm used by numpy.argsort.
reversebool: Sort in reverse order (default=False)

Returns:

index_arrayndarray, int: Array of indices that sorts the table by the specified key column(s).

as_array(keep_byteorder=False, names=None)

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

Parameters:

keep_byteorderbool, 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).
nameslist, optional:: List of column names to include for returned structured array. Default is to include all table columns.

Returns:

table_arrayarray or ~numpy.ma.MaskedArray: Copy of table as a numpy structured array. ndarray for unmasked or ~numpy.ma.MaskedArray for masked.

property colnames

convert_bytestring_to_unicode()

Convert bytestring columns (dtype.kind=’S’) to unicode (dtype.kind=’U’) using UTF-8 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 scripts to manipulate string arrays with natural syntax.

convert_unicode_to_bytestring()

Convert unicode columns (dtype.kind=’U’) to bytestring (dtype.kind=’S’) using UTF-8 encoding.

When exporting a unicode string array to a file, it may be desirable to encode unicode columns as bytestrings.

copy(copy_data=True)

Return a copy of the table.

Parameters:

copy_databool: If True (the default), copy the underlying data array and make a deep copy of the meta attribute. Otherwise, use the same data array and make a shallow (key-only) copy of meta.

property dtype

field(item): Return column[item] for recarray compatibility.

filled(fill_value=None)

Return 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_valuestr: If supplied, this fill_value is used for all masked entries in the entire table.

Returns:

filled_table~astropy.table.Table: New table with masked values filled

classmethod from_pandas(dataframe, index=False, units=None)

Create a ~astropy.table.Table from a pandas.DataFrame instance.

In addition to converting generic numeric or string columns, this supports conversion of pandas Date and Time delta columns to ~astropy.time.Time and ~astropy.time.TimeDelta columns, respectively.

Parameters:

dataframepandas.DataFrame: A pandas pandas.DataFrame instance
indexbool: Include the index column in the returned table (default=False)
units: dict: A dict mapping column names to a ~astropy.units.Unit. The columns will have the specified unit in the Table.

Returns:

table~astropy.table.Table: A ~astropy.table.Table (or subclass) instance

Raises:

ImportError: If pandas is not installed

Examples

Here we convert a pandas.DataFrame instance to a ~astropy.table.QTable.

>>> import numpy as np
>>> import pandas as pd
>>> from astropy.table import QTable

>>> time = pd.Series(['1998-01-01', '2002-01-01'], dtype='datetime64[ns]')
>>> dt = pd.Series(np.array([1, 300], dtype='timedelta64[s]'))
>>> df = pd.DataFrame({'time': time})
>>> df['dt'] = dt
>>> df['x'] = [3., 4.]
>>> with pd.option_context('display.max_columns', 20):
...     print(df)
        time              dt    x
0 1998-01-01 0 days 00:00:01  3.0
1 2002-01-01 0 days 00:05:00  4.0

>>> QTable.from_pandas(df)
<QTable length=2>
          time              dt       x
          Time          TimeDelta float64
----------------------- --------- -------
1998-01-01T00:00:00.000       1.0     3.0
2002-01-01T00:00:00.000     300.0     4.0

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 ~astropy.table.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

~astropy.table.Table with same length as this table

Parameters:

keysstr, list of str, numpy array, or ~astropy.table.Table: Key grouping object

Returns:

out~astropy.table.Table: New table with groups set

property groups

property has_masked_columns

True if table has any MaskedColumn columns.

This does not check for mixin columns that may have masked values, use the has_masked_values property in that case.

property has_masked_values

True if column in the table has values which are masked.

This may be relatively slow for large tables as it requires checking the mask values of each column.

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

property 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:

namestr: column name

Returns:

indexint: 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:

modestr: 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.

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

info

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:

valstuple, list, dict or None: Use the specified values in the new row
masktuple, list, dict or None: Use the specified mask values in the new row

items()

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.

iterrows(*names)

Iterate over rows of table returning a tuple of values for each row.

This method is especially useful when only a subset of columns are needed.

The iterrows method can be substantially faster than using the standard Table row iteration (e.g. for row in tbl:), since that returns a new ~astropy.table.Row object for each row and accessing a column in that row (e.g. row['col0']) is slower than tuple access.

Parameters:

nameslist: List of column names (default to all columns if no names provided)

Returns:

rowsiterable: Iterator returns tuples of row values

Examples

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

>>> t = Table({'a': [1, 2, 3],
...            'b': [1.0, 2.5, 3.0],
...            'c': ['x', 'y', 'z']})

To iterate row-wise using column names:

>>> for a, c in t.iterrows('a', 'c'):
...     print(a, c)
1 x
2 y
3 z

keep_columns(names)

Keep only the columns specified (remove the others).

Parameters:

namesstr or iterable of str: 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

Keep only column ‘a’ of the table:

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

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()

property 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.

property loc_indices: Return a TableLocIndices object that can be used for retrieving the row indices corresponding to given table index key value or values.

property mask

property masked

meta = None

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_linesint: Maximum number of lines in table output
max_widthint or None: Maximum character width of output
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is False.

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_linesint or None: Maximum number of rows to output
max_widthint or None: Maximum character width of output
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is True.
htmlbool: Format the output as an HTML table. Default is False.
tableidstr 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)
alignstr 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.
tableclassstr or list of str or None: CSS classes for the table; only used if html is set. Default is None.

Returns:

lineslist: Formatted table as a list of strings.

pformat_all(max_lines=-1, max_width=-1, 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 entire 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_linesint or None: Maximum number of rows to output
max_widthint or None: Maximum character width of output
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is True.
htmlbool: Format the output as an HTML table. Default is False.
tableidstr 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)
alignstr 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.
tableclassstr or list of str or None: CSS classes for the table; only used if html is set. Default is None.

Returns:

lineslist: 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_linesint or None: Maximum number of lines in table output.
max_widthint or None: Maximum character width of output.
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is False.
alignstr 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.

pprint_all(max_lines=-1, max_width=-1, show_name=True, show_unit=None, show_dtype=False, align=None)

Print a formatted string representation of the entire table.

This method is the same as astropy.table.Table.pprint except that the default max_lines and max_width are both -1 so that by default the entire table is printed instead of restricting to the size of the screen terminal.

Parameters:

max_linesint or None: Maximum number of lines in table output.
max_widthint or None: Maximum character width of output.
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is False.
alignstr 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.

pprint_exclude_names

Maintain tuple that controls table column visibility for print output.

This is a descriptor that inherits from MetaAttribute so that the attribute value is stored in the table meta[‘__attributes__’].

This gets used for the pprint_include_names and pprint_exclude_names Table attributes.

pprint_include_names

Maintain tuple that controls table column visibility for print output.

This is a descriptor that inherits from MetaAttribute so that the attribute value is stored in the table meta[‘__attributes__’].

This gets used for the pprint_include_names and pprint_exclude_names Table attributes.

property read

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')

Get help on the available readers for Table using the``help()`` method:

>>> Table.read.help()  # Get help reading Table and list supported formats
>>> Table.read.help('fits')  # Get detailed help on Table FITS reader
>>> Table.read.list_formats()  # Print list of available formats

Parameters:

*argstuple, optional: Positional arguments passed through to data reader. If supplied the first argument is typically the input filename.
formatstr: File format specifier.
unitslist, dict, optional: List or dict of units to apply to columns
descriptionslist, dict, optional: List or dict of descriptions to apply to columns
**kwargsdict, optional: Keyword arguments passed through to data reader.

Returns:

out~astropy.table.Table: Table corresponding to file contents

remove_column(name)

Remove a column from the table.

This can also be done with:

del table[name]

Parameters:

namestr: 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:

namesstr or iterable of str: 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:

colnamestr: Name of column

remove_row(index)

Remove a row from the table.

Parameters:

indexint: 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_specifierslice or int or array of int: 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 by setting the name attribute of the info property of the column:

table[name].info.name = new_name

Parameters:

namestr: The current name of the column.
new_namestr: 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

rename_columns(names, new_names)

Rename multiple columns.

Parameters:

nameslist, tuple: A list or tuple of existing column names.
new_nameslist, tuple: A list or tuple of new column names.

Examples

Create a table with three columns ‘a’, ‘b’, ‘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 columns ‘a’ to ‘aa’ and ‘b’ to ‘bb’:

>>> names = ('a','b')
>>> new_names = ('aa','bb')
>>> t.rename_columns(names, new_names)
>>> print(t)
 aa  bb   c
--- --- ---
  1   3   5
  2   4   6

replace_column(name, col, copy=True)

Replace column name with the new col object.

The behavior of copy for Column objects is: - copy=True: new class instance with a copy of data and deep copy of meta - copy=False: new class instance with same data and a key-only copy of meta

For mixin columns: - copy=True: new class instance with copy of data and deep copy of meta - copy=False: original instance (no copy at all)

Parameters:

namestr: Name of column to replace
col~astropy.table.Column or ~numpy.ndarray or sequence: New column object to replace the existing column.
copybool: Make copy of the input col, default=True