ObservationTable#

class gammapy.data.ObservationTable(data=None, masked=False, names=None, dtype=None, meta=None, copy=True, rows=None, copy_indices=True, units=None, descriptions=None, **kwargs)[source]#

Bases: Table

Observation table.

Data format specification: Observation index table.

Attributes Summary

ColumnClass

colnames

dtype

groups

has_masked_columns

True if table has any MaskedColumn columns.

has_masked_values

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

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.

indices

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

info

loc

Return a TableLoc object that can be used for retrieving rows by index in a given data range.

loc_indices

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

mask

masked

meta

pointing_galactic

Pointing positions in Galactic coordinates as a SkyCoord object.

pointing_radec

Pointing positions in ICRS as a SkyCoord object.

pprint_exclude_names

Maintain tuple that controls table column visibility for print output.

pprint_include_names

Maintain tuple that controls table column visibility for print output.

time_ref

Time reference as a Time object.

time_start

Observation start time as a Time object.

time_stop

Observation stop time as a Time object.

write

Write this Table object out in the specified format.

Methods Summary

add_column(col[, index, name, ...])

Add a new column to the table using col as input.

add_columns(cols[, indexes, names, copy, ...])

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

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, reverse])

Return the indices which would sort the table according to one or more key columns.

as_array([keep_byteorder, names])

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()

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

convert_unicode_to_bytestring()

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

copy([copy_data])

Return a copy of the table.

field(item)

Return column[item] for recarray compatibility.

filled([fill_value])

Return copy of self, with masked values filled.

from_pandas(dataframe[, index, units])

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.

items()

itercols()

Iterate over the columns of this table.

iterrows(*names)

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

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

pformat_all([max_lines, max_width, ...])

Return a list of lines for the formatted string representation of

pprint([max_lines, max_width, show_name, ...])

Print a formatted string representation of the table.

pprint_all([max_lines, max_width, ...])

Print a formatted string representation of the entire table.

read(filename, **kwargs)

Read an observation table from file.

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.

rename_columns(names, new_names)

Rename multiple columns.

replace_column(name, col[, copy])

Replace column name with the new col object.

reverse()

Reverse the row order of table rows.

round([decimals])

Round numeric columns in-place to the specified number of decimals.

select_obs_id(obs_id)

Get ObservationTable containing only obs_id.

select_observations([selections])

Select subset of observations from a list of selection criteria.

select_range(selection_variable, value_range)

Make an observation table, applying some selection.

select_sky_circle(center, radius[, inverted])

Make an observation table, applying a cone selection.

select_time_range(time_range[, ...])

Make an observation table, applying a time selection.

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, kind, reverse])

Sort the table according to one or more keys.

summary()

Summary information string.

to_pandas([index, use_nullable_int])

Return a pandas.DataFrame instance

update(other[, copy])

Perform a dictionary-style update and merge metadata.

values()

values_equal(other)

Element-wise comparison of table with another table, list, or scalar.

Attributes Documentation

ColumnClass#
colnames#
dtype#
groups#
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.

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.

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.

indices#

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

info#
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.

loc_indices#

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

mask#
masked#
meta#
pointing_galactic#

Pointing positions in Galactic coordinates as a SkyCoord object.

pointing_radec#

Pointing positions in ICRS as a SkyCoord object.

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.

time_ref#

Time reference as a Time object.

time_start#

Observation start time as a Time object.

time_stop#

Observation stop time as a Time object.

write#

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')

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

>>> Table.write.help()  # Get help writing Table and list supported formats
>>> Table.write.help('fits')  # Get detailed help on Table FITS writer
>>> Table.write.list_formats()  # Print list of available formats

The serialize_method argument is explained in the section on Table serialization methods.

See also: https://docs.astropy.org/en/stable/io/unified.html

Parameters:
*argstuple, optional

Positional arguments passed through to data writer. If supplied the first argument is the output filename.

formatstr

File format specifier.

serialize_methodstr, dict, optional

Serialization method specifier for columns.

**kwargsdict, optional

Keyword arguments passed through to data writer.

Methods Documentation

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 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 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 SortedArray, BST, or SCEngine. If the supplied argument is None (by default), use 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 MaskedArray

Copy of table as a numpy structured array. ndarray for unmasked or MaskedArray for masked.

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. Otherwise, use the same data array. 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 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_tableTable

New table with masked values filled

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

Create a 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 Time and 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 to a Unit. The columns will have the specified unit in the Table.

Returns:
tableTable

A Table (or subclass) instance

Raises:
ImportError

If pandas is not installed

Examples

Here we convert a pandas.DataFrame instance to a 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 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:
keysstr, list of str, numpy array, or Table

Key grouping object

Returns:
outTable

New table with groups set

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.

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()#
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.

classmethod read(filename, **kwargs)[source]#

Read an observation table from file.

Parameters:
filenamepathlib.Path or str

Filename.

**kwargsdict, optional

Keyword arguments passed to read.

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 with by setting the name attribute for a column:

table[name].name = new_name

TODO: this won’t work for mixins

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

colColumn or ndarray or sequence

New column object to replace the existing column.

copybool

Make copy of the input col, default=True

See also

add_columns, astropy.table.hstack, update

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
round(decimals=0)#

Round numeric columns in-place to the specified number of decimals. Non-numeric columns will be ignored.

Parameters:
decimals: int, dict

Number of decimals to round the columns to. If a dict is given, the columns will be rounded to the number specified as the value. If a certain column is not in the dict given, it will remain the same.

Examples

Create three columns with different types:

>>> t = Table([[1, 4, 5], [-25.55, 12.123, 85],
...     ['a', 'b', 'c']], names=('a', 'b', 'c'))
>>> print(t)
 a    b     c
--- ------ ---
  1 -25.55   a
  4 12.123   b
  5   85.0   c

Round them all to 0:

>>> t.round(0)
>>> print(t)
 a    b    c
--- ----- ---
  1 -26.0   a
  4  12.0   b
  5  85.0   c

Round column ‘a’ to -1 decimal:

>>> t.round({'a':-1})
>>> print(t)
 a    b    c
--- ----- ---
  0 -26.0   a
  0  12.0   b
  0  85.0   c
select_obs_id(obs_id)[source]#

Get ObservationTable containing only obs_id.

Raises KeyError if observation is not available.

Parameters:
obs_idint or list of int

Observation ids.

select_observations(selections=None)[source]#

Select subset of observations from a list of selection criteria.

Returns a new observation table representing the subset.

There are 3 main kinds of selection criteria, according to the value of the type keyword in the selection dictionary:

  • circular region

  • time intervals (min, max)

  • intervals (min, max) on any other parameter present in the observation table, that can be cast into an Quantity object

Allowed selection criteria are interpreted using the following keywords in the selection dictionary under the type key.

  • sky_circle is a circular region centered in the coordinate

    marked by the lon and lat keywords, and radius radius

  • time_box is a 1D selection criterion acting on the observation start time (TSTART); the interval is set via the time_range keyword; uses select_time_range

  • par_box is a 1D selection criterion acting on any parameter defined in the observation table that can be casted into an Quantity object; the parameter name and interval can be specified using the keywords variable and value_range respectively; min = max selects exact values of the parameter; uses select_range

In all cases, the selection can be inverted by activating the inverted flag, in which case, the selection is applied to keep all elements outside the selected range.

A few examples of selection criteria are given below.

Parameters:
selectionslist of dict, optional

Dictionary of selection criteria. Default is None.

Returns:
obs_tableObservationTable

Observation table after selection.

Examples

>>> from gammapy.data import ObservationTable
>>> obs_table = ObservationTable.read('$GAMMAPY_DATA/hess-dl3-dr1/obs-index.fits.gz')
>>> from astropy.coordinates import Angle
>>> selection = dict(type='sky_circle', frame='galactic',
...                  lon=Angle(0, 'deg'),
...                  lat=Angle(0, 'deg'),
...                  radius=Angle(5, 'deg'),
...                  border=Angle(2, 'deg'))
>>> selected_obs_table = obs_table.select_observations(selection)

>>> from astropy.time import Time
>>> time_range = Time(['2012-01-01T01:00:00', '2012-01-01T02:00:00'])
>>> selection = dict(type='time_box', time_range=time_range)
>>> selected_obs_table = obs_table.select_observations(selection)

>>> value_range = Angle([60., 70.], 'deg')
>>> selection = dict(type='par_box', variable='ALT_PNT', value_range=value_range)
>>> selected_obs_table = obs_table.select_observations(selection)

>>> selection = dict(type='par_box', variable='OBS_ID', value_range=[2, 5])
>>> selected_obs_table = obs_table.select_observations(selection)

>>> selection = dict(type='par_box', variable='N_TELS', value_range=[4, 4])
>>> selected_obs_table = obs_table.select_observations(selection)
select_range(selection_variable, value_range, inverted=False)[source]#

Make an observation table, applying some selection.

Generic function to apply a 1D box selection (min, max) to a table on any variable that is in the observation table and can be cast into a Quantity object.

If the range length is 0 (min = max), the selection is applied to the exact value indicated by the min value. This is useful for selection of exact values, for instance in discrete variables like the number of telescopes.

If the inverted flag is activated, the selection is applied to keep all elements outside the selected range.

Parameters:
selection_variablestr

Name of variable to apply a cut (it should exist on the table).

value_rangeQuantity-like

Allowed range of values (min, max). The type should be consistent with the selection_variable.

invertedbool, optional

Invert selection: keep all entries outside the (min, max) range. Default is False.

Returns:
obs_tableObservationTable

Observation table after selection.

select_sky_circle(center, radius, inverted=False)[source]#

Make an observation table, applying a cone selection.

Apply a selection based on the separation between the cone center and the observation pointing stored in the table.

If the inverted flag is activated, the selection is applied to keep all elements outside the selected range.

Parameters:
centerSkyCoord

Cone center coordinate.

radiusAngle

Cone opening angle. The maximal separation allowed between the center and the observation pointing direction.

invertedbool, optional

Invert selection: keep all entries outside the cone. Default is False.

Returns:
obs_tableObservationTable

Observation table after selection.

select_time_range(time_range, partial_overlap=False, inverted=False)[source]#

Make an observation table, applying a time selection.

Apply a 1D box selection (min, max) to a table on any time variable that is in the observation table. It supports absolute times in Time format.

If the inverted flag is activated, the selection is applied to keep all elements outside the selected range.

Parameters:
time_rangeTime

Allowed time range (min, max).

partial_overlapbool, optional

Include partially overlapping observations. Default is False.

invertedbool, optional

Invert selection: keep all entries outside the (min, max) range. Default is False.

Returns:
obs_tableObservationTable

Observation table after selection.

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

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

Parameters:
max_linesint

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.

jsviewerbool

If True, prepends some javascript headers so that the table is rendered as a DataTables data table. This allows in-browser searching & sorting.

browserstr

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.

jskwargsdict

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.

tableidstr 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_classstr 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 https://www.datatables.net/manual/styling/classes

cssstr

A valid CSS string declaring the formatting for the table. Defaults to astropy.table.jsviewer.DEFAULT_CSS.

show_row_indexstr 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='astropy-default', show_row_index='idx')#

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

Parameters:
tableidstr 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_classstr 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.

cssstr

A valid CSS string declaring the formatting for the table. Defaults to astropy.table.jsviewer.DEFAULT_CSS_NB.

display_lengthint, optional

Number or rows to show. Defaults to 50.

show_row_indexstr 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, *, kind=None, reverse=False)#

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

Parameters:
keysstr or list of str

The key(s) to order the table by. If None, use the primary index of the Table.

kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional

Sorting algorithm used by numpy.argsort.

reversebool

Sort in reverse order (default=False)

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

Sorting according to standard sorting rules, first ‘firstname’ then ‘tel’, in reverse order:

>>> t.sort(['firstname', 'tel'], reverse=True)
>>> print(t)
firstname   name  tel
--------- ------- ---
      Max  Miller  12
     John Jackson  18
       Jo  Miller  15
summary()[source]#

Summary information string.

to_pandas(index=None, use_nullable_int=True)#

Return a pandas.DataFrame instance

The index of the created DataFrame is controlled by the index argument. For index=True or the default None, an index will be specified for the DataFrame if there is a primary key index on the Table and if it corresponds to a single column. If index=False then no DataFrame index will be specified. If index is the name of a column in the table then that will be the DataFrame index.

In addition to vanilla columns or masked columns, this supports Table mixin columns like Quantity, Time, or SkyCoord. In many cases these objects have no analog in pandas and will be converted to a “encoded” representation using only Column or MaskedColumn. The exception is Time or TimeDelta columns, which will be converted to the corresponding representation in pandas using np.datetime64 or np.timedelta64. See the example below.

Parameters:
indexNone, bool, str

Specify DataFrame index mode

use_nullable_intbool, default=True

Convert integer MaskedColumn to pandas nullable integer type. If use_nullable_int=False or the pandas version does not support nullable integer types (version < 0.24), then the column is converted to float with NaN for missing elements and a warning is issued.

Returns:
dataframepandas.DataFrame

A pandas pandas.DataFrame instance

Raises:
ImportError

If pandas is not installed

ValueError

If the Table has multi-dimensional columns

Examples

Here we convert a table with a few mixins to a pandas.DataFrame instance.

>>> import pandas as pd
>>> from astropy.table import QTable
>>> import astropy.units as u
>>> from astropy.time import Time, TimeDelta
>>> from astropy.coordinates import SkyCoord

>>> q = [1, 2] * u.m
>>> tm = Time([1998, 2002], format='jyear')
>>> sc = SkyCoord([5, 6], [7, 8], unit='deg')
>>> dt = TimeDelta([3, 200] * u.s)

>>> t = QTable([q, tm, sc, dt], names=['q', 'tm', 'sc', 'dt'])

>>> df = t.to_pandas(index='tm')
>>> with pd.option_context('display.max_columns', 20):
...     print(df)
              q  sc.ra  sc.dec              dt
tm
1998-01-01  1.0    5.0     7.0 0 days 00:00:03
2002-01-01  2.0    6.0     8.0 0 days 00:03:20
update(other, copy=True)#

Perform a dictionary-style update and merge metadata.

The argument other must be a Table, or something that can be used to initialize a table. Columns from (possibly converted) other are added to this table. In case of matching column names the column from this table is replaced with the one from other.

Parameters:
othertable-like

Data to update this table with.

copybool

Whether the updated columns should be copies of or references to the originals.

See also

add_columns, astropy.table.hstack, replace_column

Examples

Update a table with another table:

>>> t1 = Table({'a': ['foo', 'bar'], 'b': [0., 0.]}, meta={'i': 0})
>>> t2 = Table({'b': [1., 2.], 'c': [7., 11.]}, meta={'n': 2})
>>> t1.update(t2)
>>> t1
<Table length=2>
 a      b       c
str3 float64 float64
---- ------- -------
 foo     1.0     7.0
 bar     2.0    11.0
>>> t1.meta
{'i': 0, 'n': 2}

Update a table with a dictionary:

>>> t = Table({'a': ['foo', 'bar'], 'b': [0., 0.]})
>>> t.update({'b': [1., 2.]})
>>> t
<Table length=2>
 a      b
str3 float64
---- -------
 foo     1.0
 bar     2.0
values()#
values_equal(other)#

Element-wise comparison of table with another table, list, or scalar.

Returns a Table with the same columns containing boolean values showing result of comparison.

Parameters:
othertable-like object or list or scalar

Object to compare with table

Examples

Compare 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.values_equal(t2)
<Table length=2>
 a     b     c
bool  bool  bool
---- ----- -----
True False False
True  True  True
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