Accessing table properties and data is generally consistent with the basic
interface for numpy structured arrays.
For a quick overview, the code below shows the basics of accessing table data. Where relevant, there is a comment about what sort of object is returned. Except where noted, table access returns objects that can be modified in order to update the original table data or properties. See also the section on :ref:`copy_versus_reference` to learn more about this topic.
Make a table
from astropy.table import Table
import numpy as np
arr = np.arange(15).reshape(5, 3)
t = Table(arr, names=('a', 'b', 'c'), meta={'keywords': {'key1': 'val1'}})
Table properties
t.columns # Dict of table columns (access by column name, index, or slice) t.colnames # List of column names t.meta # Dict of meta-data len(t) # Number of table rows
Access table data
t['a'] # Column 'a'
t['a'][1] # Row 1 of column 'a'
t[1] # Row 1
t[1]['a'] # Column 'a' of row 1
t[1][1:] # Row 1, columns b and c
t[2:5] # Table object with rows 2:5
t[[1, 3, 4]] # Table object with rows 1, 3, 4 (copy)
t[np.array([1, 3, 4])] # Table object with rows 1, 3, 4 (copy)
t[[]] # Same table definition but with no rows of data
t['a', 'c'] # Table with cols 'a', 'c' (copy)
dat = np.array(t) # Copy table data to numpy structured array object
t['a'].quantity # an astropy.units.Quantity for Column 'a'
t['a'].to('km') # an astropy.units.Quantity for Column 'a' in units of kilometers
t.columns[1] # Column 1 (which is the 'b' column)
t.columns[0:2] # New table with columns 0 and 1
Note
Although they appear nearly equivalent, there is a factor of two performance
difference between t[1]['a'] (slower, because an intermediate |Row|
object gets created) versus t['a'][1] (faster). Always use the latter
when possible.
Print table or column
print(t) # Print formatted version of table to the screen t.pprint() # Same as above t.pprint(show_unit=True) # Show column unit t.pprint(show_name=False) # Do not show column names t.pprint_all() # Print full table no matter how long / wide it is (same as t.pprint(max_lines=-1, max_width=-1)) t.more() # Interactively scroll through table like Unix "more" print(t['a']) # Formatted column values t['a'].pprint() # Same as above, with same options as Table.pprint() t['a'].more() # Interactively scroll through column t['a', 'c'].pprint() # Print columns 'a' and 'c' of table lines = t.pformat() # Formatted table as a list of lines (same options as pprint) lines = t['a'].pformat() # Formatted column values as a list
For all of the following examples it is assumed that the table has been created as follows:
>>> from astropy.table import Table, Column
>>> import numpy as np
>>> import astropy.units as u
>>> arr = np.arange(15, dtype=np.int32).reshape(5, 3)
>>> t = Table(arr, names=('a', 'b', 'c'), meta={'keywords': {'key1': 'val1'}})
>>> t['a'].format = "{:.3f}" # print with 3 digits after decimal point
>>> t['a'].unit = 'm sec^-1'
>>> t['a'].description = 'unladen swallow velocity'
>>> print(t)
a b c
m sec^-1
-------- --- ---
0.000 1 2
3.000 4 5
6.000 7 8
9.000 10 11
12.000 13 14
Note
In the example above the format, unit, and description
attributes of the |Column| were set directly. For :ref:`mixin_columns` like
|Quantity| you must set via the info attribute, for example,
t['a'].info.format = "{:.3f}". You can use the info attribute with
|Column| objects as well, so the general solution that works with any table
column is to set via the info attribute. See :ref:`mixin_attributes` for
more information.
You can get summary information about the table as follows:
>>> t.info
<Table length=5>
name dtype unit format description
---- ----- -------- ------ ------------------------
a int32 m sec^-1 {:.3f} unladen swallow velocity
b int32
c int32
If called as a function then you can supply an option that specifies
the type of information to return. The built-in option choices are
'attributes' (column attributes, which is the default) or 'stats'
(basic column statistics). The option argument can also be a list
of available options:
>>> t.info('stats') # doctest: +FLOAT_CMP
<Table length=5>
name mean std min max
---- ---- ------- --- ---
a 6 4.24264 0 12
b 7 4.24264 1 13
c 8 4.24264 2 14
>>> t.info(['attributes', 'stats']) # doctest: +FLOAT_CMP
<Table length=5>
name dtype unit format description mean std min max
---- ----- -------- ------ ------------------------ ---- ------- --- ---
a int32 m sec^-1 {:.3f} unladen swallow velocity 6 4.24264 0 12
b int32 7 4.24264 1 13
c int32 8 4.24264 2 14
Columns also have an info property that has the same behavior and
arguments, but provides information about a single column:
>>> t['a'].info
name = a
dtype = int32
unit = m sec^-1
format = {:.3f}
description = unladen swallow velocity
class = Column
n_bad = 0
length = 5
>>> t['a'].info('stats') # doctest: +FLOAT_CMP
name = a
mean = 6
std = 4.24264
min = 0
max = 12
n_bad = 0
length = 5
The code below shows accessing the table columns as a |TableColumns| object, getting the column names, table metadata, and number of table rows. The table metadata is a :class:`dict` by default.
>>> t.columns
<TableColumns names=('a','b','c')>
>>> t.colnames
['a', 'b', 'c']
>>> t.meta # Dict of meta-data
{'keywords': {'key1': 'val1'}}
>>> len(t)
5
As expected you can access a table column by name and get an element from that column with a numerical index:
>>> t['a'] # Column 'a'
<Column name='a' dtype='int32' unit='m sec^-1' format='{:.3f}' description='unladen swallow velocity' length=5>
0.000
3.000
6.000
9.000
12.000
>>> t['a'][1] # Row 1 of column 'a'
np.int32(3)
When a table column is printed, it is formatted according to the format
attribute (see :ref:`table_format_string`). Note the difference between the
column representation above and how it appears via print() or str():
>>> print(t['a']) a m sec^-1 -------- 0.000 3.000 6.000 9.000 12.000
Likewise a table row and a column from that row can be selected:
>>> t[1] # Row object corresponding to row 1 <Row index=1> a b c m sec^-1 int32 int32 int32 -------- ----- ----- 3.000 4 5 >>> t[1]['a'] # Column 'a' of row 1 np.int32(3)
A |Row| object has the same columns and metadata as its parent table:
>>> t[1].columns
<TableColumns names=('a','b','c')>
>>> t[1].meta
{'keywords': {'key1': 'val1'}}
Slicing a table returns a new table object with references to the original data within the slice region (See :ref:`copy_versus_reference`). The table metadata and column definitions are copied.
>>> t[2:5] # Table object with rows 2:5 (reference) <Table length=3> a b c m sec^-1 int32 int32 int32 -------- ----- ----- 6.000 7 8 9.000 10 11 12.000 13 14
It is possible to select table rows with an array of indexes or by specifying multiple column names. This returns a copy of the original table for the selected rows or columns.
>>> print(t[[1, 3, 4]]) # Table object with rows 1, 3, 4 (copy)
a b c
m sec^-1
-------- --- ---
3.000 4 5
9.000 10 11
12.000 13 14
>>> print(t[np.array([1, 3, 4])]) # Table object with rows 1, 3, 4 (copy)
a b c
m sec^-1
-------- --- ---
3.000 4 5
9.000 10 11
12.000 13 14
>>> print(t['a', 'c']) # or t[['a', 'c']] or t[('a', 'c')]
... # Table with cols 'a', 'c' (copy)
a c
m sec^-1
-------- ---
0.000 2
3.000 5
6.000 8
9.000 11
12.000 14
We can select rows from a table using conditionals to create boolean masks. A table indexed with a boolean array will only return rows where the mask array element is True. Different conditionals can be combined using the bitwise operators.
>>> mask = (t['a'] > 4) & (t['b'] > 8) # Table rows where column a > 4
>>> print(t[mask]) # and b > 8
...
a b c
m sec^-1
-------- --- ---
9.000 10 11
12.000 13 14
Finally, you can access the underlying table data as a native numpy
structured array by creating a copy or reference with :func:`numpy.array`:
>>> data = np.array(t) # copy of data in t as a structured array >>> data = np.array(t, copy=False) # reference to data in t
In some cases it might not be guaranteed that a column is present in a table,
but there does exist a good default value that can be used if it is not. The
columns of a |Table| can be represented as a :class:`dict` subclass instance
through the columns attribute, which means that a replacement for missing
columns can be provided using the :meth:`dict.get` method:
>>> t.columns.get("b", np.zeros(len(t)))
<Column name='b' dtype='int32' length=5>
1
4
7
10
13
>>> t.columns.get("x", np.zeros(len(t)))
array([0., 0., 0., 0., 0.])
In case of a single |Row| it is possible to use its
:meth:`~astropy.table.Row.get` method without having to go through
columns:
>>> row = t[2]
>>> row.get("c", -1)
np.int32(8)
>>> row.get("y", -1)
-1
We can check table data equality using two different methods:
- The
==comparison operators. In the general case, this returns a 1D array withdtype=boolmapping each row toTrueif and only if the entire row matches. For incomparable data (differentdtypeor unbroadcastable lengths), a booleanFalseis returned. This is in contrast to the behavior ofnumpywhere trying to compare structured arrays might raise exceptions. - Table :meth:`~astropy.table.Table.values_equal` to compare table values element-wise. This returns a boolean True or False for each table element, so you get a ~astropy.table.Table of values.
Note
both methods will report equality after broadcasting, which
matches numpy array comparison.
To check table equality:
>>> t1 = Table(rows=[[1, 2, 3], ... [4, 5, 6], ... [7, 7, 9]], names=['a', 'b', 'c']) >>> t2 = Table(rows=[[1, 2, -1], ... [4, -1, 6], ... [7, 7, 9]], names=['a', 'b', 'c']) >>> t1 == t2 array([False, False, True]) >>> t1.values_equal(t2) # Compare to another table <Table length=3> a b c bool bool bool ---- ----- ----- True True False True False True True True True >>> t1.values_equal([2, 4, 7]) # Compare to an array column-wise <Table length=3> a b c bool bool bool ----- ----- ----- False True False True False False True True False >>> t1.values_equal(7) # Compare to a scalar column-wise <Table length=3> a b c bool bool bool ----- ----- ----- False False False False False False True True False
The values in a table or column can be printed or retrieved as a formatted table using one of several methods:
- print() function.
- Table.more() <astropy.table.Table.more> or Column.more() <astropy.table.Column.more> methods to interactively scroll through table values.
- Table.pprint() <astropy.table.Table.pprint> or Column.pprint() <astropy.table.Column.pprint> methods to print a formatted version of the table to the screen.
- Table.pformat() <astropy.table.Table.pformat> or Column.pformat() <astropy.table.Column.pformat> methods to return the formatted table or column as a list of fixed-width strings. This could be used as a quick way to save a table.
These methods use :ref:`table_format_string` if available and strive to make the output readable. By default, table and column printing will not print the table larger than the available interactive screen size. If the screen size cannot be determined (in a non-interactive environment or on Windows) then a default size of 25 rows by 80 columns is used. If a table is too large, then rows and/or columns are cut from the middle so it fits.
To print a formatted table:
>>> arr = np.arange(3000).reshape(100, 30) # 100 rows x 30 columns array >>> t = Table(arr) >>> from astropy.table import conf >>> conf.max_width = 80 >>> conf.max_lines = 20 >>> print(t) col0 col1 col2 col3 col4 col5 col6 ... col23 col24 col25 col26 col27 col28 col29 ---- ---- ---- ---- ---- ---- ---- ... ----- ----- ----- ----- ----- ----- ----- 0 1 2 3 4 5 6 ... 23 24 25 26 27 28 29 30 31 32 33 34 35 36 ... 53 54 55 56 57 58 59 60 61 62 63 64 65 66 ... 83 84 85 86 87 88 89 90 91 92 93 94 95 96 ... 113 114 115 116 117 118 119 120 121 122 123 124 125 126 ... 143 144 145 146 147 148 149 150 151 152 153 154 155 156 ... 173 174 175 176 177 178 179 180 181 182 183 184 185 186 ... 203 204 205 206 207 208 209 210 211 212 213 214 215 216 ... 233 234 235 236 237 238 239 240 241 242 243 244 245 246 ... 263 264 265 266 267 268 269 270 271 272 273 274 275 276 ... 293 294 295 296 297 298 299 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 2700 2701 2702 2703 2704 2705 2706 ... 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 ... 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 ... 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 ... 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 ... 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 ... 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 ... 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 ... 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 ... 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 ... 2993 2994 2995 2996 2997 2998 2999 Length = 100 rows .. EXAMPLE END
In order to browse all rows of a table or column use the Table.more()
<astropy.table.Table.more> or Column.more() <astropy.table.Column.more>
methods. These let you interactively scroll through the rows much like the Unix
more command. Once part of the table or column is displayed the supported
navigation keys are:
In order to fully control the print output use the Table.pprint()
<astropy.table.Table.pprint> or Column.pprint()
<astropy.table.Column.pprint> methods. These have keyword arguments
max_lines, max_width, show_name, show_unit, and
show_dtype, with meanings as shown below:
>>> arr = np.arange(3000, dtype=float).reshape(100, 30)
>>> t = Table(arr)
>>> t['col0'].format = '%e'
>>> t['col0'].unit = 'km**2'
>>> t['col29'].unit = 'kg sec m**-2'
>>> t.pprint(max_lines=8, max_width=40)
col0 ... col29
km2 ... kg sec m**-2
------------ ... ------------
0.000000e+00 ... 29.0
... ... ...
2.940000e+03 ... 2969.0
2.970000e+03 ... 2999.0
Length = 100 rows
>>> t.pprint(max_lines=8, max_width=40, show_unit=False)
col0 ... col29
------------ ... ------
0.000000e+00 ... 29.0
... ... ...
2.940000e+03 ... 2969.0
2.970000e+03 ... 2999.0
Length = 100 rows
>>> t.pprint(max_lines=8, max_width=40, show_name=False)
km2 ... kg sec m**-2
------------ ... ------------
0.000000e+00 ... 29.0
3.000000e+01 ... 59.0
... ... ...
2.940000e+03 ... 2969.0
2.970000e+03 ... 2999.0
Length = 100 rows
>>> t.pprint(max_lines=8, max_width=40, show_dtype=True)
col0 col1 ... col29
km2 ... kg sec m**-2
float64 float64 ... float64
------------ ------- ... ------------
0.000000e+00 1.0 ... 29.0
... ... ... ...
2.970000e+03 2971.0 ... 2999.0
Length = 100 rows
In order to force printing all values regardless of the output length or width
use :meth:`~astropy.table.Table.pprint_all`, which is equivalent to setting
max_lines and max_width to -1 in :meth:`~astropy.table.Table.pprint`.
:meth:`~astropy.table.Table.pprint_all` takes the same arguments as :meth:`~astropy.table.Table.pprint`.
For the wide table in this example you see six lines of wrapped output like the
following:
>>> t.pprint_all(max_lines=8) # doctest: +SKIP
col0 col1 col2 col3 col4 col5 col6 col7 col8 col9 col10 col11 col12 col13 col14 col15 col16 col17 col18 col19 col20 col21 col22 col23 col24 col25 col26 col27 col28 col29
km2 kg sec m**-2
------------ ----------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------------
0.000000e+00 1.000000 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2.940000e+03 2941.000000 2942.0 2943.0 2944.0 2945.0 2946.0 2947.0 2948.0 2949.0 2950.0 2951.0 2952.0 2953.0 2954.0 2955.0 2956.0 2957.0 2958.0 2959.0 2960.0 2961.0 2962.0 2963.0 2964.0 2965.0 2966.0 2967.0 2968.0 2969.0
2.970000e+03 2971.000000 2972.0 2973.0 2974.0 2975.0 2976.0 2977.0 2978.0 2979.0 2980.0 2981.0 2982.0 2983.0 2984.0 2985.0 2986.0 2987.0 2988.0 2989.0 2990.0 2991.0 2992.0 2993.0 2994.0 2995.0 2996.0 2997.0 2998.0 2999.0
Length = 100 rows
For columns, the syntax and behavior of :func:`~astropy.table.Column.pprint` is
the same except that there is no max_width keyword argument:
>>> t['col3'].pprint(max_lines=8) col3 ------ 3.0 33.0 ... 2943.0 2973.0 Length = 100 rows
Individual columns have the ability to be aligned in a number of different ways for an enhanced viewing experience:
>>> t1 = Table()
>>> t1['long column name 1'] = [1, 2, 3]
>>> t1['long column name 2'] = [4, 5, 6]
>>> t1['long column name 3'] = [7, 8, 9]
>>> t1['long column name 4'] = [700000, 800000, 900000]
>>> t1['long column name 2'].info.format = '<'
>>> t1['long column name 3'].info.format = '0='
>>> t1['long column name 4'].info.format = '^'
>>> t1.pprint()
long column name 1 long column name 2 long column name 3 long column name 4
------------------ ------------------ ------------------ ------------------
1 4 000000000000000007 700000
2 5 000000000000000008 800000
3 6 000000000000000009 900000
Conveniently, alignment can be handled another way — by passing a list to the
keyword argument align:
>>> t1 = Table() >>> t1['column1'] = [1, 2, 3] >>> t1['column2'] = [2, 4, 6] >>> t1.pprint(align=['<', '0=']) column1 column2 ------- ------- 1 0000002 2 0000004 3 0000006
It is also possible to set the alignment of all columns with a single string value:
>>> t1.pprint(align='^') column1 column2 ------- ------- 1 2 2 4 3 6
The fill character for justification can be set as a prefix to the
alignment character (see Format Specification Mini-Language
for additional explanation). This can be done both in the align argument
and in the column format attribute. Note the interesting interaction below:
>>> t1 = Table([[1.0, 2.0], [1, 2]], names=['column1', 'column2']) >>> t1['column1'].format = '#^.2f' >>> t1.pprint() column1 column2 ------- ------- ##1.00# 1 ##2.00# 2
Now if we set a global align, it seems like our original column format got lost:
>>> t1.pprint(align='!<') column1 column2 ------- ------- 1.00!!! 1!!!!!! 2.00!!! 2!!!!!!
The way to avoid this is to explicitly specify the alignment strings for every column and use None where the column format should be used:
>>> t1.pprint(align=[None, '!<']) column1 column2 ------- ------- ##1.00# 1!!!!!! ##2.00# 2!!!!!!
In order to get the formatted output for manipulation or writing to a file use the Table.pformat() <astropy.table.Table.pformat> or Column.pformat() <astropy.table.Column.pformat> methods. These behave just as for :meth:`~astropy.table.Table.pprint` but return a list corresponding to each formatted line in the :meth:`~astropy.table.Table.pprint` output. The :meth:`~astropy.table.Table.pformat_all` method can be used to return a list for all lines in the |Table|.
>>> lines = t['col3'].pformat(max_lines=8)
The |Table| class has functionality to selectively show or hide certain columns within the table when using any of the print methods. This can be useful for columns that are very wide or else "uninteresting" for various reasons. The specification of which columns are outputted is associated with the table itself so that it persists through slicing, copying, and serialization (e.g. saving to :ref:`ecsv_format`). One use case is for specialized table subclasses that contain auxiliary columns that are not typically useful to the user.
The specification of which columns to include when printing is handled through two complementary |Table| attributes:
- ~astropy.table.Table.pprint_include_names: column names to include, where the default value of None implies including all columns.
- ~astropy.table.Table.pprint_exclude_names: column names to exclude, where the default value of None implies excluding no columns.
Typically you should use just one of the two attributes at a time. However, both can be set at once and the set of columns that actually gets printed is conceptually expressed in this pseudo-code:
include_names = (set(table.pprint_include_names() or table.colnames)
- set(table.pprint_exclude_names() or ())
Let's start with defining a simple table with one row and six columns:
>>> from astropy.table.table_helpers import simple_table >>> t = simple_table(size=1, cols=6) >>> print(t) a b c d e f --- --- --- --- --- --- 1 1.0 c 4 4.0 f
Now you can get the value of the pprint_include_names attribute by calling
it as a function, and then include some names for printing:
>>> print(t.pprint_include_names())
None
>>> t.pprint_include_names = ('a', 'c', 'e')
>>> print(t.pprint_include_names())
('a', 'c', 'e')
>>> print(t)
a c e
--- --- ---
1 c 4.0
Now you can instead exclude some columns from printing. Note that for both include and exclude, you can add column names that do not exist in the table. This allows pre-defining the attributes before the table has been fully constructed.
>>> t.pprint_include_names = None # Revert to printing all columns
>>> t.pprint_exclude_names = ('a', 'c', 'e', 'does-not-exist')
>>> print(t)
b d f
--- --- ---
1.0 4 f
Next you can add or remove names from the attribute:
>>> t = simple_table(size=1, cols=6) # Start with a fresh table
>>> t.pprint_exclude_names.add('b') # Single name
>>> t.pprint_exclude_names.add(['d', 'f']) # List or tuple of names
>>> t.pprint_exclude_names.remove('f') # Single name or list/tuple of names
>>> t.pprint_exclude_names()
('b', 'd')
Finally, you can temporarily set the attributes within a context manager. For example:
>>> t = simple_table(size=1, cols=6)
>>> t.pprint_include_names = ('a', 'b')
>>> print(t)
a b
--- ---
1 1.0
>>> # Show all (for pprint_include_names the value of None => all columns)
>>> with t.pprint_include_names.set(None):
... print(t)
a b c d e f
--- --- --- --- --- ---
1 1.0 c 4 4.0 f
The specification of names for these attributes can include Unix-style globs
like * and ?. See fnmatch for details (and in particular how to
escape those characters if needed). For example:
>>> t = Table() >>> t.pprint_exclude_names = ['boring*'] >>> t['a'] = [1] >>> t['b'] = ['b'] >>> t['boring_ra'] = [122.0] >>> t['boring_dec'] = [89.9] >>> print(t) a b --- --- 1 b
If a column has more than one dimension then each element of the column is
itself an array. In the example below there are three rows, each of which is a
2 x 2 array. The formatted output for such a column shows only the first
and last value of each row element and indicates the array dimensions in the
column name header:
>>> t = Table()
>>> arr = [ np.array([[ 1., 2.],
... [10., 20.]]),
... np.array([[ 3., 4.],
... [30., 40.]]),
... np.array([[ 5., 6.],
... [50., 60.]]) ]
>>> t['a'] = arr
>>> t['a'].shape
(3, 2, 2)
>>> t.pprint()
a
-----------
1.0 .. 20.0
3.0 .. 40.0
5.0 .. 60.0
There are two ways to see all of the data values for a multidimensional column. First,
you can set the astropy.table.conf.format_size_threshold configuration option
to a value greater than or equal to the number of items in each column row (4 in this
case). This respects any formatting options that are defined for the column.
>>> from astropy.table import conf
>>> with conf.set_temp("format_size_threshold", 4):
... t.pprint()
...
a
-----------------------
[[1.0 2.0] [10.0 20.0]]
[[3.0 4.0] [30.0 40.0]]
[[5.0 6.0] [50.0 60.0]]A second option is to print the column as a numpy array which uses the
standard numpy mechanism for printing the array:
>>> t['a'].data
array([[[ 1., 2.],
[10., 20.]],
[[ 3., 4.],
[30., 40.]],
[[ 5., 6.],
[50., 60.]]])
For columns which are structured arrays, the format string must be a string
that uses "new style" format strings with
parameter substitutions corresponding to the field names in the structured
array. Consider the example below including a column of parameters values where
the value, min and max are stored in the in the column as fields named val,
min, and max. By default the field values are shown as a tuple:
>>> pars = np.array(
... [(1.2345678, -20, 3),
... (12.345678, 4.5678, 33)],
... dtype=[('val', 'f8'), ('min', 'f8'), ('max', 'f8')]
... )
>>> t = Table()
>>> t['a'] = [1, 2]
>>> t['par'] = pars
>>> print(t)
a par [val, min, max]
--- -------------------------
1 (1.2345678, -20.0, 3.0)
2 (12.345678, 4.5678, 33.0)
However, setting the format string appropriately allows formatting each of the field values and controlling the overall output:
>>> t['par'].info.format = '{val:6.2f} ({min:5.1f}, {max:5.1f})'
>>> print(t)
a par [val, min, max]
--- ---------------------
1 1.23 (-20.0, 3.0)
2 12.35 ( 4.6, 33.0)
Note
|Table| and |QTable| instances handle entries with units differently. The following describes |Table|. :ref:`quantity_and_qtable` explains how a |QTable| differs from a |Table|.
A |Column| object with units within a standard |Table| has certain quantity-related conveniences available. To begin with, it can be converted explicitly to a |Quantity| object via the :attr:`~astropy.table.Column.quantity` property and the :meth:`~astropy.table.Column.to` method:
>>> data = [[1., 2., 3.], [40000., 50000., 60000.]]
>>> t = Table(data, names=('a', 'b'))
>>> t['a'].unit = u.m
>>> t['b'].unit = 'km/s'
>>> t['a'].quantity # doctest: +FLOAT_CMP
<Quantity [1., 2., 3.] m>
>>> t['b'].to(u.kpc/u.Myr) # doctest: +FLOAT_CMP
<Quantity [40.9084866 , 51.13560825, 61.3627299 ] kpc / Myr>
Note that the :attr:`~astropy.table.Column.quantity` property is actually a view of the data in the column, not a copy. Hence, you can set the values of a column in a way that respects units by making in-place changes to the :attr:`~astropy.table.Column.quantity` property:
>>> t['b'] <Column name='b' dtype='float64' unit='km / s' length=3> 40000.0 50000.0 60000.0 >>> t['b'].quantity[0] = 45000000*u.m/u.s >>> t['b'] <Column name='b' dtype='float64' unit='km / s' length=3> 45000.0 50000.0 60000.0
For a |MaskedColumn|, the :attr:`~astropy.table.MaskedColumn.quantity` property and the :meth:`~astropy.table.Column.to` method return a masked |Quantity| (see :ref:`utils-masked`), so masked elements stay masked. The :attr:`~astropy.table.MaskedColumn.quantity` view shares the mask as well as the data with the column:
>>> from astropy.table import MaskedColumn >>> mc = MaskedColumn([1., 2., 3.], unit=u.m, mask=[False, False, True]) >>> mc.quantity <MaskedQuantity [1., 2., ——] m> >>> mc.to(u.cm) <MaskedQuantity [100., 200., ———] cm>
Even without explicit conversion, columns with units can be treated like a |Quantity| in some arithmetic expressions (see the warning below for caveats to this):
>>> t['a'] + .005*u.km # doctest: +FLOAT_CMP <Quantity [6., 7., 8.] m> >>> from astropy.constants import c >>> (t['b'] / c).decompose() # doctest: +FLOAT_CMP <Quantity [0.15010384, 0.16678205, 0.20013846]>
Warning
|Table| columns do not always behave the same as |Quantity|. |Table|
columns act more like regular numpy arrays unless either explicitly
converted to a |Quantity| or combined with a |Quantity| using an arithmetic
operator. For example, the following does not work in the way you would
expect:
>>> data = [[30, 90]]
>>> t = Table(data, names=('angle',))
>>> t['angle'].unit = 'deg'
>>> np.sin(t['angle']) # doctest: +FLOAT_CMP
<Column name='angle' dtype='float64' unit='deg' length=2>
-0.988031624093
0.893996663601
This is wrong both in that it says the result is in degrees, and ~numpy.sin treated the values as radians rather than degrees. If at all in doubt that you will get the right result, the safest choice is to either use |QTable| or to explicitly convert to |Quantity|:
>>> np.sin(t['angle'].quantity) # doctest: +FLOAT_CMP <Quantity [0.5, 1. ]>
Using bytestring columns (numpy 'S' dtype) is possible
with astropy tables since they can be compared with the natural
Python string (str) type. See The bytes/str dichotomy in Python 3
for a very brief overview of the difference.
The standard method of representing strings in numpy is via the
unicode 'U' dtype. The problem is that this requires 4 bytes per
character, and if you have a very large number of strings this could
fill memory and impact performance. A very common use case is that these
strings are actually ASCII and can be represented with 1 byte per character.
In astropy it is possible to work directly and conveniently with
bytestring data in |Table| and |Column| operations.
Note that the bytestring issue is a particular problem when dealing with HDF5
files, where character data are read as bytestrings ('S' dtype) when using
the :ref:`table_io`. Since HDF5 files are frequently used to store very large
datasets, the memory bloat associated with conversion to 'U' dtype is
unacceptable.
The examples below illustrate dealing with bytestring data in astropy:
>>> t = Table([['abc', 'def']], names=['a'], dtype=['S'])
>>> t['a'] == 'abc' # Gives expected answer
array([ True, False])
>>> t['a'] == b'abc' # Still gives expected answer
array([ True, False])
>>> t['a'][0] == 'abc' # Expected answer
True
>>> t['a'][0] == b'abc' # Cannot compare to bytestring
False
>>> t['a'][0] = 'bä'
>>> t
<Table length=2>
a
bytes3
------
bä
def
>>> t['a'] == 'bä'
array([ True, False])
.. doctest-skip::
>>> # Round trip unicode strings through HDF5
>>> t.write('test.hdf5', format='hdf5', path='data', overwrite=True)
>>> t2 = Table.read('test.hdf5', format='hdf5', path='data')
>>> t2
<Table length=2>
col0
bytes3
------
bä
def