# Accessing a table¶

Accessing the table properties and data is straightforward and is generally consistent with the basic interface for numpy structured arrays.

Warning

Astropy 2.0 introduces an API change that affects comparison of bytestring column elements in Python 3. See Bytestring columns in Python 3 for details.

## Quick overview¶

For the impatient, 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 Copy versus Reference to learn more about this topic.

Make 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 obj for with row 1 values
t[1]['a']    # Column 'a' of row 1
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(max_lines=-1, max_width=-1)  # Print full table no matter how long / wide it is

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

lines = t.pformat()  # Formatted table as a list of lines (same options as pprint)
lines = t['a'].pformat()  # Formatted column values as a list


## Details¶

For all the following examples it is assumed that the table has been created as below:

>>> 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 = "%6.3f"  # print as a float 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


### Summary 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  %6.3f unladen swallow velocity
b int32
c int32


If called as a function then one 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')
<Table length=5>
name mean      std      min max
---- ---- ------------- --- ---
a  6.0 4.24264068712   0  12
b  7.0 4.24264068712   1  13
c  8.0 4.24264068712   2  14

>>> t.info(['attributes', 'stats'])
<Table length=5>
name dtype   unit   format       description        mean      std      min max
---- ----- -------- ------ ------------------------ ---- ------------- --- ---
a int32 m sec^-1  %6.3f unladen swallow velocity  6.0 4.24264068712   0  12
b int32                                           7.0 4.24264068712   1  13
c int32                                           8.0 4.24264068712   2  14


Columns also have an info property that has the behavior and arguments, but provides information about a single column:

>>> t['a'].info
name = a
dtype = int32
unit = m sec^-1
format = %6.3f
class = Column
length = 5

>>> t['a'].info('stats')
name = a
mean = 6.0
std = 4.24264068712
min = 0
max = 12
length = 5


### Accessing properties¶

The code below shows accessing the table columns as a TableColumns object, getting the column names, table meta-data, and number of table rows. The table meta-data is simply an ordered dictionary (OrderedDict) 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


### Accessing data¶

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='%6.3f' description='unladen swallow velocity' length=5>
0.000
3.000
6.000
9.000
12.000

>>> t['a'][1]  # Row 1 of column 'a'
3


When a table column is printed, it is formatted according to the format attribute (see Format specifier). 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
3


A Row object has the same columns and meta-data as its parent table:

>>> t[1].columns
<TableColumns names=('a','b','c')>

>>> t[1].colnames
['a', 'b', 'c']


Slicing a table returns a new table object which references to the original data within the slice region (See Copy versus Reference). The table meta-data 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


Finally, you can access the underlying table data as a native numpy structured array by creating a copy or reference with np.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


### Formatted printing¶

The values in a table or column can be printed or retrieved as a formatted table using one of several methods:

These methods use Format specifier 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. For example:

>>> arr = np.arange(3000).reshape(100, 30)  # 100 rows x 30 columns array
>>> t = Table(arr)
>>> 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
...  ...  ...  ...  ...  ...  ... ...   ...   ...   ...   ...   ...   ...   ...
2670 2671 2672 2673 2674 2675 2676 ...  2693  2694  2695  2696  2697  2698  2699
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


#### more() method¶

In order to browse all rows of a table or column use the Table more() or Column more() methods. These let you interactively scroll through the rows much like the linux more command. Once part of the table or column is displayed the supported navigation keys are:

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

#### pprint() method¶

In order to fully control the print output use the Table pprint() or Column pprint() methods. These have keyword arguments max_lines, max_width, show_name, show_unit with meaning as shown below:

>>> arr = np.arange(3000, dtype=float).reshape(100, 30)
>>> t = Table(arr)
>>> t['col0'].format = '%e'
>>> t['col1'].format = '%.6f'
>>> 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=True)
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_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


In order to force printing all values regardless of the output length or width set max_lines or max_width to -1, respectively. For the wide table in this example you see 6 lines of wrapped output like the following:

>>> t.pprint(max_lines=8, max_width=-1)
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 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


#### Column alignment¶

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'].format = '<'
>>> t1['long column name 3'].format = '0='
>>> t1['long column name 4'].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!!!!!!


#### pformat() method¶

In order to get the formatted output for manipulation or writing to a file use the Table pformat() or Column pformat() methods. These behave just as for pprint() but return a list corresponding to each formatted line in the pprint() output.

>>> lines = t['col3'].pformat(max_lines=8)


#### Multidimensional columns¶

If a column has more than one dimension then each element of the column is itself an array. In the example below there are 3 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:

>>> from astropy.table import Table, Column
>>> import numpy as np
>>> 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 [2,2]
-------
1 .. 20
3 .. 40
5 .. 60


In order to see all the data values for a multidimensional column use the column representation. This uses the standard numpy mechanism for printing any array:

>>> t['a'].data
array([[[ 1,  2],
[10, 20]],
[[ 3,  4],
[30, 40]],
[[ 5,  6],
[50, 60]]])


#### Columns with Units¶

A Column object with units within a standard Table (as opposed to a QTable) has certain quantity-related conveniences available. To begin with, it can be converted explicitly to a Quantity object via the quantity property and the 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
<Quantity [1., 2., 3.] m>
>>> t['b'].to(u.kpc/u.Myr)
<Quantity [40.9084866 , 51.13560825, 61.3627299 ] kpc / Myr>


Note that the 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 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


Even without explicit conversion, columns with units can be treated like like an Astropy Quantity in some arithmetic expressions (see the warning below for caveats to this):

>>> t['a'] + .005*u.km
<Quantity [6., 7., 8.] m>
>>> from astropy.constants import c
>>> (t['b'] / c).decompose()
<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 an Quantity using an arithmetic operator.For example, the following does not work the way you would expect:

>>> import numpy as np
>>> from astropy.table import Table
>>> data = [[30, 90]]
>>> t = Table(data, names=('angle',))
>>> t['angle'].unit = 'deg'
>>> np.sin(t['angle'])
<Column name='angle' dtype='float64' unit='deg' length=2>
-0.988031624093
0.893996663601


This is wrong both in that it says the unit is degrees, and sin treated the values and radians rather than degrees. If at all in doubt that you’ll get the right result, the safest choice is to either use QTable or to explicitly convert to Quantity:

>>> np.sin(t['angle'].quantity)
<Quantity [0.5, 1. ]>


#### Bytestring columns in Python 3¶

Prior to astropy 2.0, using bytestring columns (numpy 'S' dtype) in Python 3 was inconvenient because it was not possible to compare with the natural Python string (str) type. See The bytes/str dichotomy in Python 3 for a very brief overview of the difference. In Python 2 this is not an issue because of the language itself blurs the distinction between bytes, string, and unicode.

The standard method of representing Python 3 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 in memory 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. Starting with astropy 2.0 it is possible to work directly and conveniently with bytestring data in astropy Table and Column

Taking this further, there is an advantage to using Python 3 because it is supported to reliably store arbitrary UTF-8 encoded characters in bytestring columns. This is not possible in Python 2, and in fact it should be emphasized that astropy 2.0 makes absolutely no change the handling of bytestrings for Python 2 – this only applies to Python 3.

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 Unified file read/write interface. Since HDF5 files are frequently used to store very large datasets, the memory bloat associated with conversion to 'U' dtype is unacceptable.

##### Python 3 examples¶

The examples below highlight the change in behavior introduced by astropy 2.0 for Python 3. In particular please note the API change when comparing with a single element of a bytestring column. Previously one was required to compare with a bytes object while now one must compare with a str object. When comparing with the entire column one can use either a bytes or str.

Before astropy 2.0

>>> from astropy.table import Table
>>> t = Table([['abc', 'def']], names=['a'], dtype=['S'])

>>> t['a'] == 'abc'  # WRONG answer!
False

>>> t['a'] == b'abc'  # Must explicitly compare to bytestring
array([ True, False], dtype=bool)

>>> t = Table([['bä', 'def']], dtype=['S'])
Traceback (most recent call last):
...
UnicodeEncodeError: 'ascii' codec can't encode character '\xe4' in position 1:
ordinal not in range(128)


Astropy 2.0 or later:

>>> t = Table([['abc', 'def']], names=['a'], dtype=['S'])

>>> t['a'] == 'abc'  # Gives expected answer
array([ True, False], dtype=bool)

>>> t['a'] == b'abc'  # Still gives expected answer
array([ True, False], dtype=bool)

>>> t['a'][0] == 'abc'  # Expected answer
True

>>> t['a'][0] == b'abc'  # API change, this NO LONGER WORKS
False

>>> t['a'][0] = 'bä'
>>> t
<Table length=2>
a
bytes3
------
bä
def

>>> t['a'] == 'bä'
array([ True, False], dtype=bool)

>>> # Round trip unicode strings through HDF5
>>> t = Table([['bä', 'def']], dtype=['S'])
>>> 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