Accessing the table properties and data is straightforward and is generally consistent with the basic interface for numpy structured arrays.
For the impatient, the code below shows the basics of accessing table data. Where relevant there is a comment about what sort of object. Except where noted, the table access returns objects that can be modified in order to update table data or properties. In cases where is returned and how the data contained in that object relate to the original table data (i.e. whether it is a copy or reference, see Copy versus Reference).
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
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
>>> 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
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
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 1 of table
values=(3, 4, 5)
dtype=[('a', '<i4'), ('b', '<i8'), ('c', '<i8')]>
>>> 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 masked=False 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
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
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:
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
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)
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 that the astropy.units package understands can be converted explicitly to ~astropy.units.Quantity objects via the quantity property and the to() method:
>>> from astropy.table import Table
>>> from astropy import units as u
>>> 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 explicitly convert to Quantity:
>>> np.sin(t['angle'].quantity)
<Quantity [ 0.5, 1. ]>