# Accessing a Table¶

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

## Basics¶

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 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']    # Row 1 of column 'a'
t         # Row 1
t['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  # 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['a']` (slower, because an intermediate `Row` object gets created) versus `t['a']` (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
```

## Details¶

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 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 Mixin Attributes for more information.

### 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 {:.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')
<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'])
<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
length = 5

>>> t['a'].info('stats')
name = a
mean = 6
std = 4.24264
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 metadata, and number of table rows. The table metadata is an `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='{:.3f}' description='unladen swallow velocity' length=5>
0.000
3.000
6.000
9.000
12.000

>>> t['a']  # 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  # Row object corresponding to row 1
<Row index=1>
a       b     c
m sec^-1
int32   int32 int32
-------- ----- -----
3.000     4     5

>>> t['a']  # Column 'a' of row 1
3
```

A `Row` object has the same columns and metadata as its parent table:

```>>> t.columns
<TableColumns names=('a','b','c')>

>>> t.meta
{'keywords': {'key1': 'val1'}}
```

Slicing a table returns a new table object with references to the original data within the slice region (See 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 `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
```

### Table Equality¶

We can check table data equality using two different methods:

#### Examples¶

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
```

### 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.

#### Example¶

To print a formatted table:

```>>> 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 Unix `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`, 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 `pprint_all()`, which is equivalent to setting `max_lines` and `max_width` to `-1` in `pprint()`. `pprint_all()` takes the same arguments as `pprint()`. For the wide table in this example you see six lines of wrapped output like the following:

```>>> t.pprint_all(max_lines=8)
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'].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!!!!!!
```

#### 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. The `pformat_all()` method can be used to return a list for all lines in the `Table`.

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

#### Hiding columns¶

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 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:

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 ())
```
##### Examples¶

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'] = 
>>> t['b'] = ['b']
>>> t['boring_ra'] = [122.0]
>>> t['boring_dec'] = [89.9]
>>> print(t)
a   b
--- ---
1   b
```

#### 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 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
```

In order to see all of 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.]]])
```

#### Structured array columns¶

For columns which are structured arrays, the format string must be a 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 defaul 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., 3.)
2 (12.345678, 4.5678, 33.)
```

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)
```

#### Columns with Units¶

Note

`Table` and `QTable` instances handle entries with units differently. The following describes `Table`. 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 `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 = 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 a `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 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'])
<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 `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)
<Quantity [0.5, 1. ]>
```

#### Bytestring Columns¶

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 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.

##### Examples¶

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'] == 'abc'  # Expected answer
True

>>> t['a'] == b'abc'  # Cannot compare to bytestring
False

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

>>> t['a'] == 'bä'
array([ True, False])
```
```>>> # 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
```