Table Indexing#
Once a Table
has been created, it is possible to create indices on one or
more columns of the table. An index internally sorts the rows of a table based
on the index column(s), allowing for element retrieval by column value and
improved performance for certain table operations.
Creating an Index#
To create an index on a table, use the add_index()
method:
>>> from astropy.table import Table
>>> t = Table([(2, 3, 2, 1), (8, 7, 6, 5)], names=('a', 'b'))
>>> t.add_index('a')
The optional argument unique
may be specified to create an index with
uniquely valued elements.
To create a composite index on multiple columns, pass a list of columns instead:
>>> t.add_index(['a', 'b'])
In particular, the first index created using the
add_index()
method is considered the default index or the “primary key.” To
retrieve an index from a table, use the indices
property:
>>> t.indices['a']
<SlicedIndex original=True index=<Index columns=('a',) data=<SortedArray length=4>
a rows
--- ----
1 3
2 0
2 2
3 1>>
>>> t.indices['a', 'b']
<SlicedIndex original=True index=<Index columns=('a', 'b') data=<SortedArray length=4>
a b rows
--- --- ----
1 5 3
2 6 2
2 8 0
3 7 1>>
Row Retrieval using Indices#
Row retrieval can be accomplished using two table properties:
loc
and iloc
. The
loc
property can be indexed either by column value,
range of column values (including the bounds), or a list
or
numpy.ndarray
of column values:
>>> t = Table([(1, 2, 3, 4), (10, 1, 9, 9)], names=('a', 'b'), dtype=['i8', 'i8'])
>>> t.add_index('a')
>>> t.loc[2] # the row(s) where a == 2
<Row index=1>
a b
int64 int64
----- -----
2 1
>>> t.loc[[1, 4]] # the row(s) where a in [1, 4]
<Table length=2>
a b
int64 int64
----- -----
1 10
4 9
>>> t.loc[1:3] # the row(s) where a in [1, 2, 3]
<Table length=3>
a b
int64 int64
----- -----
1 10
2 1
3 9
>>> t.loc[:]
<Table length=4>
a b
int64 int64
----- -----
1 10
2 1
3 9
4 9
Note that by default, loc
uses the primary index, which
here is column 'a'
. To use a different index, pass the indexed column name
before the retrieval data:
>>> t.add_index('b')
>>> t.loc['b', 8:10]
<Table length=3>
a b
int64 int64
----- -----
3 9
4 9
1 10
The property iloc
works similarly, except that the
retrieval information must be either an integer or a slice
, and
relates to the sorted order of the index rather than column values. For
example:
>>> t.iloc[0] # smallest row by value 'a'
<Row index=0>
a b
int64 int64
----- -----
1 10
>>> t.iloc['b', 1:] # all but smallest value of 'b'
<Table length=3>
a b
int64 int64
----- -----
3 9
4 9
1 10
Effects on Performance#
Table operations change somewhat when indices are present, and there are a number of factors to consider when deciding whether the use of indices will improve performance. In general, indexing offers the following advantages:
Table grouping and sorting based on indexed column(s) both become faster.
Retrieving values by index is faster than custom searching.
There are certain caveats, however:
Creating an index requires time and memory.
Table modifications become slower due to automatic index updates.
Slicing a table becomes slower due to index relabeling.
See here for an IPython notebook profiling various aspects of table indexing.
Index Modes#
The index_mode()
method allows for some flexibility in the behavior of table
indexing by allowing the user to enter a specific indexing mode via a context
manager. There are currently three indexing modes: 'freeze'
,
'copy_on_getitem'
, and 'discard_on_copy'
.
The 'freeze'
mode prevents automatic index updates whenever a column of the
index is modified, and all indices refresh themselves after the context ends:
>>> with t.index_mode('freeze'):
... t['a'][0] = 0
... print(t.indices['a']) # unmodified
<SlicedIndex original=True index=<Index columns=('a',) data=<SortedArray length=4>
a rows
--- ----
1 0
2 1
3 2
4 3>>
>>> print(t.indices['a']) # modified
<SlicedIndex original=True index=<Index columns=('a',) data=<SortedArray length=4>
a rows
--- ----
0 0
2 1
3 2
4 3>>
The 'copy_on_getitem'
mode forces columns to copy and relabel their indices
upon slicing. In the absence of this mode, table slices will preserve
indices while column slices will not:
>>> ca = t['a'][[1, 3]]
>>> ca.info.indices
[]
>>> with t.index_mode('copy_on_getitem'):
... ca = t['a'][[1, 3]]
... print(ca.info.indices)
[<SlicedIndex original=True index=<Index columns=('a',) data=<SortedArray length=2>
a rows
--- ----
2 0
4 1>>]
The 'discard_on_copy'
mode prevents indices from being copied whenever a
column or table is copied:
>>> t2 = Table(t)
>>> t2.indices['a']
<SlicedIndex original=True index=<Index columns=('a',) data=<SortedArray length=4>
a rows
--- ----
0 0
2 1
3 2
4 3>>
>>> with t.index_mode('discard_on_copy'):
... t2 = Table(t)
... print(t2.indices)
[]
Updating Rows using Indices#
Row updates can be accomplished by assigning the table property
loc
a complete row or a list of rows:
>>> t = Table([('w', 'x', 'y', 'z'), (10, 1, 9, 9)], names=('a', 'b'), dtype=['str', 'i8'])
>>> t.add_index('a')
>>> t.loc['x']
<Row index=1>
a b
str1 int64
---- -----
x 1
>>> t.loc['x'] = ['a', 12]
>>> t
<Table length=4>
a b
str1 int64
---- -----
w 10
a 12
y 9
z 9
>>> t.loc[['w', 'y']]
<Table length=2>
a b
str1 int64
---- -----
w 10
y 9
>>> t.loc[['w', 'z']] = [['b', 23], ['c', 56]]
>>> t
<Table length=4>
a b
str1 int64
---- -----
b 23
a 12
y 9
c 56
Retrieving the Location of Rows using Indices#
Retrieval of the location of rows can be accomplished using a table property:
loc_indices
. The loc_indices
property can be indexed either by column value, range of column values
(including the bounds), or a list
or numpy.ndarray
of column values:
>>> t = Table([('w', 'x', 'y', 'z'), (10, 1, 9, 9)], names=('a', 'b'), dtype=['str', 'i8'])
>>> t.add_index('a')
>>> t.loc_indices['x']
1
Engines#
When creating an index via add_index()
, the keyword argument engine
may be
specified to use a particular indexing engine. The available engines are:
SortedArray
, a sorted array engine using an underlying sortedTable
.SCEngine
, a sorted list engine using the Sorted Containers package.BST
, a Python-based binary search tree engine (not recommended).
The SCEngine depends on the sortedcontainers
dependency. The most important takeaway is that
SortedArray
(the default engine) is usually best, although
SCEngine
may be more appropriate for an index created on an
empty column since adding new values is quicker.
The BST
engine demonstrates a simple pure Python implementation
of a search tree engine, but the performance is poor for larger tables. This
is available in the code largely as an implementation reference.