1====
2Rank
3====
4
5There are many ways to rank a sequence of values. agate strives to find a balance between simple, intuitive ranking and flexibility when you need it.
6
7Competition rank
8================
9
10The basic rank supported by agate is standard "competition ranking". In this model the values :code:`[3, 4, 4, 5]` would be ranked :code:`[1, 2, 2, 4]`. You can apply competition ranking using the :class:`.Rank` computation:
11
12.. code-block:: python
13
14    new_table = table.compute([
15        ('rank', agate.Rank('value'))
16    ])
17
18Rank descending
19===============
20
21Descending competition ranking is specified using the :code:`reverse` argument.
22
23.. code-block:: python
24
25    new_table = table.compute([
26        ('rank', agate.Rank('value', reverse=True))
27    ])
28
29Rank change
30===========
31
32You can compute the change from one rank to another by combining the :class:`.Rank` and :class:`.Change` computations:
33
34.. code-block:: python
35
36    new_table = table.compute([
37        ('rank2014', agate.Rank('value2014')),
38        ('rank2015', agate.Rank('value2015'))
39    ])
40
41    new_table2 = new_table.compute([
42        ('rank_change', agate.Change('rank2014', 'rank2015'))
43    ])
44
45Percentile rank
46===============
47
48"Percentile rank" is a bit of a misnomer. Really, this is the percentile in which each value in a column is located. This column can be computed for your data using the :class:`.PercentileRank` computation:
49
50.. code-block:: Python
51
52    new_table = table.compute([
53        ('percentile_rank', agate.PercentileRank('value'))
54    ])
55
56Note that there is no entirely standard method for computing percentiles. The percentiles computed in this manner may not agree precisely with those generated by other software. See the :class:`.Percentiles` class documentation for implementation details.
57