# Rank

There 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.

## Competition rank

The basic rank supported by agate is standard “competition ranking”. In this model the values `[3, 4, 4, 5]` would be ranked `[1, 2, 2, 4]`. You can apply competition ranking using the `Rank` computation:

    new_table = table.compute([
        ('rank', agate.Rank('value'))
    ])

## Rank descending

Descending competition ranking is specified using the `reverse` argument.

    new_table = table.compute([
        ('rank', agate.Rank('value', reverse=True))
    ])

## Rank change

You can compute the change from one rank to another by combining the `Rank` and `Change` computations:

    new_table = table.compute([
        ('rank2014', agate.Rank('value2014')),
        ('rank2015', agate.Rank('value2015'))
    ])

    new_table2 = new_table.compute([
        ('rank_change', agate.Change('rank2014', 'rank2015'))
    ])

## Percentile rank

“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 `PercentileRank` computation:

    new_table = table.compute([
        ('percentile_rank', agate.PercentileRank('value'))
    ])

Note 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 `Percentiles` class documentation for implementation details.