# 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.