# Statistics¶

Common descriptive and aggregate statistics are included with the core agate library. For additional statistical methods beyond the scope of agate consider using the agate-stats extension or integrating with scipy.

## Descriptive statistics¶

agate includes a full set of standard descriptive statistics that can be applied to any column containing `Number` data.

```table.aggregate(Sum('salary'))
table.aggregate(Min('salary'))
table.aggregate(Max('salary'))
table.aggregate(Mean('salary'))
table.aggregate(Median('salary'))
table.aggregate(Mode('salary'))
table.aggregate(Variance('salary'))
table.aggregate(StDev('salary'))
```

Or, get several at once:

```table.aggregate([
Min('salary'),
Mean('salary'),
Max('salary')
])
```

## Aggregate statistics¶

You can also generate aggregate statistics for subsets of data (sometimes referred to as “rolling up”):

```doctors = patients.group_by('doctor')
patient_ages = doctors.aggregate([
('patient_count', agate.Length())
('age_mean', agate.Mean('age')),
('age_median', agate.Median('age'))
])
```

The resulting table will have four columns: `doctor`, `patient_count`, `age_mean` and `age_median`.

## Identify outliers¶

The agate-stats extension adds methods for finding outliers.

```import agatestats

agatestats.patch()

outliers = table.stdev_outliers('salary', deviations=3, reject=False)
```

By specifying `reject=True` you can instead return a table including only those values not identified as outliers.

```not_outliers = table.stdev_outliers('salary', deviations=3, reject=True)
```

The second, more robust, method for identifying outliers is by identifying values which are more than some number of “median absolute deviations” from the median (typically 3).

```outliers = table.mad_outliers('salary', deviations=3, reject=False)
```

As with the first example, you can specify `reject=True` to exclude outliers in the resulting table.