# 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.

```
column = table.columns['salary']
column.aggregate(Sum())
column.aggregate(Min())
column.aggregate(Max())
column.aggregate(Mean())
column.aggregate(Median())
column.aggregate(Mode())
column.aggregate(Variance())
column.aggregate(StDev())
column.aggregate(MAD())
```

## Aggregate statistics¶

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

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

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.