# 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'))
table.aggregate(MAD('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.Count())
('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`

.

## Distribution by count (frequency)¶

Counting the number of each unique value in a column can be accomplished with the `Table.pivot()`

method:

```
# Counts of a single column's values
table.pivot('doctor')
# Counts of all combinations of more than one column's values
table.pivot(['doctor', 'hospital'])
```

The resulting tables will have a column for each key column and another `Count`

column counting the number of instances of each value.

## Distribution by percent¶

`Table.pivot()`

can also be used to calculate the distribution of values as a percentage of the total number:

```
# Percents of a single column's values
table.pivot('doctor', computation=Percent('Count'))
# Percents of all combinations of more than one column's values
table.pivot(['doctor', 'hospital'], computation=Percent('Count'))
```

The output table will be the same format as the previous example, except the value column will be named `Percent`

.

## 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.

## Custom statistics¶

You can also generate custom aggregated statistics for your data by defining your own ‘summary’ aggregation. This might be especially useful for performing calculations unique to your data. Here’s a simple example:

```
# Create a custom summary aggregation with agate.Summary
# Input a column name, a return data type and a function to apply on the column
count_millionaires = agate.Summary('salary', agate.Number(), lambda r: sum(salary > 1000000 for salary in r.values()))
table.aggregate([
count_millionaires
])
```

Your custom aggregation can be used to determine both descriptive and aggregate statistics shown above.