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.


Or, get several at once:


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

# 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


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()))


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