Welcome to canomaly’s documentation!

Project Description

This package detects specific types of anomalies with an emphasis in looking for cumulative changes.

Installation

This package can be installed through PyPi using

pip install canomaly

or

pip3 install canomaly

Example Usage

>>> import pandas as pd
>>> from canomaly.searchtools import cumrexpy
>>> # Get some data
>>> data = {
            'date': [
                '2018-11-20',
                '2018-11-21',
                '2018-11-22',
                '2018-11-22',
                '2018-11-23',
                '2018-11-24'],
            'email': [
                'john.doe@example.com',
                'jane.smith@example.com',
                'bob-johnson_123@example.com',
                'sarah@mydomain.co.uk',
                'frank@mydomain.com',
                'jessica_lee@mydomain.com'
                    ]
            }
>>> df = pd.DataFrame(data)
>>> df['date'] = pd.to_datetime(df['date'])
>>> # Take a peek at the data
>>> df
        date                        email
0 2018-11-20         john.doe@example.com
1 2018-11-21       jane.smith@example.com
2 2018-11-22  bob-johnson_123@example.com
3 2018-11-22         sarah@mydomain.co.uk
4 2018-11-23           frank@mydomain.com
5 2018-11-24     jessica_lee@mydomain.com
>>> # Extract regular expressions
>>> cumrexpy(df, 'email', 'date')
date
2018-11-20                           [^john\.doe@example\.com$]
2018-11-21                [^[a-z]{4}\.[a-z]{3,5}@example\.com$]
2018-11-22    [^[a-z]{4,5}[.@][a-z]+[.@][a-z]+\.[a-z]{2,3}$,...
2018-11-23    [^frank@mydomain\.com$, ^[a-z]{4,5}[.@][a-z]+[...
2018-11-24    [^frank@mydomain\.com$, ^[a-z]+[.@_][a-z]+[.@]...
Name: email_grouped, dtype: object

Build Documentation Locally

cd /path/to/canomaly/docs
make html

Contents:

Indices and tables