README.md

python-omniture

python-omniture is a wrapper around the Adobe Omniture web analytics API.

It is not meant to be comprehensive. Instead, it provides a high-level interface to certain common kinds of queries, and allows you to do construct other queries closer to the metal.

Installation

Through PyPI:

pip install omniture

Latest and greatest:

pip install git+git://github.com/stdbrouw/python-omniture.git

Authentication

The most straightforward way to authenticate is with:

import omniture
account = omniture.authenticate('my_username', 'my_secret')

However, to avoid hardcoding passwords, instead you can also put your username and password in unix environment variables (e.g. in your .bashrc):

export OMNITURE_USERNAME=my_username
export OMNITURE_SECRET=my_secret

With your credentials in the environment, you can then log in as follows:

import os
import omniture
account = omniture.authenticate(os.environ)

Account and suites

You can very easily access some basic information about your account and your reporting suites:

print analytics.suites
suite = analytics.suites['guardiangu-network']
print suite
print len(suite.evars)
print suite.segments
print suite.elements

You can refer to suites, segments, elements and so on using both their human-readable name or their id. So for example suite.segments['pageviews'] and suite.segments['Page Views'] will work exactly the same. This is especially useful in cases when segment or metric identifiers are long strings of gibberish. That way you don't have to riddle your code with references to evar16 or custom4 and instead can call them by their title.

Running a report

python-omniture can run ranked, trended and "over time" reports

Here's a quick example:

report = network.report \
    .over_time(metrics=['pageviews', 'visitors']) \
    .range('2013-05-01', '2013-05-31', granularity='month') \
    .sync()

Some basic features of the three kinds of reports you can run:

• over_time
  • supports multiple metrics but only one element: time
  • useful if you need information on a per-page basis
  • supports hourly reporting (and up)
• ranked
  • ranks pages in relation to the metric
  • one number (per metric) for the entire reporting period
  • only supports daily, weekly and monthly reporting
• trended
  • movement of a single element and metric over time (e.g. visits to world news over time)
  • supports hourly reporting (and up)

Accessing the data in a report works as follows:

report.data['pageviews']

Getting down to the plumbing.

This module is still in beta and you should expect some things not to work. In particular, trended reports have not seen much love (though they should work), and data warehouse reports don't work at all.

In these cases, it can be useful to use the lower-level access this module provides through mysuite.report.set -- you can pass set either a key and value, a dictionary with key-value pairs or you can pass keyword arguments. These will then be added to the raw query. You can always check what the raw query is going to be with the build method on queries.

query = network.report \
    .over_time(metrics=['pageviews', 'visitors']) \
    .set(dateGranularity='month')
    .set({'segmentId': 'social'})
    .set('name', 'my report name')

print query.build()

Running multiple reports

If you're interested in automating a large number of reports, you can speed up the execution by first queueing all the reports and only then waiting on the results.

Here's an example:

queue = []
for segment in segments:
    report = network.report \
        .range('2013-05-01', '2013-05-31', granularity='day') \
        .over_time(metrics=['pageviews']) \
        .filter(segment=segment)
    queue.append(report)

heartbeat = lambda: sys.stdout.write('.')
reports = omniture.sync(queue, heartbeat)

for report in reports:
    print report.segment
    print report.data['pageviews']

omniture.sync can queue up (and synchronize) both a list of reports, or a dictionary.