This package was originally developed by debrouwere (https://github.com/debrouwere/python-omniture - can be installed with pip install omniture
).
This is a continuation of that work with support for Omniture API 1.4, and Python 3.
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.
Through PyPI:
pip install omni2re
Latest and greatest:
pip install git+git://github.com/colemanja91/python-omniture.git
The most straightforward way to authenticate is with:
import omni2re
account = omni2re.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 omni2re
account = omni2re.authenticate(os.environ)
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.
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']
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()
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.
- Unit testing + tox