python-omniture

python-omniture is a wrapper around the Adobe Analytics API.

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

Installation

Through PyPI (older version):

pip install omniture

Latest and greatest:

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

Authentication

The most straightforward way to authenticate is with:

    import omniture
    analytics = 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
    analytics = 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['reportsuite_name']
    print suite
    print suite.metrics
    print suite.elements
    print suite.segments

You can refer to suites, segments, elements and so on using both their human-readable name or their id. So for example suite.metrics['pageviews'] and suite.metrics['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 event4 and instead can call them by their title. Just remember that if you change the friendly name in the interface it will break your script.

Running a report

python-omniture can run ranked, trended and over time reports. Pathing reports are still in the works

Here's a quick example:

    report = suite.report \
        .element('page') \
        .metric('pageviews') \
        .run()

This will generate the report definition and run the report. You can alternatively generate a report definition and save the report defintion to a variable by omitting the call to the run() method

If you call print on the report defintion it will print out the JSON that you can use in the API explorer for debugging or to use in other scripts

    report = suite.report \
        .element('page') \
        .metric('pageviews') \
        
    print report

The api seamlessly switches over to the realtime reporting api. In the example below we want more granularity than the normal reporting api provides, so it switches to the realtime api behind the scenes

    report = suite.report.element("prop73")\
        .metric("instances")\
        .from_date('-15 minutes')\
        .granularity('minute:1').run()

Report Options

Here are the options you can add to a report.

element() - element('element_id or element_name', **kwargs) Adds an element to the report. If you need to pass in additional information in to the element (e.g. top or startingWith or classification) you can use the kwargs to do so. A full list of options available is documented here. If multiple elements are present then they are broken-down by one another

breakdown() - breakdown('element_id or element_name', **kwargs) Same as element. It is included to make report queries more readable when there are multiple element. Use when there are more than one element. eg.

    report = suite.report.element('evar1').breakdown('evar2')

metric() - metric('metric') Adds a metric to the report. Can be called multiple times to add multiple metrics if needed.

range() - range('start', 'end=None', 'months=0', 'days=0', 'granularity=None') Sets the date range for the report. All dates shoudl be listed in ISO-8601 (e.g. 'YYYY-MM-DD')

Start -- Start date for the report. If no stop date is specified then the report will be for a single day
Stop -- End date for the report.
months -- Number of months back to run the report
days -- Number of days back from now to run the report
granularity -- The Granularity of the report (hour, day, week, month)

granularity() -- granularity('granularity') Set the granularity of the report

sortBy() -- sortBy('metric') Set the sortBy metric

filter() -- filter('segment') or filter(element='element', selected=[]) Set the segment to be applied to the report. Can either be an segment id/name or can be used to define an inline segment by specifying the paramtered. You can add multiple filters if needed and they will be stacked (anded together)

    report = suite.report.filter('537d509ee4b0893ab30616c7')
    report = suite.report.filter(element='page', selected=['homepage'])
    report = suite.report.filter('537d509ee4b0893ab30616c7')\
        .filter(element='page', selected=['homepage'])

currentData() --currentData() Set the currentData flag

run() -- run(defaultheartbeat=True) Run the report and check the queue until done. The defaultheartbeat writes a . (period) out to the console each time it checks on the report.

set() -- set(key, value) Set a custom attribute in the report definition

Using the Results of a report

To see the raw output of a report.

    print report

If you need an easy way to access the data in a report:

    data = report.data

This will generate a list of dicts with the metrics and elements called out by id.

Pandas Support

python-omniture can also generate a data frame of the data returned. It works as follows:

   df = report.dataframe

Pandas Data frames can be useful if you need to analyize the the data or transform it easily.

Getting down to the plumbing.

This module is still in beta and you should expect some things not to work. In particular, pathing 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 by simply printing the qeury.

    query = suite.report \
        .element('pages') 
        .metric('pageviews)
        .set(anomalyDetection='month')
        

    print query

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 = suite.report \
            .range('2013-05-01', '2013-05-31', granularity='day') \
            .metric('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

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

Making other API requests

If you need to make other API requests that are not reporting reqeusts you can do so by calling analytics.request(api, method, params) For example if I wanted to call Company.GetReportSuites I would do

    response = analytics.request('Company', 'GetReportSuites')

Contributing

Feel free to contribute by filing issues or issuing a pull reqeust.

Build

If you want to build the module

    bash build.sh

If you want to run unit tests

    python testAll.py

dylanroy / python-omniture