Gaapi 0.1.5
=================================
A light weight python wrapper for `Google's Analytics Reporting API v4 <https://developers.google.com/analytics/devguides/reporting/core/v4/>`_ written upon Google API Python Client.

Features provided by Gaapi 
---------------------------

- Analytics focussed library to handle reporting api.
- Pythonic style usage.
- Enables you to cache your response data.
- Enables you to get simplified response data. (TO-DO)

------------

Installation
---------------
To install, simply use pip or easy_install::

    $ pip install --upgrade gaapi

or::

    $ easy_install --upgrade gaapi

------------

Acquire Google credentials
--------------------------

**1.  To create a Service Account Credentials, follow the below link**


https://support.google.com/a/answer/7378726?hl=en


**2.  Get view ID in Google Analytics, follow the below link**

https://keyword-hero.com/documentation/finding-your-view-id-in-google-analytics

------------

Get Started
-----------
Instanstiate GA Client
''''''''''''''''''''''''
::

    from gaapi import Client

    GA_SERVICE_ACCOUNT_CREDENTIALS = {
       "type": "service_account",
       "project_id": "analytics-xyz",
       "private_key_id": "private_key_id",
       "private_key": "-----BEGIN PRIVATE KEY-----ASADASDONWQENLKQWEIL\nASDASDOILWQE",
       "client_email": "username@analytics-xyz.iam.gserviceaccount.com",
       "client_id": "103486406559549721528",
       "auth_uri": "https://accounts.google.com/o/oauth2/auth",
       "token_uri": "https://accounts.google.com/o/oauth2/token",
       "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
       "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/username@analytics-xyz.iam.gserviceaccount.com"
    }
    GA_VIEW_ID = '12345567890'


    ga = Client(
            credentials=GA_SERVICE_ACCOUNT_CREDENTIALS,
            view_id=GA_VIEW_ID
         )

Generate Query
''''''''''''''

Gaapi format::

    query = ga.query.date_ranges(
            start_date='2018-07-03', end_date='today'
        ).metrics(
            expression='ga:users'
        ).dimensions(
            name='ga:pagePath'
        ).dimensions(
            name='ga:eventLabel'
        ).dimension_filter_clauses(
            filters=[{
                "dimension_name": "ga:pagePath",
                "operator": "REGEXP",
                "expressions": ".*something.*"
            }],
        )

    # Clone query (Only for Gaapi format)
    # Add `clean=True` to reset a value in the query (say date_ranges)
    cloned_query = ga.query.clone(query2).date_ranges(
             start_date='2018-08-01', end_date='today', clean=True
        )

    # Read query
    print(query.json())


Python Dictionary format::


    query = {
        'date_ranges': {
            'start_date': '2018-07-03',
            'end_date': 'today'
        },
        'metrics': [
            {'expression': 'ga:users'}
        ],
        'dimensions': [
            {'name': 'ga:pagePath'}
        ],
        'dimension_filter_clauses': [{
          "filters": [{
              "dimension_name": "ga:pagePath",
              "operator": "REGEXP",
              "expressions": "\/.*something\/.*"
          }]
        }]
    }

Google's original query format::

    query = {
        'dateRanges': {
            'startDate': '2018-07-03',
            'endDate': 'today'
        },
        'metrics': [
            {'expression': 'ga:users'}
        ],
        'dimensions': [
            {'name': 'ga:pagePath'}
        ],
        'dimensionFilterClauses': [{
          "filters": [{
              "dimensionName": "ga:pagePath",
              "operator": "REGEXP",
              "expressions": "\/.*something\/.*"
          }]
        }]
    }



Request Data
''''''''''''
::

    # Using gaapi query or python dictionary or json
    response = ga.batch_get(query)
    
    # Cache for 600ms
    response = ga.batch_get(query, cache_ttl=600)
    
------------

Third Party Libraries and Dependencies
--------------------------------------
The following external libraries will be installed when you install gaapi ...

- `google-api-python-client <https://github.com/google/google-api-python-client) (Google Client Library>`_
- `google-auth <https://github.com/GoogleCloudPlatform/google-auth-library-python/) (Google Auth Library>`_
- `walrus <https://github.com/coleifer/walrus) (Light weight Caching Library>`_

To-Dos
------
- Response Object Manipulation. (This update will enable to you to generate response in Google Raw Response, Simplified Response, CSV, Panda Dataframe).
- Test cases.

Contribute
----------

1. Look for an open `issue <https://github.com/rakeshgunduka/gaapi/issues>`_ or create new issue to get a dialog going about the new feature or bug that you've discovered.

2. Fork the `repository <https://github.com/rakeshgunduka/gaapi>`_ on Github to start making your changes to the master branch (or branch off of it).

3. Write a test which shows that the bug was fixed or that the feature works as expected.

4. Make a pull request.