Google2Pandas will may eventually be a set of tools that will allow for easy querying
of various google database products (Analytics, etc.) with the results returned as
pandas.DataFrame objects (http://pandas.pydata.org/).
At this point, only queries to Google Analytics and the Multi-Channel Funnels Reporting via the core reporting API are supported.
Suggested usage:
from google2pandas import *
Install the latest version via pip:
sudo pip install Google2Pandas
or install the latest development version via:
sudo pip install git+https://github.com/panalysis/Google2Pandas
You will first need to enable the Analytics API, in particular you will need to follow Step 1 here.
Place the client_secrets_v3.json file in your dist-packages/google2pandas/ directory,
and you're ready to go! Note that if this package has been installed system-wide
(default), you will likely need to adjust the permissions/ownership of
client_secrets_v3.json as well as the created analytics.dat token file. In
particular, if you wish to create a system-wide token file (by default the class
looks in /path/to/your/dist-packages/google2pandas/analytics.dat) you will likely
need to instantiate the GoogleAnalyticsQuery class specifying a local location
for the token file, and manually relocate it later.
Alternatively, store your credentials anywhere you like and simply pass a pointer
to client_secrets_v3.json and analytics.dat when instantiating the class.
from google2pandas import *
query = {\
'ids' : <valid_ids>,
'metrics' : 'pageviews',
'dimensions' : ['date', 'pagePath', 'browser'],
'filters' : ['pagePath=~iPhone', 'and', 'browser=~Firefox'],
'start_date' : '8daysAgo',
'max_results' : 10}
conn = GoogleAnalyticsQuery(
token_file_name='my_analytics.dat',
secrets='my_client_secrets_v3.json')
df, metadata = conn.execute_query(**query)
Support has now been added for the GA Reporting API V4 as suggested in issue #21 via the GoogleAnalyticsQueryV4
class. The support is rather rough for now, the primary reason being that since I'm
not working with GA much at all these days I do not have the time to fully learn the
features present in the new API.
For now, what this means is that there is zero parsing of the queries provided,
it's down to the user to structure them correctly. As well, no guarantees are
provided as to the ability to of the resp2frame method to convert the JSON object
from GA to a pandas.DataFrame object in a manner that is generically robust. The
as_dict keyword argument causes the restructuring step to be skipped; if you find
room for improvements please do not hesitate to make a PR with your
suggestions!
To use this module, one needs to follow the new setup process
to enable acces. No more analytics.dat file, instead one needs to simply add the
generated email address to the GA view you wish to access.
I also suggest naming the client_secrets file to something that indicates it
is for the V4 API, as it is quite a different thing than the V3 version (default
behaviour is to look for client_secrets_v4.json in dist-packages/google2pandas/).
from google2pandas import *
query = {
'reportRequests': [{
'viewId' : <valid_ids>,
'dateRanges': [{
'startDate' : '8daysAgo',
'endDate' : 'today'}],
'dimensions' : [
{'name' : 'ga:date'},
{'name' : 'ga:pagePath'},
{'name' : 'ga:browser'}],
'metrics' : [
{'expression' : 'ga:pageviews'}],
'dimensionFilterClauses' : [{
'operator' : 'AND',
'filters' : [
{'dimensionName' : 'ga:browser',
'operator' : 'REGEXP',
'expressions' : ['Firefox']},
{'dimensionName' : 'ga:pagePath',
'operator' : 'REGEXP',
'expressions' : ['iPhone']}]
}]
}]
}
# Assume we have placed our client_secrets_v4.json file in the current
# working directory.
conn = GoogleAnalyticsQueryV4(secrets='my_client_secrets_v4.json')
df = conn.execute_query(query)