Google Search Console for Python
google-searchconsole takes the pain out of working with the Google Search
Console Search Analytics Query API. It is written in Python and provides
convenient features to make querying a site's search analytics data easier.
- Authentication. We provide a few different ways to make generating credentials and authenticating with your account easier. You can use stored fies as well as a way to do the OAuth2 flow interactively.
- Querying. Easier to query by date ranges and filter by various dimensions. No longer posting large nested JSON, the query object lets you make complex queries with ease.
- Exploration. You can traverse your account hierarchy, with an account containing webproperties with clear permission levels.
- Exports. Clean JSON and pandas.DataFrame outputs so you can easily analyse your data in Python or Excel.
First, install the package using:
pip3 install git+https://github.com/joshcarty/google-searchconsole
Then, create a new project in the Google Developers Console, enable the Google Search Console API under "APIs & Services". Next, create credentials for an OAuth client ID, choosing the Other Application type. Download a JSON copy of your client secrets.
After that, executing your first query is as easy as
import searchconsole account = searchconsole.authenticate(client_config='client_secrets.json') webproperty = account['https://www.example.com/'] report = webproperty.query.range('today', days=-7).dimension('query').get() print(report.rows)
The above example will use your client configuration file to interactively generate your credentials.
If you wish to save your credentials, to avoid going
through the OAuth consent screen in the future, you can specify a path to save
them by specifying
When you want to authenticate a new account you run:
account = searchconsole.authenticate(client_config='client_secrets.json', serialize='credentials.json')
Which will save your credentials to a file called
From then on, you can authenticate with:
account = searchconsole.authenticate(client_config='client_secrets.json', credentials='credentials.json')
Integration with Pandas DataFrame
If you wish to load your data directly into a pandas DataFrame, to avoid loading it manually after the extraction, you can do it easily:
report = webproperty.query.range('today',days=-7).dimension('page').get().to_dataframe()
You can specify the search type data you want to retrieve by using the search_type method with your query. The following values are currently supported by the API: news, video, image, web, discover & googleNews. If you don't use this method, the default value used will be web,
report = webproperty.query.search_type('discover').range('today',days=-7).dimension('page').get().to_dataframe()
You can apply filters while executing a query. The filter types supported by the API are the same available in the UI: contains, equals, notContains, notEquals, includingRegex & excludingRegex.
report = webproperty.query.range('today',days=-7).dimension('page').filter('page','/blog/','contains').get().to_dataframe()
Note that if you use Regex in your filter, you must follow RE2 syntax.