diff --git a/docs/howto/authentication.rst b/docs/howto/authentication.rst index c65563f8..17e6c04a 100644 --- a/docs/howto/authentication.rst +++ b/docs/howto/authentication.rst @@ -38,7 +38,20 @@ authentication methods: # The credentials and project_id arguments can be omitted. df = pandas_gbq.read_gbq("SELECT my_col FROM `my_dataset.my_table`") -2. Application Default Credentials via the :func:`google.auth.default` +2. If running on `Google Colab `_, + pandas-gbq attempts to authenticate with the + ``google.colab.auth.authenticate_user()`` method. See the `Getting started + with BigQuery on Colab notebook + `_ for an + example of using this authentication method with other libraries that use + Google BigQuery. + + .. note:: + + To use Colab authentication, install version 1.8.0 or later of the + ``pydata-google-auth`` package. + +3. Application Default Credentials via the :func:`google.auth.default` function. .. note:: @@ -48,10 +61,11 @@ authentication methods: user account credentials. A common problem with default credentials when running on Google - Compute Engine is that the VM does not have sufficient scopes to query - BigQuery. + Compute Engine is that the VM does not have sufficient `access scopes + `_ + to query BigQuery. -3. User account credentials. +4. User account credentials. pandas-gbq loads cached credentials from a hidden user folder on the operating system. @@ -214,5 +228,5 @@ more of the following circumstances: (or similar) notebook. If the conditions above apply to you, your needs may be better served -by the content in the `Authentication (Highly Constrained Development Environment) +by the content in the `Authentication (Highly Constrained Development Environment) `_ section.