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27 changes: 13 additions & 14 deletions docs/source/intro.rst
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Expand Up @@ -20,7 +20,7 @@ respectively.

This is opposite to default pandas behaviour which will promote integer
type to float in order to store NAs.
`See here for how this works in pandas <http://pandas.pydata.org/pandas-docs/stable/gotchas.html#nan-integer-na-values-and-na-type-promotions>`__
`See here for how this works in pandas <https://pandas.pydata.org/pandas-docs/stable/gotchas.html#nan-integer-na-values-and-na-type-promotions>`__

While this trade-off works well for most cases, it breaks down for storing
values greater than 2**53. Such values in BigQuery can represent identifiers
Expand All @@ -31,26 +31,25 @@ and unnoticed precision lost for identifier is what we want to avoid.
Authentication
''''''''''''''

Authentication to the Google ``BigQuery`` service is via ``OAuth 2.0``.
Is possible to authenticate with either user account credentials or service account credentials.
Authentication to the Google ``BigQuery`` service via ``OAuth 2.0``
is possible with either user or service account credentials.

Authenticating with user account credentials is as simple as following the prompts in a browser window
which will be automatically opened for you. You will be authenticated to the specified
``BigQuery`` account using the product name ``pandas GBQ``. It is only possible on local host.
The remote authentication using user account credentials is not currently supported in pandas.
Authentication via user account credentials is as simple as following the prompts in a browser window
which will automatically open for you. You authenticate to the specified
``BigQuery`` account using the product name ``pandas GBQ``.
The remote authentication is supported via specifying ``auth_local_webserver`` in ``read_gbq``.
Additional information on the authentication mechanism can be found
`here <https://developers.google.com/identity/protocols/OAuth2#clientside/>`__.

Authentication with service account credentials is possible via the `'private_key'` parameter. This method
is particularly useful when working on remote servers (eg. jupyter iPython notebook on remote host).
Authentication via service account credentials is possible through the `'private_key'` parameter. This method
is particularly useful when working on remote servers (eg. Jupyter Notebooks on remote host).
Additional information on service accounts can be found
`here <https://developers.google.com/identity/protocols/OAuth2#serviceaccount>`__.

Authentication via ``application default credentials`` is also possible. This is only valid
if the parameter ``private_key`` is not provided. This method also requires that
the credentials can be fetched from the environment the code is running in.
Otherwise, the OAuth2 client-side authentication is used.
Additional information on
Authentication via ``application default credentials`` is also possible, but only valid
if the parameter ``private_key`` is not provided. This method requires that the
credentials can be fetched from the development environment. Otherwise, the OAuth2
client-side authentication is used. Additional information can be found on
`application default credentials <https://developers.google.com/identity/protocols/application-default-credentials>`__.

.. note::
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