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.. _remote_data:
.. currentmodule:: pandas
******************
Remote Data Access
******************
.. _remote_data.pandas_datareader:
DataReader
----------
The sub-package ``pandas.io.data`` is removed in favor of a separately
installable `pandas-datareader package
<https://github.com/pandas-dev/pandas-datareader>`_. This will allow the data
modules to be independently updated to your pandas installation. The API for
``pandas-datareader v0.1.1`` is the same as in ``pandas v0.16.1``.
(:issue:`8961`)
You should replace the imports of the following:
.. code-block:: python
from pandas.io import data, wb
With:
.. code-block:: python
from pandas_datareader import data, wb
.. _remote_data.ga:
Google Analytics
----------------
The :mod:`~pandas.io.ga` module provides a wrapper for
`Google Analytics API <https://developers.google.com/analytics/devguides>`__
to simplify retrieving traffic data.
Result sets are parsed into a pandas DataFrame with a shape and data types
derived from the source table.
Configuring Access to Google Analytics
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The first thing you need to do is to setup accesses to Google Analytics API. Follow the steps below:
#. In the `Google Developers Console <https://console.developers.google.com>`__
#. enable the Analytics API
#. create a new project
#. create a new Client ID for an "Installed Application" (in the "APIs & auth / Credentials section" of the newly created project)
#. download it (JSON file)
#. On your machine
#. rename it to ``client_secrets.json``
#. move it to the ``pandas/io`` module directory
The first time you use the :func:`read_ga` function, a browser window will open to ask you to authentify to the Google API. Do proceed.
Using the Google Analytics API
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The following will fetch users and pageviews (metrics) data per day of the week, for the first semester of 2014, from a particular property.
.. code-block:: python
import pandas.io.ga as ga
ga.read_ga(
account_id = "2360420",
profile_id = "19462946",
property_id = "UA-2360420-5",
metrics = ['users', 'pageviews'],
dimensions = ['dayOfWeek'],
start_date = "2014-01-01",
end_date = "2014-08-01",
index_col = 0,
filters = "pagePath=~aboutus;ga:country==France",
)
The only mandatory arguments are ``metrics,`` ``dimensions`` and ``start_date``. We strongly recommend that you always specify the ``account_id``, ``profile_id`` and ``property_id`` to avoid accessing the wrong data bucket in Google Analytics.
The ``index_col`` argument indicates which dimension(s) has to be taken as index.
The ``filters`` argument indicates the filtering to apply to the query. In the above example, the page URL has to contain ``aboutus`` AND the visitors country has to be France.
Detailed information in the following:
* `pandas & google analytics, by yhat <http://blog.yhathq.com/posts/pandas-google-analytics.html>`__
* `Google Analytics integration in pandas, by Chang She <http://quantabee.wordpress.com/2012/12/17/google-analytics-pandas/>`__
* `Google Analytics Dimensions and Metrics Reference <https://developers.google.com/analytics/devguides/reporting/core/dimsmets>`_
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