pandas.io.gbq Version 2

ci/requirements-2.6.txt

@@ -4,7 +4,6 @@ python-dateutil==1.5
 pytz==2013b
 http://www.crummy.com/software/BeautifulSoup/bs4/download/4.2/beautifulsoup4-4.2.0.tar.gz
 html5lib==1.0b2
-bigquery==2.0.17
 numexpr==1.4.2
 sqlalchemy==0.7.1
 pymysql==0.6.0

ci/requirements-2.7.txt

@@ -19,5 +19,7 @@ lxml==3.2.1
 scipy==0.13.3
 beautifulsoup4==4.2.1
 statsmodels==0.5.0
-bigquery==2.0.17
 boto==2.26.1
+httplib2==0.8
+python-gflags==2.0
+google-api-python-client==1.2

doc/source/install.rst

@@ -112,7 +112,9 @@ Optional Dependencies
     :func:`~pandas.io.clipboard.read_clipboard`. Most package managers on Linux
     distributions will have xclip and/or xsel immediately available for
     installation.
-  * `Google bq Command Line Tool <https://developers.google.com/bigquery/bq-command-line-tool/>`__
+  * Google's `python-gflags` and `google-api-python-client`
+    * Needed for :mod:`~pandas.io.gbq`
+  * `httplib2`
     * Needed for :mod:`~pandas.io.gbq`
   * One of the following combinations of libraries is needed to use the
     top-level :func:`~pandas.io.html.read_html` function:

doc/source/io.rst

@@ -3373,83 +3373,79 @@ Google BigQuery (Experimental)
 The :mod:`pandas.io.gbq` module provides a wrapper for Google's BigQuery
 analytics web service to simplify retrieving results from BigQuery tables
 using SQL-like queries. Result sets are parsed into a pandas
-DataFrame with a shape derived from the source table. Additionally,
-DataFrames can be uploaded into BigQuery datasets as tables
-if the source datatypes are compatible with BigQuery ones.
+DataFrame with a shape and data types derived from the source table. 
+Additionally, DataFrames can be appended to existing BigQuery tables if 
+the destination table is the same shape as the DataFrame.
 
 For specifics on the service itself, see `here <https://developers.google.com/bigquery/>`__
 
-As an example, suppose you want to load all data from an existing table
-: `test_dataset.test_table`
-into BigQuery and pull it into a DataFrame.
+As an example, suppose you want to load all data from an existing BigQuery 
+table : `test_dataset.test_table` into a DataFrame using the :func:`~pandas.io.read_gbq` 
+function.
 
 .. code-block:: python
-
-   from pandas.io import gbq
-
    # Insert your BigQuery Project ID Here
-   # Can be found in the web console, or
-   # using the command line tool `bq ls`
+   # Can be found in the Google web console
    projectid = "xxxxxxxx"
 
-   data_frame = gbq.read_gbq('SELECT * FROM test_dataset.test_table', project_id = projectid)
+   data_frame = pd.read_gbq('SELECT * FROM test_dataset.test_table', project_id = projectid)
 
-The user will then be authenticated by the `bq` command line client -
-this usually involves the default browser opening to a login page,
-though the process can be done entirely from command line if necessary.
-Datasets and additional parameters can be either configured with `bq`,
-passed in as options to `read_gbq`, or set using Google's gflags (this
-is not officially supported by this module, though care was taken
-to ensure that they should be followed regardless of how you call the
-method).
+You will then be authenticated to the specified BigQuery account
+via Google's Oauth2 mechanism. In general, this is as simple as following the
+prompts in a browser window which will be opened for you. Should the browser not
+be available, or fail to launch, a code will be provided to complete the process
+manually.  Additional information on the authentication mechanism can be found
+`here <https://developers.google.com/accounts/docs/OAuth2#clientside/>`__
 
-Additionally, you can define which column to use as an index as well as a preferred column order as follows:
+You can define which column from BigQuery to use as an index in the
+destination DataFrame as well as a preferred column order as follows:
 
 .. code-block:: python
 
-   data_frame = gbq.read_gbq('SELECT * FROM test_dataset.test_table',
+   data_frame = pd.read_gbq('SELECT * FROM test_dataset.test_table',
                              index_col='index_column_name',
-                             col_order='[col1, col2, col3,...]', project_id = projectid)
-
-Finally, if you would like to create a BigQuery table, `my_dataset.my_table`, from the rows of DataFrame, `df`:
+                             col_order=['col1', 'col2', 'col3'], project_id = projectid)
+
+Finally, you can append data to a BigQuery table from a pandas DataFrame
+using the :func:`~pandas.io.to_gbq` function. This function uses the
+Google streaming API which requires that your destination table exists in
+BigQuery. Given the BigQuery table already exists, your DataFrame should
+match the destination table in column order, structure, and data types. 
+DataFrame indexes are not supported. By default, rows are streamed to 
+BigQuery in chunks of 10,000 rows, but you can pass other chuck values 
+via the ``chunksize`` argument. You can also see the progess of your 
+post via the ``verbose`` flag which defaults to ``True``. The http 
+response code of Google BigQuery can be successful (200) even if the 
+append failed. For this reason, if there is a failure to append to the 
+table, the complete error response from BigQuery is returned which 
+can be quite long given it provides a status for each row. You may want
+to start with smaller chuncks to test that the size and types of your
+dataframe match your destination table to make debugging simpler.
 
 .. code-block:: python
 
    df = pandas.DataFrame({'string_col_name' : ['hello'],
          'integer_col_name' : [1],
          'boolean_col_name' : [True]})
-   schema = ['STRING', 'INTEGER', 'BOOLEAN']
-   data_frame = gbq.to_gbq(df, 'my_dataset.my_table',
-                           if_exists='fail', schema = schema, project_id = projectid)
-
-To add more rows to this, simply:
-
-.. code-block:: python
-
-   df2 = pandas.DataFrame({'string_col_name' : ['hello2'],
-         'integer_col_name' : [2],
-         'boolean_col_name' : [False]})
-   data_frame = gbq.to_gbq(df2, 'my_dataset.my_table', if_exists='append', project_id = projectid)
+   df.to_gbq('my_dataset.my_table', project_id = projectid)
 
-.. note::
+The BigQuery SQL query language has some oddities, see `here <https://developers.google.com/bigquery/query-reference>`__
 
-   A default project id can be set using the command line:
-   `bq init`.
+While BigQuery uses SQL-like syntax, it has some important differences
+from traditional databases both in functionality, API limitations (size and
+qunatity of queries or uploads), and how Google charges for use of the service. 
+You should refer to Google documentation often as the service seems to
+be changing and evolving. BiqQuery is best for analyzing large sets of 
+data quickly, but it is not a direct replacement for a transactional database.
 
-   There is a hard cap on BigQuery result sets, at 128MB compressed. Also, the BigQuery SQL query language has some oddities,
-   see `here <https://developers.google.com/bigquery/query-reference>`__
-
-   You can access the management console to determine project id's by:
-   <https://code.google.com/apis/console/b/0/?noredirect>
+You can access the management console to determine project id's by:
+<https://code.google.com/apis/console/b/0/?noredirect>
 
 .. warning::
 
-   To use this module, you will need a BigQuery account. See
-   <https://cloud.google.com/products/big-query> for details.
-
-   As of 1/28/14, a known bug is present that could possibly cause data duplication in the resultant dataframe. A fix is imminent,
-   but any client changes will not make it into 0.13.1. See:
-   http://stackoverflow.com/questions/20984592/bigquery-results-not-including-page-token/21009144?noredirect=1#comment32090677_21009144
+   To use this module, you will need a valid BigQuery account. See
+   <https://cloud.google.com/products/big-query> for details on the
+   service.
 
 .. _io.stata:
 

doc/source/v0.14.1.txt

@@ -154,14 +154,11 @@ Performance
 Experimental
 ~~~~~~~~~~~~
 
-``pandas.io.data.Options`` has gained a ``get_all_data method``, and now consistently returns a multi-indexed ``DataFrame`` (:issue:`5602`). See :ref:`the docs<remote_data.yahoo_options>`
-
-  .. ipython:: python
-
-     from pandas.io.data import Options
-     aapl = Options('aapl', 'yahoo')
-     data = aapl.get_all_data()
-     data.iloc[0:5, 0:5]
+- ``io.gbq.read_gbq`` and ``io.gbq.to_gbq`` were refactored to remove the
+  dependency on the Google ``bq.py`` command line client. This submodule 
+  now uses ``httplib2`` and the Google ``apiclient`` and ``oauth2client`` API client 
+  libraries which should be more stable and, therefore, reliable than 
+  ``bq.py`` (:issue:`6937`).  
 
 .. _whatsnew_0141.bug_fixes:
 

pandas/core/frame.py

@@ -669,47 +669,43 @@ def to_dict(self, outtype='dict'):
         else:  # pragma: no cover
             raise ValueError("outtype %s not understood" % outtype)
 
-    def to_gbq(self, destination_table, schema=None, col_order=None,
-               if_exists='fail', **kwargs):
+    def to_gbq(self, destination_table, project_id=None, chunksize=10000,
+               verbose=True, reauth=False):
         """Write a DataFrame to a Google BigQuery table.
 
-        If the table exists, the DataFrame will be appended. If not, a new
-        table will be created, in which case the schema will have to be
-        specified. By default, rows will be written in the order they appear
-        in the DataFrame, though the user may specify an alternative order.
+        THIS IS AN EXPERIMENTAL LIBRARY
+
+        If the table exists, the dataframe will be written to the table using
+        the defined table schema and column types. For simplicity, this method
+        uses the Google BigQuery streaming API. The to_gbq method chunks data
+        into a default chunk size of 10,000. Failures return the complete error
+        response which can be quite long depending on the size of the insert. 
+        There are several important limitations of the Google streaming API 
+        which are detailed at:
+        https://developers.google.com/bigquery/streaming-data-into-bigquery.
 
         Parameters
-        ---------------
+        ----------
+        dataframe : DataFrame
+            DataFrame to be written
         destination_table : string
-             name of table to be written, in the form 'dataset.tablename'
-        schema : sequence (optional)
-             list of column types in order for data to be inserted, e.g.
-             ['INTEGER', 'TIMESTAMP', 'BOOLEAN']
-        col_order : sequence (optional)
-             order which columns are to be inserted, e.g. ['primary_key',
-             'birthday', 'username']
-        if_exists : {'fail', 'replace', 'append'} (optional)
-            - fail: If table exists, do nothing.
-            - replace: If table exists, drop it, recreate it, and insert data.
-            - append: If table exists, insert data. Create if does not exist.
-        kwargs are passed to the Client constructor
-
-        Raises
-        ------
-        SchemaMissing :
-            Raised if the 'if_exists' parameter is set to 'replace', but no
-            schema is specified
-        TableExists :
-            Raised if the specified 'destination_table' exists but the
-            'if_exists' parameter is set to 'fail' (the default)
-        InvalidSchema :
-            Raised if the 'schema' parameter does not match the provided
-            DataFrame
+            Name of table to be written, in the form 'dataset.tablename'
+        project_id : str
+            Google BigQuery Account project ID.
+        chunksize : int (default 10000)
+            Number of rows to be inserted in each chunk from the dataframe.
+        verbose : boolean (default True)
+            Show percentage complete
+        reauth : boolean (default False)
+            Force Google BigQuery to reauthenticate the user. This is useful
+            if multiple accounts are used.
+
         """
 
         from pandas.io import gbq
-        return gbq.to_gbq(self, destination_table, schema=None, col_order=None,
-                          if_exists='fail', **kwargs)
+        return gbq.to_gbq(self, destination_table, project_id=project_id,
+                          chunksize=chunksize, verbose=verbose,
+                          reauth=reauth)
 
     @classmethod
     def from_records(cls, data, index=None, exclude=None, columns=None,