DOC: fix building of gbq docs

doc/source/v0.13.0.txt

@@ -651,7 +651,7 @@ Experimental
 - ``pandas.io.gbq`` provides a simple way to extract from, and load data into,
   Google's BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high
   performance SQL-like database service, useful for performing ad-hoc queries
-  against extremely large datasets. :ref:`See the docs<io.gbq>`
+  against extremely large datasets. :ref:`See the docs <io.bigquery>`
 
   .. code-block:: python
 
@@ -684,24 +684,24 @@ Experimental
      df3 = pandas.concat([df2.min(), df2.mean(), df2.max()],
                          axis=1,keys=["Min Tem", "Mean Temp", "Max Temp"])
 
-  The resulting dataframe is:
-
-  ```
-             Min Tem  Mean Temp    Max Temp
-  MONTH                                  
-  1     -53.336667  39.827892   89.770968
-  2     -49.837500  43.685219   93.437932
-  3     -77.926087  48.708355   96.099998
-  4     -82.892858  55.070087   97.317240
-  5     -92.378261  61.428117  102.042856
-  6     -77.703334  65.858888  102.900000
-  7     -87.821428  68.169663  106.510714
-  8     -89.431999  68.614215  105.500000
-  9     -86.611112  63.436935  107.142856
-  10    -78.209677  56.880838   92.103333
-  11    -50.125000  48.861228   94.996428
-  12    -50.332258  42.286879   94.396774
-  ```
+  The resulting dataframe is::
+
+     > df3 
+                 Min Tem  Mean Temp    Max Temp
+      MONTH                                  
+      1     -53.336667  39.827892   89.770968
+      2     -49.837500  43.685219   93.437932
+      3     -77.926087  48.708355   96.099998
+      4     -82.892858  55.070087   97.317240
+      5     -92.378261  61.428117  102.042856
+      6     -77.703334  65.858888  102.900000
+      7     -87.821428  68.169663  106.510714
+      8     -89.431999  68.614215  105.500000
+      9     -86.611112  63.436935  107.142856
+      10    -78.209677  56.880838   92.103333
+      11    -50.125000  48.861228   94.996428
+      12    -50.332258  42.286879   94.396774
+
   .. warning::
 
      To use this module, you will need a BigQuery account. See

pandas/core/frame.py

@@ -672,34 +672,34 @@ def to_dict(self, outtype='dict'):
             raise ValueError("outtype %s not understood" % outtype)
 
     def to_gbq(self, destination_table, schema=None, col_order=None, if_exists='fail', **kwargs):
-        """
-        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,
+        """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.
 
         Parameters
         ---------------
-        destination_table: string
+        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)
+        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.
+        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:
+        Raises
         ------
-        SchemaMissing:
+        SchemaMissing :
             Raised if the 'if_exists' parameter is set to 'replace', but no schema is specified
-        TableExists:
+        TableExists :
             Raised if the specified 'destination_table' exists but the 'if_exists' parameter is set to 'fail' (the default)
-        InvalidSchema:
+        InvalidSchema :
             Raised if the 'schema' parameter does not match the provided DataFrame
         """
 

pandas/io/gbq.py

@@ -316,36 +316,36 @@ def _parse_data(client, job, index_col=None, col_order=None):
     return final_df
 
 def to_gbq(dataframe, destination_table, schema=None, col_order=None, if_exists='fail', **kwargs):
-    """
-    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,
+    """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.
 
     Parameters
-    ---------------
-    dataframe: DataFrame
+    ----------
+    dataframe : DataFrame
         DataFrame to be written
-    destination_table: string
+    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)
+    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.
+    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:
+    Raises
     ------
-    SchemaMissing:
+    SchemaMissing :
         Raised if the 'if_exists' parameter is set to 'replace', but no schema is specified
-    TableExists:
+    TableExists :
         Raised if the specified 'destination_table' exists but the 'if_exists' parameter is set to 'fail' (the default)
-    InvalidSchema:
+    InvalidSchema :
         Raised if the 'schema' parameter does not match the provided DataFrame
     """
 
@@ -416,35 +416,37 @@ def to_gbq(dataframe, destination_table, schema=None, col_order=None, if_exists=
         job = client.Load(table_reference, csv_file.name, schema=schema, **opts)
 
 def read_gbq(query, project_id = None, destination_table = None, index_col=None, col_order=None, **kwargs):
-    """
+    """Load data from Google BigQuery.
+
     The main method a user calls to load data from Google BigQuery into a pandas DataFrame. 
     This is a simple wrapper for Google's bq.py and bigquery_client.py, which we use
     to get the source data. Because of this, this script respects the user's bq settings 
     file, '~/.bigqueryrc', if it exists. Such a file can be generated using 'bq init'. Further,
-    additional parameters for the query can be specified as either **kwds in the command,
+    additional parameters for the query can be specified as either ``**kwds`` in the command,
     or using FLAGS provided in the 'gflags' module. Particular options can be found in
     bigquery_client.py.
 
     Parameters
     ----------
-    query: str
+    query : str
         SQL-Like Query to return data values
-    project_id: str (optional)
+    project_id : str (optional)
         Google BigQuery Account project ID. Optional, since it may be
         located in ~/.bigqueryrc
-    index_col: str (optional)
+    index_col : str (optional)
         Name of result column to use for index in results DataFrame
-    col_order: list(str) (optional)
+    col_order : list(str) (optional)
         List of BigQuery column names in the desired order for results
         DataFrame
-    destination_table: string (optional)
+    destination_table : string (optional)
         If provided, send the results to the given table.
-    **kwargs: to be passed to bq.Client.Create(). Particularly: 'trace', 'sync',
-        'api', 'api_version'
+    **kwargs : 
+        To be passed to bq.Client.Create(). Particularly: 'trace', 
+        'sync', 'api', 'api_version'
 
     Returns
     -------
-    df: pandas DataFrame
+    df: DataFrame
         DataFrame representing results of query
 
     """