ENH: Add usecols option to python parser.

doc/source/io.rst

@@ -85,11 +85,11 @@ They can take a number of arguments:
     ways to specify the file format
   - ``dtype``: A data type name or a dict of column name to data type. If not
     specified, data types will be inferred.
-  - ``header``: row number to use as the column names, and the start of the
+  - ``header``: row number(s) to use as the column names, and the start of the
     data.  Defaults to 0 if no ``names`` passed, otherwise ``None``. Explicitly
     pass ``header=0`` to be able to replace existing names. The header can be
     a list of integers that specify row locations for a multi-index on the columns
-    E.g. [0,1,3]. Interveaning rows that are not specified will be skipped.
+    E.g. [0,1,3]. Intervening rows that are not specified will be skipped.
     (E.g. 2 in this example are skipped)
   - ``skiprows``: A collection of numbers for rows in the file to skip. Can
     also be an integer to skip the first ``n`` rows
@@ -2938,7 +2938,7 @@ into BigQuery and pull it into a DataFrame.
 .. 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`
@@ -2998,7 +2998,7 @@ To add more rows to this, simply:
 
    To use this module, you will need a BigQuery account. See
    <https://cloud.google.com/products/big-query> for details.
- 
+
    As of 10/10/13, there is a bug in Google's API preventing result sets
    from being larger than 100,000 rows. A patch is scheduled for the week of
    10/14/13.

doc/source/v0.13.0.txt

@@ -505,11 +505,13 @@ Enhancements
 - :meth:`~pandas.io.json.json_normalize` is a new method to allow you to create a flat table
   from semi-structured JSON data. See :ref:`the docs<io.json_normalize>` (:issue:`1067`)
 
-
 - Added PySide support for the qtpandas DataFrameModel and DataFrameWidget.
 
+- Python csv parser now supports usecols (:issue:`4335`)
+
 - DataFrame has a new ``interpolate`` method, similar to Series (:issue:`4434`, :issue:`1892`)
 
+
   .. ipython:: python
 
       df = DataFrame({'A': [1, 2.1, np.nan, 4.7, 5.6, 6.8],
@@ -654,7 +656,7 @@ Experimental
   against extremely large datasets. :ref:`See the docs <io.bigquery>`
 
   .. code-block:: python
-  
+
      from pandas.io import gbq
 
      # A query to select the average monthly temperatures in the
@@ -665,8 +667,8 @@ Experimental
      query = """SELECT station_number as STATION,
      month as MONTH, AVG(mean_temp) as MEAN_TEMP
      FROM publicdata:samples.gsod
-     WHERE YEAR = 2000 
-     GROUP BY STATION, MONTH 
+     WHERE YEAR = 2000
+     GROUP BY STATION, MONTH
      ORDER BY STATION, MONTH ASC"""
 
      # Fetch the result set for this query
@@ -675,7 +677,7 @@ Experimental
      # To find this, see your dashboard:
      # https://code.google.com/apis/console/b/0/?noredirect
      projectid = xxxxxxxxx;
-     
+
      df = gbq.read_gbq(query, project_id = projectid)
 
      # Use pandas to process and reshape the dataset
@@ -686,9 +688,9 @@ Experimental
 
   The resulting dataframe is::
 
-     > df3 
+     > df3
                  Min Tem  Mean Temp    Max Temp
-      MONTH                                  
+      MONTH
       1     -53.336667  39.827892   89.770968
       2     -49.837500  43.685219   93.437932
       3     -77.926087  48.708355   96.099998