Set default sort_order to asc

hobson · Apr 25, 2013 · f138bba · f138bba
1 parent 2da1c01
commit f138bba
Show file tree

Hide file tree

Showing 2 changed files with 12 additions and 11 deletions.
diff --git a/Quandl/Quandl.py b/Quandl/Quandl.py
@@ -33,28 +33,29 @@ def get(dataset, **kwargs):
     :param str authtoken: Downloads are limited to 10 unless token is specified
     :param str trim_start, trim_end: Optional datefilers, otherwise entire
            dataset is returned
-    :param str frequency: Options are daily, weekly, monthly, quarterly, annual
+    :param str collapse: Options are daily, weekly, monthly, quarterly, annual
     :param str transformation: options are diff, rdiff, cumul, and normalize
     :param int rows: Number of rows which will be returned
-    :param str sort_order: options are asc, desc. Default: `desc`
-    :param str returns: specify what format you wish your dataset returned as, 
+    :param str sort_order: options are asc, desc. Default: `asc`
+    :param str returns: specify what format you wish your dataset returned as,
         either `numpy` for a numpy ndarray or `pandas`. Default: `pandas`
     :returns: :class:`pandas.DataFrame` or :class:`numpy.ndarray`
 
     Note that Pandas expects timeseries data to be sorted ascending for most
-    timeseries functionality to work. 
+    timeseries functionality to work.
 
     Any other `kwargs` passed to `get` are sent as field/value params to Quandl
     with no interference.
 
     """
+    kwargs.setdefault('sort_order', 'asc')
+
     auth_token = _getauthtoken(kwargs.pop('authtoken', ''))
     trim_start = _parse_dates(kwargs.pop('trim_start', None))
     trim_end = _parse_dates(kwargs.pop('trim_end', None))
-    returns = kwargs.pop('returns', 'pandas')
+    returns = kwargs.get('returns', 'pandas')
 
     url = QUANDL_API_URL + 'datasets/{}.csv?'.format(dataset)
-
     url = _append_query_fields(url,
                                auth_token=auth_token,
                                trim_start=trim_start,

diff --git a/README.md b/README.md
@@ -10,18 +10,18 @@ An example of creating a pandas time series for IBM stock data, with a weekly fr
 
 ```python
 import Quandl
-data = Quandl.get("GOOG/NYSE_IBM", frequency="weekly")
+data = Quandl.get('GOOG/NYSE_IBM', collapse='weekly')
 data.head()
 ```
 
 will output
 
 ```
-No authentication tokens found,usage will be limited 
+No authentication tokens found,usage will be limited
 Returning Dataframe for  GOOG/NYSE_IBM
 
               Open    High     Low   Close   Volume
-Date                                               
+Date
 2013-03-28  209.83  213.44  209.74  213.30  3752999
 2013-03-15  215.38  215.90  213.41  214.92  7937244
 2013-03-08  209.85  210.74  209.43  210.38  3700986
@@ -38,7 +38,7 @@ A request with a full list of options would be the following.
 ```python
 import Quandl
 data = Quandl.get('PRAGUESE/PX', authtoken='xxxxxx', trim_start='2001-01-01',
-                  trim_end='2010-01-01', frequency='annual', 
+                  trim_end='2010-01-01', collapse='annual',
                   transformation='rdiff', rows=4, returns='numpy')
 ```
 
@@ -58,7 +58,7 @@ Quarterly normalized crude oil prices since 2005, only returning first 4 values.
 
 ```python
 import Quandl
-data = Quandl.get('IMF/POILAPSP_INDEX', frequency='quarterly',
+data = Quandl.get('IMF/POILAPSP_INDEX', collapse='quarterly',
                    trim_start='2005', transformation='normalize', rows='4')
 data.head()
 ````