diff --git a/src/pg/sql/11_kmeans.sql b/src/pg/sql/11_kmeans.sql
index 59fcf59..2db57e0 100644
--- a/src/pg/sql/11_kmeans.sql
+++ b/src/pg/sql/11_kmeans.sql
@@ -11,7 +11,7 @@ $$ LANGUAGE plpythonu;
 -- Non-spatial k-means clustering
 -- query: sql query to retrieve all the needed data
 
-CREATE OR REPLACE FUNCTION CDB_KMeansNonspatial(query TEXT, col_names TEXT[], no_clusters INTEGER, id_col TEXT DEFAULT 'cartodb_id')
+CREATE OR REPLACE FUNCTION CDB_KMeansNonspatial(query TEXT, colnames TEXT[], num_clusters INTEGER, id_col TEXT DEFAULT 'cartodb_id')
 RETURNS TABLE(cluster_label text, cluster_center text, rowid bigint) AS $$
 
 from crankshaft.clustering import kmeans_nonspatial
diff --git a/src/py/crankshaft/crankshaft/clustering/kmeans.py b/src/py/crankshaft/crankshaft/clustering/kmeans.py
index ee2f304..091e87b 100644
--- a/src/py/crankshaft/crankshaft/clustering/kmeans.py
+++ b/src/py/crankshaft/crankshaft/clustering/kmeans.py
@@ -38,7 +38,7 @@ def kmeans_nonspatial(query, colnames, num_clusters=5, id_col='cartodb_id'):
         num_clusters (int): number of clusters (greater than zero)
         id_col (string): name of the input id_column
     """
-
+    import numpy as np
     id_colname = 'rowids'
 
     full_query = '''
@@ -55,13 +55,14 @@ def kmeans_nonspatial(query, colnames, num_clusters=5, id_col='cartodb_id'):
         plpy.notice('query: %s' % full_query)
 
         # fill array with values for kmeans clustering
-        data = np.array([d[c] for c in d if c != 'id_colname'],
-                        dtype=float).T
+        cluster_columns = np.array([data[0][c] for c in data.colnames() 
+                                    if c != 'id_colname'],
+                                   dtype=float).T
     except plpy.SPIError, err:
         plpy.error('KMeans cluster failed: %s' % err)
 
-    kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(data)
+    kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(cluster_columns)
 
     # zip(ids, labels, means)
     return zip(kmeans.labels_, map(str, kmeans.cluster_centers_),
-               d[0]['rowids'])
+               data[0]['rowids'])