Fix Some Stuff

Author: Roger Thomas

pandas/core/algorithms.py

@@ -948,21 +948,22 @@ def select_n_frame(frame, columns, n, method, keep):
     if not is_list_like(columns):
         columns = [columns]
     columns = list(columns)
-    for column in columns:
-        dtype = frame[column].dtype
-        if not issubclass(dtype.type, (np.integer, np.floating, np.datetime64,
-                                       np.timedelta64)):
-            msg = (
-                "{column!r} has dtype: {dtype}, cannot use method {method!r} "
-                "with this dtype"
-            ).format(column=column, dtype=dtype, method=method)
-            raise TypeError(msg)
+
+    def get_indexer(current_indexer, other_indexer):
+        """Helper function to concat `current_indexer` and `other_indexer`
+        depending on `method`
+        """
+        if method == 'nsmallest':
+            return current_indexer.append(other_indexer)
+        else:
+            return other_indexer.append(current_indexer)
 
     # Below we save and reset the index in case index contains duplicates
     original_index = frame.index
     cur_frame = frame = frame.reset_index(drop=True)
     cur_n = n
     indexer = Int64Index([])
+
     for i, column in enumerate(columns):
 
         # For each column we apply method to cur_frame[column]. If it is the
@@ -974,22 +975,17 @@ def select_n_frame(frame, columns, n, method, keep):
         series = cur_frame[column]
         values = getattr(series, method)(cur_n, keep=keep)
         is_last_column = len(columns) - 1 == i
-        if is_last_column or len(values.unique()) == sum(series.isin(values)):
+        if is_last_column or values.nunique() == series.isin(values).sum():
 
             # Last column in columns or values are unique in series => values
             # is all that matters
-            if method == 'nsmallest':
-                indexer = indexer.append(values.index)
-            else:
-                indexer = values.index.append(indexer)
+            indexer = get_indexer(indexer, values.index)
             break
+
         duplicated_filter = series.duplicated(keep=False)
-        non_duplicated = values[~duplicated_filter]
         duplicated = values[duplicated_filter]
-        if method == 'nsmallest':
-            indexer = indexer.append(non_duplicated.index)
-        else:
-            indexer = non_duplicated.index.append(indexer)
+        non_duplicated = values[~duplicated_filter]
+        indexer = get_indexer(indexer, non_duplicated.index)
 
         # Must set cur frame to include all duplicated values to consider for
         # the next column, we also can reduce cur_n by the current length of