simplied DataFrame.duplicated a bit

pandas-dev · Mar 3, 2015 · 7da9178 · 7da9178
1 parent 1ab0e5f
commit 7da9178
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Showing 2 changed files with 9 additions and 14 deletions.
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
@@ -95,7 +95,7 @@ def _unique_generic(values, table_type, type_caster):
 
 
 
-def factorize(values, sort=False, order=None, na_sentinel=-1):
+def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
     """
     Encode input values as an enumerated type or categorical variable
 
@@ -106,8 +106,9 @@ def factorize(values, sort=False, order=None, na_sentinel=-1):
     sort : boolean, default False
         Sort by values
     order : deprecated
-    na_sentinel: int, default -1
+    na_sentinel : int, default -1
         Value to mark "not found"
+    size_hint : hint to the hashtable sizer
 
     Returns
     -------
@@ -129,7 +130,7 @@ def factorize(values, sort=False, order=None, na_sentinel=-1):
     is_timedelta = com.is_timedelta64_dtype(vals)
     (hash_klass, vec_klass), vals = _get_data_algo(vals, _hashtables)
 
-    table = hash_klass(len(vals))
+    table = hash_klass(size_hint or len(vals))
     uniques = vec_klass()
     labels = table.get_labels(vals, uniques, 0, na_sentinel)
 

diff --git a/pandas/core/frame.py b/pandas/core/frame.py
@@ -2832,18 +2832,12 @@ def duplicated(self, subset=None, take_last=False):
         duplicated : Series
         """
         from pandas.core.groupby import get_group_index
+        from pandas.core.algorithms import factorize
         from pandas.hashtable import duplicated_int64, _SIZE_HINT_LIMIT
 
-        size_hint = min(len(self), _SIZE_HINT_LIMIT)
-
-        def factorize(vals):
-            (hash_klass, vec_klass), vals = \
-                    algos._get_data_algo(vals, algos._hashtables)
-
-            uniques, table = vec_klass(), hash_klass(size_hint)
-            labels = table.get_labels(vals, uniques, 0, -1)
-
-            return labels.astype('i8', copy=False), len(uniques)
+        def f(vals):
+            labels, shape = factorize(vals, size_hint=min(len(self), _SIZE_HINT_LIMIT))
+            return labels.astype('i8',copy=False), len(shape)
 
         if subset is None:
             subset = self.columns
@@ -2853,7 +2847,7 @@ def factorize(vals):
             subset = subset,
 
         vals = (self[col].values for col in subset)
-        labels, shape = map(list, zip( * map(factorize, vals)))
+        labels, shape = map(list, zip( * map(f, vals)))
 
         ids = get_group_index(labels, shape, sort=False, xnull=False)
         return Series(duplicated_int64(ids, take_last), index=self.index)