remove .hash() method

Author: jreback

doc/source/whatsnew/v0.19.2.txt

@@ -16,14 +16,6 @@ Highlights include:
     :backlinks: none
 
 
-.. _whatsnew_0192.enhancements:
-
-Enhancements
-~~~~~~~~~~~~
-
-- ``Series/DataFrame/Index`` gain a ``.hash()`` method to provide a row-wise unique data hash , see :meth:`pandas.DataFrame.hash` (:issue:`14729`)
-
-
 .. _whatsnew_0192.performance:
 
 Performance Improvements

pandas/core/base.py

@@ -795,99 +795,7 @@ def __unicode__(self):
         return "%s(%s, dtype='%s')" % (type(self).__name__, prepr, self.dtype)
 
 
-class HashableMixin(object):
-    """ provide methods for hashable pandas objects """
-
-    def hash(self, index=True, encoding='utf8', hash_key=None):
-        """
-        Return a data hash of the Index/Series/DataFrame
-        This is a 1-d Series of unique hashses of all of the elements in that
-        row, including the index if desired.
-
-        Parameters
-        ----------
-        index : boolean, default True
-            include the index in the hash (if Series/DataFrame)
-        encoding : string, default 'utf8'
-            encoding for data & key when strings
-        hash_key : string, must be 16 bytes length if passed
-
-        Returns
-        -------
-        Series of uint64 hash values. The index of the Series
-        will be the original index (Series/DataFrame),
-        or the original object if Index
-
-        Examples
-        --------
-        >>> pd.Index([1, 2, 3]).hash()
-        1     6238072747940578789
-        2    15839785061582574730
-        3     2185194620014831856
-        dtype: uint64
-
-        >>> pd.Series([1, 2, 3], index=[2, 3, 4]).hash()
-        2    16107259231694759481
-        3    12811061657343452814
-        4     1341665827200607204
-        dtype: uint64
-
-        >>> pd.Series([1, 2, 3]).hash(index=False)
-        0     6238072747940578789
-        1    15839785061582574730
-        2     2185194620014831856
-        dtype: uint64
-
-        >>> pd.DataFrame({'A': [1, 2, 3]}).hash()
-        0      267474170112184751
-        1    16863939785269199747
-        2     3948624847917518682
-        dtype: uint64
-
-        >>> pd.DataFrame({'A': [1, 2, 3], 'B': ['foo', 'bar', 'baz']}).hash()
-        0    11603696091789712533
-        1     5345384428795788641
-        2       46691607209239364
-        dtype: uint64
-
-        In [10]: pd.DataFrame({'A': [1, 1, 2], 'B': ['foo', 'bar', 'foo'],
-                               'C': [1, 2, 3]}).set_index(['A', 'B']).hash()
-        A  B
-        1  foo      553964757138028680
-           bar    13757638258637221887
-        2  foo     4843577173406411690
-        dtype: uint64
-
-        Notes
-        -----
-        These functions do not hash attributes attached to the object
-        e.g. name for Index/Series. Nor do they hash the columns of
-        a DataFrame.
-
-        Mixed dtypes within a Series (or a column of a DataFrame) will
-        be stringified, for example.
-
-        >>> Series(['1', 2, 3]).hash()
-        array([ 8973981985592347666, 16940873351292606887,
-               10100427194775696709], dtype=uint64)
-
-        >>> Series(['1', '2', '3']).hash()
-        array([ 8973981985592347666, 16940873351292606887,
-               10100427194775696709], dtype=uint64)
-
-        These have the same data hash, while a pure dtype is different.
-
-        >>> Series([1, 2, 3]).hash()
-        array([  267474170112184751, 16863939785269199747,
-               3948624847917518682], dtype=uint64)
-
-        """
-        from pandas.tools.hashing import hash_pandas_object
-        return hash_pandas_object(self, index=index, encoding=encoding,
-                                  hash_key=hash_key)
-
-
-class IndexOpsMixin(HashableMixin):
+class IndexOpsMixin(object):
     """ common ops mixin to support a unified inteface / docs for Series /
     Index
     """

pandas/core/frame.py

@@ -201,7 +201,7 @@
 # DataFrame class
 
 
-class DataFrame(NDFrame, base.HashableMixin):
+class DataFrame(NDFrame):
     """ Two-dimensional size-mutable, potentially heterogeneous tabular data
     structure with labeled axes (rows and columns). Arithmetic operations
     align on both row and column labels. Can be thought of as a dict-like

pandas/tools/tests/test_hashing.py

@@ -25,7 +25,7 @@ def setUp(self):
     def test_consistency(self):
         # check that our hash doesn't change because of a mistake
         # in the actual code; this is the ground truth
-        result = Index(['foo', 'bar', 'baz']).hash()
+        result = hash_pandas_object(Index(['foo', 'bar', 'baz']))
         expected = Series(np.array([3600424527151052760, 1374399572096150070,
                                     477881037637427054], dtype='uint64'),
                           index=['foo', 'bar', 'baz'])
@@ -41,22 +41,18 @@ def check_equal(self, obj, **kwargs):
         b = hash_pandas_object(obj, **kwargs)
         tm.assert_series_equal(a, b)
 
-        a = obj.hash(**kwargs)
-        b = obj.hash(**kwargs)
-        tm.assert_series_equal(a, b)
-
         kwargs.pop('index', None)
-        a = obj.hash(index=False, **kwargs)
-        b = obj.hash(index=False, **kwargs)
+        a = hash_pandas_object(obj, **kwargs)
+        b = hash_pandas_object(obj, **kwargs)
         tm.assert_series_equal(a, b)
 
     def check_not_equal_with_index(self, obj):
 
         # check that we are not hashing the same if
         # we include the index
         if not isinstance(obj, Index):
-            a = obj.hash(index=True)
-            b = obj.hash(index=False)
+            a = hash_pandas_object(obj, index=True)
+            b = hash_pandas_object(obj, index=False)
             self.assertFalse((a == b).all())
 
     def test_hash_pandas_object(self):