API: allow single element boolean Series to improve on numpy behavior( related GH4657)

doc/source/basics.rst

@@ -219,7 +219,7 @@ Boolean Reductions
 
 .. _basics.reductions:
 
-Furthermore, you can apply the reduction functions: ``any()`` and ``all()`` to provide a
+Furthermore, you can apply the reductions: ``empty``, ``any()``, ``all()``, and ``bool()`` to provide a
 way to summarize these results.
 
 .. ipython:: python
@@ -233,7 +233,7 @@ You can reduce to a final boolean value.
 
    (df>0).any().any()
 
-Finally you can test if a pandas object is empty, via the ``empty`` property.
+You can test if a pandas object is empty, via the ``empty`` property.
 
 .. ipython:: python
 
@@ -262,6 +262,15 @@ Finally you can test if a pandas object is empty, via the ``empty`` property.
        ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
 
 
+To evaluate single-element pandas objects in a boolean context, use the method ``.bool()``:
+
+   .. ipython:: python
+
+      Series([True]).bool()
+      Series([False]).bool()
+      DataFrame([[True]]).bool()
+      DataFrame([[False]]).bool()
+
 See :ref:`gotchas<gotchas.truth>` for a more detailed discussion.
 
 

doc/source/gotchas.rst

@@ -59,6 +59,15 @@ or return if ``any`` value is ``True``.
            print("I am any")
     >>> I am any
 
+To evaluate single-element pandas objects in a boolean context, use the method ``.bool()``:
+
+   .. ipython:: python
+
+       Series([True]).bool()
+       Series([False]).bool()
+       DataFrame([[True]]).bool()
+       DataFrame([[False]]).bool()
+
 See :ref:`boolean reductions<basics.reductions>` for more examples.
 
 Bitwise boolean

doc/source/release.rst

@@ -241,7 +241,7 @@ API Changes
 
   - Infer and downcast dtype if ``downcast='infer'`` is passed to ``fillna/ffill/bfill`` (:issue:`4604`)
   - ``__nonzero__`` for all NDFrame objects, will now raise a ``ValueError``, this reverts back to (:issue:`1073`, :issue:`4633`)
-    behavior.
+    behavior. Add ``.bool()`` method to ``NDFrame`` objects to facilitate evaluating of single-element boolean Series
   - ``DataFrame.update()`` no longer raises a ``DataConflictError``, it now
     will raise a ``ValueError`` instead (if necessary) (:issue:`4732`)
   - ``Series.isin()`` and ``DataFrame.isin()``  now raise a ``TypeError`` when

doc/source/v0.13.0.txt

@@ -55,10 +55,14 @@ API changes
         index.set_names(["bob", "cranberry"], inplace=True)
 
   - Infer and downcast dtype if ``downcast='infer'`` is passed to ``fillna/ffill/bfill`` (:issue:`4604`)
+  - Remove deprecated ``Factor`` (:issue:`3650`)
+  - Remove deprecated ``set_printoptions/reset_printoptions`` (:issue:``3046``)
+  - Remove deprecated ``_verbose_info`` (:issue:`3215`)
   - ``__nonzero__`` for all NDFrame objects, will now raise a ``ValueError``, this reverts back to (:issue:`1073`, :issue:`4633`)
-    behavior. See :ref:`gotchas<gotchas.truth>` for a more detailed discussion.
+    behavior. Added the ``.bool()`` method to ``NDFrame`` objects to facilitate evaluating of single-element boolean Series
+    See :ref:`gotchas<gotchas.truth>` for a more detailed discussion.
 
-    This prevent behaviors like (which will now all raise ``ValueError``)
+    This prevents behaviors like (which will now all raise ``ValueError``)
 
     .. code-block:: python
 
@@ -68,6 +72,16 @@ API changes
         df1 and df2
         s1 and s2
 
+
+    To evaluate single-element pandas objects in a boolean context, use the method ``.bool()``:
+
+    .. ipython:: python
+
+       Series([True]).bool()
+       Series([False]).bool()
+       DataFrame([[True]]).bool()
+       DataFrame([[False]]).bool()
+
   - All non-Index NDFrames (``Series``, ``DataFrame``, ``Panel``, ``Panel4D``,
     ``SparsePanel``, etc.), now support the entire set of arithmetic operators
     and arithmetic flex methods (add, sub, mul, etc.). ``SparsePanel`` does not

pandas/core/generic.py

@@ -596,10 +596,25 @@ def empty(self):
         return not all(len(self._get_axis(a)) > 0 for a in self._AXIS_ORDERS)
 
     def __nonzero__(self):
-        raise ValueError("The truth value of an array is ambiguous. Use a.empty, a.item(), a.any() or a.all().")
+        raise ValueError("The truth value of a {0} is ambiguous. "
+                         "Use a.empty, a.bool(), a.item(), a.any() or a.all().".format(self.__class__.__name__))
 
     __bool__ = __nonzero__
 
+    def bool(self):
+        """ Return the bool of a single element PandasObject
+            This must be a boolean scalar value, either True or False
+
+            Raise a ValueError if the PandasObject does not have exactly
+            1 element, or that element is not boolean """
+        v = self.squeeze()
+        if isinstance(v, (bool,np.bool_)):
+            return bool(v)
+        elif np.isscalar(v):
+            raise ValueError("bool cannot act on a non-boolean single element {0}".format(self.__class__.__name__))
+
+        self.__nonzero__()
+
     def __abs__(self):
         return self.abs()
 

pandas/core/series.py

@@ -7,6 +7,7 @@
 
 import operator
 import types
+import warnings
 
 from numpy import nan, ndarray
 import numpy as np
@@ -913,7 +914,6 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None,
         """
 
         if nanRep is not None:  # pragma: no cover
-            import warnings
             warnings.warn("nanRep is deprecated, use na_rep", FutureWarning)
             na_rep = nanRep
 

pandas/io/tests/test_pytables.py

@@ -2662,18 +2662,18 @@ def test_select_dtypes(self):
             df = DataFrame(np.random.randn(5,2), columns =['A','B'])
             df['object'] = 'foo'
             df.ix[4:5,'object'] = 'bar'
-            df['bool'] = df['A'] > 0
+            df['boolv'] = df['A'] > 0
             _maybe_remove(store, 'df')
             store.append('df', df, data_columns = True)
 
-            expected = df[df.bool == True].reindex(columns=['A','bool'])
+            expected = df[df.boolv == True].reindex(columns=['A','boolv'])
             for v in [True,'true',1]:
-                result = store.select('df', Term('bool == %s' % str(v)), columns = ['A','bool'])
+                result = store.select('df', Term('boolv == %s' % str(v)), columns = ['A','boolv'])
                 tm.assert_frame_equal(expected, result)
 
-            expected = df[df.bool == False ].reindex(columns=['A','bool'])
+            expected = df[df.boolv == False ].reindex(columns=['A','boolv'])
             for v in [False,'false',0]:
-                result = store.select('df', Term('bool == %s' % str(v)), columns = ['A','bool'])
+                result = store.select('df', Term('boolv == %s' % str(v)), columns = ['A','boolv'])
                 tm.assert_frame_equal(expected, result)
 
             # integer index

pandas/tests/test_generic.py

@@ -205,11 +205,31 @@ def test_get_numeric_data_preserve_dtype(self):
 
     def test_nonzero_single_element(self):
 
+        # allow single item via bool method
         s = Series([True])
-        self.assertRaises(ValueError, lambda : bool(s))
+        self.assert_(s.bool() is True)
 
         s = Series([False])
-        self.assertRaises(ValueError, lambda : bool(s))
+        self.assert_(s.bool() is False)
+
+        # single item nan to raise
+        for s in [ Series([np.nan]), Series([pd.NaT]), Series([True]), Series([False]) ]:
+            self.assertRaises(ValueError, lambda : bool(s))
+
+        for s in [ Series([np.nan]), Series([pd.NaT])]:
+            self.assertRaises(ValueError, lambda : s.bool())
+
+        # multiple bool are still an error
+        for s in [Series([True,True]), Series([False, False])]:
+            self.assertRaises(ValueError, lambda : bool(s))
+            self.assertRaises(ValueError, lambda : s.bool())
+
+        # single non-bool are an error
+        for s in [Series([1]), Series([0]),
+                  Series(['a']), Series([0.0])]:
+                self.assertRaises(ValueError, lambda : bool(s))
+                self.assertRaises(ValueError, lambda : s.bool())
+
 
 class TestDataFrame(unittest.TestCase, Generic):
     _typ = DataFrame
@@ -220,6 +240,19 @@ def test_rename_mi(self):
                        index=MultiIndex.from_tuples([("A",x) for x in ["a","B","c"]]))
         result = df.rename(str.lower)
 
+    def test_nonzero_single_element(self):
+
+        # allow single item via bool method
+        df = DataFrame([[True]])
+        self.assert_(df.bool() is True)
+
+        df = DataFrame([[False]])
+        self.assert_(df.bool() is False)
+
+        df = DataFrame([[False, False]])
+        self.assertRaises(ValueError, lambda : df.bool())
+        self.assertRaises(ValueError, lambda : bool(df))
+
     def test_get_numeric_data_preserve_dtype(self):
 
         # get the numeric data