Add optional ddof parameter to lar.std() and lar.var().

RELEASE.rst

@@ -32,6 +32,10 @@ Bottleneck package (http://pypi.python.org/pypi/Bottleneck).
 - la.unique(): Find the unique elements of a larry
 - larry.tofile(): Save 1d or 2d larry to text file
 
+**Enhancements**
+
+- Add optional `ddof` input parameter to larry.std() and larry.var() 
+
 **Breakage from la 0.4**
 
 - Bottleneck is now a dependency of la
@@ -42,11 +46,14 @@ Bottleneck package (http://pypi.python.org/pypi/Bottleneck).
 
 **Bugs fixes**
 
+- Please report bugs at https://github.com/kwgoodman/la/issues
 - #1 Due to a typo, la.info() crashed if h5py could not be imported
 - #2 larry.sortaxis(None) chopped off singleton dimensions
 - #5 la.farray.lastrank() choked on empty array input
 - #7 larry.quantile() choked on axis=None
-- #8 demean, demedian, zscore choked on 1d input when axis=-1 
+- #8 demean, demedian, zscore choked on 1d input when axis=-1
+- #9 cross_validation docstring refers to old name of function (cv) 
+- #10 Unit tests: "Warning: invalid value encountered in..." 
 
 
 Older versions

la/deflarry.py

@@ -1160,15 +1160,36 @@ def median(self, axis=None):
         """
         return self.__reduce(bn.nanmedian, axis=axis) 
 
-    def std(self, axis=None):
+    def std(self, axis=None, ddof=0):
         """
         Standard deviation of values along axis, ignoring NaNs.
 
+        `float64` intermediate and return values are used for integer inputs.
+
+        Instead of a faster one-pass algorithm, a more stable two-pass
+        algorithm is used.
+
+        An example of a one-pass algorithm:
+
+            >>> np.sqrt((arr*arr).mean() - arr.mean()**2)
+        
+        An example of a two-pass algorithm:    
+        
+            >>> np.sqrt(((arr - arr.mean())**2).mean())
+
+        Note in the two-pass algorithm the mean must be found (first pass)
+        before the squared deviation (second pass) can be found.
+
         Parameters
         ----------
-        axis : {None, integer}, optional
-            Axis to find the standard deviation along (integer) or the global
-            standard deviation (None, default).
+        axis : {int, None}, optional
+            Axis along which the standard deviation is computed. The default
+            (axis=None) is to compute the standard deviation of the flattened
+            array.
+        ddof : int, optional
+            Means Delta Degrees of Freedom. The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+            By default `ddof` is zero.
 
         Returns
         -------
@@ -1195,17 +1216,37 @@ def std(self, axis=None):
         array([ 0.,  1.])  
                          
         """      
-        return self.__reduce(bn.nanstd, axis=axis)  
+        return self.__reduce(bn.nanstd, axis=axis, ddof=ddof)  
 
-    def var(self, axis=None):
+    def var(self, axis=None, ddof=0):
         """
         Variance of values along axis, ignoring NaNs.
+        
+        `float64` intermediate and return values are used for integer inputs.
+
+        Instead of a faster one-pass algorithm, a more stable two-pass
+        algorithm is used.
+
+        An example of a one-pass algorithm:
+
+            >>> np.sqrt((arr*arr).mean() - arr.mean()**2)
+        
+        An example of a two-pass algorithm:    
+        
+            >>> np.sqrt(((arr - arr.mean())**2).mean())
+
+        Note in the two-pass algorithm the mean must be found (first pass)
+        before the squared deviation (second pass) can be found.
 
         Parameters
         ----------
-        axis : {None, integer}, optional
-            Axis to find the variance along (integer) or the global variance
-            (None, default).
+        axis : {int, None}, optional
+            Axis along which the variance is computed. The default (axis=None)
+            is to compute the variance of the flattened array.
+        ddof : int, optional
+            Means Delta Degrees of Freedom. The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+            By default `ddof` is zero.
 
         Returns
         -------
@@ -1232,7 +1273,7 @@ def var(self, axis=None):
         array([ 0.,  1.])
                     
         """         
-        return self.__reduce(bn.nanvar, axis=axis)  
+        return self.__reduce(bn.nanvar, axis=axis, ddof=ddof)  
 
     def max(self, axis=None):
         """