private _reduce_horizontal() become public reduce_columns()

dios/dios.py

@@ -195,7 +195,7 @@ class DictOfSeries(_DiosBase):
                     yield idx, DictOfSeries(data=row.to_dict(), index=[idx])
 
     # ------------------------------------------------------------------------------
-    # Broadcasting methods and helper
+    # Broadcasting and Reducing
 
     def for_each(self, attr_or_callable, **kwds):
         """
@@ -354,42 +354,61 @@ class DictOfSeries(_DiosBase):
             raise ValueError(axis)
         return result
 
-    def _reduce_horizontal(self, func, initializer_value):
+    def reduce_columns(self, func, initial=None, skipna=False):
         """
-        Reduce values of all columns to a single pandas.Series by a given function.
+        Reduce all columns to a single pandas.Series by a given function.
 
-        A given function is called on pairs of columns, and the result is used
-        for next pair-call. Because not all columns necessarily share the same
-        index, some indices (and its corresponding values) may just seen once.
-        Therefore,  every firstly seen index' values are reduced against a dummy
-        series of the initializer_value.
+        Apply a function of two pandas.Series as arguments, cumulatively to all
+        columns, from left to right, so as to reduce the columns to a single
+        pandas.Series. If initial is present, it is placed before the columns
+        in the calculation, and serves as a default when the columns are empty.
 
         Parameters
         ----------
-        func: function
-            The function must take two series and must return a single series.
-            Both input series will the same index and the returned one also
-            should have it.
-
-        initializer_value: Any
-            A value that is overwritten, by any(!) other value, if the
-            ``func`` is evaluated. This is mandatory and also must apply,
-            if the value is not present in any of the columns!
-            E.g. ``False`` for ``func=lambda s1,s2: s1 | s2`` or
-            ``0`` for ``func=max`` if all values are positive integers.
+        func : function
+            The function must take two identically indexed pandas.Series and should
+            return a single pandas.Series with the same index.
+
+        initial : column-label or pd.Series, default None
+            The series to start with. If None a dummy series is created, with the
+            indices of all columns and the first seen values.
+
+        skipna : bool, default False
+               If True, skip NaN values.
 
         Returns
         -------
         pandas.Series
-            A series that have a unique index with the union of indexes
-            of all columns and the function result as values.
+            A series with the reducing result and the index of the start series,
+            defined by ``initializer``.
         """
-        res = pd.Series(data=initializer_value, index=self.index_of('all'))
-        for d in self._data:
-            base = res.loc[d.index]
-            if len(base) > 0:
-                res.loc[d.index] = func(base, d)
-        return res
+        if initial is None:
+            value = pd.Series(index=self.index_of('all'))
+            for d in self._data:
+                value = value.combine_first(d)
+        elif isinstance(initial, pd.Series):
+            value = initial.copy()
+        elif initial in self.columns:
+            value = self._data.at[initial].copy()
+        else:
+            raise ValueError("initial must be pd.Series, a column label or None")
+
+        if skipna:
+            val = value.dropna()
+            data = self.dropna()._data
+        else:
+            val = value
+            data = self._data
+
+        for d in data:
+            idx = val.index & d.index
+            if len(idx) > 0:
+                l, r = val.loc[idx], d.loc[idx]
+                val.loc[idx] = func(l, r)
+
+        if skipna:
+            value.loc[val.index] = val
+        return value
 
     # ------------------------------------------------------------------------------
     # Misc methods
@@ -478,10 +497,7 @@ class DictOfSeries(_DiosBase):
             return self.for_each(pd.Series.min, skipna=skipna)
         elif axis in [1, 'columns']:
             func = lambda s1, s2: s1.where(s1 < s2, s2)
-            res = self._reduce_horizontal(func, np.inf)
-            if not skipna:
-                res.loc[self.isna().any(axis=1)] = np.nan
-            return res
+            return self.reduce_columns(func, skipna=skipna)
         raise ValueError(axis)
 
     def max(self, axis=None, skipna=None):
@@ -489,10 +505,7 @@ class DictOfSeries(_DiosBase):
             return self.for_each(pd.Series.min, skipna=skipna)
         elif axis in [1, 'columns']:
             func = lambda s1, s2: s1.where(s1 > s2, s2)
-            res = self._reduce_horizontal(func, -np.inf)
-            if not skipna:
-                res.loc[self.isna().any(axis=1)] = np.nan
-            return res
+            return self.reduce_columns(func, skipna=skipna)
         raise ValueError(axis)
 
     # ----------------------------------------------------------------------
@@ -514,7 +527,8 @@ class DictOfSeries(_DiosBase):
             return self._data.apply(all)
         elif axis in [1, 'columns']:
             func = lambda s1, s2: s1.astype(bool) & s2.astype(bool)
-            return self._reduce_horizontal(func, True)
+            init = pd.Series(True, dtype=bool, index=self.index_of('all'))
+            return self.reduce_columns(func, init)
         elif axis is None:
             return self._data.apply(all).all()
         raise ValueError(axis)
@@ -524,7 +538,8 @@ class DictOfSeries(_DiosBase):
             return self._data.apply(any)
         elif axis in [1, 'columns']:
             func = lambda s1, s2: s1.astype(bool) | s2.astype(bool)
-            return self._reduce_horizontal(func, False)
+            init = pd.Series(False, dtype=bool, index=self.index_of('all'))
+            return self.reduce_columns(func, init)
         elif axis is None:
             return self._data.apply(any).any()
         raise ValueError(axis)
@@ -554,7 +569,8 @@ class DictOfSeries(_DiosBase):
             return data.for_each('hasnans')
         elif axis in [1, 'columns']:
             func = lambda s1, s2: s1.isna() | s2.isna()
-            return data._reduce_horizontal(func, False)
+            init = pd.Series(False, dtype=bool, index=self.index_of('all'))
+            return data.reduce_columns(func, init)
         elif axis is None:
             return self.isna(drop_empty=drop_empty)
         raise ValueError(axis)