aloc - boolean series support

Readme.md

@@ -136,17 +136,19 @@ are treated similar, as they would passed to `.loc` (actually they are really pa
    the column, otherwise nothing is selected. [1]
 - *pd.Series* : align the index from the given Series with the column, what means only common indices are used. The 
    actual values of the series are ignored(!).
+- *boolean pd.Series* : like *pd.Series* but only True values are evaluated. 
+   False values are equivalent to missing indices. To treat a boolean series as a *normal* indexer series, as decribed
+   above, one can use `.aloc(usebool=False)[boolean pd.Series]`.
+   
 
 *special **2D**-indexer* are :
 - `.aloc[boolean dios-like]` : work same like `di[boolean dios-like]` (see there). 
-   Brief: full align, select where the index is present and the value is True.
-- `.aloc[dios-like, ...]` (with Ellipsis) : Align in columns and rows, ignore values. Per common column,
-   the common indices are selected. The ellipsis forces to ignore the values, so a boolean dios could be given, 
-   where, the values are not taken into account. [2]
+   Brief: full align, select items, where the index is present and the value is True.
+- `.aloc[dios-like, ...]` (with Ellipsis) : Align in columns and rows, ignore its values. Per common column,
+   the common indices are selected. The ellipsis forces `aloc`, to ignore the values, so a boolean dios could be 
+   treated as a non-boolean. Alternatively `.aloc(usebool=False)[boolean dios-like]` could be used.[2]
 - `.aloc[nested list-like]` : The inner lists are used as `aloc`-*list*-row-indexer (see there) on all columns. 
    One list for one column, which implies, that the outer list has the same length as the number of columns. 
-- `.aloc(booldios=True)[boolean dios-like]` : alias for `.aloc[boolean dios-like]`
-- `.aloc(booldios=False)[dios-like]` : alias for `.aloc[dios-like, ...]`
 
 *special handling of 1D-**values***
 
@@ -215,7 +217,7 @@ Empty DictOfSeries
 Columns: ['a', 'b']
 
 
->>> d.aloc[d,...]   # (equal to use) d.aloc(booldios=False)[d]
+>>> d.aloc[d,...]   # (equal to use) d.aloc(usebool=False)[d]
        a |     b | 
 ======== | ===== | 
 0    0.0 | 1  50 | 

dios/indexer.py

@@ -256,10 +256,10 @@ class _aLocIndexer(_Indexer):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self._use_bool_dios = True
+        self._usebool = True
 
-    def __call__(self, booldios=True):
-        self._use_bool_dios = booldios
+    def __call__(self, usebool=True):
+        self._usebool = usebool
         return self
 
     def __getitem__(self, key):
@@ -380,7 +380,7 @@ class _aLocIndexer(_Indexer):
         # (II)  .aloc(booldios=False)[dios] or
         # (III) .aloc(booldios=True)[dios]
         elif _is_dios_like(key):
-            if self._use_bool_dios:
+            if self._usebool:
                 return keys_from_bool_dios_like(key)
             else:
                 return keys_from_dios_like(key)
@@ -427,7 +427,10 @@ class _aLocIndexer(_Indexer):
 
         # row-alignable: pd.Series(), align rows to every series in colkey (columns)
         if isinstance(rowkey, pd.Series):
-            rowkey = [self._data.at[c].index.intersection(rowkey.index) for c in colkey]
+            if _is_bool_indexer(rowkey) and self._usebool:
+                rowkey = [self._data.at[c].index.intersection(rowkey[rowkey].index) for c in colkey]
+            else:
+                rowkey = [self._data.at[c].index.intersection(rowkey.index) for c in colkey]
 
         # handle gracefully: scalar, transform to row-slice
         elif _is_hashable(rowkey):