notna isna dropna

dios/dios.py

@@ -101,7 +101,7 @@ class DictOfSeries:
                 self._data = data
             else:
                 # it is significantly faster, to provide an index and fill it,
-                # than to successively build the index by adding series's
+                # than to successively build the index by adding data
                 self._data = pd.Series(dtype='O', index=columns)
         else:
 
@@ -416,13 +416,10 @@ class DictOfSeries:
         if `method` == `indexed` (default):
           every column is represented by a own index and corresponding values
 
-        if `method` == `aligned`:
+        if `method` == `aligned` [2]:
           one(!) global index is generated and values from a column appear at
           the corresponding index-location.
 
-          Note: the (common-) params are directly passed to pd.DataFrame.to_string(..)
-          under the hood
-
         Common params :
          - max_cols: not more column than `max_cols` are printed [1]
          - max_rows: see `min_rows` [1]
@@ -440,7 +437,10 @@ class DictOfSeries:
           that this should differ from `na_rep`, otherwise you cannot differ missing- from NaN- values.
 
         Notes:
-         [1] defaults to the corresponding value in `dios_options`
+          [1]: defaults to the corresponding value in `dios_options`
+          [2]: the common-params are directly passed to pd.DataFrame.to_string(..)
+               under the hood, if method is `aligned`
+
         """
         if self.empty:
             return _empty_repr(self)
@@ -505,18 +505,36 @@ class DictOfSeries:
             raise ValueError(axis)
         return new
 
+    def _fast_apply(self, attr, **kwargs):
+        data = pd.Series(dtype='O', index=self.columns)
+        call = callable(getattr(pd.Series, attr))
+        for c in self.columns:
+            s = self._data.at[c]
+            data.at[c] = getattr(s, attr)(**kwargs) if call else getattr(s, attr)
+        return data
+
     @property
     def dtypes(self):
-        s = pd.Series(index=self.columns, dtype='O')
-        for k in self.columns:
-            s.at[k] = self._data.at[k].dtype
-        return s
+        return self._fast_apply('dtype')
 
     def astype(self, dtype, copy=True, errors='raise'):
-        new = self.copy_empty(columns=True) if copy else self
-        for k in self.columns:
-            new._data.at[k] = self._data.at[k].astype(dtype=dtype, copy=copy, errors=errors)
-        return new
+        data = self._fast_apply('astype', dtype=dtype, copy=copy, errors=errors)
+        return DictOfSeries(data=data, itype=self.itype, cast_policy=self._policy, fastpath=True)
+
+    def isna(self):
+        data = self._fast_apply('isna')
+        return DictOfSeries(data=data, itype=self.itype, cast_policy=self._policy, fastpath=True)
+
+    def notna(self):
+        data = self._fast_apply('notna')
+        return DictOfSeries(data=data, itype=self.itype, cast_policy=self._policy, fastpath=True)
+
+    def dropna(self, inplace=False):
+        data = self._fast_apply('dropna', inplace=False)  # never pass inplace=True
+        if inplace:
+            self._data = data
+        else:
+            return DictOfSeries(data=data, itype=self.itype, cast_policy=self._policy, fastpath=True)
 
     def memory_usage(self, index=True, deep=False):
         mem = 0