Convert to Python 3: `dict.iteritems()` -> `dict.items()`

sznqalibs/hoover.py

         value is a list of paths.  For each key, it goes through the
         paths and if the value equals `a` it is set to `b`.
         """
-        for (oldv, newv), paths in action.iteritems():
+        for (oldv, newv), paths in action.items():
             for path in paths:
                 try:
                     curv = self.getpath(path)
         before comparison, since direct comparison of floats is unreliable
         on some architectures.
         """
-        for fmt, paths in action.iteritems():
+        for fmt, paths in action.items():
             for path in paths:
                 if self.ispath(path):
                     new = fmt % self.getpath(path)
         Expects dict with precision (ndigits, after the dot) as a key and
         list of paths as value.
         """
-        for ndigits, paths in action.iteritems():
+        for ndigits, paths in action.items():
             for path in paths:
                 try:
                     f = self.getpath(path)
 
     def _computed_stats(self):
         computed = dict.fromkeys(self.formulas.keys())
-        for fname, fml in self.formulas.iteritems():
+        for fname, fml in self.formulas.items():
             try:
                 v = fml(self.generic_stats, self.driver_stats)
             except ZeroDivisionError:
     def all_stats(self):
         """Compute stats from formulas and add them to colledted data."""
         stats = self.generic_stats
-        for dname, dstats in self.driver_stats.iteritems():
-            for key, value in dstats.iteritems():
+        for dname, dstats in self.driver_stats.items():
+            for key, value in dstats.items():
                 stats[dname + "_" + key] = value
         stats.update(self._computed_stats())
         return stats
         assert all([hasattr(o, 'iteritems') for o in [pattern, data]])
         results = []
         try:
-            for pk, pv in pattern.iteritems():
+            for pk, pv in pattern.items():
                 results.append(dataMatch(pv, data[pk], _r=_r+1))
         except KeyError:
             results.append(False)