Idx droplevel

doc/source/whatsnew/v0.23.1.txt

@@ -65,6 +65,7 @@ Indexing
 ^^^^^^^^
 
 - Bug in :meth:`Series.reset_index` where appropriate error was not raised with an invalid level name (:issue:`20925`)
+- :meth:`Index.droplevel` is now implemented also for flat indexes, for compatibility with MultiIndex (:issue:`21115`)
 -
 
 I/O

pandas/core/frame.py

@@ -4096,9 +4096,8 @@ def _maybe_casted_values(index, labels=None):
             if not isinstance(level, (tuple, list)):
                 level = [level]
             level = [self.index._get_level_number(lev) for lev in level]
-            if isinstance(self.index, MultiIndex):
-                if len(level) < self.index.nlevels:
-                    new_index = self.index.droplevel(level)
+            if len(level) < self.index.nlevels:
+                new_index = self.index.droplevel(level)
 
         if not drop:
             if isinstance(self.index, MultiIndex):

pandas/core/indexes/base.py

@@ -3158,6 +3158,60 @@ def _get_level_values(self, level):
 
     get_level_values = _get_level_values
 
+    def droplevel(self, level=0):
+        """
+        Return index with requested level(s) removed. If resulting index has
+        only 1 level left, the result will be of Index type, not MultiIndex.
+
+        .. versionadded:: 0.23.1 (support for non-MultiIndex)
+
+        Parameters
+        ----------
+        level : int, str, or list-like, default 0
+            If a string is given, must be the name of a level
+            If list-like, elements must be names or indexes of levels.
+
+        Returns
+        -------
+        index : Index or MultiIndex
+        """
+        if not isinstance(level, (tuple, list)):
+            level = [level]
+
+        levnums = sorted(self._get_level_number(lev) for lev in level)[::-1]
+
+        if len(level) == 0:
+            return self
+        if len(level) >= self.nlevels:
+            raise ValueError("Cannot remove {} levels from an index with {} "
+                             "levels: at least one level must be "
+                             "left.".format(len(level), self.nlevels))
+        # The two checks above guarantee that here self is a MultiIndex
+
+        new_levels = list(self.levels)
+        new_labels = list(self.labels)
+        new_names = list(self.names)
+
+        for i in levnums:
+            new_levels.pop(i)
+            new_labels.pop(i)
+            new_names.pop(i)
+
+        if len(new_levels) == 1:
+
+            # set nan if needed
+            mask = new_labels[0] == -1
+            result = new_levels[0].take(new_labels[0])
+            if mask.any():
+                result = result.putmask(mask, np.nan)
+
+            result.name = new_names[0]
+            return result
+        else:
+            from .multi import MultiIndex
+            return MultiIndex(levels=new_levels, labels=new_labels,
+                              names=new_names, verify_integrity=False)
+
     _index_shared_docs['get_indexer'] = """
         Compute indexer and mask for new index given the current index. The
         indexer should be then used as an input to ndarray.take to align the

pandas/core/indexes/multi.py

@@ -1761,52 +1761,6 @@ def _drop_from_level(self, labels, level):
 
         return self[mask]
 
-    def droplevel(self, level=0):
-        """
-        Return Index with requested level removed. If MultiIndex has only 2
-        levels, the result will be of Index type not MultiIndex.
-
-        Parameters
-        ----------
-        level : int/level name or list thereof
-
-        Notes
-        -----
-        Does not check if result index is unique or not
-
-        Returns
-        -------
-        index : Index or MultiIndex
-        """
-        levels = level
-        if not isinstance(levels, (tuple, list)):
-            levels = [level]
-
-        new_levels = list(self.levels)
-        new_labels = list(self.labels)
-        new_names = list(self.names)
-
-        levnums = sorted(self._get_level_number(lev) for lev in levels)[::-1]
-
-        for i in levnums:
-            new_levels.pop(i)
-            new_labels.pop(i)
-            new_names.pop(i)
-
-        if len(new_levels) == 1:
-
-            # set nan if needed
-            mask = new_labels[0] == -1
-            result = new_levels[0].take(new_labels[0])
-            if mask.any():
-                result = result.putmask(mask, np.nan)
-
-            result.name = new_names[0]
-            return result
-        else:
-            return MultiIndex(levels=new_levels, labels=new_labels,
-                              names=new_names, verify_integrity=False)
-
     def swaplevel(self, i=-2, j=-1):
         """
         Swap level i with level j.

pandas/core/series.py

@@ -1199,9 +1199,8 @@ def reset_index(self, level=None, drop=False, name=None, inplace=False):
                 if not isinstance(level, (tuple, list)):
                     level = [level]
                 level = [self.index._get_level_number(lev) for lev in level]
-                if isinstance(self.index, MultiIndex):
-                    if len(level) < self.index.nlevels:
-                        new_index = self.index.droplevel(level)
+                if len(level) < self.index.nlevels:
+                    new_index = self.index.droplevel(level)
 
             if inplace:
                 self.index = new_index
@@ -3177,7 +3176,8 @@ def apply(self, func, convert_dtype=True, args=(), **kwds):
 
         # handle ufuncs and lambdas
         if kwds or args and not isinstance(func, np.ufunc):
-            f = lambda x: func(x, *args, **kwds)
+            def f(x):
+                return func(x, *args, **kwds)
         else:
             f = func
 

pandas/tests/indexes/test_base.py

@@ -245,6 +245,25 @@ def test_constructor_int_dtype_nan(self):
         result = Index(data, dtype='float')
         tm.assert_index_equal(result, expected)
 
+    def test_droplevel(self, indices):
+        # GH 21115
+        if isinstance(indices, MultiIndex):
+            # Tested separately in test_multi.py
+            return
+
+        assert indices.droplevel([]).equals(indices)
+
+        for level in indices.name, [indices.name]:
+            if isinstance(indices.name, tuple) and level is indices.name:
+                # GH 21121 : droplevel with tuple name
+                continue
+            with pytest.raises(ValueError):
+                indices.droplevel(level)
+
+        for level in 'wrong', ['wrong']:
+            with pytest.raises(KeyError):
+                indices.droplevel(level)
+
     @pytest.mark.parametrize("dtype", ['int64', 'uint64'])
     def test_constructor_int_dtype_nan_raises(self, dtype):
         # see gh-15187

pandas/tests/indexes/test_multi.py

@@ -99,7 +99,8 @@ def test_where_array_like(self):
         cond = [False, True]
 
         for klass in klasses:
-            f = lambda: i.where(klass(cond))
+            def f():
+                return i.where(klass(cond))
             pytest.raises(NotImplementedError, f)
 
     def test_repeat(self):
@@ -2078,7 +2079,7 @@ def test_droplevel_with_names(self):
         expected = index.droplevel(1)
         assert dropped.equals(expected)
 
-    def test_droplevel_multiple(self):
+    def test_droplevel_list(self):
         index = MultiIndex(
             levels=[Index(lrange(4)), Index(lrange(4)), Index(lrange(4))],
             labels=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array(
@@ -2089,6 +2090,16 @@ def test_droplevel_multiple(self):
         expected = index[:2].droplevel(2).droplevel(0)
         assert dropped.equals(expected)
 
+        dropped = index[:2].droplevel([])
+        expected = index[:2]
+        assert dropped.equals(expected)
+
+        with pytest.raises(ValueError):
+            index[:2].droplevel(['one', 'two', 'three'])
+
+        with pytest.raises(KeyError):
+            index[:2].droplevel(['one', 'four'])
+
     def test_drop_not_lexsorted(self):
         # GH 12078
 
@@ -2405,7 +2416,8 @@ def check(nlevels, with_nulls):
 
             # with a dup
             if with_nulls:
-                f = lambda a: np.insert(a, 1000, a[0])
+                def f(a):
+                    return np.insert(a, 1000, a[0])
                 labels = list(map(f, labels))
                 index = MultiIndex(levels=levels, labels=labels)
             else: