BUG: support for "level=" when reset_index() is called with a flat Index

doc/source/whatsnew/v0.20.2.txt

@@ -42,7 +42,7 @@ Conversion
 Indexing
 ^^^^^^^^
 
-
+- Bug in ``DataFrame.reset_index(level=)`` with single level index (:issue:`16263`)
 
 
 I/O

pandas/core/frame.py

@@ -3012,12 +3012,12 @@ def _maybe_casted_values(index, labels=None):
  return values
 
  new_index = _default_index(len(new_obj))
- if isinstance(self.index, MultiIndex):
- if level is not None:
- if not isinstance(level, (tuple, list)):
-  level = [level]
-  level = [self.index._get_level_number(lev) for lev in level]
- if len(level) < len(self.index.levels):
+ if level is not 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 not drop:
@@ -3033,6 +3033,8 @@ def _maybe_casted_values(index, labels=None):
 
  multi_col = isinstance(self.columns, MultiIndex)
  for i, (lev, lab) in reversed(list(enumerate(to_insert))):
+ if not (level is None or i in level):
+ continue
  name = names[i]
  if multi_col:
  col_name = (list(name) if isinstance(name, tuple)
@@ -3049,11 +3051,9 @@ def _maybe_casted_values(index, labels=None):
  missing = self.columns.nlevels - len(name_lst)
  name_lst += [col_fill] * missing
  name = tuple(name_lst)
-
  # to ndarray and maybe infer different dtype
  level_values = _maybe_casted_values(lev, lab)
- if level is None or i in level:
- new_obj.insert(0, name, level_values)
+ new_obj.insert(0, name, level_values)
 
  new_obj.index = new_index
  if not inplace:

pandas/tests/frame/test_alter_axes.py

@@ -641,6 +641,43 @@ def test_reset_index(self):
  xp = xp.set_index(['B'], append=True)
  assert_frame_equal(rs, xp, check_names=False)
 
+ def test_reset_index_level(self):
+ df = pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]],
+ columns=['A', 'B', 'C', 'D'])
+
+ for levels in ['A', 'B'], [0, 1]:
+ # With MultiIndex
+ result = df.set_index(['A', 'B']).reset_index(level=levels[0])
+ tm.assert_frame_equal(result, df.set_index('B'))
+
+ result = df.set_index(['A', 'B']).reset_index(level=levels[:1])
+ tm.assert_frame_equal(result, df.set_index('B'))
+
+ result = df.set_index(['A', 'B']).reset_index(level=levels)
+ tm.assert_frame_equal(result, df)
+
+ result = df.set_index(['A', 'B']).reset_index(level=levels,
+ drop=True)
+ tm.assert_frame_equal(result, df[['C', 'D']])
+
+ # With flat Index (GH 16263)
+ result = df.set_index('A').reset_index(level=levels[0])
+ tm.assert_frame_equal(result, df)
+
+ result = df.set_index('A').reset_index(level=levels[:1])
+ tm.assert_frame_equal(result, df)
+
+ result = df.set_index(['A']).reset_index(level=levels[0],
+ drop=True)
+ tm.assert_frame_equal(result, df[['B', 'C', 'D']])
+
+ # Missing levels - for both MultiIndex and flat Index:
+ for idx_lev in ['A', 'B'], ['A']:
+ with tm.assert_raises_regex(KeyError, 'Level E '):
+ df.set_index(idx_lev).reset_index(level=['A', 'E'])
+ with tm.assert_raises_regex(IndexError, 'Too many levels'):
+ df.set_index(idx_lev).reset_index(level=[0, 1, 2])
+
  def test_reset_index_right_dtype(self):
  time = np.arange(0.0, 10, np.sqrt(2) / 2)
  s1 = Series((9.81 * time ** 2) / 2,

pandas/tests/series/test_alter_axes.py

@@ -141,6 +141,45 @@ def test_reset_index(self):
  tm.assert_index_equal(rs.index, Index(index.get_level_values(1)))
  assert isinstance(rs, Series)
 
+ def test_reset_index_level(self):
+ df = pd.DataFrame([[1, 2, 3], [4, 5, 6]],
+ columns=['A', 'B', 'C'])
+
+ for levels in ['A', 'B'], [0, 1]:
+ # With MultiIndex
+ s = df.set_index(['A', 'B'])['C']
+
+ result = s.reset_index(level=levels[0])
+ tm.assert_frame_equal(result, df.set_index('B'))
+
+ result = s.reset_index(level=levels[:1])
+ tm.assert_frame_equal(result, df.set_index('B'))
+
+ result = s.reset_index(level=levels)
+ tm.assert_frame_equal(result, df)
+
+ result = df.set_index(['A', 'B']).reset_index(level=levels,
+ drop=True)
+ tm.assert_frame_equal(result, df[['C']])
+
+ with tm.assert_raises_regex(KeyError, 'Level E '):
+ s.reset_index(level=['A', 'E'])
+
+ # With flat Index
+ s = df.set_index('A')['B']
+
+ result = s.reset_index(level=levels[0])
+ tm.assert_frame_equal(result, df[['A', 'B']])
+
+ result = s.reset_index(level=levels[:1])
+ tm.assert_frame_equal(result, df[['A', 'B']])
+
+ result = s.reset_index(level=levels[0], drop=True)
+ tm.assert_series_equal(result, df['B'])
+
+ with tm.assert_raises_regex(IndexError, 'Too many levels'):
+ s.reset_index(level=[0, 1, 2])
+
  def test_reset_index_range(self):
  # GH 12071
  s = pd.Series(range(2), name='A', dtype='int64')