# pandas.Series.reset_index

`Series.``reset_index`(level=None, drop=False, name=None, inplace=False)

  Analogous to the `pandas.DataFrame.reset_index()` function, see docstring there.

  Parameters:

  level : int, str, tuple, or list, default None

  > Only remove the given levels from the index. Removes all levels by default

  drop : boolean, default False

  > Do not try to insert index into dataframe columns

  name : object, default None

  > The name of the column corresponding to the Series values

  inplace : boolean, default False

  > Modify the Series in place (do not create a new object)

  Returns: | resetted : DataFrame, or Series if drop == True

  Examples

      >>> s = pd.Series([1, 2, 3, 4], index=pd.Index(['a', 'b', 'c', 'd'],
      ...                                            name = 'idx'))
      >>> s.reset_index()
         index  0
      0      0  1
      1      1  2
      2      2  3
      3      3  4

      >>> arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo',
      ...                     'foo', 'qux', 'qux']),
      ...           np.array(['one', 'two', 'one', 'two', 'one', 'two',
      ...                     'one', 'two'])]
      >>> s2 = pd.Series(
      ...     np.random.randn(8),
      ...     index=pd.MultiIndex.from_arrays(arrays,
      ...                                     names=['a', 'b']))
      >>> s2.reset_index(level='a')
             a         0
      b
      one  bar -0.286320
      two  bar -0.587934
      one  baz  0.710491
      two  baz -1.429006
      one  foo  0.790700
      two  foo  0.824863
      one  qux -0.718963
      two  qux -0.055028

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