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# pandas.Series.reset_index

Series.reset_index(level=None, *, drop=False, name=<no_default>, inplace=False, allow_duplicates=False)

  Generate a new DataFrame or Series with the index reset.

  This is useful when the index needs to be treated as a column, or when the index is meaningless and needs to be reset to the default before another operation.

  Parameters:
    level int, str, tuple, or list, default optional

      For a Series with a MultiIndex, only remove the specified levels from the index. Removes all levels by default.

    drop bool, default False

      Just reset the index, without inserting it as a column in the new DataFrame.

    name object, optional

      The name to use for the column containing the original Series values. Uses `self.name` by default. This argument is ignored when drop is True.

    inplace bool, default False

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

    allow_duplicates bool, default False

      Allow duplicate column labels to be created.

  Returns:
    Series or DataFrame or None

      When drop is False (the default), a DataFrame is returned. The newly created columns will come first in the DataFrame, followed by the original Series values. When drop is True, a Series is returned. In either case, if `inplace=True`, no value is returned.

  See also

  `DataFrame.reset_index`

    Analogous function for DataFrame.

  Examples

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

  Generate a DataFrame with default index.

      >>> s.reset_index()
        idx  foo
      0   a    1
      1   b    2
      2   c    3
      3   d    4

  To specify the name of the new column use name.

      >>> s.reset_index(name="values")
        idx  values
      0   a       1
      1   b       2
      2   c       3
      3   d       4

  To generate a new Series with the default set drop to True.

      >>> s.reset_index(drop=True)
      0    1
      1    2
      2    3
      3    4
      Name: foo, dtype: int64

  The level parameter is interesting for Series with a multi-level index.

      >>> arrays = [
      ...     np.array(["bar", "bar", "baz", "baz"]),
      ...     np.array(["one", "two", "one", "two"]),
      ... ]
      >>> s2 = pd.Series(
      ...     range(4),
      ...     name="foo",
      ...     index=pd.MultiIndex.from_arrays(arrays, names=["a", "b"]),
      ... )

  To remove a specific level from the Index, use level.

      >>> s2.reset_index(level="a")
             a  foo
      b
      one  bar    0
      two  bar    1
      one  baz    2
      two  baz    3

  If level is not set, all levels are removed from the Index.

      >>> s2.reset_index()
           a    b  foo
      0  bar  one    0
      1  bar  two    1
      2  baz  one    2
      3  baz  two    3