cudf.Series.reset_index#

Series.reset_index(level=None, drop=False, name=_NoDefault.no_default, inplace=False, allow_duplicates=False)[source]#

Reset the index of the Series, or a level of it.

Parameters:

levelint, str, tuple, or list, default None: Only remove the given levels from the index. Removes all levels by default.
dropbool, default False: Do not try to insert index into dataframe columns. This resets the index to the default integer index.
nameobject, 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.
inplacebool, default False: Modify the DataFrame in place (do not create a new object).
allow_duplicatesbool, default False: Allow duplicate column labels to be created. Currently not supported.

Returns:

Series or DataFrame or None: Series with the new index or None if inplace=True. For Series, 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.

Examples

>>> series = cudf.Series(['a', 'b', 'c', 'd'], index=[10, 11, 12, 13])
>>> series
10    a
11    b
12    c
13    d
dtype: object
>>> series.reset_index()
   index  0
0     10  a
1     11  b
2     12  c
3     13  d
>>> series.reset_index(drop=True)
0    a
1    b
2    c
3    d
dtype: object

You can also use reset_index with MultiIndex.

>>> s2 = cudf.Series(
...             range(4), name='foo',
...             index=cudf.MultiIndex.from_tuples([
...                     ('bar', 'one'), ('bar', 'two'),
...                     ('baz', 'one'), ('baz', 'two')],
...                     names=['a', 'b']
...      ))
>>> s2
a    b
bar  one    0
     two    1
baz  one    2
     two    3
Name: foo, dtype: int64
>>> s2.reset_index(level='a')
       a  foo
b
one  bar    0
two  bar    1
one  baz    2
two  baz    3