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reset_index(self, level: 'Hashable | Sequence[Hashable] | None' = None, drop: 'bool' = False, inplace: 'bool' = False, col_level: 'Hashable' = 0, col_fill: 'Hashable' = '') -> 'DataFrame | None'

Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.

Parameters

level : int, str, tuple, or list, default None: Only remove the given levels from the index. Removes all levels by default.
drop : bool, default False: Do not try to insert index into dataframe columns. This resets the index to the default integer index.
inplace : bool, default False: Modify the DataFrame in place (do not create a new object).
col_level : int or str, default 0: If the columns have multiple levels, determines which level the labels are inserted into. By default it is inserted into the first level.
col_fill : object, default '': If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated.

Returns

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

Reset the index, or a level of it.

Examples

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame([('bird', 389.0),
...                    ('bird', 24.0),
...                    ('mammal', 80.5),
...                    ('mammal', np.nan)],
...                   index=['falcon', 'parrot', 'lion', 'monkey'],
...                   columns=('class', 'max_speed'))
... df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal        NaN

When we reset the index, the old index is added as a column, and a new sequential index is used:

This example is valid syntax, but we were not able to check execution

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

We can use the drop parameter to avoid the old index being added as a column:

This example is valid syntax, but we were not able to check execution

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal        NaN

You can also use reset_index with MultiIndex .

This example is valid syntax, but we were not able to check execution

>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
...                                    ('bird', 'parrot'),
...                                    ('mammal', 'lion'),
...                                    ('mammal', 'monkey')],
...                                   names=['class', 'name'])
... columns = pd.MultiIndex.from_tuples([('speed', 'max'),
...                                      ('species', 'type')])
... df = pd.DataFrame([(389.0, 'fly'),
...                    ( 24.0, 'fly'),
...                    ( 80.5, 'run'),
...                    (np.nan, 'jump')],
...                   index=index,
...                   columns=columns)
... df
               speed species
                 max    type
class  name
bird   falcon  389.0     fly
       parrot   24.0     fly
mammal lion     80.5     run
       monkey    NaN    jump

If the index has multiple levels, we can reset a subset of them:

This example is valid syntax, but we were not able to check execution

>>> df.reset_index(level='class')
         class  speed species
                  max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:

This example is valid syntax, but we were not able to check execution

>>> df.reset_index(level='class', col_level=1)
                speed species
         class    max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

When the index is inserted under another level, we can specify under which one with the parameter :None:None:`col_fill`:

This example is valid syntax, but we were not able to check execution

>>> df.reset_index(level='class', col_level=1, col_fill='species')
              species  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

If we specify a nonexistent level for :None:None:`col_fill`, it is created:

This example is valid syntax, but we were not able to check execution

>>> df.reset_index(level='class', col_level=1, col_fill='genus')
                genus  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.generic.NDFrame.reindex pandas.core.series.Series.reset_index pandas.core.frame.DataFrame.reset_index pandas.core.frame.DataFrame.reindex pandas.core.frame.DataFrame.set_index pandas.core.series.Series.reindex pandas.core.generic.NDFrame.reindex_like

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File: /pandas/core/frame.py#5632
type: <class 'function'>
Commit:

Parameters

Returns

See Also

Examples

Back References

Local connectivity graph