modin.pandas.DataFrame.reset_index¶

DataFrame.reset_index(level: IndexLabel = None, drop: bool = False, inplace: bool = False, col_level: Hashable = 0, col_fill: Hashable = '', allow_duplicates=_NoDefault.no_default, names: Hashable | Sequence[Hashable] = None)[source]¶

Reset the index, or a level of it.

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) – Whether to modify the DataFrame rather than creating a new one.

• 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.

• allow_duplicates (bool, optional, default lib.no_default) – Allow duplicate column labels to be created.

• names (int, str or 1-dimensional list, default None) – Using the given string, rename the DataFrame column which contains the index data. If the DataFrame has a MultiIndex, this has to be a list or tuple with length equal to the number of levels.

Returns:

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

Return type:

DataFrame or None

See also

Series.reset_index

Analogous function for Series.

DataFrame.set_index

Opposite of reset_index.

DataFrame.reindex

Change to new indices or expand indices.

DataFrame.reindex_like

Change to same indices as other DataFrame.

Examples

>>> 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

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

>>> 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

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

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

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You can also use reset_index with MultiIndex.

>>> 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

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Using the names parameter, choose a name for the index column:

>>> df.reset_index(names=['classes', 'names']) 
  classes   names  speed species
                     max    type
0    bird  falcon  389.0     fly
1    bird  parrot   24.0     fly
2  mammal    lion   80.5     run
3  mammal  monkey    NaN    jump

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

>>> 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

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

>>> 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

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When the index is inserted under another level, we can specify under which one with the parameter col_fill:

>>> 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

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If we specify a nonexistent level for col_fill, it is created:

>>> 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

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