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) → DataFrame | Series | None[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.42.0/.tox/docs/lib/python3.9/site-packages/modin/pandas/base.py#L2677-L2713)

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
Copy

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
Copy

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
Copy

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
Copy

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
Copy

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
Copy

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
Copy
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