Vous êtes un professionnel et vous avez besoin d'une formation ? Machine Learning
avec Scikit-Learn Voir le programme détaillé

Classe « DataFrame »

Méthode pandas.DataFrame.reset_index

Signature de la méthode reset_index

def reset_index(self, level: 'IndexLabel | None' = None, *, drop: 'bool' = False, inplace: 'bool' = False, col_level: 'Hashable' = 0, col_fill: 'Hashable' = '', allow_duplicates: 'bool | lib.NoDefault' = <no_default>, names: 'Hashable | Sequence[Hashable] | None' = None) -> 'DataFrame | None' 

Description

help(DataFrame.reset_index)

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.

    .. versionadded:: 1.5.0

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.

    .. versionadded:: 1.5.0

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

See Also
--------
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

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

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

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

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

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

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

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

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

Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les fondamentaux Voir le programme détaillé