pandas.DataFrame.reset_index   DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='')[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


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.      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
  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