2.20 Set / Reset Index

Occasionally you will load or create a data set into a DataFrame and want to add an index after you’ve already done so. There are a couple of different ways.

2.20.1 Set an index

DataFrame has a set_index method which takes a column name (for a regular Index) or a list of column names (for a MultiIndex), to create a new, indexed DataFrame:

In [1]: data = pd.DataFrame({'a' : ['bar', 'bar', 'foo', 'foo'],
   ...:                      'b' : ['one', 'two', 'one', 'two'],
   ...:                      'c' : ['z', 'y', 'x', 'w'],
   ...:                      'd' : [1., 2., 3, 4]})
   ...: 

In [2]: data
Out[2]: 
     a    b  c    d
0  bar  one  z  1.0
1  bar  two  y  2.0
2  foo  one  x  3.0
3  foo  two  w  4.0

In [3]: indexed1 = data.set_index('c')

In [4]: indexed1
Out[4]: 
     a    b    d
c               
z  bar  one  1.0
y  bar  two  2.0
x  foo  one  3.0
w  foo  two  4.0

In [5]: indexed2 = data.set_index(['a', 'b'])

In [6]: indexed2
Out[6]: 
         c    d
a   b          
bar one  z  1.0
    two  y  2.0
foo one  x  3.0
    two  w  4.0

The append keyword option allow you to keep the existing index and append the given columns to a MultiIndex:

In [7]: frame = data.set_index('c', drop=False)

In [8]: frame
Out[8]: 
     a    b  c    d
c                  
z  bar  one  z  1.0
y  bar  two  y  2.0
x  foo  one  x  3.0
w  foo  two  w  4.0

In [9]: frame = frame.set_index(['a', 'b'], append=True)

In [10]: frame
Out[10]: 
           c    d
c a   b          
z bar one  z  1.0
y bar two  y  2.0
x foo one  x  3.0
w foo two  w  4.0

Other options in set_index allow you not drop the index columns or to add the index in-place (without creating a new object):

In [11]: data
Out[11]: 
     a    b  c    d
0  bar  one  z  1.0
1  bar  two  y  2.0
2  foo  one  x  3.0
3  foo  two  w  4.0

In [12]: data.set_index('c', drop=False)
Out[12]: 
     a    b  c    d
c                  
z  bar  one  z  1.0
y  bar  two  y  2.0
x  foo  one  x  3.0
w  foo  two  w  4.0

In [13]: data.set_index(['a', 'b'], inplace=True)

In [14]: data
Out[14]: 
         c    d
a   b          
bar one  z  1.0
    two  y  2.0
foo one  x  3.0
    two  w  4.0

2.20.2 Reset the index

As a convenience, there is a new function on DataFrame called reset_index which transfers the index values into the DataFrame’s columns and sets a simple integer index. This is the inverse operation to set_index

In [15]: data
Out[15]: 
         c    d
a   b          
bar one  z  1.0
    two  y  2.0
foo one  x  3.0
    two  w  4.0

In [16]: data.reset_index()
Out[16]: 
     a    b  c    d
0  bar  one  z  1.0
1  bar  two  y  2.0
2  foo  one  x  3.0
3  foo  two  w  4.0

The output is more similar to a SQL table or a record array. The names for the columns derived from the index are the ones stored in the names attribute.

You can use the level keyword to remove only a portion of the index:

In [17]: frame
Out[17]: 
           c    d
c a   b          
z bar one  z  1.0
y bar two  y  2.0
x foo one  x  3.0
w foo two  w  4.0

In [18]: frame.reset_index(level=1)
Out[18]: 
         a  c    d
c b               
z one  bar  z  1.0
y two  bar  y  2.0
x one  foo  x  3.0
w two  foo  w  4.0

reset_index takes an optional parameter drop which if true simply discards the index, instead of putting index values in the DataFrame’s columns.

Note

The reset_index method used to be called delevel which is now deprecated.

2.20.3 Adding an ad hoc index

If you create an index yourself, you can just assign it to the index field:

data.index = index