Update index after sorting data-frame

Question

You can reset the index using reset_index to get back a default index of 0, 1, 2, ..., n-1 (and use drop=True to indicate you want to drop the existing index instead of adding it as an additional column to your dataframe):

In [19]: df2 = df2.reset_index(drop=True)

In [20]: df2
Out[20]:
   x  y
0  0  0
1  0  1
2  0  2
3  1  0
4  1  1
5  1  2
6  2  0
7  2  1
8  2  2

Since pandas 1.0.0 df.sort_values has a new parameter ignore_index which does exactly what you need:

In [1]: df2 = df.sort_values(by=['x','y'],ignore_index=True)

In [2]: df2
Out[2]:
   x  y
0  0  0
1  0  1
2  0  2
3  1  0
4  1  1
5  1  2
6  2  0
7  2  1
8  2  2

df.sort() is deprecated, use df.sort_values(...): https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html

Then follow joris' answer by doing df.reset_index(drop=True)

You can set new indices by using set_index:

df2.set_index(np.arange(len(df2.index)))

Output:

   x  y
0  0  0
1  0  1
2  0  2
3  1  0
4  1  1
5  1  2
6  2  0
7  2  1
8  2  2

The following works!

1. If you want to change the existing dataframe itself, you may directly use

    df.sort_values(by=['col1'], inplace=True)
    df.reset_index(drop=True, inplace=True)
   
    df
    >>     col1  col2  col3 col4
        0    A     2     0    a
        1    A     1     1    B
        2    B     9     9    c
        5    C     4     3    F
        4    D     7     2    e
        3  NaN     8     4    D
   

2. Else, if you don't want to change the existing dataframe but want to store the sorted dataframe into another variable separately, you may use:

   df_sorted = df.sort_values(by=['col1']).reset_index(drop=True)
   
   df_sorted
   >>     col1  col2  col3 col4
       0    A     2     0    a
       1    A     1     1    B
       2    B     9     9    c
       3    C     4     3    F
       4    D     7     2    e
       5  NaN     8     4    D
   
   df
   >>       col1  col2  col3 col4
         0    A     2     0    a
         1    A     1     1    B
         2    B     9     9    c
         3  NaN     8     4    D
         4    D     7     2    e
         5    C     4     3    F