Rearrange rows of pandas dataframe based on list and keeping the order

Question

import numpy as np 
import pandas as pd

df = pd.DataFrame(data={'result':[-6.77,6.11,5.67,-7.679,-0.0930,4.342]}\
,index=['A','B','C','D','E','F'])
new_order = np.array([1,2,2,0,1,0])

The new_order numpy array assigns each row to one of three groups [0,1 or 2]. I would like to rearrange the rows of df so that those rows in group 0 appear first, followed by 1, and finally 2. Within each of the three groups the initial ordering should remain unchanged.

At the start the df is arranged as follows:

   result
A  -6.770
B   6.110
C   5.670
D  -7.679
E  -0.093
F   4.342

Here is the desired output given the above input data.

   result
D  -7.679
F   4.342
A  -6.770
E  -0.093
B   6.110
C   5.670

Answer 1

You could use argsort with kind='mergesort' to get sorted row indices that keeps the order and then simply index into the dataframe with those for the desired output, like so -

df.iloc[new_order.argsort(kind='mergesort')]

Sample run -

In [2]: df
Out[2]: 
   result
A  -6.770
B   6.110
C   5.670
D  -7.679
E  -0.093
F   4.342

In [3]: df.iloc[new_order.argsort(kind='mergesort')]
Out[3]: 
   result
D  -7.679
F   4.342
A  -6.770
E  -0.093
B   6.110
C   5.670