Map 值以分隔列 - pandas

Question

I'm trying to map values from a column to a separate column. Using below, the calculate_distances function measures the distance from each point to the nearest point for each Group . I also return the index value of each point for identification.

This all works fine. But instead of the index value, I'm hoping to map the corresponding ID value within the function to the output.

If I don't map the ID values across, both nearest_object cols will display the index value, instead of the actual ID value.

I'll comment out my attempt to show the output.

from sklearn.neighbors import BallTree
import pandas as pd

df = pd.DataFrame({              
    'Time' : [1,1,1,1,1,1,2,2,2,2,2,2],             
    'ID' : ['A','B','C','X','U','V','A','B','C','X','U','V'],      
    'Group' : ['Red','Red','Red','Grn','Grn','Grn','Red','Red','Red','Grn','Grn','Grn'],           
    'X' : [2.0,3.0,4.0,2.0,2.0,1.0,1.0,6.0,4.0,2.0,5.0,3.0],
    'Y' : [3.0,1.0,0.0,0.0,2.0,1.0,2.0,0.0,1.0,1.0,0.0,0.0],           
    })

def calculate_distances(df, group_column='Group'):    

    '''
    Calculate distance and id to both red and green groups.
    '''
    # unq groups
    groups = df[group_column].unique()

    all_points = df[['X','Y']].values

    for group in groups:
        group_points = df[df[group_column] == group][['X','Y']]
    
        # calculate distance between points
        tree = BallTree(group_points, leaf_size=15, metric='minkowski')

        distance, index = tree.query(all_points, k=1)
        distances = distance[:,0]
        nearest_id = group_points.index[index[:,0]]
                    
        distance_column_name = "distance_{}".format( group )
        df[ distance_column_name ] = distances
    
        distance_column_nearest_name = "nearest_object_{}".format( group )
        df[distance_column_nearest_name] = nearest_id   

    # map ID values
    #df.iloc[:,-3] = df.iloc[:,-3].map(df.set_index('index')['ID']) 
    #df.iloc[:,-1] = df.iloc[:,-1].map(df.set_index('index')['ID'])       

    return df

df = df.groupby(['Time']).apply(calculate_distances).reset_index()

out:

    Time ID Group    X    Y  distance_Red  nearest_object_Red  distance_Grn  nearest_object_Grn
0      1  A   Red  2.0  3.0      0.000000                   0      1.000000                   4
1      1  B   Red  3.0  1.0      0.000000                   1      1.414214                   3
2      1  C   Red  4.0  0.0      0.000000                   2      2.000000                   3
3      1  X   Grn  2.0  0.0      1.414214                   1      0.000000                   3
4      1  U   Grn  2.0  2.0      1.000000                   0      0.000000                   4
5      1  V   Grn  1.0  1.0      2.000000                   1      0.000000                   5
6      2  A   Red  1.0  2.0      0.000000                   6      1.414214                   9
7      2  B   Red  6.0  0.0      0.000000                   7      1.000000                  10
8      2  C   Red  4.0  1.0      0.000000                   8      1.414214                  10
9      2  X   Grn  2.0  1.0      1.414214                   6      0.000000                   9
10     2  U   Grn  5.0  0.0      1.000000                   7      0.000000                  10
11     2  V   Grn  3.0  0.0      1.414214                   8      0.000000                  11

intended output:

    Time ID Group    X    Y  distance_Red nearest_object_Red  distance_Grn nearest_object_Grn
0      1  A   Red  2.0  3.0      0.000000                  A      1.000000                  U
1      1  B   Red  3.0  1.0      0.000000                  B      1.414214                  X
2      1  C   Red  4.0  0.0      0.000000                  C      2.000000                  X
3      1  X   Grn  2.0  0.0      1.414214                  B      0.000000                  X
4      1  U   Grn  2.0  2.0      1.000000                  A      0.000000                  U
5      1  V   Grn  1.0  1.0      2.000000                  B      0.000000                  V
6      2  A   Red  1.0  2.0      0.000000                  A      1.414214                  X
7      2  B   Red  6.0  0.0      0.000000                  B      1.000000                  U
8      2  C   Red  4.0  1.0      0.000000                  C      1.414214                  U
9      2  X   Grn  2.0  1.0      1.414214                  A      0.000000                  X
10     2  U   Grn  5.0  0.0      1.000000                  B      0.000000                  U
11     2  V   Grn  3.0  0.0      1.414214                  C      0.000000                  V

Answer 1

Because the index is named Time while the dataframe already has a column with the same name. When you do reset_index , pandas try to make the index a normal column, in this case it fails due to the duplicate name. Try:

df = df.groupby(['Time']).apply(calculate_distances).reset_index(drop=True)