请考虑以下熊猫数据框：
print(df)
Id X Y Type X of Closest Y of Closest
0 201 73.91 34.84 A NaN NaN
1 201 74.67 32.64 A NaN NaN
2 201 74.00 33.20 A NaN NaN
3 201 71.46 27.70 A NaN NaN
4 201 69.32 35.42 A NaN NaN
5 201 75.06 24.00 B NaN NaN
6 201 74.11 16.64 B NaN NaN
7 201 73.37 18.73 B NaN NaN
8 201 56.63 26.90 B NaN NaN
9 201 73.35 38.83 B NaN NaN
10 512 74.15 28.90 A NaN NaN
11 512 75.82 17.56 A NaN NaN
12 512 74.78 33.21 A NaN NaN
13 512 75.43 32.41 A NaN NaN
14 512 75.90 25.12 A NaN NaN
15 512 79.76 29.49 B NaN NaN
16 512 76.47 36.91 B NaN NaN
17 512 74.70 19.19 B NaN NaN
18 512 78.75 30.53 B NaN NaN
19 512 74.60 31.88 B NaN NaN
请注意，对于每个ID，总有10行，类型A为5行，类型B为5行。
我想创建2列，“最近的X”和“最近的Y”。我的意思是说，X，Y对（每个ID的类型相反）是最短的欧几里德距离。
[第一行示例：（B型）与（73.91，34.84）最接近的对是（73.35,38.83）对-欧氏距离为4.03。
一种（可能的！？）方法是构造10列-每个ID中点之间的欧式距离，然后从相反的Type中选择最小欧式距离。不过，我敢肯定会有更快的方法。
对于快速（编码）解决方案，我们可以在groupby上使用apply：
from scipy.spatial import distance_matrix
def get_min_dist(x):
# compute distance matrix
tmp = distance_matrix(x.iloc[:5], x.iloc[5:])
# get index min of corresponding types
idx = np.concatenate((np.argmin(tmp,1)+5), # type A to type B
np.argmin(tmp, 0) # type B to type A
)
return pd.DataFrame(x.iloc[idx].values,
index=x.index,
columns=[a+'_closest' for a in x.columns])
df.groupby('Id')[['X','Y']].apply(get_min_dist)
输出：
X_closest Y_closest
0 73.35 38.83
1 73.35 38.83
2 73.35 38.83
3 75.06 24.00
4 73.35 38.83
5 71.46 27.70
6 71.46 27.70
7 71.46 27.70
8 71.46 27.70
9 73.91 34.84
10 74.60 31.88
11 74.70 19.19
12 74.60 31.88
13 74.60 31.88
14 79.76 29.49
15 75.43 32.41
16 74.78 33.21
17 75.82 17.56
18 75.43 32.41
19 75.43 32.41
这是我使用Numpy广播的解决方案
df = pd.DataFrame([[201, 73.91, 34.84, 'A', np.nan, np.nan], [201, 74.67, 32.64, 'A', np.nan, np.nan], [201, 74.0, 33.2, 'A', np.nan, np.nan], [201, 71.46, 27.7, 'A', np.nan, np.nan], [201, 69.32, 35.42, 'A', np.nan, np.nan], [201, 75.06, 24.0, 'B', np.nan, np.nan], [201, 74.11, 16.64, 'B', np.nan, np.nan], [201, 73.37, 18.73, 'B', np.nan, np.nan], [201, 56.63, 26.9, 'B', np.nan, np.nan], [201, 73.35, 38.83, 'B', np.nan, np.nan], [512, 74.15, 28.9, 'A', np.nan, np.nan], [512, 75.82, 17.56, 'A', np.nan, np.nan], [512, 74.78, 33.21, 'A', np.nan, np.nan], [512, 75.43, 32.41, 'A', np.nan, np.nan], [512, 75.9, 25.12, 'A', np.nan, np.nan], [512, 79.76, 29.49, 'B', np.nan, np.nan], [512, 76.47, 36.91, 'B', np.nan, np.nan], [512, 74.7, 19.19, 'B', np.nan, np.nan], [512, 78.75, 30.53, 'B', np.nan, np.nan], [512, 74.6, 31.88, 'B', np.nan, np.nan]], columns=('Id', 'X', 'Y', 'Type', 'X-of-Closest', 'Y-of-Closest'))
## assuming that df is sorted by ID and Type we can create this 4 dimensional array where
## dim0->no of unique ids, dim1-> 2 (type A, B), dim2->5 values of each type, dim3->X or Y
values = df[['X','Y']].values.reshape(-1,2, 5, 2).copy()
## values[:,0,:,:] will take rows of type A for all ids
## and the broadcast repeates values of type A and B 5 times each
## which represents 5X5=25 possible pairs of points of type A and B
diff = values[:,0,:,:][:,:,np.newaxis,:] - values[:,1,:,:][:,np.newaxis,:,:]
## get index of min distance for type A and B
ind1 = np.argmin(np.sum(diff**2, axis=-1), axis=-1)
ind2 = np.argmin(np.sum(diff**2, axis=-1), axis=-2)
## use the index to set point with min distance to other type
closest_points = np.empty_like(values)
closest_points[:,0] = values[0,1,ind1]
closest_points[:,1] = values[0,0,ind2]
## assign result back to df
df[["X-of-Closest","Y-of-Closest"]] = closest_points.reshape(-1,2)
print(df)
结果
Id X Y Type X-of-Closest Y-of-Closest
0 201 73.91 34.84 A 73.35 38.83
1 201 74.67 32.64 A 73.35 38.83
2 201 74.00 33.20 A 73.35 38.83
3 201 71.46 27.70 A 75.06 24.00
4 201 69.32 35.42 A 73.35 38.83
5 201 75.06 24.00 B 71.46 27.70
6 201 74.11 16.64 B 71.46 27.70
7 201 73.37 18.73 B 71.46 27.70
8 201 56.63 26.90 B 71.46 27.70
9 201 73.35 38.83 B 73.91 34.84
10 512 74.15 28.90 A 73.35 38.83
11 512 75.82 17.56 A 73.37 18.73
12 512 74.78 33.21 A 73.35 38.83
13 512 75.43 32.41 A 73.35 38.83
14 512 75.90 25.12 A 75.06 24.00
15 512 79.76 29.49 B 71.46 27.70
16 512 76.47 36.91 B 74.00 33.20
17 512 74.70 19.19 B 74.67 32.64
18 512 78.75 30.53 B 71.46 27.70
19 512 74.60 31.88 B 71.46 27.70
有关广播工作原理的详细信息，请查看this blog的广播部分