如何计算 pandas 数据帧中从一个数据点到所有其他数据点的欧氏距离之和？

Question

我有以下 pandas 数据框：

import pandas as pd
import math

df = pd.DataFrame()
df['x'] = [2, 1, 3]
df['y'] = [2, 5, 6]
df['weight'] = [11, 12, 13]
print(df)

     x    y   weight   
 0   2    2       11       
 1   1    5       12       
 2   3    6       13

假设这3个节点分别叫做{a,b,c}。我想计算一个节点到所有其他节点的总欧氏距离乘以它的权重，如下：

Sum = 11(d(a,b)+d(a,c)) + 12(d(b,a)+d(b,c)) + 13(d(c,a)+d(c,b))

Answer 1

使用SciPy's cdist-

In [72]: from scipy.spatial.distance import cdist

In [73]: a = df[['x','y']].values

In [74]: w = df.weight.values

In [100]: cdist(a,a).sum(1) * w
Out[100]: array([ 80.13921614,  64.78014765,  82.66925684])

我们也可以使用来自相同 SciPy 方法的 pdist 和 squareform 的组合来替换那里的 cdist。

用这些实际值验证 -

In [76]: from scipy.spatial.distance import euclidean

In [77]: euclidean([2,2],[1,5])*11 + euclidean([2,2],[3,6])*11
Out[77]: 80.139216143646451

In [78]: euclidean([1,5],[2,2])*12 + euclidean([1,5],[3,6])*12
Out[78]: 64.78014765201803

In [80]: euclidean([3,6],[2,2])*13 + euclidean([3,6],[1,5])*13
Out[80]: 82.669256840526856

如何计算 pandas 数据帧中从一个数据点到所有其他数据点的欧氏距离之和？

How to calculate sum of Euclidean distances from one datapoint to all other datapoints from pandas dataframe?

python

euclidean-distance

dataframe

pandas