Python 测量点和组之间的距离

我试图测量数据帧内点之间的距离。首先，我想测量一个子区域中点之间的距离，得到该组点的平均距离。然后我想测量子区域之间的距离（测量这两个向量之间的距离）。我了解如何进行测量部分（前者使用

scipy.space.distance.euclidean

，后者使用

scipy.space.distance.cdist

）。我遇到的问题是如何将函数应用于数据集。我认为我应该在函数中使用groupby.apply（）和feed，但我在概念化上遇到了困难。数据帧如下所示：

id, latitude, longitude, subregion, region

contacts = {}

for i, row in sc_walkbook.iterrows():
    if contacts.get(row['region'],0) == 0:
        contacts[row['region']] = {}
        contacts[row['region']][row['subregion']] = {}
        contacts[row['region']][row['subregion']]['coords'] = []
        contacts[row['region']][row['subregion']]['distances'] = []
    elif contacts[row['region']].get(row['subregion'],0) == 0:
        contacts[row['region']][row['subregion']] = {}
        contacts[row['region']][row['subregion']]['coords'] = []
        contacts[row['region']][row['subregion']]['distances'] = []
    else:
        pass
    contacts[row['region']][row['subregion']]['coords'].append([row['T_Latitude'],row['T_Longitude']])

for region in contacts.itervalues():
    for subregion in region.itervalues():
        for a, b in itertools.combinations(subregion['coords'], 2):
            subregion['distances'].append(euclidean(a, b))

目前我有：

import pandas as pd
import numpy as np
from scipy.spatial.distance import euclidean

df = pd.read_csv('targets.csv')
...
def calculate_distance(x,y):
    return x._get_numeric_data().apply(axis=0, func=euclidean[x,y]).mean()

df.groupby('subregion').apply(calculate_distance)

---

我知道这是不正确的，因为我想对所有行应用多个列。我的另一个想法是我使用了错误的数据结构。

我最终使用了不同的数据结构，结果如下所示：

id, latitude, longitude, subregion, region

contacts = {}

for i, row in sc_walkbook.iterrows():
    if contacts.get(row['region'],0) == 0:
        contacts[row['region']] = {}
        contacts[row['region']][row['subregion']] = {}
        contacts[row['region']][row['subregion']]['coords'] = []
        contacts[row['region']][row['subregion']]['distances'] = []
    elif contacts[row['region']].get(row['subregion'],0) == 0:
        contacts[row['region']][row['subregion']] = {}
        contacts[row['region']][row['subregion']]['coords'] = []
        contacts[row['region']][row['subregion']]['distances'] = []
    else:
        pass
    contacts[row['region']][row['subregion']]['coords'].append([row['T_Latitude'],row['T_Longitude']])

for region in contacts.itervalues():
    for subregion in region.itervalues():
        for a, b in itertools.combinations(subregion['coords'], 2):
            subregion['distances'].append(euclidean(a, b))