创建距离矩阵? [英] Creating a Distance Matrix?

问题描述

我目前正在将数据读入如下所示的数据框中.

I am currently reading in data into a dataframe that looks like this.

City         XCord    YCord   
Boston         5        2
Phoenix        7        3
New York       8        1
.....          .        .

我想根据此数据创建一个欧几里得距离矩阵，该矩阵显示所有城市对之间的距离，因此我得到一个结果矩阵，如:

I want to to create a Euclidean Distance Matrix from this data showing the distance between all city pairs so I get a resulting matrix like:

             Boston    Phoenix   New York
Boston         0        2.236      3.162
Phoenix        2.236      0        2.236
New York       3.162    2.236        0

我的实际数据框中还有更多城市和坐标，因此我需要能够以某种方式迭代所有城市对，并创建一个距离矩阵，就像上面显示的那样，但我不确定如何将所有引用配对在一起并应用欧几里德距离公式?任何帮助将不胜感激.

There are many more cities and coordinates in my actual data frame so i need to to be able to somehow iterate over all of the city pairs and create a distance matrix like the one I have shown above but I am not sure how to pair all of the cites together and apply the Euclidean Distance formula? Any help would be appreciated.

推荐答案

我认为您对例如:

创建数据:

import pandas as pd
from scipy.spatial import distance_matrix

data = [[5, 7], [7, 3], [8, 1]]
ctys = ['Boston', 'Phoenix', 'New York']
df = pd.DataFrame(data, columns=['xcord', 'ycord'], index=ctys)

输出:

          xcord ycord
Boston      5   7
Phoenix     7   3
New York    8   1

使用距离矩阵函数:

 pd.DataFrame(distance_matrix(df.values, df.values), index=df.index, columns=df.index)

结果:

          Boston    Phoenix     New York
Boston    0.000000  4.472136    6.708204
Phoenix   4.472136  0.000000    2.236068
New York  6.708204  2.236068    0.000000

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