导入经纬度数据，经纬度距离kmeans聚类代码

以下是导入经纬度数据并进行kmeans聚类的代码：

import pandas as pd
from sklearn.cluster import KMeans
from math import radians, cos, sin, asin, sqrt

# 导入经纬度数据
df = pd.read_csv('经纬度数据.csv')

# 计算经纬度距离
def haversine(lon1, lat1, lon2, lat2):
    """
    Calculate the great circle distance between two points 
    on the earth (specified in decimal degrees)
    """
    # 将十进制度数转化为弧度
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])

    # haversine公式
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a)) 
    r = 6371 # 地球平均半径，单位为公里
    return c * r

# 计算每个点之间的距离矩阵
dist_matrix = []
for i in range(0, len(df)):
    row_dist = []
    for j in range(0, len(df)):
        dist = haversine(df['经度'][i], df['纬度'][i], df['经度'][j], df['纬度'][j])
        row_dist.append(dist)
    dist_matrix.append(row_dist)

# 进行kmeans聚类
kmeans = KMeans(n_clusters=5, random_state=0).fit(dist_matrix)
labels = kmeans.labels_

# 将聚类结果加入原数据集
df['cluster'] = labels
df.to_csv('聚类结果.csv', index=False)

其中，`df` 是一个包含经度和纬度数据的 DataFrame，`haversine` 函数用来计算两个经纬度之间的距离，`dist_matrix` 存储了每个点之间的距离矩阵，`kmeans` 是进行聚类的对象，`labels` 是聚类的结果。最后，将聚类结果加入原数据集并保存到文件中。