以下是我使用的示例数据集：

   id,product,store,revenue,store_capacity,state    1,Ball,AB,222,1000,CA    1,Pen,AB,234,1452,WD    2,Books,CD,543,888,MA    2,Ink,EF,123,9865,NY

代码如下

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom sklearn.cluster import KMeansfrom scipy.spatial.distance import euclideanimport seaborn as snsfrom sklearn.preprocessing import MinMaxScaler, StandardScalersns.set(rc={'figure.figsize':(11.7,8.27)})df = pd.read_csv(r'1.csv',index_col=None)dummies = pd.get_dummies(data = df)km = KMeans(n_clusters=2).fit(dummies)labels = km.predict(dummies)dummies['cluster_id'] = km.labels_def distance_to_centroid(row, centroid):    row = row[['id', 'product', 'store', 'revenue','store_capacity', 'state_AL', 'state_CA', 'state_CH',       'state_WD', 'country_India', 'country_Japan', 'country_USA']]    return euclidean(row, centroid)dummies['distance_to_center0'] = dummies.apply(lambda r: distance_to_centroid(r,    km.cluster_centers_[0]),1)dummies['distance_to_center1'] = dummies.apply(lambda r: distance_to_centroid(r,    km.cluster_centers_[1]),1)dummies['distance_to_center2'] = dummies.apply(lambda r: distance_to_centroid(r,    km.cluster_centers_[2]),1)dummies_df = dummies[['distance_to_center0','distance_to_center1','cluster_id']]test = {0:"Blue", 1:"Red", 2:"Green"}sns.scatterplot(x="distance_to_center0", y="distance_to_center1", data=dummies_df, hue="cluster_id", palette = test)

我需要获取每个聚类中心，下面代码获取的是每个元素的中心点，意思是每个元素到聚类中心点的距离

centroids  = km.cluster_centers_centroid_labels = [centroids[i] for i in labels]centroid_label

我想获取每个聚类的中心点

回答：

致谢 @Isma

km = KMeans(n_clusters=7).fit(dummies)closest, _ = pairwise_distances_argmin_min(km.cluster_centers_, dummies)closest