Python读取csv文件做K-means分析详情

from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans, MiniBatchKMeans
import pandas as pd
 
if __name__ == "__main__":
    data = pd.read_csv(r"H:\CSDN_Test_Data\UseYourTestData.csv")
    x, y = data['Time (sec)'], data['Height (m HAE)']
    n = len(x)
    x = np.array(x)
    x = x.reshape(n, 1)#reshape 为一列
    y = np.array(y)
    y = y.reshape(n, 1)#reshape 为一列
    data = np.hstack((x, y)) #水平合并为两列
    k = 8  # 设置颜色聚类的类别个数（我们分别设置8，16，32，64，128进行对比）
    cluster = KMeans(n_clusters=k)  # 构造聚类器
    C = cluster.fit_predict(data)
    # C_Image = cluster.fit_predict(data)
    print("训练总耗时为：%s(s)" % (Trainingtime).seconds)
    plt.figure()
    plt.scatter(data[:, 0], data[:, 1], marker='o', s=2, c=C)
    plt.show()

from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans, MiniBatchKMeans
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D
 
if __name__ == "__main__":
    data = pd.read_csv(r"H:\CSDN_Test_Data\UseYourTestData.csv")
    x, y,z = data['Longitude (deg)'],data['Latitude (deg)'],  data['Height (m HAE)']
    n = len(x)
    x = np.array(x)
    x = x.reshape(n, 1)#reshape 为一列
    y = np.array(y)
    y = y.reshape(n, 1)#reshape 为一列
    z = np.array(z)
    z = z.reshape(n, 1)  # reshape 为一列
    data = np.hstack((x, y, z)) #水平合并为两列
    k = 8  # 设置颜色聚类的类别个数（我们分别设置8，16，32，64，128进行对比）
    cluster = KMeans(n_clusters=k)  # 构造聚类器
    C = cluster.fit_predict(data)
 
    # C_Image = cluster.fit_predict(data)
    print("训练总耗时为：%s(s)" % (Trainingtime).seconds)
    fig = plt.figure()
    ax = Axes3D(fig)
 
    ax.scatter(data[:, 0], data[:, 1],data[:, 2], s=1, c=C)
    # 绘制图例
    ax.legend(loc='best')
    # 添加坐标轴
    ax.set_zlabel('Z Label', fontdict={'size': 15, 'color': 'red'})
    ax.set_ylabel('Y Label', fontdict={'size': 15, 'color': 'red'})
    ax.set_xlabel('X Label', fontdict={'size': 15, 'color': 'red'})
    plt.show()