最近在用python做数据挖掘，在聚类的时候遇到了一个非常恶心的问题。话不多说，直接上代码：
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
#kmeans算法
df1=df23
kmeans = KMeans(n_clusters=5, random_state=10).fit(df1)
#贴上每个样本对应的簇类别标签
df1['level']=kmeans.labels_
#df1.to_csv('new_df.csv') df2=df1.groupby('level',as_index=False)['level'].agg({'num': np.size})
print(df2.head()) #将用于聚类的数据的特征的维度降至2维
pca = PCA(n_components=2)
new_pca = pd.DataFrame(pca.fit_transform(df1))
print(new_pca.head()) #可视化
d = new_pca[df1['level'] == 0]
plt.plot(d[0], d[1], 'gv')
d = new_pca[df1['level'] == 1]
plt.plot(d[0], d[1], 'ko')
d = new_pca[df1['level'] == 2]
plt.plot(d[0], d[1], 'b*')
d = new_pca[df1['level'] == 3]
plt.plot(d[0], d[1], 'y+')
d = new_pca[df1['level'] == 4]
plt.plot(d[0], d[1], 'c.') plt.title('the result of polymerization')
plt.show()
错误如下：
网上找了好久都没找到解决方法，明明之前成功过的。于是我查看了df23数据，发现它是这样的：
与之前成功的dataframe的唯一差别就是索引！！！重要的事情说三遍！！！索引！！！索引！！！于是乎，我去找怎么重置索引的方法，见代码：
df24=df23[["forks_count","has_issues","has_wiki","open_issues_count","stargazers_count","watchers_count","created_pushed_time","created_updated_time"]]
df24=df24.reset_index()
df24=df24[["forks_count","has_issues","has_wiki","open_issues_count","stargazers_count","watchers_count","created_pushed_time","created_updated_time"]]
然后聚类就成功了。。。心累。。。。