import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from mpl_toolkits.mplot3d import Axes3D from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler data=pd.read_csv('H:/analysis_results/mean_HN.csv') data.head() x=data.iloc[:,1:7] y=data.iloc[:,6] scaler=StandardScaler() scaler.fit(x) x_scaler=scaler.transform(x) print(x_scaler.shape) pca=PCA(n_components=3) x_pca=pca.fit_transform(x_scaler) print(x_pca.shape) #查看各个主成分对应的方差大小和占全部方差的比例 #可以看到前2个主成分已经解释了样本分布的90%的差异了 print('explained_variance_:',pca.explained_variance_) print('explained_variance_ratio_:',pca.explained_variance_ratio_) print('total explained variance ratio of first 6 principal components:',sum(pca.explained_variance_ratio_)) #将分析的结果保存成字典 result={ 'explained_variance_:',pca.explained_variance_, 'explained_variance_ratio_:',pca.explained_variance_ratio_, 'total explained variance ratio:',np.sum(pca.explained_variance_ratio_)} df=pd.DataFrame.from_dict(result,orient='index',columns=['value']) df.to_csv('H:/analysis_results/Cluster analysis/pca_explained_variance_HN.csv') #可视化各个主成分贡献的方差 #fig1=plt.figure(figsize=(10,10)) #plt.rcParams['figure.dpi'] = 300#设置像素参数值 plt.rcParams['path.simplify'] = False#禁用抗锯齿效果 plt.figure() plt.plot(np.arange(1,4),pca.explained_variance_,color='blue', linestyle='-',linewidth=2) plt.xticks(np.arange(1, 4, 1))#修改X轴间隔为1 plt.title('PCA_plot_HN') plt.xlabel('components_n',fontsize=16) plt.ylabel('explained_variance_',fontsize=16) #plt.savefig('H:/analysis_results/Cluster analysis/pca_explained_variance_HN.png') plt.show()报错unhashable type: 'numpy.ndarray'，如何修改

这个错误通常是由于字典中存在不可哈希对象 (unhashable type)，比如 numpy 数组，而字典的键必须是可哈希的对象。你可以将 numpy 数组转换为列表，然后再保存到字典中，例如：

result={     
    'explained_variance_': pca.explained_variance_.tolist(),
    'explained_variance_ratio_': pca.explained_variance_ratio_.tolist(),
    'total explained variance ratio': np.sum(pca.explained_variance_ratio_)
}

这样就可以避免 unhashable type 错误了。

相关问题

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from mpl_toolkits.mplot3d import Axes3D from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler data=pd.read_csv('H:/analysis_results/mean_HN.csv') data.head() x=data.iloc[:,1:7] y=data.iloc[:,6] scaler=StandardScaler() scaler.fit(x) x_scaler=scaler.transform(x) print(x_scaler.shape) pca=PCA(n_components=3) x_pca=pca.fit_transform(x_scaler) print(x_pca.shape) #查看各个主成分对应的方差大小和占全部方差的比例 #可以看到前2个主成分已经解释了样本分布的90%的差异了 print('explained_variance_:',pca.explained_variance_) print('explained_variance_ratio_:',pca.explained_variance_ratio_) print('total explained variance ratio of first 6 principal components:',sum(pca.explained_variance_ratio_)) #可视化各个主成分贡献的方差 #fig1=plt.figure(figsize=(10,10)) #plt.rcParams['figure.dpi'] = 300#设置像素参数值 plt.rcParams['path.simplify'] = False#禁用抗锯齿效果 plt.figure() plt.plot(np.arange(1,4),pca.explained_variance_,color='blue', linestyle='-',linewidth=2) plt.xticks(np.arange(1, 4, 1))#修改X轴间隔为1 plt.title('PCA_plot_HN') plt.xlabel('components_n',fontsize=16) plt.ylabel('explained_variance_',fontsize=16) plt.show() plt.pause(0.5) plt.savefig('H:/analysis_results/Cluster analysis/pca_explained_variance_HN.png')保存的图像中，一片空白，如何修改

可能是因为您在 `plt.savefig()` 函数之前调用了 `plt.show()` 函数，导致图片被清空。您可以将 `plt.savefig()` 函数放在 `plt.show()` 函数之前，或者注释掉 `plt.show()` 函数，只保存图片而不显示图片，如下所示：

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
from sklearn import datasets 
from mpl_toolkits.mplot3d import Axes3D 
from sklearn.decomposition import PCA 
from sklearn.preprocessing import StandardScaler 

data = pd.read_csv('H:/analysis_results/mean_HN.csv') 
data.head() 

x = data.iloc[:,1:7] 
y = data.iloc[:,6] 

scaler = StandardScaler() 
scaler.fit(x) 
x_scaler = scaler.transform(x) 
print(x_scaler.shape) 

pca = PCA(n_components=3) 
x_pca = pca.fit_transform(x_scaler) 
print(x_pca.shape) 

# 查看各个主成分对应的方差大小和占全部方差的比例 
# 可以看到前2个主成分已经解释了样本分布的90%的差异了 
print('explained_variance_:',pca.explained_variance_) 
print('explained_variance_ratio_:',pca.explained_variance_ratio_) 
print('total explained variance ratio of first 6 principal components:',sum(pca.explained_variance_ratio_)) 

# 可视化各个主成分贡献的方差 
# fig1=plt.figure(figsize=(10,10)) 
# plt.rcParams['figure.dpi'] = 300#设置像素参数值 
# plt.rcParams['path.simplify'] = False#禁用抗锯齿效果 

plt.figure() 
plt.plot(np.arange(1,4),pca.explained_variance_,color='blue', linestyle='-',linewidth=2) 
plt.xticks(np.arange(1, 4, 1))#修改X轴间隔为1 
plt.title('PCA_plot_HN') 
plt.xlabel('components_n',fontsize=16) 
plt.ylabel('explained_variance_',fontsize=16) 

# plt.show() 
plt.pause(0.5) 
plt.savefig('H:/analysis_results/Cluster analysis/pca_explained_variance_HN.png')

上述代码会在指定路径下生成一个名为 `pca_explained_variance_HN.png` 的图片文件，其中包含绘制的主成分方差贡献图。

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from pandas_profiling import ProfileReport from sklearn import datasets from mpl_toolkits.mplot3d import Axes3D from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler data=pd.read_csv('H:/analysis_results/mean_HN.csv') columns=['folder', 'volume', 'convex_volume', 'surface_area','length','max_width', 'max_depth'] data.head() values=data.iloc[:,1:7] correlation=values.corr() fig,ax=plt.subplots(figsize=(12,10)) sns.heatmap(correlation,annot=True,annot_kws={'size':16},cmap='Reds',square=True,ax=ax) sns.pairplot(data,hue='folder') plt.show()出现报错`import pandas_profiling` is going to be deprecated by April 1st. Please use `import ydata_profiling` instead. from pandas_profiling import ProfileReport，如何修改

你可以修改为以下代码来使用ydata_profiling：

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns 
from ydata_profiling import ProfileReport 
from sklearn import datasets 
from mpl_toolkits.mplot3d import Axes3D 
from sklearn.decomposition import PCA 
from sklearn.preprocessing import StandardScaler 

data=pd.read_csv('H:/analysis_results/mean_HN.csv') 
columns=['folder', 'volume', 'convex_volume', 'surface_area','length','max_width', 'max_depth'] 
data.head() 

values=data.iloc[:,1:7] 
correlation=values.corr() 

fig,ax=plt.subplots(figsize=(12,10)) 
sns.heatmap(correlation,annot=True,annot_kws={'size':16},cmap='Reds',square=True,ax=ax) 
sns.pairplot(data,hue='folder') 

profile = ProfileReport(data, title='Pandas Profiling Report', html={'style':{'full_width':True}}) 
profile.to_notebook_iframe() 

plt.show()

这里使用 `ydata_profiling` 替代 `pandas_profiling`，并且在最后添加了Pandas Profiling报告的代码。