我有一个问题。这个数据集有两列分类数据。每当我执行labelencoder_y时，它会给我IndexError: too many indices for array错误，而每当我运行最后的onehotencoder时，它会给我ValueError: could not convert string to float: 'No'错误。尽管我在处理x值时没有遇到任何问题。如何处理这些错误？以下是数据集：

Site Name,Per Article,Per Song,Per Hour,Per Test,Per Survey,Per Minute,PakistanListverse,100,0,0,0,0,0,YesPlaylist Push,0,12,0,0,0,0,NoTranscribeMe,0,0,18,0,0,0,YesIntelliZoomPanel,0,0,0,10,0,0,NoItalki,0,0,12,0,0,0,YesVindalle Research,0,0,0,0,3,0,NoRev,0,0,0,0,0,2,YesQuickRewards,0,0,0,0,5,0,No

这是我的代码：

#Importing the librariesimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns#Importing the datasetdataset = pd.read_csv('sheet.csv')X = dataset.iloc[:, :-7].valuesy = dataset.iloc[:, 7].valuesfrom sklearn.preprocessing import LabelEncoder, OneHotEncoderlabelencoder_X= LabelEncoder()X[:,0] = labelencoder_X.fit_transform(X[:,0])onehotencoder = OneHotEncoder(categorical_features = [0])X = onehotencoder.fit_transform(X).toarray()labelencoder_y = LabelEncoder()y[:,0] = labelencoder_y.fit_transform(y[:,0])onehotencoder = OneHotEncoder(categorical_features = [0])y = onehotencoder.fit_transform(y).toarray()

回答：

我认为索引过多是问题所在。去掉所有索引后，编码可以无错误地工作

labelencoder_y = LabelEncoder()y = labelencoder_y.fit_transform(y)y = y.reshape(-1, 1)onehotencoder = OneHotEncoder()y = onehotencoder.fit_transform(y).toarray()