ValueError: 错误的输入形状 (2835, 18)

Question

我是数据科学的新手，我想从分类数据中进行分类。 我希望在使用 K-means 算法之前这样做，但是当我使用 fit_transform() 并且我不知道如何修复它时，我得到了这个 'error ValueError: bad input shape (2835, 18)'。我希望有人可以帮助我。

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder

#load my data
myData = pd.read_excel('panelForOneHot.xlsx')
myData = myData.dropna()
myData.reset_index(drop = True, inplace = True)
myData

values = np.array(myData)
print(values)

#integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(values)

Answer 1

LabelEncoder() 需要一维数据。传递一个特定的字段进行编码，如下所示。

# Import label encoder 
from sklearn import preprocessing 

# label_encoder object knows how to understand word labels. 
label_encoder = preprocessing.LabelEncoder() 

# Encode labels in column 'species'. 
df['species']= label_encoder.fit_transform(df['species']) 

df['species'].unique() 

如果您打算对所有列进行编码，

df.apply(LabelEncoder().fit_transform)

如果您打算对多列而不是全部进行编码，

from sklearn.compose import make_column_transformer
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import OneHotEncoder

categorical_columns = ['country', 'gender']
numerical_columns = ['age']
column_trans = make_column_transformer(
    (categorical_columns, OneHotEncoder(handle_unknown='ignore'),
    (numerical_columns, RobustScaler())
column_trans.fit_transform(df)