如何使用sklearn.preprocessing修复此错误？

import numpy as np
from sklearn.preprocessing import StandardScaler

# 假设我们有一个包含字符串的数据集
data = np.array(['1', '2', '3', '4', '5'])

# 将字符串转换为数值型
data = data.astype(float)

# 使用StandardScaler进行标准化
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data.reshape(-1, 1))
print(scaled_data)

import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer

# 假设我们有一个包含缺失值的数据集
data = np.array([[1, 2], [np.nan, 3], [7, 6]])

# 使用SimpleImputer填充缺失值
imputer = SimpleImputer(strategy='mean')
data_imputed = imputer.fit_transform(data)

# 使用StandardScaler进行标准化
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data_imputed)
print(scaled_data)

import numpy as np
from sklearn.preprocessing import StandardScaler

# 假设我们有一个一维数据集
data = np.array([1, 2, 3, 4, 5])

# 将一维数据重塑为二维
data_reshaped = data.reshape(-1, 1)

# 使用StandardScaler进行标准化
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data_reshaped)
print(scaled_data)