File "<ipython-input-13-a93fa9f341f1>", line 2 from sklearn.preprocessing import MinMaxScaler ^ IndentationError: unexpected indent#数值型变量预处理， from sklearn.preprocessing import MinMaxScaler mn_X = MinMaxScaler() numerical_features = ['temp','atemp','hum','windspeed'] temp = mn_X.fit_transform(train[numerical_features]) X_train_num = pd.DataFrame(data=temp, columns=numerical_features, index =train.index) print(X_train_num.head())

这个错误提示显示在第二行的 `from sklearn.preprocessing import MinMaxScaler` 这一行出现了缩进错误。可能是你在该行前面多输入了空格或制表符，导致代码格式不符合 Python 的语法规定。建议你检查一下该行前面的空格或制表符，并根据情况进行删除或调整。正确的代码应该是这样的：

# 数值型变量预处理
from sklearn.preprocessing import MinMaxScaler
mn_X = MinMaxScaler()
numerical_features = ['temp','atemp','hum','windspeed']
temp = mn_X.fit_transform(train[numerical_features])
X_train_num = pd.DataFrame(data=temp, columns=numerical_features, index =train.index)
print(X_train_num.head())

注意，这里代码块的缩进都是使用四个空格，而不是制表符或其他空格数量。这是 Python 代码格式规范中的推荐做法。

相关问题

File "<ipython-input-64-596064a8c0f8>", line 5 cal_num = x_series.value_counts() ^ IndentationError: unexpected indent

This error message indicates that there is an unexpected indentation in the code. The indentation should be consistent throughout the code, and any blocks of code that are intended to be executed together should be indented at the same level.

To fix this error, check the indentation of the line of code mentioned in the error message (line 5 in this case). Make sure that it is properly indented and there are no extra spaces or tabs that could be causing the issue. Also, check the indentation of the preceding lines of code to ensure that they are consistent with the rest of the code.

AttributeError Traceback (most recent call last) <ipython-input-18-484e38e099dc> in <module>() 1 from sklearn import preprocessing ----> 2 minmiax_scale = preprocessing.MinMaxSacler((0,1)) AttributeError: module 'sklearn.preprocessing' has no attribute 'MinMaxSacler'

您好！根据错误信息，看起来是由于您拼写错误导致的。正确的类名应该是 `MinMaxScaler` 而不是 `MinMaxSacler`。请修改代码如下：

from sklearn import preprocessing
minmax_scale = preprocessing.MinMaxScaler((0,1))

请注意修改后的代码中的 `MinMaxScaler` 拼写正确。如果还有其他问题，请随时提问。