如何解决此错误？＆quot”功能＆＃x27;对象没有属性＆＃x27;标准标准器；

发布于 2025-02-06 23:56:37 字数 606 浏览 2 评论 0原文

def preprocessing(df:pd.DataFrame,scaler:str):
  standard_scaler= preprocessing.StandardScaler()
  not_uv=[]
  for column in df.columns:
    if column != 'uv': # uv is target
      not_uv.append(column)
  if scaler == 'standard':
    standard_df = pd.DataFrame(standard_scaler.fit_transfrom(df[not_uv]), columns = not_uv)
    standard_df = pd.concat([standard_df,df[['uv']]],axis=1)
    return standard_df

preprocessing(df_13,'standard')

AttributeError: 'function' object has no attribute 'StandardScaler'

我想制作预处理功能我的代码怎么了？

原文

def preprocessing(df:pd.DataFrame,scaler:str):
  standard_scaler= preprocessing.StandardScaler()
  not_uv=[]
  for column in df.columns:
    if column != 'uv': # uv is target
      not_uv.append(column)
  if scaler == 'standard':
    standard_df = pd.DataFrame(standard_scaler.fit_transfrom(df[not_uv]), columns = not_uv)
    standard_df = pd.concat([standard_df,df[['uv']]],axis=1)
    return standard_df

preprocessing(df_13,'standard')

AttributeError: 'function' object has no attribute 'StandardScaler'

i want to make preprocssing function
whats wrong with my code?

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や三分注定 2025-02-13 23:56:37

此代码在用户定义的功能预处理和Sklearn内置功能预处理之间产生冲突。只需导入并进行这样的实例。

from sklearn.preprocessing import StandardScaler

standard_scaler= StandardScaler()

或

将您的功能名称更改为预处理 预处理或其他东西。切勿声明或制作内置关键字的实例。

This code generates a conflict between user-defined function preprocessing and sklearn built-in function preprocessing. Simply import and make instance like this.

from sklearn.preprocessing import StandardScaler

standard_scaler= StandardScaler()

Change your function name preprocessing to preprocessings or something else. Never declare or make an instance of built-in keywords.

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柏拉图鍀咏恒 2025-02-13 23:56:37

函数的名称def预处理（...）：与preprocessing.standardscaler（）相同。这意味着您正在覆盖内存。更改功能的名称。例如：

def new_preprocessing(df:pd.DataFrame,scaler:str):
  standard_scaler= preprocessing.StandardScaler()
  not_uv=[]
  for column in df.columns:
    if column != 'uv': # uv is target
      not_uv.append(column)
  if scaler == 'standard':
    standard_df = pd.DataFrame(standard_scaler.fit_transfrom(df[not_uv]), columns = not_uv)
    standard_df = pd.concat([standard_df,df[['uv']]],axis=1)
    return standard_df

The name of the function def preprocessing(...): is same as preprocessing.StandardScaler(). Which means you are overwriting the memory. Change the name of the function. For Example:

def new_preprocessing(df:pd.DataFrame,scaler:str):
  standard_scaler= preprocessing.StandardScaler()
  not_uv=[]
  for column in df.columns:
    if column != 'uv': # uv is target
      not_uv.append(column)
  if scaler == 'standard':
    standard_df = pd.DataFrame(standard_scaler.fit_transfrom(df[not_uv]), columns = not_uv)
    standard_df = pd.concat([standard_df,df[['uv']]],axis=1)
    return standard_df

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