我正在写一个程序，它接受一个excel文件，重新排序列，丢弃碎片。一切都很好，除了当我试图重写一个函数，修复所有的列sio它是一个完整的混乱。结果如下：

def fixColsT(df1):
    null_check = np.eye(len(df1.iloc[:,1]),12) #create matrix
    null_check = map(np.isfinite, df1.iloc[:,1:12]) #set matrix-values.
    null_check = map(np.invert, df1.iloc[:,1:12]) #Invert for error-checking (not_added)
    df1.iloc[:,3:4] = map(str,df1.iloc[:,2:4]) #The problem.
    df1.iloc[:,3] = map(lambda entry: re.sub(r'[^0-9]', '', entry), df1.iloc[:,3])
    df1.iloc[:,4] = map(lambda x:x [len(x)-1], df1.iloc[:,4])
    df1.iloc[:,4] = map(lambda x: re.sub(r'[^A-Ö]', '', x), df1.iloc[:,4])
    df1.iloc[:,2] = (df1.iloc[:,2] - df1.iloc[:,4]) - df1.iloc[:,3] 
    df1.iloc[:,11] = map(fixnrRooms, df1.iloc[:,11])
    df1.iloc[:,12] = map(fixKitchen, df1.iloc[:,12])
    df1.columns = COLUMN_NAMES
    errorcheck = null_check.iloc[:,2]
    for i in CHECKS:
        errorcheck = errorcheck + null_check[:,CHECKS[i]]
    errors = np.where(errorcheck)[0]
    print(df1.iloc[4,:])
    return df1, errors

当我运行这个时，我得到以下错误：

Traceback (most recent call last):
  File "[...]\main.py", line 403, in <module> 
    main()
  File "[...]\main.py", line 391, in main     
    df, errors=fixColsT(dfInput)
  File "[...]\main.py", line 118, in fixColsT 
    df1.iloc[:,3:4] = map(str,df1.iloc[:,2:4])
  File "[...]\Python\Python310\lib\site-packages\pandas\core\indexing.py", line 818, in __setitem__
    iloc._setitem_with_indexer(indexer, value, self.name)
  File "[...]\Python\Python310\lib\site-packages\pandas\core\indexing.py", line 1795, in _setitem_with_indexer
    self._setitem_with_indexer_split_path(indexer, value, name)
  File "[...]\Python\Python310\lib\site-packages\pandas\core\indexing.py", line 1836, in _setitem_with_indexer_split_path
    elif len(ilocs) == 1 and lplane_indexer == len(value) and not is_scalar(pi):
TypeError: object of type 'map' has no len()

老实说，我不知道如何继续（请记住，我对python一般来说是相当陌生的），因为我需要以某种方式将这些列中的条目转换为字符串，以便其余代码工作。（以前的解决方案迭代了整个 Dataframe 的每一行，并手动转换了每个条目，这对于非常小的文件来说很好，但由于可以预期得到大约60000行的文件，这是不合理的）。如果不单独迭代每个条目或每个列，如何解决这个问题（除非绝对必要）。