我一直在尝试在Python/Pandas中对数据进行归一化，但遇到了一个有点棘手的问题。归一化数据需要把每列的值除以该列的最大值，从而得到0 到1 的区间。Pandas把我的DataFrame的所有列值都设为了int64数据类型，因此这样的操作不可行。为了解决这个问题，我尝试使用下面的代码将列值转换为float64：

import pandas as pd
import numpy as np

data = pd.read_excel("file_name.xlsx", header = [10, 11]) # read in data; values are int

for i in range(0, len(data.columns), 1):
     if i == 0: # the first column contains time values that are already dtype float64
          pass
     else:
          data.iloc[:, i] = data.iloc[:, i].astype(np.float64) # this line throws an error

然而，这会抛出一个 LossySetItemError: Invalid value 'value' Name: 'column_name', length 'number', dytype: 'float64' for dtype: 'int64。

我的第一种做法是用 dtype = float64 强制Pandas将我的Excel文件中的所有数值读作float64，但这同样会报错：Unable to convert column ('First_multi_index', 'second_multi_index') to type float64。我相信这是因为我使用了多级列名（multi-indexed column names），这是为了方便处理数据而需要的。

一些Google搜索表明，使用 astype() 才是执行此转换的正确方式，所以我不知道为什么它不起作用。坦白说，我对编程并不太熟悉。如果要猜测，可能我错过了一些显而易见的东西。任何帮助将不胜感激。我也在下面提供了我正在使用的库版本：

Python = 3.13.12
Pandas = 3.0.0
Numpy = 2.4.2

以下是确切的错误输出（我已对其中的部分个人信息进行了处理）：

LossySetitemError                         Traceback (most recent call last)
File ~\.conda\envs\stats\Lib\site-packages\pandas\core\indexing.py:2144, in _iLocIndexer._setitem_single_column(self, loc, value, plane_indexer)
   2143 try:
-> 2144     self.obj._mgr.column_setitem(
   2145         loc, plane_indexer, value, inplace_only=True
   2146     )
   2147 except (ValueError, TypeError, LossySetitemError) as exc:
   2148     # If we're setting an entire column and we can't do it inplace,
   2149     #  then we can use value's dtype (or inferred dtype)
   2150     #  instead of object

File ~\.conda\envs\stats\Lib\site-packages\pandas\core\internals\managers.py:1518, in BlockManager.column_setitem(self, loc, idx, value, inplace_only)
   1517 if inplace_only:
-> 1518     col_mgr.setitem_inplace(idx, value)
   1519 else:

File ~\.conda\envs\stats\Lib\site-packages\pandas\core\internals\managers.py:2220, in SingleBlockManager.setitem_inplace(self, indexer, value)
   2217 if isinstance(arr, np.ndarray):
   2218     # Note: checking for ndarray instead of np.dtype means we exclude
   2219     #  dt64/td64, which do their own validation.
-> 2220     value = np_can_hold_element(arr.dtype, value)
   2222 if isinstance(value, np.ndarray) and value.ndim == 1 and len(value) == 1:
   2223     # NumPy 1.25 deprecation: https://github.com/numpy/numpy/pull/10615

File ~\.conda\envs\stats\Lib\site-packages\pandas\core\dtypes\cast.py:1725, in np_can_hold_element(dtype, element)
   1724     # Anything other than integer we cannot hold
-> 1725     raise LossySetitemError
   1726 if (
   1727     dtype.kind == "u"
   1728     and isinstance(element, np.ndarray)
   1729     and element.dtype.kind == "i"
   1730 ):
   1731     # see test_where_uint64

LossySetitemError: 

The above exception was the direct cause of the following exception:

TypeError                                 Traceback (most recent call last)
File c:\users\user\onedrive - library\projects\data analysis\general data analysis program\easy_data.py:58
     56     tht_norm = dc.Tht_norm(directory)
     57 case 3: # non functional
---> 58     replicate = dc.ThT_rep(directory)
     59 case 4: 
     60     cd = dc.cd_plot(directory)

File ~\OneDrive - library\Projects\Data Analysis\General Data Analysis Program\data_classes.py:189, in ThT_rep.__init__(self, directory)
    187 self.data, self.headers, self.concentrations = super().determine_dataset(self.data)
    188 self.replicate = self.select_rep(self.headers)
--> 189 self.data = self.norm_data(self.data)
    190 self.figure = super().initialize_plot()
    191 self.plot_data(self.data, self.headers, self.concentrations, self.figure, self.replicate)

File ~\OneDrive - library\Projects\Data Analysis\General Data Analysis Program\data_classes.py:217, in ThT_rep.norm_data(self, data)
    215             data.iloc[:,i] = data.iloc[:,i] - minimum
    216             maximum = data.iloc[:,i].max()
--> 217             data.iloc[:, i] = data.iloc[:,i] / maximum
    218     break
    219 elif response == 'n' or response == 'N':

File ~\.conda\envs\stats\Lib\site-packages\pandas\core\indexing.py:938, in _LocationIndexer.__setitem__(self, key, value)
    933 self._has_valid_setitem_indexer(key)
    935 iloc: _iLocIndexer = (
    936     cast("_iLocIndexer", self) if self.name == "iloc" else self.obj.iloc
    937 )
--> 938 iloc._setitem_with_indexer(indexer, value, self.name)

File ~\.conda\envs\stats\Lib\site-packages\pandas\core\indexing.py:1953, in _iLocIndexer._setitem_with_indexer(self, indexer, value, name)
   1950 # align and set the values
   1951 if take_split_path:
   1952     # We have to operate column-wise
-> 1953     self._setitem_with_indexer_split_path(indexer, value, name)
   1954 else:
   1955     self._setitem_single_block(indexer, value, name)

File ~\.conda\envs\stats\Lib\site-packages\pandas\core\indexing.py:1997, in _iLocIndexer._setitem_with_indexer_split_path(self, indexer, value, name)
   1993     self._setitem_with_indexer_2d_value(indexer, value)
   1995 elif len(ilocs) == 1 and lplane_indexer == len(value) and not is_scalar(pi):
   1996     # We are setting multiple rows in a single column.
-> 1997     self._setitem_single_column(ilocs[0], value, pi)
   1999 elif len(ilocs) == 1 and 0 != lplane_indexer != len(value):
   2000     # We are trying to set N values into M entries of a single
   2001     #  column, which is invalid for N != M
   2002     # Exclude zero-len for e.g. boolean masking that is all-false
   2004     if len(value) == 1 and not is_integer(info_axis):
   2005         # This is a case like df.iloc[:3, [1]] = [0]
   2006         #  where we treat as df.iloc[:3, 1] = 0

File ~\.conda\envs\stats\Lib\site-packages\pandas\core\indexing.py:2163, in _iLocIndexer._setitem_single_column(self, loc, value, plane_indexer)
   2151         dtype = self.obj.dtypes.iloc[loc]
   2152         if dtype not in (np.void, object) and not self.obj.empty:
   2153             # - Exclude np.void, as that is a special case for expansion.
   2154             #   We want to raise for
   (...)   2161             # - Exclude empty initial object with enlargement,
   2162             #   as then there's nothing to be inconsistent with.
-> 2163             raise TypeError(
   2164                 f"Invalid value '{value}' for dtype '{dtype}'"
   2165             ) from exc
   2166         self.obj.isetitem(loc, value)
   2167 else:
   2168     # set value into the column (first attempting to operate inplace, then
   2169     #  falling back to casting if necessary)

TypeError: Invalid value '0       0.014300
1       0.008976
2       0.005148
3       0.009020
4       0.005676

1238    0.866068
1239    0.863472
1240    0.876716
1241    0.888596
1242    0.875308
Name: Sample X1, Length: 1243, dtype: float64' for dtype 'int64'

此外，这里有一段我正在处理的数据片段。它应该能够稳定地产生该错误：

解决方案

错误发生的原因是你的DataFrame列是 int64。但归一化会产生介于0 和1 之间的浮点数。

Pandas不允许将浮点值放入一个 int64 列，因此会报错。
一个简单的解决方案是先把DataFrame转换成浮点数类型。

data = pd.read_excel("file_name.xlsx", header=[10,11])
data = data.astype(float)

然后归一化：

data = (data - data.min()) / (data.max() - data.min())