Python Dataframe标准化ValueError：列的长度必须与键相同

我正在try 创建一个函数，用于为请求的频率对给定的数据帧进行归一化.

code:个

import numpy as np
import pandas as pd
def timeseries_dataframe_normalized(df, normalization_freq = 'complete'):
    """
    Input: 
        df : dataframe 
             input dataframe
        normalization_freq : string
            'daily', 'weekly', 'monthly','quarterly','yearly','complete' (default)
    Return: normalized dataframe
    
    """
    # auxiliary dataframe
    adf = df.copy()
    # convert columns to float
    # Ref: https://stackoverflow.com/questions/15891038/change-column-type-in-pandas
    adf = adf.astype(float)
    # normalized columns
    nor_cols = adf.columns
    # add suffix to columns and create new names for maximum columns
    max_cols = adf.add_suffix('_max').columns
    # initialize maximum columns
    adf.loc[:,max_cols] = np.nan
    # check the requested frequency
    if normalization_freq =='complete':
        adf[max_cols] = adf[nor_cols].max()
    # compute and return the normalized dataframe
    print(adf[nor_cols])
    print(adf[max_cols])
    adf[nor_cols] = adf[nor_cols]/adf[max_cols]
    # return the normalized dataframe
    return adf[nor_cols]
    
# Example
df2 = pd.DataFrame(data={'A':[20,10,30],'B':[1,2,3]})
timeseries_dataframe_normalized(df2)

Expected output:个

df2 = 
        A         B
0   0.666667    0.333333
1   0.333333    0.666667
2   1.000000    1.000000

Present output:个

我很惊讶得到了下面的错误.然而，当我计算df2/df2.max()时，我得到了预期的输出，但这个函数给了我错误的结果.

ValueError: Columns must be same length as key