import numpy as npimport pandas as pddf = pdread_csv2023_2_20No22023_2_20_19csvencoding = utf-8df = dfiloc118df_tumor = dfdfname == tumordf_peritumor = dfdfname == peritumor dfname = dfname + dfprope

这段代码可以简化如下：

1. 可以将 df_tumor_A 至 df_peritumor_H 的代码合并为一个循环，使用字典和字符串格式化来实现：

tumor_dict = {}
peritumor_dict = {}
for name in ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']:
    df_tumor_name = df_tumor[df_tumor['property'] == name]
    df_peritumor_name = df_peritumor[df_peritumor['property'] == name]
    tumor_dict[name] = df_tumor_name
    peritumor_dict[name] = df_peritumor_name

2. 可以将计算插值的部分写成一个函数，减少重复代码：

def calculate_conductivity(df, df_saline_median):
    conductivity_list = [976,1987,3850,5650,7450,9238]
    conductivity_list.reverse()
    result = []
    for i in range(len(freq_list)):   
        ans = df_saline_median.iloc[:,i:i+1]
        ans = ans.values.tolist()
        new_ans = sorted([ans[j][0] for j in range(len(ans))])
        result.append(np.interp(df.iloc[:, i+2:i+3].median(), new_ans, conductivity_list))
    return result

3. 可以将计算肿瘤和周围组织电导率的部分写成一个循环，使用上面的函数来计算：

for name in ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']:
    tumor_result = calculate_conductivity(tumor_dict[name], df_saline_median)
    peritumor_result = calculate_conductivity(peritumor_dict[name], df_saline_median)
    print(f"Tumor {name}: {tumor_result}")
    print(f"Peritumor {name}: {peritumor_result}")

这样可以将代码从重复的部分中解放出来，使其更加简洁和易于维护