如何在2个数据帧的3列中找到最接近的值？

import pandas as pd

# 创建两个示例数据帧
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
df2 = pd.DataFrame({'A': [10, 11, 12], 'B': [13, 14, 15], 'C': [16, 17, 18]})

# 合并两个数据帧
merged_df = pd.concat([df1, df2])

# 初始化最小差值和最接近值的索引
min_diff = float('inf')
closest_index = None

# 遍历每一行
for index, row in merged_df.iterrows():
    # 计算每个列的差值的绝对值，并存储在新的列中
    merged_df.loc[index, 'Diff'] = abs(row['A'] - row['B']) + abs(row['A'] - row['C']) + abs(row['B'] - row['C'])
    
    # 更新最小差值和最接近值的索引
    if merged_df.loc[index, 'Diff'] < min_diff:
        min_diff = merged_df.loc[index, 'Diff']
        closest_index = index

# 根据索引找到最接近的值所在的列，并提取其值
closest_value = merged_df.loc[closest_index, 'A']

print("最接近的值是:", closest_value)