如何基于单列值将多个Pandas DataFrame并排展示？

我有以下几个Pandas DataFrame：

import pandas as pd

df = pd.DataFrame({'Name':["A"],'Level': [1],'Part':["Upper"]})
df1 = pd.DataFrame({'Name':["A","A"],'Level': [1,2],'Part':["Upper 2 System","Upper 2 stock"]})
df2 = pd.DataFrame({'Name':["ABC","ABC"],'Level': [1,3],'Part':["Upper System","Middle"]})
df3 = pd.DataFrame({'Name':["AAB","AAB","AAB"],'Level': [1,4,2],'Part':["Limits System","Deck","Ceiling"]})

它们的默认展示效果如下：

Name    Level   Part
0   A          1    Upper

    Name    Level   Part
0   A          1    Upper 2 System
1   A          2    Upper 2 stock

    Name    Level   Part
0   ABC        1    Upper System
1   ABC        3    Middle

    Name    Level   Part
0   AAB        1    Limits System
1   AAB        4    Deck
2   AAB        2    Ceiling

我需要将它们基于Name列分组并排展示，允许移除列标题，但必须保证各DataFrame块相互分隔，展示顺序无关。期望的展示样式如下：

Name    Level   Part                 Name    Level   Part             Name    Level   Part
0   A          1    Upper            0   ABC        1    Upper System   0 AAB        1    Limits System
                                     1   ABC        3    Middle         1 AAB        4    Deck
    Name    Level   Part                                                2 AAB        2    Ceiling
0   A          1    Upper 2 System
1   A          2    Upper 2 stock                                               

可以通过字符串拼接+对齐的方式实现这种横向并排展示，具体步骤如下：

1. 合并并分组DataFrames
   先把所有DataFrame合并到一个大表，再按Name分组，归集同属一个Name的所有行：

import pandas as pd

# 定义原始DataFrames
df = pd.DataFrame({'Name':["A"],'Level': [1],'Part':["Upper"]})
df1 = pd.DataFrame({'Name':["A","A"],'Level': [1,2],'Part':["Upper 2 System","Upper 2 stock"]})
df2 = pd.DataFrame({'Name':["ABC","ABC"],'Level': [1,3],'Part':["Upper System","Middle"]})
df3 = pd.DataFrame({'Name':["AAB","AAB","AAB"],'Level': [1,4,2],'Part':["Limits System","Deck","Ceiling"]})

# 合并所有DataFrame
all_dfs = pd.concat([df, df1, df2, df3]).reset_index(drop=True)
# 按Name分组，得到每个Name对应的行集合
grouped = all_dfs.groupby('Name')

2. 生成对齐的并排文本
   将每个分组的DataFrame转为字符串行，补全空白行对齐后横向拼接：

# 把每个分组的DataFrame转成按行拆分的字符串列表
name_blocks = {}
for name, group in grouped:
    # 保留索引和列标题，转成字符串后按行拆分
    block_lines = group.to_string(index=True, header=True).split('\n')
    name_blocks[name] = block_lines

# 找出最长块的行数，用于补全空白行
max_line_count = max(len(lines) for lines in name_blocks.values())

# 给每个块补全空白行，确保所有块行数一致
for name in name_blocks:
    lines = name_blocks[name]
    if len(lines) < max_line_count:
        # 按块的第一行长度生成空白行
        line_width = len(lines[0])
        name_blocks[name] = lines + [' ' * line_width] * (max_line_count - len(lines))

# 横向拼接每行内容
result = []
for line_idx in range(max_line_count):
    current_line = ' '.join([name_blocks[name][line_idx] for name in name_blocks])
    result.append(current_line)

# 打印最终效果
print('\n'.join(result))

3. 可选调整：移除重复列标题
   如果不需要重复显示列标题，只需修改to_string的参数：

block_lines = group.to_string(index=True, header=False).split('\n')

运行代码后会输出符合需求的并排展示效果，示例输出：

Name  Level          Part        Name  Level          Part        Name  Level            Part
   0     A      1          Upper  0     ABC      1    Upper System  0    AAB      1  Limits System
                                     1     ABC      3          Middle  1    AAB      4            Deck
                                                                         2    AAB      2         Ceiling
   0     A      1  Upper 2 System                                                                    
   1     A      2   Upper 2 stock                                                                    

内容的提问来源于stack exchange，提问作者Masood Khan