如何基于Series名称合并两组分组后的Pandas Series？

问题说明 #

现有两组通过以下代码生成的、按name字段分组的Pandas Object Series：

beforeseries = dfbefore.groupby('name', dropna=True)['order'].apply(list)
print(beforeseries)

afterseries = dfafter.groupby('name', dropna=True)['order'].apply(list)
print(afterseries)

它们的输出分别为：
beforeseries：

Name1    [first, second, third]
Name2    [first, second, third]
Name_n   [first, second, third, fourth]

afterseries：

Name1    [fourth, fifth]
Name2    [fourth, fifth, sixth]
Name_n   [fifth, sixth]

需要将二者合并，得到如下格式的结果：

Name1    ['first second third', 'fourth fifth']
Name2    ['first second third', 'fourth fifth sixth']
Name_n   ['first second third fourth', 'fifth sixth']

解决方案 #

通过以下三步即可实现需求：

• 将每个Series中的列表元素用空格拼接为单个字符串
• 对齐两个Series的name索引，合并为DataFrame
• 将每行的两个字符串组合成列表，生成最终Series

具体代码 #

import pandas as pd

# 1. 把列表转为空格分隔的字符串
before_str = beforeseries.apply(lambda x: ' '.join(x))
after_str = afterseries.apply(lambda x: ' '.join(x))

# 2. 按name对齐并合并为DataFrame
merged_df = pd.DataFrame({'before': before_str, 'after': after_str})

# 3. 将每行的两个字段转为列表，生成目标Series
result = merged_df.apply(lambda row: [row['before'], row['after']], axis=1)

print(result)

输出结果 #

Name1    [first second third, fourth fifth]
Name2    [first second third, fourth fifth sixth]
Name_n   [first second third fourth, fifth sixth]

内容的提问来源于stack exchange，提问作者JTA1618