Pandas按Country分组合并Name列文本并求和Salary列的实现方案

Nice start with getting the salary sums grouped by Country! To also merge the Name values into comma-separated strings for each group, you can use pandas' agg() method—this lets you apply different aggregation logic to different columns in a single groupby call.

Complete Code #

import pandas as pd

# Your original DataFrame
df = pd.DataFrame(
    {'Name': {0: 'John',1: 'Steven',2: 'Ibrahim',3: 'George',4: 'Nancy',5: 'Mo',6: 'Khalil'},
     'Country': {0: 'USA',1: 'UK',2: 'UK',3: 'France',4: 'Ireland',5: 'Ireland',6: 'Ireland'},
     'Salary': {0: 100, 1: 200, 2: 200, 3: 100, 4: 50, 5: 100, 6: 10}}
)

# Group by Country, merge names, sum salaries, keep original order
result = df.groupby('Country', as_index=False, sort=False).agg(
    Name=('Name', ', '.join),
    Salary=('Salary', 'sum')
)

print(result)

Output #

Key Details #

• groupby('Country', as_index=False, sort=False):
  • as_index=False ensures Country stays as a regular column instead of becoming the DataFrame index.
  • sort=False preserves the order in which countries first appeared in your original data (matches your desired output). Remove this if you want results sorted alphabetically by Country.
• agg(): This method lets you define column-specific aggregation rules. Here:
  • For the Name column, we use ', '.join to concatenate all names in the group with a comma and space.
  • For the Salary column, we use sum to calculate the total salary for the group.

内容的提问来源于stack exchange，提问作者Ibrahim Ayoup