如何对Pandas DataFrame分组并计算指定列的总和？

原始数据 #

Plan         | Quantity_y
Starter      | 1
Intermediate | 1
Intermediate | 1
Intermediate | 2
Intermediate | 1
Intermediate | 14
Intermediate | 1
Advanced     | 1
Advanced     | 1
Advanced     | 2
Advanced     | 1
Incredible   | 1
Incredible   | 1
Incredible   | 1
Incredible   | 1
Incredible   | 1
Incredible   | 2
Incredible   | 2

期望结果 #

Plan         | Quantity_y
Starter      | 1
Intermediate | 20
Advanced     | 5
Incredible   | 9

解决方案（Pandas） #

直接用Pandas内置的groupby+sum方法就能高效实现，这比手动用iterrows()或自定义apply()简洁且性能更好：

import pandas as pd

# 1. 如果数据从文本/文件读取：
data = """Plan         | Quantity_y
Starter      | 1
Intermediate | 1
Intermediate | 1
Intermediate | 2
Intermediate | 1
Intermediate | 14
Intermediate | 1
Advanced     | 1
Advanced     | 1
Advanced     | 2
Advanced     | 1
Incredible   | 1
Incredible   | 1
Incredible   | 1
Incredible   | 1
Incredible   | 1
Incredible   | 2
Incredible   | 2"""

# 读取为DataFrame，自动处理分隔符和空格
df = pd.read_csv(pd.compat.StringIO(data), sep='|', skipinitialspace=True)

# 2. 确保Quantity_y是数值类型（读取自动识别失败时手动转换）
df['Quantity_y'] = pd.to_numeric(df['Quantity_y'], errors='coerce')

# 3. 分组求和，as_index=False保留Plan为普通列
result_df = df.groupby('Plan', as_index=False)['Quantity_y'].sum()

# 输出格式化结果
print(result_df.to_string(index=False))

关键说明 #

• 别用iterrows()：手动遍历行低效且没必要，Pandas分组聚合是C级优化，速度快得多
• groupby('Plan')指定分组键，['Quantity_y'].sum()只对目标列求和，避免冗余计算
• as_index=False确保输出格式和你期望的一致，Plan不会变成索引列

内容的提问来源于stack exchange，提问作者robster