如何求同年的总值？(熊猫)-编程知识-白鹭情

我正在用 Pandas 学习 Python 资料分析

我有一个游戏销售资料框，看起来像这样：

（此资料不真实，仅供提问）

Name                Year    Publisher   Total Sales
GTA V               2013    Rockstar    133000
Super Mario Bros    1985    Nintendo    430500
GTA VI              2025    Rockstar    86000
RDR 3               2025    Rockstar    129030
Super Mario Sister  1985    Nintendo    308900
Super Mario End     2000    Nintendo    112100

然后我洗掉名称并使用以下命令按发布者名称对其进行分组：

df.drop(columns='Name', inplace=True)
df.groupby(['Publisher','Year','Total Sales']).sum().reset_index()

资料框现在看起来像这样：

Publisher   Year    Total Sales
Nintendo    1985    308900
Nintendo    1985    430500
Nintendo    2000    112100
Rockstar    2013    133000
Rockstar    2025    129030
Rockstar    2025    86000

这很好，但我想总结同一出版商同年的总销售额

我希望资料框看起来像这样：

Publisher   Year    Total Sales
Nintendo    1985    739400
Nintendo    2000    86000
Rockstar    2013    129030
Rockstar    2025    215030

有没有办法做到这一点？

这是我的 df 代码：

data = {'Name':['GTA V','Super Mario Bros','GTA VI','RDR 3','Super Mario Sister','Super Mario End'],'Year':['2013','1985','2025','2025','1985','2000'],
        'Publisher':['Rockstar','Nintendo','Rockstar','Rockstar','Nintendo','Nintendo'],'Total Sales':['133000','430500','86000','129030','308900','112100']}

df = pd.DataFrame(data)
df

uj5u.com热心网友回复：

使用pivot_table：

>>> df.pivot_table('Total Sales', ['Year', 'Publisher'], aggfunc='sum').reset_index()

   Year Publisher  Total Sales
0  1985  Nintendo       739400
1  2000  Nintendo       112100
2  2013  Rockstar       133000
3  2025  Rockstar       215030

注意：如果Total Sales列包含字符串，请将其转换为int（或float）：

>>> df.astype({'Total Sales': int}).pivot_table(...)

uj5u.com热心网友回复：

import pandas as pd

data = {'Name':['GTA V','Super Mario Bros','GTA VI','RDR 3','Super Mario Sister','Super Mario End'],'Year':['2013','1985','2025','2025','1985','2000'],
        'Publisher':['Rockstar','Nintendo','Rockstar','Rockstar','Nintendo','Nintendo'],'Total Sales':['133000','430500','86000','129030','308900','112100']}

df = pd.DataFrame(data)
df['Total Sales'] = df['Total Sales'].astype(int)


df.groupby(['Year', 'Publisher'])['Total Sales'].agg('sum').reset_index()

uj5u.com热心网友回复：

这是一种方法：

df.drop(columns='Name', inplace=True)
df['Total Sales'] = pd.to_numeric(df['Total Sales'])
df2 = df.groupby(['Publisher','Year']).sum().reset_index()
df2