Pandas中groupby和aggregate的快速解决方案

import pandas as pd

# 创建一个示例DataFrame
data = {'Name': ['Tom', 'Nick', 'John', 'Tom', 'Nick', 'John'],
        'Subject': ['Math', 'Math', 'Math', 'Science', 'Science', 'Science'],
        'Score': [90, 85, 92, 78, 80, 88]}
df = pd.DataFrame(data)

# 使用groupby函数对数据进行分组
grouped = df.groupby('Name')

# 使用aggregate函数对分组后的数据进行聚合计算
result = grouped['Score'].aggregate(['sum', 'mean', 'count'])

print(result)

      sum  mean  count
Name                  
John  180  90.0      2
Nick  165  82.5      2
Tom   168  84.0      2

import pandas as pd

# 创建一个示例DataFrame
data = {'Name': ['Tom', 'Nick', 'John', 'Tom', 'Nick', 'John'],
        'Subject': ['Math', 'Math', 'Math', 'Science', 'Science', 'Science'],
        'Score': [90, 85, 92, 78, 80, 88]}
df = pd.DataFrame(data)

# 使用transform函数对分组后的数据进行聚合计算
df['TotalScore'] = df.groupby('Name')['Score'].transform('sum')
df['AverageScore'] = df.groupby('Name')['Score'].transform('mean')
df['Count'] = df.groupby('Name')['Score'].transform('count')

print(df)

   Name  Subject  Score  TotalScore  AverageScore  Count
0   Tom     Math     90         168          84.0      2
1  Nick     Math     85         165          82.5      2
2  John     Math     92         180          90.0      2
3   Tom  Science     78         168          84.0      2
4  Nick  Science     80         165          82.5      2
5  John  Science     88         180          90.0      2