在groupby对象的pandas agg方法中传递函数

import pandas as pd

# 创建DataFrame
data = {'Name': ['Tom', 'Nick', 'John', 'Tom', 'Nick', 'John'],
        'Age': [20, 21, 22, 23, 24, 25],
        'Score': [90, 95, 85, 80, 92, 88]}
df = pd.DataFrame(data)

# 按照Name列分组，并对Age列应用sum函数
result = df.groupby('Name').agg({'Age': 'sum'})
print(result)

      Age
Name     
John   47
Nick   45
Tom    43

import pandas as pd

# 创建DataFrame
data = {'Name': ['Tom', 'Nick', 'John', 'Tom', 'Nick', 'John'],
        'Age': [20, 21, 22, 23, 24, 25],
        'Score': [90, 95, 85, 80, 92, 88]}
df = pd.DataFrame(data)

# 自定义聚合函数，计算Age列的中位数
def median_age(series):
    return series.median()

# 按照Name列分组，并对Age列应用自定义聚合函数
result = df.groupby('Name').agg({'Age': median_age})
print(result)

      Age
Name     
John  23.5
Nick  22.5
Tom   21.5

import pandas as pd

# 创建DataFrame
data = {'Name': ['Tom', 'Nick', 'John', 'Tom', 'Nick', 'John'],
        'Age': [20, 21, 22, 23, 24, 25],
        'Score': [90, 95, 85, 80, 92, 88]}
df = pd.DataFrame(data)

# 按照Name列分组，并对Age列应用多个聚合函数
result = df.groupby('Name').agg({'Age': ['min', 'max', 'mean']})
print(result)

     Age          
     min max  mean
Name              
John  22  25  23.5
Nick  21  24  22.5
Tom   20  23  21.5