Pandas group by and sum

import pandas as pd

# 创建示例数据
data = {
    'Region': ['North', 'South', 'North', 'East', 'West', 'South'],
    'Product': ['A', 'B', 'A', 'C', 'B', 'C'],
    'Sales': [100, 200, 150, 75, 125, 100]
}

df = pd.DataFrame(data)

grouped_region = df.groupby('Region')['Sales'].sum()
print(grouped_region)

Region
East     75
North    250
South    300
West     125
Name: Sales, dtype: int64

grouped_region_product = df.groupby(['Region', 'Product'])['Sales'].sum()
print(grouped_region_product)

Region  Product
East    C          75
North   A          250
South   B          200
        C          100
West    B          125
Name: Sales, dtype: int64

# 使用 fillna 填充 NaN 值
result = df.groupby('Region')['Sales'].sum().fillna(0)
print(result)

# 使用 min_count 参数
result_with_min_count = df.groupby('Region')['Sales'].sum(min_count=1)
print(result_with_min_count)