Python、pandas:基于多个其他列对列求和，并将其放入新的数据框架中

import pandas as pd

data = {
    'Date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04'],
    'Product': ['A', 'B', 'A', 'B'],
    'Sales': [100, 200, 150, 250],
    'Region': ['North', 'South', 'North', 'South']
}

df = pd.DataFrame(data)

# 计算总销售额
total_sales = df['Sales'].sum()
print(f"Total Sales: {total_sales}")

# 计算产品A的总销售额
sales_product_A = df[df['Product'] == 'A']['Sales'].sum()
print(f"Total Sales for Product A: {sales_product_A}")

# 按照产品和地区分组，计算每组的总销售额
grouped_sales = df.groupby(['Product', 'Region'])['Sales'].sum().reset_index()
print(grouped_sales)

# 确保Sales列是数值型
df['Sales'] = pd.to_numeric(df['Sales'], errors='coerce')

# 再次进行求和操作
total_sales = df['Sales'].sum()
print(f"Total Sales: {total_sales}")

# 填充缺失值
df['Sales'].fillna(0, inplace=True)

# 再次进行求和操作
total_sales = df['Sales'].sum()
print(f"Total Sales: {total_sales}")