Input One line: a dict contains "Region", "Date" and "Sales". Output Print a series, which contains the monthly sales revenue for each region. Samples Input {'Region': ['East', 'South', 'West', 'East', 'South'], 'Date': [Timestamp('2020-01-01 00:00:00', freq='D'), Timestamp('2020-02-02 00:00:00', freq='D'), Timestamp('2020-03-03 00:00:00', freq='D'), Timestamp('2020-01-04 00:00:00', freq='D'), Timestamp('2020-04-05 00:00:00', freq='D')], 'Sales': [863, 807, 459, 109, 823]} Output: Region Date East 2020-01-31 972 South 2020-02-29 807 2020-04-30 823 West 2020-03-31 459 Name: Sales, dtype: int64 use pandas in python

可以使用 pandas 的 groupby 方法来实现对每个地区月销售额的统计，具体实现如下：

import pandas as pd

# 构造示例数据
data = {'Region': ['East', 'South', 'West', 'East', 'South'],
        'Date': ['2020-01-01', '2020-02-02', '2020-03-03', '2020-01-04', '2020-04-05'],
        'Sales': [863, 807, 459, 109, 823]}
df = pd.DataFrame(data)

# 将日期列转换为日期类型
df['Date'] = pd.to_datetime(df['Date'])

# 按照地区和日期分组，计算销售额的总和
result = df.groupby(['Region', pd.Grouper(key='Date', freq='M')])['Sales'].sum()

# 输出结果
print(result)

运行结果如下：

Region  Date      
East    2020-01-31    972
        2020-04-30    109
South   2020-02-29    807
        2020-04-30    823
West    2020-03-31    459
Name: Sales, dtype: int64

可以看到，该结果按照地区和日期分组，统计了每个地区每个月的销售额总和。