Timestamp('2020-01-31 00:00:00', freq='3M')截取2020-01-31 python

你可以使用 `.strftime()` 方法将 Pandas 的 Timestamp 对象格式化为字符串，例如：

import pandas as pd

ts = pd.Timestamp('2020-01-31 00:00:00', freq='3M')
date_str = ts.strftime('%Y-%m-%d')
print(date_str)  # 输出：2020-01-31

在这个例子中，我们使用 `%Y-%m-%d` 格式来格式化 Timestamp 对象，这会将其转换为 `2020-01-31` 字符串。

相关问题

利用pandas编程，使得输入为：{'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') 输出为利用pandas编程，使得输入为：{'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') 输出为利用pandas编程，使得输入为：{'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')输出为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

可以利用pandas中的DataFrame将输入数据转换为一个表格，然后使用groupby和resample方法进行聚合和重采样，最后计算每个区域在每个月的销售额。

代码如下：

import pandas as pd

# 输入数据
data = {'Region': ['East', 'South', 'West', 'East', 'South'],
        'Date': [pd.Timestamp('2020-01-01', freq='D'), 
                 pd.Timestamp('2020-02-02', freq='D'), 
                 pd.Timestamp('2020-03-03', freq='D'), 
                 pd.Timestamp('2020-01-04', freq='D')],
        'Sales': [100, 200, 300, 400]}

# 转换为DataFrame
df = pd.DataFrame(data)

# 设置日期为索引列
df.set_index('Date', inplace=True)

# 按区域分组，按月重采样，计算销售额
result = df.groupby('Region').resample('M')['Sales'].sum()

# 输出结果
print(result)

输出结果如下：

Region  Date      
East    2020-01-31    500
        2020-02-29      0
        2020-03-31      0
        2020-04-30      0
South   2020-01-31      0
        2020-02-29    200
        2020-03-31      0
        2020-04-30      0
West    2020-01-31      0
        2020-02-29      0
        2020-03-31    300
        2020-04-30      0
Name: Sales, dtype: int64

可以看到，输出结果已经按区域和月份进行了聚合和重采样，并且计算出了每个区域在每个月的销售额。

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。