Pandas可以很好地处理这个任务。将csv加载到dataframe，然后按月份运行一个组，并使用aggregate函数找到最大日期：

import pandas as pd
import numpy as np

df = pd.read_csv('/path/to/file/')          # Load a dataframe with your file
df.index = df['my_date_field']              # set the dataframe index with your date
dfg = df.groupby(pd.TimeGrouper(freq='M'))  # group by month / alternatively use MS for Month Start / referencing the previously created object

# Finally, find the max date in each month
dfg.agg({'my_date_field': np.max})

# To specifically coerce the results of the groupby to a list:
dfg.agg({'my_date_field': np.max})['my_date_field'].tolist()

对于年份y和月份m，calendar.monthrange(y, m)[1]返回该月最后一天的日期。
下面的脚本获取一个名为dates的datetime对象列表，并创建一个新列表month_last_dates，其中包含datetime对象，该对象对应于dates成员所属月份的最后一个日期。

import datetime
import calendar

tuples = [(2015, 8, 1), (2015, 9, 16), (2015, 10, 4)]
dates = [datetime.datetime(y, m, d) for y, m, d in tuples]

month_last_dates = len(dates) * [None]
for i, date in enumerate(dates):
  y, m, d = date.year, date.month, date.day
  last = calendar.monthrange(y, m)[1]
  print y, m, last  # Output for testing purposes.
  month_last_dates[i] = datetime.datetime(y, m, last)

下面是一个等效的脚本，它在列表解析的帮助下写得更简洁：

import datetime
import calendar

tuples = [(2015, 8, 1), (2015, 9, 16), (2015, 10, 4)]
dates = [datetime.datetime(y, m, d) for y, m, d in tuples]

month_last_dates = [datetime.datetime(date.year, date.month,
      calendar.monthrange(date.year, date.month)[1]) for date in dates]

# Output for testing purposes.
for date in month_last_dates:
  print date.year, date.month, date.day

在你的例子中，给定列表Dates，你可以像这样创建一个新的列表：

last_dates = [datetime.datetime(date.year, date.month,
      calendar.monthrange(date.year, date.month)[1]) for date in Dates]

这很好用，只需要一行代码：

dates = [datetime.date(2023, 3, 21), datetime.date(2023, 3, 1), datetime.date(2023, 2, 22), datetime.date(2023, 2, 14)]
eom_dates = set(pd.Series(dates,index=dates).groupby(lambda x:x.month).max())

这导致：

{datetime.date(2023, 2, 22), datetime.date(2023, 3, 21)}