Pandas 中的日期范围
问题描述
在与 NumPy 和 dateutil 斗争了几天之后，我最近发现了神奇的 Pandas 库.我一直在研究文档和源代码，但我不知道如何让 date_range() 在正确的断点处生成索引.
After fighting with NumPy and dateutil for days, I recently discovered the amazing Pandas library. I've been poring through the documentation and source code, but I can't figure out how to get date_range() to generate indices at the right breakpoints.
from datetime import date
import pandas as pd
start = date('2012-01-15')
end = date('2012-09-20')
# 'M' is month-end, instead I need same-day-of-month
date_range(start, end, freq='M')
我想要什么:
2012-01-15
2012-02-15
2012-03-15
...
2012-09-15
我得到了什么:
2012-01-31
2012-02-29
2012-03-31
...
2012-08-31
我需要一个月大小的块来说明一个月中的可变天数.这可以通过 dateutil.rrule 实现:
I need month-sized chunks that account for the variable number of days in a month. This is possible with dateutil.rrule:
rrule(freq=MONTHLY, dtstart=start, bymonthday=(start.day, -1), bysetpos=1)
丑陋且难以辨认，但它有效.我怎么能用熊猫做到这一点?我玩过 date_range() 和 period_range()，到目前为止都没有运气.
Ugly and illegible, but it works. How can do I this with pandas? I've played with both date_range() and period_range(), so far with no luck.
我的实际目标是使用 groupby、crosstab 和/或 resample 根据 sums/means/etc 计算每个时期的值期间内的个别条目.换句话说，我想从以下位置转换数据:
My actual goal is to use groupby, crosstab and/or resample to calculate values for each period based on sums/means/etc of individual entries within the period. In other words, I want to transform data from:
total
2012-01-10 00:01 50
2012-01-15 01:01 55
2012-03-11 00:01 60
2012-04-28 00:01 80
#Hypothetical usage
dataframe.resample('total', how='sum', freq='M', start='2012-01-09', end='2012-04-15')
到
total
2012-01-09 105 # Values summed
2012-02-09 0 # Missing from dataframe
2012-03-09 60
2012-04-09 0 # Data past end date, not counted
鉴于 Pandas 最初是一种财务分析工具，我几乎可以肯定有一种简单快捷的方法可以做到这一点.感谢您的帮助！
Given that Pandas originated as a financial analysis tool, I'm virtually certain that there's a simple and fast way to do this. Help appreciated!
解决方案
freq='M' 用于月末频率(参见 这里).但是您可以使用 .shift 将其移动任意天数(或任何频率):
freq='M' is for month-end frequencies (see here). But you can use .shift to shift it by any number of days (or any frequency for that matter):
pd.date_range(start, end, freq='M').shift(15, freq=pd.datetools.day)
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