Pandas DatetimeIndex:频率字符串中的周期数? [英] Pandas DatetimeIndex: Number of periods in a frequency string?

问题描述

如何使用频率字符串(偏移别名)计算 Pandas DatetimeIndex 中的周期数?例如，假设我有以下 DatetimeIndex:

How can I get a count of the number of periods in a Pandas DatetimeIndex using a frequency string (offset alias)? For example, let's say I have the following DatetimeIndex:

idx = pd.date_range("2019-03-01", periods=10000, freq='5T')

我想知道一周有多少个 5 分钟的时间段，或7D".我可以手动"计算:

I would like to know how many 5 minute periods are in a week, or '7D'. I can calculate this "manually":

periods = (7*24*60)//5

或者我可以得到一个虚拟索引的长度:

Or I can get the length of a dummy index:

len(pd.timedelta_range(start='1 day', end='8 days', freq='5T'))

这两种方法似乎都不是很有效.有没有更好的方法使用 Pandas 日期功能?

Neither approach seems very efficient. Is there a better way using Pandas date functionality?

推荐答案

我终于想出了一个合理的解决方案:

I finally figured out a reasonable solution:

pd.to_timedelta('7D')//idx.freq

这样做的好处是我可以使用频率字符串(偏移别名)指定一个范围，并且可以从数据帧中推断出周期或频率.@Terry 建议的 numpy 解决方案仍然是速度很重要的最快解决方案.

This has the advantage that I can specify a range using a frequency string (offset alias) and the period or frequency is inferred from the dataframe. The numpy solution suggested by @Terry is still the fastest solution where speed is important.

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