划分一系列关于 pandas 的时间间隔吗? [英] Split a series on time gaps in pandas?

问题描述

是否可以按时间间隔划分时间序列.例如，假设我们有以下内容:

Is it possible to split a time series on it's gaps. For example, suppose we had the following:

rng2011 = pd.date_range('1/1/2011', periods=72, freq='H')
rng2012 = pd.date_range('1/1/2012', periods=72, freq='H')
Y = rng2011.union(rng2012)

是否有可能寻找一年或更长时间的差距，并拆分它们上的数据框?

Is it possible to look for gaps of a year or more, and split the data frame on them?

我想这会像这样:

Y.groupby(Y.map(lambda x: x.year))

除了在年日期上分开，而且我有兴趣指定间隔间隔而不是行的year属性.

Except that this splits on the year date, and I'm interested in specifying an interval gap rather than the year attribute of the row.

应用程序是我从gps那里获得了旅行日志，但是没有描述一次旅行何时结束而另一次旅行何时开始.我想分开十分钟或更长时间.

The application is I've got trip logs from a gps, but no delineation of when one trip ended and another began. I'd like to split on gaps of ten minutes or longer.

推荐答案

假设Y是数据框中的一列，一种方法是使用

Assuming Y is a column in your dataframe, one way is to use diff and cumsum:

df = DataFrame(Y)
df[1] = df[0].diff() > 600000000000.0 #nanoseconds in ten minutes
df[1] = df[1].apply(lambda x: 1 if x else 0).cumsum()
df.groupby(1)

注意:如果您使用72小时内的纳秒数，它将分为两组.

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