如何使用 pandas 按 10 分钟对时间序列进行分组

2022-01-11 00:00:00 python pandas time-series

问题描述

有一个由 DatatimeIndex 索引的时间序列(ts)，想按 10 分钟分组

Have a time series(ts) indexed by DatatimeIndex, want to group it by 10 minutes

index   x  y  z

ts1     ....
ts2     ....
...

我知道如何按 1 分钟分组

I know how to group by 1 minute

def group_by_minute(timestamp):
    year = timestamp.year
    month = timestamp.month
    day = timestamp.day
    hour = timestamp.hour
    minute = timestamp.minute
    return datetime.datetime(year, month, day, hour, minute)

然后

ts.groupby(group_by_minute, axis=0)

我的自定义函数(大致)

my customized function (roughly)

def my_function(group):
    first_latitude = group['latitude'].sort_index().head(1).values[0]
    last_longitude = group['longitude'].sort_index().tail(1).values[0]
    return first_latitude - last_longitude

所以 ts DataFrame 肯定应该包含 'latitude' 和 'longitude' 列

so the ts DataFrame should definitely contains 'latitude' and 'longitude' columns

使用 TimeGrouper 时

When using TimeGrouper

   ts.groupby(pd.TimeGrouper(freq='100min')).apply(my_function)

我收到以下错误，

TypeError: cannot concatenate a non-NDFrame object

解决方案

有一个 pandas.TimeGrouper 用于这种事情，你描述的应该是这样的:

There is a pandas.TimeGrouper for this sort of thing, what you described would be some thing like:

agg_10m = df.groupby(pd.TimeGrouper(freq='10Min')).aggregate(numpy.sum) #or other function

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