如何使用 pandas 按 10 分钟对时间序列进行分组
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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