我有数据
data id url size domain subdomain
13/Jun/2016:06:27:26 30055 https://api.weather.com/v1/geocode/55.740002/37.610001/aggregate.json?apiKey=e45ff1b7c7bda231216c7ab7c33509b8&products=conditionsshort,fcstdaily10short,fcsthourly24short,nowlinks 3929 weather.com api.weather.com
13/Jun/2016:06:27:26 30055 https://api.weather.com/v1/geocode/54.720001/20.469999/aggregate.json?apiKey=e45ff1b7c7bda231216c7ab7c33509b8&products=conditionsshort,fcstdaily10short,fcsthourly24short,nowlinks 3845 weather.com api.weather.com
13/Jun/2016:06:27:27 3845 https://api.weather.com/v1/geocode/54.970001/73.370003/aggregate.json?apiKey=e45ff1b7c7bda231216c7ab7c33509b8&products=conditionsshort,fcstdaily10short,fcsthourly24short,nowlinks 30055 weather.com api.weather.com
13/Jun/2016:06:27:27 30055 https://api.weather.com/v1/geocode/59.919998/30.219999/aggregate.json?apiKey=e45ff1b7c7bda231216c7ab7c33509b8&products=conditionsshort,fcstdaily10short,fcsthourly24short,nowlinks 3914 weather.com api.weather.com
13/Jun/2016:06:27:28 30055 https://facebook.com 4005 facebook.com facebook.com
我需要以 5 分钟的间隔对其进行分组。 欲望输出
data id url size domain subdomain
13/Jun/2016:06:27:26 30055 https://api.weather.com/v1/geocode/55.740002/37.610001/aggregate.json?apiKey=e45ff1b7c7bda231216c7ab7c33509b8&products=conditionsshort,fcstdaily10short,fcsthourly24short,nowlinks 3929 weather.com api.weather.com
13/Jun/2016:06:27:27 3845 https://api.weather.com/v1/geocode/54.970001/73.370003/aggregate.json?apiKey=e45ff1b7c7bda231216c7ab7c33509b8&products=conditionsshort,fcstdaily10short,fcsthourly24short,nowlinks 30055 weather.com api.weather.com
13/Jun/2016:06:27:28 30055 https://facebook.com 4005 facebook.com facebook.com
我需要根据 id, subdomain 分组并建立间隔 5min
我尝试使用
print df.groupby([df['data'],pd.TimeGrouper(freq='Min')])
首先用分钟分组，但返回 TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'RangeIndex'
最佳答案
您需要使用 pd.to_datetime() 和适当的 format 设置来解析 data 并将结果用作 index。然后 .groupby() 同时重采样到 5Min 间隔:
df.index = pd.to_datetime(df.data, format='%d/%b/%Y:%H:%M:%S')
df.groupby(pd.TimeGrouper('5Min')).apply(lambda x: x.groupby(['id', 'subdomain']).first())
data \
data id subdomain
2016-06-13 06:25:00 3845 api.weather.com 13/Jun/2016:06:27:27
30055 api.weather.com 13/Jun/2016:06:27:26
facebook.com 13/Jun/2016:06:27:28
url \
data id subdomain
2016-06-13 06:25:00 3845 api.weather.com https://api.weather.com/v1/geocode/54.970001/7...
30055 api.weather.com https://api.weather.com/v1/geocode/55.740002/3...
facebook.com https://facebook.com
size domain
data id subdomain
2016-06-13 06:25:00 3845 api.weather.com 30055 weather.com
30055 api.weather.com 3929 weather.com
facebook.com 4005 facebook.com
关于python - 使用 Pandas 按日期时间间隔分组，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/37845617/