我想你需要先過濾數據框與boolean indexing
然後用groupby
與size
:
import pandas as pd
start = pd.to_datetime('2016-02-01')
end = pd.to_datetime('2016-02-25')
rng = pd.date_range(start, end, freq='12H')
events = pd.DataFrame({'number': [1] * 20 + [2] * 15 + [3] * 14}, index=rng)
print events
number
2016-02-01 00:00:00 1
2016-02-01 12:00:00 1
2016-02-02 00:00:00 1
2016-02-02 12:00:00 1
2016-02-03 00:00:00 1
2016-02-03 12:00:00 1
2016-02-04 00:00:00 1
2016-02-04 12:00:00 1
2016-02-05 00:00:00 1
2016-02-05 12:00:00 1
2016-02-06 00:00:00 1
2016-02-06 12:00:00 1
2016-02-07 00:00:00 1
...
...
filtered = events[events.index.weekday == 0]
print filtered
number
2016-02-01 00:00:00 1
2016-02-01 12:00:00 1
2016-02-08 00:00:00 1
2016-02-08 12:00:00 1
2016-02-15 00:00:00 2
2016-02-15 12:00:00 2
2016-02-22 00:00:00 3
2016-02-22 12:00:00 3
在0.18.1
版本,你可以使用新的方法DatetimeIndex.weekday_name
:
filtered = events[events.index.weekday_name == 'Monday']
print filtered
number
2016-02-01 00:00:00 1
2016-02-01 12:00:00 1
2016-02-08 00:00:00 1
2016-02-08 12:00:00 1
2016-02-15 00:00:00 2
2016-02-15 12:00:00 2
2016-02-22 00:00:00 3
2016-02-22 12:00:00 3
print filtered.groupby(filtered.index.hour).size()
0 4
12 4
dtype: int64
嘗試使用逗號「,」「而不是」&「 –
文檔i總是有用的:http://pandas.pydata.org/pandas-docs/stable/groupby.html –