期間(開始と終了)を持つPeriodを調べた¶

動機¶

pandas offset aliasまとめ　を書いた時に時系列系でpd.period_rangeというものを見た
DatetimeIndexとの違いがぱっと見でよくわからないので調べた
途中で出てくるPeriod Class のhelpはcapture stdoutで調べたとおり
documentation: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#period

まとめ¶

periodの名のとおり期間(開始と終了)を持つ
日次だとあまり意味がないけど、月次や４半期などはAliasから作成できるので便利そう

code¶

import pandas as pd

# module
pd.period_range
<function pandas.tseries.period.period_range>

# result
pindex = pd.period_range(start="2016-03-01", end="2016-03-31")
pindex
PeriodIndex(['2016-03-01', '2016-03-02', '2016-03-03', '2016-03-04',
             '2016-03-05', '2016-03-06', '2016-03-07', '2016-03-08',
             '2016-03-09', '2016-03-10', '2016-03-11', '2016-03-12',
             '2016-03-13', '2016-03-14', '2016-03-15', '2016-03-16',
             '2016-03-17', '2016-03-18', '2016-03-19', '2016-03-20',
             '2016-03-21', '2016-03-22', '2016-03-23', '2016-03-24',
             '2016-03-25', '2016-03-26', '2016-03-27', '2016-03-28',
             '2016-03-29', '2016-03-30', '2016-03-31'],
            dtype='int64', freq='D')


# element type
pindex[0], type(pindex[0])
(Period('2016-03-01', 'D'), pandas._period.Period)
# pd.Period も pandas._period.Period

# instance
pd.Period(year=2016, month=3, day=6, freq="D")
# or period = period = pd.Period("2016-03-06")
period
Period('2016-03-06', 'D')

# period start and end
period.start_time, period.end_time
(Timestamp('2016-03-06 00:00:00'), Timestamp('2016-03-06 23:59:59.999999999'))

# compare with a datetime
import datetime
dt_start = datetime.datetime(2016, 3, 6)
dt_end = datetime.datetime(2016, 3, 6, 23, 59, 59)
[
    period.start_time <  dt_start <= period.end_time,
    period.start_time <= dt_start <= period.end_time,
    period.start_time <= dt_end   <= period.end_time,
    period.start_time <= dt_end   <  period.end_time,
]
[False, True, True, True]


# おまけ periodがなかったらstartだけでなくendを自力で作る
date_range = pd.date_range("2016-03-01", periods=31)
end_exclude = date_range.shift(1)
date_df = pd.DataFrame({
    "start_date": date_range,
     "end_exclude": end_exclude
})
date_df.tail()

end_exclude start_date
26  2016-03-28  2016-03-27
27  2016-03-29  2016-03-28
28  2016-03-30  2016-03-29
29  2016-03-31  2016-03-30
30  2016-04-01  2016-03-31