Pandas基础(五)

Pandas时期 - Period

pd.Period()创建时期

生成一个以2017-01开始,月为频率的时间构造器:

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p = pd.Period('2017', freq = 'M')
print(p, type(p))
>>>
2017-01 <class 'pandas._period.Period'>

我们可以通过加减整数,将周期整体移动:

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p = pd.Period('2017', freq = 'M')
print(p, type(p))
print(p + 1)
print(p - 2)
>>>
2017-02
2016-11

pd.period_range()创建时期范围

创建指定时期范围:

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prng = pd.period_range('1/1/2011', '1/1/2012', freq='M')
print(prng,type(prng))
>>>
PeriodIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05', '2011-06',
'2011-07', '2011-08', '2011-09', '2011-10', '2011-11', '2011-12',
'2012-01'],
dtype='int64', freq='M') <class 'pandas.tseries.period.PeriodIndex'>

结合上面的时期序列,创建时间序列:

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ts = pd.Series(np.random.rand(len(prng)), index = prng)
print(ts,type(ts))
print(ts.index)
>>>
2011-01 0.342571
2011-02 0.826151
2011-03 0.370505
2011-04 0.137151
2011-05 0.679976
2011-06 0.265928
2011-07 0.416502
2011-08 0.874078
2011-09 0.112801
2011-10 0.112504
2011-11 0.448408
2011-12 0.851046
2012-01 0.370605
Freq: M, dtype: float64 <class 'pandas.core.series.Series'>
PeriodIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05', '2011-06',
'2011-07', '2011-08', '2011-09', '2011-10', '2011-11', '2011-12',
'2012-01'],
dtype='int64', freq='M')

pd.period - asfreq:频率转换

通过.asfreq(freq, method=None, how=None)方法可以将之前生成的频率转换成别的频率

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p = pd.Period('2017','A-DEC')
print(p)
print(p.asfreq('M', how = 'start')) # 也可写 how = 's'
print(p.asfreq('D', how = 'end')) # 也可写 how = 'e'
>>>
2017
2017-01
2017-12-31

asfreq也可以转换TIMESeries的index:

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prng = pd.period_range('2017','2018',freq = 'M')
ts1 = pd.Series(np.random.rand(len(prng)), index = prng)
ts2 = pd.Series(np.random.rand(len(prng)), index = prng.asfreq('D', how = 'start'))
print(ts1.head(),len(ts1))
print(ts2.head(),len(ts2))

时间戳与时期之间的转换

使用pd.to_period()、pd.to_timestamp()可以实现时间戳与时期之间的转换。

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rng = pd.date_range('2017/1/1', periods = 10, freq = 'M')
prng = pd.period_range('2017','2018', freq = 'M')

ts1 = pd.Series(np.random.rand(len(rng)), index = rng)
print(ts1.head())
print(ts1.to_period().head())
# 每月最后一日,转化为每月

ts2 = pd.Series(np.random.rand(len(prng)), index = prng)
print(ts2.head())
print(ts2.to_timestamp().head())
# 每月,转化为每月第一天
>>>
2017-01-31 0.125288
2017-02-28 0.497174
2017-03-31 0.573114
2017-04-30 0.665665
2017-05-31 0.263561
Freq: M, dtype: float64
2017-01 0.125288
2017-02 0.497174
2017-03 0.573114
2017-04 0.665665
2017-05 0.263561
Freq: M, dtype: float64
2017-01 0.748661
2017-02 0.095891
2017-03 0.280341
2017-04 0.569813
2017-05 0.067677
Freq: M, dtype: float64
2017-01-01 0.748661
2017-02-01 0.095891
2017-03-01 0.280341
2017-04-01 0.569813
2017-05-01 0.067677
Freq: MS, dtype: float64

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  • 本文作者: 煌金 | 微信公众号【咸鱼学Python】
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