pandas入门教程（3）

最新推荐文章于 2024-04-27 01:33:38 发布

风雪云侠

最新推荐文章于 2024-04-27 01:33:38 发布

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分类专栏： pandas问题文章标签： python

本文链接：https://blog.youkuaiyun.com/weixin_40426830/article/details/111591636

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pandas在对频率转换进行重新采样时拥有着简单，强大而且高效的功能（例如把按秒采样的数据转换为按5分钟采样的数据）。这在金融领域很常见，但又不限于此。

rng = pd.date_range('1/1/2012', periods=50, freq='S')
rng
'''
DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',
               '2012-01-01 00:00:02', '2012-01-01 00:00:03',
               '2012-01-01 00:00:04', '2012-01-01 00:00:05',
               '2012-01-01 00:00:06', '2012-01-01 00:00:07',
               '2012-01-01 00:00:08', '2012-01-01 00:00:09',
               '2012-01-01 00:00:10', '2012-01-01 00:00:11',
               '2012-01-01 00:00:12', '2012-01-01 00:00:13',
               '2012-01-01 00:00:14', '2012-01-01 00:00:15',
               '2012-01-01 00:00:16', '2012-01-01 00:00:17',
               '2012-01-01 00:00:18', '2012-01-01 00:00:19',
               '2012-01-01 00:00:20', '2012-01-01 00:00:21',
               '2012-01-01 00:00:22', '2012-01-01 00:00:23',
               '2012-01-01 00:00:24', '2012-01-01 00:00:25',
               '2012-01-01 00:00:26', '2012-01-01 00:00:27',
               '2012-01-01 00:00:28', '2012-01-01 00:00:29',
               '2012-01-01 00:00:30', '2012-01-01 00:00:31',
               '2012-01-01 00:00:32', '2012-01-01 00:00:33',
               '2012-01-01 00:00:34', '2012-01-01 00:00:35',
               '2012-01-01 00:00:36', '2012-01-01 00:00:37',
               '2012-01-01 00:00:38', '2012-01-01 00:00:39',
               '2012-01-01 00:00:40', '2012-01-01 00:00:41',
               '2012-01-01 00:00:42', '2012-01-01 00:00:43',
               '2012-01-01 00:00:44', '2012-01-01 00:00:45',
               '2012-01-01 00:00:46', '2012-01-01 00:00:47',
               '2012-01-01 00:00:48', '2012-01-01 00:00:49'],
              dtype='datetime64[ns]', freq='S')
'''

ts = pd.Series(np.random.randint(0,500,len(rng)), index=rng)
ts
'''
2012-01-01 00:00:00     72
2012-01-01 00:00:01    233
2012-01-01 00:00:02    243
2012-01-01 00:00:03    146
2012-01-01 00:00:04    136
2012-01-01 00:00:05    494
2012-01-01 00:00:06    352
2012-01-01 00:00:07    309
2012-01-01 00:00:08    414
2012-01-01 00:00:09    279
2012-01-01 00:00:10    249
2012-01-01 00:00:11    197
2012-01-01 00:00:12    321
2012-01-01 00:00:13    177
2012-01-01 00:00:14     71
2012-01-01 00:00:15    126
2012-01-01 00:00:16     18
2012-01-01 00:00:17    290
2012-01-01 00:00:18    292
2012-01-01 00:00:19    365
2012-01-01 00:00:20     23
2012-01-01 00:00:21    297
2012-01-01 00:00:22    186
2012-01-01 00:00:23     18
2012-01-01 00:00:24    145
2012-01-01 00:00:25    221
2012-01-01 00:00:26    325
2012-01-01 00:00:27    346
2012-01-01 00:00:28    490
2012-01-01 00:00:29    439
2012-01-01 00:00:30    170
2012-01-01 00:00:31    198
2012-01-01 00:00:32     19
2012-01-01 00:00:33    461
2012-01-01 00:00:34    200
2012-01-01 00:00:35    381
2012-01-01 00:00:36    382
2012-01-01 00:00:37    417
2012-01-01 00:00:38    152
2012-01-01 00:00:39    270
2012-01-01 00:00:40    492
2012-01-01 00:00:41    163
2012-01-01 00:00:42    303
2012-01-01 00:00:43    200
2012-01-01 00:00:44    317
2012-01-01 00:00:45    465
2012-01-01 00:00:46     61
2012-01-01 00:00:47    214
2012-01-01 00:00:48     39
2012-01-01 00:00:49     28
Freq: S, dtype: int32
'''

ts.resample('5Min', how='sum')
'''
2012-01-01    12454
Freq: 5T, dtype: int32
'''

时区表示

rng = pd.date_range('3/6/2012', periods=5, freq='D')
rng
'''
DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09',
               '2012-03-10'],
              dtype='datetime64[ns]', freq='D')
'''

ts = pd.Series(np.random.randn(len(rng)), index=rng)
ts
'''
2012-03-06    0.512563
2012-03-07   -1.226835
2012-03-08   -0.432962
2012-03-09   -0.332179
2012-03-10   -0.541199
Freq: D, dtype: float64
'''

ts_utc = ts.tz_localize('UTC')
ts_utc
'''
2012-03-06 00:00:00+00:00    0.512563
2012-03-07 00:00:00+00:00   -1.226835
2012-03-08 00:00:00+00:00   -0.432962
2012-03-09 00:00:00+00:00   -0.332179
2012-03-10 00:00:00+00:00   -0.541199
Freq: D, dtype: float64
'''

时区转换

ts_utc.tz_convert('US/Eastern')
'''
2012-03-05 19:00:00-05:00    0.512563
2012-03-06 19:00:00-05:00   -1.226835
2012-03-07 19:00:00-05:00   -0.432962
2012-03-08 19:00:00-05:00   -0.332179
2012-03-09 19:00:00-05:00   -0.541199
Freq: D, dtype: float64
'''

时间跨度转换

rng = pd.date_range('1/1/2012', periods=5, freq='M')
rng
'''
DatetimeIndex(['2012-01-31', '2012-02-29', '2012-03-31', '2012-04-30',
               '2012-05-31'],
              dtype='datetime64[ns]', freq='M')
'''

ts = pd.Series(np.random.randn(len(rng)), index=rng)
ts
'''
2012-01-31    1.246855
2012-02-29    0.623765
2012-03-31   -2.522536
2012-04-30    0.092127
2012-05-31    0.379715
Freq: M, dtype: float64
'''

ps = ts.to_period()
ps
'''
2012-01-31    1.246855
2012-02-29    0.623765
2012-03-31   -2.522536
2012-04-30    0.092127
2012-05-31    0.379715
Freq: M, dtype: float64
'''

ps.to_timestamp()
'''
2012-01-01    1.246855
2012-02-01    0.623765
2012-03-01   -2.522536
2012-04-01    0.092127
2012-05-01    0.379715
Freq: MS, dtype: float64
'''

日期与时间戳之间的转换使得可以使用一些方便的算术函数。例如，我们把以11月为年底的季度数据转换为当前季度末月底为始的数据。

prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
prng
'''
PeriodIndex(['1990Q1', '1990Q2', '1990Q3', '1990Q4', '1991Q1', '1991Q2',
             '1991Q3', '1991Q4', '1992Q1', '1992Q2', '1992Q3', '1992Q4',
             '1993Q1', '1993Q2', '1993Q3', '1993Q4', '1994Q1', '1994Q2',
             '1994Q3', '1994Q4', '1995Q1', '1995Q2', '1995Q3', '1995Q4',
             '1996Q1', '1996Q2', '1996Q3', '1996Q4', '1997Q1', '1997Q2',
             '1997Q3', '1997Q4', '1998Q1', '1998Q2', '1998Q3', '1998Q4',
             '1999Q1', '1999Q2', '1999Q3', '1999Q4', '2000Q1', '2000Q2',
             '2000Q3', '2000Q4'],
            dtype='period[Q-NOV]', freq='Q-NOV')
'''    

ts = pd.Series(np.random.randn(len(prng)), index = prng)
ts 
'''
1990Q1    1.272780
1990Q2    0.081477
1990Q3    0.857023
1990Q4   -0.731177
1991Q1   -2.023644
1991Q2    0.990468
1991Q3   -1.103367
1991Q4    0.700913
1992Q1    0.298691
1992Q2   -2.032672
1992Q3    0.059544
1992Q4    1.923286
1993Q1    0.108547
1993Q2   -0.587671
1993Q3    0.055746
1993Q4    0.391453
1994Q1   -0.349526
1994Q2   -1.791296
1994Q3   -0.103650
1994Q4   -0.072721
1995Q1    0.083551
1995Q2   -0.643590
1995Q3   -0.590049
1995Q4    1.097337
1996Q1    1.130710
1996Q2    0.022466
1996Q3   -0.237904
1996Q4   -1.132273
1997Q1   -1.045858
1997Q2   -0.817864
1997Q3   -0.220546
1997Q4    0.927126
1998Q1   -1.268038
1998Q2   -0.408925
1998Q3   -0.362332
1998Q4   -1.095481
1999Q1    0.502225
1999Q2   -2.800133
1999Q3   -0.274502
1999Q4    0.021417
2000Q1   -0.555724
2000Q2   -1.321482
2000Q3    1.043381
2000Q4    0.146324
Freq: Q-NOV, dtype: float64
'''  

ts.index = (prng.asfreq('M', 'end') ) .asfreq('H', 'start') +9
ts
'''
1990-02-01 09:00    1.272780
1990-05-01 09:00    0.081477
1990-08-01 09:00    0.857023
1990-11-01 09:00   -0.731177
1991-02-01 09:00   -2.023644
1991-05-01 09:00    0.990468
1991-08-01 09:00   -1.103367
1991-11-01 09:00    0.700913
1992-02-01 09:00    0.298691
1992-05-01 09:00   -2.032672
1992-08-01 09:00    0.059544
1992-11-01 09:00    1.923286
1993-02-01 09:00    0.108547
1993-05-01 09:00   -0.587671
1993-08-01 09:00    0.055746
1993-11-01 09:00    0.391453
1994-02-01 09:00   -0.349526
1994-05-01 09:00   -1.791296
1994-08-01 09:00   -0.103650
1994-11-01 09:00   -0.072721
1995-02-01 09:00    0.083551
1995-05-01 09:00   -0.643590
1995-08-01 09:00   -0.590049
1995-11-01 09:00    1.097337
1996-02-01 09:00    1.130710
1996-05-01 09:00    0.022466
1996-08-01 09:00   -0.237904
1996-11-01 09:00   -1.132273
1997-02-01 09:00   -1.045858
1997-05-01 09:00   -0.817864
1997-08-01 09:00   -0.220546
1997-11-01 09:00    0.927126
1998-02-01 09:00   -1.268038
1998-05-01 09:00   -0.408925
1998-08-01 09:00   -0.362332
1998-11-01 09:00   -1.095481
1999-02-01 09:00    0.502225
1999-05-01 09:00   -2.800133
1999-08-01 09:00   -0.274502
1999-11-01 09:00    0.021417
2000-02-01 09:00   -0.555724
2000-05-01 09:00   -1.321482
2000-08-01 09:00    1.043381
2000-11-01 09:00    0.146324
Freq: H, dtype: float64
'''