更改时间序列频率：重采样

Python 中的时间序列数据处理

Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

更改频率：重采样

DateTimeIndex：用 .asfreq() 设置与更改频率
但频率转换会影响数据
- 上采样：填充或插值缺失值
- 下采样：聚合已有数据
pandas API：
- .asfreq()、.reindex()
- .resample() + 变换方法

入门：季度数据

dates = pd.date_range(start='2016', periods=4, freq='Q')

data = range(1, 5)

quarterly = pd.Series(data=data, index=dates)

quarterly

2016-03-31    1
2016-06-30    2
2016-09-30    3
2016-12-31    4
Freq: Q-DEC, dtype: int64 # 默认：年末季度

上采样：季度 => 月度

monthly = quarterly.asfreq('M') # 转为月末频率

2016-03-31    1.0
2016-04-30    NaN
2016-05-31    NaN
2016-06-30    2.0
2016-07-31    NaN
2016-08-31    NaN
2016-09-30    3.0
2016-10-31    NaN
2016-11-30    NaN
2016-12-31    4.0
Freq: M, dtype: float64

上采样会产生缺失值

monthly = monthly.to_frame('baseline') # 转为 DataFrame

上采样：填充方法

monthly['ffill'] = quarterly.asfreq('M', method='ffill')

monthly['bfill'] = quarterly.asfreq('M', method='bfill')

monthly['value'] = quarterly.asfreq('M', fill_value=0)

上采样：填充方法

bfill：向后填充
ffill：向前填充

            baseline  ffill  bfill  value
2016-03-31       1.0      1      1      1
2016-04-30       NaN      1      2      0
2016-05-31       NaN      1      2      0
2016-06-30       2.0      2      2      2
2016-07-31       NaN      2      3      0
2016-08-31       NaN      2      3      0
2016-09-30       3.0      3      3      3
2016-10-31       NaN      3      4      0
2016-11-30       NaN      3      4      0
2016-12-31       4.0      4      4      4

补齐缺失月份：.reindex()

dates = pd.date_range(start='2016', 
                      periods=12, 
                      freq='M')

DatetimeIndex(['2016-01-31', 
               '2016-02-29', 
               ..., 
               '2016-11-30', 
               '2016-12-31'],
        dtype='datetime64[ns]', freq='M')

.reindex():
- 将 DataFrame 调整为新索引
- 与 .asfreq() 相同的填充逻辑

quarterly.reindex(dates)

2016-01-31    NaN
2016-02-29    NaN
2016-03-31    1.0
2016-04-30    NaN
2016-05-31    NaN
2016-06-30    2.0
2016-07-31    NaN
2016-08-31    NaN
2016-09-30    3.0
2016-10-31    NaN
2016-11-30    NaN
2016-12-31    4.0

Passons à la pratique !

Python 中的时间序列数据处理