變更時間序列頻率：重採樣

Manipulating Time Series Data in Python

Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

變更頻率：重採樣

DateTimeIndex：用 .asfreq() 設定與變更頻率
但頻率轉換會影響資料
- 上採樣：填補或內插遺漏值
- 下採樣：彙總現有資料
pandas API：
- .asfreq()、.reindex()
- .resample() 搭配轉換方法

開始：季資料

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 # 預設：年終季頻

上採樣：季 => 月

monthly = quarterly.asfreq('M') # 轉為月底頻率

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

上採樣會產生遺漏值

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

上採樣：填補方法

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

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

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

上採樣：填補方法

bfill：向後填補
ffill：向前填補

            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

.reindex() 加入缺少的月份

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()：
- 使 DataFrame 對齊新索引
- 與 .asfreq() 相同的填補邏輯

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

一起來練習吧！

Manipulating Time Series Data in Python