Mengubah frekuensi deret waktu: resampling

Memanipulasi Data Deret Waktu di Python

Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

Mengubah frekuensi: resampling

DateTimeIndex: atur & ubah frekuensi dengan .asfreq()
Namun konversi frekuensi memengaruhi data
- Upsampling: isi atau interpolasi data hilang
- Downsampling: agregasikan data yang ada
API pandas:
- .asfreq(), .reindex()
- .resample() + metode transformasi

Mulai: data kuartalan

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 # Default: kuartal akhir tahun

Upsampling: kuartal => bulan

monthly = quarterly.asfreq('M') # ke frekuensi akhir bulan

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

Upsampling membuat nilai hilang

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

Upsampling: metode pengisian

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

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

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

Upsampling: metode pengisian

bfill: isi mundur
ffill: isi maju

            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

Tambah bulan yang hilang: .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():
- sesuaikan DataFrame ke indeks baru
- logika isian sama seperti .asfreq()

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

Ayo berlatih!

Memanipulasi Data Deret Waktu di Python