[英]Pythonic way of creating a recursive series of 1 and 0 based on a pandas time series level
我正在嘗試清理我有一個數據框的代碼,例如:
df = pd.DataFrame({'value': {'2016-09-21': 13.30,
'2016-09-22': 12.02,
'2016-09-23': 12.28,
'2016-09-26': 14.5,
'2016-09-27': 13.1,
'2016-09-28': 12.39,
'2016-09-29': 14.02}})
我有一個基於電平的 ON 和 OFF 信號。 當“價值”向上跨越 14.39 時,我想要 1 直到向下跨越 12.50,這樣有:
df
value sig
2016-09-21 13.3000 0
2016-09-22 12.0200 0
2016-09-23 12.2800 0
2016-09-26 14.5000 1
2016-09-27 13.1000 1
2016-09-28 12.3900 0
2016-09-29 14.0200 0
我正在通過一個循環來解決這個問題,但我很確定有更好的方法來做到這一點。 這是我的方法:
off, on, sig = 14.39, 12.50, 0
log = []
for level in df.itertuples():
if level.value > off:
sig = 1
elif (sig == 1) & (level.value < on):
sig = 0
log.append([level.value, sig])
log = pd.DataFrame(log, index=df.index, columns=['value', 'sig'])
這是使用pandas.Series.where
方法的矢量化解決方案:
import numpy as np
ON, OFF = 14.39, 12.50
df['sig'] = 0 # set the initial value to be 0
df['sig'] = (df.sig.where(df.value < ON, 1) # if value > ON, set it 1
.where((df.value < OFF) | (df.value > ON), np.nan)
# if value < ON, and value > OFF, set it nan
.ffill().fillna(0)) # forward fill the nan value as they depend
# on their previous state, and fill initial
# value as 0
df
# value sig
#2016-09-21 13.30 0
#2016-09-22 12.02 0
#2016-09-23 12.28 0
#2016-09-26 14.50 1
#2016-09-27 13.10 1
#2016-09-28 12.39 0
#2016-09-29 14.02 0
一個類似的np.where()
方法,可能意圖更明確:
import numpy as np
df['sig'] = np.where(df.value > ON, 1, np.where(df.value < OFF, 0, np.nan))
df['sig'] = df.sig.ffill().fillna(0)
嘗試這個:
df['sig'] = (df['value'] < off & df['value'] > on).astype(int)
在我的頭頂上,未經證實。
v = df['value']
s = v.gt(14.39).sub(v.lt(12.5))
df['sig'] = s.where(s.ne(0). np.nan).ffill().add(1).div(2)
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