我有一个包含以下列的熊猫数据框:
##code to generate data frame
import pandas as pd
a = [2,3,4,5,6,0,8,7,1,3,4,0,6,4,0,2,4,0,4,5,0,1,7,0,1,8,5,3,6]
idx = pd.date_range("2018-01-01", periods=len(a), freq="H")
ts = pd.Series(a, index=idx)
这将创建此数据帧:
## Will create this:
Percent_change
2018-01-01 00:00:00 2
2018-01-01 01:00:00 3
2018-01-01 02:00:00 4
2018-01-01 03:00:00 5
2018-01-01 04:00:00 6
2018-01-01 05:00:00 0
2018-01-01 06:00:00 8
2018-01-01 07:00:00 7
2018-01-01 08:00:00 1
2018-01-01 09:00:00 3
2018-01-01 10:00:00 4
2018-01-01 11:00:00 0
2018-01-01 12:00:00 6
2018-01-01 13:00:00 4
2018-01-01 14:00:00 0
2018-01-01 15:00:00 2
2018-01-01 16:00:00 4
2018-01-01 17:00:00 0
2018-01-01 18:00:00 4
2018-01-01 19:00:00 5
2018-01-01 20:00:00 0
2018-01-01 21:00:00 1
2018-01-01 22:00:00 7
2018-01-01 23:00:00 0
2018-01-02 00:00:00 1
2018-01-02 01:00:00 8
2018-01-02 02:00:00 5
2018-01-02 03:00:00 3
2018-01-02 04:00:00 6
我创建了一个生成器,用于根据百分比变化值从数据映射特定条件:
def signal():
current_state = "Outside market"
while True:
pct_change = yield current_state
if (
current_state in ("Outside market", "Market exit")
and pct_change >= 5
):
current_state = "Entered market"
elif current_state == "Entered market" and pct_change > 0:
current_state = "Inside market"
elif current_state is "Market exit" and pct_change < 5:
current_state = "Outside market"
elif (
current_state in ("Entered market", "Inside market")
and pct_change <= 0
):
current_state = "Market exit"
s = signal()
next(s)
df["Signal"] = df["Percent_change"].apply(lambda x: s.send(x))
df["Timestamp"] = pd.to_datetime(
np.where(
((df["Signal"] == "Entered market") | (df["Signal"] == "Market exit")),
df.index,
pd.NaT,
)
)
print(df)
将生成以下内容:
Percent_change Signal Timestamp
2018-01-01 00:00:00 2 Outside market NaT
2018-01-01 01:00:00 3 Outside market NaT
2018-01-01 02:00:00 4 Outside market NaT
2018-01-01 03:00:00 5 Entered market 2018-01-01 03:00:00
2018-01-01 04:00:00 6 Inside market NaT
2018-01-01 05:00:00 0 Market exit 2018-01-01 05:00:00
2018-01-01 06:00:00 8 Entered market 2018-01-01 06:00:00
2018-01-01 07:00:00 7 Inside market NaT
2018-01-01 08:00:00 1 Inside market NaT
2018-01-01 09:00:00 3 Inside market NaT
2018-01-01 10:00:00 4 Inside market NaT
2018-01-01 11:00:00 0 Market exit 2018-01-01 11:00:00
2018-01-01 12:00:00 6 Entered market 2018-01-01 12:00:00
2018-01-01 13:00:00 4 Inside market NaT
2018-01-01 14:00:00 0 Market exit 2018-01-01 14:00:00
2018-01-01 15:00:00 2 Outside market NaT
2018-01-01 16:00:00 4 Outside market NaT
2018-01-01 17:00:00 0 Outside market NaT
2018-01-01 18:00:00 4 Outside market NaT
2018-01-01 19:00:00 5 Entered market 2018-01-01 19:00:00
2018-01-01 20:00:00 0 Market exit 2018-01-01 20:00:00
2018-01-01 21:00:00 1 Outside market NaT
2018-01-01 22:00:00 7 Entered market 2018-01-01 22:00:00
2018-01-01 23:00:00 0 Market exit 2018-01-01 23:00:00
2018-01-02 00:00:00 1 Outside market NaT
2018-01-02 01:00:00 8 Entered market 2018-01-02 01:00:00
2018-01-02 02:00:00 5 Inside market NaT
2018-01-02 03:00:00 3 Inside market NaT
2018-01-02 04:00:00 6 Inside market NaT
如果我有一个数据帧,在这个数据帧上应用这个函数并得到结果,那么是否有一种方法可以应用相同的机制来检查数据是否是实时的,例如来自websocket,并根据pct_变化的值从生成的信号触发其他函数
谢谢
目前没有回答
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