满足条件时用零填充 python pandas dataframe 的后续值

Question

When a value in "Returns" column is less than "m"(say m=-0.5), I want the subsequent rows to be filled with zeros only till that month end. How to accomplish this? Thanks in advance.

import pandas as pd
import numpy as np
from datetime import datetime, timedelta

date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(365), freq='D')
np.random.seed(seed=1111)
data = np.random.randint(1, high=100, size=len(days))
df = pd.DataFrame({'test': days, 'Price': data})
df = df.set_index('test')
df['Returns']=df['Price'].pct_change()

Answer 1

Use numpy.where with mask for compare Returns for less like m and aggregate Series.cummax with shift for < m per months by DatetimeIndex.to_period :

np.random.seed(123)
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(365), freq='D')
np.random.seed(seed=1111)
data = np.random.randint(1, high=100, size=len(days))
df = pd.DataFrame({'test': days, 'Price': data})
df = df.set_index('test')
df['Returns']=df['Price'].pct_change()
    
    
m = -0.5

m = (df['Returns'].lt(m)
                  .groupby(df.index.to_period('m'))
                  .transform(lambda x: x.shift().cummax())
                  .fillna(False))

---

df['Returns'] = np.where(m, 0, df['Returns'])
print (df)
                            Price   Returns
test                                       
2021-11-22 14:32:35.550767     29       NaN
2021-11-23 14:32:35.550767     56  0.931034
2021-11-24 14:32:35.550767     82  0.464286
2021-11-25 14:32:35.550767     13 -0.841463
2021-11-26 14:32:35.550767     35  0.000000
                          ...       ...
2022-11-18 14:32:35.550767     47  0.000000
2022-11-19 14:32:35.550767     90  0.000000
2022-11-20 14:32:35.550767     20  0.000000
2022-11-21 14:32:35.550767     27  0.000000
2022-11-22 14:32:35.550767     51  0.000000

[366 rows x 2 columns]