pandas vectorize one valid signal in 5 rows - pandas

Question

I want to find the first valid signal in the dataframe. A valid signal is defined that there is no signal in its preceding 5 rows.
The dataframe is like:
entry
0 0
1 1
2 0
3 0
4 1
5 0
6 0
7 0
8 1
9 0
10 0
11 0
12 0
13 0
14 0
The entry signal on row 4 is not valid because there is a signal on row 1. Every signals will negate any signal in the following 5 rows.
I implement this by using an apply function with a parameter recording the signal row counter.
The code is as following
import pandas as pd
def testfun(row, orderinfo):
if orderinfo['countrows'] > orderinfo['maxrows']:
orderinfo['countrows'] = 0
if orderinfo['countrows'] > 0:
orderinfo['countrows'] += 1
row['entry'] = 0
if row['entry'] == 1 and orderinfo['countrows'] == 0:
orderinfo['countrows'] += 1
return row
if __name__ == '__main__':
df = pd.DataFrame({'entry':[0,1,0,1,0,0,0,0,1,0,0,0,0,0,0]})
orderinfo = dict(countrows=0, maxrows=5)
df = df.apply(lambda row: testfun(row, orderinfo), axis=1)
print(df)
output is:
entry
0 0
1 1
2 0
3 0
4 0
5 0
6 0
7 0
8 1
9 0
10 0
11 0
12 0
13 0
14 0
But I am wondering if there is any vectorized way to do this? Because apply is not very efficient.

Answer 1

IIUC,
You need rolling with min_periods=1 and sum less than or equal 1 and compare against entry column
(df.entry.rolling(4, min_periods=1).sum().le(1) & df.entry).astype(int)
Out[595]:
0 0
1 1
2 0
3 0
4 0
5 0
6 0
7 0
8 1
9 0
10 0
11 0
12 0
13 0
14 0
Name: entry, dtype: int32