Pandas - Check for recurring pattern in dataframe

Question

I am currently trying to analyze network data with pandas. I have been reading other posts and the closest to my problem is Pandas - Find and index rows that match row sequence pattern.

My dataframe looks like this:

I am trying to check if some of the packages are lost and count the amount of lost packages. Therefore, I would like to define a window or matrix, here 2x2. Then define a pattern, in this case it would be .

Now I want to check if the window is exactly a recurring window. If possible this should be done in an extra column giving me false or true (or nan). I tried this already in the following examples of my code.

In the first example i tried to check it with iterating the rows. My third example is more what I was looking for: With the rolling command I define a window and a pattern the code should check the rows for, but I get an error, because the pattern is a string. This is what I would like it to look like.

import pandas as pd

df = pd.read_csv('hallo')

Here I am filtering out the interferences

   Protocol_filtered = df[df['Protocol']== 'ICMP']
   Protocol_filtered1 = Protocol_filtered[['Time','Source','Destination','Info']] 
   Protocol_filtered1 = Protocol_filtered1.reset_index(drop=True)

I start checking for the lost packages

    s0 = 0
    s1 = 1

   for row in Protocol_filtered1.iterrows():
  while s1 <= len (Protocol_filtered1):
    source = Protocol_filtered1.loc[s0,'Source']
    dest = Protocol_filtered1.loc[s1,'Destination']

    if source == dest:
        Protocol_filtered1['Check']= True
    else:
        Protocol_filtered1['Check']= False
    
    source1 = Protocol_filtered1.loc[s1,'Source']
    dest1 = Protocol_filtered1.loc[s0,'Destination']
    


    if source1 == dest1:
        Protocol_filtered1['Check1']= True
    else:
        Protocol_filtered1['Check1']= False

    s0 = s0 + 2
    s1 = s1 + 2  

The result of this code is not the result I wanted, as it gives me for example a true in the row 2 where it should be false.

The logic of the following code is correct, but it checks i for every row, while it should always check the two successive rows together (0 & 1, 2&3 ,4&5 ...):

pattern = ['192.168.20.35', '192.168.20.31']
i = (Protocol_filtered1['Source'] == '192.168.20.35') &         (Protocol_filtered1['Source'].shift(-1) == '192.168.20.31')
i &= (Protocol_filtered1['Destination'] == '192.168.20.31') & (Protocol_filtered1['Destination'].shift(-1)== '192.168.20.35')

Protocol_filtered1.index[i]

Protocol_filtered1 ['Check1'] = i

The result here is (It should be: Check: True, True, False, False, True, True):

A very elegant solution I found in the forum and that I tried to apply is:

pattern = ['192.168.20.35', '192.168.20.31']
obs = len(pattern)
Protocol_filtered1['S1'] = (Protocol_filtered1['Source']
                        .rolling(window = obs, min_periods = )
                        .apply(lambda x: (x==pattern).all())
                        .astype(bool)
                        .shift(-1*(obs-1)))

But there also seems to be a problem in my code. I prefer the last solution where I can define a certain pattern and the size of the window and let pandas go over all the dataframe where I then can count the amount of lost packages with isnull().

I would really appreciate some help! Thank you very much!