[Python] Pandas: Use the (find by 'left, mid and right' columns) method to extract data from...

I'm trying to extract a [True, False, True] pattern, in a pandas index_row. Here's my sample csv.file for reproduction:

import pandas as pd
import numpy as np


Here's a CSV file:

df = pd.read_csv('My_Boxes.csv')
pd.set_option('display.max_columns', None)
df = df.drop(columns=['Unnamed: 0'])
df = df.loc[7: 9]
df


This is how the data frame looks like on output:

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
7 0 0 0 0 0 0 0 0 0 0 0 0 0 26 27 0
8 0 0 15 0 17 0 0 0 0 0 0 0 0 26 0 28
9 0 0 15 0 0 0 0 0 0 0 23 24 0 0 0 0


I'm trying to find the presence of a formation where, two opposite columns both carry a non zero integer and are, separated by a middle column whose value is '0'.

My dataframe does carry two instances of such a formation in row[8], i.e. [15, 0, 17] and [26, 0, 28].

I want to create a function that shall iterate through a dataframe, confirm such a presence and return result(s) as [True, False, True].

Here's what I've done, so far, in trying to construct my thoughts:

row8 = df.loc[8]
row = [(row8.iloc != 0), (row8.iloc==0), (row8.iloc !=0)]
row



This how the output looks like:

`True, False, True`


My challenge is to align this output to the core function:

I've done a number of back tests and have, concluded that my constructor is responding, to the prescribed function rather than perform, an iter_df function.

My expectation was that the function would, return a true copy of the data frame representative, maybe an axis=1, inplace=True return.

My thoughts are are aligned more towards something of this outcome:

row = row[['index_name'], left_col['name'], mid_col['name'],
right_col['name']]

result1 = index_name = list(15, 0, 17)
result2 = index_name = list(26, 0, 28)


A following (or, an even better) function is, what I'm trying to achieve:

output1 = result1, i.e. (axis=0, inplace=True)
output2 = result2, i.e. (axis=0, inplace=True)


I would appreciate all the help necessary, thanking you in advance. An accurate result should, be focused on such a following; return:

match1 = ['15', '0', '17']
match2 = ['26', '0', '28']


Thanks, once more, to you all for your patience.

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