Computing on two colums

I have this snippet, which computes on two columns, and if I get some increasing pattern then I will consider it a valid Key. How can I optimize this in pandas?

Sample Code

Here there is only one group of track_id. However, I will have many track_id groups, I need to apply the same for all groups.

import pandas as pd
import numpy as np

tid = [5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,
       5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.]

j = [ 0.,  0.,  0.,  0.,  0.,  1., 52., 53., -1., -1., -1., -1., -1.,
       -1., -1., -1., -1.,  1., -1., -1., -1., -1., -1., -1., -1., -1.,
       -1., -1.]

k = [ 0.,  0.,  0.,  0.,  0., -1., -1., -1., -1., -1., 56., 57., 58.,
       59., 60., 61., 62., 63., -1., -1., -1., -1., -1., -1., -1., -1.,
       -1., -1.]
rules = ['rule_1_start', 'rule_1_end']
data = {'rule_1_start': j, 'rule_1_end': k, 'track_id': tid }
df = pd.DataFrame.from_dict(data)
for r in range(0, len(rules), 2):
    track_groups = df.groupby('track_id')
    for key, item in track_groups:
        _min = np.argmin(item[rules[r]].values)
        _max =  np.argmax(item[rules[r+1]].values)
        if _min < _max: 
            print(f"{key}")

The pattern

I am trying to find is something like this. If you see these two columns, there is increasing value pattern. Col 1 has 77, 78,79 and col 2 has 82-91. The pattern should be increasing from col1 to col2 but not vice versa.