dataframe: print entire row/s where keys in the same row hold equal values

I would like to recovery the rows in a dataframe where, in the same row, differing keys hold equal values. I can display where, for instance, the rows where col2 == col3. I would like to get this code to track across col1 matching across col2, col3 and col4. Then col2 to match across col 3 and col4. Then finally col3 across col4.

I have read through this post and I am confused if iteration is the solution to my problem. If so, how can this be done.

I can display, for instance, the rows where col2 == col3.

# -*- coding: utf-8 -*-

import pandas as pd

##      writing a dataframe
rows = {'col1':['5412','5148','5800','2122','5645','1060','4801','1039'],
        'col2':['542','512','541','412','565','562','645','152'],
        'col3':['542','3120','3410','2112','5650','5620','4801','152'],
        'col4':['5800','2122','5645','2112','412','562','562','645']
}

df = pd.DataFrame(rows)
print(f'Unsorted dataframe \n\n{df}')

##  print the rows where col2 == col3
dft = df[(df['col2'] == df['col3'])]
print('\n\nupdate - list row of matching row elements')
print(dft)

##  print all except the rows where col2 == col3
dft = df.drop(df[(df['col2'] == df['col3'])].index)
print('\n\nupdate - Dropping rows of matching row elements')
print(dft)

With this I am getting back

   col1 col2 col3  col4
0  5412  542  542  5800
7  1039  152  152   645

I would like to get back

   col1  col2 col3   col4
0  5412  542  542   5800
3  2122  412  2112  2112
4  5645  565  5650  412
5  1060  562  5620  562
6  4801  645  4801  562
7  1039  152  152   645

>Solution :

Use nunique with axis=1 and compare it to the number of columns:

import pandas as pd

rows = {
    "col1": ["5412", "5148", "5800", "2122", "5645", "1060", "4801", "1039"],
    "col2": ["542", "512", "541", "412", "565", "562", "645", "152"],
    "col3": ["542", "3120", "3410", "2112", "5650", "5620", "4801", "152"],
    "col4": ["5800", "2122", "5645", "2112", "412", "562", "562", "645"],
}

df = pd.DataFrame(rows)

df = df[df.nunique(axis=1) < len(df.columns)]

print(df)

Output:

   col1 col2  col3  col4
0  5412  542   542  5800
3  2122  412  2112  2112
5  1060  562  5620   562
6  4801  645  4801   562
7  1039  152   152   645