ZoneMaterialName1,ZoneMaterialName2,ZoneMaterialName3,ZoneMaterialName4
Copper,Silver,Gold,Selenium:[[2.5,5,12,6],[2,8,2,3],[2,8,2,3],[1,0,,]]
Aluminium,Sodium,,Titanium:[[3,14,,5],[13,5,,15],[1,0,0,],[1,0,0,]]

ZoneMaterialName1,ZoneMaterialName2,ZoneMaterialName3,ZoneMaterialName4
Copper,Silver,Gold,Selenium:[[2.5,5,12,6],[2,8,2,3],[1,0,,]]
Aluminium,Sodium,,Titanium:[[3,14,,5],[13,5,,15],[1,0,0]]

import pandas as pd
import numpy as np
df = pd.read_csv('/mnt/c/python_test/Materials.csv')

# replace nan with 'NA' for material names so they are not excluded from groupby
df[['ZoneMaterialName1','ZoneMaterialName2','ZoneMaterialName3','ZoneMaterialName4']] =df[['ZoneMaterialName1','ZoneMaterialName2','ZoneMaterialName3','ZoneMaterialName4']].fillna('NA')

# Get List of thickness all values for each row
df['combined'] = df.apply(lambda row: [row['ZoneThickness1'],row['ZoneThickness2'],row['ZoneThickness3'],row['ZoneThickness4']], axis=1)

# Groupby target columns, making a list of lists of thicknesses
df.groupby(['ZoneMaterialName1','ZoneMaterialName2','ZoneMaterialName3','ZoneMaterialName4'])['combined'].apply(list)

# Get rid of duplicates
df['combined'] = df['combined'].apply(lambda x: set(tuple(i) for i in x))