Using list to find variables from data in rows [closed]

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I am trying to find a combination where it will go through the data to find matched variables for any value from the list First_row is found, any value from the list Second_row and any value from the list Third_row is found.

import pandas as pd
import numpy as np
import csv
df= pd.read_csv ("data.csv")

    
b2=1 #assigning variables
c2=2
d2=3
b3=4
c3=5
d3=6
b4=7
c4=8
d4=9

First_row = [b2, c2, d2] #list for first row
Second_row = [b3, c3, d3] #list for second row
Third_row = [b4, c4, d4] #list for third row

for n in range(len(df)): # data
    for i in range(len(First_row)):
        if df.loc[n].isin(First_row[i]).any(): # Found a value from First_row
            for i in range(len(Second_row)):
                if df.loc[n+1].isin(Second_row[i]).any(): ## Found a value from Second_row
                    for i in range(len(Third_row)):
                        if df.loc[n+2].isin(Third_row[i]).any(): # Found a value from Third_row
                            print(First_row[i],Second_row[i],Third_row[i]) # print the variables

If the first row has any one of [b2, c2, d2], second row has any one of [b3, c3, d3] and third row has any one of [b4, c4, d4] then it will print the variables, not the assigned value.

It will search as b2,b3,b4, -> b2,b3,c4, -> b2,b3,d4, -> b2,c3,b4, etc. and print the variables (not the assigned value) when found and continue. I am trying to run it but it's showing "only list-like objects are allowed to be passed to isin()"

T3 H40 · Accepted Answer · 2025-11-26 15:08:50Z

It looks like you're trying to pass a specific value from your row into the isin() method.

If you try using

if df.loc[n] in First_row

that should get you past the first step.

In any case the issue is definitely that you're giving isin() a value and not a list. Rather than embedding these loops super deep, could you turn it into separate sections?

I expect you'll run into trouble with i being reused like this as well, so iterating through each row separately will work better and you can just save i to a unique index variable.

As it is, your final printout will use the final value of i and print out every value with whatever index df.loc[n] is present in the third_row.

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