How to find duplicate rows in a column then find out if two cells in another column sum up to a third cell in an Excel tab in Python?

Here's my answer with help of the code above from Jorge N:

import pandas as pd
import itertools

excel = pd.ExcelFile('/Users/user/Downloads/DSR-Table.xlsx')
df1 = pd.read_excel(excel, 'Table2') 
name = pd.DataFrame(df1, columns=['Name'])
amount = pd.DataFrame(df1, columns=['Amount'])
df = pd.DataFrame(list(zip(name, amount)), columns=['Name', 'Amount'])

def get_duplicates_idxs(self):
   idxs=[]
   if len(self)==3:
      amount=self.Amount
      indexes=amount.index
      idx1=indexes[0]
      idx2=indexes[1]
      idx3=indexes[2]
      a1=amount[idx1]
      a2=amount[idx2]
      a3=amount[idx3]
      if a1+a2==a3:
         idxs=[idx1,idx2]
      if a1+a3==a2:
         idxs=[idx1,idx3]
      if a2+a3==a1:
         idxs=[idx2,idx3]
   return idxs

idxs_series=df1.groupby("Name").apply(lambda x: get_duplicates_idxs(x))
idxs_duplicates=(list(itertools.chain.from_iterable(idxs_series)))
df_filtered=df1[~df1.index.isin(idxs_duplicates)]
df_filtered.to_excel("/Users/user/Downloads/DSR-Table.xlsx", index = False)

This is the quickest way to do it in my opinion.

Some made up data based in your image:

import pandas as pd
import itertools
columns=["Name", "Amount"]
name=["Humming","Stanley","James","Humming","Igo","Madden","Madden","Samuels","McCallister","Samuels","Madden"]   
amount=[478028,333543,294376,199793,224,
-4000,-6000,-7886,-9331,-15043,-10000]
extra_name=["Smith","Smith","Smith"]
extra_amount=[3000,2000,-1000]
name+=extra_name
amount+=extra_amount

Table2=list(zip(name,amount))
df1=pd.DataFrame(Table2,columns=columns)

df1 is your real dataframe.

We define a aggregated function to retrieve a list of indexes of duplicates rows for each name according your criteria:

def get_duplicates_idxs(self):
   idxs=[]
   if len(self)==3:
      amount=self.Amount
      indexes=amount.index
      idx1=indexes[0]
      idx2=indexes[1]
      idx3=indexes[2]
      a1=amount[idx1]
      a2=amount[idx2]
      a3=amount[idx3]
      if a1+a2==a3:
         idxs=[idx1,idx2]
      if a1+a3==a2:
         idxs=[idx1,idx3]
      if a2+a3==a1:
         idxs=[idx2,idx3]
   return idxs

There are two assumptions:

1. Duplicates implies 3 rows for that name.
2. The order of the 3 rows are irrelevant.

a1, a2 and a3 are the amounts of the posible duplicates rows in that order.

Then we apply this function in df1 grouped by name:

idxs_series=df1.groupby("Name").apply(lambda x: get_duplicates_idxs(x))
idxs_duplicates=(list(itertools.chain.from_iterable(idxs_series)))

df_filtered=df1[~df1.index.isin(idxs_duplicates)]

df_filtered has the desired output.

Tools used:

Pandas groupby Pandas apply itertools.chain Python lambda functions