Compare row value with sum of other rows by given categories - Pandas dataframe

Question

I have a dataframe with a code, its code category and their corresponding values. I would like to compare the total of a category (A, B ) with the sum of their subtotal (A_1, B_2..) and add a column to the dataframe if the values are the same then return 1 otherwise 0.

Month	code	code_category	Value	reporter
2021M1	A_1	A_sub	5	ADC
2021M1	A_2	A_sub	10	ADC
2021M1	A	A	15	ADC
2021M1	B_1	B_sub	4	ADC
2021M1	B_2	B_sub	13	ADC
2021M1	B	B	14	ADC
2021M1	B_1	B_sub	17	FGH
2021M1	B_2	B_sub	0	FGH
2021M1	B	B	17	FGH

The desired output should be:

Month	code	code_category	Value	reporter	subtot_correct
2021M1	A_1	A_sub	5	ADC	1
2021M1	A_2	A_sub	10	ADC	1
2021M1	A	A	15	ADC	1
2021M1	B_1	B_sub	4	ADC	0
2021M1	B_2	B_sub	13	ADC	0
2021M1	B	B	14	ADC	0
2021M1	B_1	B_sub	17	FGH	1
2021M1	B_2	B_sub	0	FGH	1
2021M1	B	B	17	FGH	1

I aim just to flag the pair of month, reporter and code that have not the same subtotal, so any other suggestions on the column SUBTOT_CORRECT is welcome.

Answer 1

Might be a more efficent/eligant way, but this is what I came up with.

Looks like the sub-categories also invlove the 'reporter' , so groupby 'code_category' and 'reporter' and sum the values. Then merge that to the dataframe.

Next combine the main category and reporter to get a "key" value.

Count up the unique values within the "key" groups

Map those values into the dataframe column

Lastly, fill with 0 where not equal to 1 , and drop those columns used to merge on.

Code:

import pandas as pd
import numpy as np

cols = ['Month','code','code_category','Value','reporter']

data = [
['2021M1',  'A_1',  'A_sub',    5,  'ADC'],
['2021M1',  'A_2',  'A_sub',    10, 'ADC'],
['2021M1',  'A',    'A',    15, 'ADC'],
['2021M1',  'B_1',  'B_sub',    4,  'ADC'],
['2021M1',  'B_2',  'B_sub',    13, 'ADC'],
['2021M1',  'B',    'B',    14, 'ADC'],
['2021M1',  'B_1',  'B_sub',    17, 'FGH'],
['2021M1',  'B_2',  'B_sub',    0,  'FGH'],
['2021M1',  'B',    'B',    17, 'FGH']]

df = pd.DataFrame(data, columns=cols)

grouped = df.groupby(['code_category', 'reporter']).sum()
grouped = grouped.reset_index(drop=False)
grouped = grouped.rename(columns=({'Value':'Sum'}))
df = df.merge(grouped, how='left', left_on=['code_category', 'reporter'], right_on=['code_category', 'reporter'])

df['key'] = df['code_category'].str.replace('_sub', '') + '_' + df['reporter']

lenUnique = dict(df.groupby(['key'])['Sum'].nunique())

df['subtot_correct'] = df['key'].map(lenUnique)
df['subtot_correct'] = np.where(df['subtot_correct'] == 1, df['subtot_correct'], 0)

df = df.drop(['Sum', 'key'], axis=1)

Output: