Sum different rows in a data frame based on multiple conditions

Question

I have the following data frame:

dataset = {
     'String': ["ABCDEF","HIJABC","ABCHIJ","DEFABC"],
    'Bool':[True,True,False,False],
    'Number':[10,20,40,50]}

df = pd.DataFrame(dataset)



      String   Bool  Number
0  ABCDEF   True      10
1  HIJABC   True      20
2  ABCHIJ  False      40
3  DEFABC  False      50

I would like to sum the rows of the column Number where Bool is False to the rows where Bool is True:

The rows can be matched and summed together if the reverse of String of one row is not equal to the String of the row.

In this case ABCHIJ where bool is False is not equal to the reverse of ABCDEF so I sum the numbers: 10+40.
HIJABC where Bool is True is summed to DEFABC where Bool is False the outcome is 70

Expected output:

    String   Bool  Number
0  ABCDEF   True      50
1  HIJABC   True      70
2  ABCHIJ  False      40
3  DEFABC  False      50

I hope my explanation was good enough, is there a way to achieve the above outcome ?

Your explanation is unclear, what is the summation condition? Also, what have you tried to do it? — jkhadka
– jkhadka, Commented Aug 15, 2022 at 17:31
How does this scale when say you have another row 4 XYZ False 70? — FoundABetterName
– FoundABetterName, Commented Aug 15, 2022 at 17:43
@FoundABetterName, there would not be a 4th row... I expect for it to be a group of 4 rows everytime. — the phoenix
– the phoenix, Commented Aug 15, 2022 at 18:06

Giovanni Abel · Accepted Answer · 2022-08-15 18:25:57Z

1

One way is like this:

df_true = df[df['Bool'] == True]
df_false = df[df['Bool'] == False]

for i in df_false['String']:
   idx = df_true[df_true['String'] != (i[3:] + i[:3]) ].index[0]
   current_num = df.loc[df.index == idx, 'Number'].values[0]
   added_num = df[df['String'] == i]['Number'].values[0]
   df.loc[df.index == idx, 'Number'] =  current_num + added_num

I hope it helps

answered Aug 15, 2022 at 18:25

Giovanni Abel

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1 Comment

the phoenix Over a year ago

thank you man, I really appreciate your help. It worked, this is what I've been looking for. :)

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