CODEWITHSUNDEEP
pandasrowsubtotal
CJ123
CJ123

Reputation: 27

Adding a row with subtotal by category to my dataframe

I have created a new aggregated dataframe by using groupby and I am having problems with adding a subtotal row under each category.

I have tried using pd.groupby and pivottable and changing the index but I don't manage to represent the data as I want it. 

df_balance['Subtotal'] = df_balance.groupby('Client')['USD_Balance'].transform('sum')

+----------+-------------+------------+
|CLient ID | USD_Balance | Subtotal   |
+----------+---------+------------+----
|       1  |     2       |     6      |      
|       1  |     2       |     6      |     
|       1  |     2       |     6      |    
+----------+-------------+------------+

|---------------------|------------------|
|      Client ID      |    USD_Balance   |
|---------------------|------------------|
|          1          |         2        |
|---------------------|------------------|
|          1          |         2        |
|---------------------|------------------|
|          1          |         2        |
|---------------------|------------------|
|        SubTotal     |         6        |
|---------------------|------------------|

I would like to add a Subtotal row, with the corresponding agg per Client ID group.

Thanks in advance for any pointers on how to present my data like this!

Upvotes: 2

Views: 6811

Answers (3)

Scott Boston
Scott Boston

Reputation: 153460

Are you okay with doing it a little different way?

dftotal = df.groupby('CLient ID')['USD_Balance'].sum().reset_index()
dftotal['CLient ID'] = 'SubTotal'
pd.concat([df, dftotal])

Output:

  CLient ID  USD_Balance
0         1            2
1         1            2
2         1            2
0  SubTotal            6

Upvotes: 0

tawab_shakeel
tawab_shakeel

Reputation: 3739

sum_res= df.groupby(['CLient ID'],as_index=False)['USD_Balance'].sum()
sum_res['grand_total'] ='Grand Total'
df.sort_values(by=['CLient ID'],ascending=[True],inplace=True)

Separate two columns from original dataframe after sorting
res = df[['CLient ID','USD_Balance']]
final_res = pd.concat([res,sum_res])
final_res = final_res.sort_values(by=['CLient ID','grand_total'],ascending=[True,True],na_position='first')
final_res['CLient ID'] =np.where(final_res['grand_total'].isnull(), final_res['CLient ID'], final_res['grand_total'])

final_res.drop(['grand_total'],axis=1,inplace=True)

Upvotes: 1

Erfan
Erfan

Reputation: 42916

You can use groupby and access each group and add an Subtotal row:

dfs = []

for _, d in df.groupby('CLient ID', as_index=False):
    d.loc['Total', 'USD_Balance'] = df['USD_Balance'].sum()
    dfs.append(d)

df_final = pd.concat(dfs, ignore_index=True)

   CLient ID  USD_Balance
0        1.0          2.0
1        1.0          2.0
2        1.0          2.0
3        NaN          6.0

Upvotes: 5

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