I have a Pandas DataFrame with customer refund reasons. It contains these example data rows:
**case_type** **claim_type**
1 service service
2 service service
3 chargeback service
4 chargeback local_charges
5 service supplier_service
6 chargeback service
7 chargeback service
8 chargeback service
9 chargeback service
10 chargeback service
11 service service_not_used
12 service service_not_used
I would like to compare the customer's reason with some sort of labeled reason. This is no problem, but I would also like to see the total number of records in a specific group (customer reason).
case_claim_type = df[["case_type", "claim_type"]]
case_claim_type.groupby(by=("case_type", "claim_type"))["case_type"].count()
Which gives me this output, for example:
**case_type** **claim_type**
service service 2
supplier_service 1
service_not_used 2
chargeback service 6
local_charges 1
I would also like to have have the sum of the output per case_type. Something like:
**case_type** **claim_type**
service service 2
supplier_service 1
service_not_used 2
total: 5
chargeback service 6
local_charges 1
total: 7
It doesn't necessarily has to be in this last output format, a column with the (aggregated) totals per case_type is also fine.
Where:
df = pd.DataFrame({'case_type':['Service']*20+['chargeback']*9,'claim_type':['service']*5+['local_charges']*5+['service_not_used']*5+['supplier_service']*5+['service']*8+['local_charges']})
df_out = df.groupby(by=("case_type", "claim_type"))["case_type"].count()
Let use pd.concat
, sum
with level parameter, and assign
:
(pd.concat([df_out.to_frame(),
df_out.sum(level=0).to_frame()
.assign(claim_type= "total")
.set_index('claim_type', append=True)])
.sort_index())
Output:
case_type
case_type claim_type
Service local_charges 5
service 5
service_not_used 5
supplier_service 5
total 20
chargeback local_charges 1
service 8
total 9
You can use:
df = case_claim_type.groupby(by=("case_type", "claim_type"))["case_type"].count()
print (df)
case_type claim_type
chargeback local_charges 1
service 1
service service 2
supplier_service 1
Name: case_type, dtype: int64
You can create new DataFrame
by aggregate sum
and add MultiIndex
by MultiIndex.from_tuples
:
df1 = df.sum(level=0)
#same as
#df1 = df.groupby(level=0).sum()
new_cols= list(zip(df1.index.get_level_values(0),['total'] * len(df.index)))
df1.index = pd.MultiIndex.from_tuples(new_cols)
print (df1)
chargeback total 2
service total 3
Name: case_type, dtype: int64
Then concat
together and last sort_index
:
df2 = pd.concat([df,df1]).sort_index()
print (df2)
case_type claim_type
chargeback local_charges 1
service 1
total 2
service service 2
supplier_service 1
total 3
Name: case_type, dtype: int64
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