Pandas groupby dataframe then return single value result (sum, total) - python-3.x

Question

Dears,
Please help me, I am stucked.I guess it should not be difficult but I feel overwhelmed.
Need to make ageing of receivables, therefore they must be separated in different buckets.
Suppose we have only 3 groups: current, above_10Days and above_20Days and the following table:
d = {'Cust': [Dfg, Ers, Dac, Vds, Mhf, Kld, Xsd, Hun],
'Amount': [10000, 100000, 4000, 5411, 756000, 524058, 4444785, 54788,
'Days': 150, 21, 30, 231, 48, 15, -4, -14 }
I need to group the amounts to a total sum, depending on the Ageing group.
Example:
Current: 4499573, etc.
For that purpose, I tried to group the receivables with such code:
above_10Days = df.groupby((df['Days'] > 0) & (df['Days'] <= 10))
above10sum = above_10Days.Amount.sum().iloc[1]
It works perfect but only when they are actual amount in this group.
When they are no such A/R it throws an exception and stop executing. I tried to use function or to make 'None' value to 0, but no success.
Hopefully someone could know the solution.
Thanks in advance

Answer 1

IIUC:
d = {'Cust': ['Dfg', 'Ers', 'Dac', 'Vds', 'Mhf', 'Kld', 'Xsd', 'Hun'],
'Amount': [10000, 100000, 4000, 5411, 756000, 524058, 4444785, 54788],
'Days': [150, 21, 30, 231, 48, 15, -4, -14] }
df = pd.DataFrame(d)
#Updated to assign to output dataframe
df_out = (df.groupby(pd.cut(df.Days,
[-np.inf,10,20,np.inf],
labels=['Current','Above 10 Days','Above 20 Days']))['Amount']
.sum())
Output:
Days
Current 4499573
Above 10 Days 524058
Above 20 Days 875411
Name: Amount, dtype: int64
Varible assignent using .loc:
varCurrent = df_out.loc['Current']
var10 = df_out.loc['Above 10 Days']
var20 = df_out.loc['Above 20 Days']
print(varCurrent,var10,var20)
Output:
4499573 524058 875411