group/merge/pivot data by varied weight ranges in Pandas - pandas

Question

Is there a way in Pandas to fit in the value according to weight ranges when pivoting the dataframe? I see some answers with setting bins but these are varied weight ranges depending on how the data is entered.
Here's my dataset.
import pandas as pd
df = pd.DataFrame({'tier': [1,1,1,1,1,1,1,1,1],
'services': ["A","A","A","A","A","A","A","A","A"],
'weight_start': [1,61,161,201,1,1,61,161,201],
'weight_end': [60,160,200,500,500,60,160,200,500],
'location': [1,1,1,1,2,3,3,3,3],
'discount': [70,30,10,0,0,60,20,5,0]})
pivot_df = df.pivot(index=['tier','services','weight_start','weight_end'],columns='location',values='discount')
display(pivot_df)
Output:
df
Desired Output:
desired df
Since location 2 is 0 percent covering the ranges 1 to 500, I want it to populate 0 based on the ranges prescribed for tier 1 service A instead of having its own row.
Edit: Mozway's answer works when there is one service. When I added a second service, the dataframe ungrouped.
Here's the new dataset with service B.
import pandas as pd
df = pd.DataFrame({'tier': [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],
'services': ["A","A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B"],
'weight_start': [1,61,161,201,1,1,61,161,201,1,1,81,101,1,61,161,201],
'weight_end': [60,160,200,500,500,60,160,200,500,500,80,100,200,60,160,200,500],
'location': [1,1,1,1,2,3,3,3,3,1,2,2,2,3,3,3,3],
'discount': [70,30,10,0,0,60,20,5,0,50,70,50,10,65,55,45,5]})
pivot_df = df.pivot(index=['tier','services','weight_start','weight_end'],columns='location',values='discount')
display(pivot_df)
Output:
location 1 2 3
tier services weight_start weight_end
1 A 1 60 70.0 NaN 60.0
500 NaN 0.0 NaN
61 160 30.0 NaN 20.0
161 200 10.0 NaN 5.0
201 500 0.0 NaN 0.0
B 1 60 NaN NaN 65.0
80 NaN 70.0 NaN
500 50.0 NaN NaN
61 160 NaN NaN 55.0
81 100 NaN 50.0 NaN
101 200 NaN 10.0 NaN
161 200 NaN NaN 45.0
201 500 NaN NaN 5.0
Desired Output:
location 1 2 3
tier services weight_start weight_end
1 A 1 60 70.0 0.0 60.0
61 160 30.0 0.0 20.0
161 200 10.0 0.0 5.0
201 500 0.0 0.0 0.0
B 1 60 50 70 65.0
80 50 70.0 55
61 160 50 NaN 55.0
81 100 50 50.0 55
101 200 50 10.0 NaN
161 200 50 10 45.0
201 500 50 NaN 5.0

Answer 1

This will work
data = (df.set_index(['tier','services','weight_start','weight_end'])
.pivot(columns='location')['discount']
.reset_index()
.rename_axis(None, axis=1)
)

Answer 2

IIUC, you can (temporarily) exclude the columns with 0/nan and check if all remaining values are only NaNs per row. If so, drop those rows:
mask = ~pivot_df.loc[:, pivot_df.any()].isna().all(1)
out = pivot_df[mask].fillna(0)
output:
location 1 2 3
tier services weight_start weight_end
1 A 1 60 70.0 0.0 60.0
61 160 30.0 0.0 20.0
161 200 10.0 0.0 5.0
201 500 0.0 0.0 0.0
per group:
def drop(d):
mask = ~ d.loc[:, d.any()].isna().all(1)
return d[mask].fillna(0)
out = pivot_df.groupby(['services']).apply(drop)
output:
location 1 2 3
services tier services weight_start weight_end
A 1 A 1 60 70.0 0.0 60.0
61 160 30.0 0.0 20.0
161 200 10.0 0.0 5.0
201 500 0.0 0.0 0.0
B 1 B 1 60 0.0 0.0 65.0
80 0.0 70.0 0.0
500 50.0 0.0 0.0
61 160 0.0 0.0 55.0
81 100 0.0 50.0 0.0
101 200 0.0 10.0 0.0
161 200 0.0 0.0 45.0
201 500 0.0 0.0 5.0