I have a dataset with weights for each observation and I want to prepare weighted summaries using groupby but am rusty as to how to best do this. I think it implies a custom aggregation function. My issue is how to properly deal with not item-wise data, but group-wise data. Perhaps it means that it is best to do this in steps rather than in one go.
In pseudo-code, I am looking for
#first, calculate weighted value
for each row:
weighted jobs = weight * jobs
#then, for each city, sum these weights and divide by the count (sum of weights)
for each city:
sum(weighted jobs)/sum(weight)
I am not sure how to work the "for each city"-part into a custom aggregate function and get access to group-level summaries.
Mock data:
import pandas as pd
import numpy as np
np.random.seed(43)
## prep mock data
N = 100
industry = ['utilities','sales','real estate','finance']
city = ['sf','san mateo','oakland']
weight = np.random.randint(low=5,high=40,size=N)
jobs = np.random.randint(low=1,high=20,size=N)
ind = np.random.choice(industry, N)
cty = np.random.choice(city, N)
df_city =pd.DataFrame({'industry':ind,'city':cty,'weight':weight,'jobs':jobs})
Simply multiply the two columns:
In [11]: df_city['weighted_jobs'] = df_city['weight'] * df_city['jobs']
Now you can groupby the city (and take the sum):
In [12]: df_city_sums = df_city.groupby('city').sum()
In [13]: df_city_sums
Out[13]:
jobs weight weighted_jobs
city
oakland 362 690 7958
san mateo 367 1017 9026
sf 253 638 6209
[3 rows x 3 columns]
Now you can divide the two sums, to get the desired result:
In [14]: df_city_sums['weighted_jobs'] / df_city_sums['jobs']
Out[14]:
city
oakland 21.983425
san mateo 24.594005
sf 24.541502
dtype: float64