Aggregating using custom function and several colums in pandas - pandas-groupby

Question

Suppose I have the following data frame:
group num value
a 3 20
a 5 5
b 5 10
b 10 5
b 2 25
Now, I want to compute the weighted average of columns num and value grouping by column group. Using tidyverse packages in R, this is straightforward:
> library(tidyverse)
> df <- tribble(
~group , ~num , ~value,
"a" , 3 , 20,
"a" , 5 , 5,
"b" , 5 , 10,
"b" , 10 , 5,
"b" , 2 , 25
)
> df %>%
group_by(group) %>%
summarise(new_value = sum(num * value) / sum(num))
# A tibble: 2 x 2
group new_value
<chr> <dbl>
1 a 10.6
2 b 8.82
Using Pandas in Python, I can make all intermediary computation beforehand, and then use sum() to sum up the variables, and then perform the division using transform() like this:
import pandas as pd
from io import StringIO
data = StringIO(
"""
group,num,value
a,3,20
a,5,5
b,5,10
b,10,5
b,2,25
""")
df = pd.read_csv(data)
df["tmp_value"] = df["num"] * df["value"]
df = df.groupby(["group"]) \
[["num", "tmp_value"]] \
.sum() \
.transform(lambda x : x["tmp_value"] / x["num"], axis="columns")
print(df)
# group
# a 10.625000
# b 8.823529
# dtype: float64
Note that we explicitly need first to subset the columns of interest ([["num", "tmp_value"]]), compute the sum (sum()), and then the average/division using transform(). In R, we write this in just one simple step, much more compact and readable, IMHO.
Now, how can I accomplish that elegancy using Pandas? In other words, can it be more clean, elegant, and mainly easy to read as we do in R?

Answer 1

#an_drade - There has been a very similar stackoverflow question that provides the solution:
Pandas DataFrame aggregate function using multiple columns
The solution to your question is based on the above post by creating a python function:
df=pd.DataFrame([['a',3,20],['a',5,5],['b',5,10],['b',10,5],['b',2,25]],columns=['group','num','value'])
def wavg(group):
d = group['num']
w = group['value']
return (d*w).sum() / d.sum()
final=df.groupby("group").apply(wavg)
group
a 10.625000
b 8.823529
dtype: float64

Answer 2

This is the "R way" you wanted:
>>> from datar import f
>>> from datar.tibble import tribble
>>> from datar.dplyr import group_by, summarise
>>> from datar.base import sum
>>> # or if you are lazy:
>>> # from datar.all import *
>>>
>>> df = tribble(
... f.group , f.num , f.value,
... "a" , 3 , 20,
... "a" , 5 , 5,
... "b" , 5 , 10,
... "b" , 10 , 5,
... "b" , 2 , 25
... )
>>> df >> \
... group_by(f.group) >> \
... summarise(new_value = sum(f.num * f.value) / sum(f.num))
group new_value
<object> <float64>
0 a 10.625000
1 b 8.823529
I am the author of the datar package. Please feel free to submit issues if you have any questions about using it.