It must not be that hard but I can’t cope with this problem.
Imagine I have a long format dataframe with some data and want to calculate a weighted average of score per person and weighted by a manager and keep it as a separate variable – ‘w_mean_m’.
df['w_mean_m'] = df.groupby('person')['score'].transform(lambda x: np.average(x['score'], weights=x['manager_weight']))
throws an error and I have no idea how to fix it.
>Solution :
Because GroupBy.transform working with each column separately is not possible select multiple columns, so is used GroupBy.apply with Series.map for new column:
s = (df.groupby('contact')
.apply(lambda x: np.average(x['score'], weights=x['manager_weight'])))
df['w_mean_m'] = df['contact'].map(s)
One hack is possible with selected values by unique index for weights:
df = df.reset_index(drop=True)
f = lambda x: np.average(x, weights=df.loc[x.index, "manager_weight"])
df['w_mean_m1'] = df.groupby('contact')['score'].transform(f)
print (df)
manager_weight score contact w_mean_m1
0 1.0 1 a 1.282609
1 1.1 1 a 1.282609
2 1.2 1 a 1.282609
3 1.3 2 a 1.282609
4 1.4 2 b 2.355556
5 1.5 2 b 2.355556
6 1.6 3 b 2.355556
7 1.7 3 c 3.770270
8 1.8 4 c 3.770270
9 1.9 4 c 3.770270
10 2.0 4 c 3.770270
Setup:
df = pd.DataFrame(
{
"manager_weight": [1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2.0],
"score": [1,1,1,2,2,2,3,3,4,4,4],
"contact": ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'c']
})