weighted average on a column of dataframe with provided weighted rate
Question
Year Week_Number DC_Zip Asin_code
0 2016 1 12206 NaN
1 2016 1 29306 NaN
2 2016 1 33426 NaN
3 2016 1 37206 NaN
4 2016 1 45216 NaN
5 2016 1 60160 NaN
6 2016 1 76110 NaN
7 2016 1 80215 NaN
8 2016 1 84105 NaN
9 2016 1 85034 NaN
10 2016 1 93711 NaN
11 2016 1 98433 NaN
12 2016 2 12206 21.0
13 2016 2 29306 10.0
14 2016 2 33426 11.0
15 2016 2 37206 1.0
16 2016 2 45216 5.0
17 2016 2 60160 7.0
18 2016 2 76110 12.0
19 2016 2 80215 NaN
20 2016 2 84105 2.0
21 2016 2 85034 1.0
22 2016 2 93711 23.0
23 2016 2 98433 7.0
24 2016 3 12206 95.0
25 2016 3 29306 26.0
26 2016 3 33426 51.0
27 2016 3 37206 18.0
28 2016 3 45216 34.0
29 2016 3 60160 30.0
... ... ... ... ...
2778 2020 29 76110 33.0
2779 2020 29 80215 5.0
2780 2020 29 84105 3.0
2781 2020 29 85034 8.0
2782 2020 29 93711 53.0
2783 2020 29 98433 15.0
2784 2020 30 12206 75.0
2785 2020 30 29306 27.0
2786 2020 30 33426 34.0
2787 2020 30 37206 12.0
2788 2020 30 45216 14.0
2789 2020 30 60160 28.0
2790 2020 30 76110 47.0
2791 2020 30 80215 11.0
2792 2020 30 84105 3.0
2793 2020 30 85034 17.0
2794 2020 30 93711 62.0
2795 2020 30 98433 13.0
2796 2020 31 12206 109.0
2797 2020 31 29306 30.0
2798 2020 31 33426 31.0
2799 2020 31 37206 14.0
2800 2020 31 45216 23.0
2801 2020 31 60160 21.0
2802 2020 31 76110 25.0
2803 2020 31 80215 7.0
2804 2020 31 84105 4.0
2805 2020 31 85034 8.0
2806 2020 31 93711 71.0
2807 2020 31 98433 9.0
2808 rows × 4 columns
This is the sales data I am dealing with. I have to perform a weighted average on Asin_code with weighted rate = [5, 5, 20, 30, 40] on respective years 2016, 2017, 2018, 2019 and 2020. I have to create a function so that it will give me a column containing the weighted average of Asin_code ."Nan" values should be dropped. We should also change the weighted rate in the future to view more patterns with the data. Any help would be appreciated.
i am trying the following code:
for i in range(len(df.Asin_code)):
df["Weighted_avg"]=rate[0]*df.Asin_code[i]/df.Asin_code.loc[(df.Year==2016)].sum()
just facing difficulties in consolidating the data for whole 5 years.
2 answers
solution1
0 2020-08-09 02:55:24
It becomes much simpler it you define your weights as a dict instead of a list then a simple use of apply() works
# define weights for year as a dict
wr = {2016:5, 2017:5, 2018:20, 2019:30, 2020:40}
df["Weighted_avg"] = df.apply(lambda r:
# numerator is weight * Asin_code[i]
( r["Asin_code"] * wr[r["Year"]]
/
# denomimator sum(Asin_code for year)
df.Asin_code.loc[(df.Year==r["Year"])].sum() ), axis=1)
output
Idx Year Week_Number DC_Zip Asin_code Weighted_avg
25 2016 3 29306 26.0 0.367232
26 2016 3 33426 51.0 0.720339
27 2016 3 37206 18.0 0.254237
28 2016 3 45216 34.0 0.480226
29 2016 3 60160 30.0 0.423729
2778 2020 29 76110 33.0 1.625616
2779 2020 29 80215 5.0 0.246305
2780 2020 29 84105 3.0 0.147783
2781 2020 29 85034 8.0 0.394089
2782 2020 29 93711 53.0 2.610837
suplementary update
Updated request: weighted_average[at index 1]=rate[for year 2016]*Asin_code[at first index of 2016]+rate[for year 2017]*Asin_code[at first index of 2017]+rate[for year 2018]*Asin_code[at first index of 2018]+rate[for year 2019]*Asin_code[at first index of 2019]+rate[for year 2020]*Asin_code[at first index of 2020]
df.dropna().groupby("Year").agg({"Asin_code":"first"}).reset_index()\
.assign(wa=lambda dfa:
dfa.apply(lambda r: r["Asin_code"]*wr[r['Year']],axis=1))["wa"].sum()
solution2
0 ACCPTED 2020-08-12 05:42:26
df["Weighted_avg"] = df.apply(lambda r: ( (r["Asin_code"] *wr[r["Year"]]).sum(axis = 0)), axis=1)
Output
12 2016 2 12206 21.0 105.0
13 2016 2 29306 10.0 50.0
14 2016 2 33426 11.0 55.0
15 2016 2 37206 1.0 5.0
16 2016 2 45216 5.0 25.0
17 2016 2 60160 7.0 35.0
18 2016 2 76110 12.0 60.0
19 2016 2 80215 NaN NaN
20 2016 2 84105 2.0 10.0
21 2016 2 85034 1.0 5.0
22 2016 2 93711 23.0 115.0
23 2016 2 98433 7.0 35.0
24 2016 3 12206 95.0 475.0
25 2016 3 29306 26.0 130.0
26 2016 3 33426 51.0 255.0
27 2016 3 37206 18.0 90.0
28 2016 3 45216 34.0 170.0
29 2016 3 60160 30.0 150.0
... ... ... ... ... ...
2778 2020 29 76110 33.0 1320.0
2779 2020 29 80215 5.0 200.0
2780 2020 29 84105 3.0 120.0
2781 2020 29 85034 8.0 320.0
2782 2020 29 93711 53.0 2120.0
2783 2020 29 98433 15.0 600.0
2784 2020 30 12206 75.0 3000.0
2785 2020 30 29306 27.0 1080.0
2786 2020 30 33426 34.0 1360.0
2787 2020 30 37206 12.0 480.0
2788 2020 30 45216 14.0 560.0
2789 2020 30 60160 28.0 1120.0
2790 2020 30 76110 47.0 1880.0
2791 2020 30 80215 11.0 440.0
2792 2020 30 84105 3.0 120.0
2793 2020 30 85034 17.0 680.0
2794 2020 30 93711 62.0 2480.0
2795 2020 30 98433 13.0 520.0
2796 2020 31 12206 109.0 4360.0
2797 2020 31 29306 30.0 1200.0
2798 2020 31 33426 31.0 1240.0
2799 2020 31 37206 14.0 560.0
2800 2020 31 45216 23.0 920.0
2801 2020 31 60160 21.0 840.0
2802 2020 31 76110 25.0 1000.0
2803 2020 31 80215 7.0 280.0
2804 2020 31 84105 4.0 160.0
2805 2020 31 85034 8.0 320.0
2806 2020 31 93711 71.0 2840.0
2807 2020 31 98433 9.0 360.0
Got my solution with this.
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