Pandas：将每月数据的第 1 个数据帧除以具有每月平均值的第 2 个数据帧
Pandas: Divide 1 dataframe of monthly data by 2nd dataframe with monthly averages
我在数据框 df1 中有月度数据，我需要除以第二个数据框 df2 来计算可变性。我的 df1 如下所示。我试图找到答案，但看不出如何将每个月度值除以此处给出的独特的月度长期 (2019-2021) 平均值。
index plant_name year month wind_speed_ms
324 CAETITE III 2019 1 8.188548
325 CAETITE III 2019 2 5.506577
326 CAETITE III 2019 3 6.449032
327 CAETITE III 2019 4 6.077875
328 CAETITE III 2019 5 6.932110
329 CAETITE III 2019 6 8.905403
330 CAETITE III 2019 7 8.180067
331 CAETITE III 2019 8 9.252164
332 CAETITE III 2019 9 8.850250
333 CAETITE III 2019 10 8.013858
334 CAETITE III 2019 11 6.940972
335 CAETITE III 2019 12 7.244973
336 CAETITE III 2020 1 5.690524
337 CAETITE III 2020 2 5.782543
338 CAETITE III 2020 3 6.387433
339 CAETITE III 2020 4 7.097597
340 CAETITE III 2020 5 7.142957
341 CAETITE III 2020 6 8.653625
342 CAETITE III 2020 7 8.509987
343 CAETITE III 2020 8 9.164261
344 CAETITE III 2020 9 9.219514
345 CAETITE III 2020 10 7.110269
346 CAETITE III 2020 11 6.884597
347 CAETITE III 2020 12 6.144812
348 CAETITE III 2021 1 7.082836
349 CAETITE III 2021 2 5.706280
350 CAETITE III 2021 3 7.029341
351 CAETITE III 2021 4 6.676764
352 CAETITE III 2021 5 7.318804
353 CAETITE III 2021 6 7.403028
354 CAETITE III 2021 7 7.811183
355 CAETITE III 2021 8 9.243642
我的第二个数据框 - df2 - 看起来像这样，这是月平均值或分母：
df2:
plant_name month wind_speed_ms
0 CAETITE III 1 6.764807
1 CAETITE III 2 6.624946
2 CAETITE III 3 6.661645
3 CAETITE III 4 7.127637
4 CAETITE III 5 7.361365
5 CAETITE III 6 8.416312
6 CAETITE III 7 8.746169
7 CAETITE III 8 8.915520
8 CAETITE III 9 8.824938
9 CAETITE III 10 8.037235
10 CAETITE III 11 6.810558
11 CAETITE III 12 6.435099
答案需要如下所示：
index plant_name year month var
0 CAETITE III 2019 1 21.046
1 CAETITE III 2019 2 -16.881
2 CAETITE III 2019 3 -3.192
3 CAETITE III 2019 4 -14.728
4 CAETITE III 2019 5 -5.831
5 CAETITE III 2019 6 5.811
6 CAETITE III 2019 7 -6.473
7 CAETITE III 2019 8 3.776
8 CAETITE III 2019 9 0.287
9 CAETITE III 2019 10 -0.291
10 CAETITE III 2019 11 1.915
11 CAETITE III 2019 12 12.585
12 CAETITE III 2020 1 -15.880
13 CAETITE III 2020 2 -12.716
14 CAETITE III 2020 3 -4.116
15 CAETITE III 2020 4 -0.421
16 CAETITE III 2020 5 -2.967
17 CAETITE III 2020 6 2.820
18 CAETITE III 2020 7 -2.700
19 CAETITE III 2020 8 2.790
20 CAETITE III 2020 9 4.471
21 CAETITE III 2020 10 -11.533
22 CAETITE III 2020 11 1.087
23 CAETITE III 2020 12 -4.511
24 CAETITE III 2021 1 4.701
25 CAETITE III 2021 2 -13.867
26 CAETITE III 2021 3 5.520
27 CAETITE III 2021 4 -6.326
28 CAETITE III 2021 5 -0.578
29 CAETITE III 2021 6 -12.040
30 CAETITE III 2021 7 -10.690
31 CAETITE III 2021 8 3.680
谢谢，
您可以匹配df1和df2的month，使用.map()得到df2的平均风速。然后，将df1的风速除以刚才映射的平均风速减1再乘以100，得到var需要的值，如下：
df1['var'] = (df1['wind_speed_ms'] / df1['month'].map(df2.set_index('month')['wind_speed_ms']) - 1) * 100
结果：
print(df1)
plant_name year month wind_speed_ms var
0 CAETITE III 2019 1 8.188548 21.046291
1 CAETITE III 2019 2 5.506577 -16.881179
2 CAETITE III 2019 3 6.449032 -3.191599
3 CAETITE III 2019 4 6.077875 -14.728051
4 CAETITE III 2019 5 6.932110 -5.831188
5 CAETITE III 2019 6 8.905403 5.811227
6 CAETITE III 2019 7 8.180067 -6.472571
7 CAETITE III 2019 8 9.252164 3.775932
8 CAETITE III 2019 9 8.850250 0.286824
9 CAETITE III 2019 10 8.013858 -0.290859
10 CAETITE III 2019 11 6.940972 1.914880
11 CAETITE III 2019 12 7.244973 12.585261
12 CAETITE III 2020 1 5.690524 -15.880468
13 CAETITE III 2020 2 5.782543 -12.715621
14 CAETITE III 2020 3 6.387433 -4.116281
15 CAETITE III 2020 4 7.097597 -0.421458
16 CAETITE III 2020 5 7.142957 -2.966950
17 CAETITE III 2020 6 8.653625 2.819679
18 CAETITE III 2020 7 8.509987 -2.700405
19 CAETITE III 2020 8 9.164261 2.789977
20 CAETITE III 2020 9 9.219514 4.471148
21 CAETITE III 2020 10 7.110269 -11.533394
22 CAETITE III 2020 11 6.884597 1.087121
23 CAETITE III 2020 12 6.144812 -4.510995
24 CAETITE III 2021 1 7.082836 4.701228
25 CAETITE III 2021 2 5.706280 -13.866770
26 CAETITE III 2021 3 7.029341 5.519598
27 CAETITE III 2021 4 6.676764 -6.325701
28 CAETITE III 2021 5 7.318804 -0.578167
29 CAETITE III 2021 6 7.403028 -12.039525
30 CAETITE III 2021 7 7.811183 -10.690235
31 CAETITE III 2021 8 9.243642 3.680346
我在数据框 df1 中有月度数据，我需要除以第二个数据框 df2 来计算可变性。我的 df1 如下所示。我试图找到答案，但看不出如何将每个月度值除以此处给出的独特的月度长期 (2019-2021) 平均值。
index plant_name year month wind_speed_ms
324 CAETITE III 2019 1 8.188548
325 CAETITE III 2019 2 5.506577
326 CAETITE III 2019 3 6.449032
327 CAETITE III 2019 4 6.077875
328 CAETITE III 2019 5 6.932110
329 CAETITE III 2019 6 8.905403
330 CAETITE III 2019 7 8.180067
331 CAETITE III 2019 8 9.252164
332 CAETITE III 2019 9 8.850250
333 CAETITE III 2019 10 8.013858
334 CAETITE III 2019 11 6.940972
335 CAETITE III 2019 12 7.244973
336 CAETITE III 2020 1 5.690524
337 CAETITE III 2020 2 5.782543
338 CAETITE III 2020 3 6.387433
339 CAETITE III 2020 4 7.097597
340 CAETITE III 2020 5 7.142957
341 CAETITE III 2020 6 8.653625
342 CAETITE III 2020 7 8.509987
343 CAETITE III 2020 8 9.164261
344 CAETITE III 2020 9 9.219514
345 CAETITE III 2020 10 7.110269
346 CAETITE III 2020 11 6.884597
347 CAETITE III 2020 12 6.144812
348 CAETITE III 2021 1 7.082836
349 CAETITE III 2021 2 5.706280
350 CAETITE III 2021 3 7.029341
351 CAETITE III 2021 4 6.676764
352 CAETITE III 2021 5 7.318804
353 CAETITE III 2021 6 7.403028
354 CAETITE III 2021 7 7.811183
355 CAETITE III 2021 8 9.243642
我的第二个数据框 - df2 - 看起来像这样，这是月平均值或分母：
df2:
plant_name month wind_speed_ms
0 CAETITE III 1 6.764807
1 CAETITE III 2 6.624946
2 CAETITE III 3 6.661645
3 CAETITE III 4 7.127637
4 CAETITE III 5 7.361365
5 CAETITE III 6 8.416312
6 CAETITE III 7 8.746169
7 CAETITE III 8 8.915520
8 CAETITE III 9 8.824938
9 CAETITE III 10 8.037235
10 CAETITE III 11 6.810558
11 CAETITE III 12 6.435099
答案需要如下所示：
index plant_name year month var
0 CAETITE III 2019 1 21.046
1 CAETITE III 2019 2 -16.881
2 CAETITE III 2019 3 -3.192
3 CAETITE III 2019 4 -14.728
4 CAETITE III 2019 5 -5.831
5 CAETITE III 2019 6 5.811
6 CAETITE III 2019 7 -6.473
7 CAETITE III 2019 8 3.776
8 CAETITE III 2019 9 0.287
9 CAETITE III 2019 10 -0.291
10 CAETITE III 2019 11 1.915
11 CAETITE III 2019 12 12.585
12 CAETITE III 2020 1 -15.880
13 CAETITE III 2020 2 -12.716
14 CAETITE III 2020 3 -4.116
15 CAETITE III 2020 4 -0.421
16 CAETITE III 2020 5 -2.967
17 CAETITE III 2020 6 2.820
18 CAETITE III 2020 7 -2.700
19 CAETITE III 2020 8 2.790
20 CAETITE III 2020 9 4.471
21 CAETITE III 2020 10 -11.533
22 CAETITE III 2020 11 1.087
23 CAETITE III 2020 12 -4.511
24 CAETITE III 2021 1 4.701
25 CAETITE III 2021 2 -13.867
26 CAETITE III 2021 3 5.520
27 CAETITE III 2021 4 -6.326
28 CAETITE III 2021 5 -0.578
29 CAETITE III 2021 6 -12.040
30 CAETITE III 2021 7 -10.690
31 CAETITE III 2021 8 3.680
谢谢，
您可以匹配df1和df2的month，使用.map()得到df2的平均风速。然后，将df1的风速除以刚才映射的平均风速减1再乘以100，得到var需要的值，如下：
df1['var'] = (df1['wind_speed_ms'] / df1['month'].map(df2.set_index('month')['wind_speed_ms']) - 1) * 100
结果：
print(df1)
plant_name year month wind_speed_ms var
0 CAETITE III 2019 1 8.188548 21.046291
1 CAETITE III 2019 2 5.506577 -16.881179
2 CAETITE III 2019 3 6.449032 -3.191599
3 CAETITE III 2019 4 6.077875 -14.728051
4 CAETITE III 2019 5 6.932110 -5.831188
5 CAETITE III 2019 6 8.905403 5.811227
6 CAETITE III 2019 7 8.180067 -6.472571
7 CAETITE III 2019 8 9.252164 3.775932
8 CAETITE III 2019 9 8.850250 0.286824
9 CAETITE III 2019 10 8.013858 -0.290859
10 CAETITE III 2019 11 6.940972 1.914880
11 CAETITE III 2019 12 7.244973 12.585261
12 CAETITE III 2020 1 5.690524 -15.880468
13 CAETITE III 2020 2 5.782543 -12.715621
14 CAETITE III 2020 3 6.387433 -4.116281
15 CAETITE III 2020 4 7.097597 -0.421458
16 CAETITE III 2020 5 7.142957 -2.966950
17 CAETITE III 2020 6 8.653625 2.819679
18 CAETITE III 2020 7 8.509987 -2.700405
19 CAETITE III 2020 8 9.164261 2.789977
20 CAETITE III 2020 9 9.219514 4.471148
21 CAETITE III 2020 10 7.110269 -11.533394
22 CAETITE III 2020 11 6.884597 1.087121
23 CAETITE III 2020 12 6.144812 -4.510995
24 CAETITE III 2021 1 7.082836 4.701228
25 CAETITE III 2021 2 5.706280 -13.866770
26 CAETITE III 2021 3 7.029341 5.519598
27 CAETITE III 2021 4 6.676764 -6.325701
28 CAETITE III 2021 5 7.318804 -0.578167
29 CAETITE III 2021 6 7.403028 -12.039525
30 CAETITE III 2021 7 7.811183 -10.690235
31 CAETITE III 2021 8 9.243642 3.680346