python - 如何划分两个不同数据帧的列，而其他两个列中的值相同？
问题描述
我有一个时间序列数据集，在每个时间步长中释放的气体量不同，如下所示，每天监控数据，在 Date 中反映采样时间，在其他列中反映释放的气体量。
import pandas as pd
from statistics import mean
import numpy as np
Data = pd.read_csv('PTR 69.csv')
Data.columns = ['Date', 'H2', 'CH4', 'C2H6', 'C2H4', 'C2H2', 'CO', 'CO2', 'O2']
Data.dropna(how='all', axis=1, inplace=True)
Data.head()
它看起来像这样：
Date H2 CH4 C2H6 C2H4 C2H2 CO CO2 O2
0 2021-04-14 2:00 8.301259 10.889560 7.205929 3.485577 0.108262 318.616211 1659.179688 866.826721
1 2021-04-13 3:00 8.190150 10.224614 7.369829 3.561115 0.130052 318.895599 1641.014526 883.500305
2 2021-04-12 4:00 8.223248 10.297009 7.571199 3.479434 0.113566 315.364594 1636.670776 896.083679
3 2021-04-11 5:00 8.342580 10.233653 7.726023 3.474085 0.234786 316.315277 1641.205078 875.664856
4 2021-04-10 6:00 8.365788 9.825816 7.640978 3.621368 0.320388 320.200409 1658.575806 880.871399
5 2021-04-09 7:00 8.113251 11.198173 7.588203 3.561790 0.200721 318.738922 1651.639038 886.923401
6 2021-04-08 8:00 7.881397 7.967482 7.382273 3.528960 0.180016 315.252838 1625.236328 878.604309
7 2021-04-07 9:00 7.833044 6.773924 7.292545 3.475330 0.401435 317.085449 1628.325562 893.305664
8 2021-04-06 10:00 7.908926 9.419571 7.018494 3.347562 0.406113 317.643768 1620.742554 912.732422
9 2021-04-05 11:00 8.192807 9.262563 7.227449 3.275920 0.133978 312.931152 1601.240 845 932.079102
10 2021-04-04 12:00 8.086914 9.480316 6.515196 3.312712 0.000000 315.486816 1609.530884 928.141907
11 2021-04-03 13:00 7.984566 9.406860 6.712120 3.476949 0.336859 312.862793 1596.182495 938.904724
12 2021-04-02 14:00 8.077889 8.335327 7.443592 3.605910 0.416443 315.546539 1605.549438 928.619568
13 2021-04-01 15:00 7.996786 9.087573 7.950811 3.626776 0.745824 311.601471 1608.987183 897.747498
14 2021-03-31 16:00 8.433417 10.078784 6.567528 3.646854 0.682301 313.811615 1619.164673 825.123596
15 2021-03-30 17:00 8.445275 9.768773 7.460344 3.712297 0.353539 314.944672 1606.494751 811.027161
16 2021-03-29 18:00 8.398427 9.607062 7.446943 3.674934 0.287205 314.554596 1599.793823 828.780090
17 2021-03-28 19:00 8.272332 9.678397 7.303371 3.617573 0.430137 311.486664 1590.122192 828.557312
18 2021-03-27 20:00 8.478241 9.364383 7.153194 3.616118 0.548547 314.538849 1578.516235 821.565125
19 2021-03-26 21:00 8.452413 10.828227 6.825691 3.260484 0.642971 314.990082 1561.811890 826.468079
首先，我使用 [ pd.to_datetime] 并根据月份和年份分隔数据框，如您所见：
Data['Date'] = pd.to_datetime(Data['Date'])
# How long is the dataset
Data['Date'].max () - Data['Date'].min ()
Reults:
```python
Timedelta('1364 days 12:49:00')
Data['Month'] = Data['Date'].dt.month Data['Year'] = Data['Date'].dt.year Data.head()
Then like this:
```python
Date H2 CH4 C2H6 C2H4 C2H2 CO CO2 O2 Month Year
0 2021-04-14 02:00:00 8.301259 10.889560 7.205929 3.485577 0.108262 318.616211 1659.179688 866.826721 4 2021
1 2021-04-13 03:00:00 8.190150 10.224614 7.369829 3.561115 0.130052 318.895599 1641.014526 883.500305 4 2021
2 2021-04-12 04:00:00 8.223248 10.297009 7.571199 3.479434 0.113566 315.364594 1636.670776 896.083679 4 2021
3 2021-04-11 05:00:00 8.342580 10.233653 7.726023 3.474085 0.234786 316.315277 1641.205078 875.664856 4 2021
4 2021-04-10 06:00:00 8.365788 9.825816 7.640978 3.621368 0.320388 320.200409 1658.575806 880.871399 4 2021
5 2021-04-09 07:00:00 8.113251 11.198173 7.588203 3.561790 0.200721 318.738922 1651.639038 886.923401 4 2021
6 2021-04-08 08:00:00 7.881397 7.967482 7.382273 3.528960 0.180016 315.252838 1625.236328 878.604309 4 2021
7 2021-04-07 09:00:00 7.833044 6.773924 7.292545 3.475330 0.401435 317.085449 1628.325562 893.305664 4 2021
8 2021-04-06 10:00:00 7.908926 9.419571 7.018494 3.347562 0.406113 317.643768 1620.742554 912.732422 4 2021
9 2021-04-05 11:00:00 8.192807 9.262563 7.227449 3.275920 0.133978 312.931152 1601.240845 932.079102 4 2021
因此，另外两列 [ Month] 和 [ Year] 被添加到数据框中。我的问题：如何计算一个月内 H2 的变化率？我知道首先，我应该计算每个月和每年 H2 的平均值，因为我的数据是时间序列的。
Mean_month = Data.set_index('Date').groupby(pd.Grouper(freq = 'M'))['H2'].mean().reset_index()
我使用前面的步骤将日期转换为 [ pd.to_datetime]：
Mean_month['Date'] = pd.to_datetime(Mean_month['Date'])
Mean_month['Month_mean'] = Mean_month['Date'].dt.month
Mean_month['Year_mean'] = Mean_month['Date'].dt.year
Mean_month.head ()
看起来像这样：
Date H2 CH4 C2H2 C2H4 C2H6 CO CO2 O2 Month_mean Year_mean
0 2017-07-31 0.892207 0.797776 0.572518 0.119328 0.203212 23.137884 230.986328 1756.658813 7 2017
1 2017-08-31 NaN NaN NaN NaN NaN NaN NaN NaN 8 2017
2 2017-09-30 NaN NaN NaN NaN NaN NaN NaN NaN 9 2017
3 2017-10-31 NaN NaN NaN NaN NaN NaN NaN NaN 10 2017
4 2017-11-30 NaN NaN NaN NaN NaN NaN NaN NaN 11 2017
5 2017-12-31 NaN NaN NaN NaN NaN NaN NaN NaN 12 2017
6 2018-01-31 NaN NaN NaN NaN NaN NaN NaN NaN 1 2018
7 2018-02-28 NaN NaN NaN NaN NaN NaN NaN NaN 2 2018
8 2018-03-31 NaN NaN NaN NaN NaN NaN NaN NaN 3 2018
9 2018-04-30 NaN NaN NaN NaN NaN NaN NaN NaN 4 2018
10 2018-05-31 NaN NaN NaN NaN NaN NaN NaN NaN 5 2018
11 2018-06-30 3.376091 1.780959 0.488345 0.431397 1.777461 59.424690 246.135108 2927.244192 6 2018
12 2018-07-31 3.785872 1.710799 0.479277 0.405084 2.416031 63.220747 256.035651 2971.905932 7 2018
13 2018-08-31 3.789915 1.874313 0.444453 0.339609 2.516580 67.629768 264.437564 3016.440033 8 2018
14 2018-09-30 3.882403 1.842717 0.443967 0.342131 2.848867 71.592693 271.972792 3073.598901 9 2018
15 2018-10-31 3.858354 2.037401 0.364234 0.358209 2.651448 75.036622 274.889362 3150.082060 10 2018
16 2018-11-30 3.861638 1.854492 0.276273 0.289241 2.813399 78.563868 289.631986 3176.243186 11 2018
17 2018-12-31 5.029865 2.526096 0.232814 0.510899 3.423260 95.641880 409.359902 2831.721010 12 2018
18 2019-01-31 6.103601 2.528294 0.177558 0.612607 4.039948 116.639744 516.362618 2423.434258 1 2019
19 2019-02-28 7.480646 3.316433 0.239254 0.959470 5.319684 142.571229 662.409360 1877.447767 2 2019
20 2019-03-31 8.363644 3.779225 0.213011 1.171834 6.179431 167.295488 815.904473 1415.431158 3 2019
21 2019-04-30 9.523452 4.620810 0.233048 1.703750 8.359211 195.914846 1044.554593 898.940531 4 2019
22 2019-05-31 10.118435 5.524447 0.311802 1.904199 9.275237 213.531002 1178.495602 657.617859 5 2019
23 2019-06-30 10.283766 6.186843 0.377420 2.165453 10.729356 226.061226 1226.489872 589.417023 6 2019
24 2019-07-31 9.943331 6.648062 0.492584 2.326774 11.791042 234.309877 1257.822071 572.162592 7 2019
25 2019-08-31 9.812387 6.681962 0.510871 2.483979 13.067311 243.440762 1302.643938 568.994610 8 2019
26 2019-09-30 9.661653 7.323367 0.420726 2.628199 13.308826 252.133648 1383.259943 550.533951 9 2019
27 2019-10-31 9.246261 7.644706 0.372446 2.673924 13.880747 257.093790 1407.996110 565.502500 10 2019
28 2019-11-30 8.226894 6.606762 0.411812 2.290050 12.958136 257.590110 1306.817593 654.086494 11 2019
29 2019-12-31 7.985734 7.461197 0.314830 2.417687 13.255049 259.519881 1309.507549 684.085808 12 2019
30 2020-01-31 7.754674 7.804206 0.336518 2.506526 13.554615 262.188585 1312.052006 700.065050 1 2020
31 2020-02-29 7.662918 7.607357 0.283796 2.483387 13.803671 264.348120 1300.252926 710.281917 2 2020
32 2020-03-31 7.602619 8.326974 0.278294 2.629290 13.983202 268.429411 1351.023144 698.012543 3 2020
33 2020-04-30 7.585870 8.028798 0.389348 2.856049 15.635886 273.859451 1426.279447 703.866225 4 2020
34 2020-05-31 7.752543 8.622809 0.329810 2.974434 16.470193 279.636700 1484.100789 685.164897 5 2020
35 2020-06-30 7.935418 8.632543 0.408732 3.410121 18.330232 287.545439 1593.554077 653.294214 6 2020
36 2020-07-31 8.226212 9.180892 0.474289 3.646311 19.746735 295.059049 1688.793476 613.164837 7 2020
37 2020-08-31 8.535027 9.583940 0.517722 3.860195 20.853958 303.025472 1759.655769 597.264223 8 2020
38 2020-09-30 8.782468 9.318198 0.447619 3.780273 21.613501 309.644693 1790.096266 594.891798 9 2020
39 2020-10-31 8.766880 17.531840 0.436720 3.671641 21.794714 312.511920 1783.446248 622.681765 10 2020
40 2020-11-30 8.535022 9.695740 0.427224 3.352291 11.561881 311.624202 1676.413354 713.680609 11 2020
41 2020-12-31 8.374398 9.114723 0.340198 3.351321 6.768138 312.902290 1642.077442 766.767532 12 2020
42 2021-01-31 8.238818 9.373566 0.344173 3.372903 6.670032 313.475182 1604.747685 788.205679 1 2021
43 2021-02-28 8.191080 9.900578 0.334562 3.352319 6.802692 314.076140 1572.294619 815.143081 2 2021
44 2021-03-31 8.317389 9.627182 0.385551 3.209554 5.862067 312.134351 1484.145511 867.169165 3 2021
45 2021-04-30 8.107043 9.457317 0.266317 3.488106 7.331760 316.181560 1627.434300 900.000397 4 2021
由于 [ Mean_month] 数据框按升序排序，我再次使用：
Srt_Mean = Mean_month.sort_values(['Date'],ascending=False)
Srt_Mean
结果是：
Date H2 CH4 C2H2 C2H4 C2H6 CO CO2 O2 Month_mean Year_mean
45 2021-04-30 8.107043 9.457317 0.266317 3.488106 7.331760 316.181560 1627.434300 900.000397 4 2021
44 2021-03-31 8.317389 9.627182 0.385551 3.209554 5.862067 312.134351 1484.145511 867.169165 3 2021
43 2021-02-28 8.191080 9.900578 0.334562 3.352319 6.802692 314.076140 1572.294619 815.143081 2 2021
42 2021-01-31 8.238818 9.373566 0.344173 3.372903 6.670032 313.475182 1604.747685 788.205679 1 2021
41 2020-12-31 8.374398 9.114723 0.340198 3.351321 6.768138 312.902290 1642.077442 766.767532 12 2020
40 2020-11-30 8.535022 9.695740 0.427224 3.352291 11.561881 311.624202 1676.413354 713.680609 11 2020
39 2020-10-31 8.766880 17.531840 0.436720 3.671641 21.794714 312.511920 1783.446248 622.681765 10 2020
38 2020-09-30 8.782468 9.318198 0.447619 3.780273 21.613501 309.644693 1790.096266 594.891798 9 2020
37 2020-08-31 8.535027 9.583940 0.517722 3.860195 20.853958 303.025472 1759.655769 597.264223 8 2020
36 2020-07-31 8.226212 9.180892 0.474289 3.646311 19.746735 295.059049 1688.793476 613.164837 7 2020
35 2020-06-30 7.935418 8.632543 0.408732 3.410121 18.330232 287.545439 1593.554077 653.294214 6 2020
34 2020-05-31 7.752543 8.622809 0.329810 2.974434 16.470193 279.636700 1484.100789 685.164897 5 2020
33 2020-04-30 7.585870 8.028798 0.389348 2.856049 15.635886 273.859451 1426.279447 703.866225 4 2020
32 2020-03-31 7.602619 8.326974 0.278294 2.629290 13.983202 268.429411 1351.023144 698.012543 3 2020
31 2020-02-29 7.662918 7.607357 0.283796 2.483387 13.803671 264.348120 1300.252926 710.281917 2 2020
30 2020-01-31 7.754674 7.804206 0.336518 2.506526 13.554615 262.188585 1312.052006 700.065050 1 2020
29 2019-12-31 7.985734 7.461197 0.314830 2.417687 13.255049 259.519881 1309.507549 684.085808 12 2019
28 2019-11-30 8.226894 6.606762 0.411812 2.290050 12.958136 257.590110 1306.817593 654.086494 11 2019
27 2019-10-31 9.246261 7.644706 0.372446 2.673924 13.880747 257.093790 1407.996110 565.502500 10 2019
26 2019-09-30 9.661653 7.323367 0.420726 2.628199 13.308826 252.133648 1383.259943 550.533951 9 2019
25 2019-08-31 9.812387 6.681962 0.510871 2.483979 13.067311 243.440762 1302.643938 568.994610 8 2019
24 2019-07-31 9.943331 6.648062 0.492584 2.326774 11.791042 234.309877 1257.822071 572.162592 7 2019
23 2019-06-30 10.283766 6.186843 0.377420 2.165453 10.729356 226.061226 1226.489872 589.417023 6 2019
22 2019-05-31 10.118435 5.524447 0.311802 1.904199 9.275237 213.531002 1178.495602 657.617859 5 2019
21 2019-04-30 9.523452 4.620810 0.233048 1.703750 8.359211 195.914846 1044.554593 898.940531 4 2019
20 2019-03-31 8.363644 3.779225 0.213011 1.171834 6.179431 167.295488 815.904473 1415.431158 3 2019
19 2019-02-28 7.480646 3.316433 0.239254 0.959470 5.319684 142.571229 662.409360 1877.447767 2 2019
18 2019-01-31 6.103601 2.528294 0.177558 0.612607 4.039948 116.639744 516.362618 2423.434258 1 2019
17 2018-12-31 5.029865 2.526096 0.232814 0.510899 3.423260 95.641880 409.359902 2831.721010 12 2018
16 2018-11-30 3.861638 1.854492 0.276273 0.289241 2.813399 78.563868 289.631986 3176.243186 11 2018
15 2018-10-31 3.858354 2.037401 0.364234 0.358209 2.651448 75.036622 274.889362 3150.082060 10 2018
14 2018-09-30 3.882403 1.842717 0.443967 0.342131 2.848867 71.592693 271.972792 3073.598901 9 2018
13 2018-08-31 3.789915 1.874313 0.444453 0.339609 2.516580 67.629768 264.437564 3016.440033 8 2018
12 2018-07-31 3.785872 1.710799 0.479277 0.405084 2.416031 63.220747 256.035651 2971.905932 7 2018
11 2018-06-30 3.376091 1.780959 0.488345 0.431397 1.777461 59.424690 246.135108 2927.244192 6 2018
10 2018-05-31 NaN NaN NaN NaN NaN NaN NaN NaN 5 2018
9 2018-04-30 NaN NaN NaN NaN NaN NaN NaN NaN 4 2018
8 2018-03-31 NaN NaN NaN NaN NaN NaN NaN NaN 3 2018
7 2018-02-28 NaN NaN NaN NaN NaN NaN NaN NaN 2 2018
6 2018-01-31 NaN NaN NaN NaN NaN NaN NaN NaN 1 2018
5 2017-12-31 NaN NaN NaN NaN NaN NaN NaN NaN 12 2017
4 2017-11-30 NaN NaN NaN NaN NaN NaN NaN NaN 11 2017
3 2017-10-31 NaN NaN NaN NaN NaN NaN NaN NaN 10 2017
2 2017-09-30 NaN NaN NaN NaN NaN NaN NaN NaN 9 2017
1 2017-08-31 NaN NaN NaN NaN NaN NaN NaN NaN 8 2017
0 2017-07-31 0.892207 0.797776 0.572518 0.119328 0.203212 23.137884 230.986328 1756.658813 7 2017
我还将两个数据帧的索引定义为最后，我想将第一个数据帧中的 [ H2] 列划分为第一个数据帧中的 [ H2] 列：
df_Data = Data.set_index(['Month', 'Year'])
df_Data.head (50)
df_Srt_Mean = Srt_Mean.set_index (['Month_mean', 'Year_mean'])
df_Srt_Mean.head (50)
Date H2 CH4 C2H6 C2H4 C2H2 CO CO2 O2
Month Year
4 2021 2021-04-14 02:00:00 8.301259 10.889560 7.205929 3.485577 0.108262 318.616211 1659.179688 866.826721
2021 2021-04-13 03:00:00 8.190150 10.224614 7.369829 3.561115 0.130052 318.895599 1641.014526 883.500305
2021 2021-04-12 04:00:00 8.223248 10.297009 7.571199 3.479434 0.113566 315.364594 1636.670776 896.083679
2021 2021-04-11 05:00:00 8.342580 10.233653 7.726023 3.474085 0.234786 316.315277 1641.205078 875.664856
2021 2021-04-10 06:00:00 8.365788 9.825816 7.640978 3.621368 0.320388 320.200409 1658.575806 880.871399
2021 2021-04-09 07:00:00 8.113251 11.198173 7.588203 3.561790 0.200721 318.738922 1651.639038 886.923401
2021 2021-04-08 08:00:00 7.881397 7.967482 7.382273 3.528960 0.180016 315.252838 1625.236328 878.604309
2021 2021-04-07 09:00:00 7.833044 6.773924 7.292545 3.475330 0.401435 317.085449 1628.325562 893.305664
2021 2021-04-06 10:00:00 7.908926 9.419571 7.018494 3.347562 0.406113 317.643768 1620.742554 912.732422
2021 2021-04-05 11:00:00 8.192807 9.262563 7.227449 3.275920 0.133978 312.931152 1601.240845 932.079102
2021 2021-04-04 12:00:00 8.086914 9.480316 6.515196 3.312712 0.000000 315.486816 1609.530884 928.141907
2021 2021-04-03 13:00:00 7.984566 9.406860 6.712120 3.476949 0.336859 312.862793 1596.182495 938.904724
2021 2021-04-02 14:00:00 8.077889 8.335327 7.443592 3.605910 0.416443 315.546539 1605.549438 928.619568
2021 2021-04-01 15:00:00 7.996786 9.087573 7.950811 3.626776 0.745824 311.601471 1608.987183 897.747498
3 2021 2021-03-31 16:00:00 8.433417 10.078784 6.567528 3.646854 0.682301 313.811615 1619.164673 825.123596
2021 2021-03-30 17:00:00 8.445275 9.768773 7.460344 3.712297 0.353539 314.944672 1606.494751 811.027161
2021 2021-03-29 18:00:00 8.398427 9.607062 7.446943 3.674934 0.287205 314.554596 1599.793823 828.780090
2021 2021-03-28 19:00:00 8.272332 9.678397 7.303371 3.617573 0.430137 311.486664 1590.122192 828.557312
2021 2021-03-27 20:00:00 8.478241 9.364383 7.153194 3.616118 0.548547 314.538849 1578.516235 821.565125
2021 2021-03-26 21:00:00 8.452413 10.828227 6.825691 3.260484 0.642971 314.990082 1561.811890 826.468079
2021 2021-03-25 22:00:00 8.420037 10.468951 6.614395 3.279383 0.442519 314.821197 1538.289673 835.261902
2021 2021-03-24 23:00:00 8.290853 9.943011 5.952219 3.263231 0.077059 313.060883 1498.917969 859.999023
2021 2021-03-24 00:00:00 8.053485 9.717534 5.773523 3.210894 0.477235 309.256561 1461.547974 867.371643
2021 2021-03-23 01:00:00 8.813514 10.700623 5.444063 2.965948 0.421797 312.926971 1437.077026 867.363709
2021 2021-03-22 02:00:00 8.149124 9.727563 4.518490 2.958276 0.368664 311.796661 1420.417358 916.602539
2021 2021-03-21 03:00:00 8.169525 8.859634 5.212233 3.129839 0.416121 312.702301 1419.987427 904.523865
2021 2021-03-20 04:00:00 7.999515 8.994797 5.137753 3.148643 0.475540 307.183685 1420.932739 913.971130
2021 2021-03-19 05:00:00 8.183563 10.373088 4.949068 3.037351 0.584536 312.275482 1440.424683 895.362122
2021 2021-03-18 06:00:00 9.914630 10.722699 4.891720 3.121366 0.364292 312.476959 1446.715210 889.638367
2021 2021-03-17 07:00:00 8.063797 9.449814 4.965353 3.158536 0.332817 307.930389 1443.011108 883.420349
2021 2021-03-16 08:00:00 8.858215 9.454753 5.053194 3.093672 0.249709 313.467071 1456.114624 902.091492
2021 2021-03-15 09:00:00 8.146770 8.423282 5.213614 3.038460 0.228652 312.719238 1443.799438 900.013672
2021 2021-03-14 10:00:00 8.160034 14.032947 5.426914 2.981697 0.391028 313.519440 1459.276245 891.870300
2021 2021-03-13 11:00:00 7.876873 5.985085 5.602545 2.998276 0.607312 311.964203 1447.259399 886.466492
2021 2021-03-12 12:00:00 8.299830 9.434842 5.768423 2.931913 0.374833 312.165375 1450.703979 893.731873
2021 2021-03-11 13:00:00 8.258931 9.164996 5.773973 2.917338 0.367790 312.416412 1447.783203 884.459534
2021 2021-03-10 14:00:00 8.285775 9.396652 5.687450 3.018778 0.367582 312.764160 1452.421875 883.869568
2021 2021-03-09 15:00:00 8.069007 9.174088 5.641685 3.134619 0.282684 307.792206 1445.247192 887.044922
2021 2021-03-08 16:00:00 8.150889 8.341151 5.952223 3.310198 0.276260 310.551758 1453.108765 881.680664
2021 2021-03-07 17:00:00 8.148776 8.571256 5.962189 3.365770 0.321035 311.439789 1450.016235 881.019348
2021 2021-03-06 18:00:00 8.235992 9.840173 5.190016 3.325249 0.390993 313.732513 1476.067505 880.206055
2021 2021-03-05 19:00:00 8.041183 8.705338 6.181820 3.528234 0.299884 308.838959 1456.264038 857.722656
2021 2021-03-04 20:00:00 8.286016 8.883926 5.667931 3.196103 0.350631 314.590729 1479.576538 861.197266
2021 2021-03-03 21:00:00 8.245660 9.066014 5.785030 3.191303 0.378657 313.044281 1479.022095 850.414856
2021 2021-03-02 22:00:00 8.386712 9.401718 6.162895 3.043518 0.363813 312.941315 1493.645142 840.161438
2021 2021-03-01 23:00:00 8.231705 10.864131 6.184435 3.010111 0.217610 309.424164 1501.307983 834.103943
2021 2021-03-01 00:00:00 8.253326 10.673305 5.977970 3.028328 0.349412 310.304413 1501.962891 825.492371
2 2021 2021-02-28 01:00:00 8.313703 10.718976 5.379131 3.017091 0.303016 313.576935 1511.731079 837.980774
2021 2021-02-27 02:00:00 8.315781 10.122794 5.632700 3.183661 0.419333 309.140228 1502.215210 855.478516
2021 2021-02-26 03:00:00 7.974852 10.396459 6.063492 3.239314 0.497979 314.248688 1523.176880 852.766907
Date H2 CH4 C2H2 C2H4 C2H6 CO CO2 O2
Month_mean Year_mean
4 2021 2021-04-30 8.107043 9.457317 0.266317 3.488106 7.331760 316.181560 1627.434300 900.000397
3 2021 2021-03-31 8.317389 9.627182 0.385551 3.209554 5.862067 312.134351 1484.145511 867.169165
2 2021 2021-02-28 8.191080 9.900578 0.334562 3.352319 6.802692 314.076140 1572.294619 815.143081
1 2021 2021-01-31 8.238818 9.373566 0.344173 3.372903 6.670032 313.475182 1604.747685 788.205679
12 2020 2020-12-31 8.374398 9.114723 0.340198 3.351321 6.768138 312.902290 1642.077442 766.767532
11 2020 2020-11-30 8.535022 9.695740 0.427224 3.352291 11.561881 311.624202 1676.413354 713.680609
10 2020 2020-10-31 8.766880 17.531840 0.436720 3.671641 21.794714 312.511920 1783.446248 622.681765
9 2020 2020-09-30 8.782468 9.318198 0.447619 3.780273 21.613501 309.644693 1790.096266 594.891798
8 2020 2020-08-31 8.535027 9.583940 0.517722 3.860195 20.853958 303.025472 1759.655769 597.264223
7 2020 2020-07-31 8.226212 9.180892 0.474289 3.646311 19.746735 295.059049 1688.793476 613.164837
6 2020 2020-06-30 7.935418 8.632543 0.408732 3.410121 18.330232 287.545439 1593.554077 653.294214
5 2020 2020-05-31 7.752543 8.622809 0.329810 2.974434 16.470193 279.636700 1484.100789 685.164897
4 2020 2020-04-30 7.585870 8.028798 0.389348 2.856049 15.635886 273.859451 1426.279447 703.866225
3 2020 2020-03-31 7.602619 8.326974 0.278294 2.629290 13.983202 268.429411 1351.023144 698.012543
2 2020 2020-02-29 7.662918 7.607357 0.283796 2.483387 13.803671 264.348120 1300.252926 710.281917
1 2020 2020-01-31 7.754674 7.804206 0.336518 2.506526 13.554615 262.188585 1312.052006 700.065050
12 2019 2019-12-31 7.985734 7.461197 0.314830 2.417687 13.255049 259.519881 1309.507549 684.085808
11 2019 2019-11-30 8.226894 6.606762 0.411812 2.290050 12.958136 257.590110 1306.817593 654.086494
10 2019 2019-10-31 9.246261 7.644706 0.372446 2.673924 13.880747 257.093790 1407.996110 565.502500
9 2019 2019-09-30 9.661653 7.323367 0.420726 2.628199 13.308826 252.133648 1383.259943 550.533951
8 2019 2019-08-31 9.812387 6.681962 0.510871 2.483979 13.067311 243.440762 1302.643938 568.994610
7 2019 2019-07-31 9.943331 6.648062 0.492584 2.326774 11.791042 234.309877 1257.822071 572.162592
6 2019 2019-06-30 10.283766 6.186843 0.377420 2.165453 10.729356 226.061226 1226.489872 589.417023
5 2019 2019-05-31 10.118435 5.524447 0.311802 1.904199 9.275237 213.531002 1178.495602 657.617859
4 2019 2019-04-30 9.523452 4.620810 0.233048 1.703750 8.359211 195.914846 1044.554593 898.940531
3 2019 2019-03-31 8.363644 3.779225 0.213011 1.171834 6.179431 167.295488 815.904473 1415.431158
2 2019 2019-02-28 7.480646 3.316433 0.239254 0.959470 5.319684 142.571229 662.409360 1877.447767
1 2019 2019-01-31 6.103601 2.528294 0.177558 0.612607 4.039948 116.639744 516.362618 2423.434258
12 2018 2018-12-31 5.029865 2.526096 0.232814 0.510899 3.423260 95.641880 409.359902 2831.721010
11 2018 2018-11-30 3.861638 1.854492 0.276273 0.289241 2.813399 78.563868 289.631986 3176.243186
10 2018 2018-10-31 3.858354 2.037401 0.364234 0.358209 2.651448 75.036622 274.889362 3150.082060
9 2018 2018-09-30 3.882403 1.842717 0.443967 0.342131 2.848867 71.592693 271.972792 3073.598901
8 2018 2018-08-31 3.789915 1.874313 0.444453 0.339609 2.516580 67.629768 264.437564 3016.440033
7 2018 2018-07-31 3.785872 1.710799 0.479277 0.405084 2.416031 63.220747 256.035651 2971.905932
6 2018 2018-06-30 3.376091 1.780959 0.488345 0.431397 1.777461 59.424690 246.135108 2927.244192
5 2018 2018-05-31 NaN NaN NaN NaN NaN NaN NaN NaN
4 2018 2018-04-30 NaN NaN NaN NaN NaN NaN NaN NaN
3 2018 2018-03-31 NaN NaN NaN NaN NaN NaN NaN NaN
2 2018 2018-02-28 NaN NaN NaN NaN NaN NaN NaN NaN
1 2018 2018-01-31 NaN NaN NaN NaN NaN NaN NaN NaN
12 2017 2017-12-31 NaN NaN NaN NaN NaN NaN NaN NaN
11 2017 2017-11-30 NaN NaN NaN NaN NaN NaN NaN NaN
10 2017 2017-10-31 NaN NaN NaN NaN NaN NaN NaN NaN
9 2017 2017-09-30 NaN NaN NaN NaN NaN NaN NaN NaN
8 2017 2017-08-31 NaN NaN NaN NaN NaN NaN NaN NaN
7 2017 2017-07-31 0.892207 0.797776 0.572518 0.119328 0.203212 23.137884 230.986328 1756.658813
现在，对于每年的每个月，我有一个平均值，如何将第一个数据框的 H2 列除以包含一个数字的这一列。例如，对于 2021 年 4 月，我们有 30 天和一个平均值，5 月2021年，我们有31天1个平均值，根据这两个数据框的索引，应该进行这个划分。
如果您能帮助我找到解决方案，我将不胜感激。