Python熊猫：计算周数与两个月重叠
我有一个包含过去五年每日数据的数据框架。除了值列之外，数据框还包含日期字段和监管年列。我想创建两列：监管周数和监管月数。监管年度从4月1日开始，至3月31日结束。因此，我使用以下代码生成监管周数和月数：Python熊猫：计算周数与两个月重叠,python,python-3.x,pandas,Python,Python 3.x,Pandas,我有一个包含过去五年每日数据的数据框架。除了值列之外，数据框还包含日期字段和监管年列。我想创建两列：监管周数和监管月数。监管年度从4月1日开始，至3月31日结束。因此，我使用以下代码生成监管周数和月数： df['Week'] = np.where(df['date'].dt.isocalendar().week > 13, df['date'].dt.isocalendar().week-13,df['date'].dt.isocalendar().week + 39) df['mont
df['Week'] = np.where(df['date'].dt.isocalendar().week > 13, df['date'].dt.isocalendar().week-13,df['date'].dt.isocalendar().week + 39)
df['month'] =df['date'].dt.month
months = ['Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec','Jan','Feb','Mar']
df['month'] = pd.CategoricalIndex(df['month'], ordered=True, categories=months)
df['month number'] = df['month'].apply(lambda x: months.index(x)+1)
RY month Week Value 1 Value 2 Value 3 Value 4 month number
2016 Apr 1 0.00000 0.00000 0.000000 0.00000 1
2016 Apr 2 1.31394 0.02961 1.313940 0.02961 1
2016 Apr 3 4.98354 0.07146 4.983540 0.07146 1
2016 Apr 4 4.30606 0.05742 4.306060 0.05742 1
2016 Apr 5 1.94634 0.01958 1.946340 0.01958 1
2016 May 5 0.25342 0.01625 0.253420 0.01625 2
2016 May 6 0.64051 0.00777 0.640510 0.00777 2
2016 May 7 1.26451 0.02994 1.264510 0.02994 2
2016 May 8 2.71035 0.08150 2.194947 0.08150 2
2016 May 9 11.95120 0.13386 1.624328 0.13386 2
2016 Jun 10 6.93051 0.08126 6.930510 0.08126 3
2016 Jun 11 1.18872 0.03953 1.188720 0.03953 3
2016 Jun 12 3.19961 0.05760 0.924562 0.05760 3
2016 Jun 13 3.90429 0.04985 0.956445 0.04985 3
2016 Jun 14 0.84002 0.01738 0.840020 0.01738 3
2016 Jul 14 0.07358 0.00562 0.073580 0.00562 4
2016 Jul 15 0.78253 0.03014 0.782530 0.03014 4
2016 Jul 16 1.23036 0.01816 1.230360 0.01816 4
2016 Jul 17 0.62948 0.01341 0.629480 0.01341 4
2016 Jul 18 0.45513 0.00552 0.455130 0.00552 4
Week Value 1 Value 2 Value 3 Value 4
1 3.013490 0.039740 1.348016 0.039740
2 3.094456 0.045142 3.094456 0.045142
3 1.615948 0.027216 1.615948 0.027216
4 2.889245 0.043998 1.903319 0.043998
5 0.431549 0.009679 0.431549 0.009679
6 1.045670 0.017302 1.045670 0.017302
7 2.444196 0.034304 2.444196 0.034304
8 1.041210 0.026464 0.938129 0.026464
9 2.068607 0.030550 0.921176 0.030550
10 2.400118 0.051476 2.400118 0.051476
11 1.738332 0.035362 1.738332 0.035362
12 1.369790 0.038576 0.914780 0.038576
13 1.921781 0.021218 0.749460 0.021218
14 1.471432 0.027367 1.471432 0.027367
15 2.722526 0.053794 1.676559 0.053794
16 3.132406 0.043520 1.195321 0.043520
17 0.733952 0.021142 0.733952 0.021142
18 0.645236 0.014454 0.645236 0.014454
19 2.466326 0.049704 0.879481 0.049704
20 2.111326 0.013262 0.682253 0.013262
21 1.301004 0.023048 1.301004 0.023048
22 0.705360 0.023439 0.705360 0.023439
23 1.323438 0.019103 1.323438 0.019103
24 0.569906 0.012540 0.569906 0.012540
25 7.898792 0.034246 1.382349 0.034246
26 0.896413 0.013013 0.896413 0.013013
27 4.478349 0.039749 1.703887 0.039749
28 5.807160 0.052526 2.036502 0.052526
29 3.308176 0.043984 2.117939 0.043984
30 1.991078 0.046058 1.991078 0.046058
31 0.806589 0.016945 0.806589 0.016945
32 2.091860 0.029234 2.091860 0.029234
33 1.149280 0.025194 1.149280 0.025194
34 4.746376 0.067742 2.863484 0.067742
35 5.128558 0.029608 1.537541 0.029608
36 2.765563 0.052125 2.765563 0.052125
37 2.314376 0.036046 2.314376 0.036046
38 2.552290 0.030626 1.483397 0.030626
39 1.456778 0.037448 1.456778 0.037448
40 1.212090 0.024698 1.212090 0.024698
41 4.729104 0.037646 1.296358 0.037646
42 3.412830 0.053132 3.412830 0.053132
43 8.916526 0.050044 1.839411 0.050044
44 2.450281 0.029806 0.942205 0.029806
45 2.156186 0.024064 2.156186 0.024064
46 2.336330 0.042538 2.336330 0.042538
47 1.798326 0.025270 1.798326 0.025270
48 1.352004 0.018382 1.352004 0.018382
49 10.220510 0.073480 1.607830 0.073480
50 2.575344 0.047760 2.575344 0.047760
51 1.226056 0.028676 1.226056 0.028676
52 0.470392 0.009991 0.466561 0.009991
mean_df = df.groupby('Week')['Value1','Value2','Value3','Value4'].mean().reset_index()
RY month Week Value 1 Value 2 Value 3 Value 4 month number
2016 Apr 1 0.00000 0.00000 0.000000 0.00000 1
2016 Apr 2 1.31394 0.02961 1.313940 0.02961 1
2016 Apr 3 4.98354 0.07146 4.983540 0.07146 1
2016 Apr 4 4.30606 0.05742 4.306060 0.05742 1
2016 Apr 5 1.94634 0.01958 1.946340 0.01958 1
2016 May 5 0.25342 0.01625 0.253420 0.01625 2
2016 May 6 0.64051 0.00777 0.640510 0.00777 2
2016 May 7 1.26451 0.02994 1.264510 0.02994 2
2016 May 8 2.71035 0.08150 2.194947 0.08150 2
2016 May 9 11.95120 0.13386 1.624328 0.13386 2
2016 Jun 10 6.93051 0.08126 6.930510 0.08126 3
2016 Jun 11 1.18872 0.03953 1.188720 0.03953 3
2016 Jun 12 3.19961 0.05760 0.924562 0.05760 3
2016 Jun 13 3.90429 0.04985 0.956445 0.04985 3
2016 Jun 14 0.84002 0.01738 0.840020 0.01738 3
2016 Jul 14 0.07358 0.00562 0.073580 0.00562 4
2016 Jul 15 0.78253 0.03014 0.782530 0.03014 4
2016 Jul 16 1.23036 0.01816 1.230360 0.01816 4
2016 Jul 17 0.62948 0.01341 0.629480 0.01341 4
2016 Jul 18 0.45513 0.00552 0.455130 0.00552 4
Week Value 1 Value 2 Value 3 Value 4
1 3.013490 0.039740 1.348016 0.039740
2 3.094456 0.045142 3.094456 0.045142
3 1.615948 0.027216 1.615948 0.027216
4 2.889245 0.043998 1.903319 0.043998
5 0.431549 0.009679 0.431549 0.009679
6 1.045670 0.017302 1.045670 0.017302
7 2.444196 0.034304 2.444196 0.034304
8 1.041210 0.026464 0.938129 0.026464
9 2.068607 0.030550 0.921176 0.030550
10 2.400118 0.051476 2.400118 0.051476
11 1.738332 0.035362 1.738332 0.035362
12 1.369790 0.038576 0.914780 0.038576
13 1.921781 0.021218 0.749460 0.021218
14 1.471432 0.027367 1.471432 0.027367
15 2.722526 0.053794 1.676559 0.053794
16 3.132406 0.043520 1.195321 0.043520
17 0.733952 0.021142 0.733952 0.021142
18 0.645236 0.014454 0.645236 0.014454
19 2.466326 0.049704 0.879481 0.049704
20 2.111326 0.013262 0.682253 0.013262
21 1.301004 0.023048 1.301004 0.023048
22 0.705360 0.023439 0.705360 0.023439
23 1.323438 0.019103 1.323438 0.019103
24 0.569906 0.012540 0.569906 0.012540
25 7.898792 0.034246 1.382349 0.034246
26 0.896413 0.013013 0.896413 0.013013
27 4.478349 0.039749 1.703887 0.039749
28 5.807160 0.052526 2.036502 0.052526
29 3.308176 0.043984 2.117939 0.043984
30 1.991078 0.046058 1.991078 0.046058
31 0.806589 0.016945 0.806589 0.016945
32 2.091860 0.029234 2.091860 0.029234
33 1.149280 0.025194 1.149280 0.025194
34 4.746376 0.067742 2.863484 0.067742
35 5.128558 0.029608 1.537541 0.029608
36 2.765563 0.052125 2.765563 0.052125
37 2.314376 0.036046 2.314376 0.036046
38 2.552290 0.030626 1.483397 0.030626
39 1.456778 0.037448 1.456778 0.037448
40 1.212090 0.024698 1.212090 0.024698
41 4.729104 0.037646 1.296358 0.037646
42 3.412830 0.053132 3.412830 0.053132
43 8.916526 0.050044 1.839411 0.050044
44 2.450281 0.029806 0.942205 0.029806
45 2.156186 0.024064 2.156186 0.024064
46 2.336330 0.042538 2.336330 0.042538
47 1.798326 0.025270 1.798326 0.025270
48 1.352004 0.018382 1.352004 0.018382
49 10.220510 0.073480 1.607830 0.073480
50 2.575344 0.047760 2.575344 0.047760
51 1.226056 0.028676 1.226056 0.028676
52 0.470392 0.009991 0.466561 0.009991
理想情况下，周数只分配给一个月。我不确定我是否以正确的方式计算了周数。有人遇到过同样的问题吗？任何关于如何计算周数的建议，以使周数不与两个月重叠。第5周可能包括4月和5月的几天。因此，不可能将第5周（作为一个整体）分配给一个月 也许您可以指定一周中第一天的月份