python - 如何为许多唯一 ID 创建多组子图

Question

我有 3214 个家庭的数据（.csv format）。每家每户unique id都有power usage读数5 min interval。数据样本如下所示：

sample_data.csv

datetime,House_id,power_Usage
2018-01-28 00:00:00,105714,409.365
2018-01-28 00:05:00,105714,409.18
2018-01-28 00:10:00,105714,2424.2399
2018-01-28 00:15:00,105714,225.4576
2018-01-28 00:20:00,105714,225.2768
2018-01-28 00:25:00,105714,2382.1309
2018-01-28 00:30:00,105714,410.685
2018-01-28 00:35:00,105714,317.1043
2018-01-28 00:40:00,105714,2486.6849
2018-01-28 00:45:00,105714,225.3672
2018-01-28 00:50:00,105714,224.644
2018-01-28 00:55:00,105714,2432.4368
2018-01-28 01:00:00,105714,317.2316
2018-01-28 01:05:00,105714,316.4678
2018-01-28 01:10:00,105714,2456.6872
2018-01-28 01:15:00,105714,192.4938
2018-01-28 01:20:00,105714,2471.535
2018-01-28 01:25:00,105714,288.8052
2018-01-28 01:30:00,105714,286.3112
2018-01-28 01:35:00,105714,2403.9675
2018-01-28 01:40:00,105714,282.933
2018-01-28 01:45:00,105714,2465.3581
2018-01-28 01:50:00,105714,289.0368
2018-01-28 01:55:00,105714,194.922
2018-01-28 02:05:00,105714,284.2938
2018-01-28 02:10:00,105714,292.3504
2018-01-28 02:15:00,105714,287.3444
2018-01-28 02:20:00,105714,192.5376
2018-01-28 02:25:00,105714,2347.572
2018-01-28 02:30:00,105714,283.9536
2018-01-28 02:35:00,105714,286.2994
2018-01-28 02:40:00,105714,292.7008
2018-01-28 02:45:00,105714,228.7521
2018-01-28 02:50:00,105714,2292.045
2018-01-28 02:55:00,105714,225.1286
2018-01-28 03:00:00,105714,2458.144
2018-01-28 03:05:00,105714,413.325
2018-01-28 03:10:00,105714,2429.9042
2018-01-28 03:15:00,105714,226.904
2018-01-28 03:20:00,105714,2386.8376
2018-01-28 03:25:00,105714,221.7956
2018-01-28 03:30:00,105714,415.996
2018-01-28 03:35:00,105714,403.627
2018-01-28 03:40:00,105714,314.628
2018-01-28 03:45:00,105714,184.1296
2018-01-28 03:50:00,105714,185.888
2018-01-28 03:55:00,105714,286.482
2018-01-28 04:00:00,105714,363.66
2018-01-28 04:05:00,105714,276.8832
2018-01-28 04:10:00,105714,274.407
2018-01-28 04:15:00,105714,183.8364
2018-01-28 04:20:00,105714,185.518
2018-01-28 04:25:00,105714,277.6936
2018-01-28 04:30:00,105714,276.6624
2018-01-28 04:35:00,105714,273.531
2018-01-28 04:40:00,105714,274.2975
2018-01-28 04:45:00,105714,185.444
2018-01-28 04:50:00,105714,277.25
2018-01-28 04:55:00,105714,185.444
2018-01-28 05:00:00,105714,2435.2544
2018-01-28 05:05:00,105714,273.093
2018-01-28 05:10:00,105714,2360.1338
2018-01-28 05:15:00,105714,281.935
2018-01-28 05:20:00,105714,274.78
2018-01-28 05:25:00,105714,151.5679
2018-01-28 05:30:00,105714,250.1482
2018-01-28 05:35:00,105714,2395.8746
2018-01-28 05:40:00,105714,240.092
2018-01-28 05:45:00,105714,2322.9184
2018-01-28 05:50:00,105714,151.75
2018-01-28 05:55:00,105714,149.5
2018-01-28 06:00:00,105714,268.035
2018-01-28 06:05:00,105714,244.2792
2018-01-28 06:10:00,105714,2475.0576
2018-01-28 06:15:00,105714,153.807
2018-01-28 06:20:00,105714,153.9298
2018-01-28 06:25:00,105714,151.7945
2018-01-28 06:30:00,105714,260.728
2018-01-28 06:35:00,105714,2546.4406
2018-01-28 06:40:00,105714,329.563
2018-01-28 06:45:00,105714,2319.5536
2018-01-28 06:50:00,105714,152.2356
2018-01-28 06:55:00,105714,2225.7051
2018-01-28 07:00:00,105714,294.5414
2018-01-28 07:05:00,105714,2726.5954
2018-01-28 07:10:00,105714,283.4541
2018-01-28 07:15:00,105714,183.1767
2018-01-28 07:20:00,105714,184.704
2018-01-28 07:25:00,105714,2714.9583
2018-01-28 07:30:00,105714,386.8514
2018-01-28 07:35:00,105714,2459.084
2018-01-28 07:40:00,105714,340.5038
2018-01-28 07:45:00,105714,2558.2018
2018-01-28 07:50:00,105714,243.8748
2018-01-28 07:55:00,105714,329.604
2018-01-28 08:00:00,105714,2653.7308
2018-01-28 08:05:00,105714,334.017
2018-01-28 08:10:00,105714,339.177
2018-01-28 08:15:00,105714,2689.1172
2018-01-28 08:20:00,105714,2614.5536
2018-01-28 08:25:00,105714,327.238
2018-01-28 08:30:00,105714,248.2488
2018-01-28 08:35:00,105714,417.2744
2018-01-28 08:40:00,105714,2667.6076
2018-01-28 08:45:00,105714,2583.151
2018-01-28 08:50:00,105714,2674.4692
2018-01-28 08:55:00,105714,2569.8958
2018-01-28 09:00:00,105714,238.3392
2018-01-28 09:05:00,105714,237.5296
2018-01-28 09:10:00,105714,324.7888
2018-01-28 09:15:00,105714,487.674
2018-01-28 09:20:00,105714,325.155
2018-01-28 09:25:00,105714,239.0224
2018-01-28 09:30:00,105714,235.4088
2018-01-28 09:35:00,105714,2540.1132
2018-01-28 09:40:00,105714,2739.0337
2018-01-28 09:45:00,105714,2692.1322
2018-01-28 09:50:00,105714,2586.99
2018-01-28 09:55:00,105714,2537.2891
2018-01-28 10:00:00,105714,2591.6432
2018-01-28 10:05:00,105714,2479.6436
2018-01-28 10:10:00,105714,2781.9711
2018-01-28 10:15:00,105714,2988.8336
2018-01-28 10:20:00,105714,248.1884
2018-01-28 10:25:00,105714,234.4482
2018-01-28 10:30:00,105714,143.856
2018-01-28 10:35:00,105714,243.2
2018-01-28 10:40:00,105714,141.2992
2018-01-28 10:45:00,105714,143.1584
2018-01-28 10:50:00,105714,239.7988
2018-01-28 10:55:00,105714,2650.8354
2018-01-28 11:00:00,105714,2694.4128
2018-01-28 11:05:00,105714,2550.4995
2018-01-28 11:10:00,105714,2561.844
2018-01-28 11:15:00,105714,2610.3827
2018-01-28 11:20:00,105714,2679.4119
2018-01-28 11:25:00,105714,2658.5614
2018-01-28 11:30:00,105714,2914.6217
2018-01-28 11:35:00,105714,2999.2815
2018-01-28 11:40:00,105714,149.2578
2018-01-28 11:45:00,105714,142.9841
2018-01-28 11:50:00,105714,250.614
2018-01-28 11:55:00,105714,324.72
2018-01-28 12:00:00,105714,237.153
2018-01-28 12:05:00,105714,237.2496
2018-01-28 12:10:00,105714,138.953
2018-01-28 12:15:00,105714,2651.06
2018-01-28 12:20:00,105714,2623.8762
2018-01-28 12:25:00,105714,2703.3641
2018-01-28 12:30:00,105714,2692.885
2018-01-28 12:35:00,105714,2687.36
2018-01-28 12:40:00,105714,2566.26
2018-01-28 12:45:00,105714,2620.85
2018-01-28 12:50:00,105714,2722.3727
2018-01-28 12:55:00,105714,3160.6148
2018-01-28 13:00:00,105714,246.3978
2018-01-28 13:05:00,105714,238.6811
2018-01-28 13:10:00,105714,242.055
2018-01-28 13:15:00,105714,141.8221
2018-01-28 13:20:00,105714,141.764
2018-01-28 13:25:00,105714,141.764
2018-01-28 13:30:00,105714,242.78
2018-01-28 13:35:00,105714,2802.144
2018-01-28 13:40:00,105714,2703.7296
2018-01-28 13:45:00,105714,2657.1762
2018-01-28 13:50:00,105714,2602.5443
2018-01-28 13:55:00,105714,2764.124
2018-01-28 14:00:00,105714,321.156
2018-01-28 14:05:00,105714,319.836
2018-01-28 14:10:00,105714,234.8346
2018-01-28 14:15:00,105714,141.1249
2018-01-28 14:20:00,105714,143.7968
2018-01-28 14:25:00,105714,249.878
2018-01-28 14:30:00,105714,2660.0563
2018-01-28 14:35:00,105714,2663.936
2018-01-28 14:40:00,105714,2695.623
2018-01-28 14:45:00,105714,2674.5223
2018-01-28 14:50:00,105714,2774.8398
2018-01-28 14:55:00,105714,2802.5028
2018-01-28 15:00:00,105714,2619.67
2018-01-28 15:05:00,105714,2823.411
2018-01-28 15:10:00,105714,298.9935
2018-01-28 15:15:00,105714,382.36
2018-01-28 15:20:00,105714,375.41
2018-01-28 15:25:00,105714,292.5768
2018-01-28 15:30:00,105714,292.3354
2018-01-28 15:35:00,105714,294.272
2018-01-28 15:40:00,105714,2794.4226
2018-01-28 15:45:00,105714,2835.2545
2018-01-28 15:50:00,105714,2702.4984
2018-01-28 15:55:00,105714,2725.0009
2018-01-28 16:00:00,105714,2711.088
2018-01-28 16:05:00,105714,2658.2652
2018-01-28 16:10:00,105714,2884.64
2018-01-28 16:15:00,105714,2773.9818
2018-01-28 16:20:00,105714,242.2825
2018-01-28 16:25:00,105714,242.78
2018-01-28 16:30:00,105714,240.584
2018-01-28 16:35:00,105714,323.988
2018-01-28 16:40:00,105714,240.2882
2018-01-28 16:45:00,105714,239.6966
2018-01-28 16:50:00,105714,2658.432
2018-01-28 16:55:00,105714,2643.1158
2018-01-28 17:00:00,105714,2809.0832
2018-01-28 17:05:00,105714,2523.0699
2018-01-28 17:10:00,105714,2668.0212
2018-01-28 17:15:00,105714,140.0256
2018-01-28 17:20:00,105714,236.682
2018-01-28 17:25:00,105714,2787.8424
2018-01-28 17:30:00,105714,2676.7299
2018-01-28 17:35:00,105714,2742.8438
2018-01-28 17:40:00,105714,141.0087
2018-01-28 17:45:00,105714,140.7182
2018-01-28 17:50:00,105714,324.146
2018-01-28 17:55:00,105714,2740.0212
2018-01-28 18:00:00,105714,2658.3432
2018-01-28 18:05:00,105714,2663.4852
2018-01-28 18:10:00,105714,2499.5212
2018-01-28 18:15:00,105714,2940.6942
2018-01-28 18:20:00,105714,239.679
2018-01-28 18:25:00,105714,238.9078
2018-01-28 18:30:00,105714,234.3516
2018-01-28 18:35:00,105714,317.2089
2018-01-28 18:40:00,105714,321.6213
2018-01-28 18:45:00,105714,408.944
2018-01-28 18:50:00,105714,2715.3042
2018-01-28 18:55:00,105714,2783.6046
2018-01-28 19:00:00,105714,2854.5996
2018-01-28 19:05:00,105714,2887.024
2018-01-28 19:10:00,105714,2880.914
2018-01-28 19:15:00,105714,2779.114
2018-01-28 19:20:00,105714,2701.6152
2018-01-28 19:25:00,105714,2819.6645
2018-01-28 19:30:00,105714,3058.5749
2018-01-28 19:35:00,105714,500.992
2018-01-28 19:40:00,105714,420.3199
2018-01-28 19:45:00,105714,415.53
2018-01-28 19:50:00,105714,417.0294
2018-01-28 19:55:00,105714,509.25
2018-01-28 20:00:00,105714,502.596
2018-01-28 20:05:00,105714,422.5078
2018-01-28 20:10:00,105714,2764.8144
2018-01-28 20:15:00,105714,2717.8081
2018-01-28 20:20:00,105714,2809.08
2018-01-28 20:25:00,105714,2833.7569
2018-01-28 20:30:00,105714,2746.3228
2018-01-28 20:35:00,105714,2739.1
2018-01-28 20:40:00,105714,2667.5248
2018-01-28 20:45:00,105714,2800.8362
2018-01-28 20:50:00,105714,3081.3952
2018-01-28 20:55:00,105714,423.054
2018-01-28 21:00:00,105714,332.4321
2018-01-28 21:05:00,105714,328.9232
2018-01-28 21:10:00,105714,499.177
2018-01-28 21:15:00,105714,510.669
2018-01-28 21:20:00,105714,385.6689
2018-01-28 21:25:00,105714,381.582
2018-01-28 21:30:00,105714,2701.0962
2018-01-28 21:35:00,105714,2818.34
2018-01-28 21:40:00,105714,2701.482
2018-01-28 21:45:00,105714,2574.804
2018-01-28 21:50:00,105714,2582.272
2018-01-28 21:55:00,105714,2656.6064
2018-01-28 22:00:00,105714,2626.4616
2018-01-28 22:05:00,105714,2788.6928
2018-01-28 22:10:00,105714,2932.2939
2018-01-28 22:15:00,105714,391.876
2018-01-28 22:20:00,105714,296.9744
2018-01-28 22:25:00,105714,299.2892
2018-01-28 22:30:00,105714,495.311
2018-01-28 22:35:00,105714,388.1601
2018-01-28 22:40:00,105714,386.5356
2018-01-28 22:45:00,105714,386.5356
2018-01-28 22:50:00,105714,2722.8672
2018-01-28 22:55:00,105714,2733.8542
2018-01-28 23:00:00,105714,2625.0912
2018-01-28 23:05:00,105714,2562.1766
2018-01-28 23:10:00,105714,2592.2008
2018-01-28 23:15:00,105714,2589.37
2018-01-28 23:20:00,105714,2657.328
2018-01-28 23:25:00,105714,2570.3145
2018-01-28 23:30:00,105714,2753.9908
2018-01-28 23:35:00,105714,221.0149
2018-01-28 23:40:00,105714,218.283
2018-01-28 23:45:00,105714,404.432
2018-01-28 23:50:00,105714,313.8699
2018-01-28 23:55:00,105714,306.3879
2018-01-28 00:00:00,105715,85.8168
2018-01-28 00:05:00,105715,32.214
2018-01-28 00:10:00,105715,11.3988
2018-01-28 00:15:00,105715,11.8752
2018-01-28 00:20:00,105715,190.575
2018-01-28 00:25:00,105715,188.1
2018-01-28 00:30:00,105715,178.8702
2018-01-28 00:35:00,105715,168.436
2018-01-28 00:40:00,105715,175.867
2018-01-28 00:45:00,105715,31.6928
2018-01-28 00:50:00,105715,11.3666
2018-01-28 00:55:00,105715,12.1226
2018-01-28 01:00:00,105715,187.796
2018-01-28 01:05:00,105715,175.015
2018-01-28 01:10:00,105715,153.14
2018-01-28 01:15:00,105715,153.078
2018-01-28 01:20:00,105715,163.152
2018-01-28 01:25:00,105715,165.624
2018-01-28 01:30:00,105715,12.1226
2018-01-28 01:35:00,105715,12.103
2018-01-28 01:40:00,105715,26.9775
2018-01-28 01:45:00,105715,190.96
2018-01-28 01:50:00,105715,156.681
2018-01-28 01:55:00,105715,153.884
2018-01-28 02:00:00,105715,163.614
2018-01-28 02:05:00,105715,166.696
2018-01-28 02:10:00,105715,149.22
2018-01-28 02:15:00,105715,12.2059
2018-01-28 02:20:00,105715,27.1192
2018-01-28 02:25:00,105715,32.5928
2018-01-28 02:30:00,105715,189.6618
2018-01-28 02:35:00,105715,187.248
2018-01-28 02:40:00,105715,202.1068
2018-01-28 02:45:00,105715,209.7422
2018-01-28 02:50:00,105715,10.1926
2018-01-28 02:55:00,105715,10.2295
2018-01-28 03:00:00,105715,179.064
2018-01-28 03:05:00,105715,206.67
2018-01-28 03:10:00,105715,169.32
2018-01-28 03:15:00,105715,164.34
2018-01-28 03:20:00,105715,171.81
2018-01-28 03:25:00,105715,174.09
2018-01-28 03:30:00,105715,154.318
2018-01-28 03:35:00,105715,154.504
2018-01-28 03:40:00,105715,164.34
2018-01-28 03:45:00,105715,32.6714
2018-01-28 03:50:00,105715,12.48
2018-01-28 03:55:00,105715,12.2255
2018-01-28 04:00:00,105715,174.44
2018-01-28 04:05:00,105715,171.81
2018-01-28 04:10:00,105715,154.318
2018-01-28 04:15:00,105715,152.012
2018-01-28 04:20:00,105715,161.98
2018-01-28 04:25:00,105715,166.629
2018-01-28 04:30:00,105715,12.9584
2018-01-28 04:35:00,105715,12.9324
2018-01-28 04:40:00,105715,27.639
2018-01-28 04:45:00,105715,181.77
2018-01-28 04:50:00,105715,156.87
2018-01-28 04:55:00,105715,154.38
2018-01-28 05:00:00,105715,164.01
2018-01-28 05:05:00,105715,166.026
2018-01-28 05:10:00,105715,12.1618
2018-01-28 05:15:00,105715,12.1373
2018-01-28 05:20:00,105715,27.5946
2018-01-28 05:25:00,105715,235.98
2018-01-28 05:30:00,105715,161.2
2018-01-28 05:35:00,105715,153.884
2018-01-28 05:40:00,105715,163.548
2018-01-28 05:45:00,105715,166.495
2018-01-28 05:50:00,105715,147.609
2018-01-28 05:55:00,105715,12.1716
2018-01-28 06:00:00,105715,27.6057
2018-01-28 06:05:00,105715,32.6583
2018-01-28 06:10:00,105715,166.763
2018-01-28 06:15:00,105715,156.933
2018-01-28 06:20:00,105715,164.472
2018-01-28 06:25:00,105715,167.165
2018-01-28 06:30:00,105715,149.52
2018-01-28 06:35:00,105715,12.48
2018-01-28 06:40:00,105715,27.9552
2018-01-28 06:45:00,105715,32.6845
2018-01-28 06:50:00,105715,164.604
2018-01-28 06:55:00,105715,156.051
2018-01-28 07:00:00,105715,163.548
2018-01-28 07:05:00,105715,168.3
2018-01-28 07:10:00,105715,149.0672
2018-01-28 07:15:00,105715,146.8962
2018-01-28 07:20:00,105715,26.9448
2018-01-28 07:25:00,105715,31.6544
2018-01-28 07:30:00,105715,11.3252
2018-01-28 07:35:00,105715,167.28
2018-01-28 07:40:00,105715,166.6
2018-01-28 07:45:00,105715,169.048
2018-01-28 07:50:00,105715,149.792
2018-01-28 07:55:00,105715,169.1108
2018-01-28 08:00:00,105715,178.92
2018-01-28 08:05:00,105715,74.0232
2018-01-28 08:10:00,105715,168.21
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• 我正在尝试根据每个家庭的唯一 ID 为每个家庭创建一个线图。日期将在x-axis、power_usagey-axis和 House_id 的每个值的一行。
• 总共有3214 households903814 个读数。我正在尝试create a subplots使用线图。例如，第一个图将显示前 20 个家庭（共 20 个子图）的用电量读数，下一个图将涵盖接下来 20 个家庭（共 20 个子图）的用电读数，依此类推。总地块将是 160 个，带有 3214 个子地块。Is there a way to automatically iterate this and get all the plots? 例如，我在下图中附上了一张演示图片。

输出应该是这样的：

标签： pythonmatplotlibplotplotlyseaborn

Answer 1

• 此实现使用seaborn.relplot

1. 创建唯一'House_id'值列表
2. 'House_id'从唯一列表中创建 20 个值的组
3. 遍历组列表：
   • 过滤df以仅包含'House_id'组中值的数据
   • 保存或绘制数据

• 以下测试集中的'Date'列已经是datetimedtype。
  • 用于df.Date = pd.to_datetime(df.Date)转换'Date'真实数据的列。用您的列名替换'Date'.
• 将'Date'列转换为正确的 dtype 后，可选择df使用排序df = df.sort_values(['House_id', 'Date'])

导入和测试数据

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# create test data
hid = range(40)
date_range = pd.bdate_range('2018-01-28', '2019-01-28', freq='2W').tolist()
Date = list()
House_id = list()
power_Usage = list()
for v in hid:
    len_dr = len(date_range)
    l = [v] * len_dr
    Date += date_range
    House_id += l
    np.random.seed(v)
    power_Usage += np.random.randint(100, 400, size=(len_dr)).tolist()

df = pd.DataFrame({'Date': Date, 'House_id': House_id, 'power_Usage': power_Usage})

# display(df.head())
        Date  House_id  power_Usage
0 2018-01-28         0          272
1 2018-02-11         0          147
2 2018-02-25         0          217
3 2018-03-11         0          292
4 2018-03-25         0          351

绘制数据框

# get the unique ids
unique_ids = df.House_id.unique()

# create groups of 20 ids
N = 20
groups = [unique_ids[n:n+N] for n in range(0, len(unique_ids), N)]

# iterate through the groups of ids and plot
for i, group in enumerate(groups):
    data = df[df.House_id.isin(group)]
    g = sns.relplot(data=data, x='Date', y='power_Usage', col='House_id', col_wrap=4, kind='line', facet_kws={'sharey': False}, height=3)
    g.set_xticklabels(rotation=90)
    g.savefig(f'group_{i}.png')
    plt.show()

第 1 组

第 2 组

使用 OP 样本数据更新

• 发布答案后添加了示例数据。
• 使用Seaborn FacetGrid 格式化日期标签来格式化 xaxis 标签。
• 24 小时周期的默认格式是'%m-%d %H'（例如'01-28 03'）。

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

# load the file
df = pd.read_csv('data_sample.csv')

# set the datetime
df.datetime = pd.to_datetime(df.datetime)

# create formatting for the xaxis labels
xformatter = mdates.DateFormatter('%Y-%m-%d %H')

# get the unique ids
unique_ids = df.House_id.unique()

# create groups of 20 ids
N = 2
groups = [unique_ids[n:n+N] for n in range(0, len(unique_ids), N)]

# iterate through the groups of ids and plot
for i, group in enumerate(groups):
    data = df[df.House_id.isin(group)]
    g = sns.relplot(data=data, x='datetime', y='power_Usage', col='House_id', col_wrap=4, kind='line', facet_kws={'sharey': False}, height=3)
    g.set_xticklabels(rotation=90)

    # set the format to the axis
    g.axes[0].xaxis.set_major_formatter(xformatter)
    g.savefig(f'group_{i}.png')
    plt.show()