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| date | kilowatt-hour | n_hosts | n_guests | temperature | |
|---|---|---|---|---|---|
| 2022-01-01 | 2.171 | 0.0 | 0.0 | 7.875000000000028 | |
| 2022-01-02 | 10.31 | 0.0 | 0.5 | 8.787500000000023 | |
| 2022-01-03 | 16.107 | 0.0 | 1.0 | 8.35000000000003 | |
| 2022-01-04 | 16.563 | 0.0 | 1.0 | 9.250000000000014 | |
| 2022-01-05 | 17.098 | 0.0 | 1.0 | 5.700000000000024 | |
| 2022-01-06 | 18.76 | 0.0 | 1.0 | 2.950000000000024 | |
| 2022-01-07 | 20.853 | 0.0 | 1.0 | 3.2500000000000284 | |
| 2022-01-08 | 19.548 | 0.0 | 1.0 | 8.887500000000031 | |
| 2022-01-09 | 20.81 | 0.0 | 1.0 | 8.72500000000003 | |
| 2022-01-10 | 19.277 | 0.0 | 1.0 | 7.837500000000027 | |
| 2022-01-11 | 18.977 | 0.0 | 1.0 | 5.85000000000003 | |
| 2022-01-12 | 10.933 | 0.0 | 0.5 | 2.275000000000013 | |
| 2022-01-13 | 15.214 | 0.0 | 0.5 | -0.1499999999999701 | |
| 2022-01-14 | 21.192 | 0.0 | 1.0 | 0.8000000000000114 | |
| 2022-01-15 | 22.991 | 0.0 | 1.0 | 1.71250000000002 | |
| 2022-01-16 | 24.476 | 0.0 | 1.0 | 1.1625000000000227 | |
| 2022-01-17 | 23.372 | 0.0 | 1.0 | -0.5874999999999773 | |
| 2022-01-18 | 26.668 | 0.0 | 1.0 | 0.4875000000000256 | |
| 2022-01-19 | 28.921 | 0.0 | 1.0 | 4.312500000000021 | |
| 2022-01-20 | 24.774 | 0.0 | 1.0 | 4.9875000000000185 | |
| 2022-01-21 | 21.979 | 0.0 | 1.0 | 2.337500000000034 | |
| 2022-01-22 | 28.041 | 0.0 | 1.0 | 1.1875000000000213 | |
| 2022-01-23 | 25.802 | 0.0 | 1.0 | 2.137500000000024 | |
| 2022-01-24 | 21.516 | 0.0 | 1.0 | 6.325000000000024 | |
| 2022-01-25 | 20.496 | 0.0 | 1.0 | 4.050000000000011 | |
| 2022-01-26 | 21.634 | 0.0 | 1.0 | 3.000000000000014 | |
| 2022-01-27 | 23.348 | 0.0 | 1.0 | 0.9500000000000242 | |
| 2022-01-28 | 25.095 | 0.0 | 1.0 | 0.2500000000000213 | |
| 2022-01-29 | 30.351 | 0.0 | 1.0 | 0.8375000000000128 | |
| 2022-01-30 | 27.926 | 0.0 | 1.0 | 0.7500000000000142 | |
| 2022-01-31 | 26.02 | 0.0 | 1.0 | 5.537500000000023 | |
| 2022-02-01 | 24.038 | 0.0 | 1.0 | 6.350000000000023 | |
| 2022-02-02 | 21.191 | 0.0 | 1.0 | 8.525000000000027 | |
| 2022-02-03 | 20.848 | 0.0 | 1.0 | 5.800000000000033 | |
| 2022-02-04 | 9.168 | 0.0 | 1.0 | 9.825000000000024 | |
| 2022-02-05 | 7.638 | 0.0 | 1.0 | 8.08750000000002 | |
| 2022-02-06 | 9.832 | 0.0 | 1.0 | 5.212500000000034 | |
| 2022-02-07 | 11.215 | 0.0 | 1.0 | 9.325000000000031 | |
| 2022-02-08 | 21.322 | 0.0 | 1.0 | 7.537500000000023 | |
| 2022-02-09 | 18.505 | 0.0 | 1.0 | 8.925000000000018 | |
| 2022-02-10 | 11.716 | 0.0 | 1.0 | 9.787500000000023 | |
| 2022-02-11 | 5.696 | 0.0 | 1.0 | 9.375000000000021 | |
| 2022-02-12 | 7.385 | 0.0 | 0.5 | 6.65000000000002 | |
| 2022-02-13 | 6.383 | 0.0 | 0.0 | 11.112500000000026 | |
| 2022-02-14 | 6.126 | 0.0 | 0.0 | 8.837500000000027 | |
| 2022-02-15 | 7.283 | 0.0 | 0.0 | 8.550000000000033 | |
| 2022-02-16 | 7.655 | 0.0 | 0.0 | 11.000000000000021 | |
| 2022-02-17 | 11.835 | 1.0 | 0.0 | 13.000000000000021 | |
| 2022-02-18 | 16.639 | 2.0 | 0.0 | 12.050000000000018 | |
| 2022-02-19 | 14.473 | 1.0 | 0.5 | 9.200000000000024 | |
| 2022-02-20 | 15.226 | 0.0 | 1.0 | 7.912500000000016 | |
| 2022-02-21 | 19.018 | 0.0 | 1.0 | 11.15000000000002 | |
| 2022-02-22 | 10.834 | 0.0 | 1.0 | 10.537500000000016 | |
| 2022-02-23 | 13.09 | 0.0 | 1.0 | 9.887500000000017 | |
| 2022-02-24 | 9.604 | 0.0 | 1.0 | 10.737500000000033 | |
| 2022-02-25 | 10.532 | 0.0 | 0.5 | 8.075000000000031 | |
| 2022-02-26 | 16.355 | 1.0 | 0.0 | 6.40000000000002 | |
| 2022-02-27 | 5.813 | 2.0 | 0.0 | 8.237500000000026 | |
| 2022-02-28 | 4.149 | 2.0 | 0.0 | 10.950000000000017 | |
| 2022-03-01 | 5.304 | 1.0 | 0.0 | 8.90000000000002 | |
| 2022-03-02 | 2.062 | 0.0 | 0.0 | 9.72500000000003 | |
| 2022-03-03 | 2.2 | 0.0 | 0.0 | 10.950000000000024 | |
| 2022-03-04 | 2.652 | 0.0 | 0.0 | 9.28750000000003 | |
| 2022-03-05 | 2.606 | 0.0 | 0.0 | 8.02500000000002 | |
| 2022-03-06 | 2.18 | 0.0 | 0.0 | 7.58750000000002 | |
| 2022-03-07 | 8.854 | 0.0 | 0.5 | 6.025000000000027 | |
| 2022-03-08 | 19.768 | 0.0 | 1.0 | 9.437500000000028 | |
| 2022-03-09 | 16.199 | 0.0 | 1.0 | 9.85000000000003 | |
| 2022-03-10 | 14.997 | 0.0 | 1.0 | 11.825000000000024 | |
| 2022-03-11 | 16.97 | 0.0 | 1.0 | 12.237500000000018 | |
| 2022-03-12 | 14.738 | 0.0 | 1.0 | 9.600000000000016 | |
| 2022-03-13 | 16.351 | 0.0 | 1.0 | 9.450000000000024 | |
| 2022-03-14 | 19.501 | 0.0 | 1.0 | 9.725000000000016 | |
| 2022-03-15 | 18.596 | 0.0 | 1.0 | 13.412500000000023 | |
| 2022-03-16 | 15.068 | 0.0 | 1.0 | 13.225000000000009 | |
| 2022-03-17 | 9.547 | 0.0 | 0.5 | 12.425000000000026 | |
| 2022-03-18 | 2.236 | 0.0 | 0.0 | 10.225000000000023 | |
| 2022-03-19 | 6.737 | 0.5 | 0.0 | 11.912500000000023 | |
| 2022-03-20 | 6.707 | 1.0 | 0.0 | 12.400000000000027 | |
| 2022-03-21 | 5.577 | 1.0 | 0.0 | 12.612500000000018 | |
| 2022-03-22 | 8.594 | 1.0 | 0.0 | 11.675000000000011 | |
| 2022-03-23 | 7.468 | 0.5 | 0.0 | 11.53750000000003 | |
| 2022-03-24 | 2.465 | 0.0 | 0.0 | 11.587500000000027 | |
| 2022-03-25 | 3.999 | 0.0 | 0.0 | 12.83750000000002 | |
| 2022-03-26 | 5.441 | 1.0 | 0.0 | 12.212500000000027 | |
| 2022-03-27 | 8.289 | 2.0 | 0.0 | 13.850000000000023 | |
| 2022-03-28 | 9.167 | 2.0 | 0.0 | 14.275000000000013 | |
| 2022-03-29 | 7.701 | 2.0 | 0.0 | 13.287500000000023 | |
| 2022-03-30 | 5.418 | 1.0 | 0.0 | 10.65000000000002 | |
| 2022-03-31 | 2.536 | 0.0 | 0.0 | 9.912500000000023 | |
| 2022-04-01 | 6.991 | 0.0 | 0.0 | 4.725000000000023 | |
| 2022-04-02 | 6.458 | 0.0 | 0.0 | 4.125000000000021 | |
| 2022-04-03 | 2.155 | 0.0 | 0.0 | 3.3500000000000227 | |
| 2022-04-04 | 2.056 | 0.0 | 0.0 | 4.937500000000021 | |
| 2022-04-05 | 2.461 | 0.0 | 0.0 | 7.625000000000028 | |
| 2022-04-06 | 2.506 | 0.0 | 0.0 | 10.000000000000014 | |
| 2022-04-07 | 3.804 | 0.0 | 0.5 | 13.737500000000018 | |
| 2022-04-08 | 7.752 | 0.0 | 1.0 | 14.937500000000014 | |
| 2022-04-09 | 8.455 | 0.0 | 1.0 | 10.762500000000024 | |
| 2022-04-10 | 11.004 | 0.0 | 1.0 | 10.725000000000023 | |
| 2022-04-11 | 7.542 | 0.0 | 1.0 | 13.52500000000002 | |
| 2022-04-12 | 7.666 | 0.0 | 1.0 | 13.825000000000017 | |
| 2022-04-13 | 12.87 | 0.0 | 1.0 | 12.662500000000023 | |
| 2022-04-14 | 7.723 | 0.0 | 1.0 | 14.15000000000002 | |
| 2022-04-15 | 4.868 | 0.0 | 1.0 | 15.437500000000021 | |
| 2022-04-16 | 4.59 | 0.0 | 1.0 | 16.600000000000023 | |
| 2022-04-17 | 6.654 | 0.0 | 1.0 | 16.512500000000024 | |
| 2022-04-18 | 6.511 | 0.0 | 1.0 | 13.862500000000018 | |
| 2022-04-19 | 4.867 | 0.0 | 1.0 | 11.412500000000023 | |
| 2022-04-20 | 7.882 | 0.0 | 1.0 | 10.16250000000003 | |
| 2022-04-21 | 7.28 | 0.0 | 1.0 | 10.812500000000021 | |
| 2022-04-22 | 7.757 | 0.0 | 1.0 | 13.800000000000018 | |
| 2022-04-23 | 9.142 | 0.0 | 1.0 | 10.987500000000033 | |
| 2022-04-24 | 12.604 | 0.0 | 1.0 | 13.037500000000023 | |
| 2022-04-25 | 9.306 | 0.0 | 1.0 | 12.837500000000027 | |
| 2022-04-26 | 7.356 | 0.0 | 1.0 | 15.725000000000023 | |
| 2022-04-27 | 5.622 | 0.0 | 1.0 | 16.47500000000003 | |
| 2022-04-28 | 3.429 | 0.0 | 1.0 | 15.612500000000033 | |
| 2022-04-29 | 2.528 | 0.0 | 1.0 | 16.950000000000024 | |
| 2022-04-30 | 2.089 | 0.0 | 1.0 | 16.987500000000026 | |
| 2022-05-01 | 2.986 | 0.0 | 1.0 | 15.587500000000013 | |
| 2022-05-02 | 5.314 | 0.0 | 1.0 | 15.487500000000018 | |
| 2022-05-03 | 8.116 | 0.0 | 1.0 | 15.40000000000002 | |
| 2022-05-04 | 4.621 | 0.0 | 1.0 | 15.287500000000016 | |
| 2022-05-05 | 6.201 | 0.0 | 1.0 | 16.175000000000026 | |
| 2022-05-06 | 4.748 | 0.0 | 1.0 | 16.325000000000017 | |
| 2022-05-07 | 2.05 | 0.0 | 1.0 | 17.187500000000036 | |
| 2022-05-08 | 3.298 | 0.0 | 1.0 | 18.700000000000024 | |
| 2022-05-09 | 3.658 | 0.0 | 1.0 | 19.725000000000016 | |
| 2022-05-10 | 5.936 | 0.0 | 1.0 | 20.58750000000002 | |
| 2022-05-11 | 8.453 | 0.0 | 1.0 | 21.225000000000023 | |
| 2022-05-12 | 4.136 | 0.0 | 1.0 | 19.587500000000034 | |
| 2022-05-13 | 4.844 | 0.0 | 1.0 | 20.25000000000002 | |
| 2022-05-14 | 4.864 | 0.0 | 1.0 | 22.725000000000023 | |
| 2022-05-15 | 4.423 | 0.0 | 1.0 | 21.675000000000026 | |
| 2022-05-16 | 6.768 | 0.0 | 1.0 | 22.450000000000017 | |
| 2022-05-17 | 6.074 | 0.0 | 1.0 | 24.60000000000003 | |
| 2022-05-18 | 2.389 | 0.0 | 1.0 | 25.93750000000002 | |
| 2022-05-19 | 3.076 | 0.0 | 1.0 | 22.862500000000026 | |
| 2022-05-20 | 3.877 | 0.0 | 1.0 | 24.2714285714286 | |
| 2022-05-21 | 4.039 | 0.0 | 1.0 | 28.325000000000017 | |
| 2022-05-22 | 4.889 | 0.0 | 1.0 | 25.02500000000002 | |
| 2022-05-23 | 5.125 | 0.0 | 1.0 | 18.975000000000023 | |
| 2022-05-24 | 5.781 | 0.0 | 1.0 | 14.850000000000016 | |
| 2022-05-25 | 6.745 | 0.0 | 1.0 | 15.925000000000018 | |
| 2022-05-26 | 4.404 | 0.0 | 1.0 | 15.925000000000026 | |
| 2022-05-27 | 6.775 | 0.0 | 1.0 | 19.58750000000002 | |
| 2022-05-28 | 3.758 | 0.0 | 1.0 | 20.40000000000002 | |
| 2022-05-29 | 4.79 | 0.0 | 1.0 | 19.550000000000026 | |
| 2022-05-30 | 5.58 | 0.0 | 1.0 | 19.287500000000023 | |
| 2022-05-31 | 8.348 | 0.0 | 1.0 | 21.112500000000026 | |
| 2022-06-01 | 3.656 | 0.0 | 0.5 | 23.950000000000024 | |
| 2022-06-02 | 2.146 | 0.0 | 0.0 | 21.85000000000003 | |
| 2022-06-03 | 2.244 | 0.0 | 1.0 | 23.81250000000002 | |
| 2022-06-04 | 5.758 | 0.0 | 2.0 | 21.50000000000003 | |
| 2022-06-05 | 5.099 | 0.0 | 2.0 | 20.550000000000026 | |
| 2022-06-06 | 3.35 | 0.0 | 2.0 | 22.46250000000002 | |
| 2022-06-07 | 6.18 | 0.0 | 1.5 | 20.575000000000024 | |
| 2022-06-08 | 2.676 | 0.0 | 1.0 | 18.00000000000003 | |
| 2022-06-09 | 2.883 | 0.0 | 1.0 | 18.887500000000017 | |
| 2022-06-10 | 5.966 | 0.0 | 1.5 | 21.62500000000002 | |
| 2022-06-11 | 3.056 | 0.0 | 2.0 | 24.912500000000023 | |
| 2022-06-12 | 5.119 | 0.0 | 2.0 | 24.737500000000033 | |
| 2022-06-13 | 3.959 | 0.0 | 2.0 | 25.775000000000013 | |
| 2022-06-14 | 3.875 | 0.0 | 2.0 | 28.43750000000003 | |
| 2022-06-15 | 3.556 | 0.0 | 2.0 | 29.162500000000023 | |
| 2022-06-16 | 4.135 | 0.0 | 2.0 | 30.150000000000027 | |
| 2022-06-17 | 3.907 | 1.0 | 1.0 | 30.637500000000024 | |
| 2022-06-18 | 2.892 | 2.0 | 0.0 | 29.287500000000016 | |
| 2022-06-19 | 3.181 | 2.0 | 0.0 | 27.825000000000024 | |
| 2022-06-20 | 3.829 | 1.0 | 0.0 | 24.18750000000003 | |
| 2022-06-21 | 2.929 | 0.0 | 0.5 | 23.15000000000002 | |
| 2022-06-22 | 3.161 | 0.0 | 1.0 | 24.075000000000017 | |
| 2022-06-23 | 4.319 | 0.0 | 1.0 | 22.062500000000014 | |
| 2022-06-24 | 3.9 | 0.0 | 0.5 | 20.42500000000002 | |
| 2022-06-25 | 2.589 | 0.0 | 0.0 | 21.300000000000033 | |
| 2022-06-26 | 2.182 | 0.0 | 1.0 | 16.925000000000026 | |
| 2022-06-27 | 3.789 | 0.0 | 2.0 | 15.937500000000021 | |
| 2022-06-28 | 4.381 | 0.0 | 2.0 | 21.00000000000003 | |
| 2022-06-29 | 4.424 | 0.0 | 1.0 | 22.15000000000002 | |
| 2022-06-30 | 2.151 | 0.0 | 0.0 | 16.637500000000017 | |
| 2022-07-01 | 2.11 | 0.0 | 0.0 | 18.637500000000024 | |
| 2022-07-02 | 2.627 | 0.0 | 0.0 | 22.662500000000023 | |
| 2022-07-03 | 2.163 | 0.0 | 0.0 | 24.162500000000023 | |
| 2022-07-04 | 2.195 | 0.0 | 0.0 | 21.925000000000026 | |
| 2022-07-05 | 2.699 | 0.0 | 0.0 | 24.71250000000002 | |
| 2022-07-06 | 2.25 | 0.5 | 0.0 | 23.975000000000023 | |
| 2022-07-07 | 3.695 | 1.0 | 0.0 | 23.86250000000002 | |
| 2022-07-08 | 4.177 | 1.0 | 0.0 | 24.112500000000026 | |
| 2022-07-09 | 3.281 | 1.0 | 0.0 | 25.375000000000036 | |
| 2022-07-10 | 2.575 | 0.5 | 0.0 | 26.68750000000002 | |
| 2022-07-11 | 3.127 | 0.0 | 1.0 | 28.12500000000002 | |
| 2022-07-12 | 4.781 | 0.0 | 2.0 | 28.975000000000016 | |
| 2022-07-13 | 3.46 | 0.0 | 1.0 | 28.837500000000013 | |
| 2022-07-14 | 3.217 | 0.0 | 1.0 | 29.700000000000024 | |
| 2022-07-15 | 4.582 | 0.0 | 2.0 | 28.825000000000024 | |
| 2022-07-16 | 4.725 | 0.0 | 2.0 | 29.387500000000024 | |
| 2022-07-17 | 4.498 | 0.0 | 2.0 | 30.175000000000026 | |
| 2022-07-18 | 5.64 | 0.0 | 2.0 | 30.96250000000002 | |
| 2022-07-19 | 5.317 | 0.0 | 2.0 | 27.962500000000034 | |
| 2022-07-20 | 3.535 | 0.0 | 2.0 | 25.450000000000017 | |
| 2022-07-21 | 3.582 | 0.0 | 2.0 | 26.48750000000002 | |
| 2022-07-22 | 2.657 | 0.0 | 2.0 | 25.850000000000023 | |
| 2022-07-23 | 4.028 | 0.0 | 1.0 | 23.787500000000023 | |
| 2022-07-24 | 3.306 | 0.0 | 1.0 | 27.98750000000002 | |
| 2022-07-25 | 5.406 | 0.0 | 2.0 | 24.287500000000023 | |
| 2022-07-26 | 2.348 | 0.0 | 1.0 | 22.825000000000024 | |
| 2022-07-27 | 2.641 | 0.0 | 0.0 | 22.93750000000003 | |
| 2022-07-28 | 2.928 | 0.0 | 0.0 | 23.350000000000016 | |
| 2022-07-29 | 3.52 | 0.5 | 0.0 | 22.275000000000027 | |
| 2022-07-30 | 2.625 | 0.0 | 0.0 | 25.075000000000017 | |
| 2022-07-31 | 2.28 | 0.0 | 0.0 | 26.38750000000003 | |
| 2022-08-01 | 3.021 | 1.0 | 0.0 | 27.43750000000002 | |
| 2022-08-02 | 4.18 | 2.0 | 0.0 | 28.700000000000017 | |
| 2022-08-03 | 2.774 | 1.0 | 0.0 | 28.98750000000002 | |
| 2022-08-04 | 3.177 | 0.0 | 1.0 | 29.00000000000002 | |
| 2022-08-05 | 3.624 | 0.0 | 2.0 | 26.450000000000024 | |
| 2022-08-06 | 3.746 | 0.0 | 1.0 | 26.91250000000003 | |
| 2022-08-07 | 2.288 | 0.0 | 0.0 | 28.25000000000003 | |
| 2022-08-08 | 2.344 | 0.0 | 0.0 | 27.81250000000003 | |
| 2022-08-09 | 2.648 | 0.0 | 1.0 | 27.787500000000023 | |
| 2022-08-10 | 3.649 | 0.0 | 2.0 | 28.925000000000033 | |
| 2022-08-11 | 4.475 | 0.0 | 2.0 | 29.575000000000024 | |
| 2022-08-12 | 4.456 | 0.0 | 2.0 | 31.212500000000027 | |
| 2022-08-13 | 3.05 | 0.0 | 2.0 | 27.837500000000034 | |
| 2022-08-14 | 3.159 | 0.0 | 2.0 | 25.26250000000003 | |
| 2022-08-15 | 3.415 | 0.0 | 1.0 | 24.43750000000003 | |
| 2022-08-16 | 2.597 | 0.0 | 0.0 | 25.08750000000002 | |
| 2022-08-17 | 2.694 | 0.0 | 1.0 | 21.087500000000013 | |
| 2022-08-18 | 6.129 | 0.0 | 2.0 | 20.512500000000024 | |
| 2022-08-19 | 4.034 | 0.0 | 1.0 | 21.57500000000003 | |
| 2022-08-20 | 2.536 | 0.0 | 0.0 | 23.712500000000027 | |
| 2022-08-21 | 2.496 | 0.0 | 0.0 | 24.600000000000023 | |
| 2022-08-22 | 2.921 | 0.0 | 1.0 | 22.31250000000003 | |
| 2022-08-23 | 6.441 | 0.0 | 2.0 | 25.00000000000003 | |
| 2022-08-24 | 3.421 | 0.0 | 2.0 | 27.287500000000023 | |
| 2022-08-25 | 2.755 | 0.0 | 1.0 | 25.587500000000027 | |
| 2022-08-26 | 4.117 | 0.0 | 1.0 | 25.050000000000026 | |
| 2022-08-27 | 5.777 | 0.0 | 2.0 | 24.86250000000002 | |
| 2022-08-28 | 6.562 | 0.0 | 2.0 | 26.787500000000023 | |
| 2022-08-29 | 4.948 | 0.0 | 2.0 | 25.45000000000003 | |
| 2022-08-30 | 7.889 | 0.0 | 2.0 | 25.175000000000026 | |
| 2022-08-31 | 5.301 | 0.0 | 2.0 | 24.162500000000016 | |
| 2022-09-01 | 6.92 | 0.0 | 1.0 | 23.737500000000026 | |
| 2022-09-02 | 2.305 | 0.0 | 0.0 | 24.05000000000001 | |
| 2022-09-03 | 2.658 | 0.0 | 1.0 | 23.00000000000003 | |
| 2022-09-04 | 4.204 | 0.0 | 2.0 | 25.48750000000002 | |
| 2022-09-05 | 2.749 | 0.0 | 2.0 | 26.80000000000002 | |
| 2022-09-06 | 4.268 | 0.0 | 2.0 | 26.900000000000034 | |
| 2022-09-07 | 3.36 | 0.0 | 2.0 | 23.46250000000002 | |
| 2022-09-08 | 4.25 | 0.0 | 1.5 | 21.512500000000024 | |
| 2022-09-09 | 4.475 | 0.0 | 1.0 | 19.350000000000023 | |
| 2022-09-10 | 4.745 | 0.0 | 1.0 | 20.68750000000003 | |
| 2022-09-11 | 3.453 | 0.0 | 1.0 | 24.362500000000026 | |
| 2022-09-12 | 3.749 | 0.0 | 1.0 | 24.975000000000023 | |
| 2022-09-13 | 3.309 | 0.0 | 1.5 | 25.850000000000016 | |
| 2022-09-14 | 4.094 | 0.0 | 2.0 | 23.90000000000002 | |
| 2022-09-15 | 2.338 | 0.0 | 2.0 | 23.975000000000016 | |
| 2022-09-16 | 3.969 | 0.0 | 1.0 | 21.58750000000002 | |
| 2022-09-17 | 3.318 | 0.0 | 0.5 | 17.837500000000027 | |
| 2022-09-18 | 2.972 | 0.0 | 1.0 | 16.912500000000023 | |
| 2022-09-19 | 3.7 | 0.0 | 1.0 | 17.75000000000002 | |
| 2022-09-20 | 3.516 | 0.0 | 1.0 | 17.737500000000026 | |
| 2022-09-21 | 3.506 | 0.0 | 1.0 | 17.71250000000002 | |
| 2022-09-22 | 2.242 | 0.0 | 1.0 | 18.275000000000013 | |
| 2022-09-23 | 3.799 | 0.0 | 1.0 | 18.475000000000016 | |
| 2022-09-24 | 4.448 | 0.0 | 1.0 | 16.825000000000017 | |
| 2022-09-25 | 2.713 | 0.0 | 1.0 | 14.925000000000026 | |
| 2022-09-26 | 2.779 | 0.0 | 1.0 | 14.58750000000002 | |
| 2022-09-27 | 7.43 | 0.0 | 1.0 | 14.887500000000024 | |
| 2022-09-28 | 1.929 | 0.0 | 1.0 | 15.987500000000033 | |
| 2022-09-29 | 4.319 | 0.0 | 1.0 | 14.450000000000031 | |
| 2022-09-30 | 3.623 | 0.0 | 1.0 | 13.987500000000026 | |
| 2022-10-01 | 4.922 | 0.0 | 1.0 | 16.52500000000002 | |
| 2022-10-02 | 2.58 | 0.0 | 1.0 | 17.36250000000002 | |
| 2022-10-03 | 5.915 | 0.0 | 1.0 | 18.262500000000017 | |
| 2022-10-04 | 4.371 | 0.0 | 1.0 | 17.775000000000027 | |
| 2022-10-05 | 4.271 | 0.0 | 1.0 | 18.375000000000036 | |
| 2022-10-06 | 7.801 | 0.0 | 1.0 | 17.800000000000026 | |
| 2022-10-07 | 6.748 | 0.0 | 1.0 | 17.71250000000002 | |
| 2022-10-08 | 4.469 | 0.0 | 1.0 | 18.600000000000023 | |
| 2022-10-09 | 3.999 | 0.0 | 1.0 | 18.06250000000003 | |
| 2022-10-10 | 4.343 | 0.0 | 1.0 | 19.675000000000026 | |
| 2022-10-11 | 5.483 | 0.0 | 1.0 | 18.287500000000023 | |
| 2022-10-12 | 2.731 | 0.0 | 1.0 | 17.71250000000002 | |
| 2022-10-13 | 5.802 | 0.0 | 1.0 | 17.92500000000002 | |
| 2022-10-14 | 2.626 | 0.0 | 1.0 | 18.775000000000034 | |
| 2022-10-15 | 2.524 | 0.0 | 1.0 | 17.91250000000003 | |
| 2022-10-16 | 7.679 | 0.0 | 1.0 | 21.18750000000002 | |
| 2022-10-17 | 4.759 | 0.0 | 1.0 | 22.02500000000002 | |
| 2022-10-18 | 3.582 | 0.0 | 1.0 | 21.93750000000003 | |
| 2022-10-19 | 4.796 | 0.0 | 1.0 | 21.000000000000014 | |
| 2022-10-20 | 2.552 | 0.0 | 1.0 | 20.77500000000002 | |
| 2022-10-21 | 5.412 | 0.0 | 1.0 | 19.75000000000002 | |
| 2022-10-22 | 2.608 | 0.0 | 1.0 | 19.80000000000002 | |
| 2022-10-23 | 4.293 | 0.0 | 1.0 | 22.42500000000002 | |
| 2022-10-24 | 2.799 | 0.0 | 1.0 | 19.63750000000003 | |
| 2022-10-25 | 4.283 | 0.0 | 1.0 | 17.862500000000026 | |
| 2022-10-26 | 2.215 | 0.0 | 1.0 | 18.037500000000023 | |
| 2022-10-27 | 3.94 | 0.0 | 1.0 | 20.150000000000034 | |
| 2022-10-28 | 5.535 | 0.0 | 1.0 | 20.600000000000016 | |
| 2022-10-29 | 2.357 | 0.0 | 0.5 | 19.587500000000013 | |
| 2022-10-30 | 1.975 | 0.0 | 0.0 | 19.62500000000002 | |
| 2022-10-31 | 2.228 | 0.0 | 0.0 | 20.37500000000002 | |
| 2022-11-01 | 2.593 | 0.0 | 0.0 | 16.85000000000003 | |
| 2022-11-02 | 1.99 | 0.0 | 0.0 | 15.937500000000021 | |
| 2022-11-03 | 2.248 | 0.0 | 0.0 | 15.412500000000023 | |
| 2022-11-04 | 2.206 | 0.0 | 0.0 | 11.762500000000017 | |
| 2022-11-05 | 2.213 | 0.0 | 0.0 | 10.937500000000021 | |
| 2022-11-06 | 1.736 | 0.0 | 0.0 | 10.862500000000011 | |
| 2022-11-07 | 2.589 | 0.0 | 0.0 | 12.725000000000016 | |
| 2022-11-08 | 1.843 | 0.0 | 0.0 | 16.975000000000016 | |
| 2022-11-09 | 2.616 | 0.0 | 0.0 | 13.90000000000002 | |
| 2022-11-10 | 1.771 | 0.0 | 0.0 | 11.262500000000017 | |
| 2022-11-11 | 5.038 | 0.0 | 1.0 | 14.52500000000002 | |
| 2022-11-12 | 8.671 | 0.0 | 2.0 | 17.600000000000023 | |
| 2022-11-13 | 10.638 | 0.0 | 2.0 | 14.000000000000023 | |
| 2022-11-14 | 7.613 | 0.0 | 1.0 | 14.887500000000024 | |
| 2022-11-15 | 6.577 | 0.0 | 0.0 | 14.587500000000027 | |
| 2022-11-16 | 5.872 | 0.0 | 0.0 | 13.60000000000003 | |
| 2022-11-17 | 7.236 | 0.5 | 0.0 | 13.937500000000028 | |
| 2022-11-18 | 5.302 | 1.0 | 0.0 | 11.125000000000014 | |
| 2022-11-19 | 2.223 | 0.5 | 0.0 | 8.40000000000002 | |
| 2022-11-20 | 1.776 | 0.0 | 0.0 | 8.237500000000033 | |
| 2022-11-21 | 8.14 | 0.5 | 0.0 | 9.712500000000027 | |
| 2022-11-22 | 19.411 | 1.0 | 0.0 | 8.137500000000017 | |
| 2022-11-23 | 15.046 | 1.0 | 0.0 | 11.612500000000033 | |
| 2022-11-24 | 10.991 | 1.0 | 0.0 | 9.437500000000028 | |
| 2022-11-25 | 14.611 | 0.5 | 0.0 | 9.087500000000006 | |
| 2022-11-26 | 14.344 | 0.5 | 0.0 | 6.22500000000003 | |
| 2022-11-27 | 17.762 | 1.0 | 0.0 | 7.5375000000000085 | |
| 2022-11-28 | 15.675 | 1.0 | 0.0 | 10.08750000000002 | |
| 2022-11-29 | 14.678 | 1.0 | 0.0 | 8.575000000000024 | |
| 2022-11-30 | 13.342 | 1.0 | 0.0 | 4.262500000000017 | |
| 2022-12-01 | 16.346 | 1.0 | 0.0 | 4.875000000000021 | |
| 2022-12-02 | 19.825 | 1.5 | 0.0 | 5.812500000000028 | |
| 2022-12-03 | 10.991 | 1.0 | 0.0 | 5.700000000000024 | |
| 2022-12-04 | 6.972 | 1.0 | 0.0 | 3.9375000000000213 | |
| 2022-12-05 | 22.081 | 2.0 | 0.0 | 4.275000000000027 | |
| 2022-12-06 | 22.108 | 2.0 | 0.0 | 7.325000000000024 | |
| 2022-12-07 | 14.034 | 2.0 | 0.0 | 8.775000000000034 | |
| 2022-12-08 | 6.779 | 1.0 | 0.0 | 6.887500000000024 | |
| 2022-12-09 | 2.198 | 0.0 | 0.0 | 8.362500000000026 | |
| 2022-12-10 | 2.198 | 0.0 | 0.0 | 1.962500000000027 | |
| 2022-12-11 | 2.244 | 0.0 | 0.0 | -0.8499999999999801 | |
| 2022-12-12 | 22.233 | 1.0 | 0.0 | 2.200000000000017 | |
| 2022-12-13 | 22.456 | 2.0 | 0.0 | 7.300000000000026 | |
| 2022-12-14 | 17.933 | 2.0 | 0.0 | 11.625000000000021 | |
| 2022-12-15 | 18.002 | 2.0 | 0.0 | 10.400000000000027 | |
| 2022-12-16 | 13.852 | 2.0 | 0.0 | 9.562500000000028 | |
| 2022-12-17 | 7.804 | 1.0 | 0.0 | 5.325000000000024 | |
| 2022-12-18 | 0.933 | 0.0 | 0.0 | 7.475000000000023 | |
| 2022-12-19 | 1.002 | 0.0 | 0.0 | 12.96250000000002 | |
| 2022-12-20 | 0.961 | 0.0 | 0.0 | 13.287500000000016 | |
| 2022-12-21 | 6.027 | 1.0 | 0.0 | 12.850000000000016 | |
| 2022-12-22 | 13.967 | 2.0 | 0.0 | 13.062500000000028 | |
| 2022-12-23 | 9.399 | 1.5 | 0.0 | 13.275000000000027 | |
| 2022-12-24 | 1.362 | 1.0 | 0.0 | 11.200000000000024 | |
| 2022-12-25 | 3.269 | 0.5 | 0.0 | 11.237500000000026 | |
| 2022-12-26 | 1.84 | 0.0 | 0.0 | 11.375000000000028 | |
| 2022-12-27 | 2.74 | 0.0 | 0.5 | 10.812500000000021 | |
| 2022-12-28 | 4.237 | 0.0 | 1.0 | 8.787500000000023 | |
| 2022-12-29 | 4.705 | 0.0 | 1.0 | 10.750000000000028 | |
| 2022-12-30 | 4.66 | 0.0 | 1.0 | 12.162500000000023 | |
| 2022-12-31 | 4.432 | 0.0 | 1.0 | 13.125000000000021 |
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| { | |
| "cells": [ | |
| { | |
| "attachments": {}, | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Are Airbnb guests less energy efficient than their host?" | |
| ] | |
| }, | |
| { | |
| "attachments": {}, | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Building a dataset" | |
| ] | |
| }, | |
| { | |
| "attachments": {}, | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Host" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import datetime as dt\n", | |
| "\n", | |
| "hosts = pd.DataFrame(\n", | |
| " [\n", | |
| " (dt.date(2022, 2, 17), dt.date(2022, 2, 19), 2), \n", | |
| " (dt.date(2022, 2, 26), dt.date(2022, 3, 1), 2), \n", | |
| " (dt.date(2022, 3, 19), dt.date(2022, 3, 23), 1), \n", | |
| " (dt.date(2022, 3, 26), dt.date(2022, 3, 30), 2), \n", | |
| " (dt.date(2022, 6, 17), dt.date(2022, 6, 20), 2), \n", | |
| " (dt.date(2022, 7, 6), dt.date(2022, 7, 10), 1), \n", | |
| " (dt.date(2022, 7, 29), dt.date(2022, 7, 29), 1), \n", | |
| " (dt.date(2022, 8, 1), dt.date(2022, 8, 3), 2), \n", | |
| " (dt.date(2022, 11, 17), dt.date(2022, 11, 19), 1), \n", | |
| " (dt.date(2022, 11, 21), dt.date(2022, 11, 25), 1), \n", | |
| " (dt.date(2022, 11, 26), dt.date(2022, 12, 2), 1), \n", | |
| " (dt.date(2022, 12, 2), dt.date(2022, 12, 3), 2), \n", | |
| " (dt.date(2022, 12, 4), dt.date(2022, 12, 8), 2), \n", | |
| " (dt.date(2022, 12, 12), dt.date(2022, 12, 17), 2), \n", | |
| " (dt.date(2022, 12, 21), dt.date(2022, 12, 23), 2), \n", | |
| " (dt.date(2022, 12, 23), dt.date(2022, 12, 25), 1), \n", | |
| " ],\n", | |
| " columns=[\"since\", \"until\", \"n_hosts\"],\n", | |
| ")\n", | |
| "hosts.to_csv(\"hosts.csv\", index=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n", | |
| "date\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">02</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.0</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">02</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">03</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">07</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>\n", | |
| "Name: n_hosts, dtype: float64\n", | |
| "</pre>\n" | |
| ], | |
| "text/plain": [ | |
| "\n", | |
| "date\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m02\u001b[0m-\u001b[1;36m18\u001b[0m \u001b[1;36m2.0\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m02\u001b[0m-\u001b[1;36m26\u001b[0m \u001b[1;36m1.0\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m03\u001b[0m-\u001b[1;36m23\u001b[0m \u001b[1;36m0.5\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m07\u001b[0m-\u001b[1;36m29\u001b[0m \u001b[1;36m0.5\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m11\u001b[0m-\u001b[1;36m29\u001b[0m \u001b[1;36m1.0\u001b[0m\n", | |
| "Name: n_hosts, dtype: float64\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "host_dates = pd.concat([\n", | |
| " pd.DataFrame({\n", | |
| " \"date\": (dates := pd.date_range(stay[\"since\"], stay[\"until\"], inclusive=\"both\", freq=\"D\")),\n", | |
| " # We count only 50% for start and end days, because people don't spend the whole day there\n", | |
| " \"n_hosts\": (\n", | |
| " [stay[\"n_hosts\"] / 2] +\n", | |
| " [stay[\"n_hosts\"]] * (len(dates) - 2) +\n", | |
| " ([stay[\"n_hosts\"] / 2] if len(dates) > 1 else [])\n", | |
| " )\n", | |
| " })\n", | |
| " for stay in hosts.to_dict(orient=\"records\")\n", | |
| "])\n", | |
| "host_dates = host_dates.groupby(\"date\")[\"n_hosts\"].sum()\n", | |
| "host_dates.sample(5)" | |
| ] | |
| }, | |
| { | |
| "attachments": {}, | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### kW/h" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>kilowatt-hour</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>date</th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>2022-09-05</th>\n", | |
| " <td>2.749</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2022-01-18</th>\n", | |
| " <td>26.668</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2022-10-10</th>\n", | |
| " <td>4.343</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2022-07-14</th>\n", | |
| " <td>3.217</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2022-07-09</th>\n", | |
| " <td>3.281</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "import glob\n", | |
| "import pandas as pd\n", | |
| "\n", | |
| "kwh = (\n", | |
| " pd.concat([\n", | |
| " pd.read_csv(p, parse_dates=[\"Date\"], dayfirst=True)\n", | |
| " for p in glob.glob(\"data/historique*.csv\")\n", | |
| " ])\n", | |
| " .rename(columns={\"Date\": \"date\", \"Conso en kWh\": \"kilowatt-hour\"})\n", | |
| " .set_index(\"date\")\n", | |
| ")\n", | |
| "kwh.sample(5)" | |
| ] | |
| }, | |
| { | |
| "attachments": {}, | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Guests" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Confirmation code</th>\n", | |
| " <th>Status</th>\n", | |
| " <th># of guests</th>\n", | |
| " <th>Start date</th>\n", | |
| " <th>End date</th>\n", | |
| " <th># of nights</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>HMM29JP58P</td>\n", | |
| " <td>Past guest</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2022-01-02</td>\n", | |
| " <td>2022-01-12</td>\n", | |
| " <td>10</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>HM34P8THWE</td>\n", | |
| " <td>Past guest</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2022-01-13</td>\n", | |
| " <td>2022-02-12</td>\n", | |
| " <td>30</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>HMMJH8NSHQ</td>\n", | |
| " <td>Past guest</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2022-02-19</td>\n", | |
| " <td>2022-02-25</td>\n", | |
| " <td>6</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>HM93S8EPCM</td>\n", | |
| " <td>Past guest</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2022-03-07</td>\n", | |
| " <td>2022-03-17</td>\n", | |
| " <td>10</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>HMHMMNSW25</td>\n", | |
| " <td>Past guest</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2022-04-07</td>\n", | |
| " <td>2022-06-01</td>\n", | |
| " <td>55</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "guests = pd.read_csv('data/guests.csv', parse_dates=[\"Start date\", \"End date\"])\n", | |
| "(\n", | |
| " pd.concat([\n", | |
| " guests[[\"Start date\", \"End date\"]],\n", | |
| " pd.DataFrame([\n", | |
| " {\"Start date\": pd.Timestamp('2022-12-27 00:00:00'), \"End date\": pd.Timestamp('2022-12-31 00:00:00')},\n", | |
| " ], index=[len(guests)])\n", | |
| " ])\n", | |
| " .rename(columns={\"Start date\": \"since\", \"End date\": \"until\"})\n", | |
| " .to_csv('guests.csv')\n", | |
| ")\n", | |
| "guests[\"# of guests\"] = guests[\"# of adults\"] + guests[\"# of children\"] + guests[\"# of infants\"]\n", | |
| "guests[['Confirmation code', 'Status', '# of guests', 'Start date', 'End date', '# of nights']].head(5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n", | |
| "date\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">06</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">08</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">08</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.0</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">05</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">01</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">04</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">08</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>\n", | |
| "Name: n_guests, dtype: float64\n", | |
| "</pre>\n" | |
| ], | |
| "text/plain": [ | |
| "\n", | |
| "date\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m06\u001b[0m-\u001b[1;36m08\u001b[0m \u001b[1;36m1.0\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m08\u001b[0m-\u001b[1;36m11\u001b[0m \u001b[1;36m2.0\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m05\u001b[0m-\u001b[1;36m28\u001b[0m \u001b[1;36m1.0\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m01\u001b[0m-\u001b[1;36m22\u001b[0m \u001b[1;36m1.0\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m04\u001b[0m-\u001b[1;36m08\u001b[0m \u001b[1;36m1.0\u001b[0m\n", | |
| "Name: n_guests, dtype: float64\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "guest_dates = pd.concat([\n", | |
| " pd.DataFrame({\n", | |
| " \"date\": (dates := pd.date_range(stay[\"Start date\"], stay[\"End date\"], inclusive=\"both\", freq=\"D\")),\n", | |
| " # We count only 50% for start and end days, because people don't spend the whole day there\n", | |
| " \"n_guests\": (\n", | |
| " [stay[\"# of guests\"] / 2] +\n", | |
| " [stay[\"# of guests\"]] * (len(dates) - 2) +\n", | |
| " ([stay[\"# of guests\"] / 2] if len(dates) > 1 else [])\n", | |
| " )\n", | |
| " })\n", | |
| " for stay in guests.to_dict(orient=\"records\")\n", | |
| "])\n", | |
| "guest_dates = guest_dates.groupby(\"date\")[\"n_guests\"].sum()\n", | |
| "\n", | |
| "# Early guest who should have arrived in 2023\n", | |
| "guest_dates.loc[pd.Timestamp('2022-12-27 00:00:00')] = 0.5\n", | |
| "guest_dates.loc[pd.Timestamp('2022-12-28 00:00:00')] = 1\n", | |
| "guest_dates.loc[pd.Timestamp('2022-12-29 00:00:00')] = 1\n", | |
| "guest_dates.loc[pd.Timestamp('2022-12-30 00:00:00')] = 1\n", | |
| "guest_dates.loc[pd.Timestamp('2022-12-31 00:00:00')] = 1\n", | |
| "\n", | |
| "guest_dates.sample(5)" | |
| ] | |
| }, | |
| { | |
| "attachments": {}, | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Weather" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n", | |
| "date\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">08</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">09</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27.7875</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">04</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17.7750</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.5250</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.9625</span>\n", | |
| "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">01</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.8375</span>\n", | |
| "Name: temperature, dtype: float64\n", | |
| "</pre>\n" | |
| ], | |
| "text/plain": [ | |
| "\n", | |
| "date\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m08\u001b[0m-\u001b[1;36m09\u001b[0m \u001b[1;36m27.7875\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m10\u001b[0m-\u001b[1;36m04\u001b[0m \u001b[1;36m17.7750\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m11\u001b[0m-\u001b[1;36m11\u001b[0m \u001b[1;36m14.5250\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m12\u001b[0m-\u001b[1;36m19\u001b[0m \u001b[1;36m12.9625\u001b[0m\n", | |
| "\u001b[1;36m2022\u001b[0m-\u001b[1;36m01\u001b[0m-\u001b[1;36m10\u001b[0m \u001b[1;36m7.8375\u001b[0m\n", | |
| "Name: temperature, dtype: float64\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "weather = (\n", | |
| " pd.read_csv(\n", | |
| " 'data/donnees-synop-essentielles-omm.csv',\n", | |
| " sep=';',\n", | |
| " parse_dates=[\"Date\"],\n", | |
| " date_parser=lambda x: pd.to_datetime(x, utc=True)\n", | |
| " )\n", | |
| " .rename(columns={'Date': 'datetime', \"Température (°C)\": 'temperature'})\n", | |
| " .assign(date=lambda df: df['datetime'].dt.date)\n", | |
| ")\n", | |
| "temperature = weather.groupby('date')['temperature'].mean()\n", | |
| "temperature = temperature[temperature.index.map(lambda d: d.year == 2022)]\n", | |
| "temperature.sample(5)" | |
| ] | |
| }, | |
| { | |
| "attachments": {}, | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Join them all" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>date</th>\n", | |
| " <th>kilowatt-hour</th>\n", | |
| " <th>n_hosts</th>\n", | |
| " <th>n_guests</th>\n", | |
| " <th>temperature</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>2022-01-01</td>\n", | |
| " <td>2.171</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>7.8750</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2022-01-02</td>\n", | |
| " <td>10.310</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.5</td>\n", | |
| " <td>8.7875</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2022-01-03</td>\n", | |
| " <td>16.107</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>8.3500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>2022-01-04</td>\n", | |
| " <td>16.563</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>9.2500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>2022-01-05</td>\n", | |
| " <td>17.098</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5.7000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "dataset = (\n", | |
| " kwh\n", | |
| " .join(host_dates)\n", | |
| " .join(guest_dates)\n", | |
| " .join(temperature)\n", | |
| ")\n", | |
| "dataset = dataset.sort_index().reset_index()\n", | |
| "dataset.n_hosts = dataset.n_hosts.fillna(0).astype(float)\n", | |
| "dataset.n_guests = dataset.n_guests.fillna(0).astype(float)\n", | |
| "dataset.head(5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">365</span>\n", | |
| "</pre>\n" | |
| ], | |
| "text/plain": [ | |
| "\u001b[1;36m365\u001b[0m\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "len(dataset)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "dataset.to_csv('dataset.csv', index=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "| date | kilowatt-hour | n_hosts | n_guests | temperature |\n", | |
| "|:--------------------|----------------:|----------:|-----------:|--------------:|\n", | |
| "| 2022-01-01 00:00:00 | 2.171 | 0 | 0 | 7.875 |\n", | |
| "| 2022-01-02 00:00:00 | 10.31 | 0 | 0.5 | 8.7875 |\n", | |
| "| 2022-01-03 00:00:00 | 16.107 | 0 | 1 | 8.35 |\n", | |
| "| 2022-01-04 00:00:00 | 16.563 | 0 | 1 | 9.25 |\n", | |
| "| 2022-01-05 00:00:00 | 17.098 | 0 | 1 | 5.7 |\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(dataset.head().to_markdown(index=False))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Regression analysis" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "hourly_temperatures = (\n", | |
| " weather\n", | |
| " .sort_values('datetime')\n", | |
| " .set_index('datetime')\n", | |
| " ['temperature']\n", | |
| " .resample('1H')\n", | |
| " .mean()\n", | |
| " .interpolate()\n", | |
| ")\n", | |
| "reference_temperature = 18\n", | |
| "heating_degree_days = (\n", | |
| " hourly_temperatures\n", | |
| " .apply(lambda t: max(0, reference_temperature - t))\n", | |
| " .to_frame()\n", | |
| " .assign(date=lambda df: df.index.date)\n", | |
| " .groupby('date')\n", | |
| " .sum()\n", | |
| " .rename(columns={'temperature': 'heating_degree_days'})\n", | |
| ")\n", | |
| "cooling_degree_days = (\n", | |
| " hourly_temperatures\n", | |
| " .apply(lambda t: max(0, t - reference_temperature))\n", | |
| " .to_frame()\n", | |
| " .assign(date=lambda df: df.index.date)\n", | |
| " .groupby('date')\n", | |
| " .sum()\n", | |
| " .rename(columns={'temperature': 'cooling_degree_days'})\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>is_weekend</th>\n", | |
| " <th>n_people</th>\n", | |
| " <th>is_guest</th>\n", | |
| " <th>heating_degree_days</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>date</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>2022-01-01</th>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>244.2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2022-01-02</th>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.5</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>219.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2022-01-03</th>\n", | |
| " <td>0.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>231.1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2022-01-04</th>\n", | |
| " <td>0.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>208.9</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2022-01-05</th>\n", | |
| " <td>0.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>301.2</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ] | |
| }, | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "X = (\n", | |
| " dataset\n", | |
| " .set_index('date')\n", | |
| " .join(heating_degree_days)\n", | |
| " .join(cooling_degree_days)\n", | |
| " .reset_index()\n", | |
| ")\n", | |
| "X['is_weekend'] = X.date.dt.dayofweek > 4\n", | |
| "X['n_people'] = X.n_hosts + X.n_guests\n", | |
| "X['is_guest'] = X.n_guests > 0\n", | |
| "X = X.set_index('date')[[\n", | |
| " 'is_weekend',\n", | |
| " 'n_people',\n", | |
| " 'is_guest',\n", | |
| " 'heating_degree_days'\n", | |
| "]].astype(float)\n", | |
| "y = dataset.set_index('date')['kilowatt-hour']\n", | |
| "X.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " OLS Regression Results \n", | |
| "=======================================================================================\n", | |
| "Dep. Variable: kilowatt-hour R-squared (uncentered): 0.844\n", | |
| "Model: OLS Adj. R-squared (uncentered): 0.843\n", | |
| "Method: Least Squares F-statistic: 489.2\n", | |
| "Date: Tue, 17 Jan 2023 Prob (F-statistic): 2.61e-144\n", | |
| "Time: 17:46:00 Log-Likelihood: -1011.5\n", | |
| "No. Observations: 365 AIC: 2031.\n", | |
| "Df Residuals: 361 BIC: 2047.\n", | |
| "Df Model: 4 \n", | |
| "Covariance Type: nonrobust \n", | |
| "=======================================================================================\n", | |
| " coef std err t P>|t| [0.025 0.975]\n", | |
| "---------------------------------------------------------------------------------------\n", | |
| "is_weekend -1.5339 0.435 -3.527 0.000 -2.389 -0.679\n", | |
| "n_people 2.4652 0.331 7.455 0.000 1.815 3.116\n", | |
| "is_guest 1.7145 0.451 3.798 0.000 0.827 2.602\n", | |
| "heating_degree_days 0.0405 0.001 27.288 0.000 0.038 0.043\n", | |
| "==============================================================================\n", | |
| "Omnibus: 6.854 Durbin-Watson: 0.729\n", | |
| "Prob(Omnibus): 0.032 Jarque-Bera (JB): 9.520\n", | |
| "Skew: 0.120 Prob(JB): 0.00856\n", | |
| "Kurtosis: 3.754 Cond. No. 425.\n", | |
| "==============================================================================\n", | |
| "\n", | |
| "Notes:\n", | |
| "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", | |
| "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "import statsmodels.api as sm\n", | |
| "\n", | |
| "mod = sm.OLS(y, X)\n", | |
| "res = mod.fit()\n", | |
| "print(res.summary())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
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| "</pre>\n" | |
| ], | |
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| "\u001b[1;36m0.972\u001b[0m\n" | |
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| "24 * 0.0405" | |
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