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vgthengane/data.csv

Last active August 9, 2023 08:24
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Example dataset for anomaly detection in IoT devices.
Raw
data.csv
MI_dir_L5_weight MI_dir_L5_mean MI_dir_L5_variance MI_dir_L3_weight MI_dir_L3_mean MI_dir_L3_variance MI_dir_L1_weight MI_dir_L1_mean MI_dir_L1_variance MI_dir_L0.1_weight MI_dir_L0.1_mean MI_dir_L0.1_variance MI_dir_L0.01_weight MI_dir_L0.01_mean MI_dir_L0.01_variance H_L5_weight H_L5_mean H_L5_variance H_L3_weight H_L3_mean H_L3_variance H_L1_weight H_L1_mean H_L1_variance H_L0.1_weight H_L0.1_mean H_L0.1_variance H_L0.01_weight H_L0.01_mean H_L0.01_variance HH_L5_weight HH_L5_mean HH_L5_std HH_L5_magnitude HH_L5_radius HH_L5_covariance HH_L5_pcc HH_L3_weight HH_L3_mean HH_L3_std HH_L3_magnitude HH_L3_radius HH_L3_covariance HH_L3_pcc HH_L1_weight HH_L1_mean HH_L1_std HH_L1_magnitude HH_L1_radius HH_L1_covariance HH_L1_pcc HH_L0.1_weight HH_L0.1_mean HH_L0.1_std HH_L0.1_magnitude HH_L0.1_radius HH_L0.1_covariance HH_L0.1_pcc HH_L0.01_weight HH_L0.01_mean HH_L0.01_std HH_L0.01_magnitude HH_L0.01_radius HH_L0.01_covariance HH_L0.01_pcc HH_jit_L5_weight HH_jit_L5_mean HH_jit_L5_variance HH_jit_L3_weight HH_jit_L3_mean HH_jit_L3_variance HH_jit_L1_weight HH_jit_L1_mean HH_jit_L1_variance HH_jit_L0.1_weight HH_jit_L0.1_mean HH_jit_L0.1_variance HH_jit_L0.01_weight HH_jit_L0.01_mean HH_jit_L0.01_variance HpHp_L5_weight HpHp_L5_mean HpHp_L5_std HpHp_L5_magnitude HpHp_L5_radius HpHp_L5_covariance HpHp_L5_pcc HpHp_L3_weight HpHp_L3_mean HpHp_L3_std HpHp_L3_magnitude HpHp_L3_radius HpHp_L3_covariance HpHp_L3_pcc HpHp_L1_weight HpHp_L1_mean HpHp_L1_std HpHp_L1_magnitude HpHp_L1_radius HpHp_L1_covariance HpHp_L1_pcc HpHp_L0.1_weight HpHp_L0.1_mean HpHp_L0.1_std HpHp_L0.1_magnitude HpHp_L0.1_radius HpHp_L0.1_covariance HpHp_L0.1_pcc HpHp_L0.01_weight HpHp_L0.01_mean HpHp_L0.01_std HpHp_L0.01_magnitude HpHp_L0.01_radius HpHp_L0.01_covariance HpHp_L0.01_pcc
1 60 0 1 60 0 1 60 0 1 60 0 1 60 0 1 60 0 1 60 0 1 60 0 1 60 0 1 60 0 1 60 0 60 0 0 0 1 60 0 60 0 0 0 1 60 0 60 0 0 0 1 60 0 60 0 0 0 1 60 0 60 0 0 0 1 1505662852 0 1 1505662852 0 1 1505662852 0 1 1505662852 0 1 1505662852 0 1 60 0 60 0 0 0 1 60 0 60 0 0 0 1 60 0 60 0 0 0 1 60 0 60 0 0 0 1 60 0 60 0 0 0
1 110 0 1 110 0 1 110 0 1 110 0 1 110 0 1 110 0 1 110 0 1 110 0 1 110 2.31E-07 1.357859886 103.6754258 120.2075458 1 110 0 110 0 0 0 1 110 0 110 0 0 0 1 110 0 110 0 0 0 1 110 0.000318773 110 1.02E-07 0 0 1.238635896 106.1467951 7.887769657 106.1467951 62.21691016 0 0 1 328.7024021 0 1 328.7024021 0 1 328.7024021 0 1 328.7024021 1.44E-05 1.238635896 307.0776013 28826.32412 1 110 0 110 0 0 0 1 110 0 110 0 0 0 1 110 0 110 0 0 0 1 110 0.000318773 110 1.02E-07 0 0 1.238635896 106.1467951 7.887769657 106.1467951 62.21691016 0 0
1.724054715 91.43910415 249.4418125 1.823877859 92.45496548 253.6128683 1.937463305 93.48355816 255.7332878 1.99356304 93.94833805 255.997331 1.999354432 93.99483379 255.9999733 1.724054715 91.43910415 249.4418125 1.823877859 92.45496548 253.6128683 1.937463305 93.48355816 255.7332878 1.993563041 93.94833805 255.997331 2.356983295 92.78208351 230.2332483 1 78 0 78 0 0 0 1 78 0 78 0 0 0 1 78 0 78 0 0 0 1 78 0 78 0 0 0 1 78 0 78 0 0 0 1 1505662852 0 1 1505662852 0 1 1505662852 0 1 1505662852 0 1 1505662852 0 1 78 0 78 0 0 0 1 78 0 78 0 0 0 1 78 0 78 0 0 0 1 78 0 78 0 0 0 1 78 0 78 0 0 0
1 342 0 1 342 0 1 342 0 1 342 0 1 342 0 1 342 0 1 342 0 1 342 0 1.00000003 342 2.74E-07 2.48747291 341.959189 53.99190792 1 342 0 342 0 0 0 1 342 0 342 0 0 0 1 342 0 342 0 0 0 1.00000003 342 0.000523532 342 2.74E-07 0 0 2.48747291 341.959189 7.347918612 341.959189 53.99190792 0 0 1 257.574775 0 1 257.574775 0 1 257.574775 0 1.00000003 257.5747678 0.001755865 2.48747291 118.2942046 14996.41736 1 342 0 342 0 0 0 1 342 0 342 0 0 0 1 342 0 342 0 0 0 1.00000003 342 0.000523532 342 2.74E-07 0 0 2.48747291 341.959189 7.347918612 341.959189 53.99190792 0 0
2.516815348 90.86730847 150.827639 2.688980957 91.54199305 160.7050595 2.888468825 92.27753574 169.9441121 2.988463423 92.62714468 173.8064386 2.998842399 92.6627085 174.1807702 2.516815348 90.86730847 150.827639 2.688980957 91.54199305 160.7050595 2.888468825 92.27753574 169.9441121 2.988463424 92.62714468 173.8064386 3.356379674 91.95318935 163.2565251 1.637019983 97.78267815 9.751076034 97.78267815 95.08348382 0 0 1.762942545 98.65533079 9.90918083 98.65533079 98.19186472 0 0 1.913756777 99.54935118 9.989840622 99.54935118 99.79691564 0 0 1.991021465 99.95490488 9.999898321 99.95490488 99.99796643 0 0 2.237519263 98.93041248 9.942634589 98.93041248 98.85598257 0 0 1.637019983 127.9887348 25662.97189 1.762942545 142.3252084 26501.92193 1.913756777 157.0127245 26935.12415 1.991021465 163.6754097 26989.38763 2.237519263 169.8955495 39231.93192 1.637019983 97.78267815 9.751076034 97.78267815 95.08348382 0 0 1.762942545 98.65533079 9.90918083 98.65533079 98.19186472 0 0 1.913756777 99.54935118 9.989840622 99.54935118 99.79691564 0 0 1.991021465 99.95490488 9.999898321 99.95490488 99.99796643 0 0 2.237519263 98.93041248 9.942634589 98.93041248 98.85598257 0 0
1.697057634 342 5.82E-11 1.805306515 342 1.46E-11 1.9303658 342 4.37E-11 1.992808242 342 0 1.999278486 342 0 1.697057634 342 5.82E-11 1.805306515 342 1.46E-11 1.9303658 342 4.37E-11 1.992808271 342 1.37E-07 3.485678163 341.9708972 38.50260636 1.697057634 342 7.63E-06 342 5.82E-11 0 0 1.805306515 342 3.81E-06 342 1.46E-11 0 0 1.9303658 342 6.61E-06 342 4.37E-11 0 0 1.992808271 342 0.000369514 342 1.37E-07 0 0 3.485678163 341.9708972 6.205046846 341.9708972 38.50260636 0 0 1.697057634 105.858864 16044.67581 1.805306515 114.9559772 16380.03236 1.9303658 124.1955756 16551.21766 1.992808271 128.3748636 16572.56736 3.485678163 84.38686458 13551.92523 1.697057634 342 7.63E-06 342 5.82E-11 0 0 1.805306515 342 3.81E-06 342 1.46E-11 0 0 1.9303658 342 6.61E-06 342 4.37E-11 0 0 1.992808271 342 0.000369514 342 1.37E-07 0 0 3.485678163 341.9708972 6.205046846 341.9708972 38.50260636 0 0
2.696687475 342 1.46E-11 2.805070243 342 0 2.930281583 342 0 2.992799547 342 2.91E-11 2.999277614 342 1.46E-11 2.696687475 342 1.46E-11 2.805070243 342 0 2.930281583 342 0 2.992799577 342 9.09E-08 4.485676643 341.9773852 29.91929747 2.696687475 342 3.81E-06 342 1.46E-11 0 0 2.805070243 342 0 342 0 0 0 2.930281583 342 0 342 0 0 0 2.992799577 342 0.000301554 342 9.09E-08 0 0 4.485676643 341.9773852 5.469853515 341.9773852 29.91929747 0 0 2.696687475 66.60374007 12709.43934 2.805070243 73.9745028 13572.17648 2.930281583 81.81210182 14370.35914 2.992799577 85.48031038 14701.72453 4.485676643 65.5743624 11764.3867 2.696687475 342 3.81E-06 342 1.46E-11 0 0 2.805070243 342 0 342 0 0 0 2.930281583 342 0 342 0 0 0 2.992799577 342 0.000301554 342 9.09E-08 0 0 4.485676643 341.9773852 5.469853515 341.9773852 29.91929747 0 0
2.254705222 342 0 2.772435311 342 1.46E-11 3.514499613 342 1.46E-11 3.947350947 342 2.91E-11 3.994691488 342 1.46E-11 2.254705222 342 0 2.772435311 342 1.46E-11 3.514499613 342 1.46E-11 3.947350976 342 6.79E-08 5.478817699 341.9815128 24.45846978 2.254705222 342 0 342 0 0 0 2.772435311 342 3.81E-06 342 1.46E-11 0 0 3.514499613 342 3.81E-06 342 1.46E-11 0 0 3.947350976 342 0.000260547 342 6.79E-08 0 0 5.478817699 341.9815128 4.945550504 341.9815128 24.45846978 0 0 2.254705222 37.16176626 8160.203378 2.772435311 47.37199398 9931.118588 3.514499613 58.59646863 11636.71028 3.947350976 63.88113085 12352.28279 5.478817699 53.64595046 10254.41513 2.254705222 342 0 342 0 0 0 2.772435311 342 3.81E-06 342 1.46E-11 0 0 3.514499613 342 3.81E-06 342 1.46E-11 0 0 3.947350976 342 0.000260547 342 6.79E-08 0 0 5.478817699 341.9815128 4.945550504 341.9815128 24.45846978 0 0
2.046253898 342 0 2.749000012 342 0 4.014206232 342 0 4.887198212 342 1.46E-11 4.988561922 342 0 2.046253898 342 0 2.749000012 342 0 4.014206232 342 0 4.887198241 342 5.40E-08 6.470410848 341.98437 20.67846548 2.046253898 342 0 342 0 0 0 2.749000012 342 0 342 0 0 0 4.014206232 342 0 342 0 0 0 4.887198241 342 0.000232384 342 5.40E-08 0 0 6.470410848 341.98437 4.547358077 341.98437 20.67846548 0 0 2.046253898 19.10915552 4513.299805 2.749000012 30.22013781 6833.020685 4.014206232 44.05438383 9375.249175 4.887198241 50.85534663 10484.35048 6.470410848 45.38922449 9042.535616 2.046253898 342 0 342 0 0 0 2.749000012 342 0 342 0 0 0 4.014206232 342 0 342 0 0 0 4.887198241 342 0.000232384 342 5.40E-08 0 0 6.470410848 341.98437 4.547358077 341.98437 20.67846548 0 0
@richasinghai
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how to remove missing values for this dataset..please provide me code

@snajme
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snajme commented Aug 9, 2023

You remove the missing values with dropna

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