Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations12250000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory747.7 MiB
Average record size in memory64.0 B

Variable types

Numeric8

Alerts

microphone is highly overall correlated with underhang_radialeHigh correlation
underhang_radiale is highly overall correlated with microphoneHigh correlation

Reproduction

Analysis started2025-02-14 00:29:01.543036
Analysis finished2025-02-14 00:34:41.249883
Duration5 minutes and 39.71 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

tachometer
Real number (ℝ)

Distinct114224
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00015652634
Minimum-1.5588
Maximum5.1078
Zeros0
Zeros (%)0.0%
Negative10657507
Negative (%)87.0%
Memory size93.5 MiB
2025-02-14T00:34:41.434008image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1.5588
5-th percentile-0.85093
Q1-0.71351
median-0.64915
Q3-0.53841
95-th percentile4.4422
Maximum5.1078
Range6.6666
Interquartile range (IQR)0.1751

Descriptive statistics

Standard deviation1.7112259
Coefficient of variation (CV)10932.51
Kurtosis2.8404697
Mean0.00015652634
Median Absolute Deviation (MAD)0.07735
Skewness2.1899036
Sum1917.4477
Variance2.928294
MonotonicityNot monotonic
2025-02-14T00:34:41.677309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3868 1567
 
< 0.1%
4.3891 1522
 
< 0.1%
4.387 1521
 
< 0.1%
4.3856 1519
 
< 0.1%
4.391 1514
 
< 0.1%
4.3898 1514
 
< 0.1%
4.3864 1514
 
< 0.1%
4.393 1511
 
< 0.1%
4.39 1509
 
< 0.1%
4.3904 1508
 
< 0.1%
Other values (114214) 12234801
99.9%
ValueCountFrequency (%)
-1.5588 1
< 0.1%
-1.522 1
< 0.1%
-1.4997 1
< 0.1%
-1.4895 1
< 0.1%
-1.4833 1
< 0.1%
-1.48 1
< 0.1%
-1.4692 1
< 0.1%
-1.4624 1
< 0.1%
-1.4603 1
< 0.1%
-1.4583 1
< 0.1%
ValueCountFrequency (%)
5.1078 1
< 0.1%
5.1021 1
< 0.1%
5.0931 1
< 0.1%
5.0927 1
< 0.1%
5.0899 1
< 0.1%
5.0856 1
< 0.1%
5.0852 1
< 0.1%
5.0797 1
< 0.1%
5.0773 1
< 0.1%
5.0764 1
< 0.1%

underhang_axial
Real number (ℝ)

Distinct246083
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0078920674
Minimum-4.4835
Maximum2.3672
Zeros0
Zeros (%)0.0%
Negative5727472
Negative (%)46.8%
Memory size93.5 MiB
2025-02-14T00:34:41.919117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-4.4835
5-th percentile-1.5031
Q1-0.559
median0.07351
Q30.65213
95-th percentile1.2764
Maximum2.3672
Range6.8507
Interquartile range (IQR)1.21113

Descriptive statistics

Standard deviation0.85366277
Coefficient of variation (CV)108.16719
Kurtosis-0.17653539
Mean0.0078920674
Median Absolute Deviation (MAD)0.60317
Skewness-0.41217494
Sum96677.826
Variance0.72874012
MonotonicityNot monotonic
2025-02-14T00:34:42.170985image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0198 446
 
< 0.1%
1.0209 446
 
< 0.1%
1.0356 440
 
< 0.1%
1.016 436
 
< 0.1%
1.0071 434
 
< 0.1%
1.0422 434
 
< 0.1%
1.0001 434
 
< 0.1%
1.0102 433
 
< 0.1%
1.01 432
 
< 0.1%
1.0093 431
 
< 0.1%
Other values (246073) 12245634
> 99.9%
ValueCountFrequency (%)
-4.4835 1
< 0.1%
-4.455 1
< 0.1%
-4.4522 1
< 0.1%
-4.4352 1
< 0.1%
-4.4094 1
< 0.1%
-4.4022 1
< 0.1%
-4.3535 1
< 0.1%
-4.3449 1
< 0.1%
-4.3409 1
< 0.1%
-4.3051 1
< 0.1%
ValueCountFrequency (%)
2.3672 1
< 0.1%
2.3503 1
< 0.1%
2.3298 1
< 0.1%
2.3175 1
< 0.1%
2.313 1
< 0.1%
2.3071 1
< 0.1%
2.3068 1
< 0.1%
2.3065 1
< 0.1%
2.2823 1
< 0.1%
2.2807 1
< 0.1%

underhang_radiale
Real number (ℝ)

High correlation 

Distinct232491
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.00046604311
Minimum-3.417
Maximum4.0998
Zeros0
Zeros (%)0.0%
Negative5986345
Negative (%)48.9%
Memory size93.5 MiB
2025-02-14T00:34:42.427063image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-3.417
5-th percentile-0.68157
Q1-0.28669
median0.013319
Q30.29799
95-th percentile0.62419
Maximum4.0998
Range7.5168
Interquartile range (IQR)0.58468

Descriptive statistics

Standard deviation0.41744548
Coefficient of variation (CV)-895.72289
Kurtosis0.74825201
Mean-0.00046604311
Median Absolute Deviation (MAD)0.291731
Skewness-0.02154646
Sum-5709.0281
Variance0.17426073
MonotonicityNot monotonic
2025-02-14T00:34:42.693675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32659 196
 
< 0.1%
0.35981 195
 
< 0.1%
0.37614 195
 
< 0.1%
0.34903 193
 
< 0.1%
0.33417 193
 
< 0.1%
0.37639 192
 
< 0.1%
0.35889 191
 
< 0.1%
0.33089 189
 
< 0.1%
0.30512 188
 
< 0.1%
0.35564 188
 
< 0.1%
Other values (232481) 12248080
> 99.9%
ValueCountFrequency (%)
-3.417 1
< 0.1%
-3.2004 1
< 0.1%
-3.0564 1
< 0.1%
-2.8099 1
< 0.1%
-2.7927 1
< 0.1%
-2.7709 1
< 0.1%
-2.7514 1
< 0.1%
-2.7004 1
< 0.1%
-2.691 1
< 0.1%
-2.689 1
< 0.1%
ValueCountFrequency (%)
4.0998 1
< 0.1%
3.6636 1
< 0.1%
3.6241 1
< 0.1%
3.6071 1
< 0.1%
3.571 1
< 0.1%
3.5548 1
< 0.1%
3.55 1
< 0.1%
3.542 1
< 0.1%
3.5344 1
< 0.1%
3.5314 1
< 0.1%

underhang_tangential
Real number (ℝ)

Distinct180965
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00037320628
Minimum-2.1717
Maximum1.933
Zeros0
Zeros (%)0.0%
Negative6089620
Negative (%)49.7%
Memory size93.5 MiB
2025-02-14T00:34:42.947073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2.1717
5-th percentile-0.30311
Q1-0.086501
median0.00084348
Q30.085292
95-th percentile0.30619
Maximum1.933
Range4.1047
Interquartile range (IQR)0.171793

Descriptive statistics

Standard deviation0.18376924
Coefficient of variation (CV)492.4066
Kurtosis2.8710231
Mean0.00037320628
Median Absolute Deviation (MAD)0.08589952
Skewness0.021370869
Sum4571.777
Variance0.033771132
MonotonicityNot monotonic
2025-02-14T00:34:43.194389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1009 337
 
< 0.1%
-0.10375 335
 
< 0.1%
-0.013859 334
 
< 0.1%
0.012623 327
 
< 0.1%
0.014312 327
 
< 0.1%
0.0094823 327
 
< 0.1%
-0.10113 326
 
< 0.1%
-0.10036 324
 
< 0.1%
0.10331 323
 
< 0.1%
0.015578 323
 
< 0.1%
Other values (180955) 12246717
> 99.9%
ValueCountFrequency (%)
-2.1717 1
< 0.1%
-1.9202 1
< 0.1%
-1.8805 1
< 0.1%
-1.6988 1
< 0.1%
-1.674 1
< 0.1%
-1.6639 1
< 0.1%
-1.6618 1
< 0.1%
-1.6395 1
< 0.1%
-1.6163 1
< 0.1%
-1.6044 1
< 0.1%
ValueCountFrequency (%)
1.933 1
< 0.1%
1.9289 1
< 0.1%
1.9074 1
< 0.1%
1.8956 1
< 0.1%
1.8531 1
< 0.1%
1.8512 1
< 0.1%
1.8282 1
< 0.1%
1.7989 1
< 0.1%
1.7764 1
< 0.1%
1.7683 1
< 0.1%

overhang_axial
Real number (ℝ)

Distinct245899
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014836568
Minimum-2.9566
Maximum3.5087
Zeros0
Zeros (%)0.0%
Negative5890414
Negative (%)48.1%
Memory size93.5 MiB
2025-02-14T00:34:43.428248image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2.9566
5-th percentile-1.0405
Q1-0.3302
median0.021706
Q30.34653
95-th percentile1.0684
Maximum3.5087
Range6.4653
Interquartile range (IQR)0.67673

Descriptive statistics

Standard deviation0.62489252
Coefficient of variation (CV)42.118402
Kurtosis1.0740658
Mean0.014836568
Median Absolute Deviation (MAD)0.338224
Skewness0.054474988
Sum181747.96
Variance0.39049066
MonotonicityNot monotonic
2025-02-14T00:34:43.685941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0133 223
 
< 0.1%
-1.0048 213
 
< 0.1%
1.0114 213
 
< 0.1%
1.0067 212
 
< 0.1%
1.008 211
 
< 0.1%
1.0065 211
 
< 0.1%
1.0403 209
 
< 0.1%
1.002 209
 
< 0.1%
-1.0174 209
 
< 0.1%
1.0053 209
 
< 0.1%
Other values (245889) 12247881
> 99.9%
ValueCountFrequency (%)
-2.9566 1
< 0.1%
-2.9455 1
< 0.1%
-2.9317 1
< 0.1%
-2.9308 1
< 0.1%
-2.9217 1
< 0.1%
-2.9203 1
< 0.1%
-2.9202 1
< 0.1%
-2.9136 1
< 0.1%
-2.9113 1
< 0.1%
-2.908 1
< 0.1%
ValueCountFrequency (%)
3.5087 1
< 0.1%
3.4948 1
< 0.1%
3.4926 1
< 0.1%
3.4907 1
< 0.1%
3.4896 1
< 0.1%
3.4874 1
< 0.1%
3.472 1
< 0.1%
3.4719 1
< 0.1%
3.4595 1
< 0.1%
3.4591 1
< 0.1%

overhang_radiale
Real number (ℝ)

Distinct51299
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0030212783
Minimum-0.37335
Maximum0.28128
Zeros0
Zeros (%)0.0%
Negative5618037
Negative (%)45.9%
Memory size93.5 MiB
2025-02-14T00:34:43.934484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.37335
5-th percentile-0.049743
Q1-0.021577
median0.0040613
Q30.028108
95-th percentile0.053393
Maximum0.28128
Range0.65463
Interquartile range (IQR)0.049685

Descriptive statistics

Standard deviation0.034797504
Coefficient of variation (CV)11.517477
Kurtosis1.4477491
Mean0.0030212783
Median Absolute Deviation (MAD)0.0247907
Skewness-0.13673961
Sum37010.659
Variance0.0012108663
MonotonicityNot monotonic
2025-02-14T00:34:44.190330image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.021036 939
 
< 0.1%
0.021106 929
 
< 0.1%
-0.0029338 924
 
< 0.1%
0.01234 923
 
< 0.1%
0.020984 921
 
< 0.1%
0.02633 915
 
< 0.1%
0.010447 914
 
< 0.1%
0.012353 913
 
< 0.1%
0.015382 911
 
< 0.1%
0.02067 907
 
< 0.1%
Other values (51289) 12240804
99.9%
ValueCountFrequency (%)
-0.37335 1
< 0.1%
-0.37323 1
< 0.1%
-0.37109 1
< 0.1%
-0.36999 1
< 0.1%
-0.36676 1
< 0.1%
-0.36609 1
< 0.1%
-0.36602 1
< 0.1%
-0.36439 1
< 0.1%
-0.36428 1
< 0.1%
-0.36352 1
< 0.1%
ValueCountFrequency (%)
0.28128 1
< 0.1%
0.28079 1
< 0.1%
0.27933 1
< 0.1%
0.27908 1
< 0.1%
0.27805 1
< 0.1%
0.27786 1
< 0.1%
0.27773 1
< 0.1%
0.27738 1
< 0.1%
0.27668 1
< 0.1%
0.27655 1
< 0.1%

overhang_tangential
Real number (ℝ)

Distinct231856
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.018773925
Minimum-2.8658
Maximum2.836
Zeros0
Zeros (%)0.0%
Negative5944867
Negative (%)48.5%
Memory size93.5 MiB
2025-02-14T00:34:44.436630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2.8658
5-th percentile-0.6486005
Q1-0.22954
median0.012973
Q30.26178
95-th percentile0.71014
Maximum2.836
Range5.7018
Interquartile range (IQR)0.49132

Descriptive statistics

Standard deviation0.41335924
Coefficient of variation (CV)22.017732
Kurtosis1.1162237
Mean0.018773925
Median Absolute Deviation (MAD)0.245577
Skewness0.056915458
Sum229980.58
Variance0.17086586
MonotonicityNot monotonic
2025-02-14T00:34:44.689510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10093 212
 
< 0.1%
0.11173 212
 
< 0.1%
0.12089 211
 
< 0.1%
-0.11193 211
 
< 0.1%
0.10423 209
 
< 0.1%
-0.10143 209
 
< 0.1%
0.10606 207
 
< 0.1%
-0.10387 206
 
< 0.1%
-0.13497 204
 
< 0.1%
-0.10956 204
 
< 0.1%
Other values (231846) 12247915
> 99.9%
ValueCountFrequency (%)
-2.8658 1
< 0.1%
-2.8032 1
< 0.1%
-2.7132 1
< 0.1%
-2.7114 1
< 0.1%
-2.692 1
< 0.1%
-2.6462 1
< 0.1%
-2.6383 1
< 0.1%
-2.6319 1
< 0.1%
-2.6272 1
< 0.1%
-2.627 1
< 0.1%
ValueCountFrequency (%)
2.836 1
< 0.1%
2.7984 1
< 0.1%
2.7727 1
< 0.1%
2.7714 1
< 0.1%
2.7705 1
< 0.1%
2.7676 1
< 0.1%
2.7628 1
< 0.1%
2.7246 1
< 0.1%
2.7101 1
< 0.1%
2.7063 1
< 0.1%

microphone
Real number (ℝ)

High correlation 

Distinct49254
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012277501
Minimum-3.3691
Maximum0.8529
Zeros0
Zeros (%)0.0%
Negative6276551
Negative (%)51.2%
Memory size93.5 MiB
2025-02-14T00:34:44.948913image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-3.3691
5-th percentile-0.23771
Q1-0.12454
median-0.0064966
Q30.1332
95-th percentile0.31705
Maximum0.8529
Range4.222
Interquartile range (IQR)0.25774

Descriptive statistics

Standard deviation0.17559198
Coefficient of variation (CV)14.301931
Kurtosis0.022390285
Mean0.012277501
Median Absolute Deviation (MAD)0.1262634
Skewness0.51772527
Sum150399.39
Variance0.030832542
MonotonicityNot monotonic
2025-02-14T00:34:45.191114image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.11813 830
 
< 0.1%
-0.11512 822
 
< 0.1%
-0.12003 818
 
< 0.1%
-0.11723 816
 
< 0.1%
-0.11801 814
 
< 0.1%
-0.11222 814
 
< 0.1%
-0.11957 808
 
< 0.1%
-0.099784 806
 
< 0.1%
-0.11499 804
 
< 0.1%
-0.12164 804
 
< 0.1%
Other values (49244) 12241864
99.9%
ValueCountFrequency (%)
-3.3691 1
< 0.1%
-3.0922 1
< 0.1%
-2.7676 1
< 0.1%
-2.7006 1
< 0.1%
-2.3784 1
< 0.1%
-2.2998 1
< 0.1%
-2.0714 1
< 0.1%
-1.8191 1
< 0.1%
-1.7019 1
< 0.1%
-1.5774 1
< 0.1%
ValueCountFrequency (%)
0.8529 1
< 0.1%
0.8499 1
< 0.1%
0.84718 1
< 0.1%
0.84543 1
< 0.1%
0.84146 1
< 0.1%
0.841 1
< 0.1%
0.83942 1
< 0.1%
0.8383 1
< 0.1%
0.83749 1
< 0.1%
0.83744 1
< 0.1%

Interactions

2025-02-14T00:33:56.641876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:10.058964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:25.141242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:40.896196image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:56.295225image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:11.856565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:26.589255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:41.534103image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:58.569483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:11.971879image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:26.978065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:42.832433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:58.239977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:13.732400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:28.478944image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:43.429278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:34:00.477034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:13.832236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:28.960331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:44.659210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:00.160946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:15.606989image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:30.351482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:45.331897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:34:02.399803image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:15.695927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:30.966145image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:46.613967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:01.962552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:17.476273image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:32.229215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:47.240914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:34:04.305017image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:17.525380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:32.997385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:48.560292image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:03.946249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:19.212783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:34.082854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:49.141844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:34:06.442851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:19.366213image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:34.973526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:50.484222image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:05.882863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:21.042046image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:35.878215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:51.032866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:34:08.419032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:21.213722image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:36.978872image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:52.431100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:07.851858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:22.913988image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:37.754989image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:52.840906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:34:10.204289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:23.048829image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:38.975013image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:32:54.379727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:10.005228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:24.763385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:39.651578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-14T00:33:54.744790image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-02-14T00:34:45.354974image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
microphoneoverhang_axialoverhang_radialeoverhang_tangentialtachometerunderhang_axialunderhang_radialeunderhang_tangential
microphone1.000-0.024-0.022-0.0590.002-0.182-0.631-0.180
overhang_axial-0.0241.0000.2890.4940.078-0.0060.0260.037
overhang_radiale-0.0220.2891.0000.2270.037-0.0050.0280.017
overhang_tangential-0.0590.4940.2271.0000.0660.0620.108-0.021
tachometer0.0020.0780.0370.0661.0000.0120.0870.015
underhang_axial-0.182-0.006-0.0050.0620.0121.0000.3710.230
underhang_radiale-0.6310.0260.0280.1080.0870.3711.0000.123
underhang_tangential-0.1800.0370.017-0.0210.0150.2300.1231.000

Missing values

2025-02-14T00:34:10.763264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-14T00:34:18.318729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

tachometerunderhang_axialunderhang_radialeunderhang_tangentialoverhang_axialoverhang_radialeoverhang_tangentialmicrophone
0-0.79198-0.323980-0.4538100.233640-0.40261-0.020101-0.104640-0.099248
1-0.958770.703680-0.2898900.258740-0.36562-0.018753-0.0045200.038236
2-0.80153-0.078640-0.463250-0.012234-0.46396-0.022398-0.0439190.125900
3-0.827260.6736700.101460-0.136560-0.38256-0.0214680.049500-0.160140
4-0.95506-0.106500-0.485760-0.470790-0.49939-0.024554-0.0243060.315180
5-0.786480.3089800.130440-0.422160-0.42019-0.0216470.139270-0.273800
6-0.888460.085564-0.418340-0.553700-0.47812-0.0217240.1451300.415570
7-0.85492-0.679910-0.022051-0.237810-0.46686-0.0225010.188690-0.185940
8-0.839320.573730-0.2628200.034744-0.45229-0.0217310.2803100.179230
9-0.93213-0.082191-0.3084900.218390-0.53129-0.0245860.315660-0.015015
tachometerunderhang_axialunderhang_radialeunderhang_tangentialoverhang_axialoverhang_radialeoverhang_tangentialmicrophone
12249990-0.883670.188390-0.450490-0.260020-0.37593-0.085476-0.949350.115240
12249991-1.060100.2607700.063838-0.167010-0.35766-0.079238-0.90423-0.126750
12249992-0.83716-0.076848-0.505040-0.139300-0.36880-0.080457-0.861480.238050
12249993-1.060400.4902400.170130-0.033615-0.34959-0.078622-0.76574-0.288380
12249994-0.737760.022539-0.585960-0.105120-0.35844-0.079687-0.798680.357280
12249995-0.854980.5683200.075671-0.141710-0.34689-0.081856-0.68099-0.247080
12249996-1.104400.164050-0.766910-0.219440-0.36255-0.085540-0.839540.195310
12249997-0.790130.611480-0.403500-0.253510-0.35477-0.084661-0.94190-0.158360
12249998-0.935770.253910-0.366820-0.271260-0.33739-0.082228-0.944300.022952
12249999-0.845930.402620-0.677210-0.328130-0.35052-0.085148-1.068500.038042