Overview

Dataset statistics

Number of variables6
Number of observations362
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 KiB
Average record size in memory48.4 B

Variable types

Numeric6

Warnings

T_Machine is highly correlated with T_AmbientHigh correlation
T_Ambient is highly correlated with T_MachineHigh correlation

Reproduction

Analysis started2021-05-28 13:50:23.083630
Analysis finished2021-05-28 13:50:36.400860
Duration13.32 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

[3,0]-[3,1]
Real number (ℝ≥0)

Distinct333
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.914286959
Minimum9.898655
Maximum9.958421
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-05-28T15:50:36.613746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.898655
5-th percentile9.90359895
Q19.908607
median9.9133915
Q39.91721
95-th percentile9.9319833
Maximum9.958421
Range0.059766
Interquartile range (IQR)0.008603

Descriptive statistics

Standard deviation0.008505248962
Coefficient of variation (CV)0.0008578780297
Kurtosis3.646080581
Mean9.914286959
Median Absolute Deviation (MAD)0.0043255
Skewness1.478415386
Sum3588.971879
Variance7.233925991 × 105
MonotocityNot monotonic
2021-05-28T15:50:36.816856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.9202383
 
0.8%
9.9129142
 
0.6%
9.917212
 
0.6%
9.9205892
 
0.6%
9.9199252
 
0.6%
9.9139482
 
0.6%
9.9141442
 
0.6%
9.9156862
 
0.6%
9.914282
 
0.6%
9.9090662
 
0.6%
Other values (323)341
94.2%
ValueCountFrequency (%)
9.8986551
0.3%
9.8991631
0.3%
9.8997691
0.3%
9.9011561
0.3%
9.9016051
0.3%
ValueCountFrequency (%)
9.9584211
0.3%
9.9528741
0.3%
9.9439481
0.3%
9.9417811
0.3%
9.9407451
0.3%

[3,2]-[3,3]
Real number (ℝ≥0)

Distinct346
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.927869425
Minimum9.907732
Maximum10.022231
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-05-28T15:50:37.051213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.907732
5-th percentile9.9169958
Q19.92261725
median9.926241
Q39.92997725
95-th percentile9.94372665
Maximum10.022231
Range0.114499
Interquartile range (IQR)0.00736

Descriptive statistics

Standard deviation0.01032907148
Coefficient of variation (CV)0.001040411698
Kurtosis24.20819171
Mean9.927869425
Median Absolute Deviation (MAD)0.003722
Skewness3.656828939
Sum3593.888732
Variance0.0001066897177
MonotocityNot monotonic
2021-05-28T15:50:37.269947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.9231853
 
0.8%
9.9294552
 
0.6%
9.9323452
 
0.6%
9.9224612
 
0.6%
9.9278342
 
0.6%
9.9261542
 
0.6%
9.9245332
 
0.6%
9.9262912
 
0.6%
9.9423852
 
0.6%
9.9293372
 
0.6%
Other values (336)341
94.2%
ValueCountFrequency (%)
9.9077321
0.3%
9.9080051
0.3%
9.9108571
0.3%
9.9108961
0.3%
9.9118531
0.3%
ValueCountFrequency (%)
10.0222311
0.3%
9.9848081
0.3%
9.9754721
0.3%
9.9751591
0.3%
9.9716841
0.3%

T_Machine
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.84889503
Minimum19.1
Maximum22.8
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-05-28T15:50:37.473084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19.1
5-th percentile20
Q120.4
median20.7
Q321.1
95-th percentile22
Maximum22.8
Range3.7
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.6394348223
Coefficient of variation (CV)0.0306699622
Kurtosis0.6252118202
Mean20.84889503
Median Absolute Deviation (MAD)0.3
Skewness0.6679942001
Sum7547.3
Variance0.408876892
MonotocityNot monotonic
2021-05-28T15:50:37.644947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20.751
14.1%
20.334
 
9.4%
20.930
 
8.3%
2126
 
7.2%
20.524
 
6.6%
20.821
 
5.8%
20.615
 
4.1%
20.115
 
4.1%
20.415
 
4.1%
21.114
 
3.9%
Other values (21)117
32.3%
ValueCountFrequency (%)
19.12
 
0.6%
19.65
1.4%
19.72
 
0.6%
19.98
2.2%
205
1.4%
ValueCountFrequency (%)
22.82
 
0.6%
22.72
 
0.6%
22.64
1.1%
22.33
0.8%
22.26
1.7%

T_Ambient
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.74309392
Minimum20.6
Maximum24.2
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-05-28T15:50:37.848035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20.6
5-th percentile21
Q121.4
median21.7
Q322
95-th percentile22.9
Maximum24.2
Range3.6
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.589538847
Coefficient of variation (CV)0.02711384355
Kurtosis1.581465254
Mean21.74309392
Median Absolute Deviation (MAD)0.3
Skewness0.972089221
Sum7871
Variance0.3475560521
MonotocityNot monotonic
2021-05-28T15:50:38.019898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
21.846
12.7%
21.643
11.9%
2226
 
7.2%
21.225
 
6.9%
21.425
 
6.9%
2124
 
6.6%
21.723
 
6.4%
21.921
 
5.8%
22.216
 
4.4%
21.315
 
4.1%
Other values (22)98
27.1%
ValueCountFrequency (%)
20.62
 
0.6%
20.75
 
1.4%
20.83
 
0.8%
20.97
 
1.9%
2124
6.6%
ValueCountFrequency (%)
24.21
0.3%
23.81
0.3%
23.72
0.6%
23.61
0.3%
23.52
0.6%

T_KJ_LT
Real number (ℝ≥0)

Distinct36
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.93756906
Minimum18.5
Maximum22.4
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-05-28T15:50:38.223031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18.5
5-th percentile18.9
Q119.5
median19.9
Q320.4
95-th percentile21.2
Maximum22.4
Range3.9
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.7174691221
Coefficient of variation (CV)0.03598578743
Kurtosis0.1293453863
Mean19.93756906
Median Absolute Deviation (MAD)0.5
Skewness0.5312054726
Sum7217.4
Variance0.5147619412
MonotocityNot monotonic
2021-05-28T15:50:38.585632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
19.736
 
9.9%
19.529
 
8.0%
19.926
 
7.2%
19.324
 
6.6%
20.222
 
6.1%
20.420
 
5.5%
19.118
 
5.0%
20.817
 
4.7%
20.315
 
4.1%
20.114
 
3.9%
Other values (26)141
39.0%
ValueCountFrequency (%)
18.51
 
0.3%
18.65
1.4%
18.77
1.9%
18.82
 
0.6%
18.99
2.5%
ValueCountFrequency (%)
22.41
 
0.3%
222
 
0.6%
21.91
 
0.3%
21.81
 
0.3%
21.66
1.7%

T_KJ_HT
Real number (ℝ≥0)

Distinct34
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.89834254
Minimum15.6
Maximum19.4
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-05-28T15:50:38.804394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum15.6
5-th percentile16
Q116.4
median16.7
Q317.3
95-th percentile18.295
Maximum19.4
Range3.8
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.7050255011
Coefficient of variation (CV)0.04172157709
Kurtosis0.6175425358
Mean16.89834254
Median Absolute Deviation (MAD)0.4
Skewness0.8469289407
Sum6117.2
Variance0.4970609571
MonotocityNot monotonic
2021-05-28T15:50:38.991858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
16.635
 
9.7%
16.730
 
8.3%
16.226
 
7.2%
16.426
 
7.2%
16.925
 
6.9%
17.121
 
5.8%
17.320
 
5.5%
1716
 
4.4%
17.415
 
4.1%
16.315
 
4.1%
Other values (24)133
36.7%
ValueCountFrequency (%)
15.63
 
0.8%
15.72
 
0.6%
15.81
 
0.3%
15.910
2.8%
1615
4.1%
ValueCountFrequency (%)
19.41
 
0.3%
19.11
 
0.3%
192
0.6%
18.93
0.8%
18.82
0.6%

Interactions

2021-05-28T15:50:29.320930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:29.556404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:29.759515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:30.025150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:30.228236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:30.446994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:30.634479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:30.821967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:31.025078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:31.228166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:31.446924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:31.650021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:31.853123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:32.071882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:32.290615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:32.509328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:32.712464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:32.915552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:33.118661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:33.337419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:33.681149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:33.899882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:34.102966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:34.322624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:34.541385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:34.775720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:34.978835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:35.181942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:35.400676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-28T15:50:35.619435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-28T15:50:39.179368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-28T15:50:39.429352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-28T15:50:39.679310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-28T15:50:39.929293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-28T15:50:35.988012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-28T15:50:36.269244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

[3,0]-[3,1][3,2]-[3,3]T_MachineT_AmbientT_KJ_LTT_KJ_HT
09.9110779.92615320.821.919.616.9
19.9096909.93027420.922.019.716.6
29.9067619.92228520.921.920.317.6
39.9035979.93306821.022.019.916.6
49.9037739.92021621.022.019.916.6
59.9041249.91839921.022.019.917.1
69.9085789.92273520.921.819.716.4
79.9125819.92933720.921.820.317.3
89.9166639.93714920.821.620.316.7
99.9057859.92076220.821.620.316.6

Last rows

[3,0]-[3,1][3,2]-[3,3]T_MachineT_AmbientT_KJ_LTT_KJ_HT
3529.9178369.91706821.221.819.917.0
3539.9122889.92044821.221.919.916.7
3549.9041059.91722421.222.020.217.1
3559.9025229.91499821.322.220.617.1
3569.9046719.94437321.422.220.617.2
3579.9011569.92099421.422.220.617.2
3589.9068019.91320121.422.220.117.0
3599.9029329.91185321.322.220.117.0
3609.9141829.93199120.922.120.818.0
3619.9049449.92031120.922.120.818.0