Overview

Dataset statistics

Number of variables12
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.9 KiB
Average record size in memory108.3 B

Variable types

Numeric11
Categorical1

Dataset

DescriptionSample
Author엔에스원소프트㈜
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT10NS1001

Alerts

GTR_YM has constant value ""Constant
DATA_SN is highly overall correlated with LA_STD_WTRON_VE and 2 other fieldsHigh correlation
SHP_LA is highly overall correlated with SHP_LO and 1 other fieldsHigh correlation
SHP_LO is highly overall correlated with SHP_LA and 1 other fieldsHigh correlation
SHP_VE is highly overall correlated with GTR_YMDHMSHigh correlation
SHP_COG is highly overall correlated with SHP_SOG and 2 other fieldsHigh correlation
SHP_SOG is highly overall correlated with SHP_COG and 4 other fieldsHigh correlation
GYRO_AGL is highly overall correlated with SHP_COG and 2 other fieldsHigh correlation
LA_STD_WTRON_VE is highly overall correlated with DATA_SN and 2 other fieldsHigh correlation
LO_STD_WTRON_VE is highly overall correlated with DATA_SN and 2 other fieldsHigh correlation
SHP_RTTRN is highly overall correlated with DATA_SN and 3 other fieldsHigh correlation
GTR_YMDHMS is highly overall correlated with SHP_LA and 2 other fieldsHigh correlation
DATA_SN has unique valuesUnique
GTR_YMDHMS has unique valuesUnique
SHP_RTTRN has 209 (41.8%) zerosZeros

Reproduction

Analysis started2024-03-13 12:43:55.455399
Analysis finished2024-03-13 12:44:14.169362
Duration18.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

DATA_SN
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144815.99
Minimum137949
Maximum160901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:44:14.284650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum137949
5-th percentile137980.95
Q1138220.75
median138345.5
Q3148606.25
95-th percentile158122.05
Maximum160901
Range22952
Interquartile range (IQR)10385.5

Descriptive statistics

Standard deviation8002.2907
Coefficient of variation (CV)0.055258336
Kurtosis-1.1542882
Mean144815.99
Median Absolute Deviation (MAD)365
Skewness0.65719294
Sum72407996
Variance64036657
MonotonicityStrictly increasing
2024-03-13T21:44:14.525396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137949 1
 
0.2%
148538 1
 
0.2%
148551 1
 
0.2%
148550 1
 
0.2%
148549 1
 
0.2%
148548 1
 
0.2%
148547 1
 
0.2%
148546 1
 
0.2%
148545 1
 
0.2%
148544 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
137949 1
0.2%
137950 1
0.2%
137951 1
0.2%
137952 1
0.2%
137953 1
0.2%
137954 1
0.2%
137955 1
0.2%
137956 1
0.2%
137957 1
0.2%
137958 1
0.2%
ValueCountFrequency (%)
160901 1
0.2%
160851 1
0.2%
158145 1
0.2%
158144 1
0.2%
158143 1
0.2%
158142 1
0.2%
158141 1
0.2%
158140 1
0.2%
158139 1
0.2%
158138 1
0.2%

GTR_YM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
202205
500 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202205
2nd row202205
3rd row202205
4th row202205
5th row202205

Common Values

ValueCountFrequency (%)
202205 500
100.0%

Length

2024-03-13T21:44:14.748658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:44:14.890361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202205 500
100.0%

SHP_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.629707
Minimum34.1165
Maximum35.49851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:44:15.002553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.1165
5-th percentile34.17491
Q134.5319
median34.75774
Q334.83621
95-th percentile34.881
Maximum35.49851
Range1.38201
Interquartile range (IQR)0.30431

Descriptive statistics

Standard deviation0.26895517
Coefficient of variation (CV)0.0077666025
Kurtosis-0.54447841
Mean34.629707
Median Absolute Deviation (MAD)0.10473
Skewness-0.71168944
Sum17314.853
Variance0.072336882
MonotonicityNot monotonic
2024-03-13T21:44:15.155357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
34.83621 176
35.2%
34.17491 66
 
13.2%
34.65301 65
 
13.0%
34.75774 38
 
7.6%
34.881 32
 
6.4%
34.65524 21
 
4.2%
34.57267 20
 
4.0%
34.26237 19
 
3.8%
34.1165 15
 
3.0%
34.21011 13
 
2.6%
Other values (8) 35
 
7.0%
ValueCountFrequency (%)
34.1165 15
 
3.0%
34.17491 66
13.2%
34.21011 13
 
2.6%
34.26237 19
 
3.8%
34.2886 9
 
1.8%
34.50107 2
 
0.4%
34.5319 10
 
2.0%
34.57267 20
 
4.0%
34.57737 1
 
0.2%
34.61131 1
 
0.2%
ValueCountFrequency (%)
35.49851 2
 
0.4%
34.90538 1
 
0.2%
34.89084 9
 
1.8%
34.881 32
 
6.4%
34.83621 176
35.2%
34.75774 38
 
7.6%
34.65524 21
 
4.2%
34.65301 65
 
13.0%
34.61131 1
 
0.2%
34.57737 1
 
0.2%

SHP_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.21035
Minimum127.02757
Maximum129.59849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:44:15.300015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.02757
5-th percentile127.02757
Q1128.1192
median128.1192
Q3128.6196
95-th percentile128.87831
Maximum129.59849
Range2.57092
Interquartile range (IQR)0.5004

Descriptive statistics

Standard deviation0.47620328
Coefficient of variation (CV)0.0037142344
Kurtosis1.3402802
Mean128.21035
Median Absolute Deviation (MAD)0.21918
Skewness-0.82369809
Sum64105.173
Variance0.22676956
MonotonicityNot monotonic
2024-03-13T21:44:15.454233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
128.1192 176
35.2%
128.6196 66
 
13.2%
128.35264 65
 
13.0%
127.02757 38
 
7.6%
127.93827 32
 
6.4%
128.33838 21
 
4.2%
128.99867 20
 
4.0%
128.87831 19
 
3.8%
128.6652 15
 
3.0%
128.04487 13
 
2.6%
Other values (8) 35
 
7.0%
ValueCountFrequency (%)
127.02757 38
 
7.6%
127.14781 9
 
1.8%
127.93827 32
 
6.4%
127.98634 10
 
2.0%
128.04487 13
 
2.6%
128.06442 1
 
0.2%
128.07854 1
 
0.2%
128.1192 176
35.2%
128.33838 21
 
4.2%
128.35264 65
 
13.0%
ValueCountFrequency (%)
129.59849 2
 
0.4%
129.26149 2
 
0.4%
128.99867 20
 
4.0%
128.87831 19
 
3.8%
128.71449 1
 
0.2%
128.68959 9
 
1.8%
128.6652 15
 
3.0%
128.6196 66
13.2%
128.35264 65
13.0%
128.33838 21
 
4.2%

SHP_VE
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10268
Minimum0.01
Maximum0.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:44:15.615041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.06
median0.06
Q30.11
95-th percentile0.22
Maximum0.49
Range0.48
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.10112403
Coefficient of variation (CV)0.98484643
Kurtosis6.2030739
Mean0.10268
Median Absolute Deviation (MAD)0
Skewness2.4264416
Sum51.34
Variance0.01022607
MonotonicityNot monotonic
2024-03-13T21:44:15.760084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.06 254
50.8%
0.22 66
 
13.2%
0.01 40
 
8.0%
0.08 32
 
6.4%
0.04 21
 
4.2%
0.49 20
 
4.0%
0.11 19
 
3.8%
0.13 15
 
3.0%
0.03 11
 
2.2%
0.2 10
 
2.0%
Other values (3) 12
 
2.4%
ValueCountFrequency (%)
0.01 40
 
8.0%
0.03 11
 
2.2%
0.04 21
 
4.2%
0.06 254
50.8%
0.08 32
 
6.4%
0.09 9
 
1.8%
0.1 1
 
0.2%
0.11 19
 
3.8%
0.13 15
 
3.0%
0.2 10
 
2.0%
ValueCountFrequency (%)
0.49 20
 
4.0%
0.35 2
 
0.4%
0.22 66
 
13.2%
0.2 10
 
2.0%
0.13 15
 
3.0%
0.11 19
 
3.8%
0.1 1
 
0.2%
0.09 9
 
1.8%
0.08 32
 
6.4%
0.06 254
50.8%

SHP_COG
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.9634
Minimum53.9
Maximum262.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:44:15.947273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53.9
5-th percentile54.1
Q155.4
median235.6
Q3252.8
95-th percentile261.9
Maximum262.7
Range208.8
Interquartile range (IQR)197.4

Descriptive statistics

Standard deviation94.241949
Coefficient of variation (CV)0.56444675
Kurtosis-1.8766561
Mean166.9634
Median Absolute Deviation (MAD)21.55
Skewness-0.31275876
Sum83481.7
Variance8881.545
MonotonicityNot monotonic
2024-03-13T21:44:16.139466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.4 27
 
5.4%
55.3 20
 
4.0%
54.1 18
 
3.6%
55.5 18
 
3.6%
253.0 15
 
3.0%
235.9 13
 
2.6%
54.5 12
 
2.4%
55.8 12
 
2.4%
253.5 11
 
2.2%
54.8 10
 
2.0%
Other values (110) 344
68.8%
ValueCountFrequency (%)
53.9 4
 
0.8%
54.0 5
 
1.0%
54.1 18
3.6%
54.3 6
 
1.2%
54.4 6
 
1.2%
54.5 12
2.4%
54.6 2
 
0.4%
54.7 3
 
0.6%
54.8 10
2.0%
54.9 8
1.6%
ValueCountFrequency (%)
262.7 3
0.6%
262.5 1
 
0.2%
262.3 3
0.6%
262.2 6
1.2%
262.1 6
1.2%
262.0 3
0.6%
261.9 4
0.8%
261.7 3
0.6%
261.6 2
 
0.4%
261.5 3
0.6%

SHP_SOG
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.8
Minimum0.1
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:44:16.316559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile11.1
Q111.2
median12.1
Q312.3
95-th percentile12.8
Maximum14.9
Range14.8
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.97846553
Coefficient of variation (CV)0.082920807
Kurtosis80.373103
Mean11.8
Median Absolute Deviation (MAD)0.6
Skewness-6.7506817
Sum5900
Variance0.95739479
MonotonicityNot monotonic
2024-03-13T21:44:16.475900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
11.1 100
20.0%
12.2 83
16.6%
11.2 71
14.2%
12.1 48
9.6%
11.5 27
 
5.4%
12.3 26
 
5.2%
12.7 22
 
4.4%
12.6 22
 
4.4%
12.5 21
 
4.2%
12.8 18
 
3.6%
Other values (12) 62
12.4%
ValueCountFrequency (%)
0.1 2
 
0.4%
10.8 2
 
0.4%
11.0 7
 
1.4%
11.1 100
20.0%
11.2 71
14.2%
11.3 2
 
0.4%
11.4 10
 
2.0%
11.5 27
 
5.4%
11.6 9
 
1.8%
11.9 1
 
0.2%
ValueCountFrequency (%)
14.9 1
 
0.2%
13.0 1
 
0.2%
12.9 16
 
3.2%
12.8 18
 
3.6%
12.7 22
 
4.4%
12.6 22
 
4.4%
12.5 21
 
4.2%
12.4 9
 
1.8%
12.3 26
 
5.2%
12.2 83
16.6%

GYRO_AGL
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.1916
Minimum29.9
Maximum262.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:44:16.661081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29.9
5-th percentile54.7
Q155.1
median234.7
Q3252.1
95-th percentile261.9
Maximum262.6
Range232.7
Interquartile range (IQR)197

Descriptive statistics

Standard deviation94.371503
Coefficient of variation (CV)0.56784761
Kurtosis-1.8810362
Mean166.1916
Median Absolute Deviation (MAD)21.45
Skewness-0.30625846
Sum83095.8
Variance8905.9806
MonotonicityNot monotonic
2024-03-13T21:44:16.931349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.0 35
 
7.0%
54.7 30
 
6.0%
55.1 30
 
6.0%
252.9 28
 
5.6%
55.2 24
 
4.8%
235.1 24
 
4.8%
54.8 19
 
3.8%
235.3 18
 
3.6%
234.7 18
 
3.6%
54.6 15
 
3.0%
Other values (53) 259
51.8%
ValueCountFrequency (%)
29.9 2
 
0.4%
54.6 15
3.0%
54.7 30
6.0%
54.8 19
3.8%
54.9 9
 
1.8%
55.0 35
7.0%
55.1 30
6.0%
55.2 24
4.8%
55.3 12
 
2.4%
55.4 5
 
1.0%
ValueCountFrequency (%)
262.6 4
 
0.8%
262.3 3
 
0.6%
262.2 2
 
0.4%
262.1 4
 
0.8%
262.0 3
 
0.6%
261.9 13
2.6%
261.7 9
1.8%
256.3 2
 
0.4%
256.0 5
 
1.0%
255.6 1
 
0.2%

LA_STD_WTRON_VE
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.6658
Minimum8.05
Maximum16.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:44:17.135377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.05
5-th percentile10.88
Q111.06
median11.14
Q312.26
95-th percentile12.7
Maximum16.04
Range7.99
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.79555778
Coefficient of variation (CV)0.068195733
Kurtosis2.0657276
Mean11.6658
Median Absolute Deviation (MAD)0.26
Skewness0.3697685
Sum5832.9
Variance0.63291218
MonotonicityNot monotonic
2024-03-13T21:44:17.303026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
11.06 176
35.2%
12.18 66
 
13.2%
10.88 65
 
13.0%
12.7 38
 
7.6%
12.68 32
 
6.4%
12.26 21
 
4.2%
11.83 20
 
4.0%
12.44 19
 
3.8%
11.32 15
 
3.0%
11.14 13
 
2.6%
Other values (8) 35
 
7.0%
ValueCountFrequency (%)
8.05 2
 
0.4%
10.88 65
 
13.0%
11.06 176
35.2%
11.14 13
 
2.6%
11.32 15
 
3.0%
11.38 2
 
0.4%
11.83 20
 
4.0%
12.18 66
 
13.2%
12.26 21
 
4.2%
12.44 19
 
3.8%
ValueCountFrequency (%)
16.04 1
 
0.2%
14.8 1
 
0.2%
13.0 9
 
1.8%
12.87 9
 
1.8%
12.73 1
 
0.2%
12.7 38
7.6%
12.68 32
6.4%
12.66 10
 
2.0%
12.44 19
3.8%
12.26 21
4.2%

LO_STD_WTRON_VE
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.93744
Minimum7.93
Maximum15.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:44:17.479189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.93
5-th percentile10.97
Q111.66
median11.66
Q312.39
95-th percentile12.69
Maximum15.35
Range7.42
Interquartile range (IQR)0.73

Descriptive statistics

Standard deviation0.62902272
Coefficient of variation (CV)0.052693268
Kurtosis6.8819082
Mean11.93744
Median Absolute Deviation (MAD)0.65
Skewness-0.78374048
Sum5968.72
Variance0.39566959
MonotonicityNot monotonic
2024-03-13T21:44:17.643320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
11.66 176
35.2%
12.31 66
 
13.2%
10.97 65
 
13.0%
12.69 38
 
7.6%
12.58 32
 
6.4%
12.34 21
 
4.2%
12.39 20
 
4.0%
12.48 19
 
3.8%
11.51 15
 
3.0%
11.91 13
 
2.6%
Other values (8) 35
 
7.0%
ValueCountFrequency (%)
7.93 2
 
0.4%
10.97 65
 
13.0%
11.51 15
 
3.0%
11.57 2
 
0.4%
11.66 176
35.2%
11.91 13
 
2.6%
12.31 66
 
13.2%
12.34 21
 
4.2%
12.39 20
 
4.0%
12.48 19
 
3.8%
ValueCountFrequency (%)
15.35 1
 
0.2%
14.15 1
 
0.2%
12.82 9
 
1.8%
12.72 1
 
0.2%
12.69 38
7.6%
12.58 32
6.4%
12.57 9
 
1.8%
12.5 10
 
2.0%
12.48 19
3.8%
12.39 20
4.0%

SHP_RTTRN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2734
Minimum-3.2
Maximum9.5
Zeros209
Zeros (%)41.8%
Negative226
Negative (%)45.2%
Memory size4.5 KiB
2024-03-13T21:44:17.864105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.2
5-th percentile-1.1
Q1-0.6
median0
Q30
95-th percentile1.1
Maximum9.5
Range12.7
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.89849152
Coefficient of variation (CV)-3.2863626
Kurtosis30.774211
Mean-0.2734
Median Absolute Deviation (MAD)0.3
Skewness1.5527037
Sum-136.7
Variance0.80728701
MonotonicityNot monotonic
2024-03-13T21:44:18.037670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 209
41.8%
-0.6 176
35.2%
0.3 38
 
7.6%
-1.1 21
 
4.2%
-3.2 20
 
4.0%
1.1 15
 
3.0%
1.8 9
 
1.8%
-0.1 9
 
1.8%
1.4 1
 
0.2%
1.9 1
 
0.2%
ValueCountFrequency (%)
-3.2 20
 
4.0%
-1.1 21
 
4.2%
-0.6 176
35.2%
-0.1 9
 
1.8%
0.0 209
41.8%
0.3 38
 
7.6%
1.1 15
 
3.0%
1.4 1
 
0.2%
1.8 9
 
1.8%
1.9 1
 
0.2%
ValueCountFrequency (%)
9.5 1
 
0.2%
1.9 1
 
0.2%
1.8 9
 
1.8%
1.4 1
 
0.2%
1.1 15
 
3.0%
0.3 38
 
7.6%
0.0 209
41.8%
-0.1 9
 
1.8%
-0.6 176
35.2%
-1.1 21
 
4.2%

GTR_YMDHMS
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0220513 × 1013
Minimum2.0220509 × 1013
Maximum2.022053 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:44:18.628296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0220509 × 1013
5-th percentile2.0220509 × 1013
Q12.0220509 × 1013
median2.022051 × 1013
Q32.022051 × 1013
95-th percentile2.0220525 × 1013
Maximum2.022053 × 1013
Range20960007
Interquartile range (IQR)814731.5

Descriptive statistics

Standard deviation6383414.4
Coefficient of variation (CV)3.1569004 × 10-7
Kurtosis0.16657004
Mean2.0220513 × 1013
Median Absolute Deviation (MAD)799940.5
Skewness1.456779
Sum1.0110256 × 1016
Variance4.074798 × 1013
MonotonicityNot monotonic
2024-03-13T21:44:18.843852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20220509180049 1
 
0.2%
20220509171336 1
 
0.2%
20220509171813 1
 
0.2%
20220509171748 1
 
0.2%
20220509171735 1
 
0.2%
20220509171719 1
 
0.2%
20220509171702 1
 
0.2%
20220509171641 1
 
0.2%
20220509171623 1
 
0.2%
20220509171606 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
20220509150006 1
0.2%
20220509150012 1
0.2%
20220509150022 1
0.2%
20220509150024 1
0.2%
20220509150041 1
0.2%
20220509150043 1
0.2%
20220509150045 1
0.2%
20220509150054 1
0.2%
20220509150104 1
0.2%
20220509150112 1
0.2%
ValueCountFrequency (%)
20220530110013 1
0.2%
20220530100203 1
0.2%
20220525203620 1
0.2%
20220525203445 1
0.2%
20220525203441 1
0.2%
20220525203428 1
0.2%
20220525203023 1
0.2%
20220525202436 1
0.2%
20220525202432 1
0.2%
20220525202400 1
0.2%

Interactions

2024-03-13T21:44:12.254225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:56.023594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:57.676055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:59.299158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:00.788456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:02.732863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:04.235910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:05.895567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:07.205145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:08.830474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:10.368258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:12.411779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:56.174260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:57.794143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:59.428608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:00.911534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:02.863348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:04.356658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:06.023271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:07.314879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:08.967447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:10.909929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:12.576686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:56.317064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:57.954203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:59.568081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:01.069958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:03.030623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:04.511530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:06.186873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:07.448450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:09.111050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:11.043272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:12.713522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:56.428885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:58.088942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:59.700000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:01.205846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:03.189247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:04.646535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:06.332056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:07.622852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:09.247241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:11.169367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:12.832630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:56.557240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:58.221120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:59.811969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:01.353130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:03.330691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:04.794114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:06.457169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:07.794510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:09.370327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:11.301757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:12.959788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:56.691808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:58.389773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:59.928805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:01.464348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:03.429650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:04.923189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:06.561144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:07.931404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:09.479244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:11.422211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:13.104342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:56.845908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:58.574738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:00.058682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:01.604578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:03.548353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:05.068274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:06.689292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:08.061706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:09.621948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:11.546715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:13.226294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:56.974758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:58.715211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:00.182976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:01.758694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:03.665364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:05.221786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:06.788274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:08.179663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:09.766061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:11.668550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:13.369414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:57.172519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:58.879758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:00.325491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:01.928524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:03.802686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:05.469398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:06.919768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:08.361606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:09.960958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:11.812671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:13.497635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:57.361341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:59.032381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:00.459453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:02.065624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:03.988266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:05.615612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:07.028005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:08.508916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:10.111953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:11.957345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:13.623980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:57.552310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:59.169028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:00.605059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:02.604750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:04.115545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:05.762786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:07.125024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:08.685399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:10.240888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:44:12.084777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:44:18.984912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DATA_SNSHP_LASHP_LOSHP_VESHP_COGSHP_SOGGYRO_AGLLA_STD_WTRON_VELO_STD_WTRON_VESHP_RTTRNGTR_YMDHMS
DATA_SN1.0000.7000.9640.9080.8470.8630.5770.8870.8830.7891.000
SHP_LA0.7001.0000.9540.9120.8620.7900.8040.8930.7770.4470.542
SHP_LO0.9640.9541.0000.8070.9920.9650.8650.7950.8480.6310.720
SHP_VE0.9080.9120.8071.0000.6840.6710.6360.9400.7310.8490.847
SHP_COG0.8470.8620.9920.6841.0000.9741.0000.8230.8780.4640.426
SHP_SOG0.8630.7900.9650.6710.9741.0000.8180.9410.9590.7570.587
GYRO_AGL0.5770.8040.8650.6361.0000.8181.0000.7680.7750.3850.483
LA_STD_WTRON_VE0.8870.8930.7950.9400.8230.9410.7681.0000.9220.8880.861
LO_STD_WTRON_VE0.8830.7770.8480.7310.8780.9590.7750.9221.0000.9470.995
SHP_RTTRN0.7890.4470.6310.8490.4640.7570.3850.8880.9471.0000.945
GTR_YMDHMS1.0000.5420.7200.8470.4260.5870.4830.8610.9950.9451.000
2024-03-13T21:44:19.263993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DATA_SNSHP_LASHP_LOSHP_VESHP_COGSHP_SOGGYRO_AGLLA_STD_WTRON_VELO_STD_WTRON_VESHP_RTTRNGTR_YMDHMS
DATA_SN1.000-0.061-0.1810.2870.3700.4980.3370.7820.7250.5150.431
SHP_LA-0.0611.000-0.685-0.405-0.292-0.296-0.308-0.144-0.010-0.4510.670
SHP_LO-0.181-0.6851.0000.485-0.003-0.075-0.020-0.187-0.2820.012-0.812
SHP_VE0.287-0.4050.4851.0000.0730.0700.0640.1630.1480.043-0.510
SHP_COG0.370-0.292-0.0030.0731.0000.8730.9580.3910.3870.6380.015
SHP_SOG0.498-0.296-0.0750.0700.8731.0000.8780.6140.5770.7100.131
GYRO_AGL0.337-0.308-0.0200.0640.9580.8781.0000.4070.3920.666-0.005
LA_STD_WTRON_VE0.782-0.144-0.1870.1630.3910.6140.4071.0000.9540.3840.237
LO_STD_WTRON_VE0.725-0.010-0.2820.1480.3870.5770.3920.9541.0000.2230.256
SHP_RTTRN0.515-0.4510.0120.0430.6380.7100.6660.3840.2231.0000.161
GTR_YMDHMS0.4310.670-0.812-0.5100.0150.131-0.0050.2370.2560.1611.000

Missing values

2024-03-13T21:44:13.796084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:44:14.046795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DATA_SNGTR_YMSHP_LASHP_LOSHP_VESHP_COGSHP_SOGGYRO_AGLLA_STD_WTRON_VELO_STD_WTRON_VESHP_RTTRNGTR_YMDHMS
013794920220534.65524128.338380.04234.212.2233.712.2612.34-1.120220509180049
113795020220534.65524128.338380.04234.212.2233.712.2612.34-1.120220509180138
213795120220534.65524128.338380.04234.712.1234.012.2612.34-1.120220509180147
313795220220534.65524128.338380.04234.712.1234.012.2612.34-1.120220509180153
413795320220534.65524128.338380.04234.712.1234.012.2612.34-1.120220509180154
513795420220534.65524128.338380.04234.712.1234.012.2612.34-1.120220509180338
613795520220534.65524128.338380.04234.712.1234.012.2612.34-1.120220509180401
713795620220534.65524128.338380.04234.712.1234.012.2612.34-1.120220509180406
813795720220534.65524128.338380.04234.512.1234.012.2612.34-1.120220509180427
913795820220534.65524128.338380.04234.512.1234.012.2612.34-1.120220509180429
DATA_SNGTR_YMSHP_LASHP_LOSHP_VESHP_COGSHP_SOGGYRO_AGLLA_STD_WTRON_VELO_STD_WTRON_VESHP_RTTRNGTR_YMDHMS
49015813820220534.75774127.027570.01262.012.7261.912.712.690.320220525202400
49115813920220534.75774127.027570.01261.912.7261.912.712.690.320220525202432
49215814020220534.75774127.027570.01262.012.7261.912.712.690.320220525202436
49315814120220534.75774127.027570.01262.512.8262.112.712.690.320220525203023
49415814220220534.75774127.027570.01262.112.9262.612.712.690.320220525203428
49515814320220534.75774127.027570.01262.112.9262.612.712.690.320220525203441
49615814420220534.75774127.027570.01262.212.9262.612.712.690.320220525203445
49715814520220534.75774127.027570.01262.112.9262.612.712.690.320220525203620
49816085120220534.90538128.714490.3572.712.873.114.814.151.920220530100203
49916090120220534.57737128.078540.3572.214.971.416.0415.359.520220530110013