Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells900
Missing cells (%)50.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.9 KiB
Average record size in memory163.3 B

Variable types

Categorical6
Numeric3
Unsupported9

Dataset

DescriptionSample
Author인하대학교 산학협력단
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT03IHU006

Alerts

WTCH_YR has constant value ""Constant
WTCH_MNTH has constant value ""Constant
WTCH_DD has constant value ""Constant
WTCH_HHS has constant value ""Constant
WTCH_MIN has constant value ""Constant
WTCH_SEC has constant value ""Constant
WTCH_LA is highly overall correlated with WTCH_SLNTYHigh correlation
WTCH_SLNTY is highly overall correlated with WTCH_LAHigh correlation
WTCH_WTRLV has 100 (100.0%) missing valuesMissing
WTCH_WTDP has 100 (100.0%) missing valuesMissing
WTCH_CRSPD has 100 (100.0%) missing valuesMissing
WTCH_CRDRC has 100 (100.0%) missing valuesMissing
WTCH_WTEM has 100 (100.0%) missing valuesMissing
SSC_CCTR has 100 (100.0%) missing valuesMissing
MVMN_DRC has 100 (100.0%) missing valuesMissing
MVMN_VE has 100 (100.0%) missing valuesMissing
WTDP_VARTION_QY has 100 (100.0%) missing valuesMissing
WTCH_LA has unique valuesUnique
WTCH_LO has unique valuesUnique
WTCH_WTRLV is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTCH_WTDP is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTCH_CRSPD is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTCH_CRDRC is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTCH_WTEM is an unsupported type, check if it needs cleaning or further analysisUnsupported
SSC_CCTR is an unsupported type, check if it needs cleaning or further analysisUnsupported
MVMN_DRC is an unsupported type, check if it needs cleaning or further analysisUnsupported
MVMN_VE is an unsupported type, check if it needs cleaning or further analysisUnsupported
WTDP_VARTION_QY is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-13 12:53:35.070181
Analysis finished2024-03-13 12:53:37.013830
Duration1.94 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WTCH_YR
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 100
100.0%

Length

2024-03-13T21:53:37.159497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:37.312536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 100
100.0%

WTCH_MNTH
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 100
100.0%

Length

2024-03-13T21:53:37.443538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:37.580961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 100
100.0%

WTCH_DD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
15
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row15
3rd row15
4th row15
5th row15

Common Values

ValueCountFrequency (%)
15 100
100.0%

Length

2024-03-13T21:53:37.733744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:37.854367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15 100
100.0%

WTCH_HHS
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2024-03-13T21:53:37.993732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:38.131662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

WTCH_MIN
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2024-03-13T21:53:38.262645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:38.390338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

WTCH_SEC
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2024-03-13T21:53:38.521797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:38.655589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

WTCH_LA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.407985
Minimum37.403537
Maximum37.41528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:53:38.810167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.403537
5-th percentile37.403988
Q137.405745
median37.407563
Q337.409845
95-th percentile37.41346
Maximum37.41528
Range0.011743017
Interquartile range (IQR)0.0041001897

Descriptive statistics

Standard deviation0.0029160368
Coefficient of variation (CV)7.7952257 × 10-5
Kurtosis-0.50405485
Mean37.407985
Median Absolute Deviation (MAD)0.0022358515
Skewness0.57472282
Sum3740.7985
Variance8.5032707 × 10-6
MonotonicityNot monotonic
2024-03-13T21:53:39.034533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.404379228696165 1
 
1.0%
37.40397670806328 1
 
1.0%
37.40849802349116 1
 
1.0%
37.40804589212915 1
 
1.0%
37.40759376072698 1
 
1.0%
37.407141629284624 1
 
1.0%
37.40668949780212 1
 
1.0%
37.40623736627943 1
 
1.0%
37.405785234716525 1
 
1.0%
37.405333103113485 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
37.40353697187789 1
1.0%
37.40393950442473 1
1.0%
37.40395190835409 1
1.0%
37.40396430956695 1
1.0%
37.40397670806328 1
1.0%
37.40398910384309 1
1.0%
37.404379228696165 1
1.0%
37.40439163554368 1
1.0%
37.40440403967463 1
1.0%
37.40441644108903 1
1.0%
ValueCountFrequency (%)
37.415279989099616 1
1.0%
37.4148278583403 1
1.0%
37.414375727540815 1
1.0%
37.41392359670116 1
1.0%
37.41347146582132 1
1.0%
37.41345906309303 1
1.0%
37.413019334901286 1
1.0%
37.413006932374536 1
1.0%
37.41256720394108 1
1.0%
37.41255480161586 1
1.0%

WTCH_LO
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.41615
Minimum126.4146
Maximum126.4176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:53:39.257907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.4146
5-th percentile126.41468
Q1126.41564
median126.41625
Q3126.41679
95-th percentile126.41753
Maximum126.4176
Range0.0030065101
Interquartile range (IQR)0.0011576242

Descriptive statistics

Standard deviation0.00083616125
Coefficient of variation (CV)6.6143549 × 10-6
Kurtosis-0.82767299
Mean126.41615
Median Absolute Deviation (MAD)0.00058268758
Skewness-0.15809686
Sum12641.615
Variance6.9916564 × 10-7
MonotonicityNot monotonic
2024-03-13T21:53:39.897780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.41473796151904 1
 
1.0%
126.41702192376468 1
 
1.0%
126.4168665999603 1
 
1.0%
126.41688213390204 1
 
1.0%
126.41689766749683 1
 
1.0%
126.41691320074464 1
 
1.0%
126.41692873364546 1
 
1.0%
126.41694426619932 1
 
1.0%
126.41695979840625 1
 
1.0%
126.41697533026625 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.4145980460985 1
1.0%
126.4146135936454 1
1.0%
126.414629140845 1
1.0%
126.41464468769735 1
1.0%
126.4146602342024 1
1.0%
126.41467578036023 1
1.0%
126.41469132617078 1
1.0%
126.41470687163412 1
1.0%
126.41472241675018 1
1.0%
126.41473796151904 1
1.0%
ValueCountFrequency (%)
126.41760455616098 1
1.0%
126.4175890290892 1
1.0%
126.4175735016706 1
1.0%
126.41755797390516 1
1.0%
126.41754244579288 1
1.0%
126.41752691733375 1
1.0%
126.41751138852774 1
1.0%
126.41749585937487 1
1.0%
126.4174803298751 1
1.0%
126.41746480002845 1
1.0%

WTCH_WTRLV
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

WTCH_WTDP
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

WTCH_CRSPD
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

WTCH_CRDRC
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

WTCH_WTEM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

WTCH_SLNTY
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.60852
Minimum31.597
Maximum31.649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:53:40.068175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.597
5-th percentile31.598
Q131.601
median31.6035
Q331.6155
95-th percentile31.63005
Maximum31.649
Range0.052
Interquartile range (IQR)0.0145

Descriptive statistics

Standard deviation0.01118845
Coefficient of variation (CV)0.00035396944
Kurtosis2.0051527
Mean31.60852
Median Absolute Deviation (MAD)0.0035
Skewness1.5105946
Sum3160.852
Variance0.00012518141
MonotonicityNot monotonic
2024-03-13T21:53:40.253921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31.603 13
13.0%
31.602 10
 
10.0%
31.604 7
 
7.0%
31.599 7
 
7.0%
31.6 7
 
7.0%
31.622 6
 
6.0%
31.601 6
 
6.0%
31.605 6
 
6.0%
31.621 5
 
5.0%
31.597 4
 
4.0%
Other values (19) 29
29.0%
ValueCountFrequency (%)
31.597 4
 
4.0%
31.598 3
 
3.0%
31.599 7
7.0%
31.6 7
7.0%
31.601 6
6.0%
31.602 10
10.0%
31.603 13
13.0%
31.604 7
7.0%
31.605 6
6.0%
31.606 1
 
1.0%
ValueCountFrequency (%)
31.649 1
 
1.0%
31.645 1
 
1.0%
31.641 1
 
1.0%
31.636 1
 
1.0%
31.631 1
 
1.0%
31.63 1
 
1.0%
31.626 2
 
2.0%
31.622 6
6.0%
31.621 5
5.0%
31.62 2
 
2.0%

SSC_CCTR
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

MVMN_DRC
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

MVMN_VE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

WTDP_VARTION_QY
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

Interactions

2024-03-13T21:53:36.060811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:35.283295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:35.665618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:36.198255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:35.406344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:35.793075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:36.334465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:35.533765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:35.924298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:53:40.381799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_LAWTCH_LOWTCH_SLNTY
WTCH_LA1.0000.6130.943
WTCH_LO0.6131.0000.702
WTCH_SLNTY0.9430.7021.000
2024-03-13T21:53:40.489919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_LAWTCH_LOWTCH_SLNTY
WTCH_LA1.000-0.0150.860
WTCH_LO-0.0151.0000.379
WTCH_SLNTY0.8600.3791.000

Missing values

2024-03-13T21:53:36.536167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:53:36.837454image/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

WTCH_YRWTCH_MNTHWTCH_DDWTCH_HHSWTCH_MINWTCH_SECWTCH_LAWTCH_LOWTCH_WTRLVWTCH_WTDPWTCH_CRSPDWTCH_CRDRCWTCH_WTEMWTCH_SLNTYSSC_CCTRMVMN_DRCMVMN_VEWTDP_VARTION_QY
0202021500037.404379126.414738<NA><NA><NA><NA><NA>31.597<NA><NA><NA><NA>
1202021500037.404831126.414722<NA><NA><NA><NA><NA>31.597<NA><NA><NA><NA>
2202021500037.405283126.414707<NA><NA><NA><NA><NA>31.598<NA><NA><NA><NA>
3202021500037.405736126.414691<NA><NA><NA><NA><NA>31.598<NA><NA><NA><NA>
4202021500037.406188126.414676<NA><NA><NA><NA><NA>31.599<NA><NA><NA><NA>
5202021500037.40664126.41466<NA><NA><NA><NA><NA>31.599<NA><NA><NA><NA>
6202021500037.407092126.414645<NA><NA><NA><NA><NA>31.599<NA><NA><NA><NA>
7202021500037.407544126.414629<NA><NA><NA><NA><NA>31.6<NA><NA><NA><NA>
8202021500037.407996126.414614<NA><NA><NA><NA><NA>31.6<NA><NA><NA><NA>
9202021500037.408448126.414598<NA><NA><NA><NA><NA>31.6<NA><NA><NA><NA>
WTCH_YRWTCH_MNTHWTCH_DDWTCH_HHSWTCH_MINWTCH_SECWTCH_LAWTCH_LOWTCH_WTRLVWTCH_WTDPWTCH_CRSPDWTCH_CRDRCWTCH_WTEMWTCH_SLNTYSSC_CCTRMVMN_DRCMVMN_VEWTDP_VARTION_QY
90202021500037.403537126.417605<NA><NA><NA><NA><NA>31.607<NA><NA><NA><NA>
91202021500037.403989126.417589<NA><NA><NA><NA><NA>31.605<NA><NA><NA><NA>
92202021500037.404441126.417574<NA><NA><NA><NA><NA>31.604<NA><NA><NA><NA>
93202021500037.404893126.417558<NA><NA><NA><NA><NA>31.603<NA><NA><NA><NA>
94202021500037.405345126.417542<NA><NA><NA><NA><NA>31.603<NA><NA><NA><NA>
95202021500037.405798126.417527<NA><NA><NA><NA><NA>31.603<NA><NA><NA><NA>
96202021500037.40625126.417511<NA><NA><NA><NA><NA>31.603<NA><NA><NA><NA>
97202021500037.406702126.417496<NA><NA><NA><NA><NA>31.604<NA><NA><NA><NA>
98202021500037.407154126.41748<NA><NA><NA><NA><NA>31.604<NA><NA><NA><NA>
99202021500037.407606126.417465<NA><NA><NA><NA><NA>31.605<NA><NA><NA><NA>