Overview

Dataset statistics

Number of variables4
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.7 KiB
Average record size in memory34.3 B

Variable types

Categorical2
Text2

Dataset

DescriptionSample
Author㈜해양정보기술
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT04MIT005

Alerts

OVT_XN has constant value ""Constant

Reproduction

Analysis started2024-03-13 12:48:16.439563
Analysis finished2024-03-13 12:48:16.771759
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MNRG_YMDHMS
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
20211002061500
329 
20211005073000
171 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20211002061500
2nd row20211002061500
3rd row20211002061500
4th row20211002061500
5th row20211002061500

Common Values

ValueCountFrequency (%)
20211002061500 329
65.8%
20211005073000 171
34.2%

Length

2024-03-13T21:48:16.873847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:48:17.035320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20211002061500 329
65.8%
20211005073000 171
34.2%

OVT_MNTR_MXM_CRDN
Text

Distinct450
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-03-13T21:48:17.552001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7
Min length6

Characters and Unicode

Total characters3500
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique407 ?
Unique (%)81.4%

Sample

1st row877,429
2nd row287,380
3rd row426,383
4th row293,377
5th row54,379
ValueCountFrequency (%)
281,379 3
 
0.6%
286,378 3
 
0.6%
281,380 3
 
0.6%
360,383 3
 
0.6%
415,385 3
 
0.6%
293,377 3
 
0.6%
293,380 3
 
0.6%
458,386 2
 
0.4%
715,397 2
 
0.4%
702,399 2
 
0.4%
Other values (440) 473
94.6%
2024-03-13T21:48:18.165613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 564
16.1%
, 500
14.3%
8 376
10.7%
4 358
10.2%
2 309
8.8%
9 309
8.8%
7 291
8.3%
1 215
 
6.1%
0 208
 
5.9%
6 194
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3000
85.7%
Other Punctuation 500
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 564
18.8%
8 376
12.5%
4 358
11.9%
2 309
10.3%
9 309
10.3%
7 291
9.7%
1 215
 
7.2%
0 208
 
6.9%
6 194
 
6.5%
5 176
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 564
16.1%
, 500
14.3%
8 376
10.7%
4 358
10.2%
2 309
8.8%
9 309
8.8%
7 291
8.3%
1 215
 
6.1%
0 208
 
5.9%
6 194
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 564
16.1%
, 500
14.3%
8 376
10.7%
4 358
10.2%
2 309
8.8%
9 309
8.8%
7 291
8.3%
1 215
 
6.1%
0 208
 
5.9%
6 194
 
5.5%

OVT_MNTR_MIM_CRDN
Text

Distinct472
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-03-13T21:48:18.601607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.904
Min length5

Characters and Unicode

Total characters3452
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique451 ?
Unique (%)90.2%

Sample

1st row834,384
2nd row265,360
3rd row392,366
4th row260,362
5th row9,360
ValueCountFrequency (%)
0,355 4
 
0.8%
394,364 3
 
0.6%
0,356 3
 
0.6%
247,357 3
 
0.6%
866,388 3
 
0.6%
263,358 3
 
0.6%
256,356 2
 
0.4%
393,365 2
 
0.4%
350,364 2
 
0.4%
140,355 2
 
0.4%
Other values (462) 473
94.6%
2024-03-13T21:48:19.223813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 741
21.5%
, 500
14.5%
6 393
11.4%
5 341
9.9%
8 277
 
8.0%
7 228
 
6.6%
2 222
 
6.4%
9 214
 
6.2%
4 210
 
6.1%
0 168
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2952
85.5%
Other Punctuation 500
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 741
25.1%
6 393
13.3%
5 341
11.6%
8 277
 
9.4%
7 228
 
7.7%
2 222
 
7.5%
9 214
 
7.2%
4 210
 
7.1%
0 168
 
5.7%
1 158
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 741
21.5%
, 500
14.5%
6 393
11.4%
5 341
9.9%
8 277
 
8.0%
7 228
 
6.6%
2 222
 
6.4%
9 214
 
6.2%
4 210
 
6.1%
0 168
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 741
21.5%
, 500
14.5%
6 393
11.4%
5 341
9.9%
8 277
 
8.0%
7 228
 
6.6%
2 222
 
6.4%
9 214
 
6.2%
4 210
 
6.1%
0 168
 
4.9%

OVT_XN
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 500
100.0%

Length

2024-03-13T21:48:19.448722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:48:19.619632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

Missing values

2024-03-13T21:48:16.586910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:48:16.715553image/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

MNRG_YMDHMSOVT_MNTR_MXM_CRDNOVT_MNTR_MIM_CRDNOVT_XN
020211002061500877,429834,3841
120211002061500287,380265,3601
220211002061500426,383392,3661
320211002061500293,377260,3621
42021100206150054,3799,3601
520211002061500869,411791,3761
620211002061500428,387394,3641
720211002061500277,378247,3611
820211002061500360,380318,3591
920211002061500355,378330,3571
MNRG_YMDHMSOVT_MNTR_MXM_CRDNOVT_MNTR_MIM_CRDNOVT_XN
49020211005073000480,385429,3651
49120211005073000511,394468,3721
49220211005073000507,391458,3611
49320211005073000498,388431,3631
49420211005073000341,380315,3631
49520211005073000462,383425,3661
49620211005073000824,411747,3681
49720211005073000454,389404,3661
49820211005073000826,414711,3661
49920211005073000288,379268,3571