Overview

Dataset statistics

Number of variables3
Number of observations58
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory28.3 B

Variable types

Categorical2
Text1

Dataset

Description한국자산관리공사 공사채권 이해관계인 설정등기 일자별 등록현황("등기년도","등기일자","등록건수") 데이터 제공
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15074413/fileData.do

Alerts

등기년도 has constant value ""Constant
등록건수 is highly imbalanced (58.9%)Imbalance
등기일자 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:10:38.920802
Analysis finished2023-12-12 23:10:39.114283
Duration0.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등기년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size596.0 B
2000
58 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2000
2nd row2000
3rd row2000
4th row2000
5th row2000

Common Values

ValueCountFrequency (%)
2000 58
100.0%

Length

2023-12-13T08:10:39.168554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:10:39.249609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2000 58
100.0%

등기일자
Text

UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-13T08:10:39.503886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters290
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

Unique58 ?
Unique (%)100.0%

Sample

1st row01-19
2nd row01-22
3rd row01-26
4th row01-27
5th row02-19
ValueCountFrequency (%)
01-19 1
 
1.7%
11-01 1
 
1.7%
12-21 1
 
1.7%
07-29 1
 
1.7%
08-09 1
 
1.7%
08-25 1
 
1.7%
09-07 1
 
1.7%
09-26 1
 
1.7%
09-28 1
 
1.7%
10-11 1
 
1.7%
Other values (48) 48
82.8%
2023-12-13T08:10:39.850259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 64
22.1%
1 58
20.0%
- 58
20.0%
2 37
12.8%
3 15
 
5.2%
7 13
 
4.5%
9 12
 
4.1%
5 9
 
3.1%
6 9
 
3.1%
8 8
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 232
80.0%
Dash Punctuation 58
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64
27.6%
1 58
25.0%
2 37
15.9%
3 15
 
6.5%
7 13
 
5.6%
9 12
 
5.2%
5 9
 
3.9%
6 9
 
3.9%
8 8
 
3.4%
4 7
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 290
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64
22.1%
1 58
20.0%
- 58
20.0%
2 37
12.8%
3 15
 
5.2%
7 13
 
4.5%
9 12
 
4.1%
5 9
 
3.1%
6 9
 
3.1%
8 8
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64
22.1%
1 58
20.0%
- 58
20.0%
2 37
12.8%
3 15
 
5.2%
7 13
 
4.5%
9 12
 
4.1%
5 9
 
3.1%
6 9
 
3.1%
8 8
 
2.8%

등록건수
Categorical

IMBALANCE 

Distinct4
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size596.0 B
1
48 
2
8 
4
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)3.4%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 48
82.8%
2 8
 
13.8%
4 1
 
1.7%
3 1
 
1.7%

Length

2023-12-13T08:10:40.007218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:10:40.110194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 48
82.8%
2 8
 
13.8%
4 1
 
1.7%
3 1
 
1.7%

Correlations

2023-12-13T08:10:40.182348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등기일자등록건수
등기일자1.0001.000
등록건수1.0001.000

Missing values

2023-12-13T08:10:39.006540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:10:39.086768image/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

등기년도등기일자등록건수
0200001-191
1200001-222
2200001-261
3200001-271
4200002-191
5200002-291
6200003-091
7200003-131
8200003-171
9200003-271
등기년도등기일자등록건수
48200011-281
49200011-301
50200012-073
51200012-091
52200012-112
53200012-121
54200012-141
55200012-181
56200012-211
57200012-301