Overview

Dataset statistics

Number of variables5
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows107
Duplicate rows (%)21.4%
Total size in memory20.1 KiB
Average record size in memory41.3 B

Variable types

Text1
Boolean3
Categorical1

Dataset

Description해당 파일은 신용보증기금이 운용하는 조사서 품질 가동 정보 관련 데이터로, 규격유무, 특허권유무, 삭제여부 등의 항목을 제공합니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15122113/fileData.do

Alerts

삭제여부 has constant value ""Constant
최종수정수 has constant value ""Constant
Dataset has 107 (21.4%) duplicate rowsDuplicates

Reproduction

Analysis started2024-04-18 06:01:56.208876
Analysis finished2024-04-18 06:01:58.589139
Duration2.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조사서ID
Text

Distinct100
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T15:01:58.793136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
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

Unique0 ?
Unique (%)0.0%

Sample

1st row********67
2nd row********68
3rd row********71
4th row********72
5th row********73
ValueCountFrequency (%)
13 6
 
1.2%
77 6
 
1.2%
07 6
 
1.2%
17 6
 
1.2%
43 6
 
1.2%
00 6
 
1.2%
01 6
 
1.2%
02 6
 
1.2%
35 6
 
1.2%
41 6
 
1.2%
Other values (90) 440
88.0%
2024-04-18T15:01:59.196899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 4000
80.0%
1 109
 
2.2%
0 104
 
2.1%
2 103
 
2.1%
9 103
 
2.1%
8 102
 
2.0%
7 102
 
2.0%
3 99
 
2.0%
6 94
 
1.9%
4 94
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 4000
80.0%
Decimal Number 1000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 109
10.9%
0 104
10.4%
2 103
10.3%
9 103
10.3%
8 102
10.2%
7 102
10.2%
3 99
9.9%
6 94
9.4%
4 94
9.4%
5 90
9.0%
Other Punctuation
ValueCountFrequency (%)
* 4000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 4000
80.0%
1 109
 
2.2%
0 104
 
2.1%
2 103
 
2.1%
9 103
 
2.1%
8 102
 
2.0%
7 102
 
2.0%
3 99
 
2.0%
6 94
 
1.9%
4 94
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 4000
80.0%
1 109
 
2.2%
0 104
 
2.1%
2 103
 
2.1%
9 103
 
2.1%
8 102
 
2.0%
7 102
 
2.0%
3 99
 
2.0%
6 94
 
1.9%
4 94
 
1.9%

규격유무
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
419 
True
81 
ValueCountFrequency (%)
False 419
83.8%
True 81
 
16.2%
2024-04-18T15:01:59.326135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

특허권유무
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
415 
True
85 
ValueCountFrequency (%)
False 415
83.0%
True 85
 
17.0%
2024-04-18T15:01:59.412561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2024-04-18T15:01:59.499563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
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

2024-04-18T15:01:59.587574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T15:01:59.674815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

Correlations

2024-04-18T15:01:59.741793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사서ID규격유무특허권유무
조사서ID1.0000.1990.000
규격유무0.1991.0000.640
특허권유무0.0000.6401.000
2024-04-18T15:01:59.827609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규격유무특허권유무
규격유무1.0000.442
특허권유무0.4421.000
2024-04-18T15:01:59.917701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규격유무특허권유무
규격유무1.0000.442
특허권유무0.4421.000

Missing values

2024-04-18T15:01:58.549427image/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

조사서ID규격유무특허권유무삭제여부최종수정수
0********67NNN1
1********68NNN1
2********71YYN1
3********72NNN1
4********73NNN1
5********74NNN1
6********75NNN1
7********77NNN1
8********78NYN1
9********79NNN1
조사서ID규격유무특허권유무삭제여부최종수정수
490********46NNN1
491********47NNN1
492********48NNN1
493********49NNN1
494********51NNN1
495********52NNN1
496********53NNN1
497********54NNN1
498********55NNN1
499********56NNN1

Duplicate rows

Most frequently occurring

조사서ID규격유무특허권유무삭제여부최종수정수# duplicates
6********05NNN16
39********35NNN16
76********72NNN16
79********75NNN16
89********84NNN16
1********01NNN15
7********06NNN15
8********07NNN15
10********09NNN15
12********11NNN15