Overview

Dataset statistics

Number of variables13
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.4 KiB
Average record size in memory109.3 B

Variable types

Text6
Categorical3
Boolean1
Numeric3

Dataset

Description해당 파일 데이터는 신용보증기금의 상품보증상품마스터에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093028/fileData.do

Alerts

상품구분코드 is highly overall correlated with 노드구분코드 and 1 other fieldsHigh correlation
레벨순서값 is highly overall correlated with 상품구분코드 and 1 other fieldsHigh correlation
노드구분코드 is highly overall correlated with 상품구분코드 and 1 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation
삭제여부 is highly imbalanced (89.4%)Imbalance
상품코드 has unique valuesUnique

Reproduction

Analysis started2024-04-17 11:46:46.150124
Analysis finished2024-04-17 11:46:47.682286
Duration1.53 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상품코드
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T20:46:47.911740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2500
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st row13522
2nd row13521
3rd row13468
4th row13520
5th row11291
ValueCountFrequency (%)
13522 1
 
0.2%
13294 1
 
0.2%
11276 1
 
0.2%
c0126 1
 
0.2%
13291 1
 
0.2%
13292 1
 
0.2%
13293 1
 
0.2%
c0127 1
 
0.2%
c0125 1
 
0.2%
13288 1
 
0.2%
Other values (490) 490
98.0%
2024-04-17T20:46:48.304481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 688
27.5%
3 562
22.5%
2 261
 
10.4%
4 193
 
7.7%
0 174
 
7.0%
5 139
 
5.6%
8 111
 
4.4%
6 105
 
4.2%
7 101
 
4.0%
9 100
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2434
97.4%
Uppercase Letter 66
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 688
28.3%
3 562
23.1%
2 261
 
10.7%
4 193
 
7.9%
0 174
 
7.1%
5 139
 
5.7%
8 111
 
4.6%
6 105
 
4.3%
7 101
 
4.1%
9 100
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
C 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2434
97.4%
Latin 66
 
2.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 688
28.3%
3 562
23.1%
2 261
 
10.7%
4 193
 
7.9%
0 174
 
7.1%
5 139
 
5.7%
8 111
 
4.6%
6 105
 
4.3%
7 101
 
4.1%
9 100
 
4.1%
Latin
ValueCountFrequency (%)
C 66
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 688
27.5%
3 562
22.5%
2 261
 
10.4%
4 193
 
7.7%
0 174
 
7.0%
5 139
 
5.6%
8 111
 
4.4%
6 105
 
4.2%
7 101
 
4.0%
9 100
 
4.0%

상품명
Text

Distinct491
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T20:46:48.542934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length30
Mean length21.6
Min length3

Characters and Unicode

Total characters10800
Distinct characters339
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique482 ?
Unique (%)96.4%

Sample

1st row재창업지원보증(사업성평가:타기관)
2nd row재창업지원보증(사업성평가:신보)
3rd row(장기분할전용)코로나19 상환유예 연착륙 조치(21.3월)
4th row문화산업정책보증(네이버)
5th row미사용
ValueCountFrequency (%)
코로나19 45
 
3.0%
지원 35
 
2.3%
협약보증 32
 
2.1%
금융지원 31
 
2.0%
특례보증('20.9월 27
 
1.8%
피해기업 22
 
1.5%
특례보증('20.3월 20
 
1.3%
뉴딜 19
 
1.3%
일자리기업 19
 
1.3%
연매출액 18
 
1.2%
Other values (582) 1248
82.3%
2024-04-17T20:46:48.912186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1017
 
9.4%
( 517
 
4.8%
) 517
 
4.8%
보 392
 
3.6%
증 376
 
3.5%
업 358
 
3.3%
기 298
 
2.8%
1 227
 
2.1%
. 202
 
1.9%
2 191
 
1.8%
Other values (329) 6705
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7086
65.6%
Space Separator 1017
 
9.4%
Decimal Number 839
 
7.8%
Open Punctuation 517
 
4.8%
Close Punctuation 517
 
4.8%
Other Punctuation 434
 
4.0%
Uppercase Letter 290
 
2.7%
Lowercase Letter 66
 
0.6%
Dash Punctuation 24
 
0.2%
Connector Punctuation 10
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 392
 
5.5%
증 376
 
5.3%
업 358
 
5.1%
기 298
 
4.2%
월 181
 
2.6%
특 176
 
2.5%
원 142
 
2.0%
지 142
 
2.0%
협 132
 
1.9%
출 118
 
1.7%
Other values (276) 4771
67.3%
Uppercase Letter
ValueCountFrequency (%)
T 42
14.5%
S 40
13.8%
I 37
12.8%
R 30
10.3%
C 21
7.2%
A 17
 
5.9%
O 16
 
5.5%
M 16
 
5.5%
W 13
 
4.5%
G 13
 
4.5%
Other values (9) 45
15.5%
Lowercase Letter
ValueCountFrequency (%)
t 16
24.2%
a 11
16.7%
u 11
16.7%
p 10
15.2%
r 8
12.1%
s 4
 
6.1%
m 2
 
3.0%
e 1
 
1.5%
n 1
 
1.5%
y 1
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 227
27.1%
2 191
22.8%
0 157
18.7%
9 138
16.4%
3 43
 
5.1%
4 24
 
2.9%
7 22
 
2.6%
8 15
 
1.8%
6 12
 
1.4%
5 10
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 202
46.5%
' 135
31.1%
, 46
 
10.6%
· 35
 
8.1%
& 6
 
1.4%
/ 4
 
0.9%
: 4
 
0.9%
% 2
 
0.5%
Space Separator
ValueCountFrequency (%)
1017
100.0%
Open Punctuation
ValueCountFrequency (%)
( 517
100.0%
Close Punctuation
ValueCountFrequency (%)
) 517
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7086
65.6%
Common 3358
31.1%
Latin 356
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 392
 
5.5%
증 376
 
5.3%
업 358
 
5.1%
기 298
 
4.2%
월 181
 
2.6%
특 176
 
2.5%
원 142
 
2.0%
지 142
 
2.0%
협 132
 
1.9%
출 118
 
1.7%
Other values (276) 4771
67.3%
Latin
ValueCountFrequency (%)
T 42
 
11.8%
S 40
 
11.2%
I 37
 
10.4%
R 30
 
8.4%
C 21
 
5.9%
A 17
 
4.8%
t 16
 
4.5%
O 16
 
4.5%
M 16
 
4.5%
W 13
 
3.7%
Other values (20) 108
30.3%
Common
ValueCountFrequency (%)
1017
30.3%
( 517
15.4%
) 517
15.4%
1 227
 
6.8%
. 202
 
6.0%
2 191
 
5.7%
0 157
 
4.7%
9 138
 
4.1%
' 135
 
4.0%
, 46
 
1.4%
Other values (13) 211
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7086
65.6%
ASCII 3679
34.1%
None 35
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1017
27.6%
( 517
14.1%
) 517
14.1%
1 227
 
6.2%
. 202
 
5.5%
2 191
 
5.2%
0 157
 
4.3%
9 138
 
3.8%
' 135
 
3.7%
, 46
 
1.3%
Other values (42) 532
14.5%
Hangul
ValueCountFrequency (%)
보 392
 
5.5%
증 376
 
5.3%
업 358
 
5.1%
기 298
 
4.2%
월 181
 
2.6%
특 176
 
2.5%
원 142
 
2.0%
지 142
 
2.0%
협 132
 
1.9%
출 118
 
1.7%
Other values (276) 4771
67.3%
None
ValueCountFrequency (%)
· 35
100.0%

상품구분코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3
359 
2
77 
44 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 359
71.8%
2 77
 
15.4%
44
 
8.8%
1 20
 
4.0%

Length

2024-04-17T20:46:49.026242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:46:49.110612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 359
78.7%
2 77
 
16.9%
1 20
 
4.4%

상위상품코드
Text

Distinct85
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T20:46:49.316918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length3.616
Min length1

Characters and Unicode

Total characters1808
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)2.4%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
c0045 23
 
7.0%
c0101 17
 
5.2%
c0110 11
 
3.4%
c0168 11
 
3.4%
c0012 10
 
3.1%
c0105 9
 
2.8%
c0052 9
 
2.8%
c0140 8
 
2.4%
c0116 8
 
2.4%
c0087 8
 
2.4%
Other values (74) 213
65.1%
2024-04-17T20:46:49.683922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 471
26.1%
C 327
18.1%
1 325
18.0%
173
 
9.6%
2 93
 
5.1%
4 89
 
4.9%
5 77
 
4.3%
6 73
 
4.0%
3 55
 
3.0%
8 45
 
2.5%
Other values (2) 80
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1308
72.3%
Uppercase Letter 327
 
18.1%
Space Separator 173
 
9.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 471
36.0%
1 325
24.8%
2 93
 
7.1%
4 89
 
6.8%
5 77
 
5.9%
6 73
 
5.6%
3 55
 
4.2%
8 45
 
3.4%
7 45
 
3.4%
9 35
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 327
100.0%
Space Separator
ValueCountFrequency (%)
173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1481
81.9%
Latin 327
 
18.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 471
31.8%
1 325
21.9%
173
 
11.7%
2 93
 
6.3%
4 89
 
6.0%
5 77
 
5.2%
6 73
 
4.9%
3 55
 
3.7%
8 45
 
3.0%
7 45
 
3.0%
Latin
ValueCountFrequency (%)
C 327
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 471
26.1%
C 327
18.1%
1 325
18.0%
173
 
9.6%
2 93
 
5.1%
4 89
 
4.9%
5 77
 
4.3%
6 73
 
4.0%
3 55
 
3.0%
8 45
 
2.5%
Other values (2) 80
 
4.4%

노드구분코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
434 
1
66 

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 (%)
2 434
86.8%
1 66
 
13.2%

Length

2024-04-17T20:46:49.804231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:46:49.882403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 434
86.8%
1 66
 
13.2%

레벨순서값
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
327 
1
105 
0
68 

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 (%)
2 327
65.4%
1 105
 
21.0%
0 68
 
13.6%

Length

2024-04-17T20:46:49.965889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:46:50.059209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 327
65.4%
1 105
 
21.0%
0 68
 
13.6%

상품표시명
Text

Distinct491
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T20:46:50.293431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length30
Mean length21.408
Min length3

Characters and Unicode

Total characters10704
Distinct characters339
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique482 ?
Unique (%)96.4%

Sample

1st row재창업지원보증(사업성평가:타기관)
2nd row재창업지원보증(사업성평가:신보)
3rd row(장기분할전용)코로나19 상환유예 연착륙 조치(21.3월)
4th row문화산업정책보증(네이버)
5th row미사용
ValueCountFrequency (%)
코로나19 45
 
3.1%
지원 32
 
2.2%
금융지원 30
 
2.0%
협약보증 28
 
1.9%
특례보증('20.9월 27
 
1.8%
피해기업 22
 
1.5%
특례보증('20.3월 20
 
1.4%
뉴딜 19
 
1.3%
일자리기업 19
 
1.3%
연매출액 18
 
1.2%
Other values (572) 1211
82.3%
2024-04-17T20:46:50.717674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
972
 
9.1%
( 517
 
4.8%
) 515
 
4.8%
보 378
 
3.5%
증 362
 
3.4%
업 358
 
3.3%
기 298
 
2.8%
1 227
 
2.1%
. 202
 
1.9%
2 191
 
1.8%
Other values (329) 6684
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7039
65.8%
Space Separator 972
 
9.1%
Decimal Number 838
 
7.8%
Open Punctuation 517
 
4.8%
Close Punctuation 515
 
4.8%
Other Punctuation 434
 
4.1%
Uppercase Letter 289
 
2.7%
Lowercase Letter 66
 
0.6%
Dash Punctuation 24
 
0.2%
Connector Punctuation 10
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 378
 
5.4%
증 362
 
5.1%
업 358
 
5.1%
기 298
 
4.2%
월 181
 
2.6%
특 176
 
2.5%
원 141
 
2.0%
지 141
 
2.0%
협 129
 
1.8%
출 118
 
1.7%
Other values (276) 4757
67.6%
Uppercase Letter
ValueCountFrequency (%)
T 42
14.5%
S 40
13.8%
I 37
12.8%
R 30
10.4%
C 21
7.3%
A 17
 
5.9%
M 16
 
5.5%
O 16
 
5.5%
G 13
 
4.5%
W 13
 
4.5%
Other values (9) 44
15.2%
Lowercase Letter
ValueCountFrequency (%)
t 16
24.2%
u 11
16.7%
a 11
16.7%
p 10
15.2%
r 8
12.1%
s 4
 
6.1%
m 2
 
3.0%
l 1
 
1.5%
y 1
 
1.5%
n 1
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 227
27.1%
2 191
22.8%
0 156
18.6%
9 138
16.5%
3 43
 
5.1%
4 24
 
2.9%
7 22
 
2.6%
8 15
 
1.8%
6 12
 
1.4%
5 10
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 202
46.5%
' 135
31.1%
, 46
 
10.6%
· 35
 
8.1%
& 6
 
1.4%
/ 4
 
0.9%
: 4
 
0.9%
% 2
 
0.5%
Space Separator
ValueCountFrequency (%)
972
100.0%
Open Punctuation
ValueCountFrequency (%)
( 517
100.0%
Close Punctuation
ValueCountFrequency (%)
) 515
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7039
65.8%
Common 3310
30.9%
Latin 355
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 378
 
5.4%
증 362
 
5.1%
업 358
 
5.1%
기 298
 
4.2%
월 181
 
2.6%
특 176
 
2.5%
원 141
 
2.0%
지 141
 
2.0%
협 129
 
1.8%
출 118
 
1.7%
Other values (276) 4757
67.6%
Latin
ValueCountFrequency (%)
T 42
 
11.8%
S 40
 
11.3%
I 37
 
10.4%
R 30
 
8.5%
C 21
 
5.9%
A 17
 
4.8%
M 16
 
4.5%
O 16
 
4.5%
t 16
 
4.5%
G 13
 
3.7%
Other values (20) 107
30.1%
Common
ValueCountFrequency (%)
972
29.4%
( 517
15.6%
) 515
15.6%
1 227
 
6.9%
. 202
 
6.1%
2 191
 
5.8%
0 156
 
4.7%
9 138
 
4.2%
' 135
 
4.1%
, 46
 
1.4%
Other values (13) 211
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7039
65.8%
ASCII 3630
33.9%
None 35
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
972
26.8%
( 517
14.2%
) 515
14.2%
1 227
 
6.3%
. 202
 
5.6%
2 191
 
5.3%
0 156
 
4.3%
9 138
 
3.8%
' 135
 
3.7%
, 46
 
1.3%
Other values (42) 531
14.6%
Hangul
ValueCountFrequency (%)
보 378
 
5.4%
증 362
 
5.1%
업 358
 
5.1%
기 298
 
4.2%
월 181
 
2.6%
특 176
 
2.5%
원 141
 
2.0%
지 141
 
2.0%
협 129
 
1.8%
출 118
 
1.7%
Other values (276) 4757
67.6%
None
ValueCountFrequency (%)
· 35
100.0%

삭제여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
493 
True
 
7
ValueCountFrequency (%)
False 493
98.6%
True 7
 
1.4%
2024-04-17T20:46:50.822419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.692
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T20:46:50.904022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum19
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6032082
Coefficient of variation (CV)0.94752258
Kurtosis33.847994
Mean1.692
Median Absolute Deviation (MAD)0
Skewness4.679545
Sum846
Variance2.5702766
MonotonicityNot monotonic
2024-04-17T20:46:51.004542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 343
68.6%
2 93
 
18.6%
4 20
 
4.0%
3 18
 
3.6%
5 10
 
2.0%
7 6
 
1.2%
6 4
 
0.8%
11 2
 
0.4%
9 2
 
0.4%
8 1
 
0.2%
ValueCountFrequency (%)
1 343
68.6%
2 93
 
18.6%
3 18
 
3.6%
4 20
 
4.0%
5 10
 
2.0%
6 4
 
0.8%
7 6
 
1.2%
8 1
 
0.2%
9 2
 
0.4%
11 2
 
0.4%
ValueCountFrequency (%)
19 1
 
0.2%
11 2
 
0.4%
9 2
 
0.4%
8 1
 
0.2%
7 6
 
1.2%
6 4
 
0.8%
5 10
 
2.0%
4 20
 
4.0%
3 18
 
3.6%
2 93
18.6%

처리시각
Text

Distinct399
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T20:46:51.308402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
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

Unique362 ?
Unique (%)72.4%

Sample

1st row58:48.1
2nd row58:10.2
3rd row20:40.9
4th row32:15.5
5th row36:11.5
ValueCountFrequency (%)
06:37.2 17
 
3.4%
44:47.4 16
 
3.2%
32:47.1 8
 
1.6%
17:11.6 7
 
1.4%
53:09.6 6
 
1.2%
26:44.7 6
 
1.2%
40:44.6 5
 
1.0%
33:55.9 4
 
0.8%
55:31.7 4
 
0.8%
34:42.5 4
 
0.8%
Other values (389) 423
84.6%
2024-04-17T20:46:51.745569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 359
10.3%
3 328
9.4%
2 310
8.9%
5 294
8.4%
1 284
8.1%
0 278
7.9%
7 197
 
5.6%
6 175
 
5.0%
Other values (2) 275
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 359
14.4%
3 328
13.1%
2 310
12.4%
5 294
11.8%
1 284
11.4%
0 278
11.1%
7 197
7.9%
6 175
7.0%
9 138
 
5.5%
8 137
 
5.5%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 359
10.3%
3 328
9.4%
2 310
8.9%
5 294
8.4%
1 284
8.1%
0 278
7.9%
7 197
 
5.6%
6 175
 
5.0%
Other values (2) 275
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 359
10.3%
3 328
9.4%
2 310
8.9%
5 294
8.4%
1 284
8.1%
0 278
7.9%
7 197
 
5.6%
6 175
 
5.0%
Other values (2) 275
7.9%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5050.814
Minimum4432
Maximum6105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T20:46:51.864862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4432
5-th percentile4432
Q14432
median5208
Q35554.25
95-th percentile6105
Maximum6105
Range1673
Interquartile range (IQR)1122.25

Descriptive statistics

Standard deviation593.52733
Coefficient of variation (CV)0.11751122
Kurtosis-1.4303769
Mean5050.814
Median Absolute Deviation (MAD)688.5
Skewness0.2514277
Sum2525407
Variance352274.69
MonotonicityNot monotonic
2024-04-17T20:46:51.961123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4432 152
30.4%
5220 76
15.2%
5823 45
 
9.0%
4444 44
 
8.8%
6105 31
 
6.2%
5573 27
 
5.4%
5552 26
 
5.2%
4496 23
 
4.6%
5416 16
 
3.2%
5873 15
 
3.0%
Other values (15) 45
 
9.0%
ValueCountFrequency (%)
4432 152
30.4%
4444 44
 
8.8%
4496 23
 
4.6%
4606 1
 
0.2%
4685 1
 
0.2%
4896 1
 
0.2%
4917 4
 
0.8%
5032 1
 
0.2%
5037 1
 
0.2%
5099 7
 
1.4%
ValueCountFrequency (%)
6105 31
6.2%
5873 15
 
3.0%
5823 45
9.0%
5797 3
 
0.6%
5637 3
 
0.6%
5573 27
5.4%
5561 1
 
0.2%
5552 26
5.2%
5472 5
 
1.0%
5416 16
 
3.2%

최초처리시각
Text

Distinct354
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T20:46:52.258734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
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

Unique293 ?
Unique (%)58.6%

Sample

1st row58:48.1
2nd row58:10.2
3rd row58:11.5
4th row32:15.5
5th row18:43.9
ValueCountFrequency (%)
06:37.2 17
 
3.4%
44:47.4 16
 
3.2%
32:47.1 8
 
1.6%
53:09.6 8
 
1.6%
42:01.9 7
 
1.4%
17:11.6 7
 
1.4%
04:39.4 6
 
1.2%
26:44.7 6
 
1.2%
40:44.6 5
 
1.0%
55:31.7 5
 
1.0%
Other values (344) 415
83.0%
2024-04-17T20:46:52.686346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 374
10.7%
3 332
9.5%
2 316
9.0%
5 293
8.4%
0 277
7.9%
1 273
7.8%
7 192
 
5.5%
6 175
 
5.0%
Other values (2) 268
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 374
15.0%
3 332
13.3%
2 316
12.6%
5 293
11.7%
0 277
11.1%
1 273
10.9%
7 192
7.7%
6 175
7.0%
9 143
 
5.7%
8 125
 
5.0%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 374
10.7%
3 332
9.5%
2 316
9.0%
5 293
8.4%
0 277
7.9%
1 273
7.8%
7 192
 
5.5%
6 175
 
5.0%
Other values (2) 268
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 374
10.7%
3 332
9.5%
2 316
9.0%
5 293
8.4%
0 277
7.9%
1 273
7.8%
7 192
 
5.5%
6 175
 
5.0%
Other values (2) 268
7.7%

최초처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4996.252
Minimum4026
Maximum6105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T20:46:52.803045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4026
5-th percentile4432
Q14432
median5076
Q35220
95-th percentile6105
Maximum6105
Range2079
Interquartile range (IQR)788

Descriptive statistics

Standard deviation586.62517
Coefficient of variation (CV)0.11741305
Kurtosis-1.2031709
Mean4996.252
Median Absolute Deviation (MAD)644
Skewness0.45683637
Sum2498126
Variance344129.09
MonotonicityNot monotonic
2024-04-17T20:46:52.895379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4432 161
32.2%
5220 116
23.2%
4444 49
 
9.8%
5823 45
 
9.0%
6105 32
 
6.4%
5573 25
 
5.0%
4496 21
 
4.2%
5873 14
 
2.8%
4917 9
 
1.8%
5099 7
 
1.4%
Other values (12) 21
 
4.2%
ValueCountFrequency (%)
4026 1
 
0.2%
4256 1
 
0.2%
4432 161
32.2%
4444 49
 
9.8%
4496 21
 
4.2%
4606 1
 
0.2%
4773 1
 
0.2%
4848 1
 
0.2%
4917 9
 
1.8%
4927 1
 
0.2%
ValueCountFrequency (%)
6105 32
 
6.4%
5873 14
 
2.8%
5823 45
 
9.0%
5797 4
 
0.8%
5573 25
 
5.0%
5561 1
 
0.2%
5552 1
 
0.2%
5220 116
23.2%
5213 1
 
0.2%
5208 4
 
0.8%

Interactions

2024-04-17T20:46:47.185982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:46:46.725581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:46:46.944415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:46:47.287829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:46:46.799912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:46:47.023490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:46:47.366854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:46:46.872611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:46:47.107392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T20:46:53.198853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상품구분코드상위상품코드노드구분코드레벨순서값삭제여부최종수정수처리직원번호최초처리직원번호
상품구분코드1.0000.6090.9970.6880.0840.0000.2480.431
상위상품코드0.6091.0000.4620.8500.5490.7570.9230.912
노드구분코드0.9970.4621.0000.7320.0000.0000.0000.136
레벨순서값0.6880.8500.7321.0000.0000.0000.2030.172
삭제여부0.0840.5490.0000.0001.0000.0000.1530.100
최종수정수0.0000.7570.0000.0000.0001.0000.4080.393
처리직원번호0.2480.9230.0000.2030.1530.4081.0000.873
최초처리직원번호0.4310.9120.1360.1720.1000.3930.8731.000
2024-04-17T20:46:53.298208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상품구분코드레벨순서값노드구분코드삭제여부
상품구분코드1.0000.7210.9460.055
레벨순서값0.7211.0000.9650.000
노드구분코드0.9460.9651.0000.000
삭제여부0.0550.0000.0001.000
2024-04-17T20:46:53.395104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수처리직원번호최초처리직원번호상품구분코드노드구분코드레벨순서값삭제여부
최종수정수1.0000.084-0.0210.0000.0000.0000.000
처리직원번호0.0841.0000.8690.1860.0320.1810.000
최초처리직원번호-0.0210.8691.0000.1990.0920.1020.060
상품구분코드0.0000.1860.1991.0000.9460.7210.055
노드구분코드0.0000.0320.0920.9461.0000.9650.000
레벨순서값0.0000.1810.1020.7210.9651.0000.000
삭제여부0.0000.0000.0600.0550.0000.0001.000

Missing values

2024-04-17T20:46:47.473188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T20:46:47.622303image/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

상품코드상품명상품구분코드상위상품코드노드구분코드레벨순서값상품표시명삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
013522재창업지원보증(사업성평가:타기관)321재창업지원보증(사업성평가:타기관)N158:48.1509958:48.15099
113521재창업지원보증(사업성평가:신보)321재창업지원보증(사업성평가:신보)N158:10.2509958:10.25099
213468(장기분할전용)코로나19 상환유예 연착륙 조치(21.3월)321(장기분할전용)코로나19 상환유예 연착륙 조치(21.3월)N220:40.9557358:11.55873
313520문화산업정책보증(네이버)321문화산업정책보증(네이버)N132:15.5509932:15.55099
411291미사용221미사용N336:11.5587318:43.94432
511290미사용221미사용N335:53.5587318:43.94432
613519(호남)전북 벤처기업육성자금 지원 협약보증121(호남)전북 벤처기업육성자금 지원 협약보증N132:28.7587332:28.75873
713518(호남)코로나19 연착륙 특례보증('21.9월)3C017222(호남)코로나19 연착륙 특례보증('21.9월)N251:36.5555224:34.25823
813517(충청)코로나19 연착륙 특례보증('21.9월)3C017222(충청)코로나19 연착륙 특례보증('21.9월)N251:28.4555224:21.45823
913516(인천)코로나19 연착륙 특례보증('21.9월)3C017222(인천)코로나19 연착륙 특례보증('21.9월)N251:19.5555224:10.95823
상품코드상품명상품구분코드상위상품코드노드구분코드레벨순서값상품표시명삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490C0109문화산업완성보증110문화산업완성보증N134:39.0444434:39.04444
49113184지식재산 가치평가 수수료 지원 보증(한국산업은행)3C005622지식재산가치평가수수료지원보증(산업)N308:27.6541614:54.85220
49213185법인기업 연대보증 면제 보증(19년 6월)3C008022법인기업연대보증면제보증(19.6월)N108:38.7522008:38.75220
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