Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.3 KiB
Average record size in memory74.3 B

Variable types

Text3
Numeric2
Categorical4

Dataset

Description해당 파일 데이터는 신용보증기금의 공통RM요구사항통계보고서상세에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093198/fileData.do

Alerts

신용정보외부제공여부 is highly overall correlated with 주민번호표시구분코드 and 2 other fieldsHigh correlation
신용보증부동의구분코드 is highly overall correlated with 신용정보외부제공여부 and 2 other fieldsHigh correlation
주민번호표시구분코드 is highly overall correlated with 신용정보외부제공여부 and 2 other fieldsHigh correlation
자료파기예정일자코드 is highly overall correlated with 신용정보외부제공여부 and 2 other fieldsHigh correlation
신용정보외부제공여부 is highly imbalanced (68.3%)Imbalance
신용보증부동의구분코드 is highly imbalanced (72.9%)Imbalance
자료파기예정일자코드 is highly imbalanced (77.1%)Imbalance

Reproduction

Analysis started2023-12-12 19:01:43.666621
Analysis finished2023-12-12 19:01:45.066294
Duration1.4 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

요구사항ID
Text

Distinct398
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T04:01:45.332447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
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

Unique341 ?
Unique (%)68.2%

Sample

1st row9dnSHY3XN8
2nd row9dnSHY3XN8
3rd row9dnM25gc2h
4th row9dnLuhyo3o
5th row9dnLppbSsL
ValueCountFrequency (%)
9din51mhb6 7
 
1.4%
9dnlbl2wfv 6
 
1.2%
9diskyjzfi 6
 
1.2%
9dcqjb1adv 6
 
1.2%
9dk8qwosbv 6
 
1.2%
9dk6rw6tkf 6
 
1.2%
9dkkjhfqgq 5
 
1.0%
9dlnq6xxcw 5
 
1.0%
9dmadbhd3u 4
 
0.8%
9dm2welhtm 4
 
0.8%
Other values (388) 445
89.0%
2023-12-13T04:01:45.801485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 556
 
11.1%
9 551
 
11.0%
l 161
 
3.2%
k 154
 
3.1%
m 151
 
3.0%
j 135
 
2.7%
i 130
 
2.6%
n 117
 
2.3%
v 75
 
1.5%
b 74
 
1.5%
Other values (52) 2896
57.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2525
50.5%
Uppercase Letter 1442
28.8%
Decimal Number 1033
20.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 556
22.0%
l 161
 
6.4%
k 154
 
6.1%
m 151
 
6.0%
j 135
 
5.3%
i 130
 
5.1%
n 117
 
4.6%
v 75
 
3.0%
b 74
 
2.9%
t 72
 
2.9%
Other values (16) 900
35.6%
Uppercase Letter
ValueCountFrequency (%)
J 74
 
5.1%
M 73
 
5.1%
B 73
 
5.1%
I 66
 
4.6%
C 64
 
4.4%
X 63
 
4.4%
S 62
 
4.3%
H 62
 
4.3%
Q 62
 
4.3%
R 61
 
4.2%
Other values (16) 782
54.2%
Decimal Number
ValueCountFrequency (%)
9 551
53.3%
6 68
 
6.6%
1 60
 
5.8%
5 56
 
5.4%
8 53
 
5.1%
2 52
 
5.0%
3 51
 
4.9%
0 50
 
4.8%
7 48
 
4.6%
4 44
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3967
79.3%
Common 1033
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 556
 
14.0%
l 161
 
4.1%
k 154
 
3.9%
m 151
 
3.8%
j 135
 
3.4%
i 130
 
3.3%
n 117
 
2.9%
v 75
 
1.9%
b 74
 
1.9%
J 74
 
1.9%
Other values (42) 2340
59.0%
Common
ValueCountFrequency (%)
9 551
53.3%
6 68
 
6.6%
1 60
 
5.8%
5 56
 
5.4%
8 53
 
5.1%
2 52
 
5.0%
3 51
 
4.9%
0 50
 
4.8%
7 48
 
4.6%
4 44
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 556
 
11.1%
9 551
 
11.0%
l 161
 
3.2%
k 154
 
3.1%
m 151
 
3.0%
j 135
 
2.7%
i 130
 
2.6%
n 117
 
2.3%
v 75
 
1.5%
b 74
 
1.5%
Other values (52) 2896
57.9%

일련번호
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.392
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T04:01:45.955087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96964758
Coefficient of variation (CV)0.69658591
Kurtosis10.066907
Mean1.392
Median Absolute Deviation (MAD)0
Skewness3.0944953
Sum696
Variance0.94021643
MonotonicityNot monotonic
2023-12-13T04:01:46.057446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 398
79.6%
2 57
 
11.4%
3 19
 
3.8%
4 11
 
2.2%
5 8
 
1.6%
6 6
 
1.2%
7 1
 
0.2%
ValueCountFrequency (%)
1 398
79.6%
2 57
 
11.4%
3 19
 
3.8%
4 11
 
2.2%
5 8
 
1.6%
6 6
 
1.2%
7 1
 
0.2%
ValueCountFrequency (%)
7 1
 
0.2%
6 6
 
1.2%
5 8
 
1.6%
4 11
 
2.2%
3 19
 
3.8%
2 57
 
11.4%
1 398
79.6%

보고서명
Text

Distinct362
Distinct (%)72.5%
Missing1
Missing (%)0.2%
Memory size4.0 KiB
2023-12-13T04:01:46.353604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length41
Mean length18.60521
Min length1

Characters and Unicode

Total characters9284
Distinct characters350
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique301 ?
Unique (%)60.3%

Sample

1st row신규증액원장명세_조사심사관련
2nd row유동화 기초자산 편입심사 명세
3rd rowQCU_0002
4th rowQCU_0011
5th row(ABP-05550-20190509-001)신기준 신성장동력산업영위기업 보증공급액(IGS용)
ValueCountFrequency (%)
명세 51
 
3.6%
및 51
 
3.6%
요청 23
 
1.6%
정보 20
 
1.4%
적용된 18
 
1.3%
이용고객 17
 
1.2%
신규 16
 
1.1%
증액보증 16
 
1.1%
채무관계자 16
 
1.1%
유동화회사보증 14
 
1.0%
Other values (678) 1185
83.0%
2023-12-13T04:01:47.255758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
957
 
10.3%
0 421
 
4.5%
보 295
 
3.2%
1 194
 
2.1%
업 183
 
2.0%
세 183
 
2.0%
명 180
 
1.9%
증 172
 
1.9%
_ 160
 
1.7%
2 160
 
1.7%
Other values (340) 6379
68.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5850
63.0%
Decimal Number 1135
 
12.2%
Space Separator 957
 
10.3%
Uppercase Letter 718
 
7.7%
Connector Punctuation 160
 
1.7%
Dash Punctuation 149
 
1.6%
Open Punctuation 108
 
1.2%
Close Punctuation 107
 
1.2%
Other Punctuation 51
 
0.5%
Lowercase Letter 47
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 295
 
5.0%
업 183
 
3.1%
세 183
 
3.1%
명 180
 
3.1%
증 172
 
2.9%
기 156
 
2.7%
자 119
 
2.0%
신 118
 
2.0%
관 110
 
1.9%
용 106
 
1.8%
Other values (279) 4228
72.3%
Uppercase Letter
ValueCountFrequency (%)
A 94
13.1%
S 75
10.4%
U 71
9.9%
Q 67
9.3%
G 55
 
7.7%
C 46
 
6.4%
B 43
 
6.0%
I 38
 
5.3%
D 38
 
5.3%
P 34
 
4.7%
Other values (15) 157
21.9%
Lowercase Letter
ValueCountFrequency (%)
a 9
19.1%
e 8
17.0%
t 5
10.6%
c 5
10.6%
h 5
10.6%
s 3
 
6.4%
x 2
 
4.3%
d 2
 
4.3%
l 2
 
4.3%
b 2
 
4.3%
Other values (3) 4
8.5%
Decimal Number
ValueCountFrequency (%)
0 421
37.1%
1 194
17.1%
2 160
 
14.1%
5 87
 
7.7%
9 66
 
5.8%
4 60
 
5.3%
7 46
 
4.1%
3 37
 
3.3%
8 33
 
2.9%
6 31
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 29
56.9%
. 10
 
19.6%
' 10
 
19.6%
· 1
 
2.0%
/ 1
 
2.0%
Open Punctuation
ValueCountFrequency (%)
( 107
99.1%
[ 1
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 106
99.1%
] 1
 
0.9%
Space Separator
ValueCountFrequency (%)
957
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 149
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5850
63.0%
Common 2669
28.7%
Latin 765
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 295
 
5.0%
업 183
 
3.1%
세 183
 
3.1%
명 180
 
3.1%
증 172
 
2.9%
기 156
 
2.7%
자 119
 
2.0%
신 118
 
2.0%
관 110
 
1.9%
용 106
 
1.8%
Other values (279) 4228
72.3%
Latin
ValueCountFrequency (%)
A 94
12.3%
S 75
 
9.8%
U 71
 
9.3%
Q 67
 
8.8%
G 55
 
7.2%
C 46
 
6.0%
B 43
 
5.6%
I 38
 
5.0%
D 38
 
5.0%
P 34
 
4.4%
Other values (28) 204
26.7%
Common
ValueCountFrequency (%)
957
35.9%
0 421
15.8%
1 194
 
7.3%
_ 160
 
6.0%
2 160
 
6.0%
- 149
 
5.6%
( 107
 
4.0%
) 106
 
4.0%
5 87
 
3.3%
9 66
 
2.5%
Other values (13) 262
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5840
62.9%
ASCII 3433
37.0%
Compat Jamo 10
 
0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
957
27.9%
0 421
12.3%
1 194
 
5.7%
_ 160
 
4.7%
2 160
 
4.7%
- 149
 
4.3%
( 107
 
3.1%
) 106
 
3.1%
A 94
 
2.7%
5 87
 
2.5%
Other values (50) 998
29.1%
Hangul
ValueCountFrequency (%)
보 295
 
5.1%
업 183
 
3.1%
세 183
 
3.1%
명 180
 
3.1%
증 172
 
2.9%
기 156
 
2.7%
자 119
 
2.0%
신 118
 
2.0%
관 110
 
1.9%
용 106
 
1.8%
Other values (276) 4218
72.2%
Compat Jamo
ValueCountFrequency (%)
ㄹ 5
50.0%
ㅇ 3
30.0%
ㄴ 2
 
20.0%
None
ValueCountFrequency (%)
· 1
100.0%

신용정보외부제공여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
N
447 
Y
52 
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 447
89.4%
Y 52
 
10.4%
1
 
0.2%

Length

2023-12-13T04:01:47.478850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:01:47.634330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 447
89.6%
y 52
 
10.4%

주민번호표시구분코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
321 
2
126 
4
51 
5
 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 321
64.2%
2 126
 
25.2%
4 51
 
10.2%
5 1
 
0.2%
1
 
0.2%

Length

2023-12-13T04:01:47.779267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:01:47.912637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 321
64.3%
2 126
 
25.3%
4 51
 
10.2%
5 1
 
0.2%

신용보증부동의구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Y
458 
E
 
41
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowE

Common Values

ValueCountFrequency (%)
Y 458
91.6%
E 41
 
8.2%
1
 
0.2%

Length

2023-12-13T04:01:48.062094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:01:48.206842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 458
91.8%
e 41
 
8.2%

최종수정수
Real number (ℝ)

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.752
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T04:01:48.371533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2560459
Coefficient of variation (CV)0.71692118
Kurtosis13.835317
Mean1.752
Median Absolute Deviation (MAD)0
Skewness2.9317124
Sum876
Variance1.5776513
MonotonicityNot monotonic
2023-12-13T04:01:48.513180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 297
59.4%
2 108
 
21.6%
3 57
 
11.4%
4 20
 
4.0%
5 9
 
1.8%
6 5
 
1.0%
9 2
 
0.4%
7 1
 
0.2%
12 1
 
0.2%
ValueCountFrequency (%)
1 297
59.4%
2 108
 
21.6%
3 57
 
11.4%
4 20
 
4.0%
5 9
 
1.8%
6 5
 
1.0%
7 1
 
0.2%
9 2
 
0.4%
12 1
 
0.2%
ValueCountFrequency (%)
12 1
 
0.2%
9 2
 
0.4%
7 1
 
0.2%
6 5
 
1.0%
5 9
 
1.8%
4 20
 
4.0%
3 57
 
11.4%
2 108
 
21.6%
1 297
59.4%

처리직원번호
Text

Distinct126
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T04:01:48.896456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.012
Min length4

Characters and Unicode

Total characters2006
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

Unique61 ?
Unique (%)12.2%

Sample

1st row4546
2nd row4546
3rd row4757
4th row5640
5th row5595
ValueCountFrequency (%)
5589 47
 
9.4%
4793 24
 
4.8%
5730 24
 
4.8%
5831 23
 
4.6%
5841 23
 
4.6%
5595 18
 
3.6%
4597 18
 
3.6%
5640 17
 
3.4%
5228 16
 
3.2%
5759 14
 
2.8%
Other values (116) 276
55.2%
2023-12-13T04:01:49.490965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 590
29.4%
4 239
11.9%
8 180
 
9.0%
7 173
 
8.6%
9 169
 
8.4%
2 162
 
8.1%
3 160
 
8.0%
0 123
 
6.1%
1 111
 
5.5%
6 93
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
99.7%
Uppercase Letter 6
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 590
29.5%
4 239
11.9%
8 180
 
9.0%
7 173
 
8.6%
9 169
 
8.5%
2 162
 
8.1%
3 160
 
8.0%
0 123
 
6.2%
1 111
 
5.5%
6 93
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
A 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
99.7%
Latin 6
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 590
29.5%
4 239
11.9%
8 180
 
9.0%
7 173
 
8.6%
9 169
 
8.5%
2 162
 
8.1%
3 160
 
8.0%
0 123
 
6.2%
1 111
 
5.5%
6 93
 
4.7%
Latin
ValueCountFrequency (%)
A 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 590
29.4%
4 239
11.9%
8 180
 
9.0%
7 173
 
8.6%
9 169
 
8.4%
2 162
 
8.1%
3 160
 
8.0%
0 123
 
6.1%
1 111
 
5.5%
6 93
 
4.6%

자료파기예정일자코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
456 
3
 
42
2
 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 456
91.2%
3 42
 
8.4%
2 1
 
0.2%
1
 
0.2%

Length

2023-12-13T04:01:49.734921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:01:49.878744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 456
91.4%
3 42
 
8.4%
2 1
 
0.2%

Interactions

2023-12-13T04:01:44.537070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:44.287874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:44.640165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:44.426392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:01:49.981225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호신용정보외부제공여부주민번호표시구분코드신용보증부동의구분코드최종수정수자료파기예정일자코드
일련번호1.0000.2000.2040.2070.0000.154
신용정보외부제공여부0.2001.0000.7440.9980.1860.680
주민번호표시구분코드0.2040.7441.0000.7550.1890.867
신용보증부동의구분코드0.2070.9980.7551.0000.0440.681
최종수정수0.0000.1860.1890.0441.0000.227
자료파기예정일자코드0.1540.6800.8670.6810.2271.000
2023-12-13T04:01:50.131693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자료파기예정일자코드주민번호표시구분코드신용정보외부제공여부신용보증부동의구분코드
자료파기예정일자코드1.0000.8530.7110.712
주민번호표시구분코드0.8531.0000.7410.757
신용정보외부제공여부0.7110.7411.0000.940
신용보증부동의구분코드0.7120.7570.9401.000
2023-12-13T04:01:50.270563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호최종수정수신용정보외부제공여부주민번호표시구분코드신용보증부동의구분코드자료파기예정일자코드
일련번호1.0000.0420.1360.1310.1410.106
최종수정수0.0421.0000.1180.1160.0270.103
신용정보외부제공여부0.1360.1181.0000.7410.9400.711
주민번호표시구분코드0.1310.1160.7411.0000.7570.853
신용보증부동의구분코드0.1410.0270.9400.7571.0000.712
자료파기예정일자코드0.1060.1030.7110.8530.7121.000

Missing values

2023-12-13T04:01:44.793205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:01:44.995286image/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

요구사항ID일련번호보고서명신용정보외부제공여부주민번호표시구분코드신용보증부동의구분코드최종수정수처리직원번호자료파기예정일자코드
09dnSHY3XN81신규증액원장명세_조사심사관련N1Y145461
19dnSHY3XN82유동화 기초자산 편입심사 명세N1Y145461
29dnM25gc2h1QCU_0002N1Y147571
39dnLuhyo3o1QCU_0011N1Y156401
49dnLppbSsL1(ABP-05550-20190509-001)신기준 신성장동력산업영위기업 보증공급액(IGS용)Y1E155951
59dnLppbSsL3(ABP-05550-20190509-001)신기준 신성장동력산업영위기업 보증공급액(IGS용)Y1E155951
69dnLppbSsL2(ABP-05550-20190509-001)신기준 신성장동력산업영위기업 보증공급액(IGS용)Y1E155951
79dnKjdvhGQ1보증업체의 조사서상 이메일주소N4Y352751
89dnJ0Zt79Q1선수수익명세N1Y146451
99dnJCyBcwJ1첨부참조Y1Y158021
요구사항ID일련번호보고서명신용정보외부제공여부주민번호표시구분코드신용보증부동의구분코드최종수정수처리직원번호자료파기예정일자코드
4909din51MHB67개인회생파산신청현황N2Y355891
4919din51MHB66구상특수채권업체채무관계자명세N2Y355891
4929din51MHB65분할상환파기자명세N2Y355891
4939din51MHB64기보및지역신보규제해제채무자명세N2Y355891
4949din51MHB63신용관리해제채무자명세N2Y355891
4959din51MHB62신용카드신규발급자명세N2Y355891
4969din5HDeSA1채무관계자 규제자 명세Y2E155891
4979dimXvDS6z1잔액보유업체의 피보험자 수N1Y258411
4989dimTUAde91매출채권보험사고원장N1Y252211
4999dimTUAde92매출채권보험사고원장N1Y252211