Overview

Dataset statistics

Number of variables12
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory104.4 B

Variable types

Categorical7
Text2
DateTime1
Numeric2

Dataset

Description샘플 데이터
Author경기신용보증재단
URLhttps://bigdata-region.kr/#/dataset/550d9463-fe03-41c3-ade6-dfcd61ffba8d

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
부실수 has constant value ""Constant
보증금액 is highly overall correlated with 보증부실발생금액High correlation
보증부실발생금액 is highly overall correlated with 보증금액High correlation
연령대코드 is highly overall correlated with 업종대분류명High correlation
업종대분류명 is highly overall correlated with 연령대코드High correlation
성별코드 is highly imbalanced (64.7%)Imbalance
업종대분류명 is highly imbalanced (61.7%)Imbalance
기업번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:45:58.730191
Analysis finished2023-12-10 13:46:00.888180
Duration2.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-04
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-04
2nd row2023-04
3rd row2023-04
4th row2023-04
5th row2023-04

Common Values

ValueCountFrequency (%)
2023-04 30
100.0%

Length

2023-12-10T22:46:00.995236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:01.287630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-04 30
100.0%

성별코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
M
28 
F
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 28
93.3%
F 2
 
6.7%

Length

2023-12-10T22:46:01.532403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:01.712737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 28
93.3%
f 2
 
6.7%

연령대코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
70
13 
60
8 
80
8 
40
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row60
2nd row60
3rd row70
4th row70
5th row70

Common Values

ValueCountFrequency (%)
70 13
43.3%
60 8
26.7%
80 8
26.7%
40 1
 
3.3%

Length

2023-12-10T22:46:01.908275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:02.123134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
70 13
43.3%
60 8
26.7%
80 8
26.7%
40 1
 
3.3%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T22:46:02.310053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:02.471995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:02.768442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.5666667
Min length3

Characters and Unicode

Total characters107
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)30.0%

Sample

1st row화성시
2nd row시흥시
3rd row파주시
4th row남양주시
5th row김포시
ValueCountFrequency (%)
화성시 4
11.8%
부천시 4
11.8%
김포시 4
11.8%
파주시 3
 
8.8%
포천시 3
 
8.8%
양주시 3
 
8.8%
처인구 1
 
2.9%
분당구 1
 
2.9%
성남시 1
 
2.9%
안성시 1
 
2.9%
Other values (9) 9
26.5%
2023-12-10T22:46:03.531884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 31
29.0%
천 8
 
7.5%
포 7
 
6.5%
주 7
 
6.5%
성 6
 
5.6%
화 4
 
3.7%
김 4
 
3.7%
부 4
 
3.7%
양 4
 
3.7%
구 4
 
3.7%
Other values (19) 28
26.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 103
96.3%
Space Separator 4
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 31
30.1%
천 8
 
7.8%
포 7
 
6.8%
주 7
 
6.8%
성 6
 
5.8%
화 4
 
3.9%
김 4
 
3.9%
부 4
 
3.9%
양 4
 
3.9%
구 4
 
3.9%
Other values (18) 24
23.3%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 103
96.3%
Common 4
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 31
30.1%
천 8
 
7.8%
포 7
 
6.8%
주 7
 
6.8%
성 6
 
5.8%
화 4
 
3.9%
김 4
 
3.9%
부 4
 
3.9%
양 4
 
3.9%
구 4
 
3.9%
Other values (18) 24
23.3%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 103
96.3%
ASCII 4
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 31
30.1%
천 8
 
7.8%
포 7
 
6.8%
주 7
 
6.8%
성 6
 
5.8%
화 4
 
3.9%
김 4
 
3.9%
부 4
 
3.9%
양 4
 
3.9%
구 4
 
3.9%
Other values (18) 24
23.3%
ASCII
ValueCountFrequency (%)
4
100.0%

업체형태명
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
주식회사
21 
개인기업
9 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row주식회사
2nd row개인기업
3rd row주식회사
4th row주식회사
5th row개인기업

Common Values

ValueCountFrequency (%)
주식회사 21
70.0%
개인기업 9
30.0%

Length

2023-12-10T22:46:03.859036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:04.138597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주식회사 21
70.0%
개인기업 9
30.0%

업종대분류명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
C제조업(10~33)
26 
G도매및소매업(45~47)
 
2
H운수업(49~52)
 
1
I숙박및음식점업(55~56)
 
1

Length

Max length15
Median length11
Mean length11.333333
Min length11

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st rowH운수업(49~52)
2nd rowG도매및소매업(45~47)
3rd rowC제조업(10~33)
4th rowC제조업(10~33)
5th rowC제조업(10~33)

Common Values

ValueCountFrequency (%)
C제조업(10~33) 26
86.7%
G도매및소매업(45~47) 2
 
6.7%
H운수업(49~52) 1
 
3.3%
I숙박및음식점업(55~56) 1
 
3.3%

Length

2023-12-10T22:46:04.425006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:04.619365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c제조업(10~33 26
86.7%
g도매및소매업(45~47 2
 
6.7%
h운수업(49~52 1
 
3.3%
i숙박및음식점업(55~56 1
 
3.3%

부실발생일자
Date

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum1996-09-02 00:00:00
Maximum2023-09-05 00:00:00
2023-12-10T22:46:04.795683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:04.991076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

보증금액
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3046667 × 108
Minimum20000000
Maximum5.45 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:46:05.176935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000000
5-th percentile34500000
Q190000000
median1 × 108
Q31.5 × 108
95-th percentile2.33 × 108
Maximum5.45 × 108
Range5.25 × 108
Interquartile range (IQR)60000000

Descriptive statistics

Standard deviation98004128
Coefficient of variation (CV)0.75118136
Kurtosis10.642978
Mean1.3046667 × 108
Median Absolute Deviation (MAD)50000000
Skewness2.7199281
Sum3.914 × 109
Variance9.6048092 × 1015
MonotonicityNot monotonic
2023-12-10T22:46:05.390384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
100000000 8
26.7%
150000000 5
16.7%
200000000 4
13.3%
50000000 4
13.3%
90000000 2
 
6.7%
20000000 1
 
3.3%
40000000 1
 
3.3%
120000000 1
 
3.3%
30000000 1
 
3.3%
169000000 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
20000000 1
 
3.3%
30000000 1
 
3.3%
40000000 1
 
3.3%
50000000 4
13.3%
90000000 2
 
6.7%
100000000 8
26.7%
120000000 1
 
3.3%
150000000 5
16.7%
169000000 1
 
3.3%
200000000 4
13.3%
ValueCountFrequency (%)
545000000 1
 
3.3%
260000000 1
 
3.3%
200000000 4
13.3%
169000000 1
 
3.3%
150000000 5
16.7%
120000000 1
 
3.3%
100000000 8
26.7%
90000000 2
 
6.7%
50000000 4
13.3%
40000000 1
 
3.3%

보증부실발생금액
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.02386 × 108
Minimum19580000
Maximum2 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:46:05.608024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19580000
5-th percentile29550000
Q163000000
median1 × 108
Q31.4 × 108
95-th percentile2 × 108
Maximum2 × 108
Range1.8042 × 108
Interquartile range (IQR)77000000

Descriptive statistics

Standard deviation52555582
Coefficient of variation (CV)0.51330828
Kurtosis-0.70699266
Mean1.02386 × 108
Median Absolute Deviation (MAD)40000000
Skewness0.39803325
Sum3.07158 × 109
Variance2.7620892 × 1015
MonotonicityNot monotonic
2023-12-10T22:46:05.814987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
100000000 5
16.7%
50000000 4
13.3%
200000000 3
10.0%
150000000 3
10.0%
70000000 3
10.0%
63000000 2
 
6.7%
140000000 2
 
6.7%
19580000 1
 
3.3%
40000000 1
 
3.3%
90000000 1
 
3.3%
Other values (5) 5
16.7%
ValueCountFrequency (%)
19580000 1
 
3.3%
21000000 1
 
3.3%
40000000 1
 
3.3%
50000000 4
13.3%
63000000 2
 
6.7%
70000000 3
10.0%
90000000 1
 
3.3%
100000000 5
16.7%
105000000 1
 
3.3%
120000000 1
 
3.3%
ValueCountFrequency (%)
200000000 3
10.0%
180000000 1
 
3.3%
150000000 3
10.0%
140000000 2
 
6.7%
130000000 1
 
3.3%
120000000 1
 
3.3%
105000000 1
 
3.3%
100000000 5
16.7%
90000000 1
 
3.3%
70000000 3
10.0%

부실수
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
1
30 

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 30
100.0%

Length

2023-12-10T22:46:06.040660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:06.216007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 30
100.0%

기업번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:06.604110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters720
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowkJq4ZAG9ui2/zbYC3gZ78Q==
2nd rowpOTyktu8AJ02qBZO0qwLug==
3rd rowxHMVBvB2mX9ck+odayFMhQ==
4th row5l6wOMDFMu5h2Zgq20pWuw==
5th rowG23RomHt2NAUJeT+9/KbgQ==
ValueCountFrequency (%)
kjq4zag9ui2/zbyc3gz78q 1
 
3.3%
potyktu8aj02qbzo0qwlug 1
 
3.3%
ojo+wsxe0frgwgq++2ccfg 1
 
3.3%
r9m2mjtuvaxzczc+cmzsa 1
 
3.3%
i/4aprzyfuoq+fdaitjf0w 1
 
3.3%
ynu8lapatclcwet3hn/g/w 1
 
3.3%
uk/gmj+qucvwegxn3ab/wq 1
 
3.3%
p1lcepcmzpninssmtc7jxw 1
 
3.3%
yx8tt/kecfzvxvtada4ncq 1
 
3.3%
ci578jxetekwspzdoz4zya 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:46:07.168946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 60
 
8.3%
Q 22
 
3.1%
w 17
 
2.4%
2 17
 
2.4%
X 16
 
2.2%
u 15
 
2.1%
M 15
 
2.1%
0 14
 
1.9%
g 14
 
1.9%
F 14
 
1.9%
Other values (55) 516
71.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 275
38.2%
Lowercase Letter 250
34.7%
Decimal Number 111
15.4%
Math Symbol 73
 
10.1%
Other Punctuation 11
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Q 22
 
8.0%
X 16
 
5.8%
M 15
 
5.5%
F 14
 
5.1%
A 14
 
5.1%
Z 14
 
5.1%
T 13
 
4.7%
K 13
 
4.7%
J 12
 
4.4%
C 12
 
4.4%
Other values (16) 130
47.3%
Lowercase Letter
ValueCountFrequency (%)
w 17
 
6.8%
u 15
 
6.0%
g 14
 
5.6%
m 14
 
5.6%
t 13
 
5.2%
c 13
 
5.2%
s 12
 
4.8%
j 11
 
4.4%
v 11
 
4.4%
a 10
 
4.0%
Other values (16) 120
48.0%
Decimal Number
ValueCountFrequency (%)
2 17
15.3%
0 14
12.6%
8 13
11.7%
1 13
11.7%
3 11
9.9%
4 10
9.0%
7 10
9.0%
5 9
8.1%
9 7
6.3%
6 7
6.3%
Math Symbol
ValueCountFrequency (%)
= 60
82.2%
+ 13
 
17.8%
Other Punctuation
ValueCountFrequency (%)
/ 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 525
72.9%
Common 195
 
27.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
Q 22
 
4.2%
w 17
 
3.2%
X 16
 
3.0%
u 15
 
2.9%
M 15
 
2.9%
g 14
 
2.7%
F 14
 
2.7%
A 14
 
2.7%
Z 14
 
2.7%
m 14
 
2.7%
Other values (42) 370
70.5%
Common
ValueCountFrequency (%)
= 60
30.8%
2 17
 
8.7%
0 14
 
7.2%
+ 13
 
6.7%
8 13
 
6.7%
1 13
 
6.7%
/ 11
 
5.6%
3 11
 
5.6%
4 10
 
5.1%
7 10
 
5.1%
Other values (3) 23
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 60
 
8.3%
Q 22
 
3.1%
w 17
 
2.4%
2 17
 
2.4%
X 16
 
2.2%
u 15
 
2.1%
M 15
 
2.1%
0 14
 
1.9%
g 14
 
1.9%
F 14
 
1.9%
Other values (55) 516
71.7%

Interactions

2023-12-10T22:45:59.824254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:59.419158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:00.024019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:59.666035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:46:07.349453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드연령대코드시군구명업체형태명업종대분류명부실발생일자보증금액보증부실발생금액기업번호
성별코드1.0000.4840.6230.0000.5290.0000.0000.0001.000
연령대코드0.4841.0000.8420.0000.8830.0000.0000.0001.000
시군구명0.6230.8421.0000.6650.8750.0000.6780.2971.000
업체형태명0.0000.0000.6651.0000.1480.5670.4770.3341.000
업종대분류명0.5290.8830.8750.1481.0000.0000.0000.0001.000
부실발생일자0.0000.0000.0000.5670.0001.0000.9471.0001.000
보증금액0.0000.0000.6780.4770.0000.9471.0000.8411.000
보증부실발생금액0.0000.0000.2970.3340.0001.0000.8411.0001.000
기업번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T22:46:07.534873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종대분류명업체형태명연령대코드성별코드
업종대분류명1.0000.0740.5530.343
업체형태명0.0741.0000.0000.000
연령대코드0.5530.0001.0000.310
성별코드0.3430.0000.3101.000
2023-12-10T22:46:07.742759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보증금액보증부실발생금액성별코드연령대코드업체형태명업종대분류명
보증금액1.0000.9600.0000.0000.3560.000
보증부실발생금액0.9601.0000.2590.0000.3170.000
성별코드0.0000.2591.0000.3100.0000.343
연령대코드0.0000.0000.3101.0000.0000.553
업체형태명0.3560.3170.0000.0001.0000.074
업종대분류명0.0000.0000.3430.5530.0741.000

Missing values

2023-12-10T22:46:00.419826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:46:00.785584image/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

기준년월성별코드연령대코드시도명시군구명업체형태명업종대분류명부실발생일자보증금액보증부실발생금액부실수기업번호
02023-04M60경기도화성시주식회사H운수업(49~52)1996-09-021000000001000000001kJq4ZAG9ui2/zbYC3gZ78Q==
12023-04F60경기도시흥시개인기업G도매및소매업(45~47)2023-09-0520000000195800001pOTyktu8AJ02qBZO0qwLug==
22023-04M70경기도파주시주식회사C제조업(10~33)1996-12-062000000002000000001xHMVBvB2mX9ck+odayFMhQ==
32023-04M70경기도남양주시주식회사C제조업(10~33)1996-12-2415000000015000000015l6wOMDFMu5h2Zgq20pWuw==
42023-04M70경기도김포시개인기업C제조업(10~33)1997-01-071000000001000000001G23RomHt2NAUJeT+9/KbgQ==
52023-04M70경기도화성시주식회사C제조업(10~33)1997-03-211000000001000000001TFoTtwmeRuwR7J67N+uLjQ==
62023-04M70경기도파주시주식회사C제조업(10~33)1997-06-16150000000150000000114GhAY3imeQXraU5IGuWkg==
72023-04M40경기도이천시개인기업I숙박및음식점업(55~56)2023-09-0540000000400000001pxf2332sZ11KhjvVa/0VxQ==
82023-04M60경기도부천시주식회사C제조업(10~33)1997-08-1290000000630000001IehKwKn5Km8XZC/IGMDTLA==
92023-04M80경기도포천시주식회사C제조업(10~33)1997-08-30100000000700000001cW7X5G3kbB1P1PzmXaA8KA==
기준년월성별코드연령대코드시도명시군구명업체형태명업종대분류명부실발생일자보증금액보증부실발생금액부실수기업번호
202023-04M80경기도김포시주식회사C제조업(10~33)1998-01-221000000001000000001SGPBVo01MQMkK04VsSDHUg==
212023-04M60경기도수원시 팔달구개인기업C제조업(10~33)1998-01-2650000000500000001CI578JXETekWspZDOZ4ZYA==
222023-04M80경기도부천시주식회사C제조업(10~33)1998-01-301690000001300000001YX8tt/KeCFzVXvTadA4NCQ==
232023-04M60경기도안성시주식회사C제조업(10~33)1998-02-0450000000500000001p1LcEpCMZpninSsmtc7Jxw==
242023-04M80경기도양주시주식회사C제조업(10~33)1998-02-105450000002000000001uk/GmJ+QuCvwEgXN3aB/WQ==
252023-04M70경기도성남시 분당구주식회사C제조업(10~33)1998-02-111500000001200000001yNu8lAPatclcwEt3Hn/G/w==
262023-04M70경기도광명시주식회사C제조업(10~33)1998-03-042600000002000000001i/4APRZyfuOq+FdaiTjf0w==
272023-04M60경기도부천시주식회사C제조업(10~33)1998-03-1150000000500000001+r9M2MjtUvaXZczc+cmZSA==
282023-04M80경기도양주시주식회사C제조업(10~33)1998-03-282000000001400000001Ojo+WsXE0fRGWgq++2CCfg==
292023-04F60경기도포천시주식회사C제조업(10~33)1998-04-061500000001500000001lvbJujzXrx2gloK5ltI26w==