Overview

Dataset statistics

Number of variables9
Number of observations30
Missing cells9
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory78.4 B

Variable types

Categorical3
Text4
Numeric2

Dataset

Description샘플 데이터
Author경기신용보증재단
URLhttps://bigdata-region.kr/#/dataset/dbcf92ed-4be4-4883-baf4-116e9f363c28

Alerts

기준년월 has constant value ""Constant
운영여부 has constant value ""Constant
설립년월 has 3 (10.0%) missing valuesMissing
폐업년월 has 6 (20.0%) missing valuesMissing
고객번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:46:15.281400
Analysis finished2023-12-10 13:46:17.879042
Duration2.6 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
Apr-23
30 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApr-23
2nd rowApr-23
3rd rowApr-23
4th rowApr-23
5th rowApr-23

Common Values

ValueCountFrequency (%)
Apr-23 30
100.0%

Length

2023-12-10T22:46:17.993576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:18.126943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
apr-23 30
100.0%

고객번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:18.378762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9333333
Min length7

Characters and Unicode

Total characters238
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

Unique30 ?
Unique (%)100.0%

Sample

1st row101304**
2nd row101617**
3rd row102375**
4th row103845**
5th row105057**
ValueCountFrequency (%)
101304 1
 
3.3%
101617 1
 
3.3%
15123 1
 
3.3%
150016 1
 
3.3%
148042 1
 
3.3%
142783 1
 
3.3%
13912 1
 
3.3%
139086 1
 
3.3%
138192 1
 
3.3%
134464 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:46:18.809207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 60
25.2%
1 44
18.5%
0 27
11.3%
2 19
 
8.0%
3 18
 
7.6%
5 16
 
6.7%
4 14
 
5.9%
8 12
 
5.0%
6 10
 
4.2%
7 10
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 178
74.8%
Other Punctuation 60
 
25.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44
24.7%
0 27
15.2%
2 19
10.7%
3 18
10.1%
5 16
 
9.0%
4 14
 
7.9%
8 12
 
6.7%
6 10
 
5.6%
7 10
 
5.6%
9 8
 
4.5%
Other Punctuation
ValueCountFrequency (%)
* 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 238
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 60
25.2%
1 44
18.5%
0 27
11.3%
2 19
 
8.0%
3 18
 
7.6%
5 16
 
6.7%
4 14
 
5.9%
8 12
 
5.0%
6 10
 
4.2%
7 10
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 60
25.2%
1 44
18.5%
0 27
11.3%
2 19
 
8.0%
3 18
 
7.6%
5 16
 
6.7%
4 14
 
5.9%
8 12
 
5.0%
6 10
 
4.2%
7 10
 
4.2%

시군명
Text

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:19.061605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0666667
Min length3

Characters and Unicode

Total characters92
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)23.3%

Sample

1st row성남시
2nd row이천시
3rd row안산시
4th row광주시
5th row평택시
ValueCountFrequency (%)
시흥시 4
13.3%
수원시 3
10.0%
광주시 2
 
6.7%
화성시 2
 
6.7%
안산시 2
 
6.7%
남양주시 2
 
6.7%
부천시 2
 
6.7%
양주시 2
 
6.7%
김포시 2
 
6.7%
평택시 2
 
6.7%
Other values (7) 7
23.3%
2023-12-10T22:46:19.512421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 34
37.0%
주 7
 
7.6%
양 6
 
6.5%
성 4
 
4.3%
안 4
 
4.3%
흥 4
 
4.3%
남 3
 
3.3%
천 3
 
3.3%
원 3
 
3.3%
수 3
 
3.3%
Other values (13) 21
22.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 34
37.0%
주 7
 
7.6%
양 6
 
6.5%
성 4
 
4.3%
안 4
 
4.3%
흥 4
 
4.3%
남 3
 
3.3%
천 3
 
3.3%
원 3
 
3.3%
수 3
 
3.3%
Other values (13) 21
22.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 34
37.0%
주 7
 
7.6%
양 6
 
6.5%
성 4
 
4.3%
안 4
 
4.3%
흥 4
 
4.3%
남 3
 
3.3%
천 3
 
3.3%
원 3
 
3.3%
수 3
 
3.3%
Other values (13) 21
22.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 34
37.0%
주 7
 
7.6%
양 6
 
6.5%
성 4
 
4.3%
안 4
 
4.3%
흥 4
 
4.3%
남 3
 
3.3%
천 3
 
3.3%
원 3
 
3.3%
수 3
 
3.3%
Other values (13) 21
22.8%

업종대분류명
Categorical

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
G 도매 및 소매업 (45~47)
12 
I 숙박 및 음식점업 (55 ~ 56)
5 
P 교육 서비스업(85)
4 
S협회및단체,수리및기타개인서비스업(94~96)
3 
H 운수업 (49~52)
2 
Other values (4)
4 

Length

Max length25
Median length22
Mean length17.866667
Min length10

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st rowH 운수업 (49~52)
2nd rowG 도매 및 소매업 (45~47)
3rd rowS협회및단체,수리및기타개인서비스업(94~96)
4th rowG 도매 및 소매업 (45~47)
5th rowI 숙박 및 음식점업 (55 ~ 56)

Common Values

ValueCountFrequency (%)
G 도매 및 소매업 (45~47) 12
40.0%
I 숙박 및 음식점업 (55 ~ 56) 5
16.7%
P 교육 서비스업(85) 4
 
13.3%
S협회및단체,수리및기타개인서비스업(94~96) 3
 
10.0%
H 운수업 (49~52) 2
 
6.7%
L 부동산업(68) 1
 
3.3%
C 제조업 (10 ~ 33) 1
 
3.3%
L 부동산업 및 임대업 (68 ~ 69) 1
 
3.3%
C 제조업 (10 ~ 34) 1
 
3.3%

Length

2023-12-10T22:46:19.785352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:20.047670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
및 18
13.3%
g 12
 
8.9%
도매 12
 
8.9%
소매업 12
 
8.9%
45~47 12
 
8.9%
8
 
5.9%
i 5
 
3.7%
숙박 5
 
3.7%
음식점업 5
 
3.7%
55 5
 
3.7%
Other values (19) 41
30.4%

업종중분류명
Text

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:20.401982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length8.8
Min length3

Characters and Unicode

Total characters264
Distinct characters75
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)63.3%

Sample

1st row일반 화물자동차 운송업
2nd row건강보조식품 소매업
3rd row두발미용업
4th row그외 기타 종합 소매업
5th row한식 일반 음식점업
ValueCountFrequency (%)
일반 8
 
10.0%
소매업 8
 
10.0%
및 5
 
6.2%
음식점업 5
 
6.2%
기타 4
 
5.0%
한식 4
 
5.0%
학원 4
 
5.0%
서적 3
 
3.8%
교과 3
 
3.8%
슈퍼마켓 2
 
2.5%
Other values (30) 34
42.5%
2023-12-10T22:46:20.965945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50
 
18.9%
업 24
 
9.1%
식 12
 
4.5%
매 9
 
3.4%
소 8
 
3.0%
일 8
 
3.0%
반 8
 
3.0%
음 6
 
2.3%
품 6
 
2.3%
점 5
 
1.9%
Other values (65) 128
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 213
80.7%
Space Separator 50
 
18.9%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 24
 
11.3%
식 12
 
5.6%
매 9
 
4.2%
소 8
 
3.8%
일 8
 
3.8%
반 8
 
3.8%
음 6
 
2.8%
품 6
 
2.8%
점 5
 
2.3%
기 5
 
2.3%
Other values (63) 122
57.3%
Space Separator
ValueCountFrequency (%)
50
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 213
80.7%
Common 51
 
19.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 24
 
11.3%
식 12
 
5.6%
매 9
 
4.2%
소 8
 
3.8%
일 8
 
3.8%
반 8
 
3.8%
음 6
 
2.8%
품 6
 
2.8%
점 5
 
2.3%
기 5
 
2.3%
Other values (63) 122
57.3%
Common
ValueCountFrequency (%)
50
98.0%
, 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 213
80.7%
ASCII 51
 
19.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50
98.0%
, 1
 
2.0%
Hangul
ValueCountFrequency (%)
업 24
 
11.3%
식 12
 
5.6%
매 9
 
4.2%
소 8
 
3.8%
일 8
 
3.8%
반 8
 
3.8%
음 6
 
2.8%
품 6
 
2.8%
점 5
 
2.3%
기 5
 
2.3%
Other values (63) 122
57.3%

주요제품명
Text

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:21.350074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length9.6
Min length4

Characters and Unicode

Total characters288
Distinct characters67
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)36.7%

Sample

1st row도로 화물 운송업
2nd row음·식료품 및 담배 소매업
3rd row미용, 욕탕 및 유사 서비스업
4th row종합 소매업
5th row음식점업
ValueCountFrequency (%)
소매업 10
 
11.2%
및 10
 
11.2%
음식점업 5
 
5.6%
종합 3
 
3.4%
일반 3
 
3.4%
교습 3
 
3.4%
학원 3
 
3.4%
음·식료품 3
 
3.4%
담배 3
 
3.4%
미용 3
 
3.4%
Other values (29) 43
48.3%
2023-12-10T22:46:22.075464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
59
20.5%
업 26
 
9.0%
매 11
 
3.8%
품 11
 
3.8%
및 10
 
3.5%
소 10
 
3.5%
식 9
 
3.1%
음 8
 
2.8%
용 7
 
2.4%
점 6
 
2.1%
Other values (57) 131
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 221
76.7%
Space Separator 59
 
20.5%
Other Punctuation 8
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 26
 
11.8%
매 11
 
5.0%
품 11
 
5.0%
및 10
 
4.5%
소 10
 
4.5%
식 9
 
4.1%
음 8
 
3.6%
용 7
 
3.2%
점 6
 
2.7%
교 4
 
1.8%
Other values (54) 119
53.8%
Other Punctuation
ValueCountFrequency (%)
, 5
62.5%
· 3
37.5%
Space Separator
ValueCountFrequency (%)
59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 221
76.7%
Common 67
 
23.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 26
 
11.8%
매 11
 
5.0%
품 11
 
5.0%
및 10
 
4.5%
소 10
 
4.5%
식 9
 
4.1%
음 8
 
3.6%
용 7
 
3.2%
점 6
 
2.7%
교 4
 
1.8%
Other values (54) 119
53.8%
Common
ValueCountFrequency (%)
59
88.1%
, 5
 
7.5%
· 3
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 221
76.7%
ASCII 64
 
22.2%
None 3
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
59
92.2%
, 5
 
7.8%
Hangul
ValueCountFrequency (%)
업 26
 
11.8%
매 11
 
5.0%
품 11
 
5.0%
및 10
 
4.5%
소 10
 
4.5%
식 9
 
4.1%
음 8
 
3.6%
용 7
 
3.2%
점 6
 
2.7%
교 4
 
1.8%
Other values (54) 119
53.8%
None
ValueCountFrequency (%)
· 3
100.0%

운영여부
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업자 30
100.0%

Length

2023-12-10T22:46:22.352868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:22.531186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업자 30
100.0%

설립년월
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)100.0%
Missing3
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean20139885
Minimum19960621
Maximum20220429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:46:22.697934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19960621
5-th percentile20025629
Q120100856
median20151116
Q320190320
95-th percentile20210669
Maximum20220429
Range259808
Interquartile range (IQR)89464.5

Descriptive statistics

Standard deviation64504.594
Coefficient of variation (CV)0.0032028283
Kurtosis0.96500146
Mean20139885
Median Absolute Deviation (MAD)40306
Skewness-1.0689994
Sum5.437769 × 108
Variance4.1608427 × 109
MonotonicityNot monotonic
2023-12-10T22:46:22.917031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
20010715 1
 
3.3%
20190415 1
 
3.3%
20090713 1
 
3.3%
20131118 1
 
3.3%
20190226 1
 
3.3%
20150417 1
 
3.3%
20160823 1
 
3.3%
20151116 1
 
3.3%
20180317 1
 
3.3%
20201026 1
 
3.3%
Other values (17) 17
56.7%
(Missing) 3
 
10.0%
ValueCountFrequency (%)
19960621 1
3.3%
20010715 1
3.3%
20060427 1
3.3%
20070207 1
3.3%
20080825 1
3.3%
20090713 1
3.3%
20090902 1
3.3%
20110810 1
3.3%
20111103 1
3.3%
20130903 1
3.3%
ValueCountFrequency (%)
20220429 1
3.3%
20210901 1
3.3%
20210129 1
3.3%
20201026 1
3.3%
20200801 1
3.3%
20200410 1
3.3%
20190415 1
3.3%
20190226 1
3.3%
20180317 1
3.3%
20180122 1
3.3%

폐업년월
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)100.0%
Missing6
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean20218664
Minimum20181015
Maximum20230414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:46:23.156667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20181015
5-th percentile20192552
Q120220612
median20220766
Q320221208
95-th percentile20230298
Maximum20230414
Range49399
Interquartile range (IQR)596.5

Descriptive statistics

Standard deviation11612.405
Coefficient of variation (CV)0.00057434088
Kurtosis5.1292657
Mean20218664
Median Absolute Deviation (MAD)251.5
Skewness-2.223375
Sum4.8524794 × 108
Variance1.3484796 × 108
MonotonicityNot monotonic
2023-12-10T22:46:23.410943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20221209 1
 
3.3%
20230328 1
 
3.3%
20230414 1
 
3.3%
20220621 1
 
3.3%
20220617 1
 
3.3%
20220914 1
 
3.3%
20201220 1
 
3.3%
20220630 1
 
3.3%
20221208 1
 
3.3%
20220826 1
 
3.3%
Other values (14) 14
46.7%
(Missing) 6
20.0%
ValueCountFrequency (%)
20181015 1
3.3%
20191022 1
3.3%
20201220 1
3.3%
20220430 1
3.3%
20220607 1
3.3%
20220608 1
3.3%
20220613 1
3.3%
20220614 1
3.3%
20220617 1
3.3%
20220621 1
3.3%
ValueCountFrequency (%)
20230414 1
3.3%
20230328 1
3.3%
20230125 1
3.3%
20230120 1
3.3%
20221226 1
3.3%
20221209 1
3.3%
20221208 1
3.3%
20221024 1
3.3%
20221011 1
3.3%
20220914 1
3.3%

Interactions

2023-12-10T22:46:16.783400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:16.072853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:17.128780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:16.474990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:46:23.914583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객번호시군명업종대분류명업종중분류명주요제품명설립년월폐업년월
고객번호1.0001.0001.0001.0001.0001.0001.000
시군명1.0001.0000.5430.7960.5150.7080.000
업종대분류명1.0000.5431.0001.0001.0000.5910.657
업종중분류명1.0000.7961.0001.0001.0000.6070.000
주요제품명1.0000.5151.0001.0001.0000.0000.528
설립년월1.0000.7080.5910.6070.0001.0000.000
폐업년월1.0000.0000.6570.0000.5280.0001.000
2023-12-10T22:46:24.169608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설립년월폐업년월업종대분류명
설립년월1.0000.0380.088
폐업년월0.0381.0000.320
업종대분류명0.0880.3201.000

Missing values

2023-12-10T22:46:17.388525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:46:17.602692image/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.
2023-12-10T22:46:17.778995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준년월고객번호시군명업종대분류명업종중분류명주요제품명운영여부설립년월폐업년월
0Apr-23101304**성남시H 운수업 (49~52)일반 화물자동차 운송업도로 화물 운송업폐업자2015060520230120
1Apr-23101617**이천시G 도매 및 소매업 (45~47)건강보조식품 소매업음·식료품 및 담배 소매업폐업자2001071520220613
2Apr-23102375**안산시S협회및단체,수리및기타개인서비스업(94~96)두발미용업미용, 욕탕 및 유사 서비스업폐업자<NA><NA>
3Apr-23103845**광주시G 도매 및 소매업 (45~47)그외 기타 종합 소매업종합 소매업폐업자2021012920221011
4Apr-23105057**평택시I 숙박 및 음식점업 (55 ~ 56)한식 일반 음식점업음식점업폐업자2009090220220608
5Apr-23105548**양주시G 도매 및 소매업 (45~47)슈퍼마켓종합 소매업폐업자2020080120220831
6Apr-23108053**시흥시P 교육 서비스업(85)일반 교과 학원일반 교습 학원폐업자2021090120220430
7Apr-23110597**부천시I 숙박 및 음식점업 (55 ~ 56)중식 음식점업음식점업폐업자2020041020221024
8Apr-23112039**화성시H 운수업 (49~52)택시 운송업육상 여객 운송업폐업자2018012220220706
9Apr-23114006**양주시G 도매 및 소매업 (45~47)음료 소매업음·식료품 및 담배 소매업폐업자2017012120230125
기준년월고객번호시군명업종대분류명업종중분류명주요제품명운영여부설립년월폐업년월
20Apr-23134000**수원시P 교육 서비스업(85)일반 교과 학원일반 교습 학원폐업자2020102620221208
21Apr-23134464**남양주시I 숙박 및 음식점업 (55 ~ 56)한식 일반 음식점업음식점업폐업자2018031720220630
22Apr-23138192**화성시G 도매 및 소매업 (45~47)슈퍼마켓종합 소매업폐업자2015111620201220
23Apr-23139086**시흥시G 도매 및 소매업 (45~47)전자상거래 소매업무점포 소매업폐업자2016082320220914
24Apr-2313912**광주시S협회및단체,수리및기타개인서비스업(94~96)두발미용업미용, 욕탕 및 유사 서비스업폐업자<NA><NA>
25Apr-23142783**안산시G 도매 및 소매업 (45~47)운동 및 경기용품 소매업문화, 오락 및 여가 용품 소매업폐업자20150417<NA>
26Apr-23148042**고양시I 숙박 및 음식점업 (55 ~ 56)한식 일반 음식점업음식점업폐업자2019022620220617
27Apr-23150016**시흥시P 교육 서비스업(85)예술 학원기타 교육기관폐업자2013111820220621
28Apr-2315123**시흥시L 부동산업 및 임대업 (68 ~ 69)서적 임대업개인 및 가정용품 임대업폐업자2009071320230414
29Apr-23151732**안양시C 제조업 (10 ~ 34)그외 기타 나무제품 제조업나무제품 제조업폐업자2019041520230328