Overview

Dataset statistics

Number of variables44
Number of observations383
Missing cells3819
Missing cells (%)22.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.4 KiB
Average record size in memory375.3 B

Variable types

Categorical19
Text7
DateTime4
Unsupported7
Numeric6
Boolean1

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,남성종사자수,여성종사자수,영업장주변구분명,등급구분명,급수시설구분명,총인원,본사종업원수,공장사무직종업원수,공장판매직종업원수,공장생산직종업원수,건물소유구분명,보증액,월세액,다중이용업소여부,시설총규모,전통업소지정번호,전통업소주된음식,홈페이지
Author구로구
URLhttps://data.seoul.go.kr/dataList/OA-18268/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
여성종사자수 is highly imbalanced (56.0%)Imbalance
영업장주변구분명 is highly imbalanced (50.0%)Imbalance
총인원 is highly imbalanced (83.9%)Imbalance
공장사무직종업원수 is highly imbalanced (52.3%)Imbalance
보증액 is highly imbalanced (59.5%)Imbalance
월세액 is highly imbalanced (59.5%)Imbalance
인허가취소일자 has 383 (100.0%) missing valuesMissing
폐업일자 has 46 (12.0%) missing valuesMissing
휴업시작일자 has 383 (100.0%) missing valuesMissing
휴업종료일자 has 383 (100.0%) missing valuesMissing
재개업일자 has 383 (100.0%) missing valuesMissing
전화번호 has 91 (23.8%) missing valuesMissing
소재지면적 has 12 (3.1%) missing valuesMissing
도로명주소 has 205 (53.5%) missing valuesMissing
도로명우편번호 has 207 (54.0%) missing valuesMissing
좌표정보(X) has 20 (5.2%) missing valuesMissing
좌표정보(Y) has 20 (5.2%) missing valuesMissing
남성종사자수 has 282 (73.6%) missing valuesMissing
공장생산직종업원수 has 187 (48.8%) missing valuesMissing
다중이용업소여부 has 34 (8.9%) missing valuesMissing
시설총규모 has 34 (8.9%) missing valuesMissing
전통업소지정번호 has 383 (100.0%) missing valuesMissing
전통업소주된음식 has 383 (100.0%) missing valuesMissing
홈페이지 has 383 (100.0%) missing valuesMissing
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소지정번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소주된음식 is an unsupported type, check if it needs cleaning or further analysisUnsupported
홈페이지 is an unsupported type, check if it needs cleaning or further analysisUnsupported
남성종사자수 has 66 (17.2%) zerosZeros
공장생산직종업원수 has 161 (42.0%) zerosZeros
시설총규모 has 340 (88.8%) zerosZeros

Reproduction

Analysis started2024-05-11 04:20:55.157573
Analysis finished2024-05-11 04:20:57.215538
Duration2.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
3160000
383 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3160000
2nd row3160000
3rd row3160000
4th row3160000
5th row3160000

Common Values

ValueCountFrequency (%)
3160000 383
100.0%

Length

2024-05-11T04:20:57.403582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:20:57.738984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3160000 383
100.0%

관리번호
Text

UNIQUE 

Distinct383
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-05-11T04:20:58.365574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters8426
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

Unique383 ?
Unique (%)100.0%

Sample

1st row3160000-106-1979-00028
2nd row3160000-106-1980-00031
3rd row3160000-106-1981-00030
4th row3160000-106-1987-00004
5th row3160000-106-1987-00005
ValueCountFrequency (%)
3160000-106-1979-00028 1
 
0.3%
3160000-106-2012-00009 1
 
0.3%
3160000-106-2012-00006 1
 
0.3%
3160000-106-2012-00005 1
 
0.3%
3160000-106-2012-00004 1
 
0.3%
3160000-106-2012-00003 1
 
0.3%
3160000-106-2012-00002 1
 
0.3%
3160000-106-2012-00001 1
 
0.3%
3160000-106-2011-00016 1
 
0.3%
3160000-106-2011-00015 1
 
0.3%
Other values (373) 373
97.4%
2024-05-11T04:20:59.331342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3699
43.9%
1 1185
 
14.1%
- 1149
 
13.6%
6 860
 
10.2%
3 497
 
5.9%
2 421
 
5.0%
9 244
 
2.9%
8 108
 
1.3%
4 97
 
1.2%
5 87
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7277
86.4%
Dash Punctuation 1149
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3699
50.8%
1 1185
 
16.3%
6 860
 
11.8%
3 497
 
6.8%
2 421
 
5.8%
9 244
 
3.4%
8 108
 
1.5%
4 97
 
1.3%
5 87
 
1.2%
7 79
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 1149
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8426
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3699
43.9%
1 1185
 
14.1%
- 1149
 
13.6%
6 860
 
10.2%
3 497
 
5.9%
2 421
 
5.0%
9 244
 
2.9%
8 108
 
1.3%
4 97
 
1.2%
5 87
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3699
43.9%
1 1185
 
14.1%
- 1149
 
13.6%
6 860
 
10.2%
3 497
 
5.9%
2 421
 
5.0%
9 244
 
2.9%
8 108
 
1.3%
4 97
 
1.2%
5 87
 
1.0%
Distinct358
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1979-08-20 00:00:00
Maximum2023-12-01 00:00:00
2024-05-11T04:20:59.776232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:21:00.168296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing383
Missing (%)100.0%
Memory size3.5 KiB
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
3
337 
1
46 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 337
88.0%
1 46
 
12.0%

Length

2024-05-11T04:21:00.512084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:00.771404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 337
88.0%
1 46
 
12.0%

영업상태명
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
폐업
337 
영업/정상
46 

Length

Max length5
Median length2
Mean length2.3603133
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 337
88.0%
영업/정상 46
 
12.0%

Length

2024-05-11T04:21:01.119949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:01.456260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 337
88.0%
영업/정상 46
 
12.0%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2
337 
1
46 

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 (%)
2 337
88.0%
1 46
 
12.0%

Length

2024-05-11T04:21:01.797147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:02.139304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 337
88.0%
1 46
 
12.0%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
폐업
337 
영업
46 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 337
88.0%
영업 46
 
12.0%

Length

2024-05-11T04:21:02.435293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:02.755062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 337
88.0%
영업 46
 
12.0%

폐업일자
Date

MISSING 

Distinct296
Distinct (%)87.8%
Missing46
Missing (%)12.0%
Memory size3.1 KiB
Minimum1997-05-19 00:00:00
Maximum2023-12-13 00:00:00
2024-05-11T04:21:03.100885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:21:03.479631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing383
Missing (%)100.0%
Memory size3.5 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing383
Missing (%)100.0%
Memory size3.5 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing383
Missing (%)100.0%
Memory size3.5 KiB

전화번호
Text

MISSING 

Distinct251
Distinct (%)86.0%
Missing91
Missing (%)23.8%
Memory size3.1 KiB
2024-05-11T04:21:04.056954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.8116438
Min length2

Characters and Unicode

Total characters2865
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

Unique245 ?
Unique (%)83.9%

Sample

1st row02
2nd row02 6784049
3rd row02
4th row02 0
5th row02 6887353
ValueCountFrequency (%)
02 192
36.4%
0 28
 
5.3%
070 16
 
3.0%
856 5
 
0.9%
863 3
 
0.6%
8304219 2
 
0.4%
8373883 2
 
0.4%
6000 2
 
0.4%
868 2
 
0.4%
8529424 2
 
0.4%
Other values (270) 274
51.9%
2024-05-11T04:21:05.256170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 511
17.8%
2 508
17.7%
367
12.8%
8 293
10.2%
6 262
9.1%
5 177
 
6.2%
3 171
 
6.0%
1 170
 
5.9%
7 162
 
5.7%
4 128
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2498
87.2%
Space Separator 367
 
12.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 511
20.5%
2 508
20.3%
8 293
11.7%
6 262
10.5%
5 177
 
7.1%
3 171
 
6.8%
1 170
 
6.8%
7 162
 
6.5%
4 128
 
5.1%
9 116
 
4.6%
Space Separator
ValueCountFrequency (%)
367
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2865
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 511
17.8%
2 508
17.7%
367
12.8%
8 293
10.2%
6 262
9.1%
5 177
 
6.2%
3 171
 
6.0%
1 170
 
5.9%
7 162
 
5.7%
4 128
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 511
17.8%
2 508
17.7%
367
12.8%
8 293
10.2%
6 262
9.1%
5 177
 
6.2%
3 171
 
6.0%
1 170
 
5.9%
7 162
 
5.7%
4 128
 
4.5%

소재지면적
Text

MISSING 

Distinct336
Distinct (%)90.6%
Missing12
Missing (%)3.1%
Memory size3.1 KiB
2024-05-11T04:21:06.201365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.3018868
Min length3

Characters and Unicode

Total characters1967
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

Unique313 ?
Unique (%)84.4%

Sample

1st row111.88
2nd row105.64
3rd row.00
4th row113.43
5th row66.35
ValueCountFrequency (%)
33.00 5
 
1.3%
00 5
 
1.3%
45.00 3
 
0.8%
60.00 3
 
0.8%
150.00 3
 
0.8%
49.50 3
 
0.8%
18.00 3
 
0.8%
40.00 3
 
0.8%
455.30 2
 
0.5%
18.96 2
 
0.5%
Other values (326) 339
91.4%
2024-05-11T04:21:07.378058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 371
18.9%
0 325
16.5%
1 239
12.2%
2 177
9.0%
3 146
 
7.4%
6 138
 
7.0%
4 134
 
6.8%
5 133
 
6.8%
8 121
 
6.2%
9 92
 
4.7%
Other values (2) 91
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1595
81.1%
Other Punctuation 372
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 325
20.4%
1 239
15.0%
2 177
11.1%
3 146
9.2%
6 138
8.7%
4 134
8.4%
5 133
8.3%
8 121
 
7.6%
9 92
 
5.8%
7 90
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 371
99.7%
, 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1967
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 371
18.9%
0 325
16.5%
1 239
12.2%
2 177
9.0%
3 146
 
7.4%
6 138
 
7.0%
4 134
 
6.8%
5 133
 
6.8%
8 121
 
6.2%
9 92
 
4.7%
Other values (2) 91
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1967
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 371
18.9%
0 325
16.5%
1 239
12.2%
2 177
9.0%
3 146
 
7.4%
6 138
 
7.0%
4 134
 
6.8%
5 133
 
6.8%
8 121
 
6.2%
9 92
 
4.7%
Other values (2) 91
 
4.6%
Distinct100
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-05-11T04:21:08.018612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0469974
Min length6

Characters and Unicode

Total characters2316
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

Unique33 ?
Unique (%)8.6%

Sample

1st row152893
2nd row152887
3rd row152872
4th row152858
5th row152824
ValueCountFrequency (%)
152848 38
 
9.9%
152801 13
 
3.4%
152872 12
 
3.1%
152800 12
 
3.1%
152815 12
 
3.1%
152885 11
 
2.9%
152883 11
 
2.9%
152847 10
 
2.6%
152887 10
 
2.6%
152823 9
 
2.3%
Other values (90) 245
64.0%
2024-05-11T04:21:09.188193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 474
20.5%
5 467
20.2%
2 450
19.4%
1 448
19.3%
0 121
 
5.2%
4 105
 
4.5%
3 81
 
3.5%
7 57
 
2.5%
6 49
 
2.1%
9 46
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2298
99.2%
Dash Punctuation 18
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 474
20.6%
5 467
20.3%
2 450
19.6%
1 448
19.5%
0 121
 
5.3%
4 105
 
4.6%
3 81
 
3.5%
7 57
 
2.5%
6 49
 
2.1%
9 46
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2316
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 474
20.5%
5 467
20.2%
2 450
19.4%
1 448
19.3%
0 121
 
5.2%
4 105
 
4.5%
3 81
 
3.5%
7 57
 
2.5%
6 49
 
2.1%
9 46
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 474
20.5%
5 467
20.2%
2 450
19.4%
1 448
19.3%
0 121
 
5.2%
4 105
 
4.5%
3 81
 
3.5%
7 57
 
2.5%
6 49
 
2.1%
9 46
 
2.0%
Distinct360
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-05-11T04:21:09.853594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length47
Mean length26.425587
Min length17

Characters and Unicode

Total characters10121
Distinct characters186
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

Unique340 ?
Unique (%)88.8%

Sample

1st row서울특별시 구로구 오류동 38-7번지
2nd row서울특별시 구로구 신도림동 396-24번지
3rd row서울특별시 구로구 구로동 734-15번지
4th row서울특별시 구로구 구로동 507-1번지
5th row서울특별시 구로구 고척동 52-35번지
ValueCountFrequency (%)
구로구 384
21.2%
서울특별시 383
21.1%
구로동 154
 
8.5%
개봉동 62
 
3.4%
고척동 57
 
3.1%
1층 31
 
1.7%
가리봉동 29
 
1.6%
궁동 26
 
1.4%
오류동 25
 
1.4%
신도림동 15
 
0.8%
Other values (523) 648
35.7%
2024-05-11T04:21:10.955284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1763
17.4%
930
 
9.2%
548
 
5.4%
1 527
 
5.2%
408
 
4.0%
386
 
3.8%
384
 
3.8%
384
 
3.8%
383
 
3.8%
383
 
3.8%
Other values (176) 4025
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5777
57.1%
Decimal Number 2096
 
20.7%
Space Separator 1763
 
17.4%
Dash Punctuation 363
 
3.6%
Open Punctuation 44
 
0.4%
Close Punctuation 43
 
0.4%
Other Punctuation 16
 
0.2%
Uppercase Letter 13
 
0.1%
Letter Number 3
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
930
16.1%
548
9.5%
408
 
7.1%
386
 
6.7%
384
 
6.6%
384
 
6.6%
383
 
6.6%
383
 
6.6%
364
 
6.3%
323
 
5.6%
Other values (151) 1284
22.2%
Decimal Number
ValueCountFrequency (%)
1 527
25.1%
2 327
15.6%
3 226
10.8%
0 173
 
8.3%
7 166
 
7.9%
4 154
 
7.3%
5 154
 
7.3%
6 134
 
6.4%
9 119
 
5.7%
8 116
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
B 8
61.5%
A 3
 
23.1%
I 1
 
7.7%
T 1
 
7.7%
Lowercase Letter
ValueCountFrequency (%)
b 1
33.3%
s 1
33.3%
k 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
. 1
 
6.2%
Letter Number
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
1763
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 363
100.0%
Open Punctuation
ValueCountFrequency (%)
( 44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5777
57.1%
Common 4325
42.7%
Latin 19
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
930
16.1%
548
9.5%
408
 
7.1%
386
 
6.7%
384
 
6.6%
384
 
6.6%
383
 
6.6%
383
 
6.6%
364
 
6.3%
323
 
5.6%
Other values (151) 1284
22.2%
Common
ValueCountFrequency (%)
1763
40.8%
1 527
 
12.2%
- 363
 
8.4%
2 327
 
7.6%
3 226
 
5.2%
0 173
 
4.0%
7 166
 
3.8%
4 154
 
3.6%
5 154
 
3.6%
6 134
 
3.1%
Other values (6) 338
 
7.8%
Latin
ValueCountFrequency (%)
B 8
42.1%
A 3
 
15.8%
2
 
10.5%
b 1
 
5.3%
1
 
5.3%
I 1
 
5.3%
T 1
 
5.3%
s 1
 
5.3%
k 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5777
57.1%
ASCII 4341
42.9%
Number Forms 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1763
40.6%
1 527
 
12.1%
- 363
 
8.4%
2 327
 
7.5%
3 226
 
5.2%
0 173
 
4.0%
7 166
 
3.8%
4 154
 
3.5%
5 154
 
3.5%
6 134
 
3.1%
Other values (13) 354
 
8.2%
Hangul
ValueCountFrequency (%)
930
16.1%
548
9.5%
408
 
7.1%
386
 
6.7%
384
 
6.6%
384
 
6.6%
383
 
6.6%
383
 
6.6%
364
 
6.3%
323
 
5.6%
Other values (151) 1284
22.2%
Number Forms
ValueCountFrequency (%)
2
66.7%
1
33.3%

도로명주소
Text

MISSING 

Distinct175
Distinct (%)98.3%
Missing205
Missing (%)53.5%
Memory size3.1 KiB
2024-05-11T04:21:11.608259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length47
Mean length35.882022
Min length22

Characters and Unicode

Total characters6387
Distinct characters166
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

Unique172 ?
Unique (%)96.6%

Sample

1st row서울특별시 구로구 개봉로1길 96, 1층 (개봉동)
2nd row서울특별시 구로구 구로동로18길 62-18 (구로동,(101호))
3rd row서울특별시 구로구 경인로23길 22 (오류동)
4th row서울특별시 구로구 경인로 518 (구로동)
5th row서울특별시 구로구 우마1길 12 (가리봉동)
ValueCountFrequency (%)
서울특별시 178
 
15.3%
구로구 178
 
15.3%
구로동 84
 
7.2%
1층 47
 
4.0%
고척동 16
 
1.4%
개봉동 15
 
1.3%
2층 14
 
1.2%
디지털로31길 12
 
1.0%
지층 12
 
1.0%
오류동 12
 
1.0%
Other values (385) 596
51.2%
2024-05-11T04:21:12.824699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
986
 
15.4%
486
 
7.6%
481
 
7.5%
1 339
 
5.3%
217
 
3.4%
, 195
 
3.1%
) 190
 
3.0%
( 190
 
3.0%
2 188
 
2.9%
183
 
2.9%
Other values (156) 2932
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3585
56.1%
Decimal Number 1170
 
18.3%
Space Separator 986
 
15.4%
Other Punctuation 195
 
3.1%
Close Punctuation 190
 
3.0%
Open Punctuation 190
 
3.0%
Dash Punctuation 55
 
0.9%
Uppercase Letter 10
 
0.2%
Letter Number 3
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
486
 
13.6%
481
 
13.4%
217
 
6.1%
183
 
5.1%
181
 
5.0%
179
 
5.0%
178
 
5.0%
178
 
5.0%
144
 
4.0%
97
 
2.7%
Other values (134) 1261
35.2%
Decimal Number
ValueCountFrequency (%)
1 339
29.0%
2 188
16.1%
3 150
12.8%
0 110
 
9.4%
5 78
 
6.7%
7 75
 
6.4%
8 71
 
6.1%
4 65
 
5.6%
6 56
 
4.8%
9 38
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
s 1
33.3%
k 1
33.3%
b 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
B 7
70.0%
A 3
30.0%
Letter Number
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
986
100.0%
Other Punctuation
ValueCountFrequency (%)
, 195
100.0%
Close Punctuation
ValueCountFrequency (%)
) 190
100.0%
Open Punctuation
ValueCountFrequency (%)
( 190
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3585
56.1%
Common 2786
43.6%
Latin 16
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
486
 
13.6%
481
 
13.4%
217
 
6.1%
183
 
5.1%
181
 
5.0%
179
 
5.0%
178
 
5.0%
178
 
5.0%
144
 
4.0%
97
 
2.7%
Other values (134) 1261
35.2%
Common
ValueCountFrequency (%)
986
35.4%
1 339
 
12.2%
, 195
 
7.0%
) 190
 
6.8%
( 190
 
6.8%
2 188
 
6.7%
3 150
 
5.4%
0 110
 
3.9%
5 78
 
2.8%
7 75
 
2.7%
Other values (5) 285
 
10.2%
Latin
ValueCountFrequency (%)
B 7
43.8%
A 3
18.8%
2
 
12.5%
1
 
6.2%
s 1
 
6.2%
k 1
 
6.2%
b 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3585
56.1%
ASCII 2799
43.8%
Number Forms 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
986
35.2%
1 339
 
12.1%
, 195
 
7.0%
) 190
 
6.8%
( 190
 
6.8%
2 188
 
6.7%
3 150
 
5.4%
0 110
 
3.9%
5 78
 
2.8%
7 75
 
2.7%
Other values (10) 298
 
10.6%
Hangul
ValueCountFrequency (%)
486
 
13.6%
481
 
13.4%
217
 
6.1%
183
 
5.1%
181
 
5.0%
179
 
5.0%
178
 
5.0%
178
 
5.0%
144
 
4.0%
97
 
2.7%
Other values (134) 1261
35.2%
Number Forms
ValueCountFrequency (%)
2
66.7%
1
33.3%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct91
Distinct (%)51.7%
Missing207
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean8309.1023
Minimum8202
Maximum8395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-05-11T04:21:13.326001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8202
5-th percentile8214.75
Q18256
median8306
Q38376
95-th percentile8389
Maximum8395
Range193
Interquartile range (IQR)120

Descriptive statistics

Standard deviation60.510502
Coefficient of variation (CV)0.0072824356
Kurtosis-1.372647
Mean8309.1023
Median Absolute Deviation (MAD)57
Skewness-0.10603934
Sum1462402
Variance3661.5209
MonotonicityNot monotonic
2024-05-11T04:21:13.743731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8381 12
 
3.1%
8376 7
 
1.8%
8238 5
 
1.3%
8280 5
 
1.3%
8249 5
 
1.3%
8389 4
 
1.0%
8378 4
 
1.0%
8375 4
 
1.0%
8311 4
 
1.0%
8278 4
 
1.0%
Other values (81) 122
31.9%
(Missing) 207
54.0%
ValueCountFrequency (%)
8202 1
 
0.3%
8203 3
0.8%
8206 2
0.5%
8209 1
 
0.3%
8214 2
0.5%
8215 2
0.5%
8219 2
0.5%
8220 2
0.5%
8222 1
 
0.3%
8223 1
 
0.3%
ValueCountFrequency (%)
8395 1
 
0.3%
8393 2
0.5%
8392 1
 
0.3%
8391 1
 
0.3%
8390 2
0.5%
8389 4
1.0%
8388 2
0.5%
8387 1
 
0.3%
8383 2
0.5%
8382 1
 
0.3%
Distinct361
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-05-11T04:21:14.459218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length25
Mean length6.151436
Min length2

Characters and Unicode

Total characters2356
Distinct characters405
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique344 ?
Unique (%)89.8%

Sample

1st row부산식품
2nd row강서두부
3rd row유한식품
4th row버들식품
5th row진식품
ValueCountFrequency (%)
주식회사 22
 
5.0%
개미식품 4
 
0.9%
roasters 4
 
0.9%
그린식품 3
 
0.7%
진식품 3
 
0.7%
이원제과 3
 
0.7%
coffee 3
 
0.7%
한양식품 3
 
0.7%
고향만두 2
 
0.5%
제2공장 2
 
0.5%
Other values (379) 395
89.0%
2024-05-11T04:21:15.887492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
146
 
6.2%
121
 
5.1%
89
 
3.8%
( 75
 
3.2%
) 75
 
3.2%
61
 
2.6%
51
 
2.2%
38
 
1.6%
37
 
1.6%
34
 
1.4%
Other values (395) 1629
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2003
85.0%
Lowercase Letter 102
 
4.3%
Open Punctuation 75
 
3.2%
Close Punctuation 75
 
3.2%
Space Separator 61
 
2.6%
Uppercase Letter 32
 
1.4%
Other Punctuation 4
 
0.2%
Decimal Number 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
146
 
7.3%
121
 
6.0%
89
 
4.4%
51
 
2.5%
38
 
1.9%
37
 
1.8%
34
 
1.7%
32
 
1.6%
30
 
1.5%
28
 
1.4%
Other values (346) 1397
69.7%
Lowercase Letter
ValueCountFrequency (%)
e 16
15.7%
o 11
10.8%
r 11
10.8%
a 10
9.8%
s 9
8.8%
f 8
 
7.8%
t 6
 
5.9%
n 4
 
3.9%
u 4
 
3.9%
c 3
 
2.9%
Other values (12) 20
19.6%
Uppercase Letter
ValueCountFrequency (%)
B 4
12.5%
T 3
 
9.4%
F 3
 
9.4%
C 3
 
9.4%
G 2
 
6.2%
P 2
 
6.2%
R 2
 
6.2%
L 2
 
6.2%
K 2
 
6.2%
A 1
 
3.1%
Other values (8) 8
25.0%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
, 1
25.0%
. 1
25.0%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
5 1
25.0%
1 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 75
100.0%
Close Punctuation
ValueCountFrequency (%)
) 75
100.0%
Space Separator
ValueCountFrequency (%)
61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2002
85.0%
Common 219
 
9.3%
Latin 134
 
5.7%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
146
 
7.3%
121
 
6.0%
89
 
4.4%
51
 
2.5%
38
 
1.9%
37
 
1.8%
34
 
1.7%
32
 
1.6%
30
 
1.5%
28
 
1.4%
Other values (345) 1396
69.7%
Latin
ValueCountFrequency (%)
e 16
 
11.9%
o 11
 
8.2%
r 11
 
8.2%
a 10
 
7.5%
s 9
 
6.7%
f 8
 
6.0%
t 6
 
4.5%
B 4
 
3.0%
n 4
 
3.0%
u 4
 
3.0%
Other values (30) 51
38.1%
Common
ValueCountFrequency (%)
( 75
34.2%
) 75
34.2%
61
27.9%
& 2
 
0.9%
2 2
 
0.9%
5 1
 
0.5%
, 1
 
0.5%
. 1
 
0.5%
1 1
 
0.5%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2001
84.9%
ASCII 353
 
15.0%
CJK 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
146
 
7.3%
121
 
6.0%
89
 
4.4%
51
 
2.5%
38
 
1.9%
37
 
1.8%
34
 
1.7%
32
 
1.6%
30
 
1.5%
28
 
1.4%
Other values (344) 1395
69.7%
ASCII
ValueCountFrequency (%)
( 75
21.2%
) 75
21.2%
61
17.3%
e 16
 
4.5%
o 11
 
3.1%
r 11
 
3.1%
a 10
 
2.8%
s 9
 
2.5%
f 8
 
2.3%
t 6
 
1.7%
Other values (39) 71
20.1%
CJK
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Distinct313
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1999-08-02 00:00:00
Maximum2024-04-19 10:10:42
2024-05-11T04:21:16.489587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:21:17.129807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
I
306 
U
77 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 306
79.9%
U 77
 
20.1%

Length

2024-05-11T04:21:17.722003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:18.138907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 306
79.9%
u 77
 
20.1%
Distinct86
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-03 22:01:00
2024-05-11T04:21:18.743951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:21:19.279889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
식품제조가공업
310 
기타 식품제조가공업
73 

Length

Max length10
Median length7
Mean length7.5718016
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 310
80.9%
기타 식품제조가공업 73
 
19.1%

Length

2024-05-11T04:21:19.793758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:20.185077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 383
84.0%
기타 73
 
16.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct296
Distinct (%)81.5%
Missing20
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean188338.97
Minimum183690.9
Maximum191173.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-05-11T04:21:20.557447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183690.9
5-th percentile184846.54
Q1186762.12
median189052.1
Q3190173.45
95-th percentile190665.57
Maximum191173.83
Range7482.9319
Interquartile range (IQR)3411.332

Descriptive statistics

Standard deviation2021.3419
Coefficient of variation (CV)0.010732468
Kurtosis-1.060845
Mean188338.97
Median Absolute Deviation (MAD)1476.3483
Skewness-0.47566472
Sum68367045
Variance4085823.2
MonotonicityNot monotonic
2024-05-11T04:21:21.067844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190461.790473157 6
 
1.6%
190415.331312828 4
 
1.0%
190496.447846852 4
 
1.0%
188394.46956161 4
 
1.0%
190594.904645635 3
 
0.8%
190423.216683488 3
 
0.8%
188923.906304468 3
 
0.8%
190609.413674522 3
 
0.8%
187619.596959162 3
 
0.8%
184846.543453888 3
 
0.8%
Other values (286) 327
85.4%
(Missing) 20
 
5.2%
ValueCountFrequency (%)
183690.902241778 2
0.5%
183725.210246235 1
0.3%
183748.774951501 1
0.3%
183807.118188697 1
0.3%
183969.570522017 1
0.3%
184064.5497836 1
0.3%
184145.671176177 1
0.3%
184459.312860748 1
0.3%
184474.46941356 1
0.3%
184482.4743791 2
0.5%
ValueCountFrequency (%)
191173.834097241 1
0.3%
191039.660930416 1
0.3%
191036.561437766 1
0.3%
191020.264430044 1
0.3%
190980.657371441 1
0.3%
190919.498006713 1
0.3%
190909.52798971 1
0.3%
190901.977946718 1
0.3%
190857.2540833 1
0.3%
190845.231594802 2
0.5%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct296
Distinct (%)81.5%
Missing20
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean443535.05
Minimum441599.36
Maximum445606.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-05-11T04:21:21.589141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum441599.36
5-th percentile442167.19
Q1442645.33
median443578.11
Q3444336.05
95-th percentile444976.77
Maximum445606.68
Range4007.3272
Interquartile range (IQR)1690.7208

Descriptive statistics

Standard deviation939.74852
Coefficient of variation (CV)0.0021187695
Kurtosis-1.1402794
Mean443535.05
Median Absolute Deviation (MAD)860.65843
Skewness-0.011572286
Sum1.6100322 × 108
Variance883127.28
MonotonicityNot monotonic
2024-05-11T04:21:22.395099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
442368.090413171 6
 
1.6%
442596.203831363 4
 
1.0%
442645.330573628 4
 
1.0%
444768.403887191 4
 
1.0%
442261.654499191 3
 
0.8%
442403.568114284 3
 
0.8%
444156.604961623 3
 
0.8%
442782.030555155 3
 
0.8%
444186.923109714 3
 
0.8%
444081.871997546 3
 
0.8%
Other values (286) 327
85.4%
(Missing) 20
 
5.2%
ValueCountFrequency (%)
441599.355055656 1
0.3%
441675.376027035 1
0.3%
441706.119585122 2
0.5%
441873.504088439 1
0.3%
441922.27353307 1
0.3%
441923.997508266 1
0.3%
441940.689088582 1
0.3%
441941.589844702 1
0.3%
441953.169448475 1
0.3%
441975.851326573 1
0.3%
ValueCountFrequency (%)
445606.682305619 1
0.3%
445601.073248063 1
0.3%
445430.97917589 1
0.3%
445355.004580319 1
0.3%
445205.147216016 1
0.3%
445195.615736292 1
0.3%
445178.860000001 1
0.3%
445165.538984481 1
0.3%
445118.362115261 1
0.3%
445089.01349184 1
0.3%

위생업태명
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
식품제조가공업
304 
기타 식품제조가공업
45 
<NA>
34 

Length

Max length10
Median length7
Mean length7.0861619
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 304
79.4%
기타 식품제조가공업 45
 
11.7%
<NA> 34
 
8.9%

Length

2024-05-11T04:21:22.899527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:23.207876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 349
81.5%
기타 45
 
10.5%
na 34
 
7.9%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)5.9%
Missing282
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean0.78217822
Minimum0
Maximum12
Zeros66
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-05-11T04:21:23.589318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0228888
Coefficient of variation (CV)2.586225
Kurtosis21.798254
Mean0.78217822
Median Absolute Deviation (MAD)0
Skewness4.5187212
Sum79
Variance4.0920792
MonotonicityNot monotonic
2024-05-11T04:21:24.118475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 66
 
17.2%
1 22
 
5.7%
2 9
 
2.3%
12 2
 
0.5%
5 1
 
0.3%
10 1
 
0.3%
(Missing) 282
73.6%
ValueCountFrequency (%)
0 66
17.2%
1 22
 
5.7%
2 9
 
2.3%
5 1
 
0.3%
10 1
 
0.3%
12 2
 
0.5%
ValueCountFrequency (%)
12 2
 
0.5%
10 1
 
0.3%
5 1
 
0.3%
2 9
 
2.3%
1 22
 
5.7%
0 66
17.2%

여성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
279 
0
81 
2
 
11
1
 
10
5
 
1

Length

Max length4
Median length4
Mean length3.1853786
Min length1

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 279
72.8%
0 81
 
21.1%
2 11
 
2.9%
1 10
 
2.6%
5 1
 
0.3%
3 1
 
0.3%

Length

2024-05-11T04:21:24.590729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:25.093256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 279
72.8%
0 81
 
21.1%
2 11
 
2.9%
1 10
 
2.6%
5 1
 
0.3%
3 1
 
0.3%

영업장주변구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
277 
기타
73 
주택가주변
28 
아파트지역
 
4
학교정화(상대)
 
1

Length

Max length8
Median length4
Mean length3.7127937
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 277
72.3%
기타 73
 
19.1%
주택가주변 28
 
7.3%
아파트지역 4
 
1.0%
학교정화(상대) 1
 
0.3%

Length

2024-05-11T04:21:25.776786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:26.393675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 277
72.3%
기타 73
 
19.1%
주택가주변 28
 
7.3%
아파트지역 4
 
1.0%
학교정화(상대 1
 
0.3%

등급구분명
Categorical

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
277 
기타
70 
자율
31 
 
5

Length

Max length4
Median length4
Mean length3.4334204
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
<NA> 277
72.3%
기타 70
 
18.3%
자율 31
 
8.1%
5
 
1.3%

Length

2024-05-11T04:21:26.885429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:27.266036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 277
72.3%
기타 70
 
18.3%
자율 31
 
8.1%
5
 
1.3%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
218 
상수도전용
165 

Length

Max length5
Median length4
Mean length4.4308094
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 218
56.9%
상수도전용 165
43.1%

Length

2024-05-11T04:21:27.614415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:28.058200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 218
56.9%
상수도전용 165
43.1%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
374 
0
 
9

Length

Max length4
Median length4
Mean length3.9295039
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 374
97.7%
0 9
 
2.3%

Length

2024-05-11T04:21:28.768098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:29.148083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 374
97.7%
0 9
 
2.3%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
194 
0
189 

Length

Max length4
Median length4
Mean length2.5195822
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 194
50.7%
0 189
49.3%

Length

2024-05-11T04:21:29.531086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:30.143842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 194
50.7%
0 189
49.3%

공장사무직종업원수
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
192 
0
186 
1
 
2
2
 
2
4
 
1

Length

Max length4
Median length4
Mean length2.5039164
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 192
50.1%
0 186
48.6%
1 2
 
0.5%
2 2
 
0.5%
4 1
 
0.3%

Length

2024-05-11T04:21:30.641024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:31.214781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 192
50.1%
0 186
48.6%
1 2
 
0.5%
2 2
 
0.5%
4 1
 
0.3%
Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
190 
0
188 
1
 
5

Length

Max length4
Median length1
Mean length2.4882507
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 190
49.6%
0 188
49.1%
1 5
 
1.3%

Length

2024-05-11T04:21:31.922276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:32.252356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 190
49.6%
0 188
49.1%
1 5
 
1.3%

공장생산직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)3.1%
Missing187
Missing (%)48.8%
Infinite0
Infinite (%)0.0%
Mean0.3877551
Minimum0
Maximum11
Zeros161
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-05-11T04:21:32.547300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.124393
Coefficient of variation (CV)2.8997503
Kurtosis41.832975
Mean0.3877551
Median Absolute Deviation (MAD)0
Skewness5.3788656
Sum76
Variance1.2642595
MonotonicityNot monotonic
2024-05-11T04:21:32.973650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 161
42.0%
2 15
 
3.9%
1 13
 
3.4%
4 4
 
1.0%
3 2
 
0.5%
11 1
 
0.3%
(Missing) 187
48.8%
ValueCountFrequency (%)
0 161
42.0%
1 13
 
3.4%
2 15
 
3.9%
3 2
 
0.5%
4 4
 
1.0%
11 1
 
0.3%
ValueCountFrequency (%)
11 1
 
0.3%
4 4
 
1.0%
3 2
 
0.5%
2 15
 
3.9%
1 13
 
3.4%
0 161
42.0%
Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
237 
임대
99 
자가
47 

Length

Max length4
Median length4
Mean length3.2375979
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 237
61.9%
임대 99
25.8%
자가 47
 
12.3%

Length

2024-05-11T04:21:33.663365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:34.184680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 237
61.9%
임대 99
25.8%
자가 47
 
12.3%

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
352 
0
 
31

Length

Max length4
Median length4
Mean length3.7571802
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 352
91.9%
0 31
 
8.1%

Length

2024-05-11T04:21:34.664232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:35.107453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 352
91.9%
0 31
 
8.1%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
<NA>
352 
0
 
31

Length

Max length4
Median length4
Mean length3.7571802
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 352
91.9%
0 31
 
8.1%

Length

2024-05-11T04:21:35.539660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:21:35.883028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 352
91.9%
0 31
 
8.1%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing34
Missing (%)8.9%
Memory size898.0 B
False
349 
(Missing)
 
34
ValueCountFrequency (%)
False 349
91.1%
(Missing) 34
 
8.9%
2024-05-11T04:21:36.162601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)2.9%
Missing34
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean1.6626648
Minimum0
Maximum225.14
Zeros340
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-05-11T04:21:36.462938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum225.14
Range225.14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.940482
Coefficient of variation (CV)9.5873095
Kurtosis132.26656
Mean1.6626648
Median Absolute Deviation (MAD)0
Skewness11.016642
Sum580.27
Variance254.09896
MonotonicityNot monotonic
2024-05-11T04:21:36.819481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 340
88.8%
2.76 1
 
0.3%
1.17 1
 
0.3%
3.06 1
 
0.3%
6.2 1
 
0.3%
3.9 1
 
0.3%
225.14 1
 
0.3%
120.12 1
 
0.3%
124.09 1
 
0.3%
93.83 1
 
0.3%
(Missing) 34
 
8.9%
ValueCountFrequency (%)
0.0 340
88.8%
1.17 1
 
0.3%
2.76 1
 
0.3%
3.06 1
 
0.3%
3.9 1
 
0.3%
6.2 1
 
0.3%
93.83 1
 
0.3%
120.12 1
 
0.3%
124.09 1
 
0.3%
225.14 1
 
0.3%
ValueCountFrequency (%)
225.14 1
 
0.3%
124.09 1
 
0.3%
120.12 1
 
0.3%
93.83 1
 
0.3%
6.2 1
 
0.3%
3.9 1
 
0.3%
3.06 1
 
0.3%
2.76 1
 
0.3%
1.17 1
 
0.3%
0.0 340
88.8%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing383
Missing (%)100.0%
Memory size3.5 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing383
Missing (%)100.0%
Memory size3.5 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing383
Missing (%)100.0%
Memory size3.5 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
031600003160000-106-1979-0002819790820<NA>3폐업2폐업20020130<NA><NA><NA>02111.88152893서울특별시 구로구 오류동 38-7번지<NA><NA>부산식품2000-12-06 00:00:00I2018-08-31 23:59:59.0식품제조가공업186100.854868443827.671883식품제조가공업00기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
131600003160000-106-1980-0003119801219<NA>3폐업2폐업20011127<NA><NA><NA>02 6784049105.64152887서울특별시 구로구 신도림동 396-24번지<NA><NA>강서두부2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업189256.849354445165.538984식품제조가공업00기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
231600003160000-106-1981-0003019810504<NA>3폐업2폐업20000828<NA><NA><NA>02.00152872서울특별시 구로구 구로동 734-15번지<NA><NA>유한식품2000-08-28 00:00:00I2018-08-31 23:59:59.0식품제조가공업189846.867634443097.802226식품제조가공업00기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
331600003160000-106-1987-0000419870220<NA>3폐업2폐업19990930<NA><NA><NA>02 0113.43152858서울특별시 구로구 구로동 507-1번지<NA><NA>버들식품1999-09-30 00:00:00I2018-08-31 23:59:59.0식품제조가공업189715.365568444005.092287식품제조가공업00기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
431600003160000-106-1987-0000519871117<NA>3폐업2폐업20020417<NA><NA><NA>02 688735366.35152824서울특별시 구로구 고척동 52-35번지<NA><NA>진식품2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업188016.917688444410.997851식품제조가공업00기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
531600003160000-106-1988-0000619880315<NA>3폐업2폐업20010328<NA><NA><NA>0268.98152844서울특별시 구로구 구로동 111-41번지<NA><NA>진영식품2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업190079.75523444239.256427식품제조가공업10기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
631600003160000-106-1991-0005719911023<NA>3폐업2폐업20061212<NA><NA><NA>02 868133399.98152844서울특별시 구로구 구로동 111-42번지<NA><NA>청하식품2006-07-19 00:00:00I2018-08-31 23:59:59.0식품제조가공업190059.913126444253.385552식품제조가공업52주택가주변자율상수도전용<NA>0000임대<NA><NA>N0.0<NA><NA><NA>
731600003160000-106-1994-0000319941201<NA>3폐업2폐업20040813<NA><NA><NA>02 8523030166.82152851서울특별시 구로구 구로동 390-180번지<NA><NA>원앙식품2002-05-10 00:00:00I2018-08-31 23:59:59.0식품제조가공업189375.413638442848.818353식품제조가공업00기타기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
831600003160000-106-1994-0001219940901<NA>3폐업2폐업20020116<NA><NA><NA>02 6866289113.42152883서울특별시 구로구 궁동 211-13번지<NA><NA>모두아베이커리2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업184984.518967443660.699408식품제조가공업00기타자율상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
931600003160000-106-1994-0005019940201<NA>3폐업2폐업20030206<NA><NA><NA>02 685077713.80152814서울특별시 구로구 개봉동 323-28번지<NA><NA>정원식품2002-05-10 00:00:00I2018-08-31 23:59:59.0식품제조가공업186940.511244443378.133229식품제조가공업20주택가주변기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
37331600003160000-106-2022-000032022-06-17<NA>3폐업2폐업2023-02-22<NA><NA><NA>02 816 944829.70152-888서울특별시 구로구 신도림동 643 신도림1차동아아파트서울특별시 구로구 신도림로 87, 상가동 지하1층 1-2호 (신도림동, 신도림1차동아아파트)8202주식회사 토브커피2023-02-22 10:52:26U2022-12-01 22:04:00.0기타 식품제조가공업189581.725137445430.979176<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
37431600003160000-106-2022-0000420220704<NA>1영업/정상1영업<NA><NA><NA><NA><NA>89.00152848서울특별시 구로구 구로동 197-48 에이스테크노타워Ⅲ서울특별시 구로구 디지털로29길 38, 에이스테크노타워Ⅲ 810-2호 (구로동)8381카페로그(Log coffee roasters)2022-07-04 11:02:44I2021-12-07 00:06:00.0기타 식품제조가공업190491.83306442477.332681<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
37531600003160000-106-2022-0000520220809<NA>1영업/정상1영업<NA><NA><NA><NA><NA>80.80152140서울특별시 구로구 항동 206-1서울특별시 구로구 부광로 96-5, 913호 (항동)8362서원식품2022-08-09 14:06:18I2021-12-07 23:01:00.0기타 식품제조가공업183807.118189441599.355056<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
37631600003160000-106-2023-000012023-04-24<NA>1영업/정상1영업<NA><NA><NA><NA><NA>61.52152-858서울특별시 구로구 구로동 494-21 1층서울특별시 구로구 구로중앙로31길 2-4, 1층 (구로동)8280유니바이오2023-04-24 16:01:18I2022-12-03 22:06:00.0기타 식품제조가공업189602.48444131.864757<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
37731600003160000-106-2023-000022023-05-12<NA>1영업/정상1영업<NA><NA><NA><NA>026952158223.00152-729서울특별시 구로구 구로동 187-10 코오롱싸이언스밸리1차 지하층 103호서울특별시 구로구 디지털로34길 43, 코오롱싸이언스밸리1차 지하층 103호 (구로동)8378진중연구관2024-02-13 14:12:00U2023-12-01 23:05:00.0기타 식품제조가공업190980.657371442547.689998<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
37831600003160000-106-2023-000032023-06-07<NA>1영업/정상1영업<NA><NA><NA><NA>0221355482300.06152-848서울특별시 구로구 구로동 197-17 에이스테크노타워1차 206호서울특별시 구로구 디지털로31길 38-9, 에이스테크노타워1차 206호 (구로동)8376퍼스트커피랩 구로공장점2023-06-07 14:15:50I2022-12-06 00:09:00.0기타 식품제조가공업190496.447847442645.330574<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
37931600003160000-106-2023-000042023-08-24<NA>1영업/정상1영업<NA><NA><NA><NA><NA>138.60152-848서울특별시 구로구 구로동 197-10 이앤씨벤처드림타워2차 1101-2서울특별시 구로구 디지털로33길 55, 이앤씨벤처드림타워2차 1101-2호 (구로동)8376주식회사 보스제이홀딩스2023-09-01 09:22:55U2022-12-09 00:03:00.0기타 식품제조가공업190479.902194442751.41954<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
38031600003160000-106-2023-000052023-11-21<NA>1영업/정상1영업<NA><NA><NA><NA><NA>133.65152-848서울특별시 구로구 구로동 197-22 에이스테크노타워5차 602호 중 607호서울특별시 구로구 디지털로31길 20, 에이스테크노타워5차 602호 중 607호 (구로동)8380주식회사 케이크로2023-11-21 11:35:05I2022-10-31 22:03:00.0기타 식품제조가공업190571.928128442598.144799<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
38131600003160000-106-2023-000062023-11-21<NA>1영업/정상1영업<NA><NA><NA><NA><NA>19.10152-891서울특별시 구로구 오류동 25-19 1층서울특별시 구로구 경인로23길 22, 1층 (오류동)8268티엠비식품2023-11-21 17:53:43I2022-10-31 22:03:00.0기타 식품제조가공업186102.94147443894.177955<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
38231600003160000-106-2023-000072023-12-01<NA>1영업/정상1영업<NA><NA><NA><NA><NA>65.00152-865서울특별시 구로구 구로동 607-1 우성빌딩 205(일부),417(일부)서울특별시 구로구 경인로55길 21, 우성빌딩 205(일부),417(일부)호 (구로동)8215사단법인 전국장애인노동조합총연합회2023-12-01 09:25:34I2022-11-02 00:03:00.0기타 식품제조가공업189065.0678444456.127223<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>