Overview

Dataset statistics

Number of variables44
Number of observations410
Missing cells4330
Missing cells (%)24.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory149.9 KiB
Average record size in memory374.3 B

Variable types

Categorical19
Text8
DateTime4
Unsupported6
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
Author강서구
URLhttps://data.seoul.go.kr/dataList/OA-18267/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
홈페이지 has constant value ""Constant
영업장주변구분명 is highly imbalanced (59.9%)Imbalance
등급구분명 is highly imbalanced (65.9%)Imbalance
총인원 is highly imbalanced (87.5%)Imbalance
공장생산직종업원수 is highly imbalanced (50.6%)Imbalance
보증액 is highly imbalanced (53.9%)Imbalance
월세액 is highly imbalanced (53.9%)Imbalance
인허가취소일자 has 410 (100.0%) missing valuesMissing
폐업일자 has 61 (14.9%) missing valuesMissing
휴업시작일자 has 410 (100.0%) missing valuesMissing
휴업종료일자 has 410 (100.0%) missing valuesMissing
재개업일자 has 410 (100.0%) missing valuesMissing
전화번호 has 94 (22.9%) missing valuesMissing
소재지면적 has 85 (20.7%) missing valuesMissing
도로명주소 has 224 (54.6%) missing valuesMissing
도로명우편번호 has 228 (55.6%) missing valuesMissing
좌표정보(X) has 22 (5.4%) missing valuesMissing
좌표정보(Y) has 22 (5.4%) missing valuesMissing
남성종사자수 has 327 (79.8%) missing valuesMissing
여성종사자수 has 328 (80.0%) missing valuesMissing
다중이용업소여부 has 34 (8.3%) missing valuesMissing
시설총규모 has 34 (8.3%) missing valuesMissing
전통업소지정번호 has 410 (100.0%) missing valuesMissing
전통업소주된음식 has 410 (100.0%) missing valuesMissing
홈페이지 has 409 (99.8%) 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
남성종사자수 has 21 (5.1%) zerosZeros
여성종사자수 has 24 (5.9%) zerosZeros
시설총규모 has 314 (76.6%) zerosZeros

Reproduction

Analysis started2024-04-17 18:14:06.310943
Analysis finished2024-04-17 18:14:06.959571
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
3150000
410 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3150000
2nd row3150000
3rd row3150000
4th row3150000
5th row3150000

Common Values

ValueCountFrequency (%)
3150000 410
100.0%

Length

2024-04-18T03:14:07.012389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:07.114672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3150000 410
100.0%

관리번호
Text

UNIQUE 

Distinct410
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2024-04-18T03:14:07.247937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters9020
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

Unique410 ?
Unique (%)100.0%

Sample

1st row3150000-106-1973-00432
2nd row3150000-106-1973-00433
3rd row3150000-106-1975-00431
4th row3150000-106-1977-00444
5th row3150000-106-1980-00404
ValueCountFrequency (%)
3150000-106-1973-00432 1
 
0.2%
3150000-106-2010-00016 1
 
0.2%
3150000-106-2011-00008 1
 
0.2%
3150000-106-2011-00007 1
 
0.2%
3150000-106-2011-00006 1
 
0.2%
3150000-106-2011-00005 1
 
0.2%
3150000-106-2011-00004 1
 
0.2%
3150000-106-2011-00003 1
 
0.2%
3150000-106-2011-00002 1
 
0.2%
3150000-106-2011-00001 1
 
0.2%
Other values (400) 400
97.6%
2024-04-18T03:14:07.496119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3969
44.0%
1 1233
 
13.7%
- 1230
 
13.6%
3 530
 
5.9%
5 517
 
5.7%
6 497
 
5.5%
2 473
 
5.2%
9 252
 
2.8%
4 141
 
1.6%
7 95
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7790
86.4%
Dash Punctuation 1230
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3969
50.9%
1 1233
 
15.8%
3 530
 
6.8%
5 517
 
6.6%
6 497
 
6.4%
2 473
 
6.1%
9 252
 
3.2%
4 141
 
1.8%
7 95
 
1.2%
8 83
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 1230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9020
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3969
44.0%
1 1233
 
13.7%
- 1230
 
13.6%
3 530
 
5.9%
5 517
 
5.7%
6 497
 
5.5%
2 473
 
5.2%
9 252
 
2.8%
4 141
 
1.6%
7 95
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3969
44.0%
1 1233
 
13.7%
- 1230
 
13.6%
3 530
 
5.9%
5 517
 
5.7%
6 497
 
5.5%
2 473
 
5.2%
9 252
 
2.8%
4 141
 
1.6%
7 95
 
1.1%
Distinct390
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Minimum1973-04-17 00:00:00
Maximum2023-12-13 00:00:00
2024-04-18T03:14:07.604543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:14:07.708781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing410
Missing (%)100.0%
Memory size3.7 KiB
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
3
349 
1
61 

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 (%)
3 349
85.1%
1 61
 
14.9%

Length

2024-04-18T03:14:07.810592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:07.882251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 349
85.1%
1 61
 
14.9%

영업상태명
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
폐업
349 
영업/정상
61 

Length

Max length5
Median length2
Mean length2.4463415
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 349
85.1%
영업/정상 61
 
14.9%

Length

2024-04-18T03:14:07.970524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:08.049432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 349
85.1%
영업/정상 61
 
14.9%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2
349 
1
61 

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 (%)
2 349
85.1%
1 61
 
14.9%

Length

2024-04-18T03:14:08.123134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:08.411687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 349
85.1%
1 61
 
14.9%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
폐업
349 
영업
61 

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 349
85.1%
영업 61
 
14.9%

Length

2024-04-18T03:14:08.485967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:08.556579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 349
85.1%
영업 61
 
14.9%

폐업일자
Date

MISSING 

Distinct310
Distinct (%)88.8%
Missing61
Missing (%)14.9%
Memory size3.3 KiB
Minimum1996-08-05 00:00:00
Maximum2024-03-29 00:00:00
2024-04-18T03:14:08.640483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:14:08.741496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing410
Missing (%)100.0%
Memory size3.7 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing410
Missing (%)100.0%
Memory size3.7 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing410
Missing (%)100.0%
Memory size3.7 KiB

전화번호
Text

MISSING 

Distinct304
Distinct (%)96.2%
Missing94
Missing (%)22.9%
Memory size3.3 KiB
2024-04-18T03:14:08.913082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.275316
Min length2

Characters and Unicode

Total characters3247
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

Unique294 ?
Unique (%)93.0%

Sample

1st row0226020735
2nd row0226482698
3rd row02 6575114
4th row0236600857
5th row0226621810
ValueCountFrequency (%)
02 107
 
23.9%
070 15
 
3.3%
6631225 3
 
0.7%
6925955 2
 
0.4%
325 2
 
0.4%
0226083144 2
 
0.4%
73988226 2
 
0.4%
0236642741 2
 
0.4%
26070918 2
 
0.4%
75041933 2
 
0.4%
Other values (307) 309
69.0%
2024-04-18T03:14:09.187773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 628
19.3%
6 550
16.9%
0 548
16.9%
3 242
 
7.5%
5 217
 
6.7%
8 192
 
5.9%
9 187
 
5.8%
4 181
 
5.6%
179
 
5.5%
7 171
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3068
94.5%
Space Separator 179
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 628
20.5%
6 550
17.9%
0 548
17.9%
3 242
 
7.9%
5 217
 
7.1%
8 192
 
6.3%
9 187
 
6.1%
4 181
 
5.9%
7 171
 
5.6%
1 152
 
5.0%
Space Separator
ValueCountFrequency (%)
179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 628
19.3%
6 550
16.9%
0 548
16.9%
3 242
 
7.5%
5 217
 
6.7%
8 192
 
5.9%
9 187
 
5.8%
4 181
 
5.6%
179
 
5.5%
7 171
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 628
19.3%
6 550
16.9%
0 548
16.9%
3 242
 
7.5%
5 217
 
6.7%
8 192
 
5.9%
9 187
 
5.8%
4 181
 
5.6%
179
 
5.5%
7 171
 
5.3%

소재지면적
Text

MISSING 

Distinct279
Distinct (%)85.8%
Missing85
Missing (%)20.7%
Memory size3.3 KiB
2024-04-18T03:14:09.499750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.2092308
Min length3

Characters and Unicode

Total characters1693
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

Unique249 ?
Unique (%)76.6%

Sample

1st row158.00
2nd row900.00
3rd row1,879.00
4th row.00
5th row50.93
ValueCountFrequency (%)
00 8
 
2.5%
24.00 4
 
1.2%
30.00 3
 
0.9%
35.00 3
 
0.9%
26.00 3
 
0.9%
15.00 3
 
0.9%
32.00 3
 
0.9%
86.00 3
 
0.9%
38.00 3
 
0.9%
85.00 3
 
0.9%
Other values (269) 289
88.9%
2024-04-18T03:14:10.050309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 371
21.9%
. 325
19.2%
1 156
9.2%
2 132
 
7.8%
5 112
 
6.6%
3 108
 
6.4%
4 106
 
6.3%
8 105
 
6.2%
7 95
 
5.6%
9 90
 
5.3%
Other values (2) 93
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1363
80.5%
Other Punctuation 330
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 371
27.2%
1 156
11.4%
2 132
 
9.7%
5 112
 
8.2%
3 108
 
7.9%
4 106
 
7.8%
8 105
 
7.7%
7 95
 
7.0%
9 90
 
6.6%
6 88
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 325
98.5%
, 5
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 371
21.9%
. 325
19.2%
1 156
9.2%
2 132
 
7.8%
5 112
 
6.6%
3 108
 
6.4%
4 106
 
6.3%
8 105
 
6.2%
7 95
 
5.6%
9 90
 
5.3%
Other values (2) 93
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 371
21.9%
. 325
19.2%
1 156
9.2%
2 132
 
7.8%
5 112
 
6.6%
3 108
 
6.4%
4 106
 
6.3%
8 105
 
6.2%
7 95
 
5.6%
9 90
 
5.3%
Other values (2) 93
 
5.5%
Distinct111
Distinct (%)27.1%
Missing1
Missing (%)0.2%
Memory size3.3 KiB
2024-04-18T03:14:10.293885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0611247
Min length6

Characters and Unicode

Total characters2479
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

Unique40 ?
Unique (%)9.8%

Sample

1st row157925
2nd row157897
3rd row157801
4th row157801
5th row157811
ValueCountFrequency (%)
157210 28
 
6.8%
157816 18
 
4.4%
157846 14
 
3.4%
157861 13
 
3.2%
157930 13
 
3.2%
157801 12
 
2.9%
157853 11
 
2.7%
157839 10
 
2.4%
157840 9
 
2.2%
157290 9
 
2.2%
Other values (101) 272
66.5%
2024-04-18T03:14:10.615929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 552
22.3%
7 467
18.8%
5 464
18.7%
8 313
12.6%
0 158
 
6.4%
9 140
 
5.6%
2 118
 
4.8%
3 87
 
3.5%
6 81
 
3.3%
4 74
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2454
99.0%
Dash Punctuation 25
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 552
22.5%
7 467
19.0%
5 464
18.9%
8 313
12.8%
0 158
 
6.4%
9 140
 
5.7%
2 118
 
4.8%
3 87
 
3.5%
6 81
 
3.3%
4 74
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2479
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 552
22.3%
7 467
18.8%
5 464
18.7%
8 313
12.6%
0 158
 
6.4%
9 140
 
5.6%
2 118
 
4.8%
3 87
 
3.5%
6 81
 
3.3%
4 74
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 552
22.3%
7 467
18.8%
5 464
18.7%
8 313
12.6%
0 158
 
6.4%
9 140
 
5.6%
2 118
 
4.8%
3 87
 
3.5%
6 81
 
3.3%
4 74
 
3.0%
Distinct381
Distinct (%)93.2%
Missing1
Missing (%)0.2%
Memory size3.3 KiB
2024-04-18T03:14:10.868629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length44
Mean length25.163814
Min length18

Characters and Unicode

Total characters10292
Distinct characters165
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique358 ?
Unique (%)87.5%

Sample

1st row서울특별시 강서구 화곡동 1081-1번지
2nd row서울특별시 강서구 화곡동 789-34번지
3rd row서울특별시 강서구 가양동 52-1번지
4th row서울특별시 강서구 가양동 92-1번지
5th row서울특별시 강서구 공항동 2-45번지
ValueCountFrequency (%)
서울특별시 409
22.1%
강서구 409
22.1%
화곡동 114
 
6.2%
등촌동 58
 
3.1%
방화동 56
 
3.0%
공항동 39
 
2.1%
마곡동 36
 
1.9%
염창동 32
 
1.7%
내발산동 32
 
1.7%
가양동 26
 
1.4%
Other values (482) 641
34.6%
2024-04-18T03:14:11.240736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1801
17.5%
823
 
8.0%
425
 
4.1%
416
 
4.0%
1 415
 
4.0%
414
 
4.0%
409
 
4.0%
409
 
4.0%
409
 
4.0%
409
 
4.0%
Other values (155) 4362
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5924
57.6%
Decimal Number 2088
 
20.3%
Space Separator 1801
 
17.5%
Dash Punctuation 374
 
3.6%
Open Punctuation 37
 
0.4%
Close Punctuation 37
 
0.4%
Uppercase Letter 16
 
0.2%
Other Punctuation 9
 
0.1%
Letter Number 4
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
823
13.9%
425
 
7.2%
416
 
7.0%
414
 
7.0%
409
 
6.9%
409
 
6.9%
409
 
6.9%
409
 
6.9%
400
 
6.8%
339
 
5.7%
Other values (129) 1471
24.8%
Decimal Number
ValueCountFrequency (%)
1 415
19.9%
2 257
12.3%
4 209
10.0%
6 203
9.7%
3 200
9.6%
0 191
9.1%
7 172
8.2%
8 159
 
7.6%
5 147
 
7.0%
9 135
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
B 8
50.0%
W 2
 
12.5%
A 2
 
12.5%
M 1
 
6.2%
I 1
 
6.2%
N 1
 
6.2%
D 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 8
88.9%
. 1
 
11.1%
Space Separator
ValueCountFrequency (%)
1801
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 374
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Letter Number
ValueCountFrequency (%)
4
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5924
57.6%
Common 4347
42.2%
Latin 21
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
823
13.9%
425
 
7.2%
416
 
7.0%
414
 
7.0%
409
 
6.9%
409
 
6.9%
409
 
6.9%
409
 
6.9%
400
 
6.8%
339
 
5.7%
Other values (129) 1471
24.8%
Common
ValueCountFrequency (%)
1801
41.4%
1 415
 
9.5%
- 374
 
8.6%
2 257
 
5.9%
4 209
 
4.8%
6 203
 
4.7%
3 200
 
4.6%
0 191
 
4.4%
7 172
 
4.0%
8 159
 
3.7%
Other values (7) 366
 
8.4%
Latin
ValueCountFrequency (%)
B 8
38.1%
4
19.0%
W 2
 
9.5%
A 2
 
9.5%
b 1
 
4.8%
M 1
 
4.8%
I 1
 
4.8%
N 1
 
4.8%
D 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5924
57.6%
ASCII 4364
42.4%
Number Forms 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1801
41.3%
1 415
 
9.5%
- 374
 
8.6%
2 257
 
5.9%
4 209
 
4.8%
6 203
 
4.7%
3 200
 
4.6%
0 191
 
4.4%
7 172
 
3.9%
8 159
 
3.6%
Other values (15) 383
 
8.8%
Hangul
ValueCountFrequency (%)
823
13.9%
425
 
7.2%
416
 
7.0%
414
 
7.0%
409
 
6.9%
409
 
6.9%
409
 
6.9%
409
 
6.9%
400
 
6.8%
339
 
5.7%
Other values (129) 1471
24.8%
Number Forms
ValueCountFrequency (%)
4
100.0%

도로명주소
Text

MISSING 

Distinct184
Distinct (%)98.9%
Missing224
Missing (%)54.6%
Memory size3.3 KiB
2024-04-18T03:14:11.467722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length47
Mean length33.311828
Min length23

Characters and Unicode

Total characters6196
Distinct characters181
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

Unique182 ?
Unique (%)97.8%

Sample

1st row서울특별시 강서구 화곡로26가길 43-5 (화곡동, 1동)
2nd row서울특별시 강서구 방화대로23길 5 (공항동)
3rd row서울특별시 강서구 하늘길 251 (공항동)
4th row서울특별시 강서구 등촌로5길 46 (화곡동)
5th row서울특별시 강서구 금낭화로7길 6, 1층 (방화동)
ValueCountFrequency (%)
서울특별시 186
 
15.8%
강서구 186
 
15.8%
1층 41
 
3.5%
화곡동 36
 
3.1%
등촌동 31
 
2.6%
방화동 26
 
2.2%
마곡동 25
 
2.1%
2층 18
 
1.5%
공항동 16
 
1.4%
공항대로 12
 
1.0%
Other values (369) 600
51.0%
2024-04-18T03:14:11.804403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
991
 
16.0%
393
 
6.3%
1 266
 
4.3%
222
 
3.6%
205
 
3.3%
( 195
 
3.1%
) 195
 
3.1%
191
 
3.1%
191
 
3.1%
186
 
3.0%
Other values (171) 3161
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3563
57.5%
Decimal Number 1019
 
16.4%
Space Separator 991
 
16.0%
Open Punctuation 195
 
3.1%
Close Punctuation 195
 
3.1%
Other Punctuation 168
 
2.7%
Dash Punctuation 38
 
0.6%
Uppercase Letter 20
 
0.3%
Letter Number 4
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
393
 
11.0%
222
 
6.2%
205
 
5.8%
191
 
5.4%
191
 
5.4%
186
 
5.2%
186
 
5.2%
186
 
5.2%
186
 
5.2%
124
 
3.5%
Other values (146) 1493
41.9%
Decimal Number
ValueCountFrequency (%)
1 266
26.1%
2 140
13.7%
4 98
 
9.6%
0 98
 
9.6%
5 97
 
9.5%
3 90
 
8.8%
6 87
 
8.5%
7 56
 
5.5%
9 46
 
4.5%
8 41
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 10
50.0%
A 4
 
20.0%
W 2
 
10.0%
D 1
 
5.0%
N 1
 
5.0%
I 1
 
5.0%
M 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 167
99.4%
. 1
 
0.6%
Space Separator
ValueCountFrequency (%)
991
100.0%
Open Punctuation
ValueCountFrequency (%)
( 195
100.0%
Close Punctuation
ValueCountFrequency (%)
) 195
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Letter Number
ValueCountFrequency (%)
4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3563
57.5%
Common 2609
42.1%
Latin 24
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
393
 
11.0%
222
 
6.2%
205
 
5.8%
191
 
5.4%
191
 
5.4%
186
 
5.2%
186
 
5.2%
186
 
5.2%
186
 
5.2%
124
 
3.5%
Other values (146) 1493
41.9%
Common
ValueCountFrequency (%)
991
38.0%
1 266
 
10.2%
( 195
 
7.5%
) 195
 
7.5%
, 167
 
6.4%
2 140
 
5.4%
4 98
 
3.8%
0 98
 
3.8%
5 97
 
3.7%
3 90
 
3.4%
Other values (7) 272
 
10.4%
Latin
ValueCountFrequency (%)
B 10
41.7%
A 4
 
16.7%
4
 
16.7%
W 2
 
8.3%
D 1
 
4.2%
N 1
 
4.2%
I 1
 
4.2%
M 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3563
57.5%
ASCII 2629
42.4%
Number Forms 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
991
37.7%
1 266
 
10.1%
( 195
 
7.4%
) 195
 
7.4%
, 167
 
6.4%
2 140
 
5.3%
4 98
 
3.7%
0 98
 
3.7%
5 97
 
3.7%
3 90
 
3.4%
Other values (14) 292
 
11.1%
Hangul
ValueCountFrequency (%)
393
 
11.0%
222
 
6.2%
205
 
5.8%
191
 
5.4%
191
 
5.4%
186
 
5.2%
186
 
5.2%
186
 
5.2%
186
 
5.2%
124
 
3.5%
Other values (146) 1493
41.9%
Number Forms
ValueCountFrequency (%)
4
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct95
Distinct (%)52.2%
Missing228
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean7643.044
Minimum7505
Maximum7807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-04-18T03:14:11.912599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7505
5-th percentile7522.05
Q17566.25
median7630.5
Q37723.75
95-th percentile7806
Maximum7807
Range302
Interquartile range (IQR)157.5

Descriptive statistics

Standard deviation91.443659
Coefficient of variation (CV)0.011964298
Kurtosis-1.0201579
Mean7643.044
Median Absolute Deviation (MAD)77
Skewness0.39818071
Sum1391034
Variance8361.9428
MonotonicityNot monotonic
2024-04-18T03:14:12.015907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7645 11
 
2.7%
7806 8
 
2.0%
7523 6
 
1.5%
7644 5
 
1.2%
7621 4
 
1.0%
7570 4
 
1.0%
7571 4
 
1.0%
7788 3
 
0.7%
7547 3
 
0.7%
7528 3
 
0.7%
Other values (85) 131
32.0%
(Missing) 228
55.6%
ValueCountFrequency (%)
7505 1
 
0.2%
7507 1
 
0.2%
7511 1
 
0.2%
7516 2
 
0.5%
7517 3
0.7%
7518 1
 
0.2%
7522 1
 
0.2%
7523 6
1.5%
7524 2
 
0.5%
7528 3
0.7%
ValueCountFrequency (%)
7807 3
 
0.7%
7806 8
2.0%
7803 1
 
0.2%
7802 1
 
0.2%
7798 3
 
0.7%
7793 1
 
0.2%
7791 2
 
0.5%
7788 3
 
0.7%
7786 1
 
0.2%
7783 1
 
0.2%
Distinct386
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2024-04-18T03:14:12.188923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length17
Mean length6.2463415
Min length2

Characters and Unicode

Total characters2561
Distinct characters403
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique365 ?
Unique (%)89.0%

Sample

1st row신곡식품
2nd row한일식품
3rd row대상주식회사
4th row제일제당주식회사
5th row원주식품
ValueCountFrequency (%)
주식회사 18
 
3.7%
신곡식품 3
 
0.6%
행촌물산 3
 
0.6%
영월식품 3
 
0.6%
주)제이디티 3
 
0.6%
런치리아 2
 
0.4%
로스팅 2
 
0.4%
빵가 2
 
0.4%
유한회사 2
 
0.4%
리트웨이 2
 
0.4%
Other values (426) 442
91.7%
2024-04-18T03:14:12.473353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
 
4.8%
98
 
3.8%
97
 
3.8%
) 84
 
3.3%
( 83
 
3.2%
74
 
2.9%
72
 
2.8%
46
 
1.8%
41
 
1.6%
39
 
1.5%
Other values (393) 1804
70.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2238
87.4%
Close Punctuation 84
 
3.3%
Open Punctuation 83
 
3.2%
Space Separator 72
 
2.8%
Uppercase Letter 72
 
2.8%
Other Punctuation 6
 
0.2%
Decimal Number 5
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
123
 
5.5%
98
 
4.4%
97
 
4.3%
74
 
3.3%
46
 
2.1%
41
 
1.8%
39
 
1.7%
37
 
1.7%
30
 
1.3%
29
 
1.3%
Other values (362) 1624
72.6%
Uppercase Letter
ValueCountFrequency (%)
E 10
13.9%
F 7
9.7%
T 7
9.7%
U 6
 
8.3%
S 5
 
6.9%
A 5
 
6.9%
N 4
 
5.6%
O 4
 
5.6%
K 4
 
5.6%
C 3
 
4.2%
Other values (11) 17
23.6%
Other Punctuation
ValueCountFrequency (%)
& 3
50.0%
? 2
33.3%
! 1
 
16.7%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
5 1
20.0%
Close Punctuation
ValueCountFrequency (%)
) 84
100.0%
Open Punctuation
ValueCountFrequency (%)
( 83
100.0%
Space Separator
ValueCountFrequency (%)
72
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2238
87.4%
Common 251
 
9.8%
Latin 72
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
123
 
5.5%
98
 
4.4%
97
 
4.3%
74
 
3.3%
46
 
2.1%
41
 
1.8%
39
 
1.7%
37
 
1.7%
30
 
1.3%
29
 
1.3%
Other values (362) 1624
72.6%
Latin
ValueCountFrequency (%)
E 10
13.9%
F 7
9.7%
T 7
9.7%
U 6
 
8.3%
S 5
 
6.9%
A 5
 
6.9%
N 4
 
5.6%
O 4
 
5.6%
K 4
 
5.6%
C 3
 
4.2%
Other values (11) 17
23.6%
Common
ValueCountFrequency (%)
) 84
33.5%
( 83
33.1%
72
28.7%
& 3
 
1.2%
? 2
 
0.8%
2 2
 
0.8%
1 2
 
0.8%
5 1
 
0.4%
- 1
 
0.4%
! 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2238
87.4%
ASCII 323
 
12.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
123
 
5.5%
98
 
4.4%
97
 
4.3%
74
 
3.3%
46
 
2.1%
41
 
1.8%
39
 
1.7%
37
 
1.7%
30
 
1.3%
29
 
1.3%
Other values (362) 1624
72.6%
ASCII
ValueCountFrequency (%)
) 84
26.0%
( 83
25.7%
72
22.3%
E 10
 
3.1%
F 7
 
2.2%
T 7
 
2.2%
U 6
 
1.9%
S 5
 
1.5%
A 5
 
1.5%
N 4
 
1.2%
Other values (21) 40
12.4%
Distinct335
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Minimum1999-03-27 00:00:00
Maximum2024-03-29 14:56:14
2024-04-18T03:14:12.576999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:14:12.682019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
I
333 
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 333
81.2%
U 77
 
18.8%

Length

2024-04-18T03:14:12.780972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:12.852217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 333
81.2%
u 77
 
18.8%
Distinct93
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-02 21:01:00
2024-04-18T03:14:12.933777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:14:13.043878image/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 size3.3 KiB
식품제조가공업
308 
기타 식품제조가공업
102 

Length

Max length10
Median length7
Mean length7.7463415
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 308
75.1%
기타 식품제조가공업 102
 
24.9%

Length

2024-04-18T03:14:13.145552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:13.224107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 410
80.1%
기타 102
 
19.9%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct329
Distinct (%)84.8%
Missing22
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean185745.33
Minimum182734.08
Maximum189098.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-04-18T03:14:13.309393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182734.08
5-th percentile183186.74
Q1184029.25
median185857.05
Q3187161.59
95-th percentile188177.66
Maximum189098.81
Range6364.7314
Interquartile range (IQR)3132.3427

Descriptive statistics

Standard deviation1681.1963
Coefficient of variation (CV)0.0090510821
Kurtosis-1.1383183
Mean185745.33
Median Absolute Deviation (MAD)1552.6724
Skewness-0.053717509
Sum72069190
Variance2826420.9
MonotonicityNot monotonic
2024-04-18T03:14:13.414706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183914.938310002 7
 
1.7%
188153.507549647 4
 
1.0%
187952.560027898 4
 
1.0%
183117.281621173 3
 
0.7%
183550.712254046 3
 
0.7%
185867.499970695 3
 
0.7%
187034.37621095 3
 
0.7%
187701.673692397 3
 
0.7%
186501.233192961 3
 
0.7%
188968.109183775 3
 
0.7%
Other values (319) 352
85.9%
(Missing) 22
 
5.4%
ValueCountFrequency (%)
182734.07539408 1
0.2%
182742.537963411 1
0.2%
182830.370717987 1
0.2%
182865.612372488 1
0.2%
182876.367858149 1
0.2%
182879.610355768 1
0.2%
182897.251870492 1
0.2%
182956.589087267 1
0.2%
182967.067219996 1
0.2%
182973.246828277 1
0.2%
ValueCountFrequency (%)
189098.806779959 1
 
0.2%
188968.109183775 3
0.7%
188954.187154407 1
 
0.2%
188917.095297226 1
 
0.2%
188856.941317403 2
0.5%
188608.831941989 1
 
0.2%
188577.994815057 1
 
0.2%
188472.277894258 1
 
0.2%
188457.614941077 1
 
0.2%
188429.106552524 1
 
0.2%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct329
Distinct (%)84.8%
Missing22
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean450152.61
Minimum447239.55
Maximum452878.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-04-18T03:14:13.523130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum447239.55
5-th percentile447619.14
Q1449334.06
median450268.48
Q3451184.97
95-th percentile452222.65
Maximum452878.72
Range5639.1642
Interquartile range (IQR)1850.9106

Descriptive statistics

Standard deviation1392.0195
Coefficient of variation (CV)0.0030923279
Kurtosis-0.66113261
Mean450152.61
Median Absolute Deviation (MAD)933.28716
Skewness-0.28525306
Sum1.7465921 × 108
Variance1937718.2
MonotonicityNot monotonic
2024-04-18T03:14:13.629717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450085.01457343 7
 
1.7%
450402.784194393 4
 
1.0%
450562.020225978 4
 
1.0%
451398.804774616 3
 
0.7%
450214.162965782 3
 
0.7%
452099.765683138 3
 
0.7%
448407.64875222 3
 
0.7%
450327.279069351 3
 
0.7%
451485.251875922 3
 
0.7%
449841.392476365 3
 
0.7%
Other values (319) 352
85.9%
(Missing) 22
 
5.4%
ValueCountFrequency (%)
447239.554007375 1
0.2%
447291.688405525 1
0.2%
447326.548615694 1
0.2%
447330.821264356 1
0.2%
447380.825061783 1
0.2%
447381.645907113 1
0.2%
447398.196837221 1
0.2%
447464.394353482 1
0.2%
447465.590147476 1
0.2%
447482.537447986 1
0.2%
ValueCountFrequency (%)
452878.718236683 1
0.2%
452849.091475392 1
0.2%
452801.707821111 1
0.2%
452771.603941827 1
0.2%
452514.491608298 2
0.5%
452451.456657006 1
0.2%
452441.541807772 1
0.2%
452436.645972319 1
0.2%
452412.972039961 1
0.2%
452412.820310013 1
0.2%

위생업태명
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
식품제조가공업
303 
기타 식품제조가공업
73 
<NA>
34 

Length

Max length10
Median length7
Mean length7.2853659
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 303
73.9%
기타 식품제조가공업 73
 
17.8%
<NA> 34
 
8.3%

Length

2024-04-18T03:14:13.733880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:13.818941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 376
77.8%
기타 73
 
15.1%
na 34
 
7.0%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)13.3%
Missing327
Missing (%)79.8%
Infinite0
Infinite (%)0.0%
Mean2.6024096
Minimum0
Maximum59
Zeros21
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-04-18T03:14:13.888484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q32
95-th percentile6.9
Maximum59
Range59
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation7.2598793
Coefficient of variation (CV)2.7896759
Kurtosis47.36494
Mean2.6024096
Median Absolute Deviation (MAD)1
Skewness6.5416551
Sum216
Variance52.705848
MonotonicityNot monotonic
2024-04-18T03:14:13.967872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 35
 
8.5%
0 21
 
5.1%
2 11
 
2.7%
3 6
 
1.5%
6 3
 
0.7%
4 2
 
0.5%
7 1
 
0.2%
9 1
 
0.2%
59 1
 
0.2%
10 1
 
0.2%
(Missing) 327
79.8%
ValueCountFrequency (%)
0 21
5.1%
1 35
8.5%
2 11
 
2.7%
3 6
 
1.5%
4 2
 
0.5%
6 3
 
0.7%
7 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
30 1
 
0.2%
ValueCountFrequency (%)
59 1
 
0.2%
30 1
 
0.2%
10 1
 
0.2%
9 1
 
0.2%
7 1
 
0.2%
6 3
 
0.7%
4 2
 
0.5%
3 6
 
1.5%
2 11
 
2.7%
1 35
8.5%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)15.9%
Missing328
Missing (%)80.0%
Infinite0
Infinite (%)0.0%
Mean3.1463415
Minimum0
Maximum60
Zeros24
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-04-18T03:14:14.047764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile14.6
Maximum60
Range60
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.4726663
Coefficient of variation (CV)3.0106924
Kurtosis29.303289
Mean3.1463415
Median Absolute Deviation (MAD)1
Skewness5.3056534
Sum258
Variance89.731406
MonotonicityNot monotonic
2024-04-18T03:14:14.126153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 34
 
8.3%
0 24
 
5.9%
2 12
 
2.9%
3 3
 
0.7%
20 1
 
0.2%
16 1
 
0.2%
5 1
 
0.2%
58 1
 
0.2%
60 1
 
0.2%
7 1
 
0.2%
Other values (3) 3
 
0.7%
(Missing) 328
80.0%
ValueCountFrequency (%)
0 24
5.9%
1 34
8.3%
2 12
 
2.9%
3 3
 
0.7%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
15 1
 
0.2%
16 1
 
0.2%
ValueCountFrequency (%)
60 1
 
0.2%
58 1
 
0.2%
20 1
 
0.2%
16 1
 
0.2%
15 1
 
0.2%
7 1
 
0.2%
6 1
 
0.2%
5 1
 
0.2%
4 1
 
0.2%
3 3
0.7%

영업장주변구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
324 
기타
45 
주택가주변
34 
아파트지역
 
5
학교정화(상대)
 
1

Length

Max length8
Median length4
Mean length3.895122
Min length2

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 324
79.0%
기타 45
 
11.0%
주택가주변 34
 
8.3%
아파트지역 5
 
1.2%
학교정화(상대) 1
 
0.2%
유흥업소밀집지역 1
 
0.2%

Length

2024-04-18T03:14:14.220880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:14.307314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 324
79.0%
기타 45
 
11.0%
주택가주변 34
 
8.3%
아파트지역 5
 
1.2%
학교정화(상대 1
 
0.2%
유흥업소밀집지역 1
 
0.2%

등급구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
324 
기타
77 
자율
 
4
 
2
지도
 
2

Length

Max length4
Median length4
Mean length3.5731707
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 324
79.0%
기타 77
 
18.8%
자율 4
 
1.0%
2
 
0.5%
지도 2
 
0.5%
1
 
0.2%

Length

2024-04-18T03:14:14.400793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:14.711457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 324
79.0%
기타 77
 
18.8%
자율 4
 
1.0%
2
 
0.5%
지도 2
 
0.5%
1
 
0.2%
Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
259 
상수도전용
148 
지하수전용
 
3

Length

Max length5
Median length4
Mean length4.3682927
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 259
63.2%
상수도전용 148
36.1%
지하수전용 3
 
0.7%

Length

2024-04-18T03:14:14.799039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:14.876481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 259
63.2%
상수도전용 148
36.1%
지하수전용 3
 
0.7%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
403 
0
 
7

Length

Max length4
Median length4
Mean length3.9487805
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> 403
98.3%
0 7
 
1.7%

Length

2024-04-18T03:14:14.957516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:15.032726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 403
98.3%
0 7
 
1.7%
Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
212 
0
195 
1
 
3

Length

Max length4
Median length4
Mean length2.5512195
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 212
51.7%
0 195
47.6%
1 3
 
0.7%

Length

2024-04-18T03:14:15.111073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:15.189878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 212
51.7%
0 195
47.6%
1 3
 
0.7%
Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
216 
0
191 
1
 
3

Length

Max length4
Median length4
Mean length2.5804878
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 216
52.7%
0 191
46.6%
1 3
 
0.7%

Length

2024-04-18T03:14:15.288608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:15.391733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 216
52.7%
0 191
46.6%
1 3
 
0.7%
Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
216 
0
192 
1
 
2

Length

Max length4
Median length4
Mean length2.5804878
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 216
52.7%
0 192
46.8%
1 2
 
0.5%

Length

2024-04-18T03:14:15.486758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:15.566152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 216
52.7%
0 192
46.8%
1 2
 
0.5%

공장생산직종업원수
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
206 
0
186 
1
 
11
2
 
5
3
 
1

Length

Max length4
Median length4
Mean length2.5073171
Min length1

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row<NA>
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 206
50.2%
0 186
45.4%
1 11
 
2.7%
2 5
 
1.2%
3 1
 
0.2%
6 1
 
0.2%

Length

2024-04-18T03:14:15.648824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:15.732230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 206
50.2%
0 186
45.4%
1 11
 
2.7%
2 5
 
1.2%
3 1
 
0.2%
6 1
 
0.2%
Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
240 
임대
89 
자가
81 

Length

Max length4
Median length4
Mean length3.1707317
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> 240
58.5%
임대 89
 
21.7%
자가 81
 
19.8%

Length

2024-04-18T03:14:15.830047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:15.915922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 240
58.5%
임대 89
 
21.7%
자가 81
 
19.8%

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
370 
0
40 

Length

Max length4
Median length4
Mean length3.7073171
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> 370
90.2%
0 40
 
9.8%

Length

2024-04-18T03:14:16.002060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:16.079890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 370
90.2%
0 40
 
9.8%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
370 
0
40 

Length

Max length4
Median length4
Mean length3.7073171
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> 370
90.2%
0 40
 
9.8%

Length

2024-04-18T03:14:16.176819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:14:16.257041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 370
90.2%
0 40
 
9.8%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing34
Missing (%)8.3%
Memory size952.0 B
False
376 
(Missing)
 
34
ValueCountFrequency (%)
False 376
91.7%
(Missing) 34
 
8.3%
2024-04-18T03:14:16.319375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  ZEROS 

Distinct52
Distinct (%)13.8%
Missing34
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean4.7945213
Minimum0
Maximum670.65
Zeros314
Zeros (%)76.6%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-04-18T03:14:16.397178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile17.6725
Maximum670.65
Range670.65
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36.909964
Coefficient of variation (CV)7.6983628
Kurtosis285.18222
Mean4.7945213
Median Absolute Deviation (MAD)0
Skewness16.087859
Sum1802.74
Variance1362.3455
MonotonicityNot monotonic
2024-04-18T03:14:16.504173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 314
76.6%
3.0 6
 
1.5%
2.0 4
 
1.0%
10.0 3
 
0.7%
8.0 2
 
0.5%
9.76 1
 
0.2%
18.19 1
 
0.2%
23.81 1
 
0.2%
1.2 1
 
0.2%
3.6 1
 
0.2%
Other values (42) 42
 
10.2%
(Missing) 34
 
8.3%
ValueCountFrequency (%)
0.0 314
76.6%
1.0 1
 
0.2%
1.2 1
 
0.2%
2.0 4
 
1.0%
2.2 1
 
0.2%
3.0 6
 
1.5%
3.3 1
 
0.2%
3.36 1
 
0.2%
3.6 1
 
0.2%
3.7 1
 
0.2%
ValueCountFrequency (%)
670.65 1
0.2%
157.09 1
0.2%
118.38 1
0.2%
106.89 1
0.2%
57.9 1
0.2%
54.65 1
0.2%
49.39 1
0.2%
38.44 1
0.2%
32.5 1
0.2%
32.31 1
0.2%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing410
Missing (%)100.0%
Memory size3.7 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing410
Missing (%)100.0%
Memory size3.7 KiB

홈페이지
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing409
Missing (%)99.8%
Memory size3.3 KiB
2024-04-18T03:14:16.624066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters20
Distinct characters17
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

Unique1 ?
Unique (%)100.0%

Sample

1st rowsung9284@hanmail.net
ValueCountFrequency (%)
sung9284@hanmail.net 1
100.0%
2024-04-18T03:14:16.866347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 3
15.0%
a 2
 
10.0%
s 1
 
5.0%
h 1
 
5.0%
e 1
 
5.0%
. 1
 
5.0%
l 1
 
5.0%
i 1
 
5.0%
m 1
 
5.0%
@ 1
 
5.0%
Other values (7) 7
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14
70.0%
Decimal Number 4
 
20.0%
Other Punctuation 2
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3
21.4%
a 2
14.3%
s 1
 
7.1%
h 1
 
7.1%
e 1
 
7.1%
l 1
 
7.1%
i 1
 
7.1%
m 1
 
7.1%
u 1
 
7.1%
g 1
 
7.1%
Decimal Number
ValueCountFrequency (%)
4 1
25.0%
8 1
25.0%
2 1
25.0%
9 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
@ 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14
70.0%
Common 6
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3
21.4%
a 2
14.3%
s 1
 
7.1%
h 1
 
7.1%
e 1
 
7.1%
l 1
 
7.1%
i 1
 
7.1%
m 1
 
7.1%
u 1
 
7.1%
g 1
 
7.1%
Common
ValueCountFrequency (%)
. 1
16.7%
@ 1
16.7%
4 1
16.7%
8 1
16.7%
2 1
16.7%
9 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3
15.0%
a 2
 
10.0%
s 1
 
5.0%
h 1
 
5.0%
e 1
 
5.0%
. 1
 
5.0%
l 1
 
5.0%
i 1
 
5.0%
m 1
 
5.0%
@ 1
 
5.0%
Other values (7) 7
35.0%

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
031500003150000-106-1973-0043220030214<NA>3폐업2폐업20090817<NA><NA><NA>0226020735<NA>157925서울특별시 강서구 화곡동 1081-1번지서울특별시 강서구 화곡로26가길 43-5 (화곡동, 1동)<NA>신곡식품2015-07-13 17:42:10I2018-08-31 23:59:59.0식품제조가공업185608.754659448663.16889식품제조가공업70기타기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
131500003150000-106-1973-0043319730417<NA>3폐업2폐업20060125<NA><NA><NA>0226482698158.00157897서울특별시 강서구 화곡동 789-34번지<NA><NA>한일식품2002-05-07 00:00:00I2018-08-31 23:59:59.0식품제조가공업187593.884783447653.507202식품제조가공업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
231500003150000-106-1975-0043119751231<NA>3폐업2폐업20051102<NA><NA><NA>02 6575114900.00157801서울특별시 강서구 가양동 52-1번지<NA><NA>대상주식회사2004-03-31 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업920기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
331500003150000-106-1977-0044419770623<NA>3폐업2폐업20020917<NA><NA><NA>02366008571,879.00157801서울특별시 강서구 가양동 92-1번지<NA><NA>제일제당주식회사2002-05-03 00:00:00I2018-08-31 23:59:59.0식품제조가공업186223.731479451794.187683식품제조가공업5916기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
431500003150000-106-1980-0040419800724<NA>3폐업2폐업20130610<NA><NA><NA>0226621810.00157811서울특별시 강서구 공항동 2-45번지서울특별시 강서구 방화대로23길 5 (공항동)7617원주식품2013-04-26 10:20:34I2018-08-31 23:59:59.0식품제조가공업183679.606717451372.227408식품제조가공업11기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
531500003150000-106-1985-0049819850611<NA>3폐업2폐업20000106<NA><NA><NA>023664422050.93157861서울특별시 강서구 염창동 242-31번지<NA><NA>일용식품상사2000-01-07 00:00:00I2018-08-31 23:59:59.0식품제조가공업188153.50755450402.784194식품제조가공업22기타지도<NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
631500003150000-106-1986-0040519861218<NA>3폐업2폐업19991203<NA><NA><NA>0236618624129.44157930서울특별시 강서구 등촌동 674-6번지<NA><NA>아리랑식품2001-09-28 00:00:00I2018-08-31 23:59:59.0식품제조가공업185798.339847450797.485746식품제조가공업35기타기타<NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
731500003150000-106-1987-0040819870702<NA>1영업/정상1영업<NA><NA><NA><NA>022656559313,448.85157240서울특별시 강서구 공항동 1373서울특별시 강서구 하늘길 251 (공항동)7505대한항공씨앤디서비스 주식회사2020-12-30 14:45:08U2021-01-01 02:40:00.0기타 식품제조가공업182974.850128450419.68456기타 식품제조가공업1058기타기타지하수전용<NA>0000자가<NA><NA>N670.65<NA><NA><NA>
831500003150000-106-1987-0077519871106<NA>3폐업2폐업20040826<NA><NA><NA>0284.38157879서울특별시 강서구 화곡동 340-41번지<NA><NA>한식품2000-03-10 00:00:00I2018-08-31 23:59:59.0식품제조가공업186387.980494447869.539782식품제조가공업21주택가주변기타<NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
931500003150000-106-1989-0039819890824<NA>3폐업2폐업19970905<NA><NA><NA>0236628085114.48157839서울특별시 강서구 등촌동 631-9번지<NA><NA>낙원식품2001-09-28 00:00:00I2018-08-31 23:59:59.0식품제조가공업187759.109876450414.448218식품제조가공업10기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
40031500003150000-106-2022-000062022-07-20<NA>3폐업2폐업2023-03-02<NA><NA><NA><NA>21.82157-838서울특별시 강서구 등촌동 628-9 가양역 더스카이밸리5차 지식산업센터 322호서울특별시 강서구 화곡로 416, 가양역 더스카이밸리5차 지식산업센터 3층 322호 (등촌동)7548해장코리아2023-03-02 09:21:46U2022-12-03 00:04:00.0기타 식품제조가공업187160.665637450873.017067<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
40131500003150000-106-2022-0000720221103<NA>1영업/정상1영업<NA><NA><NA><NA>023473582223.93157857서울특별시 강서구 방화동 830-1 에어뷰21-2오피스텔 223호서울특별시 강서구 금낭화로 136, 에어뷰21-2오피스텔 2층 223호 (방화동)7511(주)인페인터글로벌2022-11-03 15:20:31I2021-11-01 00:06:00.0기타 식품제조가공업183410.412077452801.707821<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
40231500003150000-106-2022-0000820221125<NA>1영업/정상1영업<NA><NA><NA><NA>023663102026.15157859서울특별시 강서구 염창동 56-2서울특별시 강서구 양천로67길 96-20, 1층 (염창동)7535폭포2022-11-25 09:14:11I2021-10-31 22:07:00.0기타 식품제조가공업188472.277894450310.407203<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
40331500003150000-106-2022-0000920221207<NA>1영업/정상1영업<NA><NA><NA><NA><NA>36.31157210서울특별시 강서구 마곡동 798-6 류마타워Ⅱ서울특별시 강서구 마곡중앙로 59-17, 류마타워Ⅱ 316호 (마곡동)7807(주)바이오컴2022-12-07 13:57:03I2021-11-02 00:09:00.0기타 식품제조가공업184448.542297450753.134593<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
40431500003150000-106-2023-000022023-01-30<NA>1영업/정상1영업<NA><NA><NA><NA><NA>89.70157-915서울특별시 강서구 화곡동 980-21 강서아이파크서울특별시 강서구 화곡로 296, 2층 201호 (화곡동, 강서아이파크)7663(주)어나더젠틀2023-03-17 13:15:27U2022-12-02 22:00:00.0기타 식품제조가공업186594.265613449819.183931<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
40531500003150000-106-2023-000032023-02-06<NA>1영업/정상1영업<NA><NA><NA><NA><NA>462.90157-930서울특별시 강서구 등촌동 684-1 테크노타워서울특별시 강서구 강서로 468, 테크노타워 지하1층 B001호 (등촌동)7573경원2023-02-06 16:44:18I2022-12-02 00:08:00.0기타 식품제조가공업185931.619683451668.023529<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
40631500003150000-106-2023-000042023-05-19<NA>1영업/정상1영업<NA><NA><NA><NA><NA>41.31157-210서울특별시 강서구 마곡동 760 마곡나루역보타닉푸르지오시티서울특별시 강서구 마곡중앙5로 6, 마곡나루역보타닉푸르지오시티 지하1층 B-117호 (마곡동)7788(주)오직코리아2024-02-13 14:57:54U2023-12-01 23:05:00.0기타 식품제조가공업184646.580818451684.726858<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
40731500003150000-106-2023-000052023-09-18<NA>1영업/정상1영업<NA><NA><NA><NA><NA>126.73157-884서울특별시 강서구 화곡동 368-32서울특별시 강서구 가로공원로 208, B101호 (화곡동)7762삼부자푸드 주식회사2023-09-18 13:52:00I2022-12-08 22:00:00.0기타 식품제조가공업185714.171293448339.279243<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
40831500003150000-106-2023-000062023-11-29<NA>1영업/정상1영업<NA><NA><NA><NA><NA>37.08157-853서울특별시 강서구 방화동 612-195서울특별시 강서구 개화동로27길 46, 1층 좌측호 (방화동)7620진수제누룽지2023-11-29 16:24:53I2022-11-02 00:01:00.0기타 식품제조가공업183069.162137451363.253657<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
40931500003150000-106-2023-000072023-12-13<NA>1영업/정상1영업<NA><NA><NA><NA><NA>62.35157-210서울특별시 강서구 마곡동 768-2 동익드미라벨복합빌딩서울특별시 강서구 마곡서로 101, 동익드미라벨복합빌딩 812호 (마곡동)7798주식회사 브라운바운스2023-12-13 21:49:55I2022-11-01 23:05:00.0기타 식품제조가공업184345.724013451171.287709<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>