Overview

Dataset statistics

Number of variables44
Number of observations549
Missing cells4849
Missing cells (%)20.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory201.2 KiB
Average record size in memory375.2 B

Variable types

Categorical20
Text7
DateTime4
Unsupported7
Numeric5
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
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-18270/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
여성종사자수 is highly imbalanced (66.0%)Imbalance
영업장주변구분명 is highly imbalanced (62.5%)Imbalance
등급구분명 is highly imbalanced (71.7%)Imbalance
급수시설구분명 is highly imbalanced (51.7%)Imbalance
본사종업원수 is highly imbalanced (67.1%)Imbalance
공장사무직종업원수 is highly imbalanced (72.2%)Imbalance
공장판매직종업원수 is highly imbalanced (66.1%)Imbalance
공장생산직종업원수 is highly imbalanced (75.4%)Imbalance
인허가취소일자 has 549 (100.0%) missing valuesMissing
폐업일자 has 72 (13.1%) missing valuesMissing
휴업시작일자 has 549 (100.0%) missing valuesMissing
휴업종료일자 has 549 (100.0%) missing valuesMissing
재개업일자 has 549 (100.0%) missing valuesMissing
전화번호 has 177 (32.2%) missing valuesMissing
소재지면적 has 65 (11.8%) missing valuesMissing
소재지우편번호 has 7 (1.3%) missing valuesMissing
지번주소 has 7 (1.3%) missing valuesMissing
도로명주소 has 226 (41.2%) missing valuesMissing
도로명우편번호 has 237 (43.2%) missing valuesMissing
좌표정보(X) has 16 (2.9%) missing valuesMissing
좌표정보(Y) has 16 (2.9%) missing valuesMissing
남성종사자수 has 61 (11.1%) missing valuesMissing
다중이용업소여부 has 61 (11.1%) missing valuesMissing
시설총규모 has 61 (11.1%) missing valuesMissing
전통업소지정번호 has 549 (100.0%) missing valuesMissing
전통업소주된음식 has 549 (100.0%) missing valuesMissing
홈페이지 has 549 (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 473 (86.2%) zerosZeros
시설총규모 has 450 (82.0%) zerosZeros

Reproduction

Analysis started2024-05-11 06:22:24.551870
Analysis finished2024-05-11 06:22:26.327658
Duration1.78 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
3180000
549 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3180000
2nd row3180000
3rd row3180000
4th row3180000
5th row3180000

Common Values

ValueCountFrequency (%)
3180000 549
100.0%

Length

2024-05-11T15:22:26.454001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:26.617665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3180000 549
100.0%

관리번호
Text

UNIQUE 

Distinct549
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-05-11T15:22:27.000551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters12078
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

Unique549 ?
Unique (%)100.0%

Sample

1st row3180000-106-1966-00381
2nd row3180000-106-1966-00437
3rd row3180000-106-1966-00913
4th row3180000-106-1969-00380
5th row3180000-106-1969-00436
ValueCountFrequency (%)
3180000-106-1966-00381 1
 
0.2%
3180000-106-2011-00019 1
 
0.2%
3180000-106-2013-00017 1
 
0.2%
3180000-106-2013-00016 1
 
0.2%
3180000-106-2013-00015 1
 
0.2%
3180000-106-2013-00014 1
 
0.2%
3180000-106-2013-00013 1
 
0.2%
3180000-106-2013-00012 1
 
0.2%
3180000-106-2013-00011 1
 
0.2%
3180000-106-2009-00006 1
 
0.2%
Other values (539) 539
98.2%
2024-05-11T15:22:27.618252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5304
43.9%
1 1724
 
14.3%
- 1647
 
13.6%
8 704
 
5.8%
2 698
 
5.8%
3 688
 
5.7%
6 670
 
5.5%
9 209
 
1.7%
7 170
 
1.4%
4 151
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10431
86.4%
Dash Punctuation 1647
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5304
50.8%
1 1724
 
16.5%
8 704
 
6.7%
2 698
 
6.7%
3 688
 
6.6%
6 670
 
6.4%
9 209
 
2.0%
7 170
 
1.6%
4 151
 
1.4%
5 113
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 1647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5304
43.9%
1 1724
 
14.3%
- 1647
 
13.6%
8 704
 
5.8%
2 698
 
5.8%
3 688
 
5.7%
6 670
 
5.5%
9 209
 
1.7%
7 170
 
1.4%
4 151
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5304
43.9%
1 1724
 
14.3%
- 1647
 
13.6%
8 704
 
5.8%
2 698
 
5.8%
3 688
 
5.7%
6 670
 
5.5%
9 209
 
1.7%
7 170
 
1.4%
4 151
 
1.3%
Distinct513
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum1965-09-21 00:00:00
Maximum2024-04-11 00:00:00
2024-05-11T15:22:27.907051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:28.190228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
3
477 
1
72 

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 (%)
3 477
86.9%
1 72
 
13.1%

Length

2024-05-11T15:22:28.461475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:28.676130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 477
86.9%
1 72
 
13.1%

영업상태명
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
폐업
477 
영업/정상
72 

Length

Max length5
Median length2
Mean length2.3934426
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 477
86.9%
영업/정상 72
 
13.1%

Length

2024-05-11T15:22:28.910340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:29.119686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 477
86.9%
영업/정상 72
 
13.1%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2
477 
1
72 

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 (%)
2 477
86.9%
1 72
 
13.1%

Length

2024-05-11T15:22:29.324509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:29.871487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 477
86.9%
1 72
 
13.1%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
폐업
477 
영업
72 

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 477
86.9%
영업 72
 
13.1%

Length

2024-05-11T15:22:30.051465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:30.242396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 477
86.9%
영업 72
 
13.1%

폐업일자
Date

MISSING 

Distinct424
Distinct (%)88.9%
Missing72
Missing (%)13.1%
Memory size4.4 KiB
Minimum1996-05-30 00:00:00
Maximum2024-03-28 00:00:00
2024-05-11T15:22:30.455135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:30.715177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

전화번호
Text

MISSING 

Distinct338
Distinct (%)90.9%
Missing177
Missing (%)32.2%
Memory size4.4 KiB
2024-05-11T15:22:31.258010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.3521505
Min length2

Characters and Unicode

Total characters3479
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

Unique328 ?
Unique (%)88.2%

Sample

1st row02
2nd row0226344221
3rd row0226290514
4th row0226340171
5th row0226340171
ValueCountFrequency (%)
02 167
31.5%
070 3
 
0.6%
8439277 2
 
0.4%
8351267 2
 
0.4%
6714301 2
 
0.4%
8313603 2
 
0.4%
0226329647 2
 
0.4%
0226788933 2
 
0.4%
0226340171 2
 
0.4%
0226316784 2
 
0.4%
Other values (342) 344
64.9%
2024-05-11T15:22:32.038552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 633
18.2%
0 571
16.4%
6 362
10.4%
8 342
9.8%
3 321
9.2%
7 276
7.9%
4 224
 
6.4%
5 207
 
5.9%
1 205
 
5.9%
180
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3299
94.8%
Space Separator 180
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 633
19.2%
0 571
17.3%
6 362
11.0%
8 342
10.4%
3 321
9.7%
7 276
8.4%
4 224
 
6.8%
5 207
 
6.3%
1 205
 
6.2%
9 158
 
4.8%
Space Separator
ValueCountFrequency (%)
180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3479
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 633
18.2%
0 571
16.4%
6 362
10.4%
8 342
9.8%
3 321
9.2%
7 276
7.9%
4 224
 
6.4%
5 207
 
5.9%
1 205
 
5.9%
180
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 633
18.2%
0 571
16.4%
6 362
10.4%
8 342
9.8%
3 321
9.2%
7 276
7.9%
4 224
 
6.4%
5 207
 
5.9%
1 205
 
5.9%
180
 
5.2%

소재지면적
Text

MISSING 

Distinct396
Distinct (%)81.8%
Missing65
Missing (%)11.8%
Memory size4.4 KiB
2024-05-11T15:22:32.701914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.1818182
Min length3

Characters and Unicode

Total characters2508
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

Unique344 ?
Unique (%)71.1%

Sample

1st row106.25
2nd row810.00
3rd row810.00
4th row.00
5th row240.00
ValueCountFrequency (%)
00 18
 
3.7%
33.00 7
 
1.4%
120.00 5
 
1.0%
23.00 4
 
0.8%
66.00 4
 
0.8%
22.80 3
 
0.6%
39.00 3
 
0.6%
49.50 3
 
0.6%
50.00 3
 
0.6%
45.00 3
 
0.6%
Other values (386) 431
89.0%
2024-05-11T15:22:33.751554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 531
21.2%
. 484
19.3%
1 237
9.4%
5 185
 
7.4%
3 181
 
7.2%
4 175
 
7.0%
2 172
 
6.9%
6 142
 
5.7%
8 136
 
5.4%
9 135
 
5.4%
Other values (2) 130
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2023
80.7%
Other Punctuation 485
 
19.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 531
26.2%
1 237
11.7%
5 185
 
9.1%
3 181
 
8.9%
4 175
 
8.7%
2 172
 
8.5%
6 142
 
7.0%
8 136
 
6.7%
9 135
 
6.7%
7 129
 
6.4%
Other Punctuation
ValueCountFrequency (%)
. 484
99.8%
, 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2508
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 531
21.2%
. 484
19.3%
1 237
9.4%
5 185
 
7.4%
3 181
 
7.2%
4 175
 
7.0%
2 172
 
6.9%
6 142
 
5.7%
8 136
 
5.4%
9 135
 
5.4%
Other values (2) 130
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 531
21.2%
. 484
19.3%
1 237
9.4%
5 185
 
7.4%
3 181
 
7.2%
4 175
 
7.0%
2 172
 
6.9%
6 142
 
5.7%
8 136
 
5.4%
9 135
 
5.4%
Other values (2) 130
 
5.2%

소재지우편번호
Text

MISSING 

Distinct124
Distinct (%)22.9%
Missing7
Missing (%)1.3%
Memory size4.4 KiB
2024-05-11T15:22:34.396340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0738007
Min length6

Characters and Unicode

Total characters3292
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

Unique47 ?
Unique (%)8.7%

Sample

1st row150096
2nd row150096
3rd row150096
4th row150866
5th row150866
ValueCountFrequency (%)
150841 29
 
5.4%
150105 20
 
3.7%
150103 18
 
3.3%
150800 17
 
3.1%
150866 15
 
2.8%
150867 15
 
2.8%
150035 14
 
2.6%
150092 13
 
2.4%
150863 13
 
2.4%
150095 12
 
2.2%
Other values (114) 376
69.4%
2024-05-11T15:22:35.251237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 843
25.6%
1 705
21.4%
5 656
19.9%
8 377
11.5%
3 154
 
4.7%
6 130
 
3.9%
9 127
 
3.9%
4 118
 
3.6%
2 90
 
2.7%
7 52
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3252
98.8%
Dash Punctuation 40
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 843
25.9%
1 705
21.7%
5 656
20.2%
8 377
11.6%
3 154
 
4.7%
6 130
 
4.0%
9 127
 
3.9%
4 118
 
3.6%
2 90
 
2.8%
7 52
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3292
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 843
25.6%
1 705
21.4%
5 656
19.9%
8 377
11.5%
3 154
 
4.7%
6 130
 
3.9%
9 127
 
3.9%
4 118
 
3.6%
2 90
 
2.7%
7 52
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 843
25.6%
1 705
21.4%
5 656
19.9%
8 377
11.5%
3 154
 
4.7%
6 130
 
3.9%
9 127
 
3.9%
4 118
 
3.6%
2 90
 
2.7%
7 52
 
1.6%

지번주소
Text

MISSING 

Distinct520
Distinct (%)95.9%
Missing7
Missing (%)1.3%
Memory size4.4 KiB
2024-05-11T15:22:35.783602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length46
Mean length27.129151
Min length18

Characters and Unicode

Total characters14704
Distinct characters190
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique501 ?
Unique (%)92.4%

Sample

1st row서울특별시 영등포구 문래동6가 21-0번지
2nd row서울특별시 영등포구 문래동6가 21-0번지
3rd row서울특별시 영등포구 문래동6가 21-0번지
4th row서울특별시 영등포구 양평동4가 16-1번지
5th row서울특별시 영등포구 양평동4가 16-1번지
ValueCountFrequency (%)
서울특별시 542
21.0%
영등포구 542
21.0%
신길동 80
 
3.1%
대림동 73
 
2.8%
1층 53
 
2.1%
양평동4가 36
 
1.4%
여의도동 32
 
1.2%
문래동3가 30
 
1.2%
지하1층 30
 
1.2%
양평동1가 27
 
1.0%
Other values (701) 1133
43.9%
2024-05-11T15:22:36.659011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2496
 
17.0%
1 650
 
4.4%
610
 
4.1%
606
 
4.1%
605
 
4.1%
562
 
3.8%
549
 
3.7%
542
 
3.7%
542
 
3.7%
542
 
3.7%
Other values (180) 7000
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8928
60.7%
Decimal Number 2734
 
18.6%
Space Separator 2496
 
17.0%
Dash Punctuation 437
 
3.0%
Uppercase Letter 40
 
0.3%
Close Punctuation 23
 
0.2%
Open Punctuation 23
 
0.2%
Other Punctuation 22
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
610
 
6.8%
606
 
6.8%
605
 
6.8%
562
 
6.3%
549
 
6.1%
542
 
6.1%
542
 
6.1%
542
 
6.1%
542
 
6.1%
542
 
6.1%
Other values (155) 3286
36.8%
Decimal Number
ValueCountFrequency (%)
1 650
23.8%
2 370
13.5%
3 321
11.7%
4 255
 
9.3%
5 249
 
9.1%
6 233
 
8.5%
0 219
 
8.0%
7 168
 
6.1%
8 139
 
5.1%
9 130
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
B 15
37.5%
K 8
20.0%
A 6
 
15.0%
S 6
 
15.0%
M 2
 
5.0%
V 1
 
2.5%
P 1
 
2.5%
T 1
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 18
81.8%
. 4
 
18.2%
Space Separator
ValueCountFrequency (%)
2496
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 437
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8928
60.7%
Common 5736
39.0%
Latin 40
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
610
 
6.8%
606
 
6.8%
605
 
6.8%
562
 
6.3%
549
 
6.1%
542
 
6.1%
542
 
6.1%
542
 
6.1%
542
 
6.1%
542
 
6.1%
Other values (155) 3286
36.8%
Common
ValueCountFrequency (%)
2496
43.5%
1 650
 
11.3%
- 437
 
7.6%
2 370
 
6.5%
3 321
 
5.6%
4 255
 
4.4%
5 249
 
4.3%
6 233
 
4.1%
0 219
 
3.8%
7 168
 
2.9%
Other values (7) 338
 
5.9%
Latin
ValueCountFrequency (%)
B 15
37.5%
K 8
20.0%
A 6
 
15.0%
S 6
 
15.0%
M 2
 
5.0%
V 1
 
2.5%
P 1
 
2.5%
T 1
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8928
60.7%
ASCII 5776
39.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2496
43.2%
1 650
 
11.3%
- 437
 
7.6%
2 370
 
6.4%
3 321
 
5.6%
4 255
 
4.4%
5 249
 
4.3%
6 233
 
4.0%
0 219
 
3.8%
7 168
 
2.9%
Other values (15) 378
 
6.5%
Hangul
ValueCountFrequency (%)
610
 
6.8%
606
 
6.8%
605
 
6.8%
562
 
6.3%
549
 
6.1%
542
 
6.1%
542
 
6.1%
542
 
6.1%
542
 
6.1%
542
 
6.1%
Other values (155) 3286
36.8%

도로명주소
Text

MISSING 

Distinct319
Distinct (%)98.8%
Missing226
Missing (%)41.2%
Memory size4.4 KiB
2024-05-11T15:22:37.207753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length50
Mean length34.337461
Min length24

Characters and Unicode

Total characters11091
Distinct characters181
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique315 ?
Unique (%)97.5%

Sample

1st row서울특별시 영등포구 양평로21길 25 (양평동4가)
2nd row서울특별시 영등포구 당산로41길 11, B125-127호 (당산동4가)
3rd row서울특별시 영등포구 신길로 87 (문래동3가)
4th row서울특별시 영등포구 양평로 158 (양평동5가)
5th row서울특별시 영등포구 버드나루로20길 6 (당산동)
ValueCountFrequency (%)
서울특별시 323
 
16.5%
영등포구 323
 
16.5%
1층 43
 
2.2%
신길동 40
 
2.0%
대림동 32
 
1.6%
지하1층 28
 
1.4%
여의도동 22
 
1.1%
양평동1가 20
 
1.0%
선유로 17
 
0.9%
양평동5가 17
 
0.9%
Other values (558) 1097
55.9%
2024-05-11T15:22:38.099670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1639
 
14.8%
1 504
 
4.5%
395
 
3.6%
378
 
3.4%
377
 
3.4%
345
 
3.1%
336
 
3.0%
330
 
3.0%
( 329
 
3.0%
) 329
 
3.0%
Other values (171) 6129
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6551
59.1%
Decimal Number 1848
 
16.7%
Space Separator 1639
 
14.8%
Open Punctuation 329
 
3.0%
Close Punctuation 329
 
3.0%
Other Punctuation 274
 
2.5%
Dash Punctuation 72
 
0.6%
Uppercase Letter 45
 
0.4%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
395
 
6.0%
378
 
5.8%
377
 
5.8%
345
 
5.3%
336
 
5.1%
330
 
5.0%
323
 
4.9%
323
 
4.9%
323
 
4.9%
323
 
4.9%
Other values (144) 3098
47.3%
Decimal Number
ValueCountFrequency (%)
1 504
27.3%
2 307
16.6%
3 220
11.9%
4 160
 
8.7%
0 147
 
8.0%
5 128
 
6.9%
6 106
 
5.7%
7 106
 
5.7%
8 100
 
5.4%
9 70
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
B 17
37.8%
A 8
17.8%
K 7
15.6%
S 5
 
11.1%
M 2
 
4.4%
F 2
 
4.4%
E 1
 
2.2%
V 1
 
2.2%
P 1
 
2.2%
T 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 273
99.6%
. 1
 
0.4%
Space Separator
ValueCountFrequency (%)
1639
100.0%
Open Punctuation
ValueCountFrequency (%)
( 329
100.0%
Close Punctuation
ValueCountFrequency (%)
) 329
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6551
59.1%
Common 4495
40.5%
Latin 45
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
395
 
6.0%
378
 
5.8%
377
 
5.8%
345
 
5.3%
336
 
5.1%
330
 
5.0%
323
 
4.9%
323
 
4.9%
323
 
4.9%
323
 
4.9%
Other values (144) 3098
47.3%
Common
ValueCountFrequency (%)
1639
36.5%
1 504
 
11.2%
( 329
 
7.3%
) 329
 
7.3%
2 307
 
6.8%
, 273
 
6.1%
3 220
 
4.9%
4 160
 
3.6%
0 147
 
3.3%
5 128
 
2.8%
Other values (7) 459
 
10.2%
Latin
ValueCountFrequency (%)
B 17
37.8%
A 8
17.8%
K 7
15.6%
S 5
 
11.1%
M 2
 
4.4%
F 2
 
4.4%
E 1
 
2.2%
V 1
 
2.2%
P 1
 
2.2%
T 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6551
59.1%
ASCII 4540
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1639
36.1%
1 504
 
11.1%
( 329
 
7.2%
) 329
 
7.2%
2 307
 
6.8%
, 273
 
6.0%
3 220
 
4.8%
4 160
 
3.5%
0 147
 
3.2%
5 128
 
2.8%
Other values (17) 504
 
11.1%
Hangul
ValueCountFrequency (%)
395
 
6.0%
378
 
5.8%
377
 
5.8%
345
 
5.3%
336
 
5.1%
330
 
5.0%
323
 
4.9%
323
 
4.9%
323
 
4.9%
323
 
4.9%
Other values (144) 3098
47.3%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct127
Distinct (%)40.7%
Missing237
Missing (%)43.2%
Infinite0
Infinite (%)0.0%
Mean7298.4455
Minimum7201
Maximum7448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-05-11T15:22:38.416666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7201
5-th percentile7205
Q17251
median7286.5
Q37352
95-th percentile7418.45
Maximum7448
Range247
Interquartile range (IQR)101

Descriptive statistics

Standard deviation68.342176
Coefficient of variation (CV)0.0093639359
Kurtosis-0.86066773
Mean7298.4455
Median Absolute Deviation (MAD)48.5
Skewness0.43057203
Sum2277115
Variance4670.653
MonotonicityNot monotonic
2024-05-11T15:22:38.750034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7290 11
 
2.0%
7272 11
 
2.0%
7205 9
 
1.6%
7299 9
 
1.6%
7265 9
 
1.6%
7333 8
 
1.5%
7383 7
 
1.3%
7208 7
 
1.3%
7286 6
 
1.1%
7207 6
 
1.1%
Other values (117) 229
41.7%
(Missing) 237
43.2%
ValueCountFrequency (%)
7201 3
 
0.5%
7202 3
 
0.5%
7203 1
 
0.2%
7204 1
 
0.2%
7205 9
1.6%
7206 5
0.9%
7207 6
1.1%
7208 7
1.3%
7209 4
0.7%
7212 1
 
0.2%
ValueCountFrequency (%)
7448 1
0.2%
7446 1
0.2%
7442 1
0.2%
7439 2
0.4%
7436 1
0.2%
7433 1
0.2%
7432 1
0.2%
7430 1
0.2%
7429 2
0.4%
7427 1
0.2%
Distinct521
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-05-11T15:22:39.316207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length31
Mean length6.4699454
Min length2

Characters and Unicode

Total characters3552
Distinct characters473
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique497 ?
Unique (%)90.5%

Sample

1st row(주)롯데삼강
2nd row(주)롯데삼강
3rd row(주)롯데삼강
4th row(주)롯데제과
5th row(주)롯데제과
ValueCountFrequency (%)
주식회사 15
 
2.4%
coffee 5
 
0.8%
바오담 4
 
0.6%
커피 3
 
0.5%
roasters 3
 
0.5%
주)롯데삼강 3
 
0.5%
우리식품 3
 
0.5%
서울식품 3
 
0.5%
로스터스 2
 
0.3%
삼촌과조카 2
 
0.3%
Other values (559) 582
93.1%
2024-05-11T15:22:40.209029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
143
 
4.0%
141
 
4.0%
( 139
 
3.9%
) 139
 
3.9%
124
 
3.5%
87
 
2.4%
76
 
2.1%
62
 
1.7%
62
 
1.7%
58
 
1.6%
Other values (463) 2521
71.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2956
83.2%
Open Punctuation 139
 
3.9%
Close Punctuation 139
 
3.9%
Uppercase Letter 124
 
3.5%
Lowercase Letter 97
 
2.7%
Space Separator 76
 
2.1%
Other Punctuation 14
 
0.4%
Decimal Number 6
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
143
 
4.8%
141
 
4.8%
124
 
4.2%
87
 
2.9%
62
 
2.1%
62
 
2.1%
58
 
2.0%
50
 
1.7%
48
 
1.6%
46
 
1.6%
Other values (408) 2135
72.2%
Uppercase Letter
ValueCountFrequency (%)
C 15
12.1%
E 14
11.3%
F 13
10.5%
O 12
9.7%
B 9
 
7.3%
R 9
 
7.3%
S 9
 
7.3%
T 8
 
6.5%
L 5
 
4.0%
M 5
 
4.0%
Other values (11) 25
20.2%
Lowercase Letter
ValueCountFrequency (%)
e 19
19.6%
o 12
12.4%
f 10
10.3%
a 8
 
8.2%
k 6
 
6.2%
s 5
 
5.2%
r 5
 
5.2%
i 4
 
4.1%
n 4
 
4.1%
c 4
 
4.1%
Other values (10) 20
20.6%
Other Punctuation
ValueCountFrequency (%)
& 5
35.7%
. 5
35.7%
, 2
 
14.3%
' 1
 
7.1%
? 1
 
7.1%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
2 1
16.7%
6 1
16.7%
3 1
16.7%
0 1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 139
100.0%
Close Punctuation
ValueCountFrequency (%)
) 139
100.0%
Space Separator
ValueCountFrequency (%)
76
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2955
83.2%
Common 375
 
10.6%
Latin 221
 
6.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
143
 
4.8%
141
 
4.8%
124
 
4.2%
87
 
2.9%
62
 
2.1%
62
 
2.1%
58
 
2.0%
50
 
1.7%
48
 
1.6%
46
 
1.6%
Other values (407) 2134
72.2%
Latin
ValueCountFrequency (%)
e 19
 
8.6%
C 15
 
6.8%
E 14
 
6.3%
F 13
 
5.9%
O 12
 
5.4%
o 12
 
5.4%
f 10
 
4.5%
B 9
 
4.1%
R 9
 
4.1%
S 9
 
4.1%
Other values (31) 99
44.8%
Common
ValueCountFrequency (%)
( 139
37.1%
) 139
37.1%
76
20.3%
& 5
 
1.3%
. 5
 
1.3%
, 2
 
0.5%
1 2
 
0.5%
2 1
 
0.3%
- 1
 
0.3%
' 1
 
0.3%
Other values (4) 4
 
1.1%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2955
83.2%
ASCII 596
 
16.8%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
143
 
4.8%
141
 
4.8%
124
 
4.2%
87
 
2.9%
62
 
2.1%
62
 
2.1%
58
 
2.0%
50
 
1.7%
48
 
1.6%
46
 
1.6%
Other values (407) 2134
72.2%
ASCII
ValueCountFrequency (%)
( 139
23.3%
) 139
23.3%
76
12.8%
e 19
 
3.2%
C 15
 
2.5%
E 14
 
2.3%
F 13
 
2.2%
O 12
 
2.0%
o 12
 
2.0%
f 10
 
1.7%
Other values (45) 147
24.7%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
Distinct497
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum1999-06-02 00:00:00
Maximum2024-05-03 10:57:38
2024-05-11T15:22:40.574479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:40.888334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
I
411 
U
138 

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 411
74.9%
U 138
 
25.1%

Length

2024-05-11T15:22:41.239319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:41.423299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 411
74.9%
u 138
 
25.1%
Distinct138
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:05:00
2024-05-11T15:22:41.636384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:41.945359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
식품제조가공업
392 
기타 식품제조가공업
122 
<NA>
 
31
도시락제조업
 
4

Length

Max length10
Median length7
Mean length7.4899818
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 392
71.4%
기타 식품제조가공업 122
 
22.2%
<NA> 31
 
5.6%
도시락제조업 4
 
0.7%

Length

2024-05-11T15:22:42.269368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:42.494610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 514
76.6%
기타 122
 
18.2%
na 31
 
4.6%
도시락제조업 4
 
0.6%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct432
Distinct (%)81.1%
Missing16
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean191245.57
Minimum189574.96
Maximum194632.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-05-11T15:22:42.833820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189574.96
5-th percentile189877.93
Q1190555.8
median191024.65
Q3191741.35
95-th percentile193258.38
Maximum194632.53
Range5057.5643
Interquartile range (IQR)1185.5471

Descriptive statistics

Standard deviation1004.9618
Coefficient of variation (CV)0.0052548236
Kurtosis0.87830047
Mean191245.57
Median Absolute Deviation (MAD)624.21395
Skewness1.0045827
Sum1.0193389 × 108
Variance1009948.1
MonotonicityNot monotonic
2024-05-11T15:22:43.155805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190587.567878858 6
 
1.1%
189859.071768836 5
 
0.9%
191024.651979979 5
 
0.9%
189667.63984485 5
 
0.9%
190279.297139378 5
 
0.9%
191087.758954321 4
 
0.7%
191741.345847708 4
 
0.7%
190828.268143013 4
 
0.7%
190364.652010662 4
 
0.7%
191236.216659536 3
 
0.5%
Other values (422) 488
88.9%
(Missing) 16
 
2.9%
ValueCountFrequency (%)
189574.962072527 1
 
0.2%
189641.475801911 1
 
0.2%
189667.63984485 5
0.9%
189708.67445487 1
 
0.2%
189716.809081039 1
 
0.2%
189764.477432316 1
 
0.2%
189766.368228049 1
 
0.2%
189814.860666388 3
0.5%
189818.218894743 1
 
0.2%
189835.868160807 1
 
0.2%
ValueCountFrequency (%)
194632.526367463 3
0.5%
194370.32715363 1
 
0.2%
194124.497455922 3
0.5%
194005.549728371 2
0.4%
193999.840415079 1
 
0.2%
193989.272586157 1
 
0.2%
193896.282065175 2
0.4%
193882.109246282 1
 
0.2%
193844.169062846 3
0.5%
193818.18014 1
 
0.2%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct432
Distinct (%)81.1%
Missing16
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean446179.96
Minimum442846.65
Maximum449025.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-05-11T15:22:43.486192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum442846.65
5-th percentile443539.87
Q1445267.95
median446413.92
Q3447020.96
95-th percentile448480.13
Maximum449025.25
Range6178.5964
Interquartile range (IQR)1753.0015

Descriptive statistics

Standard deviation1439.2105
Coefficient of variation (CV)0.0032256279
Kurtosis-0.51931018
Mean446179.96
Median Absolute Deviation (MAD)865.12034
Skewness-0.26164726
Sum2.3781392 × 108
Variance2071327
MonotonicityNot monotonic
2024-05-11T15:22:43.798762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448642.112414847 6
 
1.1%
445875.89672273 5
 
0.9%
446421.72002587 5
 
0.9%
446140.637415622 5
 
0.9%
448618.587308851 5
 
0.9%
446157.437289242 4
 
0.7%
445970.307641467 4
 
0.7%
445548.79737092 4
 
0.7%
447158.946262059 4
 
0.7%
446066.47341564 3
 
0.5%
Other values (422) 488
88.9%
(Missing) 16
 
2.9%
ValueCountFrequency (%)
442846.650212582 1
0.2%
442913.875052132 1
0.2%
443001.570866736 1
0.2%
443002.033200332 1
0.2%
443006.868554346 1
0.2%
443009.331056058 1
0.2%
443010.178826055 1
0.2%
443116.431732436 1
0.2%
443144.137508694 1
0.2%
443228.349006844 1
0.2%
ValueCountFrequency (%)
449025.246614702 1
 
0.2%
449021.440559054 1
 
0.2%
448991.53912287 1
 
0.2%
448953.434292828 1
 
0.2%
448906.42052912 1
 
0.2%
448900.040843925 1
 
0.2%
448788.457554535 1
 
0.2%
448779.57454276 1
 
0.2%
448667.181702536 1
 
0.2%
448656.726986041 3
0.5%

위생업태명
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
식품제조가공업
369 
<NA>
92 
기타 식품제조가공업
84 
도시락제조업
 
4

Length

Max length10
Median length7
Mean length6.9489982
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 369
67.2%
<NA> 92
 
16.8%
기타 식품제조가공업 84
 
15.3%
도시락제조업 4
 
0.7%

Length

2024-05-11T15:22:44.095978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:44.345159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 453
71.6%
na 92
 
14.5%
기타 84
 
13.3%
도시락제조업 4
 
0.6%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)1.2%
Missing61
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean0.057377049
Minimum0
Maximum6
Zeros473
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-05-11T15:22:44.655907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41132191
Coefficient of variation (CV)7.1687533
Kurtosis117.60341
Mean0.057377049
Median Absolute Deviation (MAD)0
Skewness10.006281
Sum28
Variance0.16918571
MonotonicityNot monotonic
2024-05-11T15:22:44.885385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 473
86.2%
1 10
 
1.8%
3 2
 
0.4%
6 1
 
0.2%
2 1
 
0.2%
4 1
 
0.2%
(Missing) 61
 
11.1%
ValueCountFrequency (%)
0 473
86.2%
1 10
 
1.8%
2 1
 
0.2%
3 2
 
0.4%
4 1
 
0.2%
6 1
 
0.2%
ValueCountFrequency (%)
6 1
 
0.2%
4 1
 
0.2%
3 2
 
0.4%
2 1
 
0.2%
1 10
 
1.8%
0 473
86.2%

여성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
475 
<NA>
61 
1
 
10
2
 
3

Length

Max length4
Median length1
Mean length1.3333333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 475
86.5%
<NA> 61
 
11.1%
1 10
 
1.8%
2 3
 
0.5%

Length

2024-05-11T15:22:45.106186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:45.381912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 475
86.5%
na 61
 
11.1%
1 10
 
1.8%
2 3
 
0.5%

영업장주변구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
489 
기타
 
42
주택가주변
 
18

Length

Max length5
Median length4
Mean length3.8797814
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 489
89.1%
기타 42
 
7.7%
주택가주변 18
 
3.3%

Length

2024-05-11T15:22:45.609506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:45.835904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 489
89.1%
기타 42
 
7.7%
주택가주변 18
 
3.3%

등급구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
489 
기타
 
31
자율
 
22
우수
 
6
지도
 
1

Length

Max length4
Median length4
Mean length3.7814208
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 489
89.1%
기타 31
 
5.6%
자율 22
 
4.0%
우수 6
 
1.1%
지도 1
 
0.2%

Length

2024-05-11T15:22:46.061800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:46.281438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 489
89.1%
기타 31
 
5.6%
자율 22
 
4.0%
우수 6
 
1.1%
지도 1
 
0.2%

급수시설구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
431 
상수도전용
117 
지하수전용
 
1

Length

Max length5
Median length4
Mean length4.2149362
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 431
78.5%
상수도전용 117
 
21.3%
지하수전용 1
 
0.2%

Length

2024-05-11T15:22:46.476061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:46.670609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 431
78.5%
상수도전용 117
 
21.3%
지하수전용 1
 
0.2%

총인원
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
488 
<NA>
61 

Length

Max length4
Median length1
Mean length1.3333333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 488
88.9%
<NA> 61
 
11.1%

Length

2024-05-11T15:22:47.306229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:47.494028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 488
88.9%
na 61
 
11.1%

본사종업원수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
487 
<NA>
61 
2
 
1

Length

Max length4
Median length1
Mean length1.3333333
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 487
88.7%
<NA> 61
 
11.1%
2 1
 
0.2%

Length

2024-05-11T15:22:47.718377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:47.908470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 487
88.7%
na 61
 
11.1%
2 1
 
0.2%

공장사무직종업원수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
485 
<NA>
61 
2
 
2
1
 
1

Length

Max length4
Median length1
Mean length1.3333333
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 485
88.3%
<NA> 61
 
11.1%
2 2
 
0.4%
1 1
 
0.2%

Length

2024-05-11T15:22:48.137634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:48.407411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 485
88.3%
na 61
 
11.1%
2 2
 
0.4%
1 1
 
0.2%

공장판매직종업원수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
486 
<NA>
61 
1
 
2

Length

Max length4
Median length1
Mean length1.3333333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 486
88.5%
<NA> 61
 
11.1%
1 2
 
0.4%

Length

2024-05-11T15:22:48.624376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:48.832048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 486
88.5%
na 61
 
11.1%
1 2
 
0.4%

공장생산직종업원수
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
480 
<NA>
61 
2
 
3
1
 
3
3
 
1

Length

Max length4
Median length1
Mean length1.3333333
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 480
87.4%
<NA> 61
 
11.1%
2 3
 
0.5%
1 3
 
0.5%
3 1
 
0.2%
9 1
 
0.2%

Length

2024-05-11T15:22:49.066481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:49.295056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 480
87.4%
na 61
 
11.1%
2 3
 
0.5%
1 3
 
0.5%
3 1
 
0.2%
9 1
 
0.2%
Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
348 
임대
129 
자가
72 

Length

Max length4
Median length4
Mean length3.2677596
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> 348
63.4%
임대 129
 
23.5%
자가 72
 
13.1%

Length

2024-05-11T15:22:49.552759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:49.899803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 348
63.4%
임대 129
 
23.5%
자가 72
 
13.1%

보증액
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
488 
<NA>
61 

Length

Max length4
Median length1
Mean length1.3333333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 488
88.9%
<NA> 61
 
11.1%

Length

2024-05-11T15:22:50.115653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:50.297740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 488
88.9%
na 61
 
11.1%

월세액
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
488 
<NA>
61 

Length

Max length4
Median length1
Mean length1.3333333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 488
88.9%
<NA> 61
 
11.1%

Length

2024-05-11T15:22:50.519562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:22:50.726287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 488
88.9%
na 61
 
11.1%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing61
Missing (%)11.1%
Memory size1.2 KiB
False
488 
(Missing)
61 
ValueCountFrequency (%)
False 488
88.9%
(Missing) 61
 
11.1%
2024-05-11T15:22:50.874935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  ZEROS 

Distinct38
Distinct (%)7.8%
Missing61
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean2.4556967
Minimum0
Maximum260
Zeros450
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-05-11T15:22:51.052280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7.991
Maximum260
Range260
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.103282
Coefficient of variation (CV)6.5575206
Kurtosis162.15085
Mean2.4556967
Median Absolute Deviation (MAD)0
Skewness11.639988
Sum1198.38
Variance259.31568
MonotonicityNot monotonic
2024-05-11T15:22:51.309848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.0 450
82.0%
6.0 2
 
0.4%
173.21 1
 
0.2%
36.1 1
 
0.2%
21.31 1
 
0.2%
6.6 1
 
0.2%
23.76 1
 
0.2%
31.74 1
 
0.2%
66.38 1
 
0.2%
7.9 1
 
0.2%
Other values (28) 28
 
5.1%
(Missing) 61
 
11.1%
ValueCountFrequency (%)
0.0 450
82.0%
1.2 1
 
0.2%
2.53 1
 
0.2%
2.88 1
 
0.2%
3.5 1
 
0.2%
3.61 1
 
0.2%
4.74 1
 
0.2%
4.8 1
 
0.2%
5.8 1
 
0.2%
6.0 2
 
0.4%
ValueCountFrequency (%)
260.0 1
0.2%
173.21 1
0.2%
81.61 1
0.2%
66.46 1
0.2%
66.38 1
0.2%
50.8 1
0.2%
49.44 1
0.2%
48.5 1
0.2%
39.1 1
0.2%
36.1 1
0.2%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
031800003180000-106-1966-0038119660620<NA>3폐업2폐업20010316<NA><NA><NA>02106.25150096서울특별시 영등포구 문래동6가 21-0번지<NA><NA>(주)롯데삼강2001-03-16 00:00:00I2018-08-31 23:59:59.0<NA>189814.860666446221.013702<NA>00기타자율상수도전용00000<NA>00N0.0<NA><NA><NA>
131800003180000-106-1966-0043719660620<NA>3폐업2폐업20010316<NA><NA><NA>0226344221810.00150096서울특별시 영등포구 문래동6가 21-0번지<NA><NA>(주)롯데삼강2001-03-16 00:00:00I2018-08-31 23:59:59.0도시락제조업189814.860666446221.013702도시락제조업00기타기타<NA>00000<NA>00N0.0<NA><NA><NA>
231800003180000-106-1966-0091319660620<NA>3폐업2폐업20051223<NA><NA><NA>0226290514810.00150096서울특별시 영등포구 문래동6가 21-0번지<NA><NA>(주)롯데삼강2005-06-02 00:00:00I2018-08-31 23:59:59.0식품제조가공업189814.860666446221.013702식품제조가공업00기타자율상수도전용00000<NA>00N0.0<NA><NA><NA>
331800003180000-106-1969-0038019690307<NA>3폐업2폐업20010205<NA><NA><NA>0226340171.00150866서울특별시 영등포구 양평동4가 16-1번지<NA><NA>(주)롯데제과2001-02-05 00:00:00I2018-08-31 23:59:59.0<NA>190452.870314448107.484025<NA>00기타기타<NA>00000<NA>00N0.0<NA><NA><NA>
431800003180000-106-1969-0043619690307<NA>3폐업2폐업20010205<NA><NA><NA>0226340171240.00150866서울특별시 영등포구 양평동4가 16-1번지<NA><NA>(주)롯데제과2001-02-05 00:00:00I2018-08-31 23:59:59.0도시락제조업190452.870314448107.484025도시락제조업00기타기타상수도전용00000<NA>00N0.0<NA><NA><NA>
531800003180000-106-1969-004391969-03-07<NA>1영업/정상1영업<NA><NA><NA><NA>022670665334401.25150-866서울특별시 영등포구 양평동4가 19서울특별시 영등포구 양평로21길 25 (양평동4가)7209롯데웰푸드(주)2023-04-17 15:03:52U2022-12-03 23:09:00.0식품제조가공업190508.589813448196.897432<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
631800003180000-106-1977-0038319771107<NA>3폐업2폐업20070927<NA><NA><NA>0226336695146.35150095서울특별시 영등포구 문래동5가 4-1번지<NA><NA>제니코식품(주)2005-10-12 00:00:00I2018-08-31 23:59:59.0식품제조가공업189986.498936445955.499303식품제조가공업00주택가주변자율상수도전용00000<NA>00N0.0<NA><NA><NA>
731800003180000-106-1987-0040719870501<NA>3폐업2폐업20010316<NA><NA><NA>02 833944352.92150813서울특별시 영등포구 대림동 685-9번지<NA><NA>대호식품2001-03-16 00:00:00I2018-08-31 23:59:59.0<NA>191120.253083444138.163829<NA>00주택가주변기타상수도전용00000<NA>00N0.0<NA><NA><NA>
831800003180000-106-1989-0038919890725<NA>3폐업2폐업19990818<NA><NA><NA>02 6793566114.22150807서울특별시 영등포구 당산동6가 5-0번지<NA><NA>영흥식품2001-08-02 00:00:00I2018-08-31 23:59:59.0<NA>191575.0885447795.243069<NA>00주택가주변기타상수도전용00000<NA>00N0.0<NA><NA><NA>
931800003180000-106-1990-0036919900623<NA>3폐업2폐업19970111<NA><NA><NA>02340.00150103서울특별시 영등포구 양평동3가 16-0번지<NA><NA>(주)두산농산2001-08-02 00:00:00I2018-08-31 23:59:59.0<NA>190026.251961447067.240942<NA>00기타기타상수도전용00000<NA>00N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
53931800003180000-106-2021-0001120210120<NA>1영업/정상1영업<NA><NA><NA><NA><NA>51.41150834서울특별시 영등포구 문래동3가 46 트리플렉스 1508호서울특별시 영등포구 문래북로 116, 트리플렉스 1508호 (문래동3가)7293미켈레커피2022-07-18 17:29:56I2021-12-06 22:00:00.0기타 식품제조가공업190918.714723446361.891182<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54031800003180000-106-2022-000012022-01-28<NA>1영업/정상1영업<NA><NA><NA><NA><NA>57.51150-835서울특별시 영등포구 문래동3가 55-16 영등포 SK 리더스뷰서울특별시 영등포구 문래로 164, 영등포 SK 리더스뷰 3층 E20호 (문래동3가)7297깃든식품2023-12-19 09:53:19U2022-11-01 22:01:00.0기타 식품제조가공업191087.758954446157.437289<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54131800003180000-106-2022-000022022-12-20<NA>1영업/정상1영업<NA><NA><NA><NA><NA>478.00150-103서울특별시 영등포구 양평동3가 23-5 미디어 비즈 센타서울특별시 영등포구 선유서로31길 3, 미디어 비즈 센타 5,6층 (양평동3가)7269(주)이코니크2023-07-12 10:08:18U2022-12-06 23:04:00.0기타 식품제조가공업189841.054007447123.801322<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54231800003180000-106-2023-000012023-03-23<NA>1영업/정상1영업<NA><NA><NA><NA><NA>50.00150-866서울특별시 영등포구 양평동4가 15-5서울특별시 영등포구 선유로43길 32, 1층 (양평동4가)7209(주)서현리테일2023-03-23 17:05:46I2022-12-02 22:05:00.0기타 식품제조가공업190408.447284448064.716163<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54331800003180000-106-2023-000032023-08-04<NA>1영업/정상1영업<NA><NA><NA><NA><NA>37.80150-095서울특별시 영등포구 문래동5가 14 문래 두산위브서울특별시 영등포구 선유로9나길 12, 상가동 지하1층 1,5호 (문래동5가, 문래 두산위브)7285강화발효원2023-08-04 16:54:23I2022-12-08 00:06:00.0기타 식품제조가공업189835.868161446078.046145<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54431800003180000-106-2023-000042023-08-16<NA>1영업/정상1영업<NA><NA><NA><NA><NA>117.60150-095서울특별시 영등포구 문래동5가 9 벽산디지털밸리서울특별시 영등포구 경인로71길 70, 벽산디지털밸리 A-1808호 (문래동5가)7286르비건베이킹랩2023-12-07 19:23:43U2022-11-02 00:09:00.0기타 식품제조가공업189859.071769445875.896723<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54531800003180000-106-2023-000052023-10-16<NA>1영업/정상1영업<NA><NA><NA><NA><NA>42.00150-095서울특별시 영등포구 문래동5가 9 벽산디지털밸리 A동 105호서울특별시 영등포구 경인로71길 70, 벽산디지털밸리 A동 105호 (문래동5가)7286에스디씨코리아(주)2023-10-16 21:01:48I2022-10-30 23:08:00.0기타 식품제조가공업189859.071769445875.896723<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54631800003180000-106-2024-000012024-01-08<NA>1영업/정상1영업<NA><NA><NA><NA>025363310168.00150-095서울특별시 영등포구 문래동5가 23-5 문래동 빅토리 테크노 타워 102호서울특별시 영등포구 선유서로 17, 문래동 빅토리 테크노 타워 1층 102호 (문래동5가)7284주식회사 설봄(SEOLBOM)2024-01-08 21:23:31I2023-11-30 23:00:00.0기타 식품제조가공업189667.639845446140.637416<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54731800003180000-106-2024-000022024-03-22<NA>1영업/정상1영업<NA><NA><NA><NA><NA>97.50150-095서울특별시 영등포구 문래동5가 23-5 문래동 빅토리 테크노 타워 702호서울특별시 영등포구 선유서로 17, 문래동 빅토리 테크노 타워 7층 702호 (문래동5가)7284건인약품식품사업부2024-03-22 13:34:42I2023-12-02 22:04:00.0기타 식품제조가공업189667.639845446140.637416<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54831800003180000-106-2024-000032024-04-11<NA>1영업/정상1영업<NA><NA><NA><NA><NA>22.42150-859서울특별시 영등포구 신길동 4300-23서울특별시 영등포구 대방천로 240-1, 지층 (신길동)7433랜드폴커피2024-04-11 20:01:29I2023-12-03 23:03:00.0기타 식품제조가공업192652.663385444035.593584<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>