Overview

Dataset statistics

Number of variables44
Number of observations590
Missing cells5401
Missing cells (%)20.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory216.2 KiB
Average record size in memory375.2 B

Variable types

Categorical19
Text7
DateTime4
Unsupported7
Numeric6
Boolean1

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,남성종사자수,여성종사자수,영업장주변구분명,등급구분명,급수시설구분명,총인원,본사종업원수,공장사무직종업원수,공장판매직종업원수,공장생산직종업원수,건물소유구분명,보증액,월세액,다중이용업소여부,시설총규모,전통업소지정번호,전통업소주된음식,홈페이지
Author성동구
URLhttps://data.seoul.go.kr/dataList/OA-18255/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (53.6%)Imbalance
남성종사자수 is highly imbalanced (71.3%)Imbalance
여성종사자수 is highly imbalanced (63.7%)Imbalance
영업장주변구분명 is highly imbalanced (56.2%)Imbalance
등급구분명 is highly imbalanced (58.5%)Imbalance
총인원 is highly imbalanced (70.3%)Imbalance
공장사무직종업원수 is highly imbalanced (54.8%)Imbalance
공장판매직종업원수 is highly imbalanced (52.9%)Imbalance
보증액 is highly imbalanced (75.8%)Imbalance
월세액 is highly imbalanced (78.1%)Imbalance
인허가취소일자 has 590 (100.0%) missing valuesMissing
폐업일자 has 93 (15.8%) missing valuesMissing
휴업시작일자 has 590 (100.0%) missing valuesMissing
휴업종료일자 has 590 (100.0%) missing valuesMissing
재개업일자 has 590 (100.0%) missing valuesMissing
전화번호 has 171 (29.0%) missing valuesMissing
소재지면적 has 28 (4.7%) missing valuesMissing
도로명주소 has 218 (36.9%) missing valuesMissing
도로명우편번호 has 225 (38.1%) missing valuesMissing
좌표정보(X) has 15 (2.5%) missing valuesMissing
좌표정보(Y) has 15 (2.5%) missing valuesMissing
본사종업원수 has 183 (31.0%) missing valuesMissing
공장생산직종업원수 has 189 (32.0%) missing valuesMissing
다중이용업소여부 has 67 (11.4%) missing valuesMissing
시설총규모 has 67 (11.4%) missing valuesMissing
전통업소지정번호 has 590 (100.0%) missing valuesMissing
전통업소주된음식 has 590 (100.0%) missing valuesMissing
홈페이지 has 590 (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
본사종업원수 has 388 (65.8%) zerosZeros
공장생산직종업원수 has 384 (65.1%) zerosZeros
시설총규모 has 428 (72.5%) zerosZeros

Reproduction

Analysis started2024-04-06 10:00:31.923125
Analysis finished2024-04-06 10:00:33.391469
Duration1.47 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
3030000
590 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3030000
2nd row3030000
3rd row3030000
4th row3030000
5th row3030000

Common Values

ValueCountFrequency (%)
3030000 590
100.0%

Length

2024-04-06T19:00:33.511837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:33.736549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3030000 590
100.0%

관리번호
Text

UNIQUE 

Distinct590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-04-06T19:00:34.065389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters12980
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

Unique590 ?
Unique (%)100.0%

Sample

1st row3030000-106-1968-00363
2nd row3030000-106-1968-00381
3rd row3030000-106-1972-00680
4th row3030000-106-1973-00364
5th row3030000-106-1974-00379
ValueCountFrequency (%)
3030000-106-1968-00363 1
 
0.2%
3030000-106-2015-00005 1
 
0.2%
3030000-106-2014-00026 1
 
0.2%
3030000-106-2014-00033 1
 
0.2%
3030000-106-2014-00027 1
 
0.2%
3030000-106-2014-00028 1
 
0.2%
3030000-106-2014-00029 1
 
0.2%
3030000-106-2014-00030 1
 
0.2%
3030000-106-2014-00031 1
 
0.2%
3030000-106-2014-00032 1
 
0.2%
Other values (580) 580
98.3%
2024-04-06T19:00:34.633772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6231
48.0%
- 1770
 
13.6%
3 1341
 
10.3%
1 1269
 
9.8%
2 782
 
6.0%
6 743
 
5.7%
9 266
 
2.0%
5 160
 
1.2%
7 144
 
1.1%
8 137
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11210
86.4%
Dash Punctuation 1770
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6231
55.6%
3 1341
 
12.0%
1 1269
 
11.3%
2 782
 
7.0%
6 743
 
6.6%
9 266
 
2.4%
5 160
 
1.4%
7 144
 
1.3%
8 137
 
1.2%
4 137
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 1770
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6231
48.0%
- 1770
 
13.6%
3 1341
 
10.3%
1 1269
 
9.8%
2 782
 
6.0%
6 743
 
5.7%
9 266
 
2.0%
5 160
 
1.2%
7 144
 
1.1%
8 137
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6231
48.0%
- 1770
 
13.6%
3 1341
 
10.3%
1 1269
 
9.8%
2 782
 
6.0%
6 743
 
5.7%
9 266
 
2.0%
5 160
 
1.2%
7 144
 
1.1%
8 137
 
1.1%
Distinct554
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Minimum1968-07-22 00:00:00
Maximum2024-03-27 00:00:00
2024-04-06T19:00:34.919840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:00:35.142943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing590
Missing (%)100.0%
Memory size5.3 KiB
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
3
497 
1
93 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
3 497
84.2%
1 93
 
15.8%

Length

2024-04-06T19:00:35.438579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:35.660017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 497
84.2%
1 93
 
15.8%

영업상태명
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
폐업
497 
영업/정상
93 

Length

Max length5
Median length2
Mean length2.4728814
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row폐업
3rd row폐업
4th row폐업
5th row영업/정상

Common Values

ValueCountFrequency (%)
폐업 497
84.2%
영업/정상 93
 
15.8%

Length

2024-04-06T19:00:35.898382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:36.216525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 497
84.2%
영업/정상 93
 
15.8%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2
497 
1
93 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 497
84.2%
1 93
 
15.8%

Length

2024-04-06T19:00:36.382214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:36.537015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 497
84.2%
1 93
 
15.8%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
폐업
497 
영업
93 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 497
84.2%
영업 93
 
15.8%

Length

2024-04-06T19:00:36.774103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:36.925570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 497
84.2%
영업 93
 
15.8%

폐업일자
Date

MISSING 

Distinct450
Distinct (%)90.5%
Missing93
Missing (%)15.8%
Memory size4.7 KiB
Minimum1997-01-14 00:00:00
Maximum2024-02-29 00:00:00
2024-04-06T19:00:37.108152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:00:37.362276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing590
Missing (%)100.0%
Memory size5.3 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing590
Missing (%)100.0%
Memory size5.3 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing590
Missing (%)100.0%
Memory size5.3 KiB

전화번호
Text

MISSING 

Distinct387
Distinct (%)92.4%
Missing171
Missing (%)29.0%
Memory size4.7 KiB
2024-04-06T19:00:37.848309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.181384
Min length2

Characters and Unicode

Total characters4266
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique376 ?
Unique (%)89.7%

Sample

1st row02 4643753
2nd row02 4636023
3rd row02 4635585
4th row02 4631464
5th row02 4690011
ValueCountFrequency (%)
02 221
31.5%
070 13
 
1.9%
4
 
0.6%
498 4
 
0.6%
4690011 4
 
0.6%
0222948997 3
 
0.4%
464 3
 
0.4%
553 3
 
0.4%
462 3
 
0.4%
466 3
 
0.4%
Other values (423) 441
62.8%
2024-04-06T19:00:38.751066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 808
18.9%
0 754
17.7%
380
8.9%
4 364
8.5%
9 320
 
7.5%
6 315
 
7.4%
5 289
 
6.8%
7 287
 
6.7%
1 283
 
6.6%
8 248
 
5.8%
Other values (2) 218
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3882
91.0%
Space Separator 380
 
8.9%
Other Punctuation 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 808
20.8%
0 754
19.4%
4 364
9.4%
9 320
 
8.2%
6 315
 
8.1%
5 289
 
7.4%
7 287
 
7.4%
1 283
 
7.3%
8 248
 
6.4%
3 214
 
5.5%
Space Separator
ValueCountFrequency (%)
380
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4266
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 808
18.9%
0 754
17.7%
380
8.9%
4 364
8.5%
9 320
 
7.5%
6 315
 
7.4%
5 289
 
6.8%
7 287
 
6.7%
1 283
 
6.6%
8 248
 
5.8%
Other values (2) 218
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 808
18.9%
0 754
17.7%
380
8.9%
4 364
8.5%
9 320
 
7.5%
6 315
 
7.4%
5 289
 
6.8%
7 287
 
6.7%
1 283
 
6.6%
8 248
 
5.8%
Other values (2) 218
 
5.1%

소재지면적
Text

MISSING 

Distinct515
Distinct (%)91.6%
Missing28
Missing (%)4.7%
Memory size4.7 KiB
2024-04-06T19:00:39.506400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.3238434
Min length3

Characters and Unicode

Total characters2992
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

Unique478 ?
Unique (%)85.1%

Sample

1st row534.50
2nd row111.25
3rd row263.48
4th row369.67
5th row201.12
ValueCountFrequency (%)
256.96 4
 
0.7%
90.00 3
 
0.5%
53.62 3
 
0.5%
65.00 3
 
0.5%
140.00 3
 
0.5%
00 3
 
0.5%
35.28 3
 
0.5%
30.00 3
 
0.5%
198.00 3
 
0.5%
378.40 2
 
0.4%
Other values (505) 532
94.7%
2024-04-06T19:00:40.505312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 562
18.8%
0 424
14.2%
1 336
11.2%
2 281
9.4%
5 256
8.6%
6 224
 
7.5%
3 212
 
7.1%
8 195
 
6.5%
4 185
 
6.2%
9 165
 
5.5%
Other values (2) 152
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2427
81.1%
Other Punctuation 565
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 424
17.5%
1 336
13.8%
2 281
11.6%
5 256
10.5%
6 224
9.2%
3 212
8.7%
8 195
8.0%
4 185
7.6%
9 165
 
6.8%
7 149
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 562
99.5%
, 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 562
18.8%
0 424
14.2%
1 336
11.2%
2 281
9.4%
5 256
8.6%
6 224
 
7.5%
3 212
 
7.1%
8 195
 
6.5%
4 185
 
6.2%
9 165
 
5.5%
Other values (2) 152
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 562
18.8%
0 424
14.2%
1 336
11.2%
2 281
9.4%
5 256
8.6%
6 224
 
7.5%
3 212
 
7.1%
8 195
 
6.5%
4 185
 
6.2%
9 165
 
5.5%
Other values (2) 152
 
5.1%
Distinct88
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-04-06T19:00:40.967314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0694915
Min length6

Characters and Unicode

Total characters3581
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

Unique21 ?
Unique (%)3.6%

Sample

1st row133835
2nd row133-832
3rd row133823
4th row133819
5th row133832
ValueCountFrequency (%)
133832 53
 
9.0%
133833 43
 
7.3%
133831 30
 
5.1%
133822 26
 
4.4%
133827 24
 
4.1%
133823 21
 
3.6%
133812 17
 
2.9%
133825 17
 
2.9%
133819 16
 
2.7%
133814 16
 
2.7%
Other values (78) 327
55.4%
2024-04-06T19:00:41.599058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1461
40.8%
1 730
20.4%
8 602
16.8%
2 287
 
8.0%
4 99
 
2.8%
0 97
 
2.7%
5 91
 
2.5%
7 70
 
2.0%
9 58
 
1.6%
6 45
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3540
98.9%
Dash Punctuation 41
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1461
41.3%
1 730
20.6%
8 602
17.0%
2 287
 
8.1%
4 99
 
2.8%
0 97
 
2.7%
5 91
 
2.6%
7 70
 
2.0%
9 58
 
1.6%
6 45
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3581
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1461
40.8%
1 730
20.4%
8 602
16.8%
2 287
 
8.0%
4 99
 
2.8%
0 97
 
2.7%
5 91
 
2.5%
7 70
 
2.0%
9 58
 
1.6%
6 45
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3581
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1461
40.8%
1 730
20.4%
8 602
16.8%
2 287
 
8.0%
4 99
 
2.8%
0 97
 
2.7%
5 91
 
2.5%
7 70
 
2.0%
9 58
 
1.6%
6 45
 
1.3%
Distinct543
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-04-06T19:00:42.005536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length42
Mean length28.172881
Min length19

Characters and Unicode

Total characters16622
Distinct characters219
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

Unique508 ?
Unique (%)86.1%

Sample

1st row서울특별시 성동구 성수동2가 302-4번지
2nd row서울특별시 성동구 성수동2가 280-32
3rd row서울특별시 성동구 성수동1가 656-34번지
4th row서울특별시 성동구 성수동1가 22-8번지
5th row서울특별시 성동구 성수동2가 277-60
ValueCountFrequency (%)
서울특별시 590
20.6%
성동구 590
20.6%
성수동2가 231
 
8.1%
성수동1가 120
 
4.2%
지상1층 60
 
2.1%
마장동 59
 
2.1%
지하1층 34
 
1.2%
행당동 27
 
0.9%
용답동 26
 
0.9%
옥수동 26
 
0.9%
Other values (712) 1103
38.5%
2024-04-06T19:00:42.667038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2770
 
16.7%
1219
 
7.3%
963
 
5.8%
1 735
 
4.4%
2 722
 
4.3%
627
 
3.8%
623
 
3.7%
622
 
3.7%
598
 
3.6%
591
 
3.6%
Other values (209) 7152
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9466
56.9%
Decimal Number 3565
 
21.4%
Space Separator 2770
 
16.7%
Dash Punctuation 514
 
3.1%
Uppercase Letter 116
 
0.7%
Open Punctuation 76
 
0.5%
Close Punctuation 76
 
0.5%
Other Punctuation 26
 
0.2%
Lowercase Letter 8
 
< 0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1219
12.9%
963
 
10.2%
627
 
6.6%
623
 
6.6%
622
 
6.6%
598
 
6.3%
591
 
6.2%
591
 
6.2%
590
 
6.2%
446
 
4.7%
Other values (165) 2596
27.4%
Uppercase Letter
ValueCountFrequency (%)
T 17
14.7%
B 17
14.7%
S 12
10.3%
I 11
9.5%
H 8
 
6.9%
K 7
 
6.0%
A 7
 
6.0%
R 6
 
5.2%
O 5
 
4.3%
F 4
 
3.4%
Other values (10) 22
19.0%
Decimal Number
ValueCountFrequency (%)
1 735
20.6%
2 722
20.3%
3 364
10.2%
6 295
8.3%
7 269
 
7.5%
0 262
 
7.3%
4 257
 
7.2%
5 246
 
6.9%
8 221
 
6.2%
9 194
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
w 2
25.0%
r 2
25.0%
e 2
25.0%
o 2
25.0%
Other Punctuation
ValueCountFrequency (%)
, 23
88.5%
@ 2
 
7.7%
/ 1
 
3.8%
Letter Number
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
Space Separator
ValueCountFrequency (%)
2770
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 514
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9466
56.9%
Common 7028
42.3%
Latin 128
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1219
12.9%
963
 
10.2%
627
 
6.6%
623
 
6.6%
622
 
6.6%
598
 
6.3%
591
 
6.2%
591
 
6.2%
590
 
6.2%
446
 
4.7%
Other values (165) 2596
27.4%
Latin
ValueCountFrequency (%)
T 17
13.3%
B 17
13.3%
S 12
 
9.4%
I 11
 
8.6%
H 8
 
6.2%
K 7
 
5.5%
A 7
 
5.5%
R 6
 
4.7%
O 5
 
3.9%
F 4
 
3.1%
Other values (16) 34
26.6%
Common
ValueCountFrequency (%)
2770
39.4%
1 735
 
10.5%
2 722
 
10.3%
- 514
 
7.3%
3 364
 
5.2%
6 295
 
4.2%
7 269
 
3.8%
0 262
 
3.7%
4 257
 
3.7%
5 246
 
3.5%
Other values (8) 594
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9466
56.9%
ASCII 7152
43.0%
Number Forms 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2770
38.7%
1 735
 
10.3%
2 722
 
10.1%
- 514
 
7.2%
3 364
 
5.1%
6 295
 
4.1%
7 269
 
3.8%
0 262
 
3.7%
4 257
 
3.6%
5 246
 
3.4%
Other values (32) 718
 
10.0%
Hangul
ValueCountFrequency (%)
1219
12.9%
963
 
10.2%
627
 
6.6%
623
 
6.6%
622
 
6.6%
598
 
6.3%
591
 
6.2%
591
 
6.2%
590
 
6.2%
446
 
4.7%
Other values (165) 2596
27.4%
Number Forms
ValueCountFrequency (%)
3
75.0%
1
 
25.0%

도로명주소
Text

MISSING 

Distinct363
Distinct (%)97.6%
Missing218
Missing (%)36.9%
Memory size4.7 KiB
2024-04-06T19:00:43.258609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length55
Mean length39.577957
Min length25

Characters and Unicode

Total characters14723
Distinct characters212
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

Unique354 ?
Unique (%)95.2%

Sample

1st row서울특별시 성동구 광나루로6길 7 (성수동2가, 성수동2가 280-32)
2nd row서울특별시 성동구 뚝섬로1나길 5 (성수동1가, 성수동1가 22-8)
3rd row서울특별시 성동구 아차산로13길 6 (성수동2가, 성수동2가 277-60)
4th row서울특별시 성동구 성수이로16길 21 (성수동2가, 성수동2가 269-223)
5th row서울특별시 성동구 성수이로24길 33, 1-3층 (성수동2가, 277-174)
ValueCountFrequency (%)
서울특별시 372
 
13.8%
성동구 372
 
13.8%
성수동2가 173
 
6.4%
성수동1가 94
 
3.5%
1층 86
 
3.2%
지하1층 50
 
1.9%
2층 39
 
1.4%
마장동 27
 
1.0%
7 22
 
0.8%
성수일로 20
 
0.7%
Other values (679) 1442
53.5%
2024-04-06T19:00:44.077914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2327
 
15.8%
1 887
 
6.0%
802
 
5.4%
748
 
5.1%
, 576
 
3.9%
2 574
 
3.9%
430
 
2.9%
412
 
2.8%
384
 
2.6%
383
 
2.6%
Other values (202) 7200
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7791
52.9%
Decimal Number 2983
 
20.3%
Space Separator 2327
 
15.8%
Other Punctuation 577
 
3.9%
Open Punctuation 376
 
2.6%
Close Punctuation 376
 
2.6%
Dash Punctuation 148
 
1.0%
Uppercase Letter 127
 
0.9%
Lowercase Letter 8
 
0.1%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
802
 
10.3%
748
 
9.6%
430
 
5.5%
412
 
5.3%
384
 
4.9%
383
 
4.9%
374
 
4.8%
372
 
4.8%
372
 
4.8%
344
 
4.4%
Other values (162) 3170
40.7%
Uppercase Letter
ValueCountFrequency (%)
B 75
59.1%
T 10
 
7.9%
I 7
 
5.5%
S 6
 
4.7%
V 5
 
3.9%
K 5
 
3.9%
A 5
 
3.9%
F 2
 
1.6%
R 2
 
1.6%
H 2
 
1.6%
Other values (7) 8
 
6.3%
Decimal Number
ValueCountFrequency (%)
1 887
29.7%
2 574
19.2%
0 315
 
10.6%
3 261
 
8.7%
4 204
 
6.8%
7 185
 
6.2%
5 155
 
5.2%
8 143
 
4.8%
6 139
 
4.7%
9 120
 
4.0%
Lowercase Letter
ValueCountFrequency (%)
r 2
25.0%
e 2
25.0%
w 2
25.0%
o 2
25.0%
Other Punctuation
ValueCountFrequency (%)
, 576
99.8%
? 1
 
0.2%
Letter Number
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
2327
100.0%
Open Punctuation
ValueCountFrequency (%)
( 376
100.0%
Close Punctuation
ValueCountFrequency (%)
) 376
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7791
52.9%
Common 6794
46.1%
Latin 138
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
802
 
10.3%
748
 
9.6%
430
 
5.5%
412
 
5.3%
384
 
4.9%
383
 
4.9%
374
 
4.8%
372
 
4.8%
372
 
4.8%
344
 
4.4%
Other values (162) 3170
40.7%
Latin
ValueCountFrequency (%)
B 75
54.3%
T 10
 
7.2%
I 7
 
5.1%
S 6
 
4.3%
V 5
 
3.6%
K 5
 
3.6%
A 5
 
3.6%
F 2
 
1.4%
2
 
1.4%
r 2
 
1.4%
Other values (13) 19
 
13.8%
Common
ValueCountFrequency (%)
2327
34.3%
1 887
 
13.1%
, 576
 
8.5%
2 574
 
8.4%
( 376
 
5.5%
) 376
 
5.5%
0 315
 
4.6%
3 261
 
3.8%
4 204
 
3.0%
7 185
 
2.7%
Other values (7) 713
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7791
52.9%
ASCII 6929
47.1%
Number Forms 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2327
33.6%
1 887
 
12.8%
, 576
 
8.3%
2 574
 
8.3%
( 376
 
5.4%
) 376
 
5.4%
0 315
 
4.5%
3 261
 
3.8%
4 204
 
2.9%
7 185
 
2.7%
Other values (28) 848
 
12.2%
Hangul
ValueCountFrequency (%)
802
 
10.3%
748
 
9.6%
430
 
5.5%
412
 
5.3%
384
 
4.9%
383
 
4.9%
374
 
4.8%
372
 
4.8%
372
 
4.8%
344
 
4.4%
Other values (162) 3170
40.7%
Number Forms
ValueCountFrequency (%)
2
66.7%
1
33.3%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct75
Distinct (%)20.5%
Missing225
Missing (%)38.1%
Infinite0
Infinite (%)0.0%
Mean4772.5178
Minimum4700
Maximum4808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-06T19:00:44.322422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4700
5-th percentile4707
Q14766
median4782
Q34793
95-th percentile4799
Maximum4808
Range108
Interquartile range (IQR)27

Descriptive statistics

Standard deviation28.980153
Coefficient of variation (CV)0.0060722986
Kurtosis0.29487499
Mean4772.5178
Median Absolute Deviation (MAD)11
Skewness-1.2477427
Sum1741969
Variance839.84927
MonotonicityNot monotonic
2024-04-06T19:00:44.593230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4799 26
 
4.4%
4793 21
 
3.6%
4778 20
 
3.4%
4788 17
 
2.9%
4781 15
 
2.5%
4792 14
 
2.4%
4783 14
 
2.4%
4790 13
 
2.2%
4782 13
 
2.2%
4796 12
 
2.0%
Other values (65) 200
33.9%
(Missing) 225
38.1%
ValueCountFrequency (%)
4700 2
 
0.3%
4701 2
 
0.3%
4703 2
 
0.3%
4704 10
1.7%
4705 2
 
0.3%
4707 3
 
0.5%
4708 3
 
0.5%
4709 1
 
0.2%
4710 1
 
0.2%
4713 1
 
0.2%
ValueCountFrequency (%)
4808 4
 
0.7%
4805 1
 
0.2%
4804 4
 
0.7%
4803 1
 
0.2%
4800 4
 
0.7%
4799 26
4.4%
4798 8
 
1.4%
4797 5
 
0.8%
4796 12
2.0%
4795 4
 
0.7%
Distinct568
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-04-06T19:00:44.922889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32
Mean length7.1661017
Min length1

Characters and Unicode

Total characters4228
Distinct characters533
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

Unique547 ?
Unique (%)92.7%

Sample

1st row광신냉동
2nd row경남두부
3rd row동아물산
4th row서울산업사
5th row대성식품
ValueCountFrequency (%)
주식회사 37
 
5.2%
coffee 4
 
0.6%
한양식품 3
 
0.4%
성수공장 3
 
0.4%
커피 3
 
0.4%
유한회사 3
 
0.4%
팩토리 3
 
0.4%
주)신세계푸드 3
 
0.4%
성수 3
 
0.4%
주)타르틴코리아 2
 
0.3%
Other values (627) 653
91.1%
2024-04-06T19:00:45.648649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
178
 
4.2%
) 178
 
4.2%
( 178
 
4.2%
161
 
3.8%
127
 
3.0%
112
 
2.6%
105
 
2.5%
105
 
2.5%
76
 
1.8%
71
 
1.7%
Other values (523) 2937
69.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3328
78.7%
Uppercase Letter 222
 
5.3%
Close Punctuation 178
 
4.2%
Open Punctuation 178
 
4.2%
Lowercase Letter 163
 
3.9%
Space Separator 127
 
3.0%
Decimal Number 19
 
0.4%
Other Punctuation 11
 
0.3%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
178
 
5.3%
161
 
4.8%
112
 
3.4%
105
 
3.2%
105
 
3.2%
76
 
2.3%
71
 
2.1%
69
 
2.1%
58
 
1.7%
55
 
1.7%
Other values (459) 2338
70.3%
Uppercase Letter
ValueCountFrequency (%)
E 25
11.3%
O 25
11.3%
C 21
 
9.5%
A 19
 
8.6%
F 19
 
8.6%
B 12
 
5.4%
S 12
 
5.4%
R 12
 
5.4%
T 11
 
5.0%
M 10
 
4.5%
Other values (15) 56
25.2%
Lowercase Letter
ValueCountFrequency (%)
e 27
16.6%
o 23
14.1%
n 13
 
8.0%
a 13
 
8.0%
s 9
 
5.5%
r 8
 
4.9%
i 8
 
4.9%
l 7
 
4.3%
p 7
 
4.3%
c 6
 
3.7%
Other values (12) 42
25.8%
Decimal Number
ValueCountFrequency (%)
2 4
21.1%
1 4
21.1%
3 3
15.8%
8 2
10.5%
6 2
10.5%
5 1
 
5.3%
9 1
 
5.3%
7 1
 
5.3%
0 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
? 3
27.3%
& 3
27.3%
' 3
27.3%
. 2
18.2%
Close Punctuation
ValueCountFrequency (%)
) 178
100.0%
Open Punctuation
ValueCountFrequency (%)
( 178
100.0%
Space Separator
ValueCountFrequency (%)
127
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3328
78.7%
Common 515
 
12.2%
Latin 385
 
9.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
178
 
5.3%
161
 
4.8%
112
 
3.4%
105
 
3.2%
105
 
3.2%
76
 
2.3%
71
 
2.1%
69
 
2.1%
58
 
1.7%
55
 
1.7%
Other values (459) 2338
70.3%
Latin
ValueCountFrequency (%)
e 27
 
7.0%
E 25
 
6.5%
O 25
 
6.5%
o 23
 
6.0%
C 21
 
5.5%
A 19
 
4.9%
F 19
 
4.9%
n 13
 
3.4%
a 13
 
3.4%
B 12
 
3.1%
Other values (37) 188
48.8%
Common
ValueCountFrequency (%)
) 178
34.6%
( 178
34.6%
127
24.7%
2 4
 
0.8%
1 4
 
0.8%
3 3
 
0.6%
? 3
 
0.6%
& 3
 
0.6%
' 3
 
0.6%
. 2
 
0.4%
Other values (7) 10
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3328
78.7%
ASCII 900
 
21.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
178
 
5.3%
161
 
4.8%
112
 
3.4%
105
 
3.2%
105
 
3.2%
76
 
2.3%
71
 
2.1%
69
 
2.1%
58
 
1.7%
55
 
1.7%
Other values (459) 2338
70.3%
ASCII
ValueCountFrequency (%)
) 178
19.8%
( 178
19.8%
127
14.1%
e 27
 
3.0%
E 25
 
2.8%
O 25
 
2.8%
o 23
 
2.6%
C 21
 
2.3%
A 19
 
2.1%
F 19
 
2.1%
Other values (54) 258
28.7%
Distinct513
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Minimum1999-10-16 00:00:00
Maximum2024-03-27 14:52:36
2024-04-06T19:00:45.883877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:00:46.093925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
I
412 
U
178 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 412
69.8%
U 178
30.2%

Length

2024-04-06T19:00:46.326696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:46.473201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 412
69.8%
u 178
30.2%
Distinct186
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-03 00:02:00
2024-04-06T19:00:46.630502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:00:47.319153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
식품제조가공업
411 
기타 식품제조가공업
176 
도시락제조업
 
2
PB제품 제조업체
 
1

Length

Max length10
Median length7
Mean length7.8949153
Min length6

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 411
69.7%
기타 식품제조가공업 176
29.8%
도시락제조업 2
 
0.3%
PB제품 제조업체 1
 
0.2%

Length

2024-04-06T19:00:47.738031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:47.966037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 587
76.5%
기타 176
 
22.9%
도시락제조업 2
 
0.3%
pb제품 1
 
0.1%
제조업체 1
 
0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct407
Distinct (%)70.8%
Missing15
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean204057.15
Minimum200812.99
Maximum206344.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-06T19:00:48.227056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200812.99
5-th percentile201632.44
Q1203307.99
median204323.65
Q3204895.64
95-th percentile205582.77
Maximum206344.9
Range5531.9059
Interquartile range (IQR)1587.656

Descriptive statistics

Standard deviation1206.2902
Coefficient of variation (CV)0.0059115313
Kurtosis-0.055281646
Mean204057.15
Median Absolute Deviation (MAD)751.11384
Skewness-0.73665933
Sum1.1733286 × 108
Variance1455136.1
MonotonicityNot monotonic
2024-04-06T19:00:48.514534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204461.776750011 13
 
2.2%
205214.696074247 10
 
1.7%
203887.185047227 8
 
1.4%
205557.941680639 7
 
1.2%
205422.77256038 5
 
0.8%
205058.868582609 4
 
0.7%
204730.714005417 4
 
0.7%
204025.755687334 4
 
0.7%
200951.206580662 4
 
0.7%
204304.435352975 4
 
0.7%
Other values (397) 512
86.8%
(Missing) 15
 
2.5%
ValueCountFrequency (%)
200812.992681398 3
0.5%
200951.206580662 4
0.7%
201006.532910436 1
 
0.2%
201018.896469335 1
 
0.2%
201057.801236 1
 
0.2%
201058.18634 1
 
0.2%
201061.249831 1
 
0.2%
201132.08093176 1
 
0.2%
201148.037914 1
 
0.2%
201153.981577199 1
 
0.2%
ValueCountFrequency (%)
206344.898613387 2
0.3%
206302.321886409 2
0.3%
206235.328202061 1
0.2%
206231.067970479 1
0.2%
206209.280864162 1
0.2%
205979.344645747 1
0.2%
205939.681535139 1
0.2%
205889.340997345 1
0.2%
205879.81215195 1
0.2%
205834.097987543 1
0.2%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct407
Distinct (%)70.8%
Missing15
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean449800.07
Minimum448121.69
Maximum452130.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-06T19:00:48.787540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum448121.69
5-th percentile448522.59
Q1449024.14
median449511.92
Q3450463.12
95-th percentile451856.08
Maximum452130.26
Range4008.5691
Interquartile range (IQR)1438.9829

Descriptive statistics

Standard deviation1044.3695
Coefficient of variation (CV)0.0023218526
Kurtosis-0.4867474
Mean449800.07
Median Absolute Deviation (MAD)524.70998
Skewness0.8149842
Sum2.5863504 × 108
Variance1090707.6
MonotonicityNot monotonic
2024-04-06T19:00:49.188078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
449524.558350945 13
 
2.2%
449326.791808157 10
 
1.7%
449860.057407228 8
 
1.4%
449473.46380541 7
 
1.2%
449084.126946131 5
 
0.8%
448944.318340747 4
 
0.7%
449630.289700434 4
 
0.7%
449724.395161694 4
 
0.7%
449000.920061846 4
 
0.7%
449241.757151863 4
 
0.7%
Other values (397) 512
86.8%
(Missing) 15
 
2.5%
ValueCountFrequency (%)
448121.687172695 1
0.2%
448122.666937252 1
0.2%
448169.308636228 1
0.2%
448175.694252303 1
0.2%
448179.869576203 1
0.2%
448228.610508055 1
0.2%
448236.227470207 1
0.2%
448238.821371375 1
0.2%
448241.50093862 1
0.2%
448257.988722667 1
0.2%
ValueCountFrequency (%)
452130.256229522 1
0.2%
452118.573751193 1
0.2%
452116.187204484 1
0.2%
452105.00310132 1
0.2%
452098.622697627 1
0.2%
452076.36180744 1
0.2%
452063.440192474 1
0.2%
452057.98801601 1
0.2%
452050.70248922 2
0.3%
452034.635580112 1
0.2%

위생업태명
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
식품제조가공업
398 
기타 식품제조가공업
122 
<NA>
67 
도시락제조업
 
2
PB제품 제조업체
 
1

Length

Max length10
Median length7
Mean length7.279661
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 398
67.5%
기타 식품제조가공업 122
 
20.7%
<NA> 67
 
11.4%
도시락제조업 2
 
0.3%
PB제품 제조업체 1
 
0.2%

Length

2024-04-06T19:00:49.516338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:49.719281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 520
72.9%
기타 122
 
17.1%
na 67
 
9.4%
도시락제조업 2
 
0.3%
pb제품 1
 
0.1%
제조업체 1
 
0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
<NA>
502 
0
74 
1
 
6
2
 
5
3
 
2

Length

Max length4
Median length4
Mean length3.5542373
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 502
85.1%
0 74
 
12.5%
1 6
 
1.0%
2 5
 
0.8%
3 2
 
0.3%
10 1
 
0.2%

Length

2024-04-06T19:00:49.926568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:50.120383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 502
85.1%
0 74
 
12.5%
1 6
 
1.0%
2 5
 
0.8%
3 2
 
0.3%
10 1
 
0.2%

여성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
<NA>
502 
0
74 
1
 
8
2
 
6

Length

Max length4
Median length4
Mean length3.5525424
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 502
85.1%
0 74
 
12.5%
1 8
 
1.4%
2 6
 
1.0%

Length

2024-04-06T19:00:50.314627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:50.511920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 502
85.1%
0 74
 
12.5%
1 8
 
1.4%
2 6
 
1.0%

영업장주변구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
<NA>
487 
기타
55 
주택가주변
 
44
아파트지역
 
4

Length

Max length5
Median length4
Mean length3.8949153
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 487
82.5%
기타 55
 
9.3%
주택가주변 44
 
7.5%
아파트지역 4
 
0.7%

Length

2024-04-06T19:00:50.717404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:50.924265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 487
82.5%
기타 55
 
9.3%
주택가주변 44
 
7.5%
아파트지역 4
 
0.7%

등급구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
<NA>
487 
기타
73 
자율
 
29
 
1

Length

Max length4
Median length4
Mean length3.6491525
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 487
82.5%
기타 73
 
12.4%
자율 29
 
4.9%
1
 
0.2%

Length

2024-04-06T19:00:51.121227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:51.329125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 487
82.5%
기타 73
 
12.4%
자율 29
 
4.9%
1
 
0.2%
Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
<NA>
344 
상수도전용
245 
지하수전용
 
1

Length

Max length5
Median length4
Mean length4.4169492
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 344
58.3%
상수도전용 245
41.5%
지하수전용 1
 
0.2%

Length

2024-04-06T19:00:51.542034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:51.750906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 344
58.3%
상수도전용 245
41.5%
지하수전용 1
 
0.2%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
<NA>
559 
0
 
31

Length

Max length4
Median length4
Mean length3.8423729
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> 559
94.7%
0 31
 
5.3%

Length

2024-04-06T19:00:51.964959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:52.151469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 559
94.7%
0 31
 
5.3%

본사종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)2.0%
Missing183
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean0.15970516
Minimum0
Maximum15
Zeros388
Zeros (%)65.8%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-06T19:00:52.291364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0925398
Coefficient of variation (CV)6.8409801
Kurtosis131.59904
Mean0.15970516
Median Absolute Deviation (MAD)0
Skewness10.760185
Sum65
Variance1.1936433
MonotonicityNot monotonic
2024-04-06T19:00:52.481780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 388
65.8%
2 7
 
1.2%
1 5
 
0.8%
3 3
 
0.5%
5 1
 
0.2%
4 1
 
0.2%
13 1
 
0.2%
15 1
 
0.2%
(Missing) 183
31.0%
ValueCountFrequency (%)
0 388
65.8%
1 5
 
0.8%
2 7
 
1.2%
3 3
 
0.5%
4 1
 
0.2%
5 1
 
0.2%
13 1
 
0.2%
15 1
 
0.2%
ValueCountFrequency (%)
15 1
 
0.2%
13 1
 
0.2%
5 1
 
0.2%
4 1
 
0.2%
3 3
 
0.5%
2 7
 
1.2%
1 5
 
0.8%
0 388
65.8%

공장사무직종업원수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
0
389 
<NA>
190 
1
 
8
2
 
2
3
 
1

Length

Max length4
Median length1
Mean length1.9661017
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 389
65.9%
<NA> 190
32.2%
1 8
 
1.4%
2 2
 
0.3%
3 1
 
0.2%

Length

2024-04-06T19:00:52.701088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:52.875445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 389
65.9%
na 190
32.2%
1 8
 
1.4%
2 2
 
0.3%
3 1
 
0.2%

공장판매직종업원수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
0
397 
<NA>
191 
2
 
1
1
 
1

Length

Max length4
Median length1
Mean length1.9711864
Min length1

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 397
67.3%
<NA> 191
32.4%
2 1
 
0.2%
1 1
 
0.2%

Length

2024-04-06T19:00:53.072986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:53.253085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 397
67.3%
na 191
32.4%
2 1
 
0.2%
1 1
 
0.2%

공장생산직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)2.0%
Missing189
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean0.18703242
Minimum0
Maximum18
Zeros384
Zeros (%)65.1%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-06T19:00:53.419959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3499746
Coefficient of variation (CV)7.2178642
Kurtosis136.15818
Mean0.18703242
Median Absolute Deviation (MAD)0
Skewness11.04116
Sum75
Variance1.8224314
MonotonicityNot monotonic
2024-04-06T19:00:53.588875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 384
65.1%
2 5
 
0.8%
4 4
 
0.7%
1 3
 
0.5%
3 2
 
0.3%
18 1
 
0.2%
5 1
 
0.2%
17 1
 
0.2%
(Missing) 189
32.0%
ValueCountFrequency (%)
0 384
65.1%
1 3
 
0.5%
2 5
 
0.8%
3 2
 
0.3%
4 4
 
0.7%
5 1
 
0.2%
17 1
 
0.2%
18 1
 
0.2%
ValueCountFrequency (%)
18 1
 
0.2%
17 1
 
0.2%
5 1
 
0.2%
4 4
 
0.7%
3 2
 
0.3%
2 5
 
0.8%
1 3
 
0.5%
0 384
65.1%
Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
<NA>
288 
임대
160 
자가
142 

Length

Max length4
Median length2
Mean length2.9762712
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> 288
48.8%
임대 160
27.1%
자가 142
24.1%

Length

2024-04-06T19:00:53.827569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:54.106651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 288
48.8%
임대 160
27.1%
자가 142
24.1%

보증액
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
<NA>
522 
0
64 
15000000
 
2
134500000
 
1
80000000
 
1

Length

Max length9
Median length4
Mean length3.7033898
Min length1

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 522
88.5%
0 64
 
10.8%
15000000 2
 
0.3%
134500000 1
 
0.2%
80000000 1
 
0.2%

Length

2024-04-06T19:00:54.321176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:54.492375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 522
88.5%
0 64
 
10.8%
15000000 2
 
0.3%
134500000 1
 
0.2%
80000000 1
 
0.2%

월세액
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
<NA>
522 
0
64 
10222000
 
1
4000000
 
1
1800000
 
1

Length

Max length8
Median length4
Mean length3.6966102
Min length1

Unique

Unique4 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 522
88.5%
0 64
 
10.8%
10222000 1
 
0.2%
4000000 1
 
0.2%
1800000 1
 
0.2%
1000000 1
 
0.2%

Length

2024-04-06T19:00:54.694006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:00:54.893590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 522
88.5%
0 64
 
10.8%
10222000 1
 
0.2%
4000000 1
 
0.2%
1800000 1
 
0.2%
1000000 1
 
0.2%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing67
Missing (%)11.4%
Memory size1.3 KiB
False
523 
(Missing)
67 
ValueCountFrequency (%)
False 523
88.6%
(Missing) 67
 
11.4%
2024-04-06T19:00:55.041368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  ZEROS 

Distinct90
Distinct (%)17.2%
Missing67
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean6.5186042
Minimum0
Maximum450
Zeros428
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-04-06T19:00:55.234258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23.985
Maximum450
Range450
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32.17859
Coefficient of variation (CV)4.9364233
Kurtosis101.94919
Mean6.5186042
Median Absolute Deviation (MAD)0
Skewness9.2880678
Sum3409.23
Variance1035.4616
MonotonicityNot monotonic
2024-04-06T19:00:55.470863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 428
72.5%
20.03 2
 
0.3%
12.0 2
 
0.3%
8.07 2
 
0.3%
252.78 2
 
0.3%
33.92 2
 
0.3%
8.0 2
 
0.3%
15.7 1
 
0.2%
0.82 1
 
0.2%
2.7 1
 
0.2%
Other values (80) 80
 
13.6%
(Missing) 67
 
11.4%
ValueCountFrequency (%)
0.0 428
72.5%
0.82 1
 
0.2%
1.2 1
 
0.2%
2.05 1
 
0.2%
2.7 1
 
0.2%
2.98 1
 
0.2%
3.42 1
 
0.2%
3.43 1
 
0.2%
3.84 1
 
0.2%
3.89 1
 
0.2%
ValueCountFrequency (%)
450.0 1
0.2%
325.77 1
0.2%
252.78 2
0.3%
194.84 1
0.2%
116.69 1
0.2%
92.61 1
0.2%
90.48 1
0.2%
90.0 1
0.2%
81.8 1
0.2%
75.88 1
0.2%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing590
Missing (%)100.0%
Memory size5.3 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing590
Missing (%)100.0%
Memory size5.3 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing590
Missing (%)100.0%
Memory size5.3 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
030300003030000-106-1968-0036319680722<NA>3폐업2폐업19980107<NA><NA><NA>02 4643753534.50133835서울특별시 성동구 성수동2가 302-4번지<NA><NA>광신냉동2001-09-25 00:00:00I2018-08-31 23:59:59.0식품제조가공업204591.630913449217.592025식품제조가공업<NA><NA>기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N22.5<NA><NA><NA>
130300003030000-106-1968-003811968-12-09<NA>3폐업2폐업2023-06-12<NA><NA><NA>02 4636023111.25133-832서울특별시 성동구 성수동2가 280-32서울특별시 성동구 광나루로6길 7 (성수동2가, 성수동2가 280-32)4796경남두부2023-06-12 11:54:01U2022-12-05 23:04:00.0식품제조가공업205276.22205449509.682081<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
230300003030000-106-1972-0068019720510<NA>3폐업2폐업20000608<NA><NA><NA>02 4635585263.48133823서울특별시 성동구 성수동1가 656-34번지<NA><NA>동아물산2001-10-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업00기타기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
330300003030000-106-1973-0036419731231<NA>3폐업2폐업20120618<NA><NA><NA>02 4631464369.67133819서울특별시 성동구 성수동1가 22-8번지서울특별시 성동구 뚝섬로1나길 5 (성수동1가, 성수동1가 22-8)4779서울산업사2012-02-08 10:49:34I2018-08-31 23:59:59.0식품제조가공업204151.656964449132.442019식품제조가공업<NA><NA>기타기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
430300003030000-106-1974-0037919740301<NA>1영업/정상1영업<NA><NA><NA><NA>02 4690011201.12133832서울특별시 성동구 성수동2가 277-60서울특별시 성동구 아차산로13길 6 (성수동2가, 성수동2가 277-60)4798대성식품2022-06-29 16:35:02U2021-12-07 00:01:00.0식품제조가공업205305.922412449046.689334<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
530300003030000-106-1974-0038019740301<NA>3폐업2폐업20010402<NA><NA><NA>022298084265.00133070서울특별시 성동구 행당동 37-62번지 ,98<NA><NA>한양식품2001-10-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업203552.060705450483.446406식품제조가공업<NA><NA>주택가주변자율상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
630300003030000-106-1979-0035119790426<NA>3폐업2폐업20040601<NA><NA><NA>02 4621089253.09133826서울특별시 성동구 성수동2가 261번지<NA><NA>신진식품2001-10-29 00:00:00I2018-08-31 23:59:59.0식품제조가공업205118.769912448435.996212식품제조가공업<NA><NA>기타기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
730300003030000-106-1980-0000819801011<NA>3폐업2폐업20091007<NA><NA><NA>022292515131,600.00133010서울특별시 성동구 상왕십리동 106번지 ,108 (지상1~2층)<NA><NA>건영글로벌케이2009-03-13 16:46:14I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업00기타기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
830300003030000-106-1982-000101982-03-04<NA>1영업/정상1영업<NA><NA><NA><NA>02 4646292198.00133-826서울특별시 성동구 성수동2가 269-223서울특별시 성동구 성수이로16길 21 (성수동2가, 성수동2가 269-223)4785행성식품2023-10-20 11:24:15U2022-10-30 22:02:00.0식품제조가공업205142.248196448706.514424<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
930300003030000-106-1982-0035019820705<NA>3폐업2폐업19980309<NA><NA><NA>02 2524496213.22133803서울특별시 성동구 금호동2가 534-0번지<NA><NA>상용식품2001-09-25 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업<NA><NA>주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N7.9<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
58030300003030000-106-2022-0001320220926<NA>1영업/정상1영업<NA><NA><NA><NA>02 518 6437124.84133832서울특별시 성동구 성수동2가 279-33서울특별시 성동구 성수이로26길 51-1, 3층 (성수동2가)4799애플오브디아이2022-09-26 13:40:21I2021-12-08 22:08:00.0기타 식품제조가공업205520.040517449188.424315<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
58130300003030000-106-2022-000142022-11-08<NA>1영업/정상1영업<NA><NA><NA><NA><NA>77.14133-822서울특별시 성동구 성수동1가 13-17서울특별시 성동구 성수일로 111, 지2층 B208호 (성수동1가)4791(주)베디션성수2023-07-04 12:28:37U2022-12-07 00:06:00.0기타 식품제조가공업204485.075569449779.828708<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
58230300003030000-106-2022-000152022-11-15<NA>1영업/정상1영업<NA><NA><NA><NA>02 563 4004417.45133-832서울특별시 성동구 성수동2가 280-13 삼환디지털벤처타워서울특별시 성동구 아차산로15길 52, 삼환디지털벤처타워 6층 603호 (성수동2가)4799원바이트에프엔비2024-03-15 10:27:32U2023-12-02 23:07:00.0기타 식품제조가공업205614.244467449436.909336<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
58330300003030000-106-2023-000012023-01-30<NA>1영업/정상1영업<NA><NA><NA><NA><NA>1023.18133-823서울특별시 성동구 성수동1가 656-526 성수동 도시형공장서울특별시 성동구 아차산로1길 19, 성수동 도시형공장 지1층~3층 (성수동1가)4788런던베이글뮤지엄 성수2024-02-14 16:37:23U2023-12-01 23:06:00.0기타 식품제조가공업204025.755687449724.395162<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
58430300003030000-106-2023-000022023-02-27<NA>1영업/정상1영업<NA><NA><NA><NA><NA>256.96133-832서울특별시 성동구 성수동2가 277-175 이레타워서울특별시 성동구 아차산로9길 21, 이레타워 3층 301호 (성수동2가)4797플래닛가야2023-02-27 11:02:46I2022-12-03 00:01:00.0기타 식품제조가공업205086.097993449314.492632<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
58530300003030000-106-2023-000042023-06-16<NA>1영업/정상1영업<NA><NA><NA><NA>02 541 0854252.02133-832서울특별시 성동구 성수동2가 280-13 삼환디지털벤처타워서울특별시 성동구 아차산로15길 52, 삼환디지털벤처타워 2층 204호 (성수동2가)4799더블베이크하우스2023-06-16 15:56:42I2022-12-05 23:08:00.0기타 식품제조가공업205614.244467449436.909336<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
58630300003030000-106-2023-000052023-10-10<NA>1영업/정상1영업<NA><NA><NA><NA><NA>256.96133-832서울특별시 성동구 성수동2가 277-175 이레타워서울특별시 성동구 아차산로9길 21, 이레타워 4층 401호 (성수동2가)4797홈즈 푸드 팩토리2023-10-11 10:35:40I2022-10-30 23:03:00.0기타 식품제조가공업205086.097993449314.492632<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
58730300003030000-106-2023-000062023-11-09<NA>1영업/정상1영업<NA><NA><NA><NA><NA>53.62133-832서울특별시 성동구 성수동2가 280-15 에이스성수타워1서울특별시 성동구 광나루로8길 10, 에이스성수타워1 1층 104호 (성수동2가)4799주식회사 베통(BETON)2023-11-09 10:53:08I2022-10-31 23:01:00.0기타 식품제조가공업205557.941681449473.463805<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
58830300003030000-106-2024-000012024-03-27<NA>1영업/정상1영업<NA><NA><NA><NA><NA>57.70133-823서울특별시 성동구 성수동1가 656-439 서울숲비즈포레서울특별시 성동구 왕십리로10길 6, 서울숲비즈포레 지2층 B201호 (성수동1가)4778유어네이키드치즈2024-03-27 13:56:11I2023-12-02 22:09:00.0기타 식품제조가공업203932.128445449386.913262<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
58930300003030000-106-2024-000022024-03-27<NA>1영업/정상1영업<NA><NA><NA><NA>02 541 4625112.21133-805서울특별시 성동구 금호동3가 622-2서울특별시 성동구 무수막길 45, 지1층 (금호동3가)4729웅달식품2024-03-27 14:52:36I2023-12-02 22:09:00.0기타 식품제조가공업201963.961382449840.12973<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>