Overview

Dataset statistics

Number of variables44
Number of observations48
Missing cells481
Missing cells (%)22.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.7 KiB
Average record size in memory377.8 B

Variable types

Numeric5
Text7
DateTime3
Unsupported7
Categorical21
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
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-16074/S/1/datasetView.do

Alerts

업태구분명 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
여성종사자수 is highly imbalanced (59.4%)Imbalance
총인원 is highly imbalanced (75.0%)Imbalance
보증액 is highly imbalanced (75.0%)Imbalance
월세액 is highly imbalanced (75.0%)Imbalance
인허가취소일자 has 48 (100.0%) missing valuesMissing
폐업일자 has 11 (22.9%) missing valuesMissing
휴업시작일자 has 48 (100.0%) missing valuesMissing
휴업종료일자 has 48 (100.0%) missing valuesMissing
재개업일자 has 48 (100.0%) missing valuesMissing
전화번호 has 11 (22.9%) missing valuesMissing
소재지면적 has 9 (18.8%) missing valuesMissing
도로명주소 has 22 (45.8%) missing valuesMissing
도로명우편번호 has 23 (47.9%) missing valuesMissing
좌표정보(X) has 10 (20.8%) missing valuesMissing
좌표정보(Y) has 10 (20.8%) missing valuesMissing
남성종사자수 has 39 (81.2%) missing valuesMissing
다중이용업소여부 has 10 (20.8%) missing valuesMissing
전통업소지정번호 has 48 (100.0%) missing valuesMissing
전통업소주된음식 has 48 (100.0%) missing valuesMissing
홈페이지 has 48 (100.0%) missing valuesMissing
관리번호 has unique valuesUnique
최종수정일자 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 2 (4.2%) zerosZeros

Reproduction

Analysis started2024-04-06 10:40:13.697464
Analysis finished2024-04-06 10:40:14.609547
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Real number (ℝ)

Distinct14
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3165000
Minimum3030000
Maximum3230000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-04-06T19:40:14.701589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3030000
5-th percentile3050000
Q13150000
median3165000
Q33230000
95-th percentile3230000
Maximum3230000
Range200000
Interquartile range (IQR)80000

Descriptive statistics

Standard deviation61609.615
Coefficient of variation (CV)0.019465913
Kurtosis-0.17161885
Mean3165000
Median Absolute Deviation (MAD)45000
Skewness-0.82596056
Sum1.5192 × 108
Variance3.7957447 × 109
MonotonicityNot monotonic
2024-04-06T19:40:14.963367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3230000 13
27.1%
3150000 11
22.9%
3050000 5
 
10.4%
3190000 3
 
6.2%
3210000 3
 
6.2%
3030000 2
 
4.2%
3120000 2
 
4.2%
3160000 2
 
4.2%
3170000 2
 
4.2%
3130000 1
 
2.1%
Other values (4) 4
 
8.3%
ValueCountFrequency (%)
3030000 2
 
4.2%
3050000 5
10.4%
3120000 2
 
4.2%
3130000 1
 
2.1%
3140000 1
 
2.1%
3150000 11
22.9%
3160000 2
 
4.2%
3170000 2
 
4.2%
3180000 1
 
2.1%
3190000 3
 
6.2%
ValueCountFrequency (%)
3230000 13
27.1%
3220000 1
 
2.1%
3210000 3
 
6.2%
3200000 1
 
2.1%
3190000 3
 
6.2%
3180000 1
 
2.1%
3170000 2
 
4.2%
3160000 2
 
4.2%
3150000 11
22.9%
3140000 1
 
2.1%

관리번호
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-06T19:40:15.337823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1056
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

Unique48 ?
Unique (%)100.0%

Sample

1st row3150000-116-2008-00002
2nd row3150000-116-1995-00005
3rd row3230000-116-2020-00002
4th row3030000-116-1992-00049
5th row3030000-116-1979-00048
ValueCountFrequency (%)
3150000-116-2008-00002 1
 
2.1%
3150000-116-1995-00005 1
 
2.1%
3230000-116-1997-00238 1
 
2.1%
3170000-116-2001-00278 1
 
2.1%
3180000-116-1981-00449 1
 
2.1%
3190000-116-1995-00031 1
 
2.1%
3190000-116-2003-00001 1
 
2.1%
3190000-116-1979-00383 1
 
2.1%
3200000-116-2003-00001 1
 
2.1%
3210000-116-2003-00001 1
 
2.1%
Other values (38) 38
79.2%
2024-04-06T19:40:15.907859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 422
40.0%
1 179
17.0%
- 144
 
13.6%
2 81
 
7.7%
3 75
 
7.1%
6 52
 
4.9%
9 40
 
3.8%
5 28
 
2.7%
7 15
 
1.4%
8 13
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 912
86.4%
Dash Punctuation 144
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 422
46.3%
1 179
19.6%
2 81
 
8.9%
3 75
 
8.2%
6 52
 
5.7%
9 40
 
4.4%
5 28
 
3.1%
7 15
 
1.6%
8 13
 
1.4%
4 7
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1056
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 422
40.0%
1 179
17.0%
- 144
 
13.6%
2 81
 
7.7%
3 75
 
7.1%
6 52
 
4.9%
9 40
 
3.8%
5 28
 
2.7%
7 15
 
1.4%
8 13
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 422
40.0%
1 179
17.0%
- 144
 
13.6%
2 81
 
7.7%
3 75
 
7.1%
6 52
 
4.9%
9 40
 
3.8%
5 28
 
2.7%
7 15
 
1.4%
8 13
 
1.2%

인허가일자
Date

Distinct46
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size516.0 B
Minimum1977-07-20 00:00:00
Maximum2022-10-04 00:00:00
2024-04-06T19:40:16.292990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:40:16.569664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)100.0%
Memory size564.0 B

영업상태코드
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
3
37 
1
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 37
77.1%
1 11
 
22.9%

Length

2024-04-06T19:40:16.825206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:16.978628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 37
77.1%
1 11
 
22.9%

영업상태명
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
폐업
37 
영업/정상
11 

Length

Max length5
Median length2
Mean length2.6875
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 37
77.1%
영업/정상 11
 
22.9%

Length

2024-04-06T19:40:17.151979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:17.319315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 37
77.1%
영업/정상 11
 
22.9%

상세영업상태코드
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
2
37 
1
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 37
77.1%
1 11
 
22.9%

Length

2024-04-06T19:40:17.525274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:17.718162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 37
77.1%
1 11
 
22.9%

상세영업상태명
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
폐업
37 
영업
11 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 37
77.1%
영업 11
 
22.9%

Length

2024-04-06T19:40:17.898396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:18.065869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 37
77.1%
영업 11
 
22.9%

폐업일자
Date

MISSING 

Distinct36
Distinct (%)97.3%
Missing11
Missing (%)22.9%
Memory size516.0 B
Minimum1997-08-11 00:00:00
Maximum2023-12-28 00:00:00
2024-04-06T19:40:18.273376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:40:18.525527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)100.0%
Memory size564.0 B

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)100.0%
Memory size564.0 B

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)100.0%
Memory size564.0 B

전화번호
Text

MISSING 

Distinct35
Distinct (%)94.6%
Missing11
Missing (%)22.9%
Memory size516.0 B
2024-04-06T19:40:18.908853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.27027
Min length8

Characters and Unicode

Total characters380
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)89.2%

Sample

1st row02 26636080
2nd row02 6668558
3rd row02 22510523
4th row02 4631464
5th row02 4643753
ValueCountFrequency (%)
02 21
31.8%
18335480 2
 
3.0%
42549115 2
 
3.0%
070 2
 
3.0%
031 2
 
3.0%
5776661 1
 
1.5%
4079496 1
 
1.5%
629 1
 
1.5%
1754 1
 
1.5%
4078437 1
 
1.5%
Other values (32) 32
48.5%
2024-04-06T19:40:19.608906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 64
16.8%
2 58
15.3%
6 38
10.0%
1 34
8.9%
5 33
8.7%
32
8.4%
4 31
8.2%
3 29
7.6%
8 22
 
5.8%
9 22
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 348
91.6%
Space Separator 32
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64
18.4%
2 58
16.7%
6 38
10.9%
1 34
9.8%
5 33
9.5%
4 31
8.9%
3 29
8.3%
8 22
 
6.3%
9 22
 
6.3%
7 17
 
4.9%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 380
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64
16.8%
2 58
15.3%
6 38
10.0%
1 34
8.9%
5 33
8.7%
32
8.4%
4 31
8.2%
3 29
7.6%
8 22
 
5.8%
9 22
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64
16.8%
2 58
15.3%
6 38
10.0%
1 34
8.9%
5 33
8.7%
32
8.4%
4 31
8.2%
3 29
7.6%
8 22
 
5.8%
9 22
 
5.8%

소재지면적
Text

MISSING 

Distinct36
Distinct (%)92.3%
Missing9
Missing (%)18.8%
Memory size516.0 B
2024-04-06T19:40:19.950742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.1282051
Min length3

Characters and Unicode

Total characters239
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

Unique35 ?
Unique (%)89.7%

Sample

1st row51.30
2nd row5520.69
3rd row191.63
4th row17.60
5th row29.70
ValueCountFrequency (%)
00 4
 
10.3%
270.00 1
 
2.6%
1,003.00 1
 
2.6%
42.90 1
 
2.6%
3,365.30 1
 
2.6%
66.00 1
 
2.6%
6,894.00 1
 
2.6%
15,257.00 1
 
2.6%
192.08 1
 
2.6%
2,462.00 1
 
2.6%
Other values (26) 26
66.7%
2024-04-06T19:40:20.598064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 53
22.2%
. 39
16.3%
5 23
9.6%
2 21
 
8.8%
1 21
 
8.8%
9 15
 
6.3%
6 14
 
5.9%
3 14
 
5.9%
4 13
 
5.4%
7 11
 
4.6%
Other values (2) 15
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190
79.5%
Other Punctuation 49
 
20.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53
27.9%
5 23
12.1%
2 21
 
11.1%
1 21
 
11.1%
9 15
 
7.9%
6 14
 
7.4%
3 14
 
7.4%
4 13
 
6.8%
7 11
 
5.8%
8 5
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 39
79.6%
, 10
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53
22.2%
. 39
16.3%
5 23
9.6%
2 21
 
8.8%
1 21
 
8.8%
9 15
 
6.3%
6 14
 
5.9%
3 14
 
5.9%
4 13
 
5.4%
7 11
 
4.6%
Other values (2) 15
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53
22.2%
. 39
16.3%
5 23
9.6%
2 21
 
8.8%
1 21
 
8.8%
9 15
 
6.3%
6 14
 
5.9%
3 14
 
5.9%
4 13
 
5.4%
7 11
 
4.6%
Other values (2) 15
 
6.3%

소재지우편번호
Text

Distinct32
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-06T19:40:20.988673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1458333
Min length6

Characters and Unicode

Total characters295
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

Unique26 ?
Unique (%)54.2%

Sample

1st row157-240
2nd row157815
3rd row138-926
4th row133819
5th row133835
ValueCountFrequency (%)
138926 7
 
14.6%
157240 5
 
10.4%
157815 3
 
6.2%
156800 3
 
6.2%
138-926 2
 
4.2%
130864 2
 
4.2%
153864 1
 
2.1%
135-829 1
 
2.1%
138-838 1
 
2.1%
138160 1
 
2.1%
Other values (22) 22
45.8%
2024-04-06T19:40:21.561671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 62
21.0%
8 47
15.9%
3 35
11.9%
0 30
10.2%
5 28
9.5%
2 25
8.5%
6 19
 
6.4%
9 15
 
5.1%
7 15
 
5.1%
4 12
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 288
97.6%
Dash Punctuation 7
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 62
21.5%
8 47
16.3%
3 35
12.2%
0 30
10.4%
5 28
9.7%
2 25
8.7%
6 19
 
6.6%
9 15
 
5.2%
7 15
 
5.2%
4 12
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 295
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 62
21.0%
8 47
15.9%
3 35
11.9%
0 30
10.2%
5 28
9.5%
2 25
8.5%
6 19
 
6.4%
9 15
 
5.1%
7 15
 
5.1%
4 12
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 62
21.0%
8 47
15.9%
3 35
11.9%
0 30
10.2%
5 28
9.5%
2 25
8.5%
6 19
 
6.4%
9 15
 
5.1%
7 15
 
5.1%
4 12
 
4.1%

지번주소
Text

Distinct40
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-06T19:40:22.141427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length31
Mean length24.958333
Min length18

Characters and Unicode

Total characters1198
Distinct characters115
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

Unique32 ?
Unique (%)66.7%

Sample

1st row서울특별시 강서구 공항동 1373-5 보세창고
2nd row서울특별시 강서구 공항동 281번지
3rd row서울특별시 송파구 장지동 875 서울복합물류
4th row서울특별시 성동구 성수동1가 22-8번지
5th row서울특별시 성동구 성수동2가 302-4번지
ValueCountFrequency (%)
서울특별시 48
21.1%
송파구 13
 
5.7%
강서구 11
 
4.8%
장지동 9
 
4.0%
공항동 9
 
4.0%
1층 7
 
3.1%
서울복합물류 6
 
2.6%
875 5
 
2.2%
동대문구 5
 
2.2%
875번지 4
 
1.8%
Other values (90) 110
48.5%
2024-04-06T19:40:22.883580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
216
18.0%
서 71
 
5.9%
동 62
 
5.2%
울 54
 
4.5%
구 52
 
4.3%
시 51
 
4.3%
특 48
 
4.0%
별 48
 
4.0%
지 46
 
3.8%
1 46
 
3.8%
Other values (105) 504
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 712
59.4%
Space Separator 216
 
18.0%
Decimal Number 213
 
17.8%
Dash Punctuation 36
 
3.0%
Open Punctuation 9
 
0.8%
Close Punctuation 9
 
0.8%
Other Punctuation 1
 
0.1%
Math Symbol 1
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 71
 
10.0%
동 62
 
8.7%
울 54
 
7.6%
구 52
 
7.3%
시 51
 
7.2%
특 48
 
6.7%
별 48
 
6.7%
지 46
 
6.5%
번 33
 
4.6%
송 13
 
1.8%
Other values (86) 234
32.9%
Decimal Number
ValueCountFrequency (%)
1 46
21.6%
2 26
12.2%
7 25
11.7%
8 24
11.3%
3 23
10.8%
5 20
9.4%
0 16
 
7.5%
4 14
 
6.6%
6 10
 
4.7%
9 9
 
4.2%
Open Punctuation
ValueCountFrequency (%)
( 6
66.7%
[ 3
33.3%
Close Punctuation
ValueCountFrequency (%)
) 6
66.7%
] 3
33.3%
Space Separator
ValueCountFrequency (%)
216
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 712
59.4%
Common 485
40.5%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 71
 
10.0%
동 62
 
8.7%
울 54
 
7.6%
구 52
 
7.3%
시 51
 
7.2%
특 48
 
6.7%
별 48
 
6.7%
지 46
 
6.5%
번 33
 
4.6%
송 13
 
1.8%
Other values (86) 234
32.9%
Common
ValueCountFrequency (%)
216
44.5%
1 46
 
9.5%
- 36
 
7.4%
2 26
 
5.4%
7 25
 
5.2%
8 24
 
4.9%
3 23
 
4.7%
5 20
 
4.1%
0 16
 
3.3%
4 14
 
2.9%
Other values (8) 39
 
8.0%
Latin
ValueCountFrequency (%)
C 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 712
59.4%
ASCII 486
40.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
216
44.4%
1 46
 
9.5%
- 36
 
7.4%
2 26
 
5.3%
7 25
 
5.1%
8 24
 
4.9%
3 23
 
4.7%
5 20
 
4.1%
0 16
 
3.3%
4 14
 
2.9%
Other values (9) 40
 
8.2%
Hangul
ValueCountFrequency (%)
서 71
 
10.0%
동 62
 
8.7%
울 54
 
7.6%
구 52
 
7.3%
시 51
 
7.2%
특 48
 
6.7%
별 48
 
6.7%
지 46
 
6.5%
번 33
 
4.6%
송 13
 
1.8%
Other values (86) 234
32.9%

도로명주소
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing22
Missing (%)45.8%
Memory size516.0 B
2024-04-06T19:40:23.326360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length35.5
Mean length34
Min length24

Characters and Unicode

Total characters884
Distinct characters109
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

Unique26 ?
Unique (%)100.0%

Sample

1st row서울특별시 강서구 하늘길 227, 보세창고 1층 (공항동)
2nd row서울특별시 송파구 송파대로 55, 서울복합물류 지하1층 (장지동)
3rd row서울특별시 동대문구 약령중앙로 65, 1층 (제기동)
4th row서울특별시 동대문구 왕산로22길 6, 1층 (용두동)
5th row서울특별시 동대문구 약령시로9길 39 (제기동,,133-24 (지층)[약령시로9길39])
ValueCountFrequency (%)
서울특별시 26
 
15.1%
송파구 12
 
7.0%
송파대로 10
 
5.8%
55 9
 
5.2%
장지동 9
 
5.2%
1층 7
 
4.1%
지하1층 6
 
3.5%
서울복합물류 5
 
2.9%
f동 4
 
2.3%
동대문구 4
 
2.3%
Other values (70) 80
46.5%
2024-04-06T19:40:23.985540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
146
 
16.5%
동 40
 
4.5%
서 39
 
4.4%
울 32
 
3.6%
시 31
 
3.5%
1 30
 
3.4%
, 28
 
3.2%
( 28
 
3.2%
) 28
 
3.2%
별 26
 
2.9%
Other values (99) 456
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 520
58.8%
Space Separator 146
 
16.5%
Decimal Number 115
 
13.0%
Open Punctuation 31
 
3.5%
Close Punctuation 31
 
3.5%
Other Punctuation 28
 
3.2%
Uppercase Letter 10
 
1.1%
Dash Punctuation 2
 
0.2%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 40
 
7.7%
서 39
 
7.5%
울 32
 
6.2%
시 31
 
6.0%
별 26
 
5.0%
구 26
 
5.0%
특 26
 
5.0%
로 25
 
4.8%
파 22
 
4.2%
송 22
 
4.2%
Other values (77) 231
44.4%
Decimal Number
ValueCountFrequency (%)
1 30
26.1%
5 23
20.0%
2 21
18.3%
3 10
 
8.7%
9 7
 
6.1%
7 6
 
5.2%
4 6
 
5.2%
6 6
 
5.2%
0 4
 
3.5%
8 2
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
F 4
40.0%
B 3
30.0%
A 2
20.0%
C 1
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 28
90.3%
[ 3
 
9.7%
Close Punctuation
ValueCountFrequency (%)
) 28
90.3%
] 3
 
9.7%
Space Separator
ValueCountFrequency (%)
146
100.0%
Other Punctuation
ValueCountFrequency (%)
, 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 520
58.8%
Common 354
40.0%
Latin 10
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 40
 
7.7%
서 39
 
7.5%
울 32
 
6.2%
시 31
 
6.0%
별 26
 
5.0%
구 26
 
5.0%
특 26
 
5.0%
로 25
 
4.8%
파 22
 
4.2%
송 22
 
4.2%
Other values (77) 231
44.4%
Common
ValueCountFrequency (%)
146
41.2%
1 30
 
8.5%
, 28
 
7.9%
( 28
 
7.9%
) 28
 
7.9%
5 23
 
6.5%
2 21
 
5.9%
3 10
 
2.8%
9 7
 
2.0%
7 6
 
1.7%
Other values (8) 27
 
7.6%
Latin
ValueCountFrequency (%)
F 4
40.0%
B 3
30.0%
A 2
20.0%
C 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 520
58.8%
ASCII 364
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
146
40.1%
1 30
 
8.2%
, 28
 
7.7%
( 28
 
7.7%
) 28
 
7.7%
5 23
 
6.3%
2 21
 
5.8%
3 10
 
2.7%
9 7
 
1.9%
7 6
 
1.6%
Other values (12) 37
 
10.2%
Hangul
ValueCountFrequency (%)
동 40
 
7.7%
서 39
 
7.5%
울 32
 
6.2%
시 31
 
6.0%
별 26
 
5.0%
구 26
 
5.0%
특 26
 
5.0%
로 25
 
4.8%
파 22
 
4.2%
송 22
 
4.2%
Other values (77) 231
44.4%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)64.0%
Missing23
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean6073.24
Minimum2475
Maximum8636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-04-06T19:40:24.182215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2475
5-th percentile2497.6
Q15842
median5842
Q37255
95-th percentile8405.4
Maximum8636
Range6161
Interquartile range (IQR)1413

Descriptive statistics

Standard deviation1635.4158
Coefficient of variation (CV)0.26928226
Kurtosis1.0341538
Mean6073.24
Median Absolute Deviation (MAD)263
Skewness-0.88232078
Sum151831
Variance2674584.9
MonotonicityNot monotonic
2024-04-06T19:40:24.425837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
5842 9
 
18.8%
7505 2
 
4.2%
2475 1
 
2.1%
7927 1
 
2.1%
7780 1
 
2.1%
8636 1
 
2.1%
8525 1
 
2.1%
2478 1
 
2.1%
2576 1
 
2.1%
6765 1
 
2.1%
Other values (6) 6
 
12.5%
(Missing) 23
47.9%
ValueCountFrequency (%)
2475 1
 
2.1%
2478 1
 
2.1%
2576 1
 
2.1%
5595 1
 
2.1%
5699 1
 
2.1%
5831 1
 
2.1%
5842 9
18.8%
6105 1
 
2.1%
6596 1
 
2.1%
6765 1
 
2.1%
ValueCountFrequency (%)
8636 1
 
2.1%
8525 1
 
2.1%
7927 1
 
2.1%
7780 1
 
2.1%
7505 2
 
4.2%
7255 1
 
2.1%
6765 1
 
2.1%
6596 1
 
2.1%
6105 1
 
2.1%
5842 9
18.8%

사업장명
Text

Distinct44
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-06T19:40:24.853506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length9.2916667
Min length4

Characters and Unicode

Total characters446
Distinct characters128
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)83.3%

Sample

1st row(주)우성에프아이
2nd row(사)관우회김포사무소
3rd rowGS네트웍스 송파지점
4th row서울산업사
5th row광신냉동
ValueCountFrequency (%)
주식회사 4
 
6.5%
mfc 3
 
4.8%
vroong 3
 
4.8%
아시아나항공주식회사수출화물창고 2
 
3.2%
롯데로지스틱스(주 2
 
3.2%
롯데글로벌로지스(주 2
 
3.2%
주)한진 2
 
3.2%
케이씨냉장 1
 
1.6%
주)우성에프아이 1
 
1.6%
삼우냉동 1
 
1.6%
Other values (41) 41
66.1%
2024-04-06T19:40:25.653601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 32
 
7.2%
( 29
 
6.5%
) 29
 
6.5%
14
 
3.1%
냉 13
 
2.9%
사 12
 
2.7%
스 10
 
2.2%
장 9
 
2.0%
물 9
 
2.0%
회 8
 
1.8%
Other values (118) 281
63.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 340
76.2%
Open Punctuation 29
 
6.5%
Close Punctuation 29
 
6.5%
Lowercase Letter 19
 
4.3%
Uppercase Letter 15
 
3.4%
Space Separator 14
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 32
 
9.4%
냉 13
 
3.8%
사 12
 
3.5%
스 10
 
2.9%
장 9
 
2.6%
물 9
 
2.6%
회 8
 
2.4%
로 8
 
2.4%
한 8
 
2.4%
산 7
 
2.1%
Other values (101) 224
65.9%
Lowercase Letter
ValueCountFrequency (%)
o 6
31.6%
n 4
21.1%
g 3
15.8%
r 3
15.8%
v 1
 
5.3%
i 1
 
5.3%
d 1
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
V 3
20.0%
C 3
20.0%
M 3
20.0%
F 3
20.0%
A 1
 
6.7%
G 1
 
6.7%
S 1
 
6.7%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 340
76.2%
Common 72
 
16.1%
Latin 34
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 32
 
9.4%
냉 13
 
3.8%
사 12
 
3.5%
스 10
 
2.9%
장 9
 
2.6%
물 9
 
2.6%
회 8
 
2.4%
로 8
 
2.4%
한 8
 
2.4%
산 7
 
2.1%
Other values (101) 224
65.9%
Latin
ValueCountFrequency (%)
o 6
17.6%
n 4
11.8%
g 3
8.8%
r 3
8.8%
V 3
8.8%
C 3
8.8%
M 3
8.8%
F 3
8.8%
v 1
 
2.9%
A 1
 
2.9%
Other values (4) 4
11.8%
Common
ValueCountFrequency (%)
( 29
40.3%
) 29
40.3%
14
19.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 340
76.2%
ASCII 106
 
23.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 32
 
9.4%
냉 13
 
3.8%
사 12
 
3.5%
스 10
 
2.9%
장 9
 
2.6%
물 9
 
2.6%
회 8
 
2.4%
로 8
 
2.4%
한 8
 
2.4%
산 7
 
2.1%
Other values (101) 224
65.9%
ASCII
ValueCountFrequency (%)
( 29
27.4%
) 29
27.4%
14
13.2%
o 6
 
5.7%
n 4
 
3.8%
g 3
 
2.8%
r 3
 
2.8%
V 3
 
2.8%
C 3
 
2.8%
M 3
 
2.8%
Other values (7) 9
 
8.5%

최종수정일자
Date

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
Minimum1999-12-18 00:00:00
Maximum2023-12-28 13:22:03
2024-04-06T19:40:25.935232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:40:26.250370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

데이터갱신구분
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
I
31 
U
17 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 31
64.6%
U 17
35.4%

Length

2024-04-06T19:40:26.498504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:26.649073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 31
64.6%
u 17
35.4%

데이터갱신일자
Categorical

Distinct19
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Memory size516.0 B
2018-08-31 23:59:59.0
29 
2020-06-05 02:40:00.0
 
2
2021-01-02 02:40:00.0
 
1
2022-12-02 00:08:00.0
 
1
2021-05-29 02:40:00.0
 
1
Other values (14)
14 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique17 ?
Unique (%)35.4%

Sample

1st row2022-10-30 22:08:00.0
2nd row2018-08-31 23:59:59.0
3rd row2022-12-02 00:08:00.0
4th row2018-08-31 23:59:59.0
5th row2018-08-31 23:59:59.0

Common Values

ValueCountFrequency (%)
2018-08-31 23:59:59.0 29
60.4%
2020-06-05 02:40:00.0 2
 
4.2%
2021-01-02 02:40:00.0 1
 
2.1%
2022-12-02 00:08:00.0 1
 
2.1%
2021-05-29 02:40:00.0 1
 
2.1%
2021-12-01 02:40:00.0 1
 
2.1%
2021-04-21 02:40:00.0 1
 
2.1%
2019-08-22 02:40:00.0 1
 
2.1%
2021-08-01 02:40:00.0 1
 
2.1%
2020-05-09 02:40:00.0 1
 
2.1%
Other values (9) 9
 
18.8%

Length

2024-04-06T19:40:26.822510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-08-31 29
30.2%
23:59:59.0 29
30.2%
02:40:00.0 9
 
9.4%
2022-12-05 2
 
2.1%
22:01:00.0 2
 
2.1%
22:03:00.0 2
 
2.1%
2020-06-05 2
 
2.1%
00:08:00.0 1
 
1.0%
2021-05-29 1
 
1.0%
2022-10-30 1
 
1.0%
Other values (18) 18
18.8%

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
식품냉동.냉장업
48 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row식품냉동.냉장업
2nd row식품냉동.냉장업
3rd row식품냉동.냉장업
4th row식품냉동.냉장업
5th row식품냉동.냉장업

Common Values

ValueCountFrequency (%)
식품냉동.냉장업 48
100.0%

Length

2024-04-06T19:40:27.091475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:27.270590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품냉동.냉장업 48
100.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)73.7%
Missing10
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean198769.32
Minimum182974.85
Maximum211588.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-04-06T19:40:27.428402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182974.85
5-th percentile182974.85
Q1190751.32
median201427.99
Q3209278.4
95-th percentile210857
Maximum211588.46
Range28613.608
Interquartile range (IQR)18527.079

Descriptive statistics

Standard deviation9880.1488
Coefficient of variation (CV)0.049706608
Kurtosis-1.355748
Mean198769.32
Median Absolute Deviation (MAD)9429.0053
Skewness-0.17662647
Sum7553234.2
Variance97617340
MonotonicityNot monotonic
2024-04-06T19:40:27.689460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
210857.0 7
 
14.6%
182974.850127567 3
 
6.2%
194807.845295028 3
 
6.2%
190352.321686588 1
 
2.1%
200976.724027944 1
 
2.1%
210627.756843007 1
 
2.1%
202746.523082863 1
 
2.1%
207740.703326552 1
 
2.1%
209790.959909032 1
 
2.1%
201879.265466415 1
 
2.1%
Other values (18) 18
37.5%
(Missing) 10
20.8%
ValueCountFrequency (%)
182974.850127567 3
6.2%
183007.220061564 1
 
2.1%
185458.613850038 1
 
2.1%
186169.088781303 1
 
2.1%
186724.468908444 1
 
2.1%
190317.292576603 1
 
2.1%
190352.321686588 1
 
2.1%
190671.436719919 1
 
2.1%
190990.955753109 1
 
2.1%
191256.13116601 1
 
2.1%
ValueCountFrequency (%)
211588.458111712 1
 
2.1%
210857.0 7
14.6%
210627.756843007 1
 
2.1%
209790.959909032 1
 
2.1%
207740.703326552 1
 
2.1%
204591.630913415 1
 
2.1%
204151.656963705 1
 
2.1%
203252.909483212 1
 
2.1%
203222.244537624 1
 
2.1%
203037.467959162 1
 
2.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)73.7%
Missing10
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean445644.17
Minimum438572.47
Maximum453594.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-04-06T19:40:27.975110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum438572.47
5-th percentile440337.11
Q1441446
median444924.41
Q3449724.97
95-th percentile453308.62
Maximum453594.83
Range15022.365
Interquartile range (IQR)8278.9655

Descriptive statistics

Standard deviation4379.01
Coefficient of variation (CV)0.0098262478
Kurtosis-1.1113788
Mean445644.17
Median Absolute Deviation (MAD)3478.4107
Skewness0.38970116
Sum16934478
Variance19175729
MonotonicityNot monotonic
2024-04-06T19:40:28.262967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
441446.0 7
 
14.6%
450419.684560487 3
 
6.2%
445901.413432497 3
 
6.2%
447095.641206789 1
 
2.1%
443493.629144784 1
 
2.1%
443099.315239122 1
 
2.1%
445568.821955603 1
 
2.1%
444279.999454189 1
 
2.1%
443481.212174317 1
 
2.1%
440383.505965875 1
 
2.1%
Other values (18) 18
37.5%
(Missing) 10
20.8%
ValueCountFrequency (%)
438572.467986249 1
 
2.1%
440074.205886207 1
 
2.1%
440383.505965875 1
 
2.1%
441029.937807139 1
 
2.1%
441446.0 7
14.6%
442219.137493629 1
 
2.1%
442338.842381042 1
 
2.1%
443099.315239122 1
 
2.1%
443182.382456297 1
 
2.1%
443403.478170271 1
 
2.1%
ValueCountFrequency (%)
453594.833437939 1
 
2.1%
453540.155791793 1
 
2.1%
453267.756567614 1
 
2.1%
453012.037692966 1
 
2.1%
450590.328926893 1
 
2.1%
450515.642035894 1
 
2.1%
450419.684560487 3
6.2%
449894.090010439 1
 
2.1%
449217.592024885 1
 
2.1%
449132.442018536 1
 
2.1%

위생업태명
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
식품냉동.냉장업
38 
<NA>
10 

Length

Max length8
Median length8
Mean length7.1666667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품냉동.냉장업 38
79.2%
<NA> 10
 
20.8%

Length

2024-04-06T19:40:28.605266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:28.789369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품냉동.냉장업 38
79.2%
na 10
 
20.8%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)66.7%
Missing39
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean5.8888889
Minimum0
Maximum18
Zeros2
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-04-06T19:40:28.941790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q35
95-th percentile16.8
Maximum18
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.3135656
Coefficient of variation (CV)1.0721149
Kurtosis0.64096375
Mean5.8888889
Median Absolute Deviation (MAD)1
Skewness1.327851
Sum53
Variance39.861111
MonotonicityNot monotonic
2024-04-06T19:40:29.158623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 3
 
6.2%
0 2
 
4.2%
3 1
 
2.1%
5 1
 
2.1%
18 1
 
2.1%
15 1
 
2.1%
(Missing) 39
81.2%
ValueCountFrequency (%)
0 2
4.2%
3 1
 
2.1%
4 3
6.2%
5 1
 
2.1%
15 1
 
2.1%
18 1
 
2.1%
ValueCountFrequency (%)
18 1
 
2.1%
15 1
 
2.1%
5 1
 
2.1%
4 3
6.2%
3 1
 
2.1%
0 2
4.2%

여성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
40 
0
 
4
1
 
2
3
 
1
5
 
1

Length

Max length4
Median length4
Mean length3.5
Min length1

Unique

Unique2 ?
Unique (%)4.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 40
83.3%
0 4
 
8.3%
1 2
 
4.2%
3 1
 
2.1%
5 1
 
2.1%

Length

2024-04-06T19:40:29.397943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:30.005040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 40
83.3%
0 4
 
8.3%
1 2
 
4.2%
3 1
 
2.1%
5 1
 
2.1%

영업장주변구분명
Categorical

Distinct3
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
34 
기타
9 
주택가주변
5 

Length

Max length5
Median length4
Mean length3.7291667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 34
70.8%
기타 9
 
18.8%
주택가주변 5
 
10.4%

Length

2024-04-06T19:40:30.192606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:30.362979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 34
70.8%
기타 9
 
18.8%
주택가주변 5
 
10.4%

등급구분명
Categorical

Distinct4
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
34 
기타
9 
관리
 
3
자율
 
2

Length

Max length4
Median length4
Mean length3.4166667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 34
70.8%
기타 9
 
18.8%
관리 3
 
6.2%
자율 2
 
4.2%

Length

2024-04-06T19:40:30.591260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:30.827401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 34
70.8%
기타 9
 
18.8%
관리 3
 
6.2%
자율 2
 
4.2%

급수시설구분명
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
30 
상수도전용
18 

Length

Max length5
Median length4
Mean length4.375
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 30
62.5%
상수도전용 18
37.5%

Length

2024-04-06T19:40:31.058030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:31.216844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 30
62.5%
상수도전용 18
37.5%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
46 
0
 
2

Length

Max length4
Median length4
Mean length3.875
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> 46
95.8%
0 2
 
4.2%

Length

2024-04-06T19:40:31.394112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:31.558708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 46
95.8%
0 2
 
4.2%

본사종업원수
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
32 
0
16 

Length

Max length4
Median length4
Mean length3
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> 32
66.7%
0 16
33.3%

Length

2024-04-06T19:40:31.737074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:31.910080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 32
66.7%
0 16
33.3%

공장사무직종업원수
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
32 
0
16 

Length

Max length4
Median length4
Mean length3
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> 32
66.7%
0 16
33.3%

Length

2024-04-06T19:40:32.121693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:32.323654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 32
66.7%
0 16
33.3%

공장판매직종업원수
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
32 
0
16 

Length

Max length4
Median length4
Mean length3
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> 32
66.7%
0 16
33.3%

Length

2024-04-06T19:40:32.502672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:32.686532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 32
66.7%
0 16
33.3%

공장생산직종업원수
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
32 
0
16 

Length

Max length4
Median length4
Mean length3
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> 32
66.7%
0 16
33.3%

Length

2024-04-06T19:40:32.893481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:33.103428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 32
66.7%
0 16
33.3%

건물소유구분명
Categorical

Distinct3
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
37 
임대
8 
자가
 
3

Length

Max length4
Median length4
Mean length3.5416667
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> 37
77.1%
임대 8
 
16.7%
자가 3
 
6.2%

Length

2024-04-06T19:40:33.386675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:33.631343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 37
77.1%
임대 8
 
16.7%
자가 3
 
6.2%

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
46 
0
 
2

Length

Max length4
Median length4
Mean length3.875
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> 46
95.8%
0 2
 
4.2%

Length

2024-04-06T19:40:33.877702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:34.101121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 46
95.8%
0 2
 
4.2%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
46 
0
 
2

Length

Max length4
Median length4
Mean length3.875
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> 46
95.8%
0 2
 
4.2%

Length

2024-04-06T19:40:34.322798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:34.506061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 46
95.8%
0 2
 
4.2%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)2.6%
Missing10
Missing (%)20.8%
Memory size228.0 B
False
38 
(Missing)
10 
ValueCountFrequency (%)
False 38
79.2%
(Missing) 10
 
20.8%
2024-04-06T19:40:34.650047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Categorical

Distinct4
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size516.0 B
0.0
36 
<NA>
10 
29.7
 
1
337.5
 
1

Length

Max length5
Median length3
Mean length3.2708333
Min length3

Unique

Unique2 ?
Unique (%)4.2%

Sample

1st row<NA>
2nd row0.0
3rd row<NA>
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36
75.0%
<NA> 10
 
20.8%
29.7 1
 
2.1%
337.5 1
 
2.1%

Length

2024-04-06T19:40:35.012837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:40:35.259668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36
75.0%
na 10
 
20.8%
29.7 1
 
2.1%
337.5 1
 
2.1%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)100.0%
Memory size564.0 B

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)100.0%
Memory size564.0 B

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)100.0%
Memory size564.0 B

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
031500003150000-116-2008-000022008-02-19<NA>1영업/정상1영업<NA><NA><NA><NA>02 2663608051.30157-240서울특별시 강서구 공항동 1373-5 보세창고서울특별시 강서구 하늘길 227, 보세창고 1층 (공항동)7505(주)우성에프아이2023-10-26 13:58:55U2022-10-30 22:08:00.0식품냉동.냉장업183007.220062450515.642036<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
131500003150000-116-1995-0000519950308<NA>3폐업2폐업20060126<NA><NA><NA>02 6668558<NA>157815서울특별시 강서구 공항동 281번지<NA><NA>(사)관우회김포사무소2005-10-11 00:00:00I2018-08-31 23:59:59.0식품냉동.냉장업<NA><NA>식품냉동.냉장업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
232300003230000-116-2020-000022020-11-24<NA>1영업/정상1영업<NA><NA><NA><NA>02 225105235520.69138-926서울특별시 송파구 장지동 875 서울복합물류서울특별시 송파구 송파대로 55, 서울복합물류 지하1층 (장지동)5842GS네트웍스 송파지점2023-02-06 14:04:09U2022-12-02 00:08:00.0식품냉동.냉장업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
330300003030000-116-1992-0004919920609<NA>3폐업2폐업20050803<NA><NA><NA>02 4631464191.63133819서울특별시 성동구 성수동1가 22-8번지<NA><NA>서울산업사2000-06-01 00:00:00I2018-08-31 23:59:59.0식품냉동.냉장업204151.656964449132.442019식품냉동.냉장업<NA><NA>기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
430300003030000-116-1979-0004819790407<NA>3폐업2폐업19970811<NA><NA><NA>02 464375317.60133835서울특별시 성동구 성수동2가 302-4번지<NA><NA>광신냉동2001-09-25 00:00:00I2018-08-31 23:59:59.0식품냉동.냉장업204591.630913449217.592025식품냉동.냉장업<NA><NA>기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
530500003050000-116-2009-0000120090420<NA>3폐업2폐업20170711<NA><NA><NA>02 960567429.70130864서울특별시 동대문구 제기동 886-9번지 1층서울특별시 동대문구 약령중앙로 65, 1층 (제기동)2478남부푸드2017-07-11 15:29:27I2018-08-31 23:59:59.0식품냉동.냉장업203222.244538453540.155792식품냉동.냉장업<NA><NA><NA><NA>상수도전용<NA>0000임대<NA><NA>N29.7<NA><NA><NA>
630500003050000-116-1979-0085619790529<NA>3폐업2폐업20131112<NA><NA><NA><NA>596.24130820서울특별시 동대문구 용두동 104-27번지 1층서울특별시 동대문구 왕산로22길 6, 1층 (용두동)<NA>선일물산2013-09-10 14:19:03I2018-08-31 23:59:59.0식품냉동.냉장업<NA><NA>식품냉동.냉장업31주택가주변관리상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
730500003050000-116-2009-0000220090603<NA>1영업/정상1영업<NA><NA><NA><NA>02 9292911112.56130060서울특별시 동대문구 제기동 55-4번지 ,133-24 (지층)[약령시로9길39]서울특별시 동대문구 약령시로9길 39 (제기동,,133-24 (지층)[약령시로9길39])2475(주)지엠푸드시스템2014-08-18 15:02:50I2018-08-31 23:59:59.0식품냉동.냉장업203037.467959453594.833438식품냉동.냉장업<NA><NA><NA><NA>상수도전용<NA>0000임대<NA><NA>N0.0<NA><NA><NA>
830500003050000-116-1980-0027519800426<NA>3폐업2폐업20020228<NA><NA><NA>02224541425,940.00130874서울특별시 동대문구 휘경동 43-12번지<NA><NA>축산협동조합중앙회1999-12-18 00:00:00I2018-08-31 23:59:59.0식품냉동.냉장업<NA><NA>식품냉동.냉장업51주택가주변관리상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
931200003120000-116-1977-0004219770720<NA>3폐업2폐업20120704<NA><NA><NA>02 3347002.00120828서울특별시 서대문구 연희동 218-7번지<NA><NA>서강냉동(주)2001-09-30 00:00:00I2018-08-31 23:59:59.0식품냉동.냉장업<NA><NA>식품냉동.냉장업<NA><NA>주택가주변기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
3832300003230000-116-2018-0000120180720<NA>3폐업2폐업20201231<NA><NA><NA>031 629 17543,365.30138926서울특별시 송파구 장지동 875 서울복합물류서울특별시 송파구 송파대로 55, 서울복합물류 B동 지하1층 (장지동)5842씨제이대한통운(주)2020-12-31 14:02:01U2021-01-02 02:40:00.0식품냉동.냉장업210857.0441446.0식품냉동.냉장업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>임대<NA><NA>N0.0<NA><NA><NA>
3932300003230000-116-2017-0000220170516<NA>3폐업2폐업20200507<NA><NA><NA><NA>2,251.00138926서울특별시 송파구 장지동 875번지서울특별시 송파구 송파대로 55, F동 2층 (장지동, 서울복합물류단지)5842롯데글로벌로지스(주)2020-05-07 12:07:53U2020-05-09 02:40:00.0식품냉동.냉장업210857.0441446.0식품냉동.냉장업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>자가<NA><NA>N0.0<NA><NA><NA>
4030500003050000-116-2022-0000120220614<NA>1영업/정상1영업<NA><NA><NA><NA>070 42549115271.93130864서울특별시 동대문구 제기동 1158-35서울특별시 동대문구 왕산로19마길 16, B101호 (제기동)2576제기센터2022-06-14 09:52:52I2021-12-05 23:06:00.0식품냉동.냉장업202881.503112453012.037693<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4132100003210000-116-2022-0000120220721<NA>1영업/정상1영업<NA><NA><NA><NA><NA>152.72137901서울특별시 서초구 우면동 687 우면삼성프라자-2서울특별시 서초구 성촌1길 12, 우면삼성프라자-2 제지1층 제비101호 (우면동)6765애드뱅(Advin)2022-07-21 17:18:19I2021-12-06 22:03:00.0식품냉동.냉장업201879.265466440383.505966<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4232300003230000-116-1992-0023719920525<NA>1영업/정상1영업<NA><NA><NA><NA>02 4079496591.00138160서울특별시 송파구 가락동 산 600-0서울특별시 송파구 양재대로 932 (가락동)5699서송교역 주식회사2022-08-19 11:18:02U2021-12-07 22:01:00.0식품냉동.냉장업209790.959909443481.212174<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4332300003230000-116-2021-000022021-06-22<NA>3폐업2폐업2023-12-28<NA><NA><NA><NA>1562.00138-926서울특별시 송파구 장지동 875 서울복합물류서울특별시 송파구 송파대로 55, 서울복합물류 F동 동남권자동화창고 2층 (장지동)5842롯데글로벌로지스(주)2023-12-28 13:22:03U2022-11-01 21:00:00.0식품냉동.냉장업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4432300003230000-116-2022-000012022-10-04<NA>1영업/정상1영업<NA><NA><NA><NA>070 42549115214.87138-838서울특별시 송파구 삼전동 40-5서울특별시 송파구 백제고분로22길 35, 1층 (삼전동)5595도로위냉장고 물류센터(삼전)2023-04-19 15:16:19U2022-12-03 22:01:00.0식품냉동.냉장업207740.703327444279.999454<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4532200003220000-116-2021-000012021-03-29<NA>3폐업2폐업2023-05-03<NA><NA><NA><NA>53.76135-829서울특별시 강남구 논현동 215-17 불노빌딩서울특별시 강남구 논현로 654, 불노빌딩 지상1층 (논현동)6105Vroong MFC 강남점2023-05-03 15:26:59U2022-12-05 00:05:00.0식품냉동.냉장업202746.523083445568.821956<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4632300003230000-116-2021-000012021-05-25<NA>3폐업2폐업2023-05-10<NA><NA><NA>000 1800825542.90138-806서울특별시 송파구 가락동 101-6 광진빌딩서울특별시 송파구 송파대로 222, 1층 (가락동)5831Vroong MFC 송파점2023-05-10 15:47:29U2022-12-04 23:02:00.0식품냉동.냉장업210627.756843443099.315239<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4732100003210000-116-2021-000012021-11-24<NA>1영업/정상1영업<NA><NA><NA><NA><NA>297.43137-885서울특별시 서초구 서초동 1716-6 1층서울특별시 서초구 서초대로 277, 1층 (서초동)6596Vroong MFC 서초점2023-06-21 15:10:52U2022-12-05 22:03:00.0식품냉동.냉장업200976.724028443493.629145<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>