Overview

Dataset statistics

Number of variables44
Number of observations217
Missing cells2316
Missing cells (%)24.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.6 KiB
Average record size in memory375.6 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
Author금천구
URLhttps://data.seoul.go.kr/dataList/OA-18194/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
업태구분명 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
남성종사자수 is highly imbalanced (63.6%)Imbalance
여성종사자수 is highly imbalanced (65.7%)Imbalance
영업장주변구분명 is highly imbalanced (78.7%)Imbalance
등급구분명 is highly imbalanced (78.7%)Imbalance
급수시설구분명 is highly imbalanced (63.2%)Imbalance
총인원 is highly imbalanced (57.2%)Imbalance
인허가취소일자 has 217 (100.0%) missing valuesMissing
폐업일자 has 60 (27.6%) missing valuesMissing
휴업시작일자 has 217 (100.0%) missing valuesMissing
휴업종료일자 has 217 (100.0%) missing valuesMissing
재개업일자 has 217 (100.0%) missing valuesMissing
전화번호 has 79 (36.4%) missing valuesMissing
소재지면적 has 23 (10.6%) missing valuesMissing
도로명주소 has 74 (34.1%) missing valuesMissing
도로명우편번호 has 76 (35.0%) missing valuesMissing
보증액 has 189 (87.1%) missing valuesMissing
월세액 has 190 (87.6%) missing valuesMissing
다중이용업소여부 has 51 (23.5%) missing valuesMissing
시설총규모 has 51 (23.5%) missing valuesMissing
전통업소지정번호 has 217 (100.0%) missing valuesMissing
전통업소주된음식 has 217 (100.0%) missing valuesMissing
홈페이지 has 217 (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
보증액 has 21 (9.7%) zerosZeros
월세액 has 21 (9.7%) zerosZeros
시설총규모 has 158 (72.8%) zerosZeros

Reproduction

Analysis started2024-05-11 06:06:57.860872
Analysis finished2024-05-11 06:06:58.955842
Duration1.09 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
3170000
217 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3170000
2nd row3170000
3rd row3170000
4th row3170000
5th row3170000

Common Values

ValueCountFrequency (%)
3170000 217
100.0%

Length

2024-05-11T15:06:59.049630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:06:59.199437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3170000 217
100.0%

관리번호
Text

UNIQUE 

Distinct217
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-11T15:06:59.440464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4774
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

Unique217 ?
Unique (%)100.0%

Sample

1st row3170000-109-1995-00001
2nd row3170000-109-1997-00009
3rd row3170000-109-1997-00277
4th row3170000-109-1998-00010
5th row3170000-109-1998-00011
ValueCountFrequency (%)
3170000-109-1995-00001 1
 
0.5%
3170000-109-2019-00001 1
 
0.5%
3170000-109-2020-00008 1
 
0.5%
3170000-109-2018-00001 1
 
0.5%
3170000-109-2018-00002 1
 
0.5%
3170000-109-2018-00003 1
 
0.5%
3170000-109-2018-00004 1
 
0.5%
3170000-109-2018-00005 1
 
0.5%
3170000-109-2018-00006 1
 
0.5%
3170000-109-2018-00007 1
 
0.5%
Other values (207) 207
95.4%
2024-05-11T15:06:59.962221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2195
46.0%
- 651
 
13.6%
1 636
 
13.3%
2 329
 
6.9%
3 277
 
5.8%
9 267
 
5.6%
7 253
 
5.3%
4 50
 
1.0%
5 47
 
1.0%
6 36
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4123
86.4%
Dash Punctuation 651
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2195
53.2%
1 636
 
15.4%
2 329
 
8.0%
3 277
 
6.7%
9 267
 
6.5%
7 253
 
6.1%
4 50
 
1.2%
5 47
 
1.1%
6 36
 
0.9%
8 33
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 651
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2195
46.0%
- 651
 
13.6%
1 636
 
13.3%
2 329
 
6.9%
3 277
 
5.8%
9 267
 
5.6%
7 253
 
5.3%
4 50
 
1.0%
5 47
 
1.0%
6 36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2195
46.0%
- 651
 
13.6%
1 636
 
13.3%
2 329
 
6.9%
3 277
 
5.8%
9 267
 
5.6%
7 253
 
5.3%
4 50
 
1.0%
5 47
 
1.0%
6 36
 
0.8%
Distinct211
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum1995-05-02 00:00:00
Maximum2024-03-22 00:00:00
2024-05-11T15:07:00.248316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:07:00.481669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
3
157 
1
60 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 157
72.4%
1 60
 
27.6%

Length

2024-05-11T15:07:00.684103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:00.848061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 157
72.4%
1 60
 
27.6%

영업상태명
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
폐업
157 
영업/정상
60 

Length

Max length5
Median length2
Mean length2.8294931
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 157
72.4%
영업/정상 60
 
27.6%

Length

2024-05-11T15:07:01.037203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:01.218570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 157
72.4%
영업/정상 60
 
27.6%
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2
157 
1
60 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 157
72.4%
1 60
 
27.6%

Length

2024-05-11T15:07:01.361379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:01.506359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 157
72.4%
1 60
 
27.6%
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
폐업
157 
영업
60 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 157
72.4%
영업 60
 
27.6%

Length

2024-05-11T15:07:01.663225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:01.836753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 157
72.4%
영업 60
 
27.6%

폐업일자
Date

MISSING 

Distinct145
Distinct (%)92.4%
Missing60
Missing (%)27.6%
Memory size1.8 KiB
Minimum1998-05-29 00:00:00
Maximum2023-12-28 00:00:00
2024-05-11T15:07:02.005716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:07:02.274143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

전화번호
Text

MISSING 

Distinct134
Distinct (%)97.1%
Missing79
Missing (%)36.4%
Memory size1.8 KiB
2024-05-11T15:07:02.692382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.557971
Min length2

Characters and Unicode

Total characters1457
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

Unique130 ?
Unique (%)94.2%

Sample

1st row02 8045757
2nd row0232812035
3rd row02 8613511
4th row02 8559564
5th row02 8058866
ValueCountFrequency (%)
02 90
33.6%
070 7
 
2.6%
8640001 2
 
0.7%
8512351 2
 
0.7%
8084487 2
 
0.7%
9780 2
 
0.7%
867 2
 
0.7%
0056 1
 
0.4%
031 1
 
0.4%
382 1
 
0.4%
Other values (158) 158
59.0%
2024-05-11T15:07:03.301363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 298
20.5%
2 206
14.1%
8 181
12.4%
167
11.5%
5 108
 
7.4%
7 99
 
6.8%
6 96
 
6.6%
1 90
 
6.2%
3 73
 
5.0%
9 70
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1290
88.5%
Space Separator 167
 
11.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 298
23.1%
2 206
16.0%
8 181
14.0%
5 108
 
8.4%
7 99
 
7.7%
6 96
 
7.4%
1 90
 
7.0%
3 73
 
5.7%
9 70
 
5.4%
4 69
 
5.3%
Space Separator
ValueCountFrequency (%)
167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1457
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 298
20.5%
2 206
14.1%
8 181
12.4%
167
11.5%
5 108
 
7.4%
7 99
 
6.8%
6 96
 
6.6%
1 90
 
6.2%
3 73
 
5.0%
9 70
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 298
20.5%
2 206
14.1%
8 181
12.4%
167
11.5%
5 108
 
7.4%
7 99
 
6.8%
6 96
 
6.6%
1 90
 
6.2%
3 73
 
5.0%
9 70
 
4.8%

소재지면적
Text

MISSING 

Distinct163
Distinct (%)84.0%
Missing23
Missing (%)10.6%
Memory size1.8 KiB
2024-05-11T15:07:03.858205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.9690722
Min length3

Characters and Unicode

Total characters964
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

Unique147 ?
Unique (%)75.8%

Sample

1st row83.03
2nd row40.00
3rd row221.34
4th row84.64
5th row8.10
ValueCountFrequency (%)
3.30 6
 
3.1%
10.00 5
 
2.6%
25.00 5
 
2.6%
00 4
 
2.1%
20.00 3
 
1.5%
66.00 3
 
1.5%
33.00 3
 
1.5%
22.86 2
 
1.0%
35.00 2
 
1.0%
15.00 2
 
1.0%
Other values (153) 159
82.0%
2024-05-11T15:07:04.779456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 195
20.2%
. 194
20.1%
1 99
10.3%
2 75
 
7.8%
3 74
 
7.7%
5 72
 
7.5%
6 61
 
6.3%
4 57
 
5.9%
8 52
 
5.4%
9 46
 
4.8%
Other values (2) 39
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 769
79.8%
Other Punctuation 195
 
20.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 195
25.4%
1 99
12.9%
2 75
 
9.8%
3 74
 
9.6%
5 72
 
9.4%
6 61
 
7.9%
4 57
 
7.4%
8 52
 
6.8%
9 46
 
6.0%
7 38
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 194
99.5%
, 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 195
20.2%
. 194
20.1%
1 99
10.3%
2 75
 
7.8%
3 74
 
7.7%
5 72
 
7.5%
6 61
 
6.3%
4 57
 
5.9%
8 52
 
5.4%
9 46
 
4.8%
Other values (2) 39
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 195
20.2%
. 194
20.1%
1 99
10.3%
2 75
 
7.8%
3 74
 
7.7%
5 72
 
7.5%
6 61
 
6.3%
4 57
 
5.9%
8 52
 
5.4%
9 46
 
4.8%
Other values (2) 39
 
4.0%
Distinct67
Distinct (%)31.0%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
2024-05-11T15:07:05.160103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1759259
Min length6

Characters and Unicode

Total characters1334
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

Unique32 ?
Unique (%)14.8%

Sample

1st row153802
2nd row153859
3rd row153829
4th row153802
5th row153801
ValueCountFrequency (%)
153803 32
 
14.8%
153813 24
 
11.1%
153801 14
 
6.5%
153802 13
 
6.0%
153-803 11
 
5.1%
153861 5
 
2.3%
153832 5
 
2.3%
153825 5
 
2.3%
153814 5
 
2.3%
153-801 5
 
2.3%
Other values (57) 97
44.9%
2024-05-11T15:07:05.784344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 309
23.2%
1 289
21.7%
5 247
18.5%
8 206
15.4%
0 104
 
7.8%
2 45
 
3.4%
- 38
 
2.8%
7 31
 
2.3%
6 29
 
2.2%
4 23
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296
97.2%
Dash Punctuation 38
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 309
23.8%
1 289
22.3%
5 247
19.1%
8 206
15.9%
0 104
 
8.0%
2 45
 
3.5%
7 31
 
2.4%
6 29
 
2.2%
4 23
 
1.8%
9 13
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 309
23.2%
1 289
21.7%
5 247
18.5%
8 206
15.4%
0 104
 
7.8%
2 45
 
3.4%
- 38
 
2.8%
7 31
 
2.3%
6 29
 
2.2%
4 23
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 309
23.2%
1 289
21.7%
5 247
18.5%
8 206
15.4%
0 104
 
7.8%
2 45
 
3.4%
- 38
 
2.8%
7 31
 
2.3%
6 29
 
2.2%
4 23
 
1.7%
Distinct199
Distinct (%)92.1%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
2024-05-11T15:07:06.193827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length40
Mean length28.175926
Min length18

Characters and Unicode

Total characters6086
Distinct characters198
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

Unique185 ?
Unique (%)85.6%

Sample

1st row서울특별시 금천구 가산동 345-48 (지상3층)
2nd row서울특별시 금천구 시흥동 912-20 [금천로 117]
3rd row서울특별시 금천구 독산동 1003-5
4th row서울특별시 금천구 가산동 345-48 (지상3층)
5th row서울특별시 금천구 가산동 148-38
ValueCountFrequency (%)
서울특별시 216
18.9%
금천구 216
18.9%
가산동 94
 
8.2%
독산동 77
 
6.7%
시흥동 45
 
3.9%
지하1층 15
 
1.3%
1층 14
 
1.2%
291-7 8
 
0.7%
홈플러스금천점 7
 
0.6%
지상1층 6
 
0.5%
Other values (337) 446
39.0%
2024-05-11T15:07:06.951770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1066
 
17.5%
275
 
4.5%
1 263
 
4.3%
229
 
3.8%
228
 
3.7%
227
 
3.7%
218
 
3.6%
217
 
3.6%
217
 
3.6%
217
 
3.6%
Other values (188) 2929
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3388
55.7%
Decimal Number 1291
 
21.2%
Space Separator 1066
 
17.5%
Dash Punctuation 196
 
3.2%
Open Punctuation 46
 
0.8%
Close Punctuation 46
 
0.8%
Uppercase Letter 33
 
0.5%
Other Punctuation 10
 
0.2%
Lowercase Letter 10
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
275
 
8.1%
229
 
6.8%
228
 
6.7%
227
 
6.7%
218
 
6.4%
217
 
6.4%
217
 
6.4%
217
 
6.4%
216
 
6.4%
208
 
6.1%
Other values (149) 1136
33.5%
Uppercase Letter
ValueCountFrequency (%)
B 7
21.2%
T 6
18.2%
I 5
15.2%
A 3
9.1%
H 2
 
6.1%
C 2
 
6.1%
Y 2
 
6.1%
V 1
 
3.0%
G 1
 
3.0%
O 1
 
3.0%
Other values (3) 3
9.1%
Decimal Number
ValueCountFrequency (%)
1 263
20.4%
3 147
11.4%
4 143
11.1%
2 141
10.9%
0 141
10.9%
9 110
8.5%
5 95
 
7.4%
8 94
 
7.3%
6 80
 
6.2%
7 77
 
6.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
30.0%
r 2
20.0%
n 1
 
10.0%
u 1
 
10.0%
t 1
 
10.0%
w 1
 
10.0%
o 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
/ 5
50.0%
, 4
40.0%
: 1
 
10.0%
Open Punctuation
ValueCountFrequency (%)
[ 24
52.2%
( 22
47.8%
Close Punctuation
ValueCountFrequency (%)
] 24
52.2%
) 22
47.8%
Space Separator
ValueCountFrequency (%)
1066
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3388
55.7%
Common 2655
43.6%
Latin 43
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
275
 
8.1%
229
 
6.8%
228
 
6.7%
227
 
6.7%
218
 
6.4%
217
 
6.4%
217
 
6.4%
217
 
6.4%
216
 
6.4%
208
 
6.1%
Other values (149) 1136
33.5%
Latin
ValueCountFrequency (%)
B 7
16.3%
T 6
14.0%
I 5
11.6%
e 3
 
7.0%
A 3
 
7.0%
H 2
 
4.7%
C 2
 
4.7%
Y 2
 
4.7%
r 2
 
4.7%
n 1
 
2.3%
Other values (10) 10
23.3%
Common
ValueCountFrequency (%)
1066
40.2%
1 263
 
9.9%
- 196
 
7.4%
3 147
 
5.5%
4 143
 
5.4%
2 141
 
5.3%
0 141
 
5.3%
9 110
 
4.1%
5 95
 
3.6%
8 94
 
3.5%
Other values (9) 259
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3388
55.7%
ASCII 2698
44.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1066
39.5%
1 263
 
9.7%
- 196
 
7.3%
3 147
 
5.4%
4 143
 
5.3%
2 141
 
5.2%
0 141
 
5.2%
9 110
 
4.1%
5 95
 
3.5%
8 94
 
3.5%
Other values (29) 302
 
11.2%
Hangul
ValueCountFrequency (%)
275
 
8.1%
229
 
6.8%
228
 
6.7%
227
 
6.7%
218
 
6.4%
217
 
6.4%
217
 
6.4%
217
 
6.4%
216
 
6.4%
208
 
6.1%
Other values (149) 1136
33.5%

도로명주소
Text

MISSING 

Distinct143
Distinct (%)100.0%
Missing74
Missing (%)34.1%
Memory size1.8 KiB
2024-05-11T15:07:07.359198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length45
Mean length38.342657
Min length23

Characters and Unicode

Total characters5483
Distinct characters177
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

Unique143 ?
Unique (%)100.0%

Sample

1st row서울특별시 금천구 시흥대로 201 (시흥동, 홈플러스 시흥점)
2nd row서울특별시 금천구 시흥대로123길 94 (독산동,지상1층)
3rd row서울특별시 금천구 문성로5길 30 (독산동,[면화2길 23])
4th row서울특별시 금천구 탑골로 36, 1층 102호 (시흥동)
5th row서울특별시 금천구 시흥대로126길 15, 1층 (독산동)
ValueCountFrequency (%)
서울특별시 143
 
14.3%
금천구 143
 
14.3%
가산동 76
 
7.6%
독산동 34
 
3.4%
시흥동 26
 
2.6%
가산디지털1로 25
 
2.5%
1층 22
 
2.2%
가산디지털2로 15
 
1.5%
벚꽃로 13
 
1.3%
서부샛길 10
 
1.0%
Other values (332) 495
49.4%
2024-05-11T15:07:08.075702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
859
 
15.7%
1 278
 
5.1%
207
 
3.8%
202
 
3.7%
, 174
 
3.2%
162
 
3.0%
155
 
2.8%
147
 
2.7%
145
 
2.6%
( 144
 
2.6%
Other values (167) 3010
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3126
57.0%
Decimal Number 951
 
17.3%
Space Separator 859
 
15.7%
Other Punctuation 178
 
3.2%
Open Punctuation 148
 
2.7%
Close Punctuation 148
 
2.7%
Uppercase Letter 43
 
0.8%
Dash Punctuation 18
 
0.3%
Lowercase Letter 12
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
207
 
6.6%
202
 
6.5%
162
 
5.2%
155
 
5.0%
147
 
4.7%
145
 
4.6%
143
 
4.6%
143
 
4.6%
143
 
4.6%
143
 
4.6%
Other values (126) 1536
49.1%
Uppercase Letter
ValueCountFrequency (%)
B 9
20.9%
T 6
14.0%
A 6
14.0%
I 5
11.6%
C 3
 
7.0%
H 2
 
4.7%
Y 2
 
4.7%
K 2
 
4.7%
S 2
 
4.7%
G 1
 
2.3%
Other values (5) 5
11.6%
Decimal Number
ValueCountFrequency (%)
1 278
29.2%
2 125
13.1%
0 109
 
11.5%
3 78
 
8.2%
5 78
 
8.2%
6 73
 
7.7%
4 69
 
7.3%
9 51
 
5.4%
8 45
 
4.7%
7 45
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
e 3
25.0%
r 2
16.7%
b 2
16.7%
t 1
 
8.3%
w 1
 
8.3%
o 1
 
8.3%
n 1
 
8.3%
u 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
, 174
97.8%
/ 4
 
2.2%
Open Punctuation
ValueCountFrequency (%)
( 144
97.3%
[ 4
 
2.7%
Close Punctuation
ValueCountFrequency (%)
) 144
97.3%
] 4
 
2.7%
Space Separator
ValueCountFrequency (%)
859
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3126
57.0%
Common 2302
42.0%
Latin 55
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
207
 
6.6%
202
 
6.5%
162
 
5.2%
155
 
5.0%
147
 
4.7%
145
 
4.6%
143
 
4.6%
143
 
4.6%
143
 
4.6%
143
 
4.6%
Other values (126) 1536
49.1%
Latin
ValueCountFrequency (%)
B 9
16.4%
T 6
 
10.9%
A 6
 
10.9%
I 5
 
9.1%
C 3
 
5.5%
e 3
 
5.5%
H 2
 
3.6%
Y 2
 
3.6%
r 2
 
3.6%
b 2
 
3.6%
Other values (13) 15
27.3%
Common
ValueCountFrequency (%)
859
37.3%
1 278
 
12.1%
, 174
 
7.6%
( 144
 
6.3%
) 144
 
6.3%
2 125
 
5.4%
0 109
 
4.7%
3 78
 
3.4%
5 78
 
3.4%
6 73
 
3.2%
Other values (8) 240
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3126
57.0%
ASCII 2357
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
859
36.4%
1 278
 
11.8%
, 174
 
7.4%
( 144
 
6.1%
) 144
 
6.1%
2 125
 
5.3%
0 109
 
4.6%
3 78
 
3.3%
5 78
 
3.3%
6 73
 
3.1%
Other values (31) 295
 
12.5%
Hangul
ValueCountFrequency (%)
207
 
6.6%
202
 
6.5%
162
 
5.2%
155
 
5.0%
147
 
4.7%
145
 
4.6%
143
 
4.6%
143
 
4.6%
143
 
4.6%
143
 
4.6%
Other values (126) 1536
49.1%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct71
Distinct (%)50.4%
Missing76
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean8560.4184
Minimum8500
Maximum8657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-11T15:07:08.341686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8500
5-th percentile8502
Q18511
median8566
Q38595
95-th percentile8639
Maximum8657
Range157
Interquartile range (IQR)84

Descriptive statistics

Standard deviation48.609693
Coefficient of variation (CV)0.0056784248
Kurtosis-1.3330404
Mean8560.4184
Median Absolute Deviation (MAD)50
Skewness0.23037708
Sum1207019
Variance2362.9022
MonotonicityNot monotonic
2024-05-11T15:07:08.636182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8504 9
 
4.1%
8513 7
 
3.2%
8595 6
 
2.8%
8501 6
 
2.8%
8511 6
 
2.8%
8584 5
 
2.3%
8592 4
 
1.8%
8594 4
 
1.8%
8590 4
 
1.8%
8506 3
 
1.4%
Other values (61) 87
40.1%
(Missing) 76
35.0%
ValueCountFrequency (%)
8500 1
 
0.5%
8501 6
2.8%
8502 3
 
1.4%
8503 1
 
0.5%
8504 9
4.1%
8505 3
 
1.4%
8506 3
 
1.4%
8507 3
 
1.4%
8508 1
 
0.5%
8510 1
 
0.5%
ValueCountFrequency (%)
8657 1
 
0.5%
8655 1
 
0.5%
8652 1
 
0.5%
8651 1
 
0.5%
8649 1
 
0.5%
8639 3
1.4%
8638 1
 
0.5%
8637 1
 
0.5%
8635 1
 
0.5%
8634 2
0.9%
Distinct212
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-11T15:07:09.041902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length6.7235023
Min length2

Characters and Unicode

Total characters1459
Distinct characters320
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique207 ?
Unique (%)95.4%

Sample

1st row시골종합식품
2nd row올마트
3rd row(주)누리
4th row시골종합식품(주)
5th row반도상사
ValueCountFrequency (%)
주식회사 16
 
6.4%
지우유통 2
 
0.8%
우리차테라피 2
 
0.8%
제이에프에프 2
 
0.8%
삼성홈플러스금천점 2
 
0.8%
위니비니 2
 
0.8%
루크 2
 
0.8%
커피베이 2
 
0.8%
현대유통 2
 
0.8%
꽃가마폐백 2
 
0.8%
Other values (214) 215
86.3%
2024-05-11T15:07:09.740034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
 
6.1%
) 72
 
4.9%
( 72
 
4.9%
40
 
2.7%
38
 
2.6%
33
 
2.3%
32
 
2.2%
27
 
1.9%
27
 
1.9%
25
 
1.7%
Other values (310) 1004
68.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1250
85.7%
Close Punctuation 72
 
4.9%
Open Punctuation 72
 
4.9%
Space Separator 32
 
2.2%
Uppercase Letter 27
 
1.9%
Other Punctuation 3
 
0.2%
Lowercase Letter 2
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
89
 
7.1%
40
 
3.2%
38
 
3.0%
33
 
2.6%
27
 
2.2%
27
 
2.2%
25
 
2.0%
22
 
1.8%
21
 
1.7%
20
 
1.6%
Other values (288) 908
72.6%
Uppercase Letter
ValueCountFrequency (%)
B 4
14.8%
T 3
11.1%
E 3
11.1%
L 3
11.1%
M 2
7.4%
I 2
7.4%
A 2
7.4%
F 2
7.4%
U 1
 
3.7%
K 1
 
3.7%
Other values (4) 4
14.8%
Other Punctuation
ValueCountFrequency (%)
& 2
66.7%
. 1
33.3%
Lowercase Letter
ValueCountFrequency (%)
h 1
50.0%
e 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 72
100.0%
Open Punctuation
ValueCountFrequency (%)
( 72
100.0%
Space Separator
ValueCountFrequency (%)
32
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1250
85.7%
Common 180
 
12.3%
Latin 29
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
89
 
7.1%
40
 
3.2%
38
 
3.0%
33
 
2.6%
27
 
2.2%
27
 
2.2%
25
 
2.0%
22
 
1.8%
21
 
1.7%
20
 
1.6%
Other values (288) 908
72.6%
Latin
ValueCountFrequency (%)
B 4
13.8%
T 3
10.3%
E 3
10.3%
L 3
10.3%
M 2
 
6.9%
I 2
 
6.9%
A 2
 
6.9%
F 2
 
6.9%
h 1
 
3.4%
e 1
 
3.4%
Other values (6) 6
20.7%
Common
ValueCountFrequency (%)
) 72
40.0%
( 72
40.0%
32
17.8%
& 2
 
1.1%
. 1
 
0.6%
- 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1250
85.7%
ASCII 209
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
89
 
7.1%
40
 
3.2%
38
 
3.0%
33
 
2.6%
27
 
2.2%
27
 
2.2%
25
 
2.0%
22
 
1.8%
21
 
1.7%
20
 
1.6%
Other values (288) 908
72.6%
ASCII
ValueCountFrequency (%)
) 72
34.4%
( 72
34.4%
32
15.3%
B 4
 
1.9%
T 3
 
1.4%
E 3
 
1.4%
L 3
 
1.4%
M 2
 
1.0%
I 2
 
1.0%
A 2
 
1.0%
Other values (12) 14
 
6.7%
Distinct211
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum1999-09-15 00:00:00
Maximum2024-03-22 16:48:21
2024-05-11T15:07:09.999321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:07:10.261779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
I
152 
U
65 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 152
70.0%
U 65
30.0%

Length

2024-05-11T15:07:10.521994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:10.718259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 152
70.0%
u 65
30.0%
Distinct101
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-02 23:04:00
2024-05-11T15:07:11.246578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:07:11.477525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
식품소분업
217 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품소분업 217
100.0%

Length

2024-05-11T15:07:11.764739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:11.927482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 217
100.0%

좌표정보(X)
Real number (ℝ)

Distinct150
Distinct (%)69.4%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean190431.76
Minimum188968.19
Maximum192754.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-11T15:07:12.124175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum188968.19
5-th percentile189127.98
Q1189788.85
median190308.87
Q3191226.29
95-th percentile191730.65
Maximum192754.35
Range3786.1565
Interquartile range (IQR)1437.4379

Descriptive statistics

Standard deviation868.70766
Coefficient of variation (CV)0.0045617793
Kurtosis-0.94343462
Mean190431.76
Median Absolute Deviation (MAD)747.51175
Skewness0.17622114
Sum41133260
Variance754652.99
MonotonicityNot monotonic
2024-05-11T15:07:12.393142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190809.507840375 8
 
3.7%
189127.981104583 6
 
2.8%
189055.138252216 6
 
2.8%
190704.904312713 5
 
2.3%
189829.504142672 4
 
1.8%
191226.287379467 4
 
1.8%
189561.360525634 4
 
1.8%
189788.849463856 3
 
1.4%
189686.475000033 3
 
1.4%
189936.854290909 3
 
1.4%
Other values (140) 170
78.3%
ValueCountFrequency (%)
188968.189711073 2
 
0.9%
188979.225789508 1
 
0.5%
189055.138252216 6
2.8%
189065.818334596 1
 
0.5%
189127.981104583 6
2.8%
189202.600232089 1
 
0.5%
189228.282678816 2
 
0.9%
189232.30642848 1
 
0.5%
189282.557380259 2
 
0.9%
189285.310103951 2
 
0.9%
ValueCountFrequency (%)
192754.34619252 2
0.9%
192233.382815227 1
 
0.5%
191924.359561902 1
 
0.5%
191917.450107657 1
 
0.5%
191838.476380966 1
 
0.5%
191823.026849977 3
1.4%
191811.064576976 1
 
0.5%
191794.387105728 1
 
0.5%
191709.402700355 1
 
0.5%
191707.092726258 2
0.9%

좌표정보(Y)
Real number (ℝ)

Distinct150
Distinct (%)69.4%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean440762.89
Minimum437601.75
Maximum442478.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-11T15:07:12.657042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437601.75
5-th percentile438426.03
Q1440264.87
median440940.38
Q3441680.53
95-th percentile442316.42
Maximum442478.36
Range4876.6027
Interquartile range (IQR)1415.6647

Descriptive statistics

Standard deviation1201.2452
Coefficient of variation (CV)0.0027253774
Kurtosis-0.0049793321
Mean440762.89
Median Absolute Deviation (MAD)727.17832
Skewness-0.85043838
Sum95204784
Variance1442990.1
MonotonicityNot monotonic
2024-05-11T15:07:12.897317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
440728.382273075 8
 
3.7%
442460.505542105 6
 
2.8%
441958.334400683 6
 
2.8%
440940.384414334 5
 
2.3%
441618.071646624 4
 
1.8%
437914.06299827 4
 
1.8%
440925.40762197 4
 
1.8%
441731.610478567 3
 
1.4%
440870.092608694 3
 
1.4%
440349.985884412 3
 
1.4%
Other values (140) 170
78.3%
ValueCountFrequency (%)
437601.753603288 1
 
0.5%
437607.039565128 2
0.9%
437720.092885096 1
 
0.5%
437861.221701954 1
 
0.5%
437914.06299827 4
1.8%
438266.70898504 1
 
0.5%
438395.806833518 1
 
0.5%
438436.109390506 1
 
0.5%
438509.377572834 1
 
0.5%
438588.637827119 1
 
0.5%
ValueCountFrequency (%)
442478.356256941 1
 
0.5%
442470.576702573 1
 
0.5%
442460.505542105 6
2.8%
442417.955057116 2
 
0.9%
442338.144130222 1
 
0.5%
442309.174987731 2
 
0.9%
442205.867457803 1
 
0.5%
442174.424111843 1
 
0.5%
442119.780901928 2
 
0.9%
442035.622560657 1
 
0.5%

위생업태명
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
식품소분업
166 
<NA>
51 

Length

Max length5
Median length5
Mean length4.764977
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품소분업 166
76.5%
<NA> 51
 
23.5%

Length

2024-05-11T15:07:13.123806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:13.319277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 166
76.5%
na 51
 
23.5%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
192 
0
22 
2
 
3

Length

Max length4
Median length4
Mean length3.6543779
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> 192
88.5%
0 22
 
10.1%
2 3
 
1.4%

Length

2024-05-11T15:07:13.525228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:13.700152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 192
88.5%
0 22
 
10.1%
2 3
 
1.4%

여성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
192 
0
24 
1
 
1

Length

Max length4
Median length4
Mean length3.6543779
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 192
88.5%
0 24
 
11.1%
1 1
 
0.5%

Length

2024-05-11T15:07:13.898827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:14.122851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 192
88.5%
0 24
 
11.1%
1 1
 
0.5%

영업장주변구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
206 
기타
 
7
주택가주변
 
4

Length

Max length5
Median length4
Mean length3.9539171
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 206
94.9%
기타 7
 
3.2%
주택가주변 4
 
1.8%

Length

2024-05-11T15:07:14.329255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:14.534799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 206
94.9%
기타 7
 
3.2%
주택가주변 4
 
1.8%

등급구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
206 
기타
 
7
자율
 
4

Length

Max length4
Median length4
Mean length3.8986175
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 206
94.9%
기타 7
 
3.2%
자율 4
 
1.8%

Length

2024-05-11T15:07:14.780874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:14.978348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 206
94.9%
기타 7
 
3.2%
자율 4
 
1.8%

급수시설구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
189 
상수도전용
27 
상수도(음용)지하수(주방용)겸용
 
1

Length

Max length17
Median length4
Mean length4.1843318
Min length4

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 189
87.1%
상수도전용 27
 
12.4%
상수도(음용)지하수(주방용)겸용 1
 
0.5%

Length

2024-05-11T15:07:15.163536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:15.374747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 189
87.1%
상수도전용 27
 
12.4%
상수도(음용)지하수(주방용)겸용 1
 
0.5%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
198 
0
 
19

Length

Max length4
Median length4
Mean length3.7373272
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> 198
91.2%
0 19
 
8.8%

Length

2024-05-11T15:07:15.582469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:15.787363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 198
91.2%
0 19
 
8.8%
Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
136 
0
78 
2
 
2
1
 
1

Length

Max length4
Median length4
Mean length2.8801843
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 136
62.7%
0 78
35.9%
2 2
 
0.9%
1 1
 
0.5%

Length

2024-05-11T15:07:15.976748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:16.173534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 136
62.7%
0 78
35.9%
2 2
 
0.9%
1 1
 
0.5%
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
136 
0
81 

Length

Max length4
Median length4
Mean length2.8801843
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 136
62.7%
0 81
37.3%

Length

2024-05-11T15:07:16.419523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:16.642183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 136
62.7%
0 81
37.3%
Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
136 
0
81 

Length

Max length4
Median length4
Mean length2.8801843
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 136
62.7%
0 81
37.3%

Length

2024-05-11T15:07:16.854829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:17.058477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 136
62.7%
0 81
37.3%
Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
136 
0
80 
11
 
1

Length

Max length4
Median length4
Mean length2.8847926
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row<NA>
4th row11
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 136
62.7%
0 80
36.9%
11 1
 
0.5%

Length

2024-05-11T15:07:17.275975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:17.500810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 136
62.7%
0 80
36.9%
11 1
 
0.5%
Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
128 
자가
61 
임대
28 

Length

Max length4
Median length4
Mean length3.1797235
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> 128
59.0%
자가 61
28.1%
임대 28
 
12.9%

Length

2024-05-11T15:07:17.726307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:07:17.914812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 128
59.0%
자가 61
28.1%
임대 28
 
12.9%

보증액
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)21.4%
Missing189
Missing (%)87.1%
Infinite0
Infinite (%)0.0%
Mean12321429
Minimum0
Maximum2 × 108
Zeros21
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-11T15:07:18.080166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31250000
95-th percentile38250000
Maximum2 × 108
Range2 × 108
Interquartile range (IQR)1250000

Descriptive statistics

Standard deviation38717451
Coefficient of variation (CV)3.1422859
Kurtosis22.214257
Mean12321429
Median Absolute Deviation (MAD)0
Skewness4.5474897
Sum3.45 × 108
Variance1.499041 × 1015
MonotonicityNot monotonic
2024-05-11T15:07:18.293917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 21
 
9.7%
35000000 2
 
0.9%
5000000 2
 
0.9%
200000000 1
 
0.5%
25000000 1
 
0.5%
40000000 1
 
0.5%
(Missing) 189
87.1%
ValueCountFrequency (%)
0 21
9.7%
5000000 2
 
0.9%
25000000 1
 
0.5%
35000000 2
 
0.9%
40000000 1
 
0.5%
200000000 1
 
0.5%
ValueCountFrequency (%)
200000000 1
 
0.5%
40000000 1
 
0.5%
35000000 2
 
0.9%
25000000 1
 
0.5%
5000000 2
 
0.9%
0 21
9.7%

월세액
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)22.2%
Missing190
Missing (%)87.6%
Infinite0
Infinite (%)0.0%
Mean588888.89
Minimum0
Maximum8000000
Zeros21
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-11T15:07:18.478833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3020000
Maximum8000000
Range8000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1683022.3
Coefficient of variation (CV)2.8579624
Kurtosis15.237332
Mean588888.89
Median Absolute Deviation (MAD)0
Skewness3.7383897
Sum15900000
Variance2.8325641 × 1012
MonotonicityNot monotonic
2024-05-11T15:07:18.695580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 21
 
9.7%
400000 2
 
0.9%
8000000 1
 
0.5%
3200000 1
 
0.5%
1300000 1
 
0.5%
2600000 1
 
0.5%
(Missing) 190
87.6%
ValueCountFrequency (%)
0 21
9.7%
400000 2
 
0.9%
1300000 1
 
0.5%
2600000 1
 
0.5%
3200000 1
 
0.5%
8000000 1
 
0.5%
ValueCountFrequency (%)
8000000 1
 
0.5%
3200000 1
 
0.5%
2600000 1
 
0.5%
1300000 1
 
0.5%
400000 2
 
0.9%
0 21
9.7%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.6%
Missing51
Missing (%)23.5%
Memory size566.0 B
False
166 
(Missing)
51 
ValueCountFrequency (%)
False 166
76.5%
(Missing) 51
 
23.5%
2024-05-11T15:07:18.959243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)5.4%
Missing51
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean2.3366867
Minimum0
Maximum105
Zeros158
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-11T15:07:19.147709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum105
Range105
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.595367
Coefficient of variation (CV)5.8182241
Kurtosis40.671525
Mean2.3366867
Median Absolute Deviation (MAD)0
Skewness6.3786762
Sum387.89
Variance184.83401
MonotonicityNot monotonic
2024-05-11T15:07:19.329550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 158
72.8%
8.4 1
 
0.5%
90.35 1
 
0.5%
8.2 1
 
0.5%
6.3 1
 
0.5%
105.0 1
 
0.5%
18.56 1
 
0.5%
63.68 1
 
0.5%
87.4 1
 
0.5%
(Missing) 51
 
23.5%
ValueCountFrequency (%)
0.0 158
72.8%
6.3 1
 
0.5%
8.2 1
 
0.5%
8.4 1
 
0.5%
18.56 1
 
0.5%
63.68 1
 
0.5%
87.4 1
 
0.5%
90.35 1
 
0.5%
105.0 1
 
0.5%
ValueCountFrequency (%)
105.0 1
 
0.5%
90.35 1
 
0.5%
87.4 1
 
0.5%
63.68 1
 
0.5%
18.56 1
 
0.5%
8.4 1
 
0.5%
8.2 1
 
0.5%
6.3 1
 
0.5%
0.0 158
72.8%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
031700003170000-109-1995-0000119950502<NA>3폐업2폐업20180827<NA><NA><NA><NA><NA>153802서울특별시 금천구 가산동 345-48 (지상3층)<NA><NA>시골종합식품2018-08-27 09:34:04I2018-08-31 23:59:59.0식품소분업189561.360526440925.407622식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
131700003170000-109-1997-0000919970725<NA>3폐업2폐업20081002<NA><NA><NA>02 804575783.03153859서울특별시 금천구 시흥동 912-20 [금천로 117]<NA><NA>올마트2007-10-15 11:55:02I2018-08-31 23:59:59.0식품소분업191917.450108438699.774609식품소분업<NA><NA>주택가주변자율상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
231700003170000-109-1997-0027719970415<NA>3폐업2폐업19980529<NA><NA><NA>023281203540.00153829서울특별시 금천구 독산동 1003-5<NA><NA>(주)누리2002-01-13 00:00:00I2018-08-31 23:59:59.0식품소분업190278.498288440453.72736식품소분업<NA><NA>주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
331700003170000-109-1998-0001019980625<NA>3폐업2폐업20060707<NA><NA><NA>02 8613511221.34153802서울특별시 금천구 가산동 345-48 (지상3층)<NA><NA>시골종합식품(주)2003-12-12 00:00:00I2018-08-31 23:59:59.0식품소분업189561.360526440925.407622식품소분업<NA><NA>기타기타상수도전용<NA>00011<NA><NA><NA>N0.0<NA><NA><NA>
431700003170000-109-1998-0001119980418<NA>3폐업2폐업19980702<NA><NA><NA>02 855956484.64153801서울특별시 금천구 가산동 148-38<NA><NA>반도상사2002-01-13 00:00:00I2018-08-31 23:59:59.0식품소분업190750.791233441510.209347식품소분업<NA><NA>기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
531700003170000-109-1998-0001219980618<NA>3폐업2폐업20040412<NA><NA><NA>02 80588668.10153856서울특별시 금천구 시흥동 850-6<NA><NA>(주)한빛유통농심가2004-06-25 00:00:00I2018-08-31 23:59:59.0식품소분업191445.240967439494.602878식품소분업<NA><NA>주택가주변자율상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
631700003170000-109-1998-0027619980911<NA>3폐업2폐업20040412<NA><NA><NA>02 8576381129.91153814서울특별시 금천구 독산동 336-31<NA><NA>일양유통수산2002-01-13 00:00:00I2018-08-31 23:59:59.0식품소분업190024.84396440964.89284식품소분업<NA><NA>기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
731700003170000-109-1998-0035319980211<NA>3폐업2폐업20020919<NA><NA><NA>025.25153864서울특별시 금천구 시흥동 986-4<NA><NA>(주)이천일아울렛2000-11-16 00:00:00I2018-08-31 23:59:59.0식품소분업191298.607041438436.109391식품소분업00주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
831700003170000-109-1999-0034919990127<NA>3폐업2폐업20020612<NA><NA><NA>02 8940167108.28153813서울특별시 금천구 독산동 331-57<NA><NA>대신참치2002-03-15 00:00:00I2018-08-31 23:59:59.0식품소분업190480.150689440578.960289식품소분업21기타자율상수도(음용)지하수(주방용)겸용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
931700003170000-109-1999-0035619990306<NA>3폐업2폐업20011018<NA><NA><NA>02 894940513.76153839서울특별시 금천구 시흥동 119-1<NA><NA>금천식품2001-10-29 00:00:00I2018-08-31 23:59:59.0식품소분업190947.552731439204.21536식품소분업20기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
20731700003170000-109-2023-000132023-10-27<NA>1영업/정상1영업<NA><NA><NA><NA>0707012711282.13153-803서울특별시 금천구 가산동 543-1 대성디폴리스지식산업센터서울특별시 금천구 서부샛길 606, 대성디폴리스지식산업센터 A동 1610호 (가산동)8504디앙뜨2023-10-27 15:46:21I2022-10-30 22:09:00.0식품소분업189055.138252441958.334401<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20831700003170000-109-2023-000142023-12-06<NA>1영업/정상1영업<NA><NA><NA><NA><NA>9.84153-857서울특별시 금천구 시흥동 881-56서울특별시 금천구 시흥대로62길 31, 1층 일부호 (시흥동)8626기라성2023-12-06 10:02:10I2022-11-02 00:08:00.0식품소분업191233.745849439320.466929<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20931700003170000-109-2024-000012024-01-03<NA>1영업/정상1영업<NA><NA><NA><NA>18009258131.20153-709서울특별시 금천구 가산동 60-5 갑을그레이트밸리서울특별시 금천구 디지털로9길 32, 갑을그레이트밸리 지하105호(일부)호 (가산동)8512주식회사 금해코리아2024-01-03 14:23:37I2023-12-01 00:05:00.0식품소분업189982.344533441928.690402<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21031700003170000-109-2024-000022024-01-17<NA>1영업/정상1영업<NA><NA><NA><NA>0707585609810.00153-814서울특별시 금천구 독산동 336-23 4층 일부호서울특별시 금천구 벚꽃로 190, 4층 일부호 (독산동)8526썸푸드2024-01-17 13:37:29I2023-11-30 23:09:00.0식품소분업189972.552916441080.376789<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21131700003170000-109-2024-000032024-01-23<NA>1영업/정상1영업<NA><NA><NA><NA><NA>23.40153-864서울특별시 금천구 시흥동 991-5서울특별시 금천구 시흥대로 189, 8층 809호 (시흥동)8635화화푸드2024-01-23 15:10:20I2023-11-30 22:05:00.0식품소분업191231.71641438732.994399<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21231700003170000-109-2024-000042024-01-26<NA>1영업/정상1영업<NA><NA><NA><NA><NA>10.00153-803서울특별시 금천구 가산동 459-23 에이스 비즈포레 지식산업센터서울특별시 금천구 가산디지털2로 173, 에이스 비즈포레 지식산업센터 3층 310호 (가산동)8500메리고라운드 기프트2024-01-26 17:00:13I2023-11-30 22:08:00.0식품소분업189065.818335442338.14413<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21331700003170000-109-2024-000052024-02-21<NA>1영업/정상1영업<NA><NA><NA><NA><NA>9.23153-803서울특별시 금천구 가산동 481-10 벽산/경인디지털밸리2서울특별시 금천구 가산디지털2로 184, 벽산/경인디지털밸리2 15층 1511호 (가산동)8501주식회사 제이에프에프2024-02-21 17:28:43I2023-12-01 22:03:00.0식품소분업189127.981105442460.505542<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21431700003170000-109-2024-000062024-02-22<NA>1영업/정상1영업<NA><NA><NA><NA><NA>63.45153-818서울특별시 금천구 독산동 881-44서울특별시 금천구 남부순환로126길 48, 1층 (독산동)8547준유통2024-02-22 17:30:57I2023-12-01 22:04:00.0식품소분업191683.573321441778.351919<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21531700003170000-109-2024-000072024-03-12<NA>1영업/정상1영업<NA><NA><NA><NA><NA>99.00153-814서울특별시 금천구 독산동 336-1서울특별시 금천구 가산로3길 115, 102,103호 (독산동)8526주식회사 오에스케이에프앤지2024-03-12 17:20:19I2023-12-02 23:04:00.0식품소분업190114.172267441080.23872<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21631700003170000-109-2024-000082024-03-22<NA>1영업/정상1영업<NA><NA><NA><NA>02837889062.00153-803서울특별시 금천구 가산동 371-37 에스티엑스브이타워서울특별시 금천구 가산디지털1로 128, 에스티엑스브이타워 11층 1105-2호 (가산동)8507언더레이2024-03-22 16:48:21I2023-12-02 22:04:00.0식품소분업189647.046752441677.096205<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>