Overview

Dataset statistics

Number of variables44
Number of observations310
Missing cells3292
Missing cells (%)24.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.0 KiB
Average record size in memory376.4 B

Variable types

Categorical19
Text6
DateTime4
Unsupported7
Numeric7
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
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-18191/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
업태구분명 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
남성종사자수 is highly imbalanced (73.3%)Imbalance
여성종사자수 is highly imbalanced (76.5%)Imbalance
영업장주변구분명 is highly imbalanced (69.5%)Imbalance
등급구분명 is highly imbalanced (69.8%)Imbalance
급수시설구분명 is highly imbalanced (65.4%)Imbalance
총인원 is highly imbalanced (81.0%)Imbalance
인허가취소일자 has 310 (100.0%) missing valuesMissing
폐업일자 has 46 (14.8%) missing valuesMissing
휴업시작일자 has 310 (100.0%) missing valuesMissing
휴업종료일자 has 310 (100.0%) missing valuesMissing
재개업일자 has 310 (100.0%) missing valuesMissing
전화번호 has 95 (30.6%) missing valuesMissing
소재지면적 has 38 (12.3%) missing valuesMissing
도로명주소 has 170 (54.8%) missing valuesMissing
도로명우편번호 has 171 (55.2%) missing valuesMissing
좌표정보(X) has 4 (1.3%) missing valuesMissing
좌표정보(Y) has 4 (1.3%) missing valuesMissing
보증액 has 246 (79.4%) missing valuesMissing
월세액 has 246 (79.4%) missing valuesMissing
다중이용업소여부 has 51 (16.5%) missing valuesMissing
시설총규모 has 51 (16.5%) missing valuesMissing
전통업소지정번호 has 310 (100.0%) missing valuesMissing
전통업소주된음식 has 310 (100.0%) missing valuesMissing
홈페이지 has 310 (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 54 (17.4%) zerosZeros
월세액 has 54 (17.4%) zerosZeros
시설총규모 has 230 (74.2%) zerosZeros

Reproduction

Analysis started2024-04-29 19:38:41.061718
Analysis finished2024-04-29 19:38:42.001448
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
3140000
310 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3140000
2nd row3140000
3rd row3140000
4th row3140000
5th row3140000

Common Values

ValueCountFrequency (%)
3140000 310
100.0%

Length

2024-04-30T04:38:42.072194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:42.156538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3140000 310
100.0%

관리번호
Text

UNIQUE 

Distinct310
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-30T04:38:42.298842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters6820
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

Unique310 ?
Unique (%)100.0%

Sample

1st row3140000-109-1984-00341
2nd row3140000-109-1989-00607
3rd row3140000-109-1995-00685
4th row3140000-109-1996-00001
5th row3140000-109-1997-00342
ValueCountFrequency (%)
3140000-109-1984-00341 1
 
0.3%
3140000-109-2010-00001 1
 
0.3%
3140000-109-2010-00008 1
 
0.3%
3140000-109-2010-00007 1
 
0.3%
3140000-109-2010-00006 1
 
0.3%
3140000-109-2010-00005 1
 
0.3%
3140000-109-2010-00004 1
 
0.3%
3140000-109-2010-00003 1
 
0.3%
3140000-109-2009-00014 1
 
0.3%
3140000-109-2009-00021 1
 
0.3%
Other values (300) 300
96.8%
2024-04-30T04:38:42.578358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3158
46.3%
- 930
 
13.6%
1 848
 
12.4%
2 442
 
6.5%
9 396
 
5.8%
4 392
 
5.7%
3 384
 
5.6%
6 70
 
1.0%
5 70
 
1.0%
8 68
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5890
86.4%
Dash Punctuation 930
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3158
53.6%
1 848
 
14.4%
2 442
 
7.5%
9 396
 
6.7%
4 392
 
6.7%
3 384
 
6.5%
6 70
 
1.2%
5 70
 
1.2%
8 68
 
1.2%
7 62
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 930
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3158
46.3%
- 930
 
13.6%
1 848
 
12.4%
2 442
 
6.5%
9 396
 
5.8%
4 392
 
5.7%
3 384
 
5.6%
6 70
 
1.0%
5 70
 
1.0%
8 68
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3158
46.3%
- 930
 
13.6%
1 848
 
12.4%
2 442
 
6.5%
9 396
 
5.8%
4 392
 
5.7%
3 384
 
5.6%
6 70
 
1.0%
5 70
 
1.0%
8 68
 
1.0%

인허가일자
Date

Distinct295
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum1984-02-25 00:00:00
Maximum2024-04-09 00:00:00
2024-04-30T04:38:42.695966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:38:42.809346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing310
Missing (%)100.0%
Memory size2.9 KiB

영업상태코드
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
3
264 
1
46 

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 (%)
3 264
85.2%
1 46
 
14.8%

Length

2024-04-30T04:38:42.908776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:42.990075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 264
85.2%
1 46
 
14.8%

영업상태명
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
폐업
264 
영업/정상
46 

Length

Max length5
Median length2
Mean length2.4451613
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 264
85.2%
영업/정상 46
 
14.8%

Length

2024-04-30T04:38:43.081089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:43.163544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 264
85.2%
영업/정상 46
 
14.8%

상세영업상태코드
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2
264 
1
46 

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 (%)
2 264
85.2%
1 46
 
14.8%

Length

2024-04-30T04:38:43.253236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:43.331719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 264
85.2%
1 46
 
14.8%

상세영업상태명
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
폐업
264 
영업
46 

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 264
85.2%
영업 46
 
14.8%

Length

2024-04-30T04:38:43.418802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:43.495213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 264
85.2%
영업 46
 
14.8%

폐업일자
Date

MISSING 

Distinct234
Distinct (%)88.6%
Missing46
Missing (%)14.8%
Memory size2.6 KiB
Minimum1999-07-19 00:00:00
Maximum2024-01-04 00:00:00
2024-04-30T04:38:43.594188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:38:43.723325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing310
Missing (%)100.0%
Memory size2.9 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing310
Missing (%)100.0%
Memory size2.9 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing310
Missing (%)100.0%
Memory size2.9 KiB

전화번호
Text

MISSING 

Distinct203
Distinct (%)94.4%
Missing95
Missing (%)30.6%
Memory size2.6 KiB
2024-04-30T04:38:43.927493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.9116279
Min length2

Characters and Unicode

Total characters2131
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

Unique195 ?
Unique (%)90.7%

Sample

1st row0226975257
2nd row0226454939
3rd row0226064409
4th row02 6045755
5th row02 6944880
ValueCountFrequency (%)
02 33
 
12.7%
031 5
 
1.9%
032 4
 
1.5%
0226544411 4
 
1.5%
0226910014 2
 
0.8%
0226977123 2
 
0.8%
15220772 2
 
0.8%
0220658868 2
 
0.8%
0226063052 2
 
0.8%
0266789618 2
 
0.8%
Other values (202) 202
77.7%
2024-04-30T04:38:44.274871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 480
22.5%
0 369
17.3%
6 286
13.4%
4 159
 
7.5%
5 157
 
7.4%
1 144
 
6.8%
9 131
 
6.1%
8 120
 
5.6%
3 116
 
5.4%
7 107
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2069
97.1%
Space Separator 62
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 480
23.2%
0 369
17.8%
6 286
13.8%
4 159
 
7.7%
5 157
 
7.6%
1 144
 
7.0%
9 131
 
6.3%
8 120
 
5.8%
3 116
 
5.6%
7 107
 
5.2%
Space Separator
ValueCountFrequency (%)
62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2131
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 480
22.5%
0 369
17.3%
6 286
13.4%
4 159
 
7.5%
5 157
 
7.4%
1 144
 
6.8%
9 131
 
6.1%
8 120
 
5.6%
3 116
 
5.4%
7 107
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 480
22.5%
0 369
17.3%
6 286
13.4%
4 159
 
7.5%
5 157
 
7.4%
1 144
 
6.8%
9 131
 
6.1%
8 120
 
5.6%
3 116
 
5.4%
7 107
 
5.0%

소재지면적
Real number (ℝ)

MISSING 

Distinct145
Distinct (%)53.3%
Missing38
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean36.300184
Minimum0
Maximum600
Zeros2
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-30T04:38:44.409855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.3
Q16.6
median16.35
Q342.45
95-th percentile117.3375
Maximum600
Range600
Interquartile range (IQR)35.85

Descriptive statistics

Standard deviation58.336286
Coefficient of variation (CV)1.6070521
Kurtosis42.506008
Mean36.300184
Median Absolute Deviation (MAD)11.375
Skewness5.4474144
Sum9873.65
Variance3403.1223
MonotonicityNot monotonic
2024-04-30T04:38:44.521194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 22
 
7.1%
6.0 13
 
4.2%
33.0 12
 
3.9%
10.0 11
 
3.5%
9.9 10
 
3.2%
3.3 8
 
2.6%
4.0 6
 
1.9%
66.0 5
 
1.6%
30.0 4
 
1.3%
5.0 4
 
1.3%
Other values (135) 177
57.1%
(Missing) 38
 
12.3%
ValueCountFrequency (%)
0.0 2
 
0.6%
1.0 2
 
0.6%
2.0 2
 
0.6%
2.1 1
 
0.3%
2.2 1
 
0.3%
2.5 1
 
0.3%
3.0 4
1.3%
3.3 8
2.6%
3.4 1
 
0.3%
3.58 1
 
0.3%
ValueCountFrequency (%)
600.0 1
0.3%
462.0 1
0.3%
264.0 1
0.3%
190.08 2
0.6%
187.0 1
0.3%
186.95 1
0.3%
145.0 1
0.3%
143.55 1
0.3%
142.82 1
0.3%
142.0 1
0.3%

소재지우편번호
Text

Distinct82
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-30T04:38:44.721392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1032258
Min length6

Characters and Unicode

Total characters1892
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

Unique28 ?
Unique (%)9.0%

Sample

1st row158848
2nd row158806
3rd row158836
4th row158833
5th row158856
ValueCountFrequency (%)
158050 56
 
18.1%
158860 14
 
4.5%
158827 11
 
3.5%
158822 9
 
2.9%
158829 9
 
2.9%
158846 8
 
2.6%
158845 8
 
2.6%
158070 8
 
2.6%
158857 7
 
2.3%
158861 6
 
1.9%
Other values (72) 174
56.1%
2024-04-30T04:38:45.046697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 571
30.2%
5 426
22.5%
1 363
19.2%
0 180
 
9.5%
2 71
 
3.8%
6 65
 
3.4%
7 62
 
3.3%
4 56
 
3.0%
3 34
 
1.8%
9 32
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1860
98.3%
Dash Punctuation 32
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 571
30.7%
5 426
22.9%
1 363
19.5%
0 180
 
9.7%
2 71
 
3.8%
6 65
 
3.5%
7 62
 
3.3%
4 56
 
3.0%
3 34
 
1.8%
9 32
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 571
30.2%
5 426
22.5%
1 363
19.2%
0 180
 
9.5%
2 71
 
3.8%
6 65
 
3.4%
7 62
 
3.3%
4 56
 
3.0%
3 34
 
1.8%
9 32
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 571
30.2%
5 426
22.5%
1 363
19.2%
0 180
 
9.5%
2 71
 
3.8%
6 65
 
3.4%
7 62
 
3.3%
4 56
 
3.0%
3 34
 
1.8%
9 32
 
1.7%

지번주소
Text

Distinct283
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-30T04:38:45.317986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length37
Mean length25.66129
Min length17

Characters and Unicode

Total characters7955
Distinct characters167
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

Unique267 ?
Unique (%)86.1%

Sample

1st row서울특별시 양천구 신월동 1014-7 1층2층
2nd row서울특별시 양천구 목동 404-5
3rd row서울특별시 양천구 신월동 465-18
4th row서울특별시 양천구 신월동 440-6
5th row서울특별시 양천구 신정동 879-7
ValueCountFrequency (%)
서울특별시 310
19.5%
양천구 310
19.5%
목동 111
 
7.0%
신월동 104
 
6.5%
신정동 96
 
6.0%
1층 39
 
2.5%
지하1층 35
 
2.2%
916 30
 
1.9%
지하2층 26
 
1.6%
현대백화점 17
 
1.1%
Other values (369) 512
32.2%
2024-04-30T04:38:45.711526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1524
19.2%
1 460
 
5.8%
동 362
 
4.6%
시 317
 
4.0%
서 315
 
4.0%
특 311
 
3.9%
별 311
 
3.9%
양 311
 
3.9%
울 311
 
3.9%
천 310
 
3.9%
Other values (157) 3423
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4485
56.4%
Decimal Number 1607
 
20.2%
Space Separator 1524
 
19.2%
Dash Punctuation 250
 
3.1%
Open Punctuation 35
 
0.4%
Close Punctuation 35
 
0.4%
Uppercase Letter 14
 
0.2%
Other Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 362
 
8.1%
시 317
 
7.1%
서 315
 
7.0%
특 311
 
6.9%
별 311
 
6.9%
양 311
 
6.9%
울 311
 
6.9%
천 310
 
6.9%
구 310
 
6.9%
신 213
 
4.7%
Other values (134) 1414
31.5%
Decimal Number
ValueCountFrequency (%)
1 460
28.6%
2 226
14.1%
9 197
12.3%
0 127
 
7.9%
6 120
 
7.5%
7 107
 
6.7%
3 104
 
6.5%
4 94
 
5.8%
5 91
 
5.7%
8 81
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
B 7
50.0%
A 2
 
14.3%
C 1
 
7.1%
D 1
 
7.1%
K 1
 
7.1%
G 1
 
7.1%
S 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
/ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
1524
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 250
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4485
56.4%
Common 3456
43.4%
Latin 14
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 362
 
8.1%
시 317
 
7.1%
서 315
 
7.0%
특 311
 
6.9%
별 311
 
6.9%
양 311
 
6.9%
울 311
 
6.9%
천 310
 
6.9%
구 310
 
6.9%
신 213
 
4.7%
Other values (134) 1414
31.5%
Common
ValueCountFrequency (%)
1524
44.1%
1 460
 
13.3%
- 250
 
7.2%
2 226
 
6.5%
9 197
 
5.7%
0 127
 
3.7%
6 120
 
3.5%
7 107
 
3.1%
3 104
 
3.0%
4 94
 
2.7%
Other values (6) 247
 
7.1%
Latin
ValueCountFrequency (%)
B 7
50.0%
A 2
 
14.3%
C 1
 
7.1%
D 1
 
7.1%
K 1
 
7.1%
G 1
 
7.1%
S 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4485
56.4%
ASCII 3470
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1524
43.9%
1 460
 
13.3%
- 250
 
7.2%
2 226
 
6.5%
9 197
 
5.7%
0 127
 
3.7%
6 120
 
3.5%
7 107
 
3.1%
3 104
 
3.0%
4 94
 
2.7%
Other values (13) 261
 
7.5%
Hangul
ValueCountFrequency (%)
동 362
 
8.1%
시 317
 
7.1%
서 315
 
7.0%
특 311
 
6.9%
별 311
 
6.9%
양 311
 
6.9%
울 311
 
6.9%
천 310
 
6.9%
구 310
 
6.9%
신 213
 
4.7%
Other values (134) 1414
31.5%

도로명주소
Text

MISSING 

Distinct136
Distinct (%)97.1%
Missing170
Missing (%)54.8%
Memory size2.6 KiB
2024-04-30T04:38:45.966118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length47
Mean length33.971429
Min length22

Characters and Unicode

Total characters4756
Distinct characters153
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

Unique132 ?
Unique (%)94.3%

Sample

1st row서울특별시 양천구 목동서로 100, 105호 (목동, 목동아파트 3단지 관리동상가)
2nd row서울특별시 양천구 목동동로 100 (신정동,13단지B상가101호)
3rd row서울특별시 양천구 오목로7길 28, 지층 (신월동)
4th row서울특별시 양천구 목동로21길 24, 나동 지상1층 (신정동)
5th row서울특별시 양천구 목동중앙북로 7 (목동,신한이모르젠지하1층)
ValueCountFrequency (%)
서울특별시 140
 
15.3%
양천구 140
 
15.3%
신정동 44
 
4.8%
신월동 42
 
4.6%
목동 35
 
3.8%
1층 31
 
3.4%
지하1층 16
 
1.7%
2층 10
 
1.1%
목동동로 10
 
1.1%
지상1층 10
 
1.1%
Other values (274) 438
47.8%
2024-04-30T04:38:46.356918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
776
 
16.3%
동 238
 
5.0%
1 201
 
4.2%
, 170
 
3.6%
서 154
 
3.2%
양 147
 
3.1%
) 147
 
3.1%
( 147
 
3.1%
시 146
 
3.1%
로 144
 
3.0%
Other values (143) 2486
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2815
59.2%
Space Separator 776
 
16.3%
Decimal Number 678
 
14.3%
Other Punctuation 170
 
3.6%
Close Punctuation 147
 
3.1%
Open Punctuation 147
 
3.1%
Dash Punctuation 14
 
0.3%
Uppercase Letter 9
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 238
 
8.5%
서 154
 
5.5%
양 147
 
5.2%
시 146
 
5.2%
로 144
 
5.1%
별 141
 
5.0%
울 141
 
5.0%
특 141
 
5.0%
천 140
 
5.0%
구 140
 
5.0%
Other values (125) 1283
45.6%
Decimal Number
ValueCountFrequency (%)
1 201
29.6%
2 108
15.9%
0 77
 
11.4%
3 70
 
10.3%
5 52
 
7.7%
7 46
 
6.8%
6 40
 
5.9%
4 38
 
5.6%
9 26
 
3.8%
8 20
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 5
55.6%
A 3
33.3%
C 1
 
11.1%
Space Separator
ValueCountFrequency (%)
776
100.0%
Other Punctuation
ValueCountFrequency (%)
, 170
100.0%
Close Punctuation
ValueCountFrequency (%)
) 147
100.0%
Open Punctuation
ValueCountFrequency (%)
( 147
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2815
59.2%
Common 1932
40.6%
Latin 9
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 238
 
8.5%
서 154
 
5.5%
양 147
 
5.2%
시 146
 
5.2%
로 144
 
5.1%
별 141
 
5.0%
울 141
 
5.0%
특 141
 
5.0%
천 140
 
5.0%
구 140
 
5.0%
Other values (125) 1283
45.6%
Common
ValueCountFrequency (%)
776
40.2%
1 201
 
10.4%
, 170
 
8.8%
) 147
 
7.6%
( 147
 
7.6%
2 108
 
5.6%
0 77
 
4.0%
3 70
 
3.6%
5 52
 
2.7%
7 46
 
2.4%
Other values (5) 138
 
7.1%
Latin
ValueCountFrequency (%)
B 5
55.6%
A 3
33.3%
C 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2815
59.2%
ASCII 1941
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
776
40.0%
1 201
 
10.4%
, 170
 
8.8%
) 147
 
7.6%
( 147
 
7.6%
2 108
 
5.6%
0 77
 
4.0%
3 70
 
3.6%
5 52
 
2.7%
7 46
 
2.4%
Other values (8) 147
 
7.6%
Hangul
ValueCountFrequency (%)
동 238
 
8.5%
서 154
 
5.5%
양 147
 
5.2%
시 146
 
5.2%
로 144
 
5.1%
별 141
 
5.0%
울 141
 
5.0%
특 141
 
5.0%
천 140
 
5.0%
구 140
 
5.0%
Other values (125) 1283
45.6%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct82
Distinct (%)59.0%
Missing171
Missing (%)55.2%
Infinite0
Infinite (%)0.0%
Mean7990.6547
Minimum7900
Maximum8104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-30T04:38:46.495391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7900
5-th percentile7908.8
Q17934.5
median7993
Q38037.5
95-th percentile8096.2
Maximum8104
Range204
Interquartile range (IQR)103

Descriptive statistics

Standard deviation61.100857
Coefficient of variation (CV)0.0076465395
Kurtosis-1.1083963
Mean7990.6547
Median Absolute Deviation (MAD)51
Skewness0.2832641
Sum1110701
Variance3733.3147
MonotonicityNot monotonic
2024-04-30T04:38:46.618419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7998 6
 
1.9%
7902 5
 
1.6%
7922 4
 
1.3%
8082 4
 
1.3%
8104 4
 
1.3%
8027 3
 
1.0%
7942 3
 
1.0%
7931 3
 
1.0%
7910 3
 
1.0%
7944 3
 
1.0%
Other values (72) 101
32.6%
(Missing) 171
55.2%
ValueCountFrequency (%)
7900 1
 
0.3%
7902 5
1.6%
7907 1
 
0.3%
7909 1
 
0.3%
7910 3
1.0%
7912 1
 
0.3%
7917 1
 
0.3%
7918 1
 
0.3%
7919 1
 
0.3%
7920 2
 
0.6%
ValueCountFrequency (%)
8104 4
1.3%
8101 2
0.6%
8098 1
 
0.3%
8096 1
 
0.3%
8095 2
0.6%
8087 1
 
0.3%
8086 3
1.0%
8082 4
1.3%
8080 1
 
0.3%
8078 1
 
0.3%

사업장명
Text

Distinct298
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-30T04:38:47.004043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length6.483871
Min length1

Characters and Unicode

Total characters2010
Distinct characters354
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

Unique287 ?
Unique (%)92.6%

Sample

1st row대웅실업
2nd row정선식품
3rd row한화식품
4th row창현실업
5th row수색식품
ValueCountFrequency (%)
목동점 6
 
1.7%
주식회사 6
 
1.7%
북파주농협김치서부영업소 3
 
0.9%
처가집수산 2
 
0.6%
미래식품 2
 
0.6%
과자바구니 2
 
0.6%
진희상사 2
 
0.6%
부자식품 2
 
0.6%
컴퍼니 2
 
0.6%
주)제이이아이 2
 
0.6%
Other values (312) 318
91.6%
2024-04-30T04:38:47.338094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 86
 
4.3%
) 82
 
4.1%
( 80
 
4.0%
식 50
 
2.5%
트 47
 
2.3%
마 45
 
2.2%
이 39
 
1.9%
품 39
 
1.9%
37
 
1.8%
점 32
 
1.6%
Other values (344) 1473
73.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1736
86.4%
Close Punctuation 82
 
4.1%
Open Punctuation 80
 
4.0%
Lowercase Letter 44
 
2.2%
Space Separator 37
 
1.8%
Uppercase Letter 16
 
0.8%
Decimal Number 11
 
0.5%
Other Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 86
 
5.0%
식 50
 
2.9%
트 47
 
2.7%
마 45
 
2.6%
이 39
 
2.2%
품 39
 
2.2%
점 32
 
1.8%
스 31
 
1.8%
사 29
 
1.7%
한 29
 
1.7%
Other values (309) 1309
75.4%
Lowercase Letter
ValueCountFrequency (%)
e 7
15.9%
o 6
13.6%
r 6
13.6%
a 4
9.1%
t 3
6.8%
l 3
6.8%
s 3
6.8%
i 2
 
4.5%
n 2
 
4.5%
y 2
 
4.5%
Other values (5) 6
13.6%
Uppercase Letter
ValueCountFrequency (%)
S 4
25.0%
B 3
18.8%
G 3
18.8%
F 2
12.5%
D 1
 
6.2%
E 1
 
6.2%
L 1
 
6.2%
M 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 5
45.5%
4 2
 
18.2%
0 1
 
9.1%
5 1
 
9.1%
3 1
 
9.1%
7 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
' 2
50.0%
? 1
25.0%
& 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 82
100.0%
Open Punctuation
ValueCountFrequency (%)
( 80
100.0%
Space Separator
ValueCountFrequency (%)
37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1736
86.4%
Common 214
 
10.6%
Latin 60
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 86
 
5.0%
식 50
 
2.9%
트 47
 
2.7%
마 45
 
2.6%
이 39
 
2.2%
품 39
 
2.2%
점 32
 
1.8%
스 31
 
1.8%
사 29
 
1.7%
한 29
 
1.7%
Other values (309) 1309
75.4%
Latin
ValueCountFrequency (%)
e 7
 
11.7%
o 6
 
10.0%
r 6
 
10.0%
a 4
 
6.7%
S 4
 
6.7%
B 3
 
5.0%
t 3
 
5.0%
l 3
 
5.0%
s 3
 
5.0%
G 3
 
5.0%
Other values (13) 18
30.0%
Common
ValueCountFrequency (%)
) 82
38.3%
( 80
37.4%
37
17.3%
1 5
 
2.3%
4 2
 
0.9%
' 2
 
0.9%
? 1
 
0.5%
0 1
 
0.5%
5 1
 
0.5%
3 1
 
0.5%
Other values (2) 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1736
86.4%
ASCII 274
 
13.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 86
 
5.0%
식 50
 
2.9%
트 47
 
2.7%
마 45
 
2.6%
이 39
 
2.2%
품 39
 
2.2%
점 32
 
1.8%
스 31
 
1.8%
사 29
 
1.7%
한 29
 
1.7%
Other values (309) 1309
75.4%
ASCII
ValueCountFrequency (%)
) 82
29.9%
( 80
29.2%
37
13.5%
e 7
 
2.6%
o 6
 
2.2%
r 6
 
2.2%
1 5
 
1.8%
a 4
 
1.5%
S 4
 
1.5%
B 3
 
1.1%
Other values (25) 40
14.6%

최종수정일자
Date

Distinct285
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum1999-07-30 00:00:00
Maximum2024-04-09 09:49:01
2024-04-30T04:38:47.463965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:38:47.576966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터갱신구분
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
I
253 
U
57 

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 253
81.6%
U 57
 
18.4%

Length

2024-04-30T04:38:47.699728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:47.790608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 253
81.6%
u 57
 
18.4%

데이터갱신일자
Date

Distinct79
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-03 23:01:00
2024-04-30T04:38:47.891520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:38:48.000065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
식품소분업
310 

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 310
100.0%

Length

2024-04-30T04:38:48.118513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:48.195021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 310
100.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct210
Distinct (%)68.6%
Missing4
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean187193.09
Minimum184298.44
Maximum189512.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-30T04:38:48.286807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184298.44
5-th percentile184783.69
Q1185625.64
median187508.53
Q3188717.9
95-th percentile188977.17
Maximum189512.05
Range5213.6066
Interquartile range (IQR)3092.2514

Descriptive statistics

Standard deviation1513.9777
Coefficient of variation (CV)0.0080877864
Kurtosis-1.3560654
Mean187193.09
Median Absolute Deviation (MAD)1375.5454
Skewness-0.32671492
Sum57281086
Variance2292128.6
MonotonicityNot monotonic
2024-04-30T04:38:48.404155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188884.075622342 30
 
9.7%
188729.190478822 14
 
4.5%
188977.171050288 9
 
2.9%
184796.999053109 4
 
1.3%
188039.543424546 4
 
1.3%
188472.759197224 3
 
1.0%
185222.73244302 3
 
1.0%
187133.113562825 3
 
1.0%
187954.189632117 3
 
1.0%
186679.515116362 3
 
1.0%
Other values (200) 230
74.2%
(Missing) 4
 
1.3%
ValueCountFrequency (%)
184298.438528197 2
0.6%
184373.07604881 1
0.3%
184391.766990487 1
0.3%
184448.497335143 1
0.3%
184537.639604228 1
0.3%
184618.337375826 1
0.3%
184669.507945979 2
0.6%
184672.622849866 1
0.3%
184687.669056499 1
0.3%
184698.566377343 1
0.3%
ValueCountFrequency (%)
189512.045139098 1
0.3%
189469.901305589 1
0.3%
189423.528553032 1
0.3%
189332.773428473 1
0.3%
189309.239544413 1
0.3%
189274.417717013 1
0.3%
189086.756192965 1
0.3%
189077.657991611 1
0.3%
189055.134431663 1
0.3%
189042.496526196 1
0.3%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct210
Distinct (%)68.6%
Missing4
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean447228.1
Minimum445021.4
Maximum449649.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-30T04:38:48.521817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum445021.4
5-th percentile445771.77
Q1446577.36
median447186.89
Q3447806.02
95-th percentile448737.75
Maximum449649.02
Range4627.6122
Interquartile range (IQR)1228.6604

Descriptive statistics

Standard deviation941.57839
Coefficient of variation (CV)0.002105365
Kurtosis0.015545955
Mean447228.1
Median Absolute Deviation (MAD)619.75935
Skewness0.18115925
Sum1.368518 × 108
Variance886569.86
MonotonicityNot monotonic
2024-04-30T04:38:48.633572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
447186.888604306 30
 
9.7%
447572.610039376 14
 
4.5%
447466.355031447 9
 
2.9%
448625.389554748 4
 
1.3%
446149.197185122 4
 
1.3%
447091.855963511 3
 
1.0%
447665.545654592 3
 
1.0%
446945.479664548 3
 
1.0%
445124.131588947 3
 
1.0%
447124.449385415 3
 
1.0%
Other values (200) 230
74.2%
(Missing) 4
 
1.3%
ValueCountFrequency (%)
445021.404026362 1
 
0.3%
445081.396522145 3
1.0%
445091.494659491 1
 
0.3%
445124.131588947 3
1.0%
445309.994400633 1
 
0.3%
445372.017178876 1
 
0.3%
445477.850556888 1
 
0.3%
445532.444934761 1
 
0.3%
445546.556343923 1
 
0.3%
445613.501447784 1
 
0.3%
ValueCountFrequency (%)
449649.016215774 1
0.3%
449631.367669118 1
0.3%
449573.933274253 1
0.3%
449556.749869777 1
0.3%
449545.69876539 1
0.3%
449372.235544483 1
0.3%
449338.07750047 1
0.3%
449326.620979065 1
0.3%
449316.524841341 1
0.3%
449313.867697582 1
0.3%

위생업태명
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
식품소분업
259 
<NA>
51 

Length

Max length5
Median length5
Mean length4.8354839
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품소분업 259
83.5%
<NA> 51
 
16.5%

Length

2024-04-30T04:38:48.732892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:48.828173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 259
83.5%
na 51
 
16.5%

남성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
276 
0
 
25
1
 
7
2
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.6709677
Min length1

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row1
2nd row0
3rd row1
4th row<NA>
5th row1

Common Values

ValueCountFrequency (%)
<NA> 276
89.0%
0 25
 
8.1%
1 7
 
2.3%
2 1
 
0.3%
3 1
 
0.3%

Length

2024-04-30T04:38:48.922877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:49.016688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 276
89.0%
0 25
 
8.1%
1 7
 
2.3%
2 1
 
0.3%
3 1
 
0.3%

여성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
278 
0
 
24
1
 
4
6
 
2
10
 
1

Length

Max length4
Median length4
Mean length3.6935484
Min length1

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row6
2nd row10
3rd row0
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 278
89.7%
0 24
 
7.7%
1 4
 
1.3%
6 2
 
0.6%
10 1
 
0.3%
2 1
 
0.3%

Length

2024-04-30T04:38:49.129272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:49.219493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 278
89.7%
0 24
 
7.7%
1 4
 
1.3%
6 2
 
0.6%
10 1
 
0.3%
2 1
 
0.3%

영업장주변구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
280 
기타
 
12
주택가주변
 
9
아파트지역
 
9

Length

Max length5
Median length4
Mean length3.9806452
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 280
90.3%
기타 12
 
3.9%
주택가주변 9
 
2.9%
아파트지역 9
 
2.9%

Length

2024-04-30T04:38:49.328113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:49.417895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 280
90.3%
기타 12
 
3.9%
주택가주변 9
 
2.9%
아파트지역 9
 
2.9%

등급구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
280 
기타
29 
자율
 
1

Length

Max length4
Median length4
Mean length3.8064516
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 280
90.3%
기타 29
 
9.4%
자율 1
 
0.3%

Length

2024-04-30T04:38:49.512412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:49.599689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 280
90.3%
기타 29
 
9.4%
자율 1
 
0.3%

급수시설구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
273 
상수도전용
36 
상수도(음용)지하수(주방용)겸용
 
1

Length

Max length17
Median length4
Mean length4.1580645
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 273
88.1%
상수도전용 36
 
11.6%
상수도(음용)지하수(주방용)겸용 1
 
0.3%

Length

2024-04-30T04:38:49.688452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:49.781352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 273
88.1%
상수도전용 36
 
11.6%
상수도(음용)지하수(주방용)겸용 1
 
0.3%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
301 
0
 
9

Length

Max length4
Median length4
Mean length3.9129032
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> 301
97.1%
0 9
 
2.9%

Length

2024-04-30T04:38:49.872966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:49.999811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 301
97.1%
0 9
 
2.9%

본사종업원수
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
0
168 
<NA>
142 

Length

Max length4
Median length1
Mean length2.3741935
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 168
54.2%
<NA> 142
45.8%

Length

2024-04-30T04:38:50.117415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:50.216883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 168
54.2%
na 142
45.8%

공장사무직종업원수
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
0
168 
<NA>
142 

Length

Max length4
Median length1
Mean length2.3741935
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 168
54.2%
<NA> 142
45.8%

Length

2024-04-30T04:38:50.324562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:50.420373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 168
54.2%
na 142
45.8%

공장판매직종업원수
Categorical

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
0
167 
<NA>
142 
1
 
1

Length

Max length4
Median length1
Mean length2.3741935
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 167
53.9%
<NA> 142
45.8%
1 1
 
0.3%

Length

2024-04-30T04:38:50.516988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:50.611776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 167
53.9%
na 142
45.8%
1 1
 
0.3%

공장생산직종업원수
Categorical

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
0
167 
<NA>
142 
1
 
1

Length

Max length4
Median length1
Mean length2.3741935
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 167
53.9%
<NA> 142
45.8%
1 1
 
0.3%

Length

2024-04-30T04:38:50.698433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:50.792069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 167
53.9%
na 142
45.8%
1 1
 
0.3%

건물소유구분명
Categorical

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
124 
자가
112 
임대
74 

Length

Max length4
Median length2
Mean length2.8
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> 124
40.0%
자가 112
36.1%
임대 74
23.9%

Length

2024-04-30T04:38:50.901711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:38:50.999619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 124
40.0%
자가 112
36.1%
임대 74
23.9%

보증액
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)10.9%
Missing246
Missing (%)79.4%
Infinite0
Infinite (%)0.0%
Mean4000000
Minimum0
Maximum80000000
Zeros54
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-30T04:38:51.078290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20000000
Maximum80000000
Range80000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13508375
Coefficient of variation (CV)3.3770937
Kurtosis19.11853
Mean4000000
Median Absolute Deviation (MAD)0
Skewness4.2477172
Sum2.56 × 108
Variance1.8247619 × 1014
MonotonicityNot monotonic
2024-04-30T04:38:51.174618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 54
 
17.4%
10000000 3
 
1.0%
20000000 2
 
0.6%
50000000 2
 
0.6%
4000000 1
 
0.3%
80000000 1
 
0.3%
2000000 1
 
0.3%
(Missing) 246
79.4%
ValueCountFrequency (%)
0 54
17.4%
2000000 1
 
0.3%
4000000 1
 
0.3%
10000000 3
 
1.0%
20000000 2
 
0.6%
50000000 2
 
0.6%
80000000 1
 
0.3%
ValueCountFrequency (%)
80000000 1
 
0.3%
50000000 2
 
0.6%
20000000 2
 
0.6%
10000000 3
 
1.0%
4000000 1
 
0.3%
2000000 1
 
0.3%
0 54
17.4%

월세액
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)15.6%
Missing246
Missing (%)79.4%
Infinite0
Infinite (%)0.0%
Mean304518.75
Minimum0
Maximum7500000
Zeros54
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-30T04:38:51.275775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile885000
Maximum7500000
Range7500000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1181931.9
Coefficient of variation (CV)3.8813107
Kurtosis25.818277
Mean304518.75
Median Absolute Deviation (MAD)0
Skewness4.9539987
Sum19489200
Variance1.3969629 × 1012
MonotonicityNot monotonic
2024-04-30T04:38:51.381243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 54
 
17.4%
300000 2
 
0.6%
800000 1
 
0.3%
7500000 1
 
0.3%
3200000 1
 
0.3%
5000000 1
 
0.3%
260000 1
 
0.3%
600000 1
 
0.3%
900000 1
 
0.3%
629200 1
 
0.3%
(Missing) 246
79.4%
ValueCountFrequency (%)
0 54
17.4%
260000 1
 
0.3%
300000 2
 
0.6%
600000 1
 
0.3%
629200 1
 
0.3%
800000 1
 
0.3%
900000 1
 
0.3%
3200000 1
 
0.3%
5000000 1
 
0.3%
7500000 1
 
0.3%
ValueCountFrequency (%)
7500000 1
 
0.3%
5000000 1
 
0.3%
3200000 1
 
0.3%
900000 1
 
0.3%
800000 1
 
0.3%
629200 1
 
0.3%
600000 1
 
0.3%
300000 2
 
0.6%
260000 1
 
0.3%
0 54
17.4%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing51
Missing (%)16.5%
Memory size752.0 B
False
259 
(Missing)
51 
ValueCountFrequency (%)
False 259
83.5%
(Missing) 51
 
16.5%
2024-04-30T04:38:51.464420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)8.5%
Missing51
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean4.3566409
Minimum0
Maximum190.08
Zeros230
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-30T04:38:51.536534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13.03
Maximum190.08
Range190.08
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.319042
Coefficient of variation (CV)4.8934585
Kurtosis47.863255
Mean4.3566409
Median Absolute Deviation (MAD)0
Skewness6.5739845
Sum1128.37
Variance454.50153
MonotonicityNot monotonic
2024-04-30T04:38:51.868227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 230
74.2%
6.6 4
 
1.3%
10.0 2
 
0.6%
16.5 2
 
0.6%
60.0 2
 
0.6%
4.0 2
 
0.6%
3.3 2
 
0.6%
9.0 1
 
0.3%
190.08 1
 
0.3%
59.61 1
 
0.3%
Other values (12) 12
 
3.9%
(Missing) 51
 
16.5%
ValueCountFrequency (%)
0.0 230
74.2%
2.06 1
 
0.3%
3.3 2
 
0.6%
4.0 2
 
0.6%
6.0 1
 
0.3%
6.6 4
 
1.3%
7.9 1
 
0.3%
9.0 1
 
0.3%
9.9 1
 
0.3%
10.0 2
 
0.6%
ValueCountFrequency (%)
190.08 1
0.3%
187.0 1
0.3%
114.56 1
0.3%
96.06 1
0.3%
93.84 1
0.3%
82.06 1
0.3%
60.0 2
0.6%
59.61 1
0.3%
27.6 1
0.3%
16.5 2
0.6%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing310
Missing (%)100.0%
Memory size2.9 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing310
Missing (%)100.0%
Memory size2.9 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing310
Missing (%)100.0%
Memory size2.9 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
031400003140000-109-1984-0034119840225<NA>3폐업2폐업20100113<NA><NA><NA>0226975257462.0158848서울특별시 양천구 신월동 1014-7 1층2층<NA><NA>대웅실업2006-09-20 00:00:00I2018-08-31 23:59:59.0식품소분업185761.348412445925.808222식품소분업16기타기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
131400003140000-109-1989-0060719890711<NA>3폐업2폐업20070618<NA><NA><NA>0226454939142.82158806서울특별시 양천구 목동 404-5<NA><NA>정선식품2004-09-16 00:00:00I2018-08-31 23:59:59.0식품소분업189086.756193446851.320186식품소분업010기타기타<NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
231400003140000-109-1995-0068519951201<NA>3폐업2폐업20001227<NA><NA><NA>022606440954.7158836서울특별시 양천구 신월동 465-18<NA><NA>한화식품2000-12-27 00:00:00I2018-08-31 23:59:59.0식품소분업186121.14563447043.889416식품소분업10주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
331400003140000-109-1996-0000119960813<NA>3폐업2폐업20030410<NA><NA><NA>02 6045755<NA>158833서울특별시 양천구 신월동 440-6<NA><NA>창현실업2001-12-13 00:00:00I2018-08-31 23:59:59.0식품소분업185723.821511446684.726858식품소분업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
431400003140000-109-1997-0034219970522<NA>3폐업2폐업19990719<NA><NA><NA>02 694488021.0158856서울특별시 양천구 신정동 879-7<NA><NA>수색식품2001-09-28 00:00:00I2018-08-31 23:59:59.0식품소분업187430.22398447268.969891식품소분업1<NA>주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
531400003140000-109-1997-0034319970902<NA>3폐업2폐업19990805<NA><NA><NA>02 652432612.0158820서울특별시 양천구 목동 933-0 목동삼익아파트 상가 지하1층<NA><NA>정유통1999-10-22 00:00:00I2018-08-31 23:59:59.0식품소분업189021.797866446740.576355식품소분업00아파트지역기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
631400003140000-109-1998-0034419980612<NA>3폐업2폐업20000508<NA><NA><NA>0218.49158858서울특별시 양천구 신정동 925-33<NA><NA>주영식품2000-05-08 00:00:00I2018-08-31 23:59:59.0식품소분업187008.403671447132.556479식품소분업20주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
731400003140000-109-1998-0034519981118<NA>3폐업2폐업20000623<NA><NA><NA>0224.01158806서울특별시 양천구 목동 406-68 3층<NA><NA>진희상사2000-06-23 00:00:00I2018-08-31 23:59:59.0식품소분업188651.920734447058.617853식품소분업11주택가주변자율상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
831400003140000-109-1998-0034619981030<NA>3폐업2폐업20101231<NA><NA><NA>0226935260<NA>158845서울특별시 양천구 신월동 928-1<NA><NA>성신마트2006-09-20 00:00:00I2018-08-31 23:59:59.0식품소분업185221.95791446727.354964식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
931400003140000-109-1999-0044919990320<NA>3폐업2폐업20061121<NA><NA><NA>022651526120.0158819서울특별시 양천구 목동 798-17<NA><NA>꾸이꾸이2004-09-16 00:00:00I2018-08-31 23:59:59.0식품소분업188264.656941447848.689726식품소분업11주택가주변기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
30031400003140000-109-2023-000042023-07-04<NA>1영업/정상1영업<NA><NA><NA><NA><NA>20.0158-827서울특별시 양천구 신월동 114-18 신영시장고객지원센터 지하1층서울특별시 양천구 월정로 161-5, 신영시장고객지원센터 지하1층 (신월동)7922뉴제로마켓2023-07-04 14:51:51I2022-12-07 00:07:00.0식품소분업185437.131797447877.122121<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
30131400003140000-109-2023-000052023-07-10<NA>1영업/정상1영업<NA><NA><NA><NA>1522001330.0158-849서울특별시 양천구 신정동 86-7 3층 301호서울특별시 양천구 신목로 85, 3층 301호 (신정동)8010(주)다완보령건강2023-07-10 13:08:52I2022-12-06 23:03:00.0식품소분업188818.834501446711.238421<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
30231400003140000-109-2023-000062023-07-21<NA>1영업/정상1영업<NA><NA><NA><NA><NA>5.0158-861서울특별시 양천구 신정동 1028-8 101호, 102호서울특별시 양천구 은행정로5길 26, 101호,102호 (신정동)8082떡창고 신정네거리점2023-07-21 09:33:07I2022-12-06 22:03:00.0식품소분업187274.561185446549.331748<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
30331400003140000-109-2023-000072023-12-06<NA>1영업/정상1영업<NA><NA><NA><NA><NA>42.18158-865서울특별시 양천구 신정동 1284-2 동일프라자서울특별시 양천구 신정로7길 60-5, 동일프라자 109호 (신정동)8053순수한뻥튀기2023-12-06 15:01:35I2022-11-02 00:08:00.0식품소분업185614.002019445613.501448<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
30431400003140000-109-2023-000082023-12-13<NA>1영업/정상1영업<NA><NA><NA><NA><NA>6.0158-827서울특별시 양천구 신월동 115-15서울특별시 양천구 월정로25길 6, 1층 (신월동)7923옛날한과2023-12-13 09:52:11I2022-11-01 23:05:00.0식품소분업185505.991871447713.471208<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
30531400003140000-109-2024-000012024-01-05<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.0158-847서울특별시 양천구 신월동 988-3서울특별시 양천구 지양로5길 5, 3층 (신월동)8038오늘담2024-01-05 14:01:52I2023-12-01 00:07:00.0식품소분업185246.464487446499.196966<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
30631400003140000-109-2024-000022024-02-14<NA>1영업/정상1영업<NA><NA><NA><NA><NA>24.2158-818서울특별시 양천구 목동 788-12 삼보빌딩서울특별시 양천구 목동중앙서로7가길 38, 삼보빌딩 3층 (목동)7965대운차향2024-02-14 15:24:41I2023-12-01 23:06:00.0식품소분업187921.763712447932.347635<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
30731400003140000-109-2024-000032024-03-11<NA>1영업/정상1영업<NA><NA><NA><NA>022677070068.0158-859서울특별시 양천구 신정동 945-1 홍원빌딩서울특별시 양천구 오목로 136, 홍원빌딩 201호, B102호 (신정동)8019(주) 신세계파트너스2024-03-11 09:55:52I2023-12-02 23:03:00.0식품소분업186890.187645446898.922116<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
30831400003140000-109-2024-000042024-03-13<NA>1영업/정상1영업<NA><NA><NA><NA><NA>12.0158-050서울특별시 양천구 목동 925 목동신시가지아파트7단지서울특별시 양천구 목동로 186, 상가A동 지층 2호 (목동, 목동신시가지아파트7단지)8002광장산업2024-03-13 14:52:55I2023-12-02 23:06:00.0식품소분업188248.454327447406.301288<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
30931400003140000-109-2024-000052024-04-09<NA>1영업/정상1영업<NA><NA><NA><NA><NA>25.0158-807서울특별시 양천구 목동 506-4 가온서울특별시 양천구 목동중앙본로 117, 1층 103호 (목동, 가온)7948어부네 간식2024-04-09 09:49:01I2023-12-03 23:01:00.0식품소분업188552.828848449326.620979<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>