Overview

Dataset statistics

Number of variables44
Number of observations399
Missing cells4508
Missing cells (%)25.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory146.6 KiB
Average record size in memory376.3 B

Variable types

Categorical18
Text6
DateTime4
Unsupported7
Numeric8
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-18266/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
영업상태코드 is highly imbalanced (59.7%)Imbalance
영업상태명 is highly imbalanced (59.7%)Imbalance
상세영업상태코드 is highly imbalanced (59.7%)Imbalance
상세영업상태명 is highly imbalanced (59.7%)Imbalance
위생업태명 is highly imbalanced (52.8%)Imbalance
남성종사자수 is highly imbalanced (51.8%)Imbalance
영업장주변구분명 is highly imbalanced (52.2%)Imbalance
총인원 is highly imbalanced (91.9%)Imbalance
인허가취소일자 has 399 (100.0%) missing valuesMissing
폐업일자 has 32 (8.0%) missing valuesMissing
휴업시작일자 has 399 (100.0%) missing valuesMissing
휴업종료일자 has 399 (100.0%) missing valuesMissing
재개업일자 has 399 (100.0%) missing valuesMissing
전화번호 has 104 (26.1%) missing valuesMissing
소재지면적 has 31 (7.8%) missing valuesMissing
도로명주소 has 244 (61.2%) missing valuesMissing
도로명우편번호 has 246 (61.7%) missing valuesMissing
좌표정보(X) has 14 (3.5%) missing valuesMissing
좌표정보(Y) has 14 (3.5%) missing valuesMissing
여성종사자수 has 302 (75.7%) missing valuesMissing
보증액 has 320 (80.2%) missing valuesMissing
월세액 has 320 (80.2%) missing valuesMissing
다중이용업소여부 has 44 (11.0%) missing valuesMissing
시설총규모 has 44 (11.0%) missing valuesMissing
전통업소지정번호 has 399 (100.0%) missing valuesMissing
전통업소주된음식 has 399 (100.0%) missing valuesMissing
홈페이지 has 399 (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
여성종사자수 has 38 (9.5%) zerosZeros
보증액 has 68 (17.0%) zerosZeros
월세액 has 68 (17.0%) zerosZeros
시설총규모 has 334 (83.7%) zerosZeros

Reproduction

Analysis started2024-04-29 19:40:46.180347
Analysis finished2024-04-29 19:40:47.116804
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 size3.2 KiB
3140000
399 

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 399
100.0%

Length

2024-04-30T04:40:47.175353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:47.247536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3140000 399
100.0%

관리번호
Text

UNIQUE 

Distinct399
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-04-30T04:40:47.391673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters8778
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

Unique399 ?
Unique (%)100.0%

Sample

1st row3140000-106-1978-00038
2nd row3140000-106-1984-00001
3rd row3140000-106-1984-00002
4th row3140000-106-1986-00592
5th row3140000-106-1987-00637
ValueCountFrequency (%)
3140000-106-1978-00038 1
 
0.3%
3140000-106-2009-00007 1
 
0.3%
3140000-106-2010-00014 1
 
0.3%
3140000-106-2010-00013 1
 
0.3%
3140000-106-2010-00012 1
 
0.3%
3140000-106-2010-00011 1
 
0.3%
3140000-106-2010-00010 1
 
0.3%
3140000-106-2010-00009 1
 
0.3%
3140000-106-2010-00008 1
 
0.3%
3140000-106-2010-00007 1
 
0.3%
Other values (389) 389
97.5%
2024-04-30T04:40:47.688464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3874
44.1%
- 1197
 
13.6%
1 1193
 
13.6%
4 526
 
6.0%
3 495
 
5.6%
2 490
 
5.6%
6 483
 
5.5%
9 232
 
2.6%
5 106
 
1.2%
7 91
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7581
86.4%
Dash Punctuation 1197
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3874
51.1%
1 1193
 
15.7%
4 526
 
6.9%
3 495
 
6.5%
2 490
 
6.5%
6 483
 
6.4%
9 232
 
3.1%
5 106
 
1.4%
7 91
 
1.2%
8 91
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 1197
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8778
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3874
44.1%
- 1197
 
13.6%
1 1193
 
13.6%
4 526
 
6.0%
3 495
 
5.6%
2 490
 
5.6%
6 483
 
5.5%
9 232
 
2.6%
5 106
 
1.2%
7 91
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8778
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3874
44.1%
- 1197
 
13.6%
1 1193
 
13.6%
4 526
 
6.0%
3 495
 
5.6%
2 490
 
5.6%
6 483
 
5.5%
9 232
 
2.6%
5 106
 
1.2%
7 91
 
1.0%

인허가일자
Date

Distinct377
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Minimum1978-10-17 00:00:00
Maximum2024-04-16 00:00:00
2024-04-30T04:40:47.872889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:40:48.066262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing399
Missing (%)100.0%
Memory size3.6 KiB

영업상태코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
3
367 
1
 
32

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 (%)
3 367
92.0%
1 32
 
8.0%

Length

2024-04-30T04:40:48.204924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:48.293474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 367
92.0%
1 32
 
8.0%

영업상태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
폐업
367 
영업/정상
 
32

Length

Max length5
Median length2
Mean length2.2406015
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 367
92.0%
영업/정상 32
 
8.0%

Length

2024-04-30T04:40:48.383292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:48.471717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 367
92.0%
영업/정상 32
 
8.0%

상세영업상태코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2
367 
1
 
32

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 (%)
2 367
92.0%
1 32
 
8.0%

Length

2024-04-30T04:40:48.556173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:48.632883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 367
92.0%
1 32
 
8.0%

상세영업상태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
폐업
367 
영업
 
32

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 367
92.0%
영업 32
 
8.0%

Length

2024-04-30T04:40:48.714347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:48.799882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 367
92.0%
영업 32
 
8.0%

폐업일자
Date

MISSING 

Distinct336
Distinct (%)91.6%
Missing32
Missing (%)8.0%
Memory size3.2 KiB
Minimum1996-08-06 00:00:00
Maximum2024-03-19 00:00:00
2024-04-30T04:40:48.907744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:40:49.023893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing399
Missing (%)100.0%
Memory size3.6 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing399
Missing (%)100.0%
Memory size3.6 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing399
Missing (%)100.0%
Memory size3.6 KiB

전화번호
Text

MISSING 

Distinct270
Distinct (%)91.5%
Missing104
Missing (%)26.1%
Memory size3.2 KiB
2024-04-30T04:40:49.207753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.5118644
Min length2

Characters and Unicode

Total characters2806
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

Unique261 ?
Unique (%)88.5%

Sample

1st row02 6044800
2nd row02 6925970
3rd row02 6929283
4th row0226983337
5th row0226916910
ValueCountFrequency (%)
02 84
 
23.0%
0226916910 2
 
0.5%
0 2
 
0.5%
0226952466 2
 
0.5%
0226083141 2
 
0.5%
0226430559 2
 
0.5%
0226472229 2
 
0.5%
0226465587 2
 
0.5%
0226074151 2
 
0.5%
0226969913 1
 
0.3%
Other values (265) 265
72.4%
2024-04-30T04:40:49.518562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 591
21.1%
0 518
18.5%
6 362
12.9%
4 199
 
7.1%
9 199
 
7.1%
5 183
 
6.5%
7 182
 
6.5%
1 170
 
6.1%
3 161
 
5.7%
8 155
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2720
96.9%
Space Separator 86
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 591
21.7%
0 518
19.0%
6 362
13.3%
4 199
 
7.3%
9 199
 
7.3%
5 183
 
6.7%
7 182
 
6.7%
1 170
 
6.2%
3 161
 
5.9%
8 155
 
5.7%
Space Separator
ValueCountFrequency (%)
86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 591
21.1%
0 518
18.5%
6 362
12.9%
4 199
 
7.1%
9 199
 
7.1%
5 183
 
6.5%
7 182
 
6.5%
1 170
 
6.1%
3 161
 
5.7%
8 155
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 591
21.1%
0 518
18.5%
6 362
12.9%
4 199
 
7.1%
9 199
 
7.1%
5 183
 
6.5%
7 182
 
6.5%
1 170
 
6.1%
3 161
 
5.7%
8 155
 
5.5%

소재지면적
Real number (ℝ)

MISSING 

Distinct274
Distinct (%)74.5%
Missing31
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean74.271277
Minimum0
Maximum1092
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-30T04:40:49.677318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.3575
Q126.525
median49.75
Q387.82
95-th percentile213.433
Maximum1092
Range1092
Interquartile range (IQR)61.295

Descriptive statistics

Standard deviation91.038573
Coefficient of variation (CV)1.2257575
Kurtosis45.393392
Mean74.271277
Median Absolute Deviation (MAD)26.685
Skewness5.2353542
Sum27331.83
Variance8288.0217
MonotonicityNot monotonic
2024-04-30T04:40:49.798515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.0 14
 
3.5%
99.0 10
 
2.5%
33.0 10
 
2.5%
40.0 6
 
1.5%
27.0 6
 
1.5%
30.0 5
 
1.3%
50.0 5
 
1.3%
77.0 4
 
1.0%
49.5 4
 
1.0%
26.4 3
 
0.8%
Other values (264) 301
75.4%
(Missing) 31
 
7.8%
ValueCountFrequency (%)
0.0 2
0.5%
3.0 1
0.3%
4.0 1
0.3%
6.27 1
0.3%
7.82 1
0.3%
8.5 1
0.3%
9.0 1
0.3%
9.69 1
0.3%
10.0 1
0.3%
10.5 1
0.3%
ValueCountFrequency (%)
1092.0 1
0.3%
481.85 1
0.3%
480.0 1
0.3%
402.39 1
0.3%
385.44 1
0.3%
374.5 1
0.3%
363.92 1
0.3%
360.0 1
0.3%
355.16 1
0.3%
350.0 1
0.3%

소재지우편번호
Text

Distinct105
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-04-30T04:40:50.086802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.085213
Min length6

Characters and Unicode

Total characters2428
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

Unique45 ?
Unique (%)11.3%

Sample

1st row158856
2nd row158822
3rd row158856
4th row158828
5th row158828
ValueCountFrequency (%)
158860 23
 
5.8%
158811 15
 
3.8%
158827 15
 
3.8%
158830 13
 
3.3%
158861 13
 
3.3%
158819 12
 
3.0%
158829 11
 
2.8%
158859 11
 
2.8%
158848 10
 
2.5%
158849 10
 
2.5%
Other values (95) 266
66.7%
2024-04-30T04:40:50.435501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 810
33.4%
1 511
21.0%
5 491
20.2%
0 123
 
5.1%
2 95
 
3.9%
6 85
 
3.5%
4 82
 
3.4%
3 72
 
3.0%
9 65
 
2.7%
7 60
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2394
98.6%
Dash Punctuation 34
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 810
33.8%
1 511
21.3%
5 491
20.5%
0 123
 
5.1%
2 95
 
4.0%
6 85
 
3.6%
4 82
 
3.4%
3 72
 
3.0%
9 65
 
2.7%
7 60
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 810
33.4%
1 511
21.0%
5 491
20.2%
0 123
 
5.1%
2 95
 
3.9%
6 85
 
3.5%
4 82
 
3.4%
3 72
 
3.0%
9 65
 
2.7%
7 60
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 810
33.4%
1 511
21.0%
5 491
20.2%
0 123
 
5.1%
2 95
 
3.9%
6 85
 
3.5%
4 82
 
3.4%
3 72
 
3.0%
9 65
 
2.7%
7 60
 
2.5%

지번주소
Text

Distinct373
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-04-30T04:40:50.669637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length39
Mean length26.213033
Min length18

Characters and Unicode

Total characters10459
Distinct characters147
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

Unique350 ?
Unique (%)87.7%

Sample

1st row서울특별시 양천구 신정동 883-1번지
2nd row서울특별시 양천구 신월동 13-15번지
3rd row서울특별시 양천구 신정동 879-7번지
4th row서울특별시 양천구 신월동 133-13번지
5th row서울특별시 양천구 신월동 139-7번지 13,14,15
ValueCountFrequency (%)
서울특별시 399
20.5%
양천구 399
20.5%
신월동 162
 
8.3%
신정동 133
 
6.8%
목동 105
 
5.4%
1층 83
 
4.3%
지하1층 31
 
1.6%
지층 25
 
1.3%
지상1층 17
 
0.9%
2층 16
 
0.8%
Other values (468) 576
29.6%
2024-04-30T04:40:51.060909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1887
18.0%
1 615
 
5.9%
지 453
 
4.3%
동 426
 
4.1%
천 403
 
3.9%
양 402
 
3.8%
시 401
 
3.8%
구 400
 
3.8%
서 399
 
3.8%
특 399
 
3.8%
Other values (137) 4674
44.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5831
55.8%
Decimal Number 2172
 
20.8%
Space Separator 1887
 
18.0%
Dash Punctuation 380
 
3.6%
Open Punctuation 80
 
0.8%
Close Punctuation 80
 
0.8%
Other Punctuation 19
 
0.2%
Uppercase Letter 9
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 453
 
7.8%
동 426
 
7.3%
천 403
 
6.9%
양 402
 
6.9%
시 401
 
6.9%
구 400
 
6.9%
서 399
 
6.8%
특 399
 
6.8%
별 399
 
6.8%
울 399
 
6.8%
Other values (118) 1750
30.0%
Decimal Number
ValueCountFrequency (%)
1 615
28.3%
2 288
13.3%
0 209
 
9.6%
9 193
 
8.9%
3 168
 
7.7%
5 155
 
7.1%
7 151
 
7.0%
4 141
 
6.5%
6 139
 
6.4%
8 113
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
B 7
77.8%
C 1
 
11.1%
A 1
 
11.1%
Space Separator
ValueCountFrequency (%)
1887
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 380
100.0%
Open Punctuation
ValueCountFrequency (%)
( 80
100.0%
Close Punctuation
ValueCountFrequency (%)
) 80
100.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5831
55.8%
Common 4619
44.2%
Latin 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 453
 
7.8%
동 426
 
7.3%
천 403
 
6.9%
양 402
 
6.9%
시 401
 
6.9%
구 400
 
6.9%
서 399
 
6.8%
특 399
 
6.8%
별 399
 
6.8%
울 399
 
6.8%
Other values (118) 1750
30.0%
Common
ValueCountFrequency (%)
1887
40.9%
1 615
 
13.3%
- 380
 
8.2%
2 288
 
6.2%
0 209
 
4.5%
9 193
 
4.2%
3 168
 
3.6%
5 155
 
3.4%
7 151
 
3.3%
4 141
 
3.1%
Other values (6) 432
 
9.4%
Latin
ValueCountFrequency (%)
B 7
77.8%
C 1
 
11.1%
A 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5831
55.8%
ASCII 4628
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1887
40.8%
1 615
 
13.3%
- 380
 
8.2%
2 288
 
6.2%
0 209
 
4.5%
9 193
 
4.2%
3 168
 
3.6%
5 155
 
3.3%
7 151
 
3.3%
4 141
 
3.0%
Other values (9) 441
 
9.5%
Hangul
ValueCountFrequency (%)
지 453
 
7.8%
동 426
 
7.3%
천 403
 
6.9%
양 402
 
6.9%
시 401
 
6.9%
구 400
 
6.9%
서 399
 
6.8%
특 399
 
6.8%
별 399
 
6.8%
울 399
 
6.8%
Other values (118) 1750
30.0%

도로명주소
Text

MISSING 

Distinct152
Distinct (%)98.1%
Missing244
Missing (%)61.2%
Memory size3.2 KiB
2024-04-30T04:40:51.311368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length41
Mean length31.522581
Min length23

Characters and Unicode

Total characters4886
Distinct characters122
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

Unique149 ?
Unique (%)96.1%

Sample

1st row서울특별시 양천구 남부순환로79길 11 (신월동)
2nd row서울특별시 양천구 곰달래로13길 7-1, 1층 (신월동)
3rd row서울특별시 양천구 오목로 106 (신월동, 지층)
4th row서울특별시 양천구 목동중앙북로5길 10, 1층 (목동)
5th row서울특별시 양천구 남부순환로88길 5-8 (신정동)
ValueCountFrequency (%)
서울특별시 155
 
15.9%
양천구 155
 
15.9%
1층 55
 
5.7%
신월동 54
 
5.5%
신정동 50
 
5.1%
목동 38
 
3.9%
2층 15
 
1.5%
지층 14
 
1.4%
오목로 10
 
1.0%
지상1층 9
 
0.9%
Other values (261) 418
43.0%
2024-04-30T04:40:51.691289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
818
 
16.7%
동 211
 
4.3%
1 202
 
4.1%
) 171
 
3.5%
( 171
 
3.5%
서 168
 
3.4%
양 165
 
3.4%
로 162
 
3.3%
천 159
 
3.3%
시 156
 
3.2%
Other values (112) 2503
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2827
57.9%
Space Separator 818
 
16.7%
Decimal Number 710
 
14.5%
Close Punctuation 171
 
3.5%
Open Punctuation 171
 
3.5%
Other Punctuation 156
 
3.2%
Dash Punctuation 29
 
0.6%
Uppercase Letter 3
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 211
 
7.5%
서 168
 
5.9%
양 165
 
5.8%
로 162
 
5.7%
천 159
 
5.6%
시 156
 
5.5%
특 155
 
5.5%
별 155
 
5.5%
구 155
 
5.5%
울 155
 
5.5%
Other values (95) 1186
42.0%
Decimal Number
ValueCountFrequency (%)
1 202
28.5%
2 112
15.8%
0 82
11.5%
3 65
 
9.2%
5 51
 
7.2%
6 49
 
6.9%
8 44
 
6.2%
4 40
 
5.6%
7 37
 
5.2%
9 28
 
3.9%
Space Separator
ValueCountFrequency (%)
818
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Other Punctuation
ValueCountFrequency (%)
, 156
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2827
57.9%
Common 2056
42.1%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 211
 
7.5%
서 168
 
5.9%
양 165
 
5.8%
로 162
 
5.7%
천 159
 
5.6%
시 156
 
5.5%
특 155
 
5.5%
별 155
 
5.5%
구 155
 
5.5%
울 155
 
5.5%
Other values (95) 1186
42.0%
Common
ValueCountFrequency (%)
818
39.8%
1 202
 
9.8%
) 171
 
8.3%
( 171
 
8.3%
, 156
 
7.6%
2 112
 
5.4%
0 82
 
4.0%
3 65
 
3.2%
5 51
 
2.5%
6 49
 
2.4%
Other values (6) 179
 
8.7%
Latin
ValueCountFrequency (%)
B 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2827
57.9%
ASCII 2059
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
818
39.7%
1 202
 
9.8%
) 171
 
8.3%
( 171
 
8.3%
, 156
 
7.6%
2 112
 
5.4%
0 82
 
4.0%
3 65
 
3.2%
5 51
 
2.5%
6 49
 
2.4%
Other values (7) 182
 
8.8%
Hangul
ValueCountFrequency (%)
동 211
 
7.5%
서 168
 
5.9%
양 165
 
5.8%
로 162
 
5.7%
천 159
 
5.6%
시 156
 
5.5%
특 155
 
5.5%
별 155
 
5.5%
구 155
 
5.5%
울 155
 
5.5%
Other values (95) 1186
42.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct83
Distinct (%)54.2%
Missing246
Missing (%)61.7%
Infinite0
Infinite (%)0.0%
Mean7987.9673
Minimum7902
Maximum8101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-30T04:40:51.809767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7902
5-th percentile7909
Q17932
median7969
Q38033
95-th percentile8086.4
Maximum8101
Range199
Interquartile range (IQR)101

Descriptive statistics

Standard deviation59.776053
Coefficient of variation (CV)0.0074832621
Kurtosis-1.2922751
Mean7987.9673
Median Absolute Deviation (MAD)51
Skewness0.2335665
Sum1222159
Variance3573.1766
MonotonicityNot monotonic
2024-04-30T04:40:51.931921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8020 5
 
1.3%
7915 5
 
1.3%
7966 5
 
1.3%
8079 4
 
1.0%
7909 4
 
1.0%
7917 4
 
1.0%
8034 3
 
0.8%
8087 3
 
0.8%
7955 3
 
0.8%
7923 3
 
0.8%
Other values (73) 114
28.6%
(Missing) 246
61.7%
ValueCountFrequency (%)
7902 2
 
0.5%
7906 1
 
0.3%
7907 3
0.8%
7909 4
1.0%
7910 1
 
0.3%
7911 2
 
0.5%
7915 5
1.3%
7917 4
1.0%
7918 1
 
0.3%
7920 3
0.8%
ValueCountFrequency (%)
8101 1
 
0.3%
8095 1
 
0.3%
8093 1
 
0.3%
8092 1
 
0.3%
8091 1
 
0.3%
8087 3
0.8%
8086 1
 
0.3%
8082 2
0.5%
8079 4
1.0%
8077 1
 
0.3%

사업장명
Text

Distinct384
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-04-30T04:40:52.172742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length19
Mean length6.1553885
Min length1

Characters and Unicode

Total characters2456
Distinct characters413
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique370 ?
Unique (%)92.7%

Sample

1st row동원식품
2nd row충남기름집
3rd row상진제과
4th row장호식품
5th row일미식품
ValueCountFrequency (%)
주식회사 7
 
1.5%
커피 4
 
0.9%
대신식품 3
 
0.7%
coffee 3
 
0.7%
손찬락의장수이야기 2
 
0.4%
로스터스 2
 
0.4%
소문난맛집 2
 
0.4%
우리식품 2
 
0.4%
신선자연식품 2
 
0.4%
탄도리푸드서비스 2
 
0.4%
Other values (421) 431
93.7%
2024-04-30T04:40:52.548335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 121
 
4.9%
품 106
 
4.3%
( 69
 
2.8%
) 69
 
2.8%
주 62
 
2.5%
61
 
2.5%
스 60
 
2.4%
이 51
 
2.1%
드 40
 
1.6%
리 37
 
1.5%
Other values (403) 1780
72.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2083
84.8%
Uppercase Letter 93
 
3.8%
Open Punctuation 69
 
2.8%
Close Punctuation 69
 
2.8%
Space Separator 61
 
2.5%
Lowercase Letter 58
 
2.4%
Decimal Number 13
 
0.5%
Other Punctuation 10
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 121
 
5.8%
품 106
 
5.1%
주 62
 
3.0%
스 60
 
2.9%
이 51
 
2.4%
드 40
 
1.9%
리 37
 
1.8%
푸 33
 
1.6%
피 25
 
1.2%
산 24
 
1.2%
Other values (354) 1524
73.2%
Uppercase Letter
ValueCountFrequency (%)
C 10
10.8%
S 9
9.7%
A 8
 
8.6%
R 7
 
7.5%
O 7
 
7.5%
E 7
 
7.5%
K 7
 
7.5%
F 7
 
7.5%
T 7
 
7.5%
U 4
 
4.3%
Other values (10) 20
21.5%
Lowercase Letter
ValueCountFrequency (%)
e 13
22.4%
f 7
12.1%
o 6
10.3%
s 5
 
8.6%
i 5
 
8.6%
a 4
 
6.9%
r 3
 
5.2%
c 3
 
5.2%
h 2
 
3.4%
t 2
 
3.4%
Other values (6) 8
13.8%
Decimal Number
ValueCountFrequency (%)
2 4
30.8%
0 3
23.1%
5 2
15.4%
1 2
15.4%
3 1
 
7.7%
9 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
& 4
40.0%
' 3
30.0%
, 2
20.0%
＆ 1
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 69
100.0%
Close Punctuation
ValueCountFrequency (%)
) 69
100.0%
Space Separator
ValueCountFrequency (%)
61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2082
84.8%
Common 222
 
9.0%
Latin 151
 
6.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 121
 
5.8%
품 106
 
5.1%
주 62
 
3.0%
스 60
 
2.9%
이 51
 
2.4%
드 40
 
1.9%
리 37
 
1.8%
푸 33
 
1.6%
피 25
 
1.2%
산 24
 
1.2%
Other values (353) 1523
73.2%
Latin
ValueCountFrequency (%)
e 13
 
8.6%
C 10
 
6.6%
S 9
 
6.0%
A 8
 
5.3%
R 7
 
4.6%
O 7
 
4.6%
E 7
 
4.6%
K 7
 
4.6%
F 7
 
4.6%
f 7
 
4.6%
Other values (26) 69
45.7%
Common
ValueCountFrequency (%)
( 69
31.1%
) 69
31.1%
61
27.5%
2 4
 
1.8%
& 4
 
1.8%
' 3
 
1.4%
0 3
 
1.4%
5 2
 
0.9%
1 2
 
0.9%
, 2
 
0.9%
Other values (3) 3
 
1.4%
Han
ValueCountFrequency (%)
福 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2082
84.8%
ASCII 372
 
15.1%
None 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 121
 
5.8%
품 106
 
5.1%
주 62
 
3.0%
스 60
 
2.9%
이 51
 
2.4%
드 40
 
1.9%
리 37
 
1.8%
푸 33
 
1.6%
피 25
 
1.2%
산 24
 
1.2%
Other values (353) 1523
73.2%
ASCII
ValueCountFrequency (%)
( 69
18.5%
) 69
18.5%
61
16.4%
e 13
 
3.5%
C 10
 
2.7%
S 9
 
2.4%
A 8
 
2.2%
R 7
 
1.9%
O 7
 
1.9%
E 7
 
1.9%
Other values (38) 112
30.1%
None
ValueCountFrequency (%)
＆ 1
100.0%
CJK
ValueCountFrequency (%)
福 1
100.0%

최종수정일자
Date

Distinct347
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Minimum1999-01-20 00:00:00
Maximum2024-04-16 10:42:00
2024-04-30T04:40:52.660495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:40:52.953951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터갱신구분
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
I
329 
U
70 

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 329
82.5%
U 70
 
17.5%

Length

2024-04-30T04:40:53.058344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:53.139131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 329
82.5%
u 70
 
17.5%

데이터갱신일자
Date

Distinct66
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-03 23:08:00
2024-04-30T04:40:53.228822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:40:53.347532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
식품제조가공업
355 
기타 식품제조가공업
44 

Length

Max length10
Median length7
Mean length7.3308271
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 355
89.0%
기타 식품제조가공업 44
 
11.0%

Length

2024-04-30T04:40:53.463019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:53.571202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 399
90.1%
기타 44
 
9.9%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct317
Distinct (%)82.3%
Missing14
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean186872.94
Minimum184298.44
Maximum189473.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-30T04:40:53.673246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184298.44
5-th percentile184616.2
Q1185398.81
median187122.72
Q3188049.18
95-th percentile188986.78
Maximum189473.53
Range5175.0923
Interquartile range (IQR)2650.3618

Descriptive statistics

Standard deviation1461.0528
Coefficient of variation (CV)0.0078184288
Kurtosis-1.3477724
Mean186872.94
Median Absolute Deviation (MAD)1256.0159
Skewness-0.08973837
Sum71946082
Variance2134675.2
MonotonicityNot monotonic
2024-04-30T04:40:53.784763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188175.450195493 4
 
1.0%
185011.388536671 4
 
1.0%
186723.502831215 4
 
1.0%
184373.07604881 3
 
0.8%
188884.075622342 3
 
0.8%
188965.738829492 3
 
0.8%
188650.113584937 3
 
0.8%
185479.616483618 3
 
0.8%
187740.662222313 3
 
0.8%
184481.466072194 3
 
0.8%
Other values (307) 352
88.2%
(Missing) 14
 
3.5%
ValueCountFrequency (%)
184298.438528197 1
 
0.3%
184373.07604881 3
0.8%
184391.766990487 1
 
0.3%
184445.432090297 1
 
0.3%
184449.386660638 1
 
0.3%
184480.430654989 2
0.5%
184481.466072194 3
0.8%
184516.314305204 1
 
0.3%
184565.919100857 1
 
0.3%
184569.781919895 1
 
0.3%
ValueCountFrequency (%)
189473.530827695 2
0.5%
189471.306217651 1
 
0.3%
189444.834114092 3
0.8%
189280.689807363 1
 
0.3%
189274.417717013 2
0.5%
189212.074951921 1
 
0.3%
189081.187941353 1
 
0.3%
189056.077484585 1
 
0.3%
189055.134431663 1
 
0.3%
189048.740098848 1
 
0.3%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct317
Distinct (%)82.3%
Missing14
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean447331.45
Minimum444842.87
Maximum449727.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-30T04:40:53.889376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum444842.87
5-th percentile445768.15
Q1446527.62
median447140.02
Q3448150.86
95-th percentile449367.15
Maximum449727.48
Range4884.6058
Interquartile range (IQR)1623.24

Descriptive statistics

Standard deviation1108.1046
Coefficient of variation (CV)0.0024771445
Kurtosis-0.57801561
Mean447331.45
Median Absolute Deviation (MAD)781.89896
Skewness0.28707773
Sum1.7222261 × 108
Variance1227895.9
MonotonicityNot monotonic
2024-04-30T04:40:53.996313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448339.835468341 4
 
1.0%
447492.493293458 4
 
1.0%
445569.639465968 4
 
1.0%
448174.209450269 3
 
0.8%
447186.888604306 3
 
0.8%
448365.713974147 3
 
0.8%
446358.120598616 3
 
0.8%
445976.962071391 3
 
0.8%
446792.1255132 3
 
0.8%
447756.19747093 3
 
0.8%
Other values (307) 352
88.2%
(Missing) 14
 
3.5%
ValueCountFrequency (%)
444842.873729184 2
0.5%
444958.276659499 1
0.3%
445021.404026362 1
0.3%
445039.928375227 1
0.3%
445091.494659491 1
0.3%
445104.207668176 1
0.3%
445229.76179636 1
0.3%
445241.514474936 1
0.3%
445347.715893152 2
0.5%
445430.359091548 2
0.5%
ValueCountFrequency (%)
449727.47955699 1
0.3%
449675.516469489 1
0.3%
449649.016215774 2
0.5%
449646.517348931 1
0.3%
449627.418341306 1
0.3%
449626.380102558 2
0.5%
449610.71549451 2
0.5%
449583.899391857 1
0.3%
449578.800268858 1
0.3%
449568.73556008 1
0.3%

위생업태명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
식품제조가공업
338 
<NA>
44 
기타 식품제조가공업
 
17

Length

Max length10
Median length7
Mean length6.7969925
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 338
84.7%
<NA> 44
 
11.0%
기타 식품제조가공업 17
 
4.3%

Length

2024-04-30T04:40:54.111281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:54.222820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 355
85.3%
na 44
 
10.6%
기타 17
 
4.1%

남성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
301 
1
47 
0
 
25
2
 
18
3
 
7

Length

Max length4
Median length4
Mean length3.2631579
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row2
2nd row<NA>
3rd row<NA>
4th row1
5th row2

Common Values

ValueCountFrequency (%)
<NA> 301
75.4%
1 47
 
11.8%
0 25
 
6.3%
2 18
 
4.5%
3 7
 
1.8%
4 1
 
0.3%

Length

2024-04-30T04:40:54.317515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:54.408844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 301
75.4%
1 47
 
11.8%
0 25
 
6.3%
2 18
 
4.5%
3 7
 
1.8%
4 1
 
0.3%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)6.2%
Missing302
Missing (%)75.7%
Infinite0
Infinite (%)0.0%
Mean0.95876289
Minimum0
Maximum6
Zeros38
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-30T04:40:54.489560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3.2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1807265
Coefficient of variation (CV)1.2315105
Kurtosis6.1929891
Mean0.95876289
Median Absolute Deviation (MAD)1
Skewness2.1746844
Sum93
Variance1.3941151
MonotonicityNot monotonic
2024-04-30T04:40:54.574749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 41
 
10.3%
0 38
 
9.5%
2 11
 
2.8%
4 3
 
0.8%
3 2
 
0.5%
6 2
 
0.5%
(Missing) 302
75.7%
ValueCountFrequency (%)
0 38
9.5%
1 41
10.3%
2 11
 
2.8%
3 2
 
0.5%
4 3
 
0.8%
6 2
 
0.5%
ValueCountFrequency (%)
6 2
 
0.5%
4 3
 
0.8%
3 2
 
0.5%
2 11
 
2.8%
1 41
10.3%
0 38
9.5%

영업장주변구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
299 
주택가주변
69 
기타
 
23
아파트지역
 
7
결혼예식장주변
 
1

Length

Max length7
Median length4
Mean length4.0827068
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 299
74.9%
주택가주변 69
 
17.3%
기타 23
 
5.8%
아파트지역 7
 
1.8%
결혼예식장주변 1
 
0.3%

Length

2024-04-30T04:40:54.680813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:54.783834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 299
74.9%
주택가주변 69
 
17.3%
기타 23
 
5.8%
아파트지역 7
 
1.8%
결혼예식장주변 1
 
0.3%

등급구분명
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
299 
기타
96 
자율
 
4

Length

Max length4
Median length4
Mean length3.4987469
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 299
74.9%
기타 96
 
24.1%
자율 4
 
1.0%

Length

2024-04-30T04:40:54.901880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:54.993946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 299
74.9%
기타 96
 
24.1%
자율 4
 
1.0%

급수시설구분명
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
234 
상수도전용
165 

Length

Max length5
Median length4
Mean length4.4135338
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 234
58.6%
상수도전용 165
41.4%

Length

2024-04-30T04:40:55.081671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:55.174050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 234
58.6%
상수도전용 165
41.4%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
395 
0
 
4

Length

Max length4
Median length4
Mean length3.9699248
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> 395
99.0%
0 4
 
1.0%

Length

2024-04-30T04:40:55.266145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:55.354654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 395
99.0%
0 4
 
1.0%

본사종업원수
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
206 
0
193 

Length

Max length4
Median length4
Mean length2.5488722
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 206
51.6%
0 193
48.4%

Length

2024-04-30T04:40:55.436393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:55.517115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 206
51.6%
0 193
48.4%

공장사무직종업원수
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
201 
0
190 
1
 
8

Length

Max length4
Median length4
Mean length2.5112782
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 201
50.4%
0 190
47.6%
1 8
 
2.0%

Length

2024-04-30T04:40:55.611559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:55.701440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 201
50.4%
0 190
47.6%
1 8
 
2.0%

공장판매직종업원수
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
205 
0
193 
7
 
1

Length

Max length4
Median length4
Mean length2.5413534
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 205
51.4%
0 193
48.4%
7 1
 
0.3%

Length

2024-04-30T04:40:55.969432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:56.266651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 205
51.4%
0 193
48.4%
7 1
 
0.3%

공장생산직종업원수
Categorical

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
193 
0
180 
1
 
11
2
 
6
3
 
5

Length

Max length4
Median length1
Mean length2.4511278
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 193
48.4%
0 180
45.1%
1 11
 
2.8%
2 6
 
1.5%
3 5
 
1.3%
4 4
 
1.0%

Length

2024-04-30T04:40:56.566272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:56.811341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 193
48.4%
0 180
45.1%
1 11
 
2.8%
2 6
 
1.5%
3 5
 
1.3%
4 4
 
1.0%

건물소유구분명
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
169 
임대
132 
자가
98 

Length

Max length4
Median length2
Mean length2.8471178
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> 169
42.4%
임대 132
33.1%
자가 98
24.6%

Length

2024-04-30T04:40:56.923177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:40:57.023343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 169
42.4%
임대 132
33.1%
자가 98
24.6%

보증액
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)8.9%
Missing320
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean1607594.9
Minimum0
Maximum20000000
Zeros68
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-30T04:40:57.131308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10500000
Maximum20000000
Range20000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4614408.1
Coefficient of variation (CV)2.8703798
Kurtosis8.9152878
Mean1607594.9
Median Absolute Deviation (MAD)0
Skewness3.0848322
Sum1.27 × 108
Variance2.1292762 × 1013
MonotonicityNot monotonic
2024-04-30T04:40:57.266267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 68
 
17.0%
20000000 3
 
0.8%
10000000 3
 
0.8%
5000000 2
 
0.5%
3000000 1
 
0.3%
15000000 1
 
0.3%
9000000 1
 
0.3%
(Missing) 320
80.2%
ValueCountFrequency (%)
0 68
17.0%
3000000 1
 
0.3%
5000000 2
 
0.5%
9000000 1
 
0.3%
10000000 3
 
0.8%
15000000 1
 
0.3%
20000000 3
 
0.8%
ValueCountFrequency (%)
20000000 3
 
0.8%
15000000 1
 
0.3%
10000000 3
 
0.8%
9000000 1
 
0.3%
5000000 2
 
0.5%
3000000 1
 
0.3%
0 68
17.0%

월세액
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)13.9%
Missing320
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean75316.456
Minimum0
Maximum1000000
Zeros68
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-30T04:40:57.376583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile565000
Maximum1000000
Range1000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation214666.32
Coefficient of variation (CV)2.8501915
Kurtosis8.7304579
Mean75316.456
Median Absolute Deviation (MAD)0
Skewness3.0526042
Sum5950000
Variance4.6081629 × 1010
MonotonicityNot monotonic
2024-04-30T04:40:57.490809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 68
 
17.0%
300000 2
 
0.5%
1000000 1
 
0.3%
250000 1
 
0.3%
700000 1
 
0.3%
150000 1
 
0.3%
850000 1
 
0.3%
900000 1
 
0.3%
450000 1
 
0.3%
500000 1
 
0.3%
(Missing) 320
80.2%
ValueCountFrequency (%)
0 68
17.0%
150000 1
 
0.3%
250000 1
 
0.3%
300000 2
 
0.5%
450000 1
 
0.3%
500000 1
 
0.3%
550000 1
 
0.3%
700000 1
 
0.3%
850000 1
 
0.3%
900000 1
 
0.3%
ValueCountFrequency (%)
1000000 1
0.3%
900000 1
0.3%
850000 1
0.3%
700000 1
0.3%
550000 1
0.3%
500000 1
0.3%
450000 1
0.3%
300000 2
0.5%
250000 1
0.3%
150000 1
0.3%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing44
Missing (%)11.0%
Memory size930.0 B
False
355 
(Missing)
44 
ValueCountFrequency (%)
False 355
89.0%
(Missing) 44
 
11.0%
2024-04-30T04:40:57.583056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)5.9%
Missing44
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean1.6015211
Minimum0
Maximum92.2
Zeros334
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-30T04:40:57.673113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.516
Maximum92.2
Range92.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.6555825
Coefficient of variation (CV)6.0290073
Kurtosis56.754962
Mean1.6015211
Median Absolute Deviation (MAD)0
Skewness7.3230307
Sum568.54
Variance93.230274
MonotonicityNot monotonic
2024-04-30T04:40:57.783478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 334
83.7%
4.5 2
 
0.5%
2.88 1
 
0.3%
12.0 1
 
0.3%
0.11 1
 
0.3%
16.0 1
 
0.3%
92.2 1
 
0.3%
12.54 1
 
0.3%
2.36 1
 
0.3%
6.23 1
 
0.3%
Other values (11) 11
 
2.8%
(Missing) 44
 
11.0%
ValueCountFrequency (%)
0.0 334
83.7%
0.11 1
 
0.3%
1.6 1
 
0.3%
2.36 1
 
0.3%
2.88 1
 
0.3%
4.5 2
 
0.5%
5.0 1
 
0.3%
6.23 1
 
0.3%
7.78 1
 
0.3%
10.0 1
 
0.3%
ValueCountFrequency (%)
92.2 1
0.3%
90.48 1
0.3%
70.68 1
0.3%
61.64 1
0.3%
53.02 1
0.3%
49.5 1
0.3%
42.38 1
0.3%
23.14 1
0.3%
16.0 1
0.3%
12.54 1
0.3%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing399
Missing (%)100.0%
Memory size3.6 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing399
Missing (%)100.0%
Memory size3.6 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing399
Missing (%)100.0%
Memory size3.6 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
031400003140000-106-1978-0003819781017<NA>3폐업2폐업20000515<NA><NA><NA>02 6044800104.62158856서울특별시 양천구 신정동 883-1번지<NA><NA>동원식품2001-09-28 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업23기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
131400003140000-106-1984-0000119841204<NA>3폐업2폐업20020510<NA><NA><NA>02 6925970<NA>158822서울특별시 양천구 신월동 13-15번지<NA><NA>충남기름집2001-11-22 00:00:00I2018-08-31 23:59:59.0식품제조가공업184677.330469448781.295027식품제조가공업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
231400003140000-106-1984-0000219841016<NA>3폐업2폐업20030908<NA><NA><NA>02 6929283<NA>158856서울특별시 양천구 신정동 879-7번지<NA><NA>상진제과2001-12-12 00:00:00I2018-08-31 23:59:59.0식품제조가공업187430.22398447268.969891식품제조가공업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
331400003140000-106-1986-0059219860206<NA>3폐업2폐업20090922<NA><NA><NA>0226983337103.62158828서울특별시 양천구 신월동 133-13번지<NA><NA>장호식품2006-01-27 00:00:00I2018-08-31 23:59:59.0식품제조가공업185219.569419447715.338113식품제조가공업12기타기타<NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
431400003140000-106-1987-0063719870829<NA>3폐업2폐업20090325<NA><NA><NA>0226916910183.37158828서울특별시 양천구 신월동 139-7번지 13,14,15<NA><NA>일미식품2003-07-10 00:00:00I2018-08-31 23:59:59.0식품제조가공업184942.414852447971.327813식품제조가공업24주택가주변기타<NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
531400003140000-106-1989-0049319890607<NA>3폐업2폐업20120622<NA><NA><NA>0226955969188.33158840서울특별시 양천구 신월동 535-4번지 지층<NA><NA>풍미식품2008-12-22 11:44:52I2018-08-31 23:59:59.0식품제조가공업186110.201186446397.80603식품제조가공업32기타기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
631400003140000-106-1992-0000119920818<NA>3폐업2폐업20100608<NA><NA><NA>0226483031330.0158851서울특별시 양천구 신정동 202-10번지<NA><NA>우리식품2009-07-31 11:46:45I2018-08-31 23:59:59.0식품제조가공업187961.25482445021.404026식품제조가공업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
731400003140000-106-1993-0070619930203<NA>3폐업2폐업20040210<NA><NA><NA>022606974966.0158845서울특별시 양천구 신월동 952-1번지<NA><NA>아가랜드2002-07-29 00:00:00I2018-08-31 23:59:59.0식품제조가공업185177.642854446600.090923식품제조가공업30주택가주변기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
831400003140000-106-1994-0048419940506<NA>3폐업2폐업20090401<NA><NA><NA>0226015562123.42158846서울특별시 양천구 신월동 961-8번지<NA><NA>도원외식산업1999-06-22 00:00:00I2018-08-31 23:59:59.0식품제조가공업185285.643528446621.187867식품제조가공업10주택가주변기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
931400003140000-106-1994-0048519940104<NA>3폐업2폐업20150518<NA><NA><NA>0226953741133.92158841서울특별시 양천구 신월동 549-7번지서울특별시 양천구 남부순환로79길 11 (신월동)8065영광물산2005-06-17 00:00:00I2018-08-31 23:59:59.0식품제조가공업185883.835446098.02식품제조가공업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
38931400003140000-106-2022-000032022-03-29<NA>1영업/정상1영업<NA><NA><NA><NA>02 2603887766.0158-831서울특별시 양천구 신월동 226-1서울특별시 양천구 곰달래로 22, 2층 (신월동)7925주식회사 한바다식품2023-02-28 09:41:21U2022-12-03 00:03:00.0기타 식품제조가공업185356.793613447511.624012<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
39031400003140000-106-2022-000042022-08-30<NA>1영업/정상1영업<NA><NA><NA><NA><NA>138.0158-840서울특별시 양천구 신월동 547-7서울특별시 양천구 남부순환로 571, 101호 (신월동)8032나래푸드2023-08-08 16:27:53U2022-12-07 23:00:00.0기타 식품제조가공업185744.924948446190.192625<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
39131400003140000-106-2022-000052022-10-24<NA>1영업/정상1영업<NA><NA><NA><NA><NA>138.0158-806서울특별시 양천구 목동 404-84 지하1층서울특별시 양천구 목동동로12길 43, 지하1층 (목동)8007(주)디에스영웅2023-02-28 13:26:04U2022-12-03 00:03:00.0기타 식품제조가공업188961.777666446721.956484<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
39231400003140000-106-2022-000062022-11-03<NA>1영업/정상1영업<NA><NA><NA><NA>0269568906173.32158-828서울특별시 양천구 신월동 136-5 지층서울특별시 양천구 남부순환로 395, 지층 (신월동)7920(주)비움랩스2023-08-17 14:09:14U2022-12-07 23:09:00.0기타 식품제조가공업185005.792215447796.176428<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
39331400003140000-106-2022-0000720221109<NA>1영업/정상1영업<NA><NA><NA><NA>022690138590.0158827서울특별시 양천구 신월동 104-21 1층서울특별시 양천구 월정로 183-1, 1층 (신월동)7918민속두부공장2022-11-09 17:27:02I2021-10-31 23:02:00.0기타 식품제조가공업185356.872847448099.895949<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
39431400003140000-106-2022-000082022-11-14<NA>3폐업2폐업2023-11-07<NA><NA><NA><NA>52.9158-833서울특별시 양천구 신월동 438-3 1층서울특별시 양천구 오목로 1, 1층 (신월동)7928어라운지(AROUNZ)2023-11-07 16:18:02U2022-11-01 00:09:00.0기타 식품제조가공업185630.941986446573.571253<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
39531400003140000-106-2023-000012023-02-02<NA>1영업/정상1영업<NA><NA><NA><NA>022643298820.0158-818서울특별시 양천구 목동 775-31 1층서울특별시 양천구 목동중앙서로 33, 1층 (목동)7964빈도 로스터스 커피2023-04-21 09:33:23U2022-12-03 22:03:00.0기타 식품제조가공업188227.336978447874.771387<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
39631400003140000-106-2023-000022023-03-22<NA>1영업/정상1영업<NA><NA><NA><NA>07088013029183.64158-819서울특별시 양천구 목동 807-5 가인빌딩 지하1층 비02호서울특별시 양천구 등촌로 2, 가인빌딩 지하1층 비02호 (목동)7966주식회사 렌위치코리아 공장2024-02-27 11:37:01U2023-12-03 00:01:00.0기타 식품제조가공업187943.072004447573.990028<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
39731400003140000-106-2023-000032023-09-18<NA>1영업/정상1영업<NA><NA><NA><NA><NA>14.5158-833서울특별시 양천구 신월동 439-11 2층 일부호서울특별시 양천구 오목로 7, 2층 일부호 (신월동)7928니카커피2023-09-18 10:19:44I2022-12-08 22:00:00.0기타 식품제조가공업185656.554697446582.33596<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
39831400003140000-106-2024-000012024-04-16<NA>1영업/정상1영업<NA><NA><NA><NA><NA>100.0158-070서울특별시 양천구 신정동 1254 양천벤처타운서울특별시 양천구 신정로 267, 양천벤처타운 3층 301호 (신정동)8079희망일굼터2024-04-16 10:42:00I2023-12-03 23:08:00.0기타 식품제조가공업186723.502831445569.639466<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>