Overview

Dataset statistics

Number of variables33
Number of observations53
Missing cells669
Missing cells (%)38.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 KiB
Average record size in memory286.5 B

Variable types

Categorical10
Numeric4
DateTime4
Unsupported11
Text4

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),업무구분,건축물명,건축물연면적,건축물상태명,청소대상시작일자,청소대상종료일자,휴업폐지사유,업무구분명
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-19491/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
건축물연면적 is highly imbalanced (76.8%)Imbalance
인허가취소일자 has 53 (100.0%) missing valuesMissing
폐업일자 has 30 (56.6%) missing valuesMissing
휴업시작일자 has 53 (100.0%) missing valuesMissing
휴업종료일자 has 53 (100.0%) missing valuesMissing
재개업일자 has 53 (100.0%) missing valuesMissing
전화번호 has 9 (17.0%) missing valuesMissing
소재지면적 has 53 (100.0%) missing valuesMissing
소재지우편번호 has 53 (100.0%) missing valuesMissing
도로명우편번호 has 47 (88.7%) missing valuesMissing
업태구분명 has 53 (100.0%) missing valuesMissing
건축물명 has 53 (100.0%) missing valuesMissing
건축물상태명 has 53 (100.0%) missing valuesMissing
청소대상시작일자 has 53 (100.0%) missing valuesMissing
청소대상종료일자 has 53 (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
소재지면적 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지우편번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
업태구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건축물명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건축물상태명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
청소대상시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
청소대상종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-10 23:58:04.024014
Analysis finished2024-05-10 23:58:04.937644
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
3140000
53 

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 53
100.0%

Length

2024-05-10T23:58:05.173113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:58:05.531939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3140000 53
100.0%

관리번호
Real number (ℝ)

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1400003 × 1017
Minimum3.1400003 × 1017
Maximum3.1400003 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-05-10T23:58:05.869584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1400003 × 1017
5-th percentile3.1400003 × 1017
Q13.1400003 × 1017
median3.1400003 × 1017
Q33.1400003 × 1017
95-th percentile3.1400003 × 1017
Maximum3.1400003 × 1017
Range2100000
Interquartile range (IQR)800000

Descriptive statistics

Standard deviation601770.45
Coefficient of variation (CV)1.9164662 × 10-12
Kurtosis-0.2543223
Mean3.1400003 × 1017
Median Absolute Deviation (MAD)500032
Skewness0.88214394
Sum-1.8047424 × 1018
Variance3.6212768 × 1011
MonotonicityStrictly increasing
2024-05-10T23:58:06.284425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
314000031200200001 1
 
1.9%
314000031201100001 1
 
1.9%
314000031200800001 1
 
1.9%
314000031200800002 1
 
1.9%
314000031200800003 1
 
1.9%
314000031200800004 1
 
1.9%
314000031200800005 1
 
1.9%
314000031200800006 1
 
1.9%
314000031200800007 1
 
1.9%
314000031200800008 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
314000031200200001 1
1.9%
314000031200200002 1
1.9%
314000031200200003 1
1.9%
314000031200200004 1
1.9%
314000031200200005 1
1.9%
314000031200200006 1
1.9%
314000031200200007 1
1.9%
314000031200200008 1
1.9%
314000031200200009 1
1.9%
314000031200200010 1
1.9%
ValueCountFrequency (%)
314000031202300001 1
1.9%
314000031202200001 1
1.9%
314000031201700005 1
1.9%
314000031201700004 1
1.9%
314000031201700003 1
1.9%
314000031201700002 1
1.9%
314000031201700001 1
1.9%
314000031201600002 1
1.9%
314000031201600001 1
1.9%
314000031201500002 1
1.9%

인허가일자
Date

Distinct47
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Memory size556.0 B
Minimum1995-09-26 00:00:00
Maximum2023-06-05 00:00:00
2024-05-10T23:58:06.726396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:58:07.145868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

영업상태코드
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
3
34 
1
19 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 34
64.2%
1 19
35.8%

Length

2024-05-10T23:58:07.558615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:58:07.862318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 34
64.2%
1 19
35.8%

영업상태명
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
폐업
34 
영업/정상
19 

Length

Max length5
Median length2
Mean length3.0754717
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 34
64.2%
영업/정상 19
35.8%

Length

2024-05-10T23:58:08.250971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:58:08.646007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 34
64.2%
영업/정상 19
35.8%

상세영업상태코드
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
2
34 
11
19 

Length

Max length2
Median length1
Mean length1.3584906
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row11
3rd row11
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 34
64.2%
11 19
35.8%

Length

2024-05-10T23:58:09.000003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:58:09.255996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 34
64.2%
11 19
35.8%

상세영업상태명
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
폐업
34 
정상
19 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 34
64.2%
정상 19
35.8%

Length

2024-05-10T23:58:09.624544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:58:09.914839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 34
64.2%
정상 19
35.8%

폐업일자
Date

MISSING 

Distinct19
Distinct (%)82.6%
Missing30
Missing (%)56.6%
Memory size556.0 B
Minimum2004-07-21 00:00:00
Maximum2023-05-19 00:00:00
2024-05-10T23:58:10.238121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:58:10.605235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

전화번호
Text

MISSING 

Distinct43
Distinct (%)97.7%
Missing9
Missing (%)17.0%
Memory size556.0 B
2024-05-10T23:58:11.196061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.7954545
Min length8

Characters and Unicode

Total characters431
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

Unique42 ?
Unique (%)95.5%

Sample

1st row2646-2701
2nd row02-2654-1394
3rd row2646-1117
4th row692-8765
5th row2608-1454
ValueCountFrequency (%)
2604-8477 2
 
4.5%
2691-5459 1
 
2.3%
2646-2701 1
 
2.3%
2607-8662 1
 
2.3%
2694-6610 1
 
2.3%
2694-0930 1
 
2.3%
2698-0180 1
 
2.3%
2696-6234 1
 
2.3%
02-2696-6954 1
 
2.3%
2684-3793 1
 
2.3%
Other values (33) 33
75.0%
2024-05-10T23:58:12.395504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 75
17.4%
2 61
14.2%
- 59
13.7%
0 51
11.8%
4 39
9.0%
9 32
7.4%
8 29
 
6.7%
5 23
 
5.3%
1 22
 
5.1%
7 21
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 372
86.3%
Dash Punctuation 59
 
13.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 75
20.2%
2 61
16.4%
0 51
13.7%
4 39
10.5%
9 32
8.6%
8 29
 
7.8%
5 23
 
6.2%
1 22
 
5.9%
7 21
 
5.6%
3 19
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 75
17.4%
2 61
14.2%
- 59
13.7%
0 51
11.8%
4 39
9.0%
9 32
7.4%
8 29
 
6.7%
5 23
 
5.3%
1 22
 
5.1%
7 21
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 75
17.4%
2 61
14.2%
- 59
13.7%
0 51
11.8%
4 39
9.0%
9 32
7.4%
8 29
 
6.7%
5 23
 
5.3%
1 22
 
5.1%
7 21
 
4.9%

소재지면적
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

소재지우편번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

지번주소
Text

Distinct44
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2024-05-10T23:58:13.169252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length32
Mean length22.698113
Min length18

Characters and Unicode

Total characters1203
Distinct characters62
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

Unique38 ?
Unique (%)71.7%

Sample

1st row서울특별시 양천구 신월동 547-1 명산그린빌 지2층 비201호
2nd row서울특별시 양천구 목동 ***-*
3rd row서울특별시 양천구 목동 ***-** ***호
4th row서울특별시 양천구 신월동 507-11 3층
5th row서울특별시 양천구 신월동 996-24
ValueCountFrequency (%)
서울특별시 53
21.5%
양천구 53
21.5%
신월동 32
13.0%
19
 
7.7%
목동 12
 
4.9%
신정동 9
 
3.7%
90-10 5
 
2.0%
3층 3
 
1.2%
4층 2
 
0.8%
305호 2
 
0.8%
Other values (54) 56
22.8%
2024-05-10T23:58:14.417103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
224
18.6%
* 91
 
7.6%
동 55
 
4.6%
서 53
 
4.4%
특 53
 
4.4%
별 53
 
4.4%
시 53
 
4.4%
양 53
 
4.4%
천 53
 
4.4%
구 53
 
4.4%
Other values (52) 462
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 655
54.4%
Space Separator 224
 
18.6%
Decimal Number 180
 
15.0%
Other Punctuation 91
 
7.6%
Dash Punctuation 50
 
4.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 55
8.4%
서 53
 
8.1%
특 53
 
8.1%
별 53
 
8.1%
시 53
 
8.1%
양 53
 
8.1%
천 53
 
8.1%
구 53
 
8.1%
울 53
 
8.1%
신 42
 
6.4%
Other values (36) 134
20.5%
Decimal Number
ValueCountFrequency (%)
1 37
20.6%
0 25
13.9%
9 24
13.3%
4 24
13.3%
5 22
12.2%
7 15
8.3%
2 13
 
7.2%
3 11
 
6.1%
6 5
 
2.8%
8 4
 
2.2%
Space Separator
ValueCountFrequency (%)
224
100.0%
Other Punctuation
ValueCountFrequency (%)
* 91
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 655
54.4%
Common 547
45.5%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 55
8.4%
서 53
 
8.1%
특 53
 
8.1%
별 53
 
8.1%
시 53
 
8.1%
양 53
 
8.1%
천 53
 
8.1%
구 53
 
8.1%
울 53
 
8.1%
신 42
 
6.4%
Other values (36) 134
20.5%
Common
ValueCountFrequency (%)
224
41.0%
* 91
16.6%
- 50
 
9.1%
1 37
 
6.8%
0 25
 
4.6%
9 24
 
4.4%
4 24
 
4.4%
5 22
 
4.0%
7 15
 
2.7%
2 13
 
2.4%
Other values (5) 22
 
4.0%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 655
54.4%
ASCII 548
45.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
224
40.9%
* 91
16.6%
- 50
 
9.1%
1 37
 
6.8%
0 25
 
4.6%
9 24
 
4.4%
4 24
 
4.4%
5 22
 
4.0%
7 15
 
2.7%
2 13
 
2.4%
Other values (6) 23
 
4.2%
Hangul
ValueCountFrequency (%)
동 55
8.4%
서 53
 
8.1%
특 53
 
8.1%
별 53
 
8.1%
시 53
 
8.1%
양 53
 
8.1%
천 53
 
8.1%
구 53
 
8.1%
울 53
 
8.1%
신 42
 
6.4%
Other values (36) 134
20.5%

도로명주소
Text

Distinct49
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size556.0 B
2024-05-10T23:58:15.270954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length35
Mean length28.584906
Min length21

Characters and Unicode

Total characters1515
Distinct characters79
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

Unique45 ?
Unique (%)84.9%

Sample

1st row서울특별시 양천구 남부순환로 565 (신월동,명산그린빌
2nd row서울특별시 양천구 등촌로 ** (목동)
3rd row서울특별시 양천구 목동중앙북로**길 *-** (목동)
4th row서울특별시 양천구 신월로27길 6 (신월동,3층)
5th row서울특별시 양천구 남부순환로74길 6 (신월동)
ValueCountFrequency (%)
서울특별시 53
18.3%
양천구 53
18.3%
신월동 20
 
6.9%
19
 
6.6%
월정로 8
 
2.8%
목동 8
 
2.8%
층 6
 
2.1%
신정동 6
 
2.1%
남부순환로 5
 
1.7%
193 5
 
1.7%
Other values (91) 106
36.7%
2024-05-10T23:58:16.785321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
263
 
17.4%
* 76
 
5.0%
동 64
 
4.2%
양 57
 
3.8%
서 56
 
3.7%
로 56
 
3.7%
시 53
 
3.5%
천 53
 
3.5%
구 53
 
3.5%
울 53
 
3.5%
Other values (69) 731
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 888
58.6%
Space Separator 263
 
17.4%
Decimal Number 145
 
9.6%
Other Punctuation 110
 
7.3%
Open Punctuation 53
 
3.5%
Close Punctuation 51
 
3.4%
Dash Punctuation 4
 
0.3%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 64
 
7.2%
양 57
 
6.4%
서 56
 
6.3%
로 56
 
6.3%
시 53
 
6.0%
천 53
 
6.0%
구 53
 
6.0%
울 53
 
6.0%
별 53
 
6.0%
특 53
 
6.0%
Other values (52) 337
38.0%
Decimal Number
ValueCountFrequency (%)
1 28
19.3%
0 19
13.1%
3 17
11.7%
2 15
10.3%
4 14
9.7%
6 14
9.7%
9 12
8.3%
7 12
8.3%
5 10
 
6.9%
8 4
 
2.8%
Other Punctuation
ValueCountFrequency (%)
* 76
69.1%
, 34
30.9%
Space Separator
ValueCountFrequency (%)
263
100.0%
Open Punctuation
ValueCountFrequency (%)
( 53
100.0%
Close Punctuation
ValueCountFrequency (%)
) 51
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 888
58.6%
Common 626
41.3%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 64
 
7.2%
양 57
 
6.4%
서 56
 
6.3%
로 56
 
6.3%
시 53
 
6.0%
천 53
 
6.0%
구 53
 
6.0%
울 53
 
6.0%
별 53
 
6.0%
특 53
 
6.0%
Other values (52) 337
38.0%
Common
ValueCountFrequency (%)
263
42.0%
* 76
 
12.1%
( 53
 
8.5%
) 51
 
8.1%
, 34
 
5.4%
1 28
 
4.5%
0 19
 
3.0%
3 17
 
2.7%
2 15
 
2.4%
4 14
 
2.2%
Other values (6) 56
 
8.9%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 888
58.6%
ASCII 627
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
263
41.9%
* 76
 
12.1%
( 53
 
8.5%
) 51
 
8.1%
, 34
 
5.4%
1 28
 
4.5%
0 19
 
3.0%
3 17
 
2.7%
2 15
 
2.4%
4 14
 
2.2%
Other values (7) 57
 
9.1%
Hangul
ValueCountFrequency (%)
동 64
 
7.2%
양 57
 
6.4%
서 56
 
6.3%
로 56
 
6.3%
시 53
 
6.0%
천 53
 
6.0%
구 53
 
6.0%
울 53
 
6.0%
별 53
 
6.0%
특 53
 
6.0%
Other values (52) 337
38.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)100.0%
Missing47
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean7993.1667
Minimum7938
Maximum8040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-05-10T23:58:17.261722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7938
5-th percentile7939.75
Q17958
median8003
Q38024.75
95-th percentile8037.5
Maximum8040
Range102
Interquartile range (IQR)66.75

Descriptive statistics

Standard deviation42.845848
Coefficient of variation (CV)0.0053603096
Kurtosis-1.813445
Mean7993.1667
Median Absolute Deviation (MAD)32
Skewness-0.46883406
Sum47959
Variance1835.7667
MonotonicityNot monotonic
2024-05-10T23:58:17.762832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8040 1
 
1.9%
7938 1
 
1.9%
8030 1
 
1.9%
7945 1
 
1.9%
7997 1
 
1.9%
8009 1
 
1.9%
(Missing) 47
88.7%
ValueCountFrequency (%)
7938 1
1.9%
7945 1
1.9%
7997 1
1.9%
8009 1
1.9%
8030 1
1.9%
8040 1
1.9%
ValueCountFrequency (%)
8040 1
1.9%
8030 1
1.9%
8009 1
1.9%
7997 1
1.9%
7945 1
1.9%
7938 1
1.9%

사업장명
Text

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size556.0 B
2024-05-10T23:58:18.529197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length7.0754717
Min length2

Characters and Unicode

Total characters375
Distinct characters128
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)96.2%

Sample

1st row(주)심천기업
2nd row(주)서광안전시스템
3rd row(주)에녹플랜트엔지니어링
4th row(주)신양아이에스
5th row(주)삼화그린
ValueCountFrequency (%)
성석환경 2
 
3.6%
주)심천기업 1
 
1.8%
신풍종합개발(주 1
 
1.8%
인웅공영(주 1
 
1.8%
주)세웅씨에스 1
 
1.8%
대백건설(주 1
 
1.8%
민덕공영(주 1
 
1.8%
신웅건설 1
 
1.8%
주)보우사 1
 
1.8%
수연환경 1
 
1.8%
Other values (44) 44
80.0%
2024-05-10T23:58:19.882080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 29
 
7.7%
( 28
 
7.5%
) 28
 
7.5%
환 11
 
2.9%
경 11
 
2.9%
에 11
 
2.9%
스 10
 
2.7%
설 8
 
2.1%
합 7
 
1.9%
이 7
 
1.9%
Other values (118) 225
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 317
84.5%
Open Punctuation 28
 
7.5%
Close Punctuation 28
 
7.5%
Space Separator 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 29
 
9.1%
환 11
 
3.5%
경 11
 
3.5%
에 11
 
3.5%
스 10
 
3.2%
설 8
 
2.5%
합 7
 
2.2%
이 7
 
2.2%
업 7
 
2.2%
종 7
 
2.2%
Other values (115) 209
65.9%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 317
84.5%
Common 58
 
15.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 29
 
9.1%
환 11
 
3.5%
경 11
 
3.5%
에 11
 
3.5%
스 10
 
3.2%
설 8
 
2.5%
합 7
 
2.2%
이 7
 
2.2%
업 7
 
2.2%
종 7
 
2.2%
Other values (115) 209
65.9%
Common
ValueCountFrequency (%)
( 28
48.3%
) 28
48.3%
2
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 317
84.5%
ASCII 58
 
15.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 29
 
9.1%
환 11
 
3.5%
경 11
 
3.5%
에 11
 
3.5%
스 10
 
3.2%
설 8
 
2.5%
합 7
 
2.2%
이 7
 
2.2%
업 7
 
2.2%
종 7
 
2.2%
Other values (115) 209
65.9%
ASCII
ValueCountFrequency (%)
( 28
48.3%
) 28
48.3%
2
 
3.4%

최종수정일자
Date

Distinct27
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
Minimum2015-01-05 21:09:18
Maximum2023-12-28 15:11:16
2024-05-10T23:58:20.237628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:58:20.693679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

데이터갱신구분
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
I
31 
U
22 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 31
58.5%
U 22
41.5%

Length

2024-05-10T23:58:21.278463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:58:21.758049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 31
58.5%
u 22
41.5%

데이터갱신일자
Date

Distinct12
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Memory size556.0 B
Minimum2018-08-31 23:59:59
Maximum2022-12-04 22:01:00
2024-05-10T23:58:22.182234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:58:22.824335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

업태구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

좌표정보(X)
Real number (ℝ)

Distinct47
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186516.82
Minimum184534.62
Maximum189038.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-05-10T23:58:23.540169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184534.62
5-th percentile185034.65
Q1185324.56
median186082.18
Q3187800.02
95-th percentile188916.45
Maximum189038.04
Range4503.4186
Interquartile range (IQR)2475.453

Descriptive statistics

Standard deviation1331.5843
Coefficient of variation (CV)0.0071392184
Kurtosis-1.1954377
Mean186516.82
Median Absolute Deviation (MAD)863.43747
Skewness0.48455434
Sum9885391.7
Variance1773116.8
MonotonicityNot monotonic
2024-05-10T23:58:24.279900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
185313.79572279 5
 
9.4%
186404.624501079 3
 
5.7%
185711.687468509 1
 
1.9%
187855.506684725 1
 
1.9%
186083.619969467 1
 
1.9%
185157.799726791 1
 
1.9%
188538.244361038 1
 
1.9%
189038.037681199 1
 
1.9%
188338.449364244 1
 
1.9%
185324.563960692 1
 
1.9%
Other values (37) 37
69.8%
ValueCountFrequency (%)
184534.619036954 1
 
1.9%
184748.532781957 1
 
1.9%
184996.592192739 1
 
1.9%
185060.029130323 1
 
1.9%
185099.7875285 1
 
1.9%
185157.799726791 1
 
1.9%
185203.154210446 1
 
1.9%
185218.738248638 1
 
1.9%
185313.79572279 5
9.4%
185324.563960692 1
 
1.9%
ValueCountFrequency (%)
189038.037681199 1
1.9%
188955.415701667 1
1.9%
188953.066831076 1
1.9%
188892.040932506 1
1.9%
188538.244361038 1
1.9%
188478.330682072 1
1.9%
188338.449364244 1
1.9%
188107.288080788 1
1.9%
188048.537721509 1
1.9%
187887.974778566 1
1.9%

좌표정보(Y)
Real number (ℝ)

Distinct47
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean447337.77
Minimum445569.64
Maximum449476.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-05-10T23:58:25.016088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum445569.64
5-th percentile446146.31
Q1446510.42
median447166.86
Q3448186.51
95-th percentile449262.65
Maximum449476.4
Range3906.7574
Interquartile range (IQR)1676.0958

Descriptive statistics

Standard deviation1021.6294
Coefficient of variation (CV)0.0022837987
Kurtosis-0.79053257
Mean447337.77
Median Absolute Deviation (MAD)788.80995
Skewness0.46274549
Sum23708902
Variance1043726.7
MonotonicityNot monotonic
2024-05-10T23:58:25.750884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
448186.514400952 5
 
9.4%
446729.087344494 3
 
5.7%
446261.058527886 1
 
1.9%
449476.396868849 1
 
1.9%
446221.085000927 1
 
1.9%
446846.524836683 1
 
1.9%
447476.275717561 1
 
1.9%
449399.489126654 1
 
1.9%
448558.704682233 1
 
1.9%
446594.893517371 1
 
1.9%
Other values (37) 37
69.8%
ValueCountFrequency (%)
445569.639465968 1
1.9%
445827.441199077 1
1.9%
446145.094734253 1
1.9%
446147.124980307 1
1.9%
446152.066202692 1
1.9%
446190.192625377 1
1.9%
446221.085000927 1
1.9%
446261.058527886 1
1.9%
446375.762284772 1
1.9%
446378.049578238 1
1.9%
ValueCountFrequency (%)
449476.396868849 1
1.9%
449399.489126654 1
1.9%
449349.681855253 1
1.9%
449204.635182585 1
1.9%
449059.768341526 1
1.9%
448733.208855264 1
1.9%
448558.704682233 1
1.9%
448382.261752916 1
1.9%
448285.09277095 1
1.9%
448279.633016419 1
1.9%

업무구분
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
31
33 
<NA>
20 

Length

Max length4
Median length2
Mean length2.754717
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31
2nd row<NA>
3rd row<NA>
4th row31
5th row31

Common Values

ValueCountFrequency (%)
31 33
62.3%
<NA> 20
37.7%

Length

2024-05-10T23:58:26.407970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:58:27.068214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31 33
62.3%
na 20
37.7%

건축물명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

건축물연면적
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
<NA>
51 
0
 
2

Length

Max length4
Median length4
Mean length3.8867925
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> 51
96.2%
0 2
 
3.8%

Length

2024-05-10T23:58:27.766640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:58:28.147577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 51
96.2%
0 2
 
3.8%

건축물상태명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

청소대상시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

청소대상종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

휴업폐지사유
Categorical

Distinct14
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Memory size556.0 B
<NA>
22 
영업부진
10 
사업부진
7 
자진폐업
3 
사업장 이전
 
2
Other values (9)
9 

Length

Max length21
Median length4
Mean length4.9056604
Min length2

Unique

Unique9 ?
Unique (%)17.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 22
41.5%
영업부진 10
18.9%
사업부진 7
 
13.2%
자진폐업 3
 
5.7%
사업장 이전 2
 
3.8%
파산 1
 
1.9%
업종 변경 1
 
1.9%
영업장 타구로 이전 1
 
1.9%
양천구에서 강서구로 영업소 소재지 이전 1
 
1.9%
영등포로 이전 1
 
1.9%
Other values (4) 4
 
7.5%

Length

2024-05-10T23:58:28.810976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 22
31.9%
영업부진 10
14.5%
사업부진 7
 
10.1%
이전 6
 
8.7%
자진폐업 3
 
4.3%
사업장 2
 
2.9%
변경 2
 
2.9%
소재지 2
 
2.9%
영등포로 1
 
1.4%
인한 1
 
1.4%
Other values (13) 13
18.8%

업무구분명
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
저수조청소업
33 
<NA>
20 

Length

Max length6
Median length6
Mean length5.245283
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
저수조청소업 33
62.3%
<NA> 20
37.7%

Length

2024-05-10T23:58:29.400615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:58:30.015973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
저수조청소업 33
62.3%
na 20
37.7%

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)업무구분건축물명건축물연면적건축물상태명청소대상시작일자청소대상종료일자휴업폐지사유업무구분명
0314000031400003120020000120140520<NA>3폐업2폐업20140520<NA><NA><NA>2646-2701<NA><NA>서울특별시 양천구 신월동 547-1 명산그린빌 지2층 비201호서울특별시 양천구 남부순환로 565 (신월동,명산그린빌<NA>(주)심천기업2015-01-05 21:09:18I2018-08-31 23:59:59.0<NA>185711.687469446261.05852831<NA><NA><NA><NA><NA>사업부진저수조청소업
131400003140000312002000022012-04-03<NA>1영업/정상11정상<NA><NA><NA><NA>02-2654-1394<NA><NA>서울특별시 양천구 목동 ***-*서울특별시 양천구 등촌로 ** (목동)<NA>(주)서광안전시스템2023-12-18 16:04:19U2022-11-01 22:00:00.0<NA>187887.974779448285.092771<NA><NA><NA><NA><NA><NA><NA><NA>
231400003140000312002000032014-09-22<NA>1영업/정상11정상<NA><NA><NA><NA>2646-1117<NA><NA>서울특별시 양천구 목동 ***-** ***호서울특별시 양천구 목동중앙북로**길 *-** (목동)<NA>(주)에녹플랜트엔지니어링2023-12-19 15:14:22U2022-11-01 22:01:00.0<NA>188478.330682449349.681855<NA><NA><NA><NA><NA><NA><NA><NA>
3314000031400003120020000420111226<NA>3폐업2폐업20111226<NA><NA><NA>692-8765<NA><NA>서울특별시 양천구 신월동 507-11 3층서울특별시 양천구 신월로27길 6 (신월동,3층)<NA>(주)신양아이에스2017-10-25 13:39:14I2018-08-31 23:59:59.0<NA>186404.624501446729.08734431<NA><NA><NA><NA><NA>자진폐업저수조청소업
4314000031400003120020000520030905<NA>3폐업2폐업<NA><NA><NA><NA>2608-1454<NA><NA>서울특별시 양천구 신월동 996-24서울특별시 양천구 남부순환로74길 6 (신월동)<NA>(주)삼화그린2015-01-05 21:09:18I2018-08-31 23:59:59.0<NA>185556.147769446386.11265831<NA><NA><NA><NA><NA>사업부진저수조청소업
5314000031400003120020000619950926<NA>3폐업2폐업20040721<NA><NA><NA>690-9156<NA><NA>서울특별시 양천구 신월동 444-20서울특별시 양천구 월정로 41 (신월동)<NA>엑스포환경사2018-02-09 17:56:03I2018-08-31 23:59:59.0<NA>185979.011837446807.99561731<NA><NA><NA><NA><NA><NA>저수조청소업
631400003140000312002000072007-12-14<NA>1영업/정상11정상<NA><NA><NA><NA>2606-1392<NA><NA>서울특별시 양천구 신정동 ***-**서울특별시 양천구 중앙로 *** (신정동)<NA>상호환경개발(주)2023-12-19 15:09:58U2022-11-01 22:01:00.0<NA>186824.358676446781.210398<NA><NA><NA><NA><NA><NA><NA><NA>
731400003140000312002000082007-12-11<NA>1영업/정상11정상<NA><NA><NA><NA>02-2692-8336<NA><NA>서울특별시 양천구 신월동 ***-** *층서울특별시 양천구 신월로**길 * (신월동,*층)<NA>신양환경2023-12-15 13:17:04U2022-11-01 23:07:00.0<NA>186404.624501446729.087344<NA><NA><NA><NA><NA><NA><NA><NA>
8314000031400003120020000920120726<NA>3폐업2폐업20120726<NA><NA><NA>694-9806<NA><NA>서울특별시 양천구 신월동 964-15서울특별시 양천구 남부순환로70길 10 (신월동)<NA>대한환경2015-01-05 21:09:18I2018-08-31 23:59:59.0<NA>185400.534118446658.19097831<NA><NA><NA><NA><NA>파산저수조청소업
9314000031400003120020001020120104<NA>3폐업2폐업20120104<NA><NA><NA>2605-8885<NA><NA>서울특별시 양천구 신정동 991-7서울특별시 양천구 오목로48길 2 (신정동)<NA>원영기공2015-01-05 21:09:18I2018-08-31 23:59:59.0<NA>187800.01694447035.34218431<NA><NA><NA><NA><NA>영업부진저수조청소업
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)업무구분건축물명건축물연면적건축물상태명청소대상시작일자청소대상종료일자휴업폐지사유업무구분명
4331400003140000312015000022023-06-05<NA>1영업/정상11정상<NA><NA><NA><NA><NA><NA><NA>서울특별시 양천구 신정동 ***-*서울특별시 양천구 은행정로**길 *, *층 (신정동)7938(주)금화산업2023-12-21 09:13:29U2022-11-01 22:03:00.0<NA>187477.476438447312.0184<NA><NA><NA><NA><NA><NA><NA><NA>
4431400003140000312016000012019-12-27<NA>1영업/정상11정상<NA><NA><NA><NA><NA><NA><NA>서울특별시 양천구 신월동 ***-* 로즈그린타운 ***호서울특별시 양천구 신월로 ***, *층 ***호 (신월동, 로즈그린타운)8030명성종합위생2023-12-06 14:23:10U2022-11-02 00:08:00.0<NA>186082.175714446378.049578<NA><NA><NA><NA><NA><NA><NA><NA>
45314000031400003120160000220160714<NA>3폐업2폐업20220215<NA><NA><NA>02-2060-2290<NA><NA>서울특별시 양천구 신정동 905-3서울특별시 양천구 신정중앙로 70, 201호 (신정동)7945주식회사 프라임서비스2022-02-15 17:42:27U2022-02-17 02:40:00.0<NA>187518.445217447166.85953131<NA>0<NA><NA><NA>사무실 이전으로 인한 관할지역 변경저수조청소업
46314000031400003120170000120170112<NA>3폐업2폐업20180123<NA><NA><NA><NA><NA><NA>서울특별시 양천구 신월동 115-31서울특별시 양천구 월정로 151 (신월동)<NA>장수종합관리2018-01-23 15:48:46I2018-08-31 23:59:59.0<NA>185500.434915447805.43934531<NA><NA><NA><NA><NA>영업부진저수조청소업
4731400003140000312017000022017-01-16<NA>1영업/정상11정상<NA><NA><NA><NA>2601-6008<NA><NA>서울특별시 양천구 신월동 ***-*서울특별시 양천구 남부순환로**길 * (신월동)<NA>(주)더루코리아2023-12-28 15:01:52U2022-11-01 21:00:00.0<NA>184996.592193447587.094714<NA><NA><NA><NA><NA><NA><NA><NA>
4831400003140000312017000032017-04-26<NA>3폐업2폐업2023-05-19<NA><NA><NA>02-2068-2343<NA><NA>서울특별시 양천구 신월동 ***-*서울특별시 양천구 남부순환로**길 **, *층 (신월동)<NA>(주)광원종합상사2023-05-19 15:36:06U2022-12-04 22:01:00.0<NA>185455.955218446403.495156<NA><NA><NA><NA><NA><NA><NA><NA>
4931400003140000312017000042017-07-11<NA>1영업/정상11정상<NA><NA><NA><NA>02-6404-7039<NA><NA>서울특별시 양천구 신월동 **-*서울특별시 양천구 월정로 *** (신월동)<NA>(주)삼심성업2023-12-20 16:05:29U2022-11-01 22:03:00.0<NA>185218.738249448382.261753<NA><NA><NA><NA><NA><NA><NA><NA>
5031400003140000312017000052017-08-21<NA>1영업/정상11정상<NA><NA><NA><NA>02-868-6547<NA><NA>서울특별시 양천구 목동 ***-* (지상*층)서울특별시 양천구 목동중앙서로*길 **, *층 (목동)<NA>오행종합개발(주)2023-12-06 14:36:30U2022-11-02 00:08:00.0<NA>188048.537722447718.194936<NA><NA><NA><NA><NA><NA><NA><NA>
51314000031400003120220000120220609<NA>3폐업2폐업20220609<NA><NA><NA>02-467-5705<NA><NA>서울특별시 양천구 목동 917-9 현대41타워서울특별시 양천구 목동동로 293, 현대41타워 16층 1604호 (목동)7997가인종합건설(주)2022-06-10 16:12:36U2021-12-05 23:04:00.0<NA>188953.066831447333.569188<NA><NA><NA><NA><NA><NA><NA><NA>
5231400003140000312023000012023-03-23<NA>1영업/정상11정상<NA><NA><NA><NA>02-834-4454<NA><NA>서울특별시 양천구 신정동 ***-*서울특별시 양천구 신목로**길 **-**, *층 (신정동)8009모든서비스2023-12-06 14:28:10U2022-11-02 00:08:00.0<NA>188955.415702446510.418628<NA><NA><NA><NA><NA><NA><NA><NA>