Overview

Dataset statistics

Number of variables17
Number of observations125
Missing cells49
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.5 KiB
Average record size in memory143.1 B

Variable types

Categorical6
DateTime1
Text4
Numeric6

Dataset

Description경기도 내 세탁업 현황 입니다. 빨래방, 세탁소, 운동화빨래방 등 의류 기타섬유제품이나 피혁제품등을 세탁하는 업소 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15087326/fileData.do

Alerts

시군명 has constant value ""Constant
위생업태명 is highly overall correlated with 소재지면적 and 2 other fieldsHigh correlation
통합영업상태명 is highly overall correlated with 영업상태명High correlation
영업상태명 is highly overall correlated with 통합영업상태명High correlation
업태구분명정보 is highly overall correlated with 소재지면적 and 2 other fieldsHigh correlation
소재지면적 is highly overall correlated with 업태구분명정보 and 1 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
위도 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
경도 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
좌표(X) is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
좌표(Y) is highly overall correlated with 소재지우편번호 and 5 other fieldsHigh correlation
업태구분명정보 is highly imbalanced (88.3%)Imbalance
위생업태명 is highly imbalanced (88.3%)Imbalance
소재지면적 has 6 (4.8%) missing valuesMissing
소재지우편번호 has 13 (10.4%) missing valuesMissing
좌표(X) has 15 (12.0%) missing valuesMissing
좌표(Y) has 15 (12.0%) missing valuesMissing
소재지면적 has 57 (45.6%) zerosZeros

Reproduction

Analysis started2023-12-12 15:17:16.608891
Analysis finished2023-12-12 15:17:21.613747
Duration5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
의왕시
125 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의왕시 125
100.0%

Length

2023-12-13T00:17:21.692176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:17:21.787080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의왕시 125
100.0%

인허가일자
Date

Distinct100
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2000-09-29 00:00:00
Maximum2020-07-02 00:00:00
2023-12-13T00:17:21.913013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:22.520013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사업장명
Text

Distinct105
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T00:17:22.849376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length5.064
Min length3

Characters and Unicode

Total characters633
Distinct characters130
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)72.8%

Sample

1st rowLG진달래 세탁소
2nd rowLG진달래세탁소
3rd rowe-편한세탁
4th row개나리세탁
5th row굿모닝세탁소
ValueCountFrequency (%)
백양세탁소 5
 
3.6%
세탁소 5
 
3.6%
대명세탁 4
 
2.9%
명품세탁 4
 
2.9%
현대세탁소 3
 
2.2%
현대세탁 2
 
1.4%
크린가가 2
 
1.4%
대우세탁 2
 
1.4%
우성세탁소 2
 
1.4%
푸른세탁 2
 
1.4%
Other values (102) 107
77.5%
2023-12-13T00:17:23.301590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 104
 
16.4%
탁 104
 
16.4%
소 56
 
8.8%
대 16
 
2.5%
명 16
 
2.5%
크 13
 
2.1%
13
 
2.1%
백 11
 
1.7%
화 11
 
1.7%
양 10
 
1.6%
Other values (120) 279
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 614
97.0%
Space Separator 13
 
2.1%
Uppercase Letter 4
 
0.6%
Lowercase Letter 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 104
 
16.9%
탁 104
 
16.9%
소 56
 
9.1%
대 16
 
2.6%
명 16
 
2.6%
크 13
 
2.1%
백 11
 
1.8%
화 11
 
1.8%
양 10
 
1.6%
성 10
 
1.6%
Other values (115) 263
42.8%
Uppercase Letter
ValueCountFrequency (%)
L 2
50.0%
G 2
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 614
97.0%
Common 14
 
2.2%
Latin 5
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 104
 
16.9%
탁 104
 
16.9%
소 56
 
9.1%
대 16
 
2.6%
명 16
 
2.6%
크 13
 
2.1%
백 11
 
1.8%
화 11
 
1.8%
양 10
 
1.6%
성 10
 
1.6%
Other values (115) 263
42.8%
Latin
ValueCountFrequency (%)
L 2
40.0%
G 2
40.0%
e 1
20.0%
Common
ValueCountFrequency (%)
13
92.9%
- 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 614
97.0%
ASCII 19
 
3.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 104
 
16.9%
탁 104
 
16.9%
소 56
 
9.1%
대 16
 
2.6%
명 16
 
2.6%
크 13
 
2.1%
백 11
 
1.8%
화 11
 
1.8%
양 10
 
1.6%
성 10
 
1.6%
Other values (115) 263
42.8%
ASCII
ValueCountFrequency (%)
13
68.4%
L 2
 
10.5%
G 2
 
10.5%
e 1
 
5.3%
- 1
 
5.3%

통합영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
폐업
87 
영업/정상
38 

Length

Max length5
Median length2
Mean length2.912
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 87
69.6%
영업/정상 38
30.4%

Length

2023-12-13T00:17:23.450911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:17:23.554499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 87
69.6%
영업/정상 38
30.4%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
폐업
87 
영업
38 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 87
69.6%
영업 38
30.4%

Length

2023-12-13T00:17:23.667871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:17:23.784082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 87
69.6%
영업 38
30.4%

폐업일자
Categorical

Distinct47
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
데이터 미집계
79 
2020-01-28
 
1
2004-03-08
 
1
2020-04-28
 
1
2019-06-21
 
1
Other values (42)
42 

Length

Max length10
Median length7
Mean length8.104
Min length7

Unique

Unique46 ?
Unique (%)36.8%

Sample

1st row2016-03-09
2nd row2004-12-13
3rd row데이터 미집계
4th row데이터 미집계
5th row2020-04-28

Common Values

ValueCountFrequency (%)
데이터 미집계 79
63.2%
2020-01-28 1
 
0.8%
2004-03-08 1
 
0.8%
2020-04-28 1
 
0.8%
2019-06-21 1
 
0.8%
2015-05-13 1
 
0.8%
2015-05-21 1
 
0.8%
2020-07-24 1
 
0.8%
2009-01-02 1
 
0.8%
2019-11-11 1
 
0.8%
Other values (37) 37
29.6%

Length

2023-12-13T00:17:23.917425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
데이터 79
38.7%
미집계 79
38.7%
2012-09-19 1
 
0.5%
2018-07-16 1
 
0.5%
2009-09-09 1
 
0.5%
2014-09-24 1
 
0.5%
2018-02-07 1
 
0.5%
2015-02-24 1
 
0.5%
2012-11-22 1
 
0.5%
2007-01-11 1
 
0.5%
Other values (38) 38
18.6%

소재지시설전화번호
Text

Distinct104
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T00:17:24.209120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.064
Min length7

Characters and Unicode

Total characters1383
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)80.0%

Sample

1st row031 457 5939
2nd row031 457 5939
3rd row031 424 2977
4th row031 457 8017
5th row031 4249325
ValueCountFrequency (%)
031 105
31.5%
데이터 19
 
5.7%
미집계 19
 
5.7%
425 19
 
5.7%
461 12
 
3.6%
457 6
 
1.8%
424 5
 
1.5%
421 5
 
1.5%
455 4
 
1.2%
426 3
 
0.9%
Other values (117) 136
40.8%
2023-12-13T00:17:24.694901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
209
15.1%
0 172
12.4%
3 168
12.1%
1 167
12.1%
4 139
10.1%
5 94
6.8%
2 93
6.7%
7 68
 
4.9%
9 58
 
4.2%
6 54
 
3.9%
Other values (7) 161
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1060
76.6%
Space Separator 209
 
15.1%
Other Letter 114
 
8.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
16.2%
3 168
15.8%
1 167
15.8%
4 139
13.1%
5 94
8.9%
2 93
8.8%
7 68
 
6.4%
9 58
 
5.5%
6 54
 
5.1%
8 47
 
4.4%
Other Letter
ValueCountFrequency (%)
이 19
16.7%
계 19
16.7%
집 19
16.7%
미 19
16.7%
터 19
16.7%
데 19
16.7%
Space Separator
ValueCountFrequency (%)
209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1269
91.8%
Hangul 114
 
8.2%

Most frequent character per script

Common
ValueCountFrequency (%)
209
16.5%
0 172
13.6%
3 168
13.2%
1 167
13.2%
4 139
11.0%
5 94
7.4%
2 93
7.3%
7 68
 
5.4%
9 58
 
4.6%
6 54
 
4.3%
Hangul
ValueCountFrequency (%)
이 19
16.7%
계 19
16.7%
집 19
16.7%
미 19
16.7%
터 19
16.7%
데 19
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1269
91.8%
Hangul 114
 
8.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
209
16.5%
0 172
13.6%
3 168
13.2%
1 167
13.2%
4 139
11.0%
5 94
7.4%
2 93
7.3%
7 68
 
5.4%
9 58
 
4.6%
6 54
 
4.3%
Hangul
ValueCountFrequency (%)
이 19
16.7%
계 19
16.7%
집 19
16.7%
미 19
16.7%
터 19
16.7%
데 19
16.7%

소재지면적
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct49
Distinct (%)41.2%
Missing6
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean17.730588
Minimum0
Maximum108
Zeros57
Zeros (%)45.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:17:24.865199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q331.05
95-th percentile55.224
Maximum108
Range108
Interquartile range (IQR)31.05

Descriptive statistics

Standard deviation21.668618
Coefficient of variation (CV)1.2221037
Kurtosis3.1563426
Mean17.730588
Median Absolute Deviation (MAD)15
Skewness1.5205407
Sum2109.94
Variance469.52902
MonotonicityNot monotonic
2023-12-13T00:17:25.036450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0 57
45.6%
33.0 6
 
4.8%
24.0 3
 
2.4%
32.64 2
 
1.6%
21.0 2
 
1.6%
29.7 2
 
1.6%
20.0 2
 
1.6%
43.0 2
 
1.6%
23.1 2
 
1.6%
31.05 2
 
1.6%
Other values (39) 39
31.2%
(Missing) 6
 
4.8%
ValueCountFrequency (%)
0.0 57
45.6%
9.91 1
 
0.8%
10.0 1
 
0.8%
15.0 1
 
0.8%
16.5 1
 
0.8%
16.72 1
 
0.8%
18.72 1
 
0.8%
19.81 1
 
0.8%
20.0 2
 
1.6%
20.4 1
 
0.8%
ValueCountFrequency (%)
108.0 1
0.8%
99.36 1
0.8%
77.55 1
0.8%
73.5 1
0.8%
66.0 1
0.8%
60.84 1
0.8%
54.6 1
0.8%
48.6 1
0.8%
46.2 1
0.8%
43.0 2
1.6%

소재지도로명주소
Text

Distinct116
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T00:17:25.334679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length39
Mean length28.464
Min length14

Characters and Unicode

Total characters3558
Distinct characters181
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

Unique108 ?
Unique (%)86.4%

Sample

1st row경기도 의왕시 모락로 134
2nd row경기도 의왕시 모락로 134
3rd row경기도 의왕시 양지편1로 4-10, 103호 (청계동)
4th row경기도 의왕시 오전로 47, 101호 (오전동, 개나리상가)
5th row경기도 의왕시 갈미1로 17, 201호 (내손동, LG 아파트상가)
ValueCountFrequency (%)
경기도 125
 
16.6%
의왕시 125
 
16.6%
내손동 26
 
3.4%
오전동 22
 
2.9%
삼동 15
 
2.0%
상가 14
 
1.9%
103호 12
 
1.6%
오전로 11
 
1.5%
상가동 11
 
1.5%
내손로 8
 
1.1%
Other values (222) 385
51.1%
2023-12-13T00:17:25.849455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
629
 
17.7%
1 141
 
4.0%
동 137
 
3.9%
왕 135
 
3.8%
도 131
 
3.7%
시 130
 
3.7%
경 128
 
3.6%
의 128
 
3.6%
기 125
 
3.5%
( 104
 
2.9%
Other values (171) 1770
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2089
58.7%
Space Separator 629
 
17.7%
Decimal Number 503
 
14.1%
Open Punctuation 104
 
2.9%
Close Punctuation 104
 
2.9%
Other Punctuation 103
 
2.9%
Uppercase Letter 16
 
0.4%
Dash Punctuation 10
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 137
 
6.6%
왕 135
 
6.5%
도 131
 
6.3%
시 130
 
6.2%
경 128
 
6.1%
의 128
 
6.1%
기 125
 
6.0%
로 80
 
3.8%
호 69
 
3.3%
내 61
 
2.9%
Other values (145) 965
46.2%
Decimal Number
ValueCountFrequency (%)
1 141
28.0%
0 77
15.3%
2 68
13.5%
3 47
 
9.3%
4 44
 
8.7%
6 31
 
6.2%
5 29
 
5.8%
7 27
 
5.4%
8 21
 
4.2%
9 18
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
A 4
25.0%
B 3
18.8%
T 2
12.5%
S 1
 
6.2%
K 1
 
6.2%
C 1
 
6.2%
I 1
 
6.2%
P 1
 
6.2%
G 1
 
6.2%
L 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 99
96.1%
@ 4
 
3.9%
Space Separator
ValueCountFrequency (%)
629
100.0%
Open Punctuation
ValueCountFrequency (%)
( 104
100.0%
Close Punctuation
ValueCountFrequency (%)
) 104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2089
58.7%
Common 1453
40.8%
Latin 16
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 137
 
6.6%
왕 135
 
6.5%
도 131
 
6.3%
시 130
 
6.2%
경 128
 
6.1%
의 128
 
6.1%
기 125
 
6.0%
로 80
 
3.8%
호 69
 
3.3%
내 61
 
2.9%
Other values (145) 965
46.2%
Common
ValueCountFrequency (%)
629
43.3%
1 141
 
9.7%
( 104
 
7.2%
) 104
 
7.2%
, 99
 
6.8%
0 77
 
5.3%
2 68
 
4.7%
3 47
 
3.2%
4 44
 
3.0%
6 31
 
2.1%
Other values (6) 109
 
7.5%
Latin
ValueCountFrequency (%)
A 4
25.0%
B 3
18.8%
T 2
12.5%
S 1
 
6.2%
K 1
 
6.2%
C 1
 
6.2%
I 1
 
6.2%
P 1
 
6.2%
G 1
 
6.2%
L 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2089
58.7%
ASCII 1469
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
629
42.8%
1 141
 
9.6%
( 104
 
7.1%
) 104
 
7.1%
, 99
 
6.7%
0 77
 
5.2%
2 68
 
4.6%
3 47
 
3.2%
4 44
 
3.0%
6 31
 
2.1%
Other values (16) 125
 
8.5%
Hangul
ValueCountFrequency (%)
동 137
 
6.6%
왕 135
 
6.5%
도 131
 
6.3%
시 130
 
6.2%
경 128
 
6.1%
의 128
 
6.1%
기 125
 
6.0%
로 80
 
3.8%
호 69
 
3.3%
내 61
 
2.9%
Other values (145) 965
46.2%

소재지지번주소
Text

Distinct120
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T00:17:26.223277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length35
Mean length26.8
Min length18

Characters and Unicode

Total characters3350
Distinct characters141
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

Unique115 ?
Unique (%)92.0%

Sample

1st row경기도 의왕시 오전동 222-1번지
2nd row경기도 의왕시 오전동 222-1번지 LG진달래아파트 종합상가 206호
3rd row경기도 의왕시 청계동 986-3 용진
4th row경기도 의왕시 오전동 856번지 개나리상가101호
5th row경기도 의왕시 내손동 790번지 LG 아파트상가 201호
ValueCountFrequency (%)
경기도 125
18.7%
의왕시 125
18.7%
오전동 39
 
5.8%
내손동 36
 
5.4%
삼동 23
 
3.4%
103호 13
 
1.9%
포일동 13
 
1.9%
상가동 12
 
1.8%
왕곡동 6
 
0.9%
206호 5
 
0.7%
Other values (215) 270
40.5%
2023-12-13T00:17:26.768729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
643
19.2%
동 157
 
4.7%
1 138
 
4.1%
왕 134
 
4.0%
의 128
 
3.8%
도 127
 
3.8%
시 126
 
3.8%
경 125
 
3.7%
지 125
 
3.7%
기 125
 
3.7%
Other values (131) 1522
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1922
57.4%
Decimal Number 696
 
20.8%
Space Separator 643
 
19.2%
Dash Punctuation 61
 
1.8%
Uppercase Letter 16
 
0.5%
Other Punctuation 10
 
0.3%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 157
 
8.2%
왕 134
 
7.0%
의 128
 
6.7%
도 127
 
6.6%
시 126
 
6.6%
경 125
 
6.5%
지 125
 
6.5%
기 125
 
6.5%
번 116
 
6.0%
호 74
 
3.9%
Other values (106) 685
35.6%
Decimal Number
ValueCountFrequency (%)
1 138
19.8%
2 106
15.2%
0 103
14.8%
6 65
9.3%
3 60
8.6%
4 55
 
7.9%
8 53
 
7.6%
5 49
 
7.0%
7 35
 
5.0%
9 32
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
B 4
25.0%
G 2
12.5%
A 2
12.5%
L 2
12.5%
T 2
12.5%
S 1
 
6.2%
K 1
 
6.2%
I 1
 
6.2%
P 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
@ 8
80.0%
, 2
 
20.0%
Space Separator
ValueCountFrequency (%)
643
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1922
57.4%
Common 1412
42.1%
Latin 16
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 157
 
8.2%
왕 134
 
7.0%
의 128
 
6.7%
도 127
 
6.6%
시 126
 
6.6%
경 125
 
6.5%
지 125
 
6.5%
기 125
 
6.5%
번 116
 
6.0%
호 74
 
3.9%
Other values (106) 685
35.6%
Common
ValueCountFrequency (%)
643
45.5%
1 138
 
9.8%
2 106
 
7.5%
0 103
 
7.3%
6 65
 
4.6%
- 61
 
4.3%
3 60
 
4.2%
4 55
 
3.9%
8 53
 
3.8%
5 49
 
3.5%
Other values (6) 79
 
5.6%
Latin
ValueCountFrequency (%)
B 4
25.0%
G 2
12.5%
A 2
12.5%
L 2
12.5%
T 2
12.5%
S 1
 
6.2%
K 1
 
6.2%
I 1
 
6.2%
P 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1922
57.4%
ASCII 1428
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
643
45.0%
1 138
 
9.7%
2 106
 
7.4%
0 103
 
7.2%
6 65
 
4.6%
- 61
 
4.3%
3 60
 
4.2%
4 55
 
3.9%
8 53
 
3.7%
5 49
 
3.4%
Other values (15) 95
 
6.7%
Hangul
ValueCountFrequency (%)
동 157
 
8.2%
왕 134
 
7.0%
의 128
 
6.7%
도 127
 
6.6%
시 126
 
6.6%
경 125
 
6.5%
지 125
 
6.5%
기 125
 
6.5%
번 116
 
6.0%
호 74
 
3.9%
Other values (106) 685
35.6%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct59
Distinct (%)52.7%
Missing13
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean16051.429
Minimum16002
Maximum16103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:17:26.993838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16002
5-th percentile16012.95
Q116032
median16050
Q316064
95-th percentile16098
Maximum16103
Range101
Interquartile range (IQR)32

Descriptive statistics

Standard deviation25.819789
Coefficient of variation (CV)0.0016085664
Kurtosis-0.59763932
Mean16051.429
Median Absolute Deviation (MAD)16.5
Skewness0.28782423
Sum1797760
Variance666.66152
MonotonicityNot monotonic
2023-12-13T00:17:27.196560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16056 5
 
4.0%
16057 5
 
4.0%
16032 4
 
3.2%
16044 4
 
3.2%
16052 4
 
3.2%
16045 4
 
3.2%
16031 4
 
3.2%
16085 4
 
3.2%
16017 4
 
3.2%
16040 3
 
2.4%
Other values (49) 71
56.8%
(Missing) 13
 
10.4%
ValueCountFrequency (%)
16002 2
1.6%
16004 1
 
0.8%
16006 1
 
0.8%
16007 1
 
0.8%
16008 1
 
0.8%
16017 4
3.2%
16018 1
 
0.8%
16019 1
 
0.8%
16020 1
 
0.8%
16021 1
 
0.8%
ValueCountFrequency (%)
16103 1
 
0.8%
16102 1
 
0.8%
16101 3
2.4%
16098 2
1.6%
16096 1
 
0.8%
16095 1
 
0.8%
16094 1
 
0.8%
16093 2
1.6%
16090 1
 
0.8%
16089 1
 
0.8%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.361205
Minimum37.315044
Maximum37.403041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:17:27.418270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.315044
5-th percentile37.31739
Q137.348107
median37.361701
Q337.384233
95-th percentile37.393141
Maximum37.403041
Range0.08799669
Interquartile range (IQR)0.03612662

Descriptive statistics

Standard deviation0.025832139
Coefficient of variation (CV)0.00069141611
Kurtosis-1.011309
Mean37.361205
Median Absolute Deviation (MAD)0.02178105
Skewness-0.38477646
Sum4670.1506
Variance0.0006672994
MonotonicityNot monotonic
2023-12-13T00:17:27.629948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3511712 3
 
2.4%
37.35819937 3
 
2.4%
37.36993748 2
 
1.6%
37.3931409 2
 
1.6%
37.34928604 2
 
1.6%
37.32011913 2
 
1.6%
37.38423312 2
 
1.6%
37.38476885 2
 
1.6%
37.39182364 2
 
1.6%
37.40304104 2
 
1.6%
Other values (100) 103
82.4%
ValueCountFrequency (%)
37.31504435 1
0.8%
37.3161633 1
0.8%
37.31671442 1
0.8%
37.3169249 1
0.8%
37.31702613 1
0.8%
37.3170336 1
0.8%
37.3173122 1
0.8%
37.3177005 1
0.8%
37.3177348 1
0.8%
37.31783746 1
0.8%
ValueCountFrequency (%)
37.40304104 2
1.6%
37.40101519 1
0.8%
37.39820411 1
0.8%
37.3970175 1
0.8%
37.39376038 1
0.8%
37.3931409 2
1.6%
37.39273509 1
0.8%
37.3923571 1
0.8%
37.39224811 1
0.8%
37.39184191 1
0.8%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct109
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97169
Minimum126.94895
Maximum127.00087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:17:27.832054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.94895
5-th percentile126.95209
Q1126.96585
median126.97376
Q3126.97917
95-th percentile126.98377
Maximum127.00087
Range0.0519189
Interquartile range (IQR)0.0133183

Descriptive statistics

Standard deviation0.010976853
Coefficient of variation (CV)8.6451185 × 10-5
Kurtosis-0.17831254
Mean126.97169
Median Absolute Deviation (MAD)0.0066302
Skewness-0.29138654
Sum15871.461
Variance0.0001204913
MonotonicityNot monotonic
2023-12-13T00:17:28.009950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9789772 3
 
2.4%
126.9694219 3
 
2.4%
126.9816403 2
 
1.6%
126.9550347 2
 
1.6%
126.9543704 2
 
1.6%
126.9756453 2
 
1.6%
126.9836303 2
 
1.6%
126.9737582 2
 
1.6%
126.9803884 2
 
1.6%
126.9864582 2
 
1.6%
Other values (99) 103
82.4%
ValueCountFrequency (%)
126.9489489 1
0.8%
126.9500614 1
0.8%
126.9511232 1
0.8%
126.9512003 1
0.8%
126.9517094 1
0.8%
126.9519575 1
0.8%
126.952068 1
0.8%
126.9521632 1
0.8%
126.9523127 1
0.8%
126.9532231 1
0.8%
ValueCountFrequency (%)
127.0008678 1
0.8%
126.9997959 1
0.8%
126.9945151 1
0.8%
126.9913554 1
0.8%
126.9864582 2
1.6%
126.9838088 1
0.8%
126.9836303 2
1.6%
126.9832981 1
0.8%
126.9829509 1
0.8%
126.9829095 1
0.8%

업태구분명정보
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
일반세탁업
122 
운동화전문세탁업
 
2
빨래방업
 
1

Length

Max length8
Median length5
Mean length5.04
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
일반세탁업 122
97.6%
운동화전문세탁업 2
 
1.6%
빨래방업 1
 
0.8%

Length

2023-12-13T00:17:28.169526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:17:28.279853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반세탁업 122
97.6%
운동화전문세탁업 2
 
1.6%
빨래방업 1
 
0.8%

좌표(X)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)76.4%
Missing15
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean197447.59
Minimum195409.44
Maximum200062.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:17:28.398461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum195409.44
5-th percentile195688.32
Q1196916.8
median197682.96
Q3198134.54
95-th percentile198491.97
Maximum200062.72
Range4653.2816
Interquartile range (IQR)1217.7429

Descriptive statistics

Standard deviation985.49684
Coefficient of variation (CV)0.0049911819
Kurtosis-0.23341715
Mean197447.59
Median Absolute Deviation (MAD)580.34035
Skewness-0.3446687
Sum21719235
Variance971204.02
MonotonicityNot monotonic
2023-12-13T00:17:28.546947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198071.6977 4
 
3.2%
197365.8924 4
 
3.2%
197014.5373 3
 
2.4%
198309.2212 3
 
2.4%
197778.3174 2
 
1.6%
197826.6741 2
 
1.6%
197496.2127 2
 
1.6%
197772.3072 2
 
1.6%
197412.5338 2
 
1.6%
197730.9443 2
 
1.6%
Other values (74) 84
67.2%
(Missing) 15
 
12.0%
ValueCountFrequency (%)
195409.4408 1
0.8%
195507.455 1
0.8%
195600.3561 1
0.8%
195609.4314 1
0.8%
195676.5128 1
0.8%
195686.5676 1
0.8%
195690.4721 1
0.8%
195786.7688 1
0.8%
195832.0572 1
0.8%
195850.3828 1
0.8%
ValueCountFrequency (%)
200062.7224 1
0.8%
199902.415 1
0.8%
199121.9205 1
0.8%
198835.0791 2
1.6%
198501.8215 1
0.8%
198479.9222 2
1.6%
198452.4332 1
0.8%
198425.186 1
0.8%
198414.9154 1
0.8%
198384.9678 1
0.8%

좌표(Y)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)76.4%
Missing15
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean428774.97
Minimum423796.86
Maximum433472.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:17:28.700135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum423796.86
5-th percentile423975.58
Q1427232.58
median428844.65
Q3431394.56
95-th percentile432368.3
Maximum433472.77
Range9675.9062
Interquartile range (IQR)4161.9831

Descriptive statistics

Standard deviation2871.4368
Coefficient of variation (CV)0.0066968387
Kurtosis-1.0272
Mean428774.97
Median Absolute Deviation (MAD)2394.6756
Skewness-0.33869482
Sum47165246
Variance8245149.4
MonotonicityNot monotonic
2023-12-13T00:17:28.835814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
427690.1287 4
 
3.2%
428294.0194 4
 
3.2%
428859.4804 3
 
2.4%
432349.0816 3
 
2.4%
427617.2897 2
 
1.6%
430600.4541 2
 
1.6%
427933.7354 2
 
1.6%
427471.8453 2
 
1.6%
430696.5622 2
 
1.6%
431232.0712 2
 
1.6%
Other values (74) 84
67.2%
(Missing) 15
 
12.0%
ValueCountFrequency (%)
423796.8617 1
0.8%
423829.4213 1
0.8%
423889.1683 1
0.8%
423889.9559 1
0.8%
423932.6646 1
0.8%
423975.5775 2
1.6%
423978.6093 1
0.8%
424114.1844 1
0.8%
424116.8659 1
0.8%
424199.8067 1
0.8%
ValueCountFrequency (%)
433472.7679 2
1.6%
433212.8289 1
 
0.8%
432899.8687 1
 
0.8%
432857.9457 1
 
0.8%
432384.0153 1
 
0.8%
432349.0816 3
2.4%
432262.1404 1
 
0.8%
432253.9663 1
 
0.8%
432223.7255 1
 
0.8%
432197.341 1
 
0.8%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
일반세탁업
122 
운동화전문세탁업
 
2
빨래방업
 
1

Length

Max length8
Median length5
Mean length5.04
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
일반세탁업 122
97.6%
운동화전문세탁업 2
 
1.6%
빨래방업 1
 
0.8%

Length

2023-12-13T00:17:28.982755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:17:29.095931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반세탁업 122
97.6%
운동화전문세탁업 2
 
1.6%
빨래방업 1
 
0.8%

Interactions

2023-12-13T00:17:20.540076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:17.615149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:18.159856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:18.801243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.376924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.899610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:20.621555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:17.719187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:18.268756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:18.899093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.462425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:20.036513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:20.744419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:17.801261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:18.377238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.035684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.544982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:20.131313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:20.827656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:17.886706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:18.482269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.136379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.631694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:20.241966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:20.922038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:17.985136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:18.591659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.224577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.717172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:20.346851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:21.000739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:18.073616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:18.710011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.296280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:19.812896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:20.461462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:17:29.191930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자통합영업상태명영업상태명폐업일자소재지면적소재지우편번호위도경도업태구분명정보좌표(X)좌표(Y)위생업태명
인허가일자1.0000.0000.0000.9730.9500.7690.9690.9261.0000.8790.9871.000
통합영업상태명0.0001.0001.0000.0000.0000.0000.0000.0000.0590.0000.1950.059
영업상태명0.0001.0001.0000.0000.0000.0000.0000.0000.0590.0000.1950.059
폐업일자0.9730.0000.0001.0000.0000.0000.4610.0000.0000.0000.0000.000
소재지면적0.9500.0000.0000.0001.0000.3750.5040.4470.8680.6700.6900.868
소재지우편번호0.7690.0000.0000.0000.3751.0000.8840.8400.6600.9150.9230.660
위도0.9690.0000.0000.4610.5040.8841.0000.7950.7970.9180.9920.797
경도0.9260.0000.0000.0000.4470.8400.7951.0000.4710.9840.8120.471
업태구분명정보1.0000.0590.0590.0000.8680.6600.7970.4711.0000.3320.9431.000
좌표(X)0.8790.0000.0000.0000.6700.9150.9180.9840.3321.0000.8380.332
좌표(Y)0.9870.1950.1950.0000.6900.9230.9920.8120.9430.8381.0000.943
위생업태명1.0000.0590.0590.0000.8680.6600.7970.4711.0000.3320.9431.000
2023-12-13T00:17:29.343027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐업일자위생업태명통합영업상태명영업상태명업태구분명정보
폐업일자1.0000.0000.0000.0000.000
위생업태명0.0001.0000.0960.0961.000
통합영업상태명0.0000.0961.0000.9810.096
영업상태명0.0000.0960.9811.0000.096
업태구분명정보0.0001.0000.0960.0961.000
2023-12-13T00:17:29.436158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지면적소재지우편번호위도경도좌표(X)좌표(Y)통합영업상태명영업상태명폐업일자업태구분명정보위생업태명
소재지면적1.000-0.1940.1910.1000.1920.2740.0000.0000.0000.5740.574
소재지우편번호-0.1941.000-0.977-0.706-0.724-0.9890.0000.0000.0000.4710.471
위도0.191-0.9771.0000.7050.7130.9870.0000.0000.1400.4900.490
경도0.100-0.7060.7051.0000.9870.7070.0000.0000.0000.3110.311
좌표(X)0.192-0.7240.7130.9871.0000.7260.0000.0000.0000.2380.238
좌표(Y)0.274-0.9890.9870.7070.7261.0000.1750.1750.0000.6980.698
통합영업상태명0.0000.0000.0000.0000.0000.1751.0000.9810.0000.0960.096
영업상태명0.0000.0000.0000.0000.0000.1750.9811.0000.0000.0960.096
폐업일자0.0000.0000.1400.0000.0000.0000.0000.0001.0000.0000.000
업태구분명정보0.5740.4710.4900.3110.2380.6980.0960.0960.0001.0001.000
위생업태명0.5740.4710.4900.3110.2380.6980.0960.0960.0001.0001.000

Missing values

2023-12-13T00:17:21.135541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:17:21.385772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T00:17:21.534885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군명인허가일자사업장명통합영업상태명영업상태명폐업일자소재지시설전화번호소재지면적소재지도로명주소소재지지번주소소재지우편번호위도경도업태구분명정보좌표(X)좌표(Y)위생업태명
0의왕시2005-01-03LG진달래 세탁소폐업폐업2016-03-09031 457 593933.0경기도 의왕시 모락로 134경기도 의왕시 오전동 222-1번지<NA>37.369937126.967006일반세탁업<NA><NA>일반세탁업
1의왕시2003-11-26LG진달래세탁소폐업폐업2004-12-13031 457 59390.0경기도 의왕시 모락로 134경기도 의왕시 오전동 222-1번지 LG진달래아파트 종합상가 206호<NA>37.369937126.967006일반세탁업<NA><NA>일반세탁업
2의왕시2008-08-21e-편한세탁영업/정상영업데이터 미집계031 424 297729.7경기도 의왕시 양지편1로 4-10, 103호 (청계동)경기도 의왕시 청계동 986-3 용진1600737.390779126.994515일반세탁업198338.7864431928.5689일반세탁업
3의왕시2003-12-12개나리세탁폐업폐업데이터 미집계031 457 80170.0경기도 의왕시 오전로 47, 101호 (오전동, 개나리상가)경기도 의왕시 오전동 856번지 개나리상가101호1606237.350021126.980557일반세탁업198219.7829427517.1677일반세탁업
4의왕시2003-10-30굿모닝세탁소폐업폐업2020-04-28031 42493250.0경기도 의왕시 갈미1로 17, 201호 (내손동, LG 아파트상가)경기도 의왕시 내손동 790번지 LG 아파트상가 201호1604137.378154126.973055일반세탁업197570.1006430657.7976일반세탁업
5의왕시2005-11-02그린 세탁소폐업폐업2019-06-21031 457 741836.0경기도 의왕시 오전천로 52, 103호 (오전동, 이삭민들레@상가)경기도 의왕시 오전동 388-1번지 이삭민들레@상가 103호1606237.349526126.978787일반세탁업198117.3559427473.573일반세탁업
6의왕시2011-12-01그린명품세탁폐업폐업2015-05-13031 456 170125.92경기도 의왕시 현충탑길 6 상가동 104호 (왕곡동,인스빌2단지)경기도 의왕시 왕곡동 593번지 인스빌@ 상가동 104호1606437.346729126.97917일반세탁업198089.5761427196.659일반세탁업
7의왕시2003-07-30까치세탁소폐업폐업2015-05-21031 461 8925<NA>경기도 의왕시 까치골길 14 (삼동)경기도 의왕시 삼동 122-1번지<NA>37.317034126.955591일반세탁업196062.5424206.171일반세탁업
8의왕시2004-03-31내손컴퓨터세탁소폐업폐업2020-07-24031 426207046.2경기도 의왕시 내손중앙로 74 (내손동)경기도 의왕시 내손동 678-111603237.384734126.98291일반세탁업198414.9154431406.3456일반세탁업
9의왕시2003-11-26늘푸른세탁소영업/정상영업데이터 미집계031 452 57000.0경기도 의왕시 호성로 50 (오전동)경기도 의왕시 오전동 23번지 진달래상가B동105호1604437.366243126.965121일반세탁업196843.1879429357.4345일반세탁업
시군명인허가일자사업장명통합영업상태명영업상태명폐업일자소재지시설전화번호소재지면적소재지도로명주소소재지지번주소소재지우편번호위도경도업태구분명정보좌표(X)좌표(Y)위생업태명
115의왕시2011-08-04현대세탁폐업폐업데이터 미집계031 421 531710.0경기도 의왕시 내손순환로 82 (내손동)경기도 의왕시 내손동 696-1번지1603137.38331126.980609일반세탁업198214.1623431246.5704일반세탁업
116의왕시2012-12-03현대세탁영업/정상영업데이터 미집계데이터 미집계0.0경기도 의왕시 오리나무로 8, 102호 (내손동)경기도 의왕시 내손동 814-7번지1604337.374785126.968326일반세탁업197126.5259430302.5983일반세탁업
117의왕시2003-07-30현대세탁소폐업폐업데이터 미집계데이터 미집계0.0경기도 의왕시 부곡시장2길 10 (삼동,한양빌댕내)경기도 의왕시 삼동 192-36번지 한양빌댕내1608937.319794126.9512일반세탁업195609.4314424199.8067일반세탁업
118의왕시2004-08-11현대세탁소폐업폐업데이터 미집계031 459 80030.0경기도 의왕시 오전동 원골로 43경기도 의왕시 오전동 100번지 모락산현대상가 211호1604937.361864126.969063일반세탁업197193.4817428876.2142일반세탁업
119의왕시2003-05-29현대세탁소폐업폐업데이터 미집계031 425 011029.7경기도 의왕시 등칙골1길 7 (오전동)경기도 의왕시 오전동 281-3번지1605637.352608126.972505일반세탁업197495.4448427842.6412일반세탁업
120의왕시2010-07-27형제세탁소영업/정상영업데이터 미집계031 424 778943.0경기도 의왕시 덕장로 78, 상가동 101호 (청계동, 휴먼시아청계마을)경기도 의왕시 청계동 966번지 휴먼시아청계마을 상가동 101호1600837.39376127.000868일반세탁업200062.7224432384.0153일반세탁업
121의왕시2003-07-30혜성세탁소영업/정상영업데이터 미집계031 461 91370.0경기도 의왕시 부곡초등3길 41 (삼동)경기도 의왕시 삼동 144-22번지1609437.317312126.953921일반세탁업195850.3828423932.6646일반세탁업
122의왕시2003-10-08화성세탁소폐업폐업데이터 미집계031 456 42270.0경기도 의왕시 원골로 10, 상가동 1층 105호 (오전동, 신안아파트)경기도 의왕시 오전동 216-1번지 신안아파트 상가동 105호1605337.358241126.967643일반세탁업197066.7171428406.9217일반세탁업
123의왕시2009-04-07화이트영업/정상영업데이터 미집계031 427 010824.0경기도 의왕시 오전로 178, 103호 (오전동, 동백@ 분산상가)경기도 의왕시 오전동 849 동백@ 분산상가 103호1605237.356995126.969792일반세탁업197365.8924428294.0194일반세탁업
124의왕시2013-04-04효성세탁소폐업폐업데이터 미집계031 437036831.5경기도 의왕시 신장승길 12, 103호 (삼동, 장안마을주공아파트상가)경기도 의왕시 삼동 556번지 장안마을주공아파트상가 103호1609337.320119126.95437일반세탁업195922.9568424217.3546일반세탁업