Overview

Dataset statistics

Number of variables15
Number of observations309
Missing cells342
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.5 KiB
Average record size in memory127.4 B

Variable types

Categorical4
Text3
Numeric7
Boolean1

Dataset

Description세탁업(빨래방업) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=57FQ7EDWAJNMVXPIWA3214134562&infSeq=1

Alerts

시군명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
위생업태명 is highly overall correlated with 소재지우편번호 and 10 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
위생업종명 is highly overall correlated with 소재지우편번호 and 10 other fieldsHigh correlation
다중이용업소여부 is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84경도 and 3 other fieldsHigh correlation
인허가일자 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
폐업일자 is highly overall correlated with 인허가일자 and 3 other fieldsHigh correlation
세탁기수(대) is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
회수건조수(대) is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
다중이용업소여부 is highly imbalanced (96.7%)Imbalance
위생업종명 is highly imbalanced (70.6%)Imbalance
위생업태명 is highly imbalanced (70.6%)Imbalance
소재지도로명주소 has 9 (2.9%) missing valuesMissing
폐업일자 has 172 (55.7%) missing valuesMissing
다중이용업소여부 has 16 (5.2%) missing valuesMissing
세탁기수(대) has 57 (18.4%) missing valuesMissing
회수건조수(대) has 80 (25.9%) missing valuesMissing
WGS84위도 has 4 (1.3%) missing valuesMissing
WGS84경도 has 4 (1.3%) missing valuesMissing
세탁기수(대) has 29 (9.4%) zerosZeros
회수건조수(대) has 150 (48.5%) zerosZeros

Reproduction

Analysis started2023-12-10 22:49:02.982691
Analysis finished2023-12-10 22:49:08.624638
Duration5.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
용인시
26 
고양시
26 
성남시
25 
수원시
22 
남양주시
22 
Other values (25)
188 

Length

Max length4
Median length3
Mean length3.1521036
Min length3

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row가평군
2nd row가평군
3rd row고양시
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
용인시 26
 
8.4%
고양시 26
 
8.4%
성남시 25
 
8.1%
수원시 22
 
7.1%
남양주시 22
 
7.1%
부천시 21
 
6.8%
의정부시 19
 
6.1%
안양시 17
 
5.5%
화성시 15
 
4.9%
평택시 14
 
4.5%
Other values (20) 102
33.0%

Length

2023-12-11T07:49:08.684440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용인시 26
 
8.4%
고양시 26
 
8.4%
성남시 25
 
8.1%
수원시 22
 
7.1%
남양주시 22
 
7.1%
부천시 21
 
6.8%
의정부시 19
 
6.1%
안양시 17
 
5.5%
화성시 15
 
4.9%
평택시 14
 
4.5%
Other values (20) 102
33.0%

사업장명
Text

Distinct273
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-11T07:49:08.880540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length8.5954693
Min length2

Characters and Unicode

Total characters2656
Distinct characters271
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254 ?
Unique (%)82.2%

Sample

1st row시민코인빨래방
2nd row뉴세탁나라
3rd row크린토피아코인워시능곡현대홈타운점
4th row뽀송뽀송24시셀프빨래방
5th row향기로운세탁소
ValueCountFrequency (%)
크린토피아 31
 
7.7%
코인워시 16
 
4.0%
빨래방 8
 
2.0%
월풀빨래방 7
 
1.7%
셀프빨래방 6
 
1.5%
마마운동화이불빨래방 5
 
1.2%
그린빨래방 4
 
1.0%
워시엔조이 4
 
1.0%
크린크린빨래방 3
 
0.7%
크린업셀프빨래방 3
 
0.7%
Other values (291) 316
78.4%
2023-12-11T07:49:09.220180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
빨 131
 
4.9%
래 131
 
4.9%
방 121
 
4.6%
크 115
 
4.3%
린 112
 
4.2%
시 98
 
3.7%
94
 
3.5%
점 82
 
3.1%
아 74
 
2.8%
피 68
 
2.6%
Other values (261) 1630
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2340
88.1%
Decimal Number 109
 
4.1%
Space Separator 94
 
3.5%
Close Punctuation 33
 
1.2%
Open Punctuation 33
 
1.2%
Lowercase Letter 25
 
0.9%
Uppercase Letter 19
 
0.7%
Other Punctuation 2
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빨 131
 
5.6%
래 131
 
5.6%
방 121
 
5.2%
크 115
 
4.9%
린 112
 
4.8%
시 98
 
4.2%
점 82
 
3.5%
아 74
 
3.2%
피 68
 
2.9%
워 68
 
2.9%
Other values (229) 1340
57.3%
Lowercase Letter
ValueCountFrequency (%)
h 6
24.0%
s 3
12.0%
a 3
12.0%
i 3
12.0%
e 3
12.0%
t 2
 
8.0%
c 2
 
8.0%
w 1
 
4.0%
n 1
 
4.0%
o 1
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
W 5
26.3%
T 3
15.8%
C 3
15.8%
E 2
 
10.5%
Z 1
 
5.3%
K 1
 
5.3%
A 1
 
5.3%
O 1
 
5.3%
L 1
 
5.3%
S 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
2 49
45.0%
4 47
43.1%
5 4
 
3.7%
6 3
 
2.8%
3 3
 
2.8%
1 2
 
1.8%
9 1
 
0.9%
Space Separator
ValueCountFrequency (%)
94
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2340
88.1%
Common 272
 
10.2%
Latin 44
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빨 131
 
5.6%
래 131
 
5.6%
방 121
 
5.2%
크 115
 
4.9%
린 112
 
4.8%
시 98
 
4.2%
점 82
 
3.5%
아 74
 
3.2%
피 68
 
2.9%
워 68
 
2.9%
Other values (229) 1340
57.3%
Latin
ValueCountFrequency (%)
h 6
13.6%
W 5
11.4%
s 3
 
6.8%
a 3
 
6.8%
i 3
 
6.8%
e 3
 
6.8%
T 3
 
6.8%
C 3
 
6.8%
t 2
 
4.5%
c 2
 
4.5%
Other values (10) 11
25.0%
Common
ValueCountFrequency (%)
94
34.6%
2 49
18.0%
4 47
17.3%
) 33
 
12.1%
( 33
 
12.1%
5 4
 
1.5%
6 3
 
1.1%
3 3
 
1.1%
& 2
 
0.7%
1 2
 
0.7%
Other values (2) 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2339
88.1%
ASCII 316
 
11.9%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
빨 131
 
5.6%
래 131
 
5.6%
방 121
 
5.2%
크 115
 
4.9%
린 112
 
4.8%
시 98
 
4.2%
점 82
 
3.5%
아 74
 
3.2%
피 68
 
2.9%
워 68
 
2.9%
Other values (228) 1339
57.2%
ASCII
ValueCountFrequency (%)
94
29.7%
2 49
15.5%
4 47
14.9%
) 33
 
10.4%
( 33
 
10.4%
h 6
 
1.9%
W 5
 
1.6%
5 4
 
1.3%
s 3
 
0.9%
a 3
 
0.9%
Other values (22) 39
12.3%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct243
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416052.09
Minimum12437
Maximum487851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T07:49:09.601949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12437
5-th percentile14668.8
Q1425848
median446596
Q3464070
95-th percentile482026
Maximum487851
Range475414
Interquartile range (IQR)38222

Descriptive statistics

Standard deviation116072.09
Coefficient of variation (CV)0.27898451
Kurtosis7.8854543
Mean416052.09
Median Absolute Deviation (MAD)18330
Skewness-3.0596315
Sum1.285601 × 108
Variance1.347273 × 1010
MonotonicityNot monotonic
2023-12-11T07:49:09.755387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
415060 4
 
1.3%
442835 4
 
1.3%
446906 4
 
1.3%
459813 4
 
1.3%
448130 3
 
1.0%
14548 3
 
1.0%
441400 3
 
1.0%
472864 3
 
1.0%
446911 3
 
1.0%
486903 3
 
1.0%
Other values (233) 275
89.0%
ValueCountFrequency (%)
12437 1
 
0.3%
14408 1
 
0.3%
14465 1
 
0.3%
14529 1
 
0.3%
14543 1
 
0.3%
14546 1
 
0.3%
14548 3
1.0%
14576 1
 
0.3%
14598 1
 
0.3%
14620 1
 
0.3%
ValueCountFrequency (%)
487851 2
0.6%
487823 1
 
0.3%
487020 1
 
0.3%
486903 3
1.0%
483801 1
 
0.3%
483800 1
 
0.3%
483080 1
 
0.3%
483020 2
0.6%
483010 1
 
0.3%
482110 1
 
0.3%

소재지도로명주소
Text

MISSING 

Distinct298
Distinct (%)99.3%
Missing9
Missing (%)2.9%
Memory size2.5 KiB
2023-12-11T07:49:10.015641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length45
Mean length30.88
Min length15

Characters and Unicode

Total characters9264
Distinct characters304
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

Unique296 ?
Unique (%)98.7%

Sample

1st row경기도 가평군 가평읍 연인2길 22
2nd row경기도 가평군 조종면 연인산로 20-5
3rd row경기도 고양시 덕양구 능곡로 16, 101동 1층 101호 (토당동)
4th row경기도 고양시 일산동구 중산로 149, 1층 일부호 (중산동, 스카이빌딩)
5th row경기도 고양시 일산동구 일산로463번길 48-20 (정발산동,1층전체,지하일부)
ValueCountFrequency (%)
경기도 300
 
15.6%
1층 89
 
4.6%
용인시 26
 
1.4%
고양시 26
 
1.4%
성남시 25
 
1.3%
부천시 21
 
1.1%
수원시 21
 
1.1%
남양주시 19
 
1.0%
의정부시 18
 
0.9%
기흥구 18
 
0.9%
Other values (821) 1362
70.8%
2023-12-11T07:49:10.419863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1625
 
17.5%
1 479
 
5.2%
기 335
 
3.6%
경 317
 
3.4%
도 316
 
3.4%
시 313
 
3.4%
동 310
 
3.3%
로 281
 
3.0%
, 245
 
2.6%
( 243
 
2.6%
Other values (294) 4800
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5236
56.5%
Space Separator 1625
 
17.5%
Decimal Number 1578
 
17.0%
Other Punctuation 246
 
2.7%
Open Punctuation 243
 
2.6%
Close Punctuation 243
 
2.6%
Dash Punctuation 79
 
0.9%
Uppercase Letter 13
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 335
 
6.4%
경 317
 
6.1%
도 316
 
6.0%
시 313
 
6.0%
동 310
 
5.9%
로 281
 
5.4%
길 178
 
3.4%
번 146
 
2.8%
구 134
 
2.6%
층 128
 
2.4%
Other values (273) 2778
53.1%
Decimal Number
ValueCountFrequency (%)
1 479
30.4%
2 221
14.0%
0 134
 
8.5%
4 130
 
8.2%
3 129
 
8.2%
6 119
 
7.5%
5 102
 
6.5%
8 93
 
5.9%
7 93
 
5.9%
9 78
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
B 6
46.2%
A 5
38.5%
F 1
 
7.7%
C 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 245
99.6%
. 1
 
0.4%
Space Separator
ValueCountFrequency (%)
1625
100.0%
Open Punctuation
ValueCountFrequency (%)
( 243
100.0%
Close Punctuation
ValueCountFrequency (%)
) 243
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5236
56.5%
Common 4015
43.3%
Latin 13
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 335
 
6.4%
경 317
 
6.1%
도 316
 
6.0%
시 313
 
6.0%
동 310
 
5.9%
로 281
 
5.4%
길 178
 
3.4%
번 146
 
2.8%
구 134
 
2.6%
층 128
 
2.4%
Other values (273) 2778
53.1%
Common
ValueCountFrequency (%)
1625
40.5%
1 479
 
11.9%
, 245
 
6.1%
( 243
 
6.1%
) 243
 
6.1%
2 221
 
5.5%
0 134
 
3.3%
4 130
 
3.2%
3 129
 
3.2%
6 119
 
3.0%
Other values (7) 447
 
11.1%
Latin
ValueCountFrequency (%)
B 6
46.2%
A 5
38.5%
F 1
 
7.7%
C 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5236
56.5%
ASCII 4028
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1625
40.3%
1 479
 
11.9%
, 245
 
6.1%
( 243
 
6.0%
) 243
 
6.0%
2 221
 
5.5%
0 134
 
3.3%
4 130
 
3.2%
3 129
 
3.2%
6 119
 
3.0%
Other values (11) 460
 
11.4%
Hangul
ValueCountFrequency (%)
기 335
 
6.4%
경 317
 
6.1%
도 316
 
6.0%
시 313
 
6.0%
동 310
 
5.9%
로 281
 
5.4%
길 178
 
3.4%
번 146
 
2.8%
구 134
 
2.6%
층 128
 
2.4%
Other values (273) 2778
53.1%

소재지지번주소
Text

Distinct306
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-11T07:49:10.724294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length45
Mean length26.915858
Min length17

Characters and Unicode

Total characters8317
Distinct characters274
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

Unique303 ?
Unique (%)98.1%

Sample

1st row경기도 가평군 가평읍 읍내리 489-3번지
2nd row경기도 가평군 조종면 현리 253-3번지
3rd row경기도 고양시 덕양구 토당동 345-48번지 101동 1층 101호
4th row경기도 고양시 일산동구 중산동 1657-5번지 스카이빌딩 1층 일부호
5th row경기도 고양시 일산동구 정발산동 1168-2번지 1층전체,지하일부
ValueCountFrequency (%)
경기도 309
 
17.6%
1층 77
 
4.4%
용인시 26
 
1.5%
고양시 26
 
1.5%
성남시 25
 
1.4%
수원시 22
 
1.3%
남양주시 22
 
1.3%
부천시 21
 
1.2%
의정부시 19
 
1.1%
기흥구 18
 
1.0%
Other values (711) 1186
67.7%
2023-12-11T07:49:11.164674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1442
 
17.3%
1 491
 
5.9%
지 349
 
4.2%
기 340
 
4.1%
도 322
 
3.9%
동 321
 
3.9%
시 314
 
3.8%
경 313
 
3.8%
번 310
 
3.7%
- 242
 
2.9%
Other values (264) 3873
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4878
58.7%
Decimal Number 1677
 
20.2%
Space Separator 1442
 
17.3%
Dash Punctuation 242
 
2.9%
Other Punctuation 23
 
0.3%
Close Punctuation 20
 
0.2%
Open Punctuation 20
 
0.2%
Uppercase Letter 14
 
0.2%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 349
 
7.2%
기 340
 
7.0%
도 322
 
6.6%
동 321
 
6.6%
시 314
 
6.4%
경 313
 
6.4%
번 310
 
6.4%
구 133
 
2.7%
층 109
 
2.2%
양 99
 
2.0%
Other values (243) 2268
46.5%
Decimal Number
ValueCountFrequency (%)
1 491
29.3%
2 189
 
11.3%
0 169
 
10.1%
4 165
 
9.8%
3 135
 
8.1%
7 117
 
7.0%
5 113
 
6.7%
6 108
 
6.4%
9 97
 
5.8%
8 93
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
B 6
42.9%
A 6
42.9%
F 1
 
7.1%
C 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 22
95.7%
. 1
 
4.3%
Space Separator
ValueCountFrequency (%)
1442
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 242
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4878
58.7%
Common 3425
41.2%
Latin 14
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 349
 
7.2%
기 340
 
7.0%
도 322
 
6.6%
동 321
 
6.6%
시 314
 
6.4%
경 313
 
6.4%
번 310
 
6.4%
구 133
 
2.7%
층 109
 
2.2%
양 99
 
2.0%
Other values (243) 2268
46.5%
Common
ValueCountFrequency (%)
1442
42.1%
1 491
 
14.3%
- 242
 
7.1%
2 189
 
5.5%
0 169
 
4.9%
4 165
 
4.8%
3 135
 
3.9%
7 117
 
3.4%
5 113
 
3.3%
6 108
 
3.2%
Other values (7) 254
 
7.4%
Latin
ValueCountFrequency (%)
B 6
42.9%
A 6
42.9%
F 1
 
7.1%
C 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4878
58.7%
ASCII 3439
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1442
41.9%
1 491
 
14.3%
- 242
 
7.0%
2 189
 
5.5%
0 169
 
4.9%
4 165
 
4.8%
3 135
 
3.9%
7 117
 
3.4%
5 113
 
3.3%
6 108
 
3.1%
Other values (11) 268
 
7.8%
Hangul
ValueCountFrequency (%)
지 349
 
7.2%
기 340
 
7.0%
도 322
 
6.6%
동 321
 
6.6%
시 314
 
6.4%
경 313
 
6.4%
번 310
 
6.4%
구 133
 
2.7%
층 109
 
2.2%
양 99
 
2.0%
Other values (243) 2268
46.5%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct291
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20052162
Minimum199011
Maximum20180706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T07:49:11.291315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum199011
5-th percentile20030264
Q120091104
median20130314
Q320150729
95-th percentile20170919
Maximum20180706
Range19981695
Interquartile range (IQR)59625

Descriptive statistics

Standard deviation1134138
Coefficient of variation (CV)0.056559384
Kurtosis307.82994
Mean20052162
Median Absolute Deviation (MAD)29584
Skewness-17.528738
Sum6.1961182 × 109
Variance1.2862689 × 1012
MonotonicityNot monotonic
2023-12-11T07:49:11.498113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20100105 2
 
0.6%
20160725 2
 
0.6%
20120712 2
 
0.6%
20120427 2
 
0.6%
20081202 2
 
0.6%
20111109 2
 
0.6%
20151007 2
 
0.6%
20130314 2
 
0.6%
20131010 2
 
0.6%
20100630 2
 
0.6%
Other values (281) 289
93.5%
ValueCountFrequency (%)
199011 1
0.3%
19920821 1
0.3%
19921221 1
0.3%
19930503 1
0.3%
19940426 1
0.3%
19940704 1
0.3%
19941025 1
0.3%
19960426 1
0.3%
19970404 1
0.3%
19970708 1
0.3%
ValueCountFrequency (%)
20180706 1
0.3%
20180702 1
0.3%
20180601 1
0.3%
20180510 1
0.3%
20180427 1
0.3%
20180411 1
0.3%
20180410 1
0.3%
20180403 1
0.3%
20180219 1
0.3%
20180111 1
0.3%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
운영중
172 
폐업 등
137 

Length

Max length4
Median length3
Mean length3.4433657
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 172
55.7%
폐업 등 137
44.3%

Length

2023-12-11T07:49:11.642684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:49:11.727843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 172
38.6%
폐업 137
30.7%
등 137
30.7%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct130
Distinct (%)94.9%
Missing172
Missing (%)55.7%
Infinite0
Infinite (%)0.0%
Mean20145594
Minimum20070806
Maximum20180823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T07:49:11.844002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070806
5-th percentile20090930
Q120120910
median20151210
Q320170608
95-th percentile20180706
Maximum20180823
Range110017
Interquartile range (IQR)49698

Descriptive statistics

Standard deviation29212.974
Coefficient of variation (CV)0.0014500924
Kurtosis-0.56526931
Mean20145594
Median Absolute Deviation (MAD)20181
Skewness-0.6450792
Sum2.7599464 × 109
Variance8.5339784 × 108
MonotonicityNot monotonic
2023-12-11T07:49:12.009904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170106 3
 
1.0%
20160825 2
 
0.6%
20150703 2
 
0.6%
20120927 2
 
0.6%
20080418 2
 
0.6%
20180212 2
 
0.6%
20140227 1
 
0.3%
20111011 1
 
0.3%
20131224 1
 
0.3%
20170524 1
 
0.3%
Other values (120) 120
38.8%
(Missing) 172
55.7%
ValueCountFrequency (%)
20070806 1
0.3%
20071011 1
0.3%
20080418 2
0.6%
20080918 1
0.3%
20081002 1
0.3%
20090601 1
0.3%
20091012 1
0.3%
20100331 1
0.3%
20100527 1
0.3%
20100603 1
0.3%
ValueCountFrequency (%)
20180823 1
0.3%
20180822 1
0.3%
20180817 1
0.3%
20180806 1
0.3%
20180730 1
0.3%
20180716 1
0.3%
20180710 1
0.3%
20180705 1
0.3%
20180702 1
0.3%
20180629 1
0.3%

다중이용업소여부
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.7%
Missing16
Missing (%)5.2%
Memory size750.0 B
False
292 
True
 
1
(Missing)
 
16
ValueCountFrequency (%)
False 292
94.5%
True 1
 
0.3%
(Missing) 16
 
5.2%
2023-12-11T07:49:12.121141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

세탁기수(대)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)3.6%
Missing57
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean2.5039683
Minimum0
Maximum14
Zeros29
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T07:49:12.192150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile4
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5703884
Coefficient of variation (CV)0.62715986
Kurtosis10.917247
Mean2.5039683
Median Absolute Deviation (MAD)1
Skewness1.6102888
Sum631
Variance2.4661196
MonotonicityNot monotonic
2023-12-11T07:49:12.279807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 97
31.4%
2 52
16.8%
4 32
 
10.4%
1 30
 
9.7%
0 29
 
9.4%
5 5
 
1.6%
6 3
 
1.0%
7 3
 
1.0%
14 1
 
0.3%
(Missing) 57
18.4%
ValueCountFrequency (%)
0 29
 
9.4%
1 30
 
9.7%
2 52
16.8%
3 97
31.4%
4 32
 
10.4%
5 5
 
1.6%
6 3
 
1.0%
7 3
 
1.0%
14 1
 
0.3%
ValueCountFrequency (%)
14 1
 
0.3%
7 3
 
1.0%
6 3
 
1.0%
5 5
 
1.6%
4 32
 
10.4%
3 97
31.4%
2 52
16.8%
1 30
 
9.7%
0 29
 
9.4%

회수건조수(대)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)3.1%
Missing80
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean0.95196507
Minimum0
Maximum7
Zeros150
Zeros (%)48.5%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T07:49:12.372739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4756379
Coefficient of variation (CV)1.5500966
Kurtosis0.58225293
Mean0.95196507
Median Absolute Deviation (MAD)0
Skewness1.2896603
Sum218
Variance2.1775071
MonotonicityNot monotonic
2023-12-11T07:49:12.486023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 150
48.5%
3 29
 
9.4%
4 18
 
5.8%
2 17
 
5.5%
1 13
 
4.2%
7 1
 
0.3%
5 1
 
0.3%
(Missing) 80
25.9%
ValueCountFrequency (%)
0 150
48.5%
1 13
 
4.2%
2 17
 
5.5%
3 29
 
9.4%
4 18
 
5.8%
5 1
 
0.3%
7 1
 
0.3%
ValueCountFrequency (%)
7 1
 
0.3%
5 1
 
0.3%
4 18
 
5.8%
3 29
 
9.4%
2 17
 
5.5%
1 13
 
4.2%
0 150
48.5%

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
세탁업
293 
<NA>
 
16

Length

Max length4
Median length3
Mean length3.0517799
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세탁업 293
94.8%
<NA> 16
 
5.2%

Length

2023-12-11T07:49:12.610718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:49:12.700677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 293
94.8%
na 16
 
5.2%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
빨래방업
293 
<NA>
 
16

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
빨래방업 293
94.8%
<NA> 16
 
5.2%

Length

2023-12-11T07:49:12.796589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:49:12.897063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
빨래방업 293
94.8%
na 16
 
5.2%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct297
Distinct (%)97.4%
Missing4
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean37.453698
Minimum36.978588
Maximum38.025851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T07:49:13.051984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.978588
5-th percentile37.037634
Q137.281263
median37.422117
Q337.662317
95-th percentile37.828958
Maximum38.025851
Range1.0472633
Interquartile range (IQR)0.38105353

Descriptive statistics

Standard deviation0.23890016
Coefficient of variation (CV)0.0063785468
Kurtosis-0.68562238
Mean37.453698
Median Absolute Deviation (MAD)0.17779798
Skewness0.091727435
Sum11423.378
Variance0.057073288
MonotonicityNot monotonic
2023-12-11T07:49:13.215811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2443188859 3
 
1.0%
37.0620972627 2
 
0.6%
37.5014360537 2
 
0.6%
37.6494247379 2
 
0.6%
37.7342087759 2
 
0.6%
37.3374923833 2
 
0.6%
37.2579768845 2
 
0.6%
37.2135200785 1
 
0.3%
37.7976063291 1
 
0.3%
37.2584892947 1
 
0.3%
Other values (287) 287
92.9%
(Missing) 4
 
1.3%
ValueCountFrequency (%)
36.9785880452 1
0.3%
36.9843913859 1
0.3%
36.9871189595 1
0.3%
36.987627825 1
0.3%
36.9880136416 1
0.3%
36.9883150515 1
0.3%
36.9944915135 1
0.3%
36.9956961338 1
0.3%
36.9993026277 1
0.3%
36.9995641462 1
0.3%
ValueCountFrequency (%)
38.0258513641 1
0.3%
38.0249032265 1
0.3%
38.0180428102 1
0.3%
37.9556073292 1
0.3%
37.9554205845 1
0.3%
37.9260023368 1
0.3%
37.9107494385 1
0.3%
37.9066524548 1
0.3%
37.8972135866 1
0.3%
37.8915863816 1
0.3%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct297
Distinct (%)97.4%
Missing4
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean127.01364
Minimum126.58611
Maximum127.64582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T07:49:13.393219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.58611
5-th percentile126.74733
Q1126.83581
median127.04971
Q3127.13261
95-th percentile127.31722
Maximum127.64582
Range1.0597052
Interquartile range (IQR)0.29679891

Descriptive statistics

Standard deviation0.19231872
Coefficient of variation (CV)0.001514158
Kurtosis0.17786661
Mean127.01364
Median Absolute Deviation (MAD)0.11470021
Skewness0.28760077
Sum38739.161
Variance0.036986492
MonotonicityNot monotonic
2023-12-11T07:49:13.561130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0765400669 3
 
1.0%
127.0541253594 2
 
0.6%
126.7698413545 2
 
0.6%
127.247885027 2
 
0.6%
127.0450186418 2
 
0.6%
127.2963227839 2
 
0.6%
127.0338361891 2
 
0.6%
126.9756725345 1
 
0.3%
127.101945789 1
 
0.3%
127.64581505 1
 
0.3%
Other values (287) 287
92.9%
(Missing) 4
 
1.3%
ValueCountFrequency (%)
126.5861098556 1
0.3%
126.5958031627 1
0.3%
126.6663319357 1
0.3%
126.6729148921 1
0.3%
126.6737263117 1
0.3%
126.678547796 1
0.3%
126.6794040798 1
0.3%
126.6840758718 1
0.3%
126.6986141136 1
0.3%
126.7008753428 1
0.3%
ValueCountFrequency (%)
127.64581505 1
0.3%
127.6424534343 1
0.3%
127.6294374711 1
0.3%
127.5113523295 1
0.3%
127.4972058429 1
0.3%
127.4941481849 1
0.3%
127.4780830475 1
0.3%
127.4556709026 1
0.3%
127.3778773401 1
0.3%
127.3748398512 1
0.3%

Interactions

2023-12-11T07:49:07.614560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:03.790066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:04.561171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.196356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.821361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.501279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.083967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.692538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:03.871193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:04.645680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.286666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.902940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.594100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.163009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.764990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:03.952654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:04.745152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.389139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.000769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.681021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.234071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.838134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:04.023497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:04.857312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.471555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.101676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.759425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.306444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.931464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:04.100406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:04.945064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.558609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.218673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.847229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.388167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:08.009308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:04.399538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.031127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.661765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.325537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.928622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.466278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:08.087834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:04.483930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.120075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:05.752189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:06.412196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.006508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:07.538660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:49:13.679469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명소재지우편번호인허가일자영업상태명폐업일자다중이용업소여부세탁기수(대)회수건조수(대)WGS84위도WGS84경도
시군명1.0001.000NaN0.2340.3230.0000.0000.0000.9830.970
소재지우편번호1.0001.000NaN0.0000.3600.0000.1700.0000.6770.785
인허가일자NaNNaN1.000NaNNaNNaNNaNNaNNaNNaN
영업상태명0.2340.000NaN1.000NaN0.0000.0000.1000.1310.179
폐업일자0.3230.360NaNNaN1.0000.0000.4730.1740.0000.341
다중이용업소여부0.0000.000NaN0.0000.0001.0000.0000.2050.0000.000
세탁기수(대)0.0000.170NaN0.0000.4730.0001.0000.8310.1360.284
회수건조수(대)0.0000.000NaN0.1000.1740.2050.8311.0000.0000.000
WGS84위도0.9830.677NaN0.1310.0000.0000.1360.0001.0000.705
WGS84경도0.9700.785NaN0.1790.3410.0000.2840.0000.7051.000
2023-12-11T07:49:13.829900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명위생업태명영업상태명위생업종명다중이용업소여부
시군명1.0001.0000.1771.0000.000
위생업태명1.0001.0001.0001.0001.000
영업상태명0.1771.0001.0001.0000.000
위생업종명1.0001.0001.0001.0001.000
다중이용업소여부0.0001.0000.0001.0001.000
2023-12-11T07:49:13.952821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호인허가일자폐업일자세탁기수(대)회수건조수(대)WGS84위도WGS84경도시군명영업상태명다중이용업소여부위생업종명위생업태명
소재지우편번호1.000-0.012-0.223-0.0450.0250.1110.8010.9360.0000.0001.0001.000
인허가일자-0.0121.0000.6840.1280.232-0.039-0.1380.0000.0000.0001.0001.000
폐업일자-0.2230.6841.0000.3110.279-0.054-0.2300.1061.0000.0001.0001.000
세탁기수(대)-0.0450.1280.3111.0000.2570.006-0.0810.0000.0000.0001.0001.000
회수건조수(대)0.0250.2320.2790.2571.000-0.0350.0310.0000.1060.2161.0001.000
WGS84위도0.111-0.039-0.0540.006-0.0351.000-0.1580.7680.0990.0001.0001.000
WGS84경도0.801-0.138-0.230-0.0810.031-0.1581.0000.7100.1350.0001.0001.000
시군명0.9360.0000.1060.0000.0000.7680.7101.0000.1770.0001.0001.000
영업상태명0.0000.0001.0000.0000.1060.0990.1350.1771.0000.0001.0001.000
다중이용업소여부0.0000.0000.0000.0000.2160.0000.0000.0000.0001.0001.0001.000
위생업종명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위생업태명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T07:49:08.193874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:49:08.374030image/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.
2023-12-11T07:49:08.533044image/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

시군명사업장명소재지우편번호소재지도로명주소소재지지번주소인허가일자영업상태명폐업일자다중이용업소여부세탁기수(대)회수건조수(대)위생업종명위생업태명WGS84위도WGS84경도
0가평군시민코인빨래방477801경기도 가평군 가평읍 연인2길 22경기도 가평군 가평읍 읍내리 489-3번지20180510운영중<NA>N20세탁업빨래방업37.829842127.511352
1가평군뉴세탁나라12437경기도 가평군 조종면 연인산로 20-5경기도 가평군 조종면 현리 253-3번지20010725운영중<NA>N<NA><NA>세탁업빨래방업37.822034127.351799
2고양시크린토피아코인워시능곡현대홈타운점412818경기도 고양시 덕양구 능곡로 16, 101동 1층 101호 (토당동)경기도 고양시 덕양구 토당동 345-48번지 101동 1층 101호20160829운영중<NA>N33세탁업빨래방업37.621539126.821134
3고양시뽀송뽀송24시셀프빨래방410831경기도 고양시 일산동구 중산로 149, 1층 일부호 (중산동, 스카이빌딩)경기도 고양시 일산동구 중산동 1657-5번지 스카이빌딩 1층 일부호20141016운영중<NA>N33세탁업빨래방업37.68543126.7808
4고양시향기로운세탁소410829경기도 고양시 일산동구 일산로463번길 48-20 (정발산동,1층전체,지하일부)경기도 고양시 일산동구 정발산동 1168-2번지 1층전체,지하일부20041020운영중<NA>N01세탁업빨래방업37.672324126.773318
5고양시경인세탁소411803경기도 고양시 일산서구 성저로 69-8 (대화동, 1층)경기도 고양시 일산서구 대화동 2075-3번지 1층20120627운영중<NA>N10세탁업빨래방업37.685555126.754521
6고양시119운동화빨래방412470경기도 고양시 덕양구 통일로802번길 82, 1(일부)층 (관산동)경기도 고양시 덕양구 관산동 1009번지 외1필지 1층일부20161019운영중<NA>N20세탁업빨래방업37.691081126.865654
7고양시크린업셀프빨래방411860경기도 고양시 일산서구 탄중로 494 (일산동, 1층일부)경기도 고양시 일산서구 일산동 2031번지 외3필지 1층(일부)20111103운영중<NA>N30세탁업빨래방업37.689069126.771624
8고양시크린토피아코인워시365홈플러스일산점410837경기도 고양시 일산동구 중앙로1275번길 64, 지하1층 일부호 (장항동, 홈플러스)경기도 고양시 일산동구 장항동 755번지 홈플러스 지하1일부호20150423운영중<NA>N43세탁업빨래방업37.659065126.768867
9고양시마마운동화이불빨래방410315경기도 고양시 일산동구 하늘마을로93번길 22-12, 1층 전체호 (중산동)경기도 고양시 일산동구 중산동 1707번지20161021운영중<NA>N20세탁업빨래방업37.680265126.785239
시군명사업장명소재지우편번호소재지도로명주소소재지지번주소인허가일자영업상태명폐업일자다중이용업소여부세탁기수(대)회수건조수(대)위생업종명위생업태명WGS84위도WGS84경도
299화성시크린토피아코인워시 신동탄이지더원점445130경기도 화성시 지산1길 25-10, 1층 (영천동)경기도 화성시 영천동 산 27-60번지20150918운영중<NA>N33세탁업빨래방업37.208263127.110866
300화성시24시무인빨래방향남점445926경기도 화성시 향남읍 행정서로1길 38, 102호경기도 화성시 향남읍 행정리 445-11번지 102호20150407운영중<NA>N30세탁업빨래방업37.128016126.914773
301화성시크린토피아 화성진안점445390경기도 화성시 병점4로 58, 1층 일부호 (진안동)경기도 화성시 진안동 862-3번지 1층 일부호20160725운영중<NA>N33세탁업빨래방업37.214389127.037215
302화성시크린토피아 코인워시 봉담와우점445897경기도 화성시 봉담읍 와우로 90, 121호경기도 화성시 봉담읍 와우리 31-6번지 121호20170918운영중<NA>N<NA><NA>세탁업빨래방업37.21352126.975673
303화성시크린토피아 코인워시 화성동탄능동점445320경기도 화성시 동탄원천로 354-28, 109호 (능동)경기도 화성시 능동 1064-5번지 109호20160219운영중<NA>N34세탁업빨래방업37.218275127.058731
304화성시워시앤드라이445897경기도 화성시 봉담읍 와우로73번길 6, 114호경기도 화성시 봉담읍 와우리 26-12번지 114호20151116폐업 등20180614N32세탁업빨래방업37.214145126.974248
305화성시코인워시24셀프빨래방(동탄점)445160경기도 화성시 동탄문화센터로 71-17 (반송동, 연세프라자 101호)경기도 화성시 반송동 107-4번지 연세프라자 101호20130719폐업 등20160122Y11세탁업빨래방업37.200254127.072741
306화성시월풀빨래방445861경기도 화성시 마도면 석교로157번길 6-20경기도 화성시 마도면 석교리 219-1번지20070920폐업 등20081002N0<NA>세탁업빨래방업37.205831126.774051
307화성시워시엔조이셀프빨래방향남점445926경기도 화성시 향남읍 행정죽전로1길 47경기도 화성시 향남읍 행정리 503-1번지20150518폐업 등20160817N33세탁업빨래방업37.12508126.922665
308화성시일렉트로룩스 코인워시 셀프빨래방(병점점)445390경기도 화성시 병점중앙로156번길 15-7 (진안동, 1층 일부)경기도 화성시 진안동 884-11번지 1층 일부20121031폐업 등20140825N34세탁업빨래방업37.211696127.039948