Overview

Dataset statistics

Number of variables15
Number of observations150
Missing cells132
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.7 KiB
Average record size in memory127.9 B

Variable types

Categorical4
Text3
Numeric7
Boolean1

Dataset

Description세탁업(세탁업기타) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=WWP5RIQ6I7NGI8Q21G9F14159163&infSeq=1

Alerts

다중이용업소여부 has constant value ""Constant
위생업종명 is highly overall correlated with 인허가일자 and 9 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
위생업태명 is highly overall correlated with 인허가일자 and 9 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 4 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
소재지우편번호 is highly overall correlated with WGS84경도 and 3 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
위생업종명 is highly imbalanced (89.8%)Imbalance
위생업태명 is highly imbalanced (89.8%)Imbalance
폐업일자 has 106 (70.7%) missing valuesMissing
다중이용업소여부 has 2 (1.3%) missing valuesMissing
세탁기수(대) has 7 (4.7%) missing valuesMissing
회수건조수(대) has 15 (10.0%) missing valuesMissing
세탁기수(대) has 17 (11.3%) zerosZeros
회수건조수(대) has 77 (51.3%) zerosZeros

Reproduction

Analysis started2023-12-10 21:04:44.189138
Analysis finished2023-12-10 21:04:50.767329
Duration6.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
파주시
38 
고양시
15 
화성시
12 
김포시
9 
수원시
8 
Other values (20)
68 

Length

Max length4
Median length3
Mean length3.0866667
Min length3

Unique

Unique3 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
파주시 38
25.3%
고양시 15
 
10.0%
화성시 12
 
8.0%
김포시 9
 
6.0%
수원시 8
 
5.3%
남양주시 7
 
4.7%
광주시 6
 
4.0%
용인시 6
 
4.0%
하남시 5
 
3.3%
부천시 5
 
3.3%
Other values (15) 39
26.0%

Length

2023-12-11T06:04:50.835145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
파주시 38
25.3%
고양시 15
 
10.0%
화성시 12
 
8.0%
김포시 9
 
6.0%
수원시 8
 
5.3%
남양주시 7
 
4.7%
광주시 6
 
4.0%
용인시 6
 
4.0%
하남시 5
 
3.3%
부천시 5
 
3.3%
Other values (15) 39
26.0%

사업장명
Text

Distinct146
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T06:04:51.094514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16.5
Mean length6.8933333
Min length2

Characters and Unicode

Total characters1034
Distinct characters235
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

Unique143 ?
Unique (%)95.3%

Sample

1st row손빨래방
2nd row크린토피아수색지사
3rd row대륙세탁
4th row대성
5th row크린에이드
ValueCountFrequency (%)
크린토피아 13
 
6.8%
코인워시 6
 
3.1%
크린에이드 3
 
1.6%
주식회사 3
 
1.6%
그린 2
 
1.0%
대성기업 2
 
1.0%
주)크린토피아 2
 
1.0%
황토인 2
 
1.0%
대원세탁 1
 
0.5%
능안세탁 1
 
0.5%
Other values (157) 157
81.8%
2023-12-11T06:04:51.476534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
린 44
 
4.3%
42
 
4.1%
크 40
 
3.9%
세 30
 
2.9%
탁 29
 
2.8%
토 28
 
2.7%
아 28
 
2.7%
주 27
 
2.6%
) 25
 
2.4%
( 25
 
2.4%
Other values (225) 716
69.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 897
86.8%
Space Separator 42
 
4.1%
Uppercase Letter 26
 
2.5%
Close Punctuation 25
 
2.4%
Open Punctuation 25
 
2.4%
Lowercase Letter 10
 
1.0%
Decimal Number 4
 
0.4%
Other Punctuation 4
 
0.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
린 44
 
4.9%
크 40
 
4.5%
세 30
 
3.3%
탁 29
 
3.2%
토 28
 
3.1%
아 28
 
3.1%
주 27
 
3.0%
피 23
 
2.6%
이 22
 
2.5%
점 18
 
2.0%
Other values (196) 608
67.8%
Uppercase Letter
ValueCountFrequency (%)
O 4
15.4%
K 3
11.5%
T 3
11.5%
C 3
11.5%
P 2
7.7%
N 2
7.7%
B 2
7.7%
E 2
7.7%
W 1
 
3.8%
A 1
 
3.8%
Other values (3) 3
11.5%
Lowercase Letter
ValueCountFrequency (%)
e 5
50.0%
w 1
 
10.0%
n 1
 
10.0%
o 1
 
10.0%
t 1
 
10.0%
s 1
 
10.0%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 1
25.0%
7 1
25.0%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
. 1
25.0%
' 1
25.0%
Space Separator
ValueCountFrequency (%)
42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 897
86.8%
Common 101
 
9.8%
Latin 36
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
린 44
 
4.9%
크 40
 
4.5%
세 30
 
3.3%
탁 29
 
3.2%
토 28
 
3.1%
아 28
 
3.1%
주 27
 
3.0%
피 23
 
2.6%
이 22
 
2.5%
점 18
 
2.0%
Other values (196) 608
67.8%
Latin
ValueCountFrequency (%)
e 5
13.9%
O 4
11.1%
K 3
 
8.3%
T 3
 
8.3%
C 3
 
8.3%
P 2
 
5.6%
N 2
 
5.6%
B 2
 
5.6%
E 2
 
5.6%
W 1
 
2.8%
Other values (9) 9
25.0%
Common
ValueCountFrequency (%)
42
41.6%
) 25
24.8%
( 25
24.8%
1 2
 
2.0%
& 2
 
2.0%
2 1
 
1.0%
- 1
 
1.0%
7 1
 
1.0%
. 1
 
1.0%
' 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 897
86.8%
ASCII 137
 
13.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
린 44
 
4.9%
크 40
 
4.5%
세 30
 
3.3%
탁 29
 
3.2%
토 28
 
3.1%
아 28
 
3.1%
주 27
 
3.0%
피 23
 
2.6%
이 22
 
2.5%
점 18
 
2.0%
Other values (196) 608
67.8%
ASCII
ValueCountFrequency (%)
42
30.7%
) 25
18.2%
( 25
18.2%
e 5
 
3.6%
O 4
 
2.9%
K 3
 
2.2%
T 3
 
2.2%
C 3
 
2.2%
P 2
 
1.5%
N 2
 
1.5%
Other values (19) 23
16.8%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct143
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20129867
Minimum19981112
Maximum20180521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:04:51.613325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19981112
5-th percentile20080724
Q120110245
median20140260
Q320150927
95-th percentile20170963
Maximum20180521
Range199409
Interquartile range (IQR)40681.75

Descriptive statistics

Standard deviation32054.908
Coefficient of variation (CV)0.0015924054
Kurtosis1.7396394
Mean20129867
Median Absolute Deviation (MAD)20297
Skewness-0.85554408
Sum3.01948 × 109
Variance1.0275171 × 109
MonotonicityNot monotonic
2023-12-11T06:04:51.755707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20090601 2
 
1.3%
20170904 2
 
1.3%
20090716 2
 
1.3%
20150529 2
 
1.3%
20150316 2
 
1.3%
20121012 2
 
1.3%
20130418 2
 
1.3%
20120625 1
 
0.7%
20150917 1
 
0.7%
20060922 1
 
0.7%
Other values (133) 133
88.7%
ValueCountFrequency (%)
19981112 1
0.7%
20060922 1
0.7%
20071010 1
0.7%
20071019 1
0.7%
20071127 1
0.7%
20080313 1
0.7%
20080714 1
0.7%
20080723 1
0.7%
20080725 1
0.7%
20081103 1
0.7%
ValueCountFrequency (%)
20180521 1
0.7%
20180509 1
0.7%
20180504 1
0.7%
20180417 1
0.7%
20180327 1
0.7%
20180102 1
0.7%
20171204 1
0.7%
20171011 1
0.7%
20170904 2
1.3%
20170901 1
0.7%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
운영중
106 
폐업 등
44 

Length

Max length4
Median length3
Mean length3.2933333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 106
70.7%
폐업 등 44
29.3%

Length

2023-12-11T06:04:51.885913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:04:51.976009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 106
54.6%
폐업 44
22.7%
등 44
22.7%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)93.2%
Missing106
Missing (%)70.7%
Infinite0
Infinite (%)0.0%
Mean20150450
Minimum20090910
Maximum20180813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:04:52.088762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090910
5-th percentile20100625
Q120137894
median20160565
Q320170508
95-th percentile20180702
Maximum20180813
Range89903
Interquartile range (IQR)32614.5

Descriptive statistics

Standard deviation26267.327
Coefficient of variation (CV)0.0013035603
Kurtosis-0.37411836
Mean20150450
Median Absolute Deviation (MAD)14953.5
Skewness-0.83133747
Sum8.8661981 × 108
Variance6.8997244 × 108
MonotonicityNot monotonic
2023-12-11T06:04:52.214507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
20160617 2
 
1.3%
20180615 2
 
1.3%
20170508 2
 
1.3%
20100705 1
 
0.7%
20110919 1
 
0.7%
20170904 1
 
0.7%
20140625 1
 
0.7%
20180717 1
 
0.7%
20150331 1
 
0.7%
20151126 1
 
0.7%
Other values (31) 31
 
20.7%
(Missing) 106
70.7%
ValueCountFrequency (%)
20090910 1
0.7%
20100316 1
0.7%
20100611 1
0.7%
20100705 1
0.7%
20100827 1
0.7%
20110623 1
0.7%
20110919 1
0.7%
20120619 1
0.7%
20130115 1
0.7%
20130619 1
0.7%
ValueCountFrequency (%)
20180813 1
0.7%
20180723 1
0.7%
20180717 1
0.7%
20180615 2
1.3%
20180409 1
0.7%
20180119 1
0.7%
20170918 1
0.7%
20170904 1
0.7%
20170713 1
0.7%
20170508 2
1.3%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.7%
Missing2
Missing (%)1.3%
Memory size432.0 B
False
148 
(Missing)
 
2
ValueCountFrequency (%)
False 148
98.7%
(Missing) 2
 
1.3%
2023-12-11T06:04:52.318885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

세탁기수(대)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)6.3%
Missing7
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean2.2657343
Minimum0
Maximum14
Zeros17
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:04:52.392445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4.9
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7072796
Coefficient of variation (CV)0.75352154
Kurtosis14.8706
Mean2.2657343
Median Absolute Deviation (MAD)1
Skewness2.5135543
Sum324
Variance2.9148035
MonotonicityNot monotonic
2023-12-11T06:04:52.490417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 47
31.3%
3 29
19.3%
1 26
17.3%
0 17
 
11.3%
4 16
 
10.7%
5 4
 
2.7%
6 2
 
1.3%
7 1
 
0.7%
14 1
 
0.7%
(Missing) 7
 
4.7%
ValueCountFrequency (%)
0 17
 
11.3%
1 26
17.3%
2 47
31.3%
3 29
19.3%
4 16
 
10.7%
5 4
 
2.7%
6 2
 
1.3%
7 1
 
0.7%
14 1
 
0.7%
ValueCountFrequency (%)
14 1
 
0.7%
7 1
 
0.7%
6 2
 
1.3%
5 4
 
2.7%
4 16
 
10.7%
3 29
19.3%
2 47
31.3%
1 26
17.3%
0 17
 
11.3%

회수건조수(대)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)5.9%
Missing15
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean0.97777778
Minimum0
Maximum9
Zeros77
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:04:52.605503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3.3
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5232853
Coefficient of variation (CV)1.5579054
Kurtosis8.1541194
Mean0.97777778
Median Absolute Deviation (MAD)0
Skewness2.4167898
Sum132
Variance2.320398
MonotonicityNot monotonic
2023-12-11T06:04:52.725047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 77
51.3%
2 27
 
18.0%
1 17
 
11.3%
3 7
 
4.7%
5 3
 
2.0%
4 2
 
1.3%
9 1
 
0.7%
8 1
 
0.7%
(Missing) 15
 
10.0%
ValueCountFrequency (%)
0 77
51.3%
1 17
 
11.3%
2 27
 
18.0%
3 7
 
4.7%
4 2
 
1.3%
5 3
 
2.0%
8 1
 
0.7%
9 1
 
0.7%
ValueCountFrequency (%)
9 1
 
0.7%
8 1
 
0.7%
5 3
 
2.0%
4 2
 
1.3%
3 7
 
4.7%
2 27
 
18.0%
1 17
 
11.3%
0 77
51.3%

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
세탁업
148 
<NA>
 
2

Length

Max length4
Median length3
Mean length3.0133333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세탁업 148
98.7%
<NA> 2
 
1.3%

Length

2023-12-11T06:04:52.868673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:04:52.968572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 148
98.7%
na 2
 
1.3%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
세탁업 기타
148 
<NA>
 
2

Length

Max length6
Median length6
Mean length5.9733333
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세탁업 기타 148
98.7%
<NA> 2
 
1.3%

Length

2023-12-11T06:04:53.092349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:04:53.198831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 148
49.7%
기타 148
49.7%
na 2
 
0.7%

소재지도로명주소
Text

Distinct149
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T06:04:53.472609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length41
Mean length29.333333
Min length14

Characters and Unicode

Total characters4400
Distinct characters263
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)98.7%

Sample

1st row경기도 가평군 가평읍 보납로 35 ([한국통신 2층 직원휴게실내])
2nd row경기도 고양시 덕양구 중앙로78번안길 123 (화전동,A동 1층)
3rd row경기도 고양시 덕양구 고골길116번길 14-45 (관산동,나동)
4th row경기도 고양시 덕양구 고골길116번길 14-45, 1층 (관산동, 가동)
5th row경기도 고양시 일산서구 일중로 17, 101호,102호,103호 (일산동, 포오스프라자)
ValueCountFrequency (%)
경기도 150
 
15.9%
1층 48
 
5.1%
파주시 38
 
4.0%
조리읍 16
 
1.7%
고양시 15
 
1.6%
화성시 12
 
1.3%
가동 10
 
1.1%
김포시 9
 
1.0%
광탄면 8
 
0.8%
수원시 8
 
0.8%
Other values (454) 628
66.7%
2023-12-11T06:04:53.942950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
792
 
18.0%
1 228
 
5.2%
도 156
 
3.5%
시 154
 
3.5%
경 153
 
3.5%
기 153
 
3.5%
동 150
 
3.4%
로 122
 
2.8%
2 116
 
2.6%
, 106
 
2.4%
Other values (253) 2270
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2474
56.2%
Space Separator 792
 
18.0%
Decimal Number 785
 
17.8%
Other Punctuation 107
 
2.4%
Close Punctuation 87
 
2.0%
Open Punctuation 87
 
2.0%
Dash Punctuation 57
 
1.3%
Uppercase Letter 9
 
0.2%
Math Symbol 1
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 156
 
6.3%
시 154
 
6.2%
경 153
 
6.2%
기 153
 
6.2%
동 150
 
6.1%
로 122
 
4.9%
길 95
 
3.8%
번 65
 
2.6%
층 61
 
2.5%
주 60
 
2.4%
Other values (228) 1305
52.7%
Decimal Number
ValueCountFrequency (%)
1 228
29.0%
2 116
14.8%
3 85
 
10.8%
0 72
 
9.2%
6 65
 
8.3%
4 54
 
6.9%
5 50
 
6.4%
9 47
 
6.0%
8 37
 
4.7%
7 31
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
A 3
33.3%
B 2
22.2%
C 2
22.2%
G 1
 
11.1%
D 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 106
99.1%
@ 1
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 86
98.9%
] 1
 
1.1%
Open Punctuation
ValueCountFrequency (%)
( 86
98.9%
[ 1
 
1.1%
Space Separator
ValueCountFrequency (%)
792
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2474
56.2%
Common 1916
43.5%
Latin 10
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 156
 
6.3%
시 154
 
6.2%
경 153
 
6.2%
기 153
 
6.2%
동 150
 
6.1%
로 122
 
4.9%
길 95
 
3.8%
번 65
 
2.6%
층 61
 
2.5%
주 60
 
2.4%
Other values (228) 1305
52.7%
Common
ValueCountFrequency (%)
792
41.3%
1 228
 
11.9%
2 116
 
6.1%
, 106
 
5.5%
) 86
 
4.5%
( 86
 
4.5%
3 85
 
4.4%
0 72
 
3.8%
6 65
 
3.4%
- 57
 
3.0%
Other values (9) 223
 
11.6%
Latin
ValueCountFrequency (%)
A 3
30.0%
B 2
20.0%
C 2
20.0%
G 1
 
10.0%
D 1
 
10.0%
Ⅰ 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2474
56.2%
ASCII 1925
43.8%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
792
41.1%
1 228
 
11.8%
2 116
 
6.0%
, 106
 
5.5%
) 86
 
4.5%
( 86
 
4.5%
3 85
 
4.4%
0 72
 
3.7%
6 65
 
3.4%
- 57
 
3.0%
Other values (14) 232
 
12.1%
Hangul
ValueCountFrequency (%)
도 156
 
6.3%
시 154
 
6.2%
경 153
 
6.2%
기 153
 
6.2%
동 150
 
6.1%
로 122
 
4.9%
길 95
 
3.8%
번 65
 
2.6%
층 61
 
2.5%
주 60
 
2.4%
Other values (228) 1305
52.7%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
100.0%

소재지지번주소
Text

Distinct149
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T06:04:54.244599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length41
Mean length26.226667
Min length17

Characters and Unicode

Total characters3934
Distinct characters225
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)98.7%

Sample

1st row경기도 가평군 가평읍 읍내리 426-10번지 [한국통신 2층 직원휴게실내]
2nd row경기도 고양시 덕양구 화전동 234-6번지 A동 1층
3rd row경기도 고양시 덕양구 관산동 561-2번지 나동
4th row경기도 고양시 덕양구 관산동 561-2번지 가동 1층 전체
5th row경기도 고양시 일산서구 일산동 524-16번지 포오스프라자 101호,102호,103호
ValueCountFrequency (%)
경기도 150
 
17.3%
파주시 38
 
4.4%
1층 35
 
4.0%
조리읍 16
 
1.8%
고양시 15
 
1.7%
능안리 14
 
1.6%
화성시 12
 
1.4%
가동 11
 
1.3%
나동 9
 
1.0%
김포시 9
 
1.0%
Other values (397) 557
64.3%
2023-12-11T06:04:54.737990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
716
 
18.2%
1 189
 
4.8%
지 172
 
4.4%
시 154
 
3.9%
도 153
 
3.9%
기 152
 
3.9%
경 150
 
3.8%
번 150
 
3.8%
동 149
 
3.8%
- 116
 
2.9%
Other values (215) 1833
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2340
59.5%
Decimal Number 727
 
18.5%
Space Separator 716
 
18.2%
Dash Punctuation 116
 
2.9%
Uppercase Letter 11
 
0.3%
Other Punctuation 9
 
0.2%
Open Punctuation 6
 
0.2%
Close Punctuation 6
 
0.2%
Math Symbol 2
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 172
 
7.4%
시 154
 
6.6%
도 153
 
6.5%
기 152
 
6.5%
경 150
 
6.4%
번 150
 
6.4%
동 149
 
6.4%
리 88
 
3.8%
주 59
 
2.5%
층 46
 
2.0%
Other values (190) 1067
45.6%
Decimal Number
ValueCountFrequency (%)
1 189
26.0%
2 86
11.8%
3 83
11.4%
0 77
10.6%
5 73
 
10.0%
4 55
 
7.6%
8 47
 
6.5%
6 47
 
6.5%
9 40
 
5.5%
7 30
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
A 5
45.5%
B 2
 
18.2%
C 2
 
18.2%
G 1
 
9.1%
D 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 8
88.9%
@ 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 5
83.3%
[ 1
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 5
83.3%
] 1
 
16.7%
Space Separator
ValueCountFrequency (%)
716
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 116
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2340
59.5%
Common 1582
40.2%
Latin 12
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 172
 
7.4%
시 154
 
6.6%
도 153
 
6.5%
기 152
 
6.5%
경 150
 
6.4%
번 150
 
6.4%
동 149
 
6.4%
리 88
 
3.8%
주 59
 
2.5%
층 46
 
2.0%
Other values (190) 1067
45.6%
Common
ValueCountFrequency (%)
716
45.3%
1 189
 
11.9%
- 116
 
7.3%
2 86
 
5.4%
3 83
 
5.2%
0 77
 
4.9%
5 73
 
4.6%
4 55
 
3.5%
8 47
 
3.0%
6 47
 
3.0%
Other values (9) 93
 
5.9%
Latin
ValueCountFrequency (%)
A 5
41.7%
B 2
 
16.7%
C 2
 
16.7%
G 1
 
8.3%
D 1
 
8.3%
Ⅰ 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2340
59.5%
ASCII 1593
40.5%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
716
44.9%
1 189
 
11.9%
- 116
 
7.3%
2 86
 
5.4%
3 83
 
5.2%
0 77
 
4.8%
5 73
 
4.6%
4 55
 
3.5%
8 47
 
3.0%
6 47
 
3.0%
Other values (14) 104
 
6.5%
Hangul
ValueCountFrequency (%)
지 172
 
7.4%
시 154
 
6.6%
도 153
 
6.5%
기 152
 
6.5%
경 150
 
6.4%
번 150
 
6.4%
동 149
 
6.4%
리 88
 
3.8%
주 59
 
2.5%
층 46
 
2.0%
Other values (190) 1067
45.6%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean423976.91
Minimum14406
Maximum487818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:04:54.893993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14406
5-th percentile410689.25
Q1413822
median430839
Q3463580.25
95-th percentile482862
Maximum487818
Range473412
Interquartile range (IQR)49758.25

Descriptive statistics

Standard deviation80205.091
Coefficient of variation (CV)0.18917325
Kurtosis20.697166
Mean423976.91
Median Absolute Deviation (MAD)17017.5
Skewness-4.4525274
Sum63596536
Variance6.4328566 × 109
MonotonicityNot monotonic
2023-12-11T06:04:55.038441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
413822 14
 
9.3%
413852 3
 
2.0%
445902 3
 
2.0%
413872 2
 
1.3%
413855 2
 
1.3%
465816 2
 
1.3%
482862 2
 
1.3%
411803 2
 
1.3%
413812 2
 
1.3%
472852 2
 
1.3%
Other values (111) 116
77.3%
ValueCountFrequency (%)
14406 1
0.7%
14576 1
0.7%
14598 1
0.7%
14620 1
0.7%
14784 1
0.7%
410330 1
0.7%
410540 1
0.7%
410570 1
0.7%
410835 1
0.7%
410837 1
0.7%
ValueCountFrequency (%)
487818 1
0.7%
487813 1
0.7%
487804 1
0.7%
483100 1
0.7%
483040 1
0.7%
483030 1
0.7%
483010 1
0.7%
482862 2
1.3%
482845 1
0.7%
480856 1
0.7%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)94.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean37.548511
Minimum36.960431
Maximum37.950948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:04:55.197861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.960431
5-th percentile37.174275
Q137.341439
median37.633916
Q337.737002
95-th percentile37.865324
Maximum37.950948
Range0.99051754
Interquartile range (IQR)0.39556362

Descriptive statistics

Standard deviation0.23527142
Coefficient of variation (CV)0.0062657989
Kurtosis-0.99212545
Mean37.548511
Median Absolute Deviation (MAD)0.17459322
Skewness-0.4296613
Sum5594.7282
Variance0.05535264
MonotonicityNot monotonic
2023-12-11T06:04:55.365716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.737002356 4
 
2.7%
37.7356142833 2
 
1.3%
37.7115219401 2
 
1.3%
37.8723445535 2
 
1.3%
37.5341121476 2
 
1.3%
37.737542767 2
 
1.3%
37.7396972299 2
 
1.3%
37.8313382415 1
 
0.7%
37.6961387961 1
 
0.7%
37.758002725 1
 
0.7%
Other values (130) 130
86.7%
ValueCountFrequency (%)
36.9604306516 1
0.7%
36.9838556341 1
0.7%
37.0669655903 1
0.7%
37.0860662363 1
0.7%
37.1559298786 1
0.7%
37.1577045373 1
0.7%
37.169493028 1
0.7%
37.1730116807 1
0.7%
37.1761698962 1
0.7%
37.176191056 1
0.7%
ValueCountFrequency (%)
37.9509481886 1
0.7%
37.9004411766 1
0.7%
37.9001203159 1
0.7%
37.8955763919 1
0.7%
37.8874110987 1
0.7%
37.8817378389 1
0.7%
37.8723445535 2
1.3%
37.8547944038 1
0.7%
37.8545337422 1
0.7%
37.8313382415 1
0.7%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)94.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean126.96766
Minimum126.56108
Maximum127.66521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:04:55.523525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.56108
5-th percentile126.70564
Q1126.78624
median126.89239
Q3127.12655
95-th percentile127.36845
Maximum127.66521
Range1.1041308
Interquartile range (IQR)0.34031118

Descriptive statistics

Standard deviation0.22565929
Coefficient of variation (CV)0.0017772974
Kurtosis0.38901112
Mean126.96766
Median Absolute Deviation (MAD)0.13024225
Skewness0.81454565
Sum18918.181
Variance0.050922116
MonotonicityNot monotonic
2023-12-11T06:04:55.708495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7836838347 4
 
2.7%
126.7896327472 2
 
1.3%
126.8637778939 2
 
1.3%
127.0223967079 2
 
1.3%
127.2225012518 2
 
1.3%
126.783621881 2
 
1.3%
126.7941335899 2
 
1.3%
127.5145230843 1
 
0.7%
126.6959309586 1
 
0.7%
126.8571134465 1
 
0.7%
Other values (130) 130
86.7%
ValueCountFrequency (%)
126.5610800845 1
0.7%
126.5697877465 1
0.7%
126.5853710504 1
0.7%
126.5979783742 1
0.7%
126.600370888 1
0.7%
126.6562251337 1
0.7%
126.6959309586 1
0.7%
126.6997590171 1
0.7%
126.7144648849 1
0.7%
126.722957616 1
0.7%
ValueCountFrequency (%)
127.6652108574 1
0.7%
127.6485268246 1
0.7%
127.6302849846 1
0.7%
127.5145230843 1
0.7%
127.4661895342 1
0.7%
127.4325512293 1
0.7%
127.3919346057 1
0.7%
127.3893031032 1
0.7%
127.3371704048 1
0.7%
127.3217093428 1
0.7%

Interactions

2023-12-11T06:04:49.238798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:45.056108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:45.808669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T06:04:47.178349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:47.804689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:48.536235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:49.358248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:45.202060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:45.925439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T06:04:47.279547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T06:04:49.460902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:45.310825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:46.055775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:46.725458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:47.382701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:48.012620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:48.778222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:49.569751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T06:04:46.160673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:46.816845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:47.471480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:48.142042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:48.882320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:49.661003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:45.507068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:46.252841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:46.896587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:47.542842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:48.233297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:48.960663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:49.747728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:45.598827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:46.331171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:46.991435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:47.626256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:48.347517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:49.063856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:49.832844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:45.690380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:46.423008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:47.082387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:47.712605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:48.449222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:49.151941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:04:55.837972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자세탁기수(대)회수건조수(대)소재지우편번호WGS84위도WGS84경도
시군명1.0000.2050.3370.2610.3120.2250.9950.9470.943
인허가일자0.2051.0000.1810.8440.0160.0000.3660.1770.280
영업상태명0.3370.1811.000NaN0.0000.0000.0850.2230.283
폐업일자0.2610.844NaN1.0000.2990.0000.0000.1900.103
세탁기수(대)0.3120.0160.0000.2991.0000.7360.0000.2400.000
회수건조수(대)0.2250.0000.0000.0000.7361.0000.0000.1220.000
소재지우편번호0.9950.3660.0850.0000.0000.0001.0000.7600.771
WGS84위도0.9470.1770.2230.1900.2400.1220.7601.0000.664
WGS84경도0.9430.2800.2830.1030.0000.0000.7710.6641.000
2023-12-11T06:04:55.998670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위생업종명영업상태명위생업태명시군명
위생업종명1.0001.0001.0001.000
영업상태명1.0001.0001.0000.266
위생업태명1.0001.0001.0001.000
시군명1.0000.2661.0001.000
2023-12-11T06:04:56.136467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자세탁기수(대)회수건조수(대)소재지우편번호WGS84위도WGS84경도시군명영업상태명위생업종명위생업태명
인허가일자1.0000.764-0.0210.210-0.071-0.021-0.1880.0000.1591.0001.000
폐업일자0.7641.0000.2460.052-0.2560.034-0.3050.1731.0001.0001.000
세탁기수(대)-0.0210.2461.0000.3180.0030.142-0.0550.0720.0001.0001.000
회수건조수(대)0.2100.0520.3181.000-0.0130.1220.0280.0670.0001.0001.000
소재지우편번호-0.071-0.2560.003-0.0131.000-0.2170.8170.9150.0891.0001.000
WGS84위도-0.0210.0340.1420.122-0.2171.000-0.3630.6790.1651.0001.000
WGS84경도-0.188-0.305-0.0550.0280.817-0.3631.0000.6660.2101.0001.000
시군명0.0000.1730.0720.0670.9150.6790.6661.0000.2661.0001.000
영업상태명0.1591.0000.0000.0000.0890.1650.2100.2661.0001.0001.000
위생업종명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위생업태명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T06:04:50.006942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:04:50.239109image/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.
2023-12-11T06:04:50.673109image/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

시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부세탁기수(대)회수건조수(대)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군손빨래방20110929운영중<NA>N20세탁업세탁업 기타경기도 가평군 가평읍 보납로 35 ([한국통신 2층 직원휴게실내])경기도 가평군 가평읍 읍내리 426-10번지 [한국통신 2층 직원휴게실내]47780137.831338127.514523
1고양시크린토피아수색지사20100729운영중<NA>N22세탁업세탁업 기타경기도 고양시 덕양구 중앙로78번안길 123 (화전동,A동 1층)경기도 고양시 덕양구 화전동 234-6번지 A동 1층41216037.600277126.875047
2고양시대륙세탁20090713운영중<NA>N30세탁업세탁업 기타경기도 고양시 덕양구 고골길116번길 14-45 (관산동,나동)경기도 고양시 덕양구 관산동 561-2번지 나동41280437.711522126.863778
3고양시대성20151106운영중<NA>N20세탁업세탁업 기타경기도 고양시 덕양구 고골길116번길 14-45, 1층 (관산동, 가동)경기도 고양시 덕양구 관산동 561-2번지 가동 1층 전체41280437.711522126.863778
4고양시크린에이드20121012운영중<NA>N42세탁업세탁업 기타경기도 고양시 일산서구 일중로 17, 101호,102호,103호 (일산동, 포오스프라자)경기도 고양시 일산서구 일산동 524-16번지 포오스프라자 101호,102호,103호41186337.681342126.774194
5고양시크린토피아 코인워시 일산웨스턴돔점20140730운영중<NA>N30세탁업세탁업 기타경기도 고양시 일산동구 정발산로42번길 38 (장항동, 라이저 오피스텔 101호, 102호일부)경기도 고양시 일산동구 장항동 853번지 라이저 오피스텔 101호, 102호41083737.655573126.774116
6고양시올크린20130814운영중<NA>N10세탁업세탁업 기타경기도 고양시 일산동구 성석로146번길 85-30 (성석동, 가동 1층전체)경기도 고양시 일산동구 성석동 848-10번지 가동 1층전체41057037.686896126.801269
7고양시오성세탁20110614운영중<NA>N30세탁업세탁업 기타경기도 고양시 일산동구 통일로1267번길 99-27 (지영동)경기도 고양시 일산동구 지영동 1020-6번지41054037.722388126.838449
8고양시유진기업20100709운영중<NA>N22세탁업세탁업 기타경기도 고양시 덕양구 강매로 148 (강매동,가동 1층)경기도 고양시 덕양구 강매동 251-5번지 가동 1층41229037.601123126.843184
9고양시크린에이드20160831운영중<NA>N00세탁업세탁업 기타경기도 고양시 덕양구 호국로 860, 1층 108호 (성사동, 레미안 휴레스트 근린생활시설-Ⅰ 정문상가)경기도 고양시 덕양구 성사동 860번지 레미안 휴레스트 근린생활시설-Ⅰ 정문상가 1층 108호41202037.660496126.841981
시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부세탁기수(대)회수건조수(대)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
140화성시New 새롬20160629운영중<NA>N22세탁업세탁업 기타경기도 화성시 배양북길 47, 나동 1층 (배양동)경기도 화성시 배양동 35-22번지 1층 나동44535037.225527126.996533
141화성시대성기업20140410운영중<NA>N22세탁업세탁업 기타경기도 화성시 정남면 발산5길 34-18, 1동경기도 화성시 정남면 발산리 594-3번지 1동44596737.15593126.98401
142화성시크린토피아 봉담지사20120202운영중<NA>N22세탁업세탁업 기타경기도 화성시 봉담읍 동화길 161경기도 화성시 봉담읍 분천리 60-1번지 나동 1층44589437.210661126.961177
143화성시나이스크리닝20130418운영중<NA>N10세탁업세탁업 기타경기도 화성시 반정로 141-12 (반정동)경기도 화성시 반정동 108-1번지 1층 전체44534037.234479127.03893
144화성시그린 크리닝20121023운영중<NA>N12세탁업세탁업 기타경기도 화성시 봉담읍 갈담초교길 40-1 (유리 98번지)경기도 화성시 봉담읍 유리 98번지44590237.17617126.93157
145화성시하나세탁20090929운영중<NA>N00세탁업세탁업 기타경기도 화성시 병점동로 147 (진안동,(104호))경기도 화성시 진안동 193-1번지 (104호)44539037.216462127.037
146화성시한성기업20080725운영중<NA>N4<NA>세탁업세탁업 기타경기도 화성시 봉담읍 상방길 32, 가동 1층경기도 화성시 봉담읍 마하리 294번지 가동44590237.177663126.947972
147화성시주식회사에스에스씨20080714운영중<NA>N2<NA>세탁업세탁업 기타경기도 화성시 반정로150번길 15-20 (반정동)경기도 화성시 반정동 146-1번지44534037.233403127.040368
148화성시빨래박사20170517운영중<NA>N22세탁업세탁업 기타경기도 화성시 봉담읍 갈담초교길 40-5경기도 화성시 봉담읍 유리 98-2번지44590237.176191126.932223
149화성시가나실업20120905운영중<NA>N42세탁업세탁업 기타경기도 화성시 기산말길 69 (기산동, 369-20번지)경기도 화성시 기산동 369-20번지44530037.225409127.043066