Overview

Dataset statistics

Number of variables15
Number of observations236
Missing cells178
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.4 KiB
Average record size in memory127.6 B

Variable types

Categorical5
Text3
Numeric6
Boolean1

Dataset

Description세탁업(운동화전문세탁업) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=R9W3INB8U1ZYYCWCC20M14148818&infSeq=1

Alerts

다중이용업소여부 has constant value ""Constant
시군명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
위생업태명 is highly overall correlated with 인허가일자 and 9 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
위생업종명 is highly overall correlated with 인허가일자 and 9 other fieldsHigh correlation
회수건조수(대) is highly overall correlated with 위생업종명 and 1 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
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 (59.6%)Imbalance
위생업태명 is highly imbalanced (59.6%)Imbalance
폐업일자 has 111 (47.0%) missing valuesMissing
다중이용업소여부 has 19 (8.1%) missing valuesMissing
세탁기수(대) has 40 (16.9%) missing valuesMissing
소재지도로명주소 has 6 (2.5%) missing valuesMissing
세탁기수(대) has 19 (8.1%) zerosZeros

Reproduction

Analysis started2023-12-10 22:50:03.617597
Analysis finished2023-12-10 22:50:08.515329
Duration4.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
부천시
26 
고양시
23 
안산시
22 
화성시
20 
평택시
19 
Other values (21)
126 

Length

Max length4
Median length3
Mean length3.059322
Min length3

Unique

Unique4 ?
Unique (%)1.7%

Sample

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

Common Values

ValueCountFrequency (%)
부천시 26
 
11.0%
고양시 23
 
9.7%
안산시 22
 
9.3%
화성시 20
 
8.5%
평택시 19
 
8.1%
수원시 12
 
5.1%
시흥시 12
 
5.1%
파주시 11
 
4.7%
안양시 11
 
4.7%
용인시 9
 
3.8%
Other values (16) 71
30.1%

Length

2023-12-11T07:50:08.587633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부천시 26
 
11.0%
고양시 23
 
9.7%
안산시 22
 
9.3%
화성시 20
 
8.5%
평택시 19
 
8.1%
수원시 12
 
5.1%
시흥시 12
 
5.1%
파주시 11
 
4.7%
안양시 11
 
4.7%
용인시 9
 
3.8%
Other values (16) 71
30.1%

사업장명
Text

Distinct164
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-11T07:50:08.820984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length13
Mean length7.1483051
Min length2

Characters and Unicode

Total characters1687
Distinct characters206
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)57.2%

Sample

1st row물소운동화,이불세탁전문점
2nd row물소 운동화세탁
3rd row운동화빨래방
4th row운동화 빠는집
5th row운동화박사119
ValueCountFrequency (%)
운동화빨래방 14
 
4.9%
운동화 12
 
4.2%
슈즈크린 8
 
2.8%
빨래방 7
 
2.4%
화이트운동화빨래방 7
 
2.4%
운동화빠는날 6
 
2.1%
슈즈쿨 5
 
1.7%
슈오투 5
 
1.7%
닥터워시 4
 
1.4%
옥색신 4
 
1.4%
Other values (170) 214
74.8%
2023-12-11T07:50:09.241210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
화 152
 
9.0%
운 134
 
7.9%
동 134
 
7.9%
빨 80
 
4.7%
래 78
 
4.6%
방 75
 
4.4%
50
 
3.0%
이 50
 
3.0%
린 45
 
2.7%
탁 45
 
2.7%
Other values (196) 844
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1570
93.1%
Space Separator 50
 
3.0%
Uppercase Letter 18
 
1.1%
Lowercase Letter 14
 
0.8%
Decimal Number 10
 
0.6%
Close Punctuation 9
 
0.5%
Open Punctuation 9
 
0.5%
Other Punctuation 7
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 152
 
9.7%
운 134
 
8.5%
동 134
 
8.5%
빨 80
 
5.1%
래 78
 
5.0%
방 75
 
4.8%
이 50
 
3.2%
린 45
 
2.9%
탁 45
 
2.9%
세 44
 
2.8%
Other values (163) 733
46.7%
Uppercase Letter
ValueCountFrequency (%)
O 3
16.7%
K 3
16.7%
D 2
11.1%
S 2
11.1%
L 1
 
5.6%
C 1
 
5.6%
M 1
 
5.6%
Y 1
 
5.6%
E 1
 
5.6%
H 1
 
5.6%
Other values (2) 2
11.1%
Lowercase Letter
ValueCountFrequency (%)
e 2
14.3%
s 2
14.3%
n 1
7.1%
g 1
7.1%
i 1
7.1%
k 1
7.1%
y 1
7.1%
h 1
7.1%
o 1
7.1%
c 1
7.1%
Other values (2) 2
14.3%
Decimal Number
ValueCountFrequency (%)
1 6
60.0%
9 3
30.0%
2 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
& 5
71.4%
· 1
 
14.3%
, 1
 
14.3%
Space Separator
ValueCountFrequency (%)
50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1570
93.1%
Common 85
 
5.0%
Latin 32
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
화 152
 
9.7%
운 134
 
8.5%
동 134
 
8.5%
빨 80
 
5.1%
래 78
 
5.0%
방 75
 
4.8%
이 50
 
3.2%
린 45
 
2.9%
탁 45
 
2.9%
세 44
 
2.8%
Other values (163) 733
46.7%
Latin
ValueCountFrequency (%)
O 3
 
9.4%
K 3
 
9.4%
e 2
 
6.2%
D 2
 
6.2%
s 2
 
6.2%
S 2
 
6.2%
L 1
 
3.1%
n 1
 
3.1%
C 1
 
3.1%
g 1
 
3.1%
Other values (14) 14
43.8%
Common
ValueCountFrequency (%)
50
58.8%
) 9
 
10.6%
( 9
 
10.6%
1 6
 
7.1%
& 5
 
5.9%
9 3
 
3.5%
· 1
 
1.2%
2 1
 
1.2%
, 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1570
93.1%
ASCII 116
 
6.9%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
화 152
 
9.7%
운 134
 
8.5%
동 134
 
8.5%
빨 80
 
5.1%
래 78
 
5.0%
방 75
 
4.8%
이 50
 
3.2%
린 45
 
2.9%
탁 45
 
2.9%
세 44
 
2.8%
Other values (163) 733
46.7%
ASCII
ValueCountFrequency (%)
50
43.1%
) 9
 
7.8%
( 9
 
7.8%
1 6
 
5.2%
& 5
 
4.3%
O 3
 
2.6%
9 3
 
2.6%
K 3
 
2.6%
e 2
 
1.7%
D 2
 
1.7%
Other values (22) 24
20.7%
None
ValueCountFrequency (%)
· 1
100.0%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct223
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20122114
Minimum20021017
Maximum20180817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:50:09.376148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20021017
5-th percentile20067936
Q120100563
median20120956
Q320150346
95-th percentile20170912
Maximum20180817
Range159800
Interquartile range (IQR)49782.5

Descriptive statistics

Standard deviation33471.383
Coefficient of variation (CV)0.0016634129
Kurtosis-0.23123738
Mean20122114
Median Absolute Deviation (MAD)24996.5
Skewness-0.35503982
Sum4.7488189 × 109
Variance1.1203335 × 109
MonotonicityNot monotonic
2023-12-11T07:50:09.509226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170925 2
 
0.8%
20150618 2
 
0.8%
20091214 2
 
0.8%
20121113 2
 
0.8%
20131216 2
 
0.8%
20101018 2
 
0.8%
20140919 2
 
0.8%
20080328 2
 
0.8%
20110405 2
 
0.8%
20120716 2
 
0.8%
Other values (213) 216
91.5%
ValueCountFrequency (%)
20021017 1
0.4%
20030722 1
0.4%
20040709 1
0.4%
20040817 1
0.4%
20041126 1
0.4%
20041223 1
0.4%
20050329 1
0.4%
20051014 1
0.4%
20060125 1
0.4%
20060710 1
0.4%
ValueCountFrequency (%)
20180817 2
0.8%
20180724 1
0.4%
20180703 1
0.4%
20180306 1
0.4%
20180122 1
0.4%
20180115 1
0.4%
20171219 1
0.4%
20171113 1
0.4%
20170927 1
0.4%
20170925 2
0.8%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
폐업 등
125 
운영중
111 

Length

Max length4
Median length4
Mean length3.529661
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 등 125
53.0%
운영중 111
47.0%

Length

2023-12-11T07:50:09.631231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:50:09.716994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 125
34.6%
등 125
34.6%
운영중 111
30.7%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct116
Distinct (%)92.8%
Missing111
Missing (%)47.0%
Infinite0
Infinite (%)0.0%
Mean20138177
Minimum20070914
Maximum20180810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:50:09.810370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070914
5-th percentile20090913
Q120111216
median20140414
Q320161201
95-th percentile20180666
Maximum20180810
Range109896
Interquartile range (IQR)49985

Descriptive statistics

Standard deviation29020.346
Coefficient of variation (CV)0.0014410612
Kurtosis-0.9762846
Mean20138177
Median Absolute Deviation (MAD)20807
Skewness-0.23613834
Sum2.5172721 × 109
Variance8.421805 × 108
MonotonicityNot monotonic
2023-12-11T07:50:09.936347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20141222 3
 
1.3%
20150629 2
 
0.8%
20180716 2
 
0.8%
20161221 2
 
0.8%
20150312 2
 
0.8%
20160302 2
 
0.8%
20120131 2
 
0.8%
20180703 2
 
0.8%
20161222 1
 
0.4%
20161201 1
 
0.4%
Other values (106) 106
44.9%
(Missing) 111
47.0%
ValueCountFrequency (%)
20070914 1
0.4%
20071212 1
0.4%
20090413 1
0.4%
20090527 1
0.4%
20090622 1
0.4%
20090820 1
0.4%
20090911 1
0.4%
20090922 1
0.4%
20091102 1
0.4%
20100106 1
0.4%
ValueCountFrequency (%)
20180810 1
0.4%
20180807 1
0.4%
20180723 1
0.4%
20180716 2
0.8%
20180703 2
0.8%
20180518 1
0.4%
20180510 1
0.4%
20180423 1
0.4%
20180418 1
0.4%
20180220 1
0.4%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing19
Missing (%)8.1%
Memory size604.0 B
False
217 
(Missing)
 
19
ValueCountFrequency (%)
False 217
91.9%
(Missing) 19
 
8.1%
2023-12-11T07:50:10.043029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

세탁기수(대)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)3.1%
Missing40
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean1.5867347
Minimum0
Maximum5
Zeros19
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:50:10.115892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91574689
Coefficient of variation (CV)0.57712666
Kurtosis1.1236448
Mean1.5867347
Median Absolute Deviation (MAD)1
Skewness0.55034503
Sum311
Variance0.83859236
MonotonicityNot monotonic
2023-12-11T07:50:10.198568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 78
33.1%
1 74
31.4%
3 21
 
8.9%
0 19
 
8.1%
4 2
 
0.8%
5 2
 
0.8%
(Missing) 40
16.9%
ValueCountFrequency (%)
0 19
 
8.1%
1 74
31.4%
2 78
33.1%
3 21
 
8.9%
4 2
 
0.8%
5 2
 
0.8%
ValueCountFrequency (%)
5 2
 
0.8%
4 2
 
0.8%
3 21
 
8.9%
2 78
33.1%
1 74
31.4%
0 19
 
8.1%

회수건조수(대)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
121 
<NA>
58 
1
32 
2
16 
3
 
7

Length

Max length4
Median length1
Mean length1.7372881
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row0
5th row<NA>

Common Values

ValueCountFrequency (%)
0 121
51.3%
<NA> 58
24.6%
1 32
 
13.6%
2 16
 
6.8%
3 7
 
3.0%
4 2
 
0.8%

Length

2023-12-11T07:50:10.304761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:50:10.413803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 121
51.3%
na 58
24.6%
1 32
 
13.6%
2 16
 
6.8%
3 7
 
3.0%
4 2
 
0.8%

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
세탁업
217 
<NA>
 
19

Length

Max length4
Median length3
Mean length3.0805085
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세탁업 217
91.9%
<NA> 19
 
8.1%

Length

2023-12-11T07:50:10.516444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:50:10.600245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 217
91.9%
na 19
 
8.1%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
운동화전문세탁업
217 
<NA>
 
19

Length

Max length8
Median length8
Mean length7.6779661
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운동화전문세탁업 217
91.9%
<NA> 19
 
8.1%

Length

2023-12-11T07:50:10.698081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:50:10.785765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운동화전문세탁업 217
91.9%
na 19
 
8.1%

소재지도로명주소
Text

MISSING 

Distinct223
Distinct (%)97.0%
Missing6
Missing (%)2.5%
Memory size2.0 KiB
2023-12-11T07:50:10.963476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length40
Mean length28.626087
Min length14

Characters and Unicode

Total characters6584
Distinct characters254
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

Unique216 ?
Unique (%)93.9%

Sample

1st row경기도 고양시 덕양구 은빛로77번길 69-7, 1(일부)층 (화정동)
2nd row경기도 고양시 일산동구 중앙로 1129 (장항동, 호수마을 중관 1055호)
3rd row경기도 고양시 일산서구 일현로 128, 1층 116호 (탄현동, 탄현마을8단지 상가동)
4th row경기도 고양시 덕양구 화중로 220 (화정동, 달빛마을 상가동 B112호)
5th row경기도 고양시 덕양구 고양대로 1359-8
ValueCountFrequency (%)
경기도 230
 
16.1%
1층 70
 
4.9%
부천시 26
 
1.8%
안산시 22
 
1.5%
고양시 22
 
1.5%
화성시 19
 
1.3%
평택시 18
 
1.3%
일부 14
 
1.0%
상록구 13
 
0.9%
시흥시 12
 
0.8%
Other values (589) 986
68.9%
2023-12-11T07:50:11.301579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1202
 
18.3%
1 328
 
5.0%
시 247
 
3.8%
도 237
 
3.6%
경 235
 
3.6%
기 235
 
3.6%
로 215
 
3.3%
동 210
 
3.2%
( 174
 
2.6%
) 174
 
2.6%
Other values (244) 3327
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3712
56.4%
Space Separator 1202
 
18.3%
Decimal Number 1098
 
16.7%
Open Punctuation 174
 
2.6%
Close Punctuation 174
 
2.6%
Other Punctuation 165
 
2.5%
Dash Punctuation 52
 
0.8%
Uppercase Letter 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 247
 
6.7%
도 237
 
6.4%
경 235
 
6.3%
기 235
 
6.3%
로 215
 
5.8%
동 210
 
5.7%
길 104
 
2.8%
층 104
 
2.8%
구 84
 
2.3%
호 82
 
2.2%
Other values (227) 1959
52.8%
Decimal Number
ValueCountFrequency (%)
1 328
29.9%
2 140
12.8%
3 110
 
10.0%
0 95
 
8.7%
4 88
 
8.0%
6 84
 
7.7%
5 82
 
7.5%
9 61
 
5.6%
7 58
 
5.3%
8 52
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
B 4
57.1%
A 3
42.9%
Space Separator
ValueCountFrequency (%)
1202
100.0%
Open Punctuation
ValueCountFrequency (%)
( 174
100.0%
Close Punctuation
ValueCountFrequency (%)
) 174
100.0%
Other Punctuation
ValueCountFrequency (%)
, 165
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3712
56.4%
Common 2865
43.5%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 247
 
6.7%
도 237
 
6.4%
경 235
 
6.3%
기 235
 
6.3%
로 215
 
5.8%
동 210
 
5.7%
길 104
 
2.8%
층 104
 
2.8%
구 84
 
2.3%
호 82
 
2.2%
Other values (227) 1959
52.8%
Common
ValueCountFrequency (%)
1202
42.0%
1 328
 
11.4%
( 174
 
6.1%
) 174
 
6.1%
, 165
 
5.8%
2 140
 
4.9%
3 110
 
3.8%
0 95
 
3.3%
4 88
 
3.1%
6 84
 
2.9%
Other values (5) 305
 
10.6%
Latin
ValueCountFrequency (%)
B 4
57.1%
A 3
42.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3712
56.4%
ASCII 2872
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1202
41.9%
1 328
 
11.4%
( 174
 
6.1%
) 174
 
6.1%
, 165
 
5.7%
2 140
 
4.9%
3 110
 
3.8%
0 95
 
3.3%
4 88
 
3.1%
6 84
 
2.9%
Other values (7) 312
 
10.9%
Hangul
ValueCountFrequency (%)
시 247
 
6.7%
도 237
 
6.4%
경 235
 
6.3%
기 235
 
6.3%
로 215
 
5.8%
동 210
 
5.7%
길 104
 
2.8%
층 104
 
2.8%
구 84
 
2.3%
호 82
 
2.2%
Other values (227) 1959
52.8%

소재지지번주소
Text

Distinct230
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-11T07:50:11.558501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length37
Mean length26.957627
Min length17

Characters and Unicode

Total characters6362
Distinct characters226
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

Unique224 ?
Unique (%)94.9%

Sample

1st row경기도 고양시 덕양구 화정동 883-16번지 1(일부)층
2nd row경기도 고양시 일산동구 장항동 902번지 호수마을 중관 1055호
3rd row경기도 고양시 일산서구 탄현동 1476번지 탄현마을8단지 상가동 116호
4th row경기도 고양시 덕양구 화정동 851번지 달빛마을 상가동 B112호
5th row경기도 고양시 덕양구 성사동 700-10번지
ValueCountFrequency (%)
경기도 236
 
17.5%
1층 73
 
5.4%
부천시 26
 
1.9%
고양시 23
 
1.7%
안산시 22
 
1.6%
화성시 20
 
1.5%
평택시 19
 
1.4%
일부 16
 
1.2%
상록구 13
 
1.0%
102호 12
 
0.9%
Other values (521) 892
66.0%
2023-12-11T07:50:12.032079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1116
 
17.5%
1 398
 
6.3%
지 274
 
4.3%
시 249
 
3.9%
동 244
 
3.8%
도 242
 
3.8%
기 241
 
3.8%
경 236
 
3.7%
번 236
 
3.7%
- 192
 
3.0%
Other values (216) 2934
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3631
57.1%
Decimal Number 1345
 
21.1%
Space Separator 1116
 
17.5%
Dash Punctuation 192
 
3.0%
Close Punctuation 29
 
0.5%
Open Punctuation 29
 
0.5%
Other Punctuation 13
 
0.2%
Uppercase Letter 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 274
 
7.5%
시 249
 
6.9%
동 244
 
6.7%
도 242
 
6.7%
기 241
 
6.6%
경 236
 
6.5%
번 236
 
6.5%
층 107
 
2.9%
호 86
 
2.4%
구 86
 
2.4%
Other values (199) 1630
44.9%
Decimal Number
ValueCountFrequency (%)
1 398
29.6%
0 134
 
10.0%
2 123
 
9.1%
4 113
 
8.4%
3 106
 
7.9%
6 103
 
7.7%
5 98
 
7.3%
7 97
 
7.2%
8 92
 
6.8%
9 81
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
B 4
57.1%
A 3
42.9%
Space Separator
ValueCountFrequency (%)
1116
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 192
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3631
57.1%
Common 2724
42.8%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 274
 
7.5%
시 249
 
6.9%
동 244
 
6.7%
도 242
 
6.7%
기 241
 
6.6%
경 236
 
6.5%
번 236
 
6.5%
층 107
 
2.9%
호 86
 
2.4%
구 86
 
2.4%
Other values (199) 1630
44.9%
Common
ValueCountFrequency (%)
1116
41.0%
1 398
 
14.6%
- 192
 
7.0%
0 134
 
4.9%
2 123
 
4.5%
4 113
 
4.1%
3 106
 
3.9%
6 103
 
3.8%
5 98
 
3.6%
7 97
 
3.6%
Other values (5) 244
 
9.0%
Latin
ValueCountFrequency (%)
B 4
57.1%
A 3
42.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3631
57.1%
ASCII 2731
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1116
40.9%
1 398
 
14.6%
- 192
 
7.0%
0 134
 
4.9%
2 123
 
4.5%
4 113
 
4.1%
3 106
 
3.9%
6 103
 
3.8%
5 98
 
3.6%
7 97
 
3.6%
Other values (7) 251
 
9.2%
Hangul
ValueCountFrequency (%)
지 274
 
7.5%
시 249
 
6.9%
동 244
 
6.7%
도 242
 
6.7%
기 241
 
6.6%
경 236
 
6.5%
번 236
 
6.5%
층 107
 
2.9%
호 86
 
2.4%
구 86
 
2.4%
Other values (199) 1630
44.9%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct180
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean391673.26
Minimum10845
Maximum483010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:50:12.200018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10845
5-th percentile14610
Q1413903.5
median440827.5
Q3456240
95-th percentile480070
Maximum483010
Range472165
Interquartile range (IQR)42336.5

Descriptive statistics

Standard deviation140027.19
Coefficient of variation (CV)0.35751021
Kurtosis3.4226363
Mean391673.26
Median Absolute Deviation (MAD)18989.5
Skewness-2.2823196
Sum92434890
Variance1.9607614 × 1010
MonotonicityNot monotonic
2023-12-11T07:50:12.335495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
445390 4
 
1.7%
425844 4
 
1.7%
459813 4
 
1.7%
412826 3
 
1.3%
445851 3
 
1.3%
413902 3
 
1.3%
480070 3
 
1.3%
415060 3
 
1.3%
463856 3
 
1.3%
447140 3
 
1.3%
Other values (170) 203
86.0%
ValueCountFrequency (%)
10845 1
0.4%
14420 2
0.8%
14460 1
0.4%
14463 1
0.4%
14540 1
0.4%
14544 1
0.4%
14548 1
0.4%
14551 1
0.4%
14574 1
0.4%
14580 1
0.4%
ValueCountFrequency (%)
483010 1
 
0.4%
482862 1
 
0.4%
482110 1
 
0.4%
482050 2
0.8%
482030 1
 
0.4%
482010 1
 
0.4%
480861 1
 
0.4%
480848 1
 
0.4%
480808 1
 
0.4%
480070 3
1.3%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)91.9%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean37.404317
Minimum36.97896
Maximum37.955906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:50:12.493345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.97896
5-th percentile37.006441
Q137.254222
median37.382459
Q337.606685
95-th percentile37.796509
Maximum37.955906
Range0.97694623
Interquartile range (IQR)0.35246235

Descriptive statistics

Standard deviation0.22754161
Coefficient of variation (CV)0.0060832982
Kurtosis-0.61555456
Mean37.404317
Median Absolute Deviation (MAD)0.14666591
Skewness0.13583021
Sum8790.0145
Variance0.051775186
MonotonicityNot monotonic
2023-12-11T07:50:12.630957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2334091365 3
 
1.3%
37.3429555953 2
 
0.8%
37.3421594952 2
 
0.8%
37.2363323922 2
 
0.8%
37.445793347 2
 
0.8%
37.4116584178 2
 
0.8%
37.5241925266 2
 
0.8%
37.053799301 2
 
0.8%
37.3564998907 2
 
0.8%
37.5487309578 2
 
0.8%
Other values (206) 214
90.7%
ValueCountFrequency (%)
36.9789601868 1
0.4%
36.9862821541 1
0.4%
36.988499113 1
0.4%
36.9885846334 1
0.4%
36.9913720418 1
0.4%
36.9931871719 1
0.4%
36.9971436989 1
0.4%
36.9980517707 1
0.4%
37.0010816621 1
0.4%
37.0027103636 1
0.4%
ValueCountFrequency (%)
37.9559064137 1
0.4%
37.8941677319 1
0.4%
37.8822907325 1
0.4%
37.8665671047 1
0.4%
37.8626227338 1
0.4%
37.8617226685 1
0.4%
37.8586559393 1
0.4%
37.8316613142 1
0.4%
37.8197379918 1
0.4%
37.8111563637 1
0.4%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)91.9%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean126.97154
Minimum126.63327
Maximum127.64265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:50:12.798707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63327
5-th percentile126.74558
Q1126.80804
median126.92515
Q3127.07425
95-th percentile127.26956
Maximum127.64265
Range1.0093781
Interquartile range (IQR)0.26621732

Descriptive statistics

Standard deviation0.20125285
Coefficient of variation (CV)0.0015850233
Kurtosis1.1000433
Mean126.97154
Median Absolute Deviation (MAD)0.13595276
Skewness1.0137814
Sum29838.313
Variance0.040502709
MonotonicityNot monotonic
2023-12-11T07:50:13.219536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0633547945 3
 
1.3%
126.8207031527 2
 
0.8%
126.8186330459 2
 
0.8%
127.2151636047 2
 
0.8%
127.1293557911 2
 
0.8%
127.1381008398 2
 
0.8%
126.8083574119 2
 
0.8%
127.0576491774 2
 
0.8%
127.2438770673 2
 
0.8%
127.2107300242 2
 
0.8%
Other values (206) 214
90.7%
ValueCountFrequency (%)
126.6332701138 1
0.4%
126.6718767242 1
0.4%
126.6722168676 1
0.4%
126.6722813205 1
0.4%
126.7097375952 1
0.4%
126.7145710135 1
0.4%
126.7235721658 1
0.4%
126.7335822388 1
0.4%
126.735754493 1
0.4%
126.7377244407 2
0.8%
ValueCountFrequency (%)
127.642648189 1
0.4%
127.6414410364 1
0.4%
127.6366062093 1
0.4%
127.6316604758 1
0.4%
127.6102936259 1
0.4%
127.5454702727 1
0.4%
127.4754072569 1
0.4%
127.4600889385 1
0.4%
127.4578362104 1
0.4%
127.3051317471 1
0.4%

Interactions

2023-12-11T07:50:07.490424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:04.449890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.038913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.577004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.117572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.690842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:07.607810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:04.545463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.138894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.669074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.241406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.798115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:07.690051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:04.631030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.225793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.762325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.348957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.884361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:07.779691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:04.723703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.300104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.855293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.431034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.976519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:07.877543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:04.838074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.403495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.935770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.526476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:07.060676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:07.964099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:04.949488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:05.485543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.028301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:06.606187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:50:07.388120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:50:13.318663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자세탁기수(대)회수건조수(대)소재지우편번호WGS84위도WGS84경도
시군명1.0000.4640.3240.4280.2060.3980.9940.9650.960
인허가일자0.4641.0000.5450.7590.0000.0000.1580.3270.090
영업상태명0.3240.5451.000NaN0.0840.1260.0000.0000.193
폐업일자0.4280.759NaN1.0000.0000.0000.0000.4460.000
세탁기수(대)0.2060.0000.0840.0001.0000.0970.0000.0000.000
회수건조수(대)0.3980.0000.1260.0000.0971.0000.0000.0920.000
소재지우편번호0.9940.1580.0000.0000.0000.0001.0000.8390.905
WGS84위도0.9650.3270.0000.4460.0000.0920.8391.0000.511
WGS84경도0.9600.0900.1930.0000.0000.0000.9050.5111.000
2023-12-11T07:50:13.469728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명위생업태명영업상태명위생업종명회수건조수(대)
시군명1.0001.0000.2431.0000.170
위생업태명1.0001.0001.0001.0001.000
영업상태명0.2431.0001.0001.0000.152
위생업종명1.0001.0001.0001.0001.000
회수건조수(대)0.1701.0000.1521.0001.000
2023-12-11T07:50:13.572322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자세탁기수(대)소재지우편번호WGS84위도WGS84경도시군명영업상태명회수건조수(대)위생업종명위생업태명
인허가일자1.0000.6750.015-0.062-0.005-0.0890.1560.4170.0001.0001.000
폐업일자0.6751.0000.074-0.074-0.107-0.1360.1711.0000.0001.0001.000
세탁기수(대)0.0150.0741.0000.098-0.0070.0650.0850.0590.0641.0001.000
소재지우편번호-0.062-0.0740.0981.000-0.3070.8560.9270.0000.0001.0001.000
WGS84위도-0.005-0.107-0.007-0.3071.000-0.3970.7800.0000.0341.0001.000
WGS84경도-0.089-0.1360.0650.856-0.3971.0000.7620.1890.0001.0001.000
시군명0.1560.1710.0850.9270.7800.7621.0000.2430.1701.0001.000
영업상태명0.4171.0000.0590.0000.0000.1890.2431.0000.1521.0001.000
회수건조수(대)0.0000.0000.0640.0000.0340.0000.1700.1521.0001.0001.000
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

2023-12-11T07:50:08.081601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:50:08.277590image/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.
2023-12-11T07:50:08.419276image/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
0고양시물소운동화,이불세탁전문점20170925운영중<NA>N11세탁업운동화전문세탁업경기도 고양시 덕양구 은빛로77번길 69-7, 1(일부)층 (화정동)경기도 고양시 덕양구 화정동 883-16번지 1(일부)층41282637.639821126.835884
1고양시물소 운동화세탁20160729운영중<NA>N21세탁업운동화전문세탁업경기도 고양시 일산동구 중앙로 1129 (장항동, 호수마을 중관 1055호)경기도 고양시 일산동구 장항동 902번지 호수마을 중관 1055호41083737.648926126.779282
2고양시운동화빨래방20160831운영중<NA>N12세탁업운동화전문세탁업경기도 고양시 일산서구 일현로 128, 1층 116호 (탄현동, 탄현마을8단지 상가동)경기도 고양시 일산서구 탄현동 1476번지 탄현마을8단지 상가동 116호41132037.698887126.767762
3고양시운동화 빠는집20120109운영중<NA>N20세탁업운동화전문세탁업경기도 고양시 덕양구 화중로 220 (화정동, 달빛마을 상가동 B112호)경기도 고양시 덕양구 화정동 851번지 달빛마을 상가동 B112호41282637.646475126.834714
4고양시운동화박사11920040817운영중<NA>N<NA><NA>세탁업운동화전문세탁업경기도 고양시 덕양구 고양대로 1359-8경기도 고양시 덕양구 성사동 700-10번지41202037.655143126.835827
5고양시메탈 팩토리20170105운영중<NA>N24세탁업운동화전문세탁업경기도 고양시 일산동구 공릉천로 3, 2층 (사리현동)경기도 고양시 일산동구 사리현동 114-7번지41053037.694877126.842856
6고양시착한운동화20161123운영중<NA>N11세탁업운동화전문세탁업경기도 고양시 덕양구 행당로 45, 1(일부)층 (행신동)경기도 고양시 덕양구 행신동 650-7번지 1층(일부)41282437.619298126.829483
7고양시운동화빠는날20110401폐업 등20130412N10세탁업운동화전문세탁업경기도 고양시 일산동구 일산로463번길 51-2 (정발산동,1층 일부)경기도 고양시 일산동구 정발산동 1162번지 1층 일부41082937.672005126.774262
8고양시크린워커20130628폐업 등20150820N21세탁업운동화전문세탁업경기도 고양시 일산동구 호수로446번길 56, 1층 일부호 (백석동)경기도 고양시 일산동구 백석동 1418번지 1층 일부호41036037.645886126.780343
9고양시크린운동화 세탁20111031폐업 등20131219N11세탁업운동화전문세탁업경기도 고양시 덕양구 은빛로 909-3 (화정동)경기도 고양시 덕양구 화정동 909-3번지 (화정꽃무리조합프라자 112호)41282637.63811126.83302
시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부세탁기수(대)회수건조수(대)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
226화성시하얀기쁨20150713폐업 등20170810N23세탁업운동화전문세탁업경기도 화성시 남양읍 남양시장로42번길 7-3, 1층경기도 화성시 남양읍 남양리 658번지44585137.208343126.81453
227화성시운동화빨래20150303폐업 등20150713N23세탁업운동화전문세탁업경기도 화성시 남양읍 남양시장로42번길 7-3, 103호경기도 화성시 남양읍 남양리 658번지44585137.208343126.81453
228화성시운동화빨래방(운빨)20141210폐업 등20161222N00세탁업운동화전문세탁업경기도 화성시 동탄원천로 315-15 (능동, 동탄능동마을 주공아파트 가상가동 104호)경기도 화성시 능동 1083번지 동탄능동마을 주공아파트 가상가동 104호44532037.213029127.061105
229화성시운동화를 부탁해20131216폐업 등20141222N10세탁업운동화전문세탁업경기도 화성시 병점중앙로 195 (진안동, 삼동 107호 전부)경기도 화성시 진안동 431-13번지 삼동 107호 전부44539037.214636127.035771
230화성시운동화를 부탁해20120716폐업 등20141222N00세탁업운동화전문세탁업경기도 화성시 병점중앙로 195 (진안동, 431-13 (삼동 106호, 107호))경기도 화성시 진안동 431-13번지 (삼동 106호, 107호)44539037.214636127.035771
231화성시크린슈즈케어20121227폐업 등20180703N20세탁업운동화전문세탁업경기도 화성시 동탄숲속로 98 (능동, 동탄숲속마을모아미래도1단지상가 102호)경기도 화성시 능동 1132번지 (동탄숲속마을모아미래도1단지상가 102호)44532037.206907127.05838
232화성시슈즈쿨20091005폐업 등20180703N10세탁업운동화전문세탁업경기도 화성시 영통로27번길 8 (반월동,(1층 일부))경기도 화성시 반월동 160-14번지 (1층 일부)44533037.233409127.063355
233화성시슈즈쿨20090803폐업 등20090922N10세탁업운동화전문세탁업경기도 화성시 영통로27번길 8경기도 화성시 반월동 160-14번지 (1층 일부)44533037.233409127.063355
234화성시스피드운동화빨래터20090420폐업 등20090911N21세탁업운동화전문세탁업<NA>경기도 화성시 진안동 496번지 (화남상가 102호)44539037.213144127.036738
235화성시슈즈쿨20090130폐업 등20090622N1<NA>세탁업운동화전문세탁업경기도 화성시 영통로27번길 8경기도 화성시 반월동 160-14번지44533037.233409127.063355