Overview

Dataset statistics

Number of variables15
Number of observations284
Missing cells274
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.1 KiB
Average record size in memory126.5 B

Variable types

Categorical4
Text3
Numeric6
Boolean2

Dataset

Description목욕장업(공동탕업 찜질 시설 서비스 영업) 현황_인허가
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=BFUGJ7554MHXY19ZITH014384743&infSeq=1

Alerts

위생업태명 is highly overall correlated with 인허가일자 and 10 other fieldsHigh correlation
발한실여부 is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
다중이용업소여부 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
위생업종명 is highly overall correlated with 인허가일자 and 10 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 5 other fieldsHigh correlation
인허가일자 is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
폐업일자 is highly overall correlated with 영업상태명 and 2 other fieldsHigh correlation
욕실수(개) 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 (73.2%)Imbalance
위생업태명 is highly imbalanced (73.2%)Imbalance
폐업일자 has 188 (66.2%) missing valuesMissing
다중이용업소여부 has 13 (4.6%) missing valuesMissing
발한실여부 has 14 (4.9%) missing valuesMissing
욕실수(개) has 53 (18.7%) missing valuesMissing
소재지도로명주소 has 6 (2.1%) missing valuesMissing
소재지지번주소 has unique valuesUnique
욕실수(개) has 47 (16.5%) zerosZeros

Reproduction

Analysis started2023-12-10 22:18:31.574947
Analysis finished2023-12-10 22:18:35.838804
Duration4.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
수원시
31 
용인시
30 
성남시
26 
부천시
24 
의정부시
23 
Other values (19)
150 

Length

Max length4
Median length3
Mean length3.1373239
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
수원시 31
10.9%
용인시 30
10.6%
성남시 26
 
9.2%
부천시 24
 
8.5%
의정부시 23
 
8.1%
안산시 19
 
6.7%
시흥시 18
 
6.3%
고양시 17
 
6.0%
광명시 14
 
4.9%
평택시 13
 
4.6%
Other values (14) 69
24.3%

Length

2023-12-11T07:18:35.908184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 31
10.9%
용인시 30
10.6%
성남시 26
 
9.2%
부천시 24
 
8.5%
의정부시 23
 
8.1%
안산시 19
 
6.7%
시흥시 18
 
6.3%
고양시 17
 
6.0%
광명시 14
 
4.9%
평택시 13
 
4.6%
Other values (14) 69
24.3%

사업장명
Text

Distinct280
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-11T07:18:36.118887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length7.556338
Min length2

Characters and Unicode

Total characters2146
Distinct characters277
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

Unique276 ?
Unique (%)97.2%

Sample

1st row테크노사우나
2nd row북한산온천비젠
3rd row마그넷대중사우나
4th row탑성원사우나
5th row탄현불가마사우나
ValueCountFrequency (%)
사우나 17
 
5.0%
스파랜드 3
 
0.9%
불가마 3
 
0.9%
금강산사우나 2
 
0.6%
에코사우나 2
 
0.6%
목간통 2
 
0.6%
수리산참숯불가마 2
 
0.6%
24시 2
 
0.6%
불한증막 2
 
0.6%
스파 2
 
0.6%
Other values (301) 301
89.1%
2023-12-11T07:18:36.466013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 156
 
7.3%
우 156
 
7.3%
나 154
 
7.2%
스 97
 
4.5%
불 63
 
2.9%
55
 
2.6%
파 54
 
2.5%
마 47
 
2.2%
한 46
 
2.1%
드 44
 
2.1%
Other values (267) 1274
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1979
92.2%
Decimal Number 69
 
3.2%
Space Separator 55
 
2.6%
Uppercase Letter 12
 
0.6%
Open Punctuation 10
 
0.5%
Close Punctuation 10
 
0.5%
Other Punctuation 6
 
0.3%
Lowercase Letter 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 156
 
7.9%
우 156
 
7.9%
나 154
 
7.8%
스 97
 
4.9%
불 63
 
3.2%
파 54
 
2.7%
마 47
 
2.4%
한 46
 
2.3%
드 44
 
2.2%
가 42
 
2.1%
Other values (244) 1120
56.6%
Uppercase Letter
ValueCountFrequency (%)
B 2
16.7%
G 1
8.3%
K 1
8.3%
W 1
8.3%
A 1
8.3%
M 1
8.3%
C 1
8.3%
E 1
8.3%
H 1
8.3%
O 1
8.3%
Decimal Number
ValueCountFrequency (%)
4 34
49.3%
2 34
49.3%
3 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
& 4
66.7%
, 1
 
16.7%
. 1
 
16.7%
Lowercase Letter
ValueCountFrequency (%)
a 3
60.0%
i 1
 
20.0%
r 1
 
20.0%
Space Separator
ValueCountFrequency (%)
55
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1979
92.2%
Common 150
 
7.0%
Latin 17
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 156
 
7.9%
우 156
 
7.9%
나 154
 
7.8%
스 97
 
4.9%
불 63
 
3.2%
파 54
 
2.7%
마 47
 
2.4%
한 46
 
2.3%
드 44
 
2.2%
가 42
 
2.1%
Other values (244) 1120
56.6%
Latin
ValueCountFrequency (%)
a 3
17.6%
B 2
11.8%
G 1
 
5.9%
i 1
 
5.9%
K 1
 
5.9%
r 1
 
5.9%
W 1
 
5.9%
A 1
 
5.9%
M 1
 
5.9%
C 1
 
5.9%
Other values (4) 4
23.5%
Common
ValueCountFrequency (%)
55
36.7%
4 34
22.7%
2 34
22.7%
( 10
 
6.7%
) 10
 
6.7%
& 4
 
2.7%
, 1
 
0.7%
3 1
 
0.7%
. 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1979
92.2%
ASCII 167
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 156
 
7.9%
우 156
 
7.9%
나 154
 
7.8%
스 97
 
4.9%
불 63
 
3.2%
파 54
 
2.7%
마 47
 
2.4%
한 46
 
2.3%
드 44
 
2.2%
가 42
 
2.1%
Other values (244) 1120
56.6%
ASCII
ValueCountFrequency (%)
55
32.9%
4 34
20.4%
2 34
20.4%
( 10
 
6.0%
) 10
 
6.0%
& 4
 
2.4%
a 3
 
1.8%
B 2
 
1.2%
, 1
 
0.6%
G 1
 
0.6%
Other values (13) 13
 
7.8%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct267
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20056592
Minimum19851014
Maximum20180425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-11T07:18:36.586159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19851014
5-th percentile19962008
Q120030530
median20050967
Q320080816
95-th percentile20150904
Maximum20180425
Range329411
Interquartile range (IQR)50286

Descriptive statistics

Standard deviation54750.132
Coefficient of variation (CV)0.0027297823
Kurtosis1.8011742
Mean20056592
Median Absolute Deviation (MAD)20847.5
Skewness-0.4522131
Sum5.6960722 × 109
Variance2.9975769 × 109
MonotonicityNot monotonic
2023-12-11T07:18:36.707125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030424 7
 
2.5%
20030226 2
 
0.7%
20030530 2
 
0.7%
20070614 2
 
0.7%
20041105 2
 
0.7%
20080502 2
 
0.7%
20040419 2
 
0.7%
20020926 2
 
0.7%
20040202 2
 
0.7%
20080908 2
 
0.7%
Other values (257) 259
91.2%
ValueCountFrequency (%)
19851014 1
0.4%
19881229 1
0.4%
19890729 1
0.4%
19890922 1
0.4%
19891020 1
0.4%
19900223 1
0.4%
19900407 1
0.4%
19900602 1
0.4%
19901020 1
0.4%
19920123 1
0.4%
ValueCountFrequency (%)
20180425 1
0.4%
20180122 1
0.4%
20180104 1
0.4%
20171201 1
0.4%
20171013 1
0.4%
20170926 1
0.4%
20170921 1
0.4%
20170728 1
0.4%
20170710 1
0.4%
20170223 1
0.4%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
운영중
188 
폐업 등
96 

Length

Max length4
Median length3
Mean length3.3380282
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 188
66.2%
폐업 등 96
33.8%

Length

2023-12-11T07:18:36.837792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:18:36.946384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 188
49.5%
폐업 96
25.3%
등 96
25.3%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)92.7%
Missing188
Missing (%)66.2%
Infinite0
Infinite (%)0.0%
Mean20135372
Minimum20070731
Maximum20180716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-11T07:18:37.053306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070731
5-th percentile20090315
Q120120221
median20131212
Q320161060
95-th percentile20173447
Maximum20180716
Range109985
Interquartile range (IQR)40839.25

Descriptive statistics

Standard deviation28285.332
Coefficient of variation (CV)0.0014047584
Kurtosis-0.80323845
Mean20135372
Median Absolute Deviation (MAD)20495.5
Skewness-0.18913306
Sum1.9329957 × 109
Variance8.0006002 × 108
MonotonicityNot monotonic
2023-12-11T07:18:37.395276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20161219 3
 
1.1%
20180220 2
 
0.7%
20151028 2
 
0.7%
20170410 2
 
0.7%
20171228 2
 
0.7%
20140429 2
 
0.7%
20110428 1
 
0.4%
20111124 1
 
0.4%
20140304 1
 
0.4%
20140310 1
 
0.4%
Other values (79) 79
27.8%
(Missing) 188
66.2%
ValueCountFrequency (%)
20070731 1
0.4%
20071026 1
0.4%
20080311 1
0.4%
20080609 1
0.4%
20090302 1
0.4%
20090319 1
0.4%
20090803 1
0.4%
20090907 1
0.4%
20100104 1
0.4%
20100108 1
0.4%
ValueCountFrequency (%)
20180716 1
0.4%
20180312 1
0.4%
20180220 2
0.7%
20180105 1
0.4%
20171228 2
0.7%
20171215 1
0.4%
20171201 1
0.4%
20171114 1
0.4%
20171110 1
0.4%
20170707 1
0.4%

다중이용업소여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.7%
Missing13
Missing (%)4.6%
Memory size700.0 B
False
209 
True
62 
(Missing)
 
13
ValueCountFrequency (%)
False 209
73.6%
True 62
 
21.8%
(Missing) 13
 
4.6%
2023-12-11T07:18:37.497368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

발한실여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.7%
Missing14
Missing (%)4.9%
Memory size700.0 B
True
150 
False
120 
(Missing)
 
14
ValueCountFrequency (%)
True 150
52.8%
False 120
42.3%
(Missing) 14
 
4.9%
2023-12-11T07:18:37.583109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

욕실수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)6.5%
Missing53
Missing (%)18.7%
Infinite0
Infinite (%)0.0%
Mean3.1991342
Minimum0
Maximum15
Zeros47
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-11T07:18:37.666682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q34
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.3859299
Coefficient of variation (CV)1.0583894
Kurtosis1.8470737
Mean3.1991342
Median Absolute Deviation (MAD)1
Skewness1.5522705
Sum739
Variance11.464521
MonotonicityNot monotonic
2023-12-11T07:18:37.786373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 111
39.1%
0 47
16.5%
6 23
 
8.1%
8 10
 
3.5%
10 7
 
2.5%
1 7
 
2.5%
4 6
 
2.1%
3 4
 
1.4%
9 4
 
1.4%
12 4
 
1.4%
Other values (5) 8
 
2.8%
(Missing) 53
18.7%
ValueCountFrequency (%)
0 47
16.5%
1 7
 
2.5%
2 111
39.1%
3 4
 
1.4%
4 6
 
2.1%
6 23
 
8.1%
7 1
 
0.4%
8 10
 
3.5%
9 4
 
1.4%
10 7
 
2.5%
ValueCountFrequency (%)
15 2
 
0.7%
14 2
 
0.7%
13 2
 
0.7%
12 4
 
1.4%
11 1
 
0.4%
10 7
 
2.5%
9 4
 
1.4%
8 10
3.5%
7 1
 
0.4%
6 23
8.1%

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
목욕장업
271 
<NA>
 
13

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
목욕장업 271
95.4%
<NA> 13
 
4.6%

Length

2023-12-11T07:18:37.894241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:18:37.974170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
목욕장업 271
95.4%
na 13
 
4.6%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
공동탕업+찜질시설서비스영업
271 
<NA>
 
13

Length

Max length14
Median length14
Mean length13.542254
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공동탕업+찜질시설서비스영업
2nd row공동탕업+찜질시설서비스영업
3rd row공동탕업+찜질시설서비스영업
4th row공동탕업+찜질시설서비스영업
5th row공동탕업+찜질시설서비스영업

Common Values

ValueCountFrequency (%)
공동탕업+찜질시설서비스영업 271
95.4%
<NA> 13
 
4.6%

Length

2023-12-11T07:18:38.054087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:18:38.131178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업+찜질시설서비스영업 271
95.4%
na 13
 
4.6%

소재지도로명주소
Text

MISSING 

Distinct277
Distinct (%)99.6%
Missing6
Missing (%)2.1%
Memory size2.3 KiB
2023-12-11T07:18:38.394255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length43
Mean length31.579137
Min length14

Characters and Unicode

Total characters8779
Distinct characters315
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)99.3%

Sample

1st row경기도 고양시 일산동구 일산로 138 (백석동,일산테크노타운부대동 B101호)
2nd row경기도 고양시 덕양구 중고개길 88-10 (지축동, 지하1층, 1층, 2층)
3rd row경기도 고양시 일산서구 중앙로 1493 (주엽동,번지 문촌마을복합상가 지하1층)
4th row경기도 고양시 덕양구 능곡로13번길 15, 지하1층 B101, B106호 (토당동, 한강 능곡역 성원상떼빌)
5th row경기도 고양시 일산서구 탄중로233번길 25, B101호,B201호 (탄현동, 양우프라자)
ValueCountFrequency (%)
경기도 278
 
15.6%
수원시 31
 
1.7%
용인시 30
 
1.7%
성남시 26
 
1.5%
부천시 23
 
1.3%
의정부시 23
 
1.3%
지하1층 19
 
1.1%
안산시 19
 
1.1%
시흥시 18
 
1.0%
고양시 17
 
1.0%
Other values (842) 1296
72.8%
2023-12-11T07:18:38.814739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1508
 
17.2%
1 390
 
4.4%
시 307
 
3.5%
기 294
 
3.3%
경 287
 
3.3%
도 287
 
3.3%
, 274
 
3.1%
로 266
 
3.0%
동 252
 
2.9%
) 249
 
2.8%
Other values (305) 4665
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4895
55.8%
Space Separator 1508
 
17.2%
Decimal Number 1478
 
16.8%
Other Punctuation 284
 
3.2%
Close Punctuation 249
 
2.8%
Open Punctuation 249
 
2.8%
Dash Punctuation 54
 
0.6%
Uppercase Letter 49
 
0.6%
Math Symbol 12
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 307
 
6.3%
기 294
 
6.0%
경 287
 
5.9%
도 287
 
5.9%
로 266
 
5.4%
동 252
 
5.1%
구 149
 
3.0%
층 122
 
2.5%
지 115
 
2.3%
원 87
 
1.8%
Other values (272) 2729
55.8%
Uppercase Letter
ValueCountFrequency (%)
B 33
67.3%
I 2
 
4.1%
A 2
 
4.1%
U 2
 
4.1%
N 1
 
2.0%
V 1
 
2.0%
T 1
 
2.0%
O 1
 
2.0%
W 1
 
2.0%
E 1
 
2.0%
Other values (4) 4
 
8.2%
Decimal Number
ValueCountFrequency (%)
1 390
26.4%
0 181
12.2%
2 176
11.9%
5 127
 
8.6%
3 121
 
8.2%
4 121
 
8.2%
8 98
 
6.6%
6 97
 
6.6%
7 96
 
6.5%
9 71
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 274
96.5%
. 10
 
3.5%
Math Symbol
ValueCountFrequency (%)
~ 11
91.7%
+ 1
 
8.3%
Space Separator
ValueCountFrequency (%)
1508
100.0%
Close Punctuation
ValueCountFrequency (%)
) 249
100.0%
Open Punctuation
ValueCountFrequency (%)
( 249
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4895
55.8%
Common 3834
43.7%
Latin 50
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 307
 
6.3%
기 294
 
6.0%
경 287
 
5.9%
도 287
 
5.9%
로 266
 
5.4%
동 252
 
5.1%
구 149
 
3.0%
층 122
 
2.5%
지 115
 
2.3%
원 87
 
1.8%
Other values (272) 2729
55.8%
Common
ValueCountFrequency (%)
1508
39.3%
1 390
 
10.2%
, 274
 
7.1%
) 249
 
6.5%
( 249
 
6.5%
0 181
 
4.7%
2 176
 
4.6%
5 127
 
3.3%
3 121
 
3.2%
4 121
 
3.2%
Other values (8) 438
 
11.4%
Latin
ValueCountFrequency (%)
B 33
66.0%
I 2
 
4.0%
A 2
 
4.0%
U 2
 
4.0%
N 1
 
2.0%
e 1
 
2.0%
V 1
 
2.0%
T 1
 
2.0%
O 1
 
2.0%
W 1
 
2.0%
Other values (5) 5
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4895
55.8%
ASCII 3884
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1508
38.8%
1 390
 
10.0%
, 274
 
7.1%
) 249
 
6.4%
( 249
 
6.4%
0 181
 
4.7%
2 176
 
4.5%
5 127
 
3.3%
3 121
 
3.1%
4 121
 
3.1%
Other values (23) 488
 
12.6%
Hangul
ValueCountFrequency (%)
시 307
 
6.3%
기 294
 
6.0%
경 287
 
5.9%
도 287
 
5.9%
로 266
 
5.4%
동 252
 
5.1%
구 149
 
3.0%
층 122
 
2.5%
지 115
 
2.3%
원 87
 
1.8%
Other values (272) 2729
55.8%

소재지지번주소
Text

UNIQUE 

Distinct284
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-11T07:18:39.073210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length44
Mean length29.440141
Min length16

Characters and Unicode

Total characters8361
Distinct characters295
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique284 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 일산동구 백석동 1141-1번지 일산테크노타운부대동 B101호
2nd row경기도 고양시 덕양구 지축동 207-1번지 지하1층,1층,2층
3rd row경기도 고양시 일산서구 주엽동 138번지 번지 문촌마을복합상가 지하1층
4th row경기도 고양시 덕양구 토당동 342-7번지 한강 능곡역 성원상떼빌 1단지 지하1층 B101, B106호
5th row경기도 고양시 일산서구 탄현동 1490-1번지 외 1필지 양우프라자 B101호, B201호
ValueCountFrequency (%)
경기도 284
 
16.8%
수원시 31
 
1.8%
용인시 30
 
1.8%
성남시 26
 
1.5%
지하1층 26
 
1.5%
부천시 24
 
1.4%
의정부시 23
 
1.4%
안산시 19
 
1.1%
시흥시 18
 
1.1%
고양시 17
 
1.0%
Other values (748) 1189
70.5%
2023-12-11T07:18:39.443796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1404
 
16.8%
1 439
 
5.3%
지 413
 
4.9%
시 303
 
3.6%
기 296
 
3.5%
도 291
 
3.5%
번 289
 
3.5%
동 287
 
3.4%
경 286
 
3.4%
- 225
 
2.7%
Other values (285) 4128
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4802
57.4%
Decimal Number 1680
 
20.1%
Space Separator 1404
 
16.8%
Dash Punctuation 225
 
2.7%
Other Punctuation 122
 
1.5%
Uppercase Letter 53
 
0.6%
Open Punctuation 32
 
0.4%
Close Punctuation 32
 
0.4%
Math Symbol 10
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 413
 
8.6%
시 303
 
6.3%
기 296
 
6.2%
도 291
 
6.1%
번 289
 
6.0%
동 287
 
6.0%
경 286
 
6.0%
구 143
 
3.0%
층 132
 
2.7%
호 83
 
1.7%
Other values (251) 2279
47.5%
Uppercase Letter
ValueCountFrequency (%)
B 35
66.0%
U 2
 
3.8%
I 2
 
3.8%
A 2
 
3.8%
N 1
 
1.9%
W 1
 
1.9%
O 1
 
1.9%
T 1
 
1.9%
V 1
 
1.9%
F 1
 
1.9%
Other values (6) 6
 
11.3%
Decimal Number
ValueCountFrequency (%)
1 439
26.1%
2 197
11.7%
0 186
11.1%
3 160
 
9.5%
5 147
 
8.8%
4 134
 
8.0%
6 123
 
7.3%
8 115
 
6.8%
7 107
 
6.4%
9 72
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 112
91.8%
. 10
 
8.2%
Space Separator
ValueCountFrequency (%)
1404
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 225
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4802
57.4%
Common 3505
41.9%
Latin 54
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 413
 
8.6%
시 303
 
6.3%
기 296
 
6.2%
도 291
 
6.1%
번 289
 
6.0%
동 287
 
6.0%
경 286
 
6.0%
구 143
 
3.0%
층 132
 
2.7%
호 83
 
1.7%
Other values (251) 2279
47.5%
Common
ValueCountFrequency (%)
1404
40.1%
1 439
 
12.5%
- 225
 
6.4%
2 197
 
5.6%
0 186
 
5.3%
3 160
 
4.6%
5 147
 
4.2%
4 134
 
3.8%
6 123
 
3.5%
8 115
 
3.3%
Other values (7) 375
 
10.7%
Latin
ValueCountFrequency (%)
B 35
64.8%
U 2
 
3.7%
I 2
 
3.7%
A 2
 
3.7%
N 1
 
1.9%
W 1
 
1.9%
O 1
 
1.9%
T 1
 
1.9%
V 1
 
1.9%
F 1
 
1.9%
Other values (7) 7
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4802
57.4%
ASCII 3559
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1404
39.4%
1 439
 
12.3%
- 225
 
6.3%
2 197
 
5.5%
0 186
 
5.2%
3 160
 
4.5%
5 147
 
4.1%
4 134
 
3.8%
6 123
 
3.5%
8 115
 
3.2%
Other values (24) 429
 
12.1%
Hangul
ValueCountFrequency (%)
지 413
 
8.6%
시 303
 
6.3%
기 296
 
6.2%
도 291
 
6.1%
번 289
 
6.0%
동 287
 
6.0%
경 286
 
6.0%
구 143
 
3.0%
층 132
 
2.7%
호 83
 
1.7%
Other values (251) 2279
47.5%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct234
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411403.24
Minimum14419
Maximum483120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-11T07:18:39.574009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14419
5-th percentile14573.3
Q1425839.25
median445335
Q3463825.75
95-th percentile480864.85
Maximum483120
Range468701
Interquartile range (IQR)37986.5

Descriptive statistics

Standard deviation122495.15
Coefficient of variation (CV)0.2977496
Kurtosis6.5357014
Mean411403.24
Median Absolute Deviation (MAD)18535
Skewness-2.8540639
Sum1.1683852 × 108
Variance1.5005062 × 1010
MonotonicityNot monotonic
2023-12-11T07:18:39.696247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
411838 3
 
1.1%
435060 3
 
1.1%
472901 3
 
1.1%
448833 3
 
1.1%
425830 3
 
1.1%
423851 3
 
1.1%
429862 2
 
0.7%
480848 2
 
0.7%
443400 2
 
0.7%
463825 2
 
0.7%
Other values (224) 258
90.8%
ValueCountFrequency (%)
14419 1
0.4%
14420 1
0.4%
14424 1
0.4%
14427 1
0.4%
14459 1
0.4%
14473 2
0.7%
14480 1
0.4%
14535 1
0.4%
14542 2
0.7%
14548 1
0.4%
ValueCountFrequency (%)
483120 1
0.4%
483030 2
0.7%
483020 2
0.7%
482862 1
0.4%
482841 1
0.4%
482812 2
0.7%
482110 1
0.4%
482060 2
0.7%
482030 1
0.4%
480867 1
0.4%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.438117
Minimum36.953136
Maximum37.908101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-11T07:18:39.806393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.953136
5-th percentile37.132777
Q137.299321
median37.409484
Q337.595967
95-th percentile37.75128
Maximum37.908101
Range0.95496519
Interquartile range (IQR)0.29664663

Descriptive statistics

Standard deviation0.20412658
Coefficient of variation (CV)0.0054523731
Kurtosis-0.31937967
Mean37.438117
Median Absolute Deviation (MAD)0.11562241
Skewness0.18187782
Sum10632.425
Variance0.041667662
MonotonicityNot monotonic
2023-12-11T07:18:39.918366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3333823033 2
 
0.7%
37.2939104045 2
 
0.7%
37.8920770782 2
 
0.7%
37.3236231493 2
 
0.7%
37.3493123982 2
 
0.7%
37.7390513745 2
 
0.7%
37.5915734632 2
 
0.7%
37.4094843844 2
 
0.7%
37.4051438061 1
 
0.4%
37.3622442111 1
 
0.4%
Other values (266) 266
93.7%
ValueCountFrequency (%)
36.9531360169 1
0.4%
36.9541890474 1
0.4%
36.9789601868 1
0.4%
36.9851085055 1
0.4%
36.9950110282 1
0.4%
36.9957065917 1
0.4%
36.9969566257 1
0.4%
36.9978463285 1
0.4%
37.0016092209 1
0.4%
37.0167520265 1
0.4%
ValueCountFrequency (%)
37.9081012046 1
0.4%
37.9053640991 1
0.4%
37.8997058221 1
0.4%
37.89480182 1
0.4%
37.8920770782 2
0.7%
37.8567108119 1
0.4%
37.8372449352 1
0.4%
37.8371727581 1
0.4%
37.8230327602 1
0.4%
37.8060454114 1
0.4%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98565
Minimum126.62453
Maximum127.51562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-11T07:18:40.029531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.62453
5-th percentile126.75225
Q1126.81533
median127.01813
Q3127.11008
95-th percentile127.22249
Maximum127.51562
Range0.89109223
Interquartile range (IQR)0.29474205

Descriptive statistics

Standard deviation0.1642632
Coefficient of variation (CV)0.0012935571
Kurtosis-0.41110375
Mean126.98565
Median Absolute Deviation (MAD)0.11897871
Skewness0.14376444
Sum36063.925
Variance0.026982398
MonotonicityNot monotonic
2023-12-11T07:18:40.150649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9168751172 2
 
0.7%
127.1207716299 2
 
0.7%
127.0530108245 2
 
0.7%
127.0764566581 2
 
0.7%
127.1104895238 2
 
0.7%
127.0899110657 2
 
0.7%
127.2224919294 2
 
0.7%
127.1259640132 2
 
0.7%
126.9193362718 1
 
0.4%
126.96249994 1
 
0.4%
Other values (266) 266
93.7%
ValueCountFrequency (%)
126.624531483 1
0.4%
126.6268123819 1
0.4%
126.6808188925 1
0.4%
126.7109579245 1
0.4%
126.7235297297 1
0.4%
126.7323768636 1
0.4%
126.7334942325 1
0.4%
126.7335822388 1
0.4%
126.7340103423 1
0.4%
126.7390098298 1
0.4%
ValueCountFrequency (%)
127.5156237102 1
0.4%
127.4965159269 1
0.4%
127.4408952766 1
0.4%
127.3774927749 1
0.4%
127.3187127842 1
0.4%
127.3016667255 1
0.4%
127.2975786015 1
0.4%
127.2904583572 1
0.4%
127.2595083063 1
0.4%
127.2583766322 1
0.4%

Interactions

2023-12-11T07:18:34.812480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:32.402233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:32.961219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.420751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.892843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.346640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.911666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:32.533046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.033652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.500684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.971119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.422554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.998007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:32.628537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.108606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.571958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.044281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.495243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:35.122435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:32.724985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.187881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.651999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.123382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.568075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:35.238542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:32.815826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.261443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.726970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.194723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.641390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:35.335556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:32.887723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.342510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:33.809504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.267492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:34.720539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:18:40.223912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자다중이용업소여부발한실여부욕실수(개)소재지우편번호WGS84위도WGS84경도
시군명1.0000.4630.0900.3160.7320.2780.2901.0000.9560.954
인허가일자0.4631.0000.3060.0000.0000.4730.0000.0000.0000.417
영업상태명0.0900.3061.000NaN0.4730.0230.1350.0300.0000.000
폐업일자0.3160.000NaN1.0000.0000.2210.2730.2660.2960.142
다중이용업소여부0.7320.0000.4730.0001.0000.2020.1770.1290.3160.359
발한실여부0.2780.4730.0230.2210.2021.0000.2930.0750.1730.126
욕실수(개)0.2900.0000.1350.2730.1770.2931.0000.0000.2650.000
소재지우편번호1.0000.0000.0300.2660.1290.0750.0001.0000.6750.785
WGS84위도0.9560.0000.0000.2960.3160.1730.2650.6751.0000.715
WGS84경도0.9540.4170.0000.1420.3590.1260.0000.7850.7151.000
2023-12-11T07:18:40.326890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위생업태명발한실여부다중이용업소여부위생업종명영업상태명시군명
위생업태명1.0001.0001.0001.0001.0001.000
발한실여부1.0001.0000.1291.0000.0140.210
다중이용업소여부1.0000.1291.0001.0000.3130.570
위생업종명1.0001.0001.0001.0001.0001.000
영업상태명1.0000.0140.3131.0001.0000.066
시군명1.0000.2100.5701.0000.0661.000
2023-12-11T07:18:40.414919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자욕실수(개)소재지우편번호WGS84위도WGS84경도시군명영업상태명다중이용업소여부발한실여부위생업종명위생업태명
인허가일자1.0000.0860.1290.0950.0620.0610.1960.2180.0000.3521.0001.000
폐업일자0.0861.000-0.1080.144-0.0550.1260.1271.0000.0000.1461.0001.000
욕실수(개)0.129-0.1081.000-0.035-0.1320.0100.0650.1330.2030.2761.0001.000
소재지우편번호0.0950.144-0.0351.0000.1270.7980.9620.0490.2350.1311.0001.000
WGS84위도0.062-0.055-0.1320.1271.000-0.1360.7580.0000.2390.1301.0001.000
WGS84경도0.0610.1260.0100.798-0.1361.0000.7500.0000.2710.0941.0001.000
시군명0.1960.1270.0650.9620.7580.7501.0000.0660.5700.2101.0001.000
영업상태명0.2181.0000.1330.0490.0000.0000.0661.0000.3130.0141.0001.000
다중이용업소여부0.0000.0000.2030.2350.2390.2710.5700.3131.0000.1291.0001.000
발한실여부0.3520.1460.2760.1310.1300.0940.2100.0140.1291.0001.0001.000
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

2023-12-11T07:18:35.442131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:18:35.609373image/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.
2023-12-11T07:18:35.760023image/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

시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부발한실여부욕실수(개)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0고양시테크노사우나20090202운영중<NA>YY6목욕장업공동탕업+찜질시설서비스영업경기도 고양시 일산동구 일산로 138 (백석동,일산테크노타운부대동 B101호)경기도 고양시 일산동구 백석동 1141-1번지 일산테크노타운부대동 B101호41083537.650785126.794625
1고양시북한산온천비젠19951207운영중<NA>NY2목욕장업공동탕업+찜질시설서비스영업경기도 고양시 덕양구 중고개길 88-10 (지축동, 지하1층, 1층, 2층)경기도 고양시 덕양구 지축동 207-1번지 지하1층,1층,2층41281437.659174126.932896
2고양시마그넷대중사우나20030225운영중<NA>YY10목욕장업공동탕업+찜질시설서비스영업경기도 고양시 일산서구 중앙로 1493 (주엽동,번지 문촌마을복합상가 지하1층)경기도 고양시 일산서구 주엽동 138번지 번지 문촌마을복합상가 지하1층41183837.672341126.754483
3고양시탑성원사우나20180104운영중<NA>YN0목욕장업공동탕업+찜질시설서비스영업경기도 고양시 덕양구 능곡로13번길 15, 지하1층 B101, B106호 (토당동, 한강 능곡역 성원상떼빌)경기도 고양시 덕양구 토당동 342-7번지 한강 능곡역 성원상떼빌 1단지 지하1층 B101, B106호41281837.622019126.819883
4고양시탄현불가마사우나20000203운영중<NA>YN0목욕장업공동탕업+찜질시설서비스영업경기도 고양시 일산서구 탄중로233번길 25, B101호,B201호 (탄현동, 양우프라자)경기도 고양시 일산서구 탄현동 1490-1번지 외 1필지 양우프라자 B101호, B201호41184137.694622126.77039
5고양시대성여성전용사우나19950904운영중<NA>YN0목욕장업공동탕업+찜질시설서비스영업경기도 고양시 일산서구 일산로 576, B106호 (일산동, 대성프라자)경기도 고양시 일산서구 일산동 1065-1번지 대성프라자 B106호41182737.679497126.764482
6고양시행신불한증막20111109운영중<NA>NY6목욕장업공동탕업+찜질시설서비스영업경기도 고양시 덕양구 중앙로 454 (행신동, 401호,402호,501호,502호,601호,602호)경기도 고양시 덕양구 행신동 1082번지 401호,402호,501호,502호,601호,602호41222037.618619126.845001
7고양시대화사우나20030821운영중<NA>YY7목욕장업공동탕업+찜질시설서비스영업경기도 고양시 일산서구 송포로 35 (대화동,대륙종합상가 지하2,3층)경기도 고양시 일산서구 대화동 1464-7번지 대륙종합상가 지하2,3층41180237.669631126.732377
8고양시설문스파랜드20170710운영중<NA>NY2목욕장업공동탕업+찜질시설서비스영업경기도 고양시 일산동구 은마길 239-3, 1동 (설문동)경기도 고양시 일산동구 설문동 583-19번지 외 1필지 1동41081937.724908126.800322
9고양시백송24시 불가마 사우나20061120운영중<NA>YN0목욕장업공동탕업+찜질시설서비스영업경기도 고양시 일산서구 덕이로 212 (덕이동,외 2필지 신일산 골프연습장 지하2층(일부))경기도 고양시 일산서구 덕이동 1047-1번지 외 2필지 신일산 골프연습장 지하2층(일부)41180937.696688126.739358
시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부발한실여부욕실수(개)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
274하남시황산한증막사우나20080814폐업 등20120813NY2목욕장업공동탕업+찜질시설서비스영업경기도 하남시 조정대로 72경기도 하남시 풍산동 286번지 외3필지46517037.550267127.187573
275화성시향남힐링스파20121214운영중<NA>NY10목욕장업공동탕업+찜질시설서비스영업경기도 화성시 향남읍 향남로 430-18 ((행정리 472-2,3호 4층 5층))경기도 화성시 향남읍 행정리 472-3번지 4층 5층44592637.132672126.923035
276화성시정남숯가마20100120운영중<NA>NN2목욕장업공동탕업+찜질시설서비스영업경기도 화성시 정남면 만은동1길 1, 1층경기도 화성시 정남면 190-1번지44596037.176851126.973549
277화성시향남스파렌드(향남사우나,찜질방)20080908운영중<NA>NN2목욕장업공동탕업+찜질시설서비스영업경기도 화성시 향남읍 3.1만세로 1124 (외 2필지)경기도 화성시 향남읍 평리 35-14번지 외 2필지44593937.133373126.909823
278화성시구봉사우나20051227운영중<NA>NY2목욕장업공동탕업+찜질시설서비스영업경기도 화성시 병점중앙로 87 (병점동, 외 15필지 태안병점 V-TOWN II 110호)경기도 화성시 병점동 292번지 외 15필지 태안병점 V-TOWN II 110호44536037.206286127.041896
279화성시송산24시사우나20060508운영중<NA>NY2목욕장업공동탕업+찜질시설서비스영업경기도 화성시 송산면 사강시장길25번길 14경기도 화성시 송산면 사강리 693-5번지44587437.213799126.73401
280화성시하피랜드20080417운영중<NA>NN2목욕장업공동탕업+찜질시설서비스영업경기도 화성시 팔탄면 시청로 888경기도 화성시 팔탄면 율암리 483-26번지44591337.168573126.88852
281화성시병점불한증막사우나20041025운영중<NA>NN2목욕장업공동탕업+찜질시설서비스영업경기도 화성시 병점로 37-6 (진안동,(메트로프라자 지하 1층))경기도 화성시 진안동 524-1번지 (메트로프라자 지하 1층)44539037.210932127.038341
282화성시보보스 스파랜드&휘트니스20051021운영중<NA>NY4목욕장업공동탕업+찜질시설서비스영업경기도 화성시 효행로 287 (기안동,외 4필지)경기도 화성시 기안동 371-2번지 외 4필지44531037.222736126.974346
283화성시봉담 알카리사우나20080908폐업 등20171228NY2목욕장업공동탕업+찜질시설서비스영업경기도 화성시 봉담읍 동화길 85, 801호,901호 (이원타워)경기도 화성시 봉담읍 동화리 599-2번지 이원타워 801호,901호44589337.216949126.958382