Overview

Dataset statistics

Number of variables14
Number of observations339
Missing cells155
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.9 KiB
Average record size in memory120.4 B

Variable types

Categorical3
Text3
Numeric8

Dataset

Description경기도 의료 유사업 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=3Z49464FE1Z29Q3172A82203953&infSeq=1

Alerts

의료유사업종별명 has constant value ""Constant
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
영업상태명 is highly imbalanced (52.9%)Imbalance
폐업일자 has 136 (40.1%) missing valuesMissing
소재지도로명주소 has 9 (2.7%) missing valuesMissing
WGS84위도 has 4 (1.2%) missing valuesMissing
WGS84경도 has 4 (1.2%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:37:34.424291
Analysis finished2023-12-10 22:37:40.937687
Duration6.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
성남시
43 
수원시
40 
부천시
39 
고양시
29 
안산시
24 
Other values (24)
164 

Length

Max length4
Median length3
Mean length3.0589971
Min length3

Unique

Unique2 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
성남시 43
12.7%
수원시 40
11.8%
부천시 39
11.5%
고양시 29
 
8.6%
안산시 24
 
7.1%
안양시 22
 
6.5%
화성시 20
 
5.9%
시흥시 18
 
5.3%
용인시 13
 
3.8%
평택시 12
 
3.5%
Other values (19) 79
23.3%

Length

2023-12-11T07:37:40.994777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 43
12.7%
수원시 40
11.8%
부천시 39
11.5%
고양시 29
 
8.6%
안산시 24
 
7.1%
안양시 22
 
6.5%
화성시 20
 
5.9%
시흥시 18
 
5.3%
용인시 13
 
3.8%
평택시 12
 
3.5%
Other values (19) 79
23.3%

사업장명
Text

Distinct247
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T07:37:41.223492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length7
Mean length7.020649
Min length1

Characters and Unicode

Total characters2380
Distinct characters252
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

Unique194 ?
Unique (%)57.2%

Sample

1st row서원안마시술소
2nd row리노안마시술소
3rd row뉴필안마시술소
4th row원당여우안마시술소
5th row탑안마시술소
ValueCountFrequency (%)
킹안마시술소 9
 
2.6%
스타안마시술소 7
 
2.0%
수안마시술소 6
 
1.7%
일심안마시술소 5
 
1.4%
안마시술소 5
 
1.4%
에이스안마시술소 4
 
1.2%
휠안마시술소 4
 
1.2%
초이스안마시술소 4
 
1.2%
동경안마시술소 4
 
1.2%
주몽안마시술소 3
 
0.9%
Other values (239) 294
85.2%
2023-12-11T07:37:41.585849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
안 333
14.0%
마 331
13.9%
시 330
13.9%
술 327
13.7%
소 327
13.7%
스 37
 
1.6%
이 35
 
1.5%
로 14
 
0.6%
에 13
 
0.5%
타 11
 
0.5%
Other values (242) 622
26.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2333
98.0%
Uppercase Letter 28
 
1.2%
Space Separator 6
 
0.3%
Close Punctuation 5
 
0.2%
Open Punctuation 5
 
0.2%
Math Symbol 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 333
14.3%
마 331
14.2%
시 330
14.1%
술 327
14.0%
소 327
14.0%
스 37
 
1.6%
이 35
 
1.5%
로 14
 
0.6%
에 13
 
0.6%
타 11
 
0.5%
Other values (224) 575
24.6%
Uppercase Letter
ValueCountFrequency (%)
V 5
17.9%
S 3
10.7%
P 3
10.7%
C 3
10.7%
F 3
10.7%
E 2
 
7.1%
J 2
 
7.1%
I 2
 
7.1%
N 2
 
7.1%
A 1
 
3.6%
Other values (2) 2
 
7.1%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
% 1
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2333
98.0%
Latin 28
 
1.2%
Common 19
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 333
14.3%
마 331
14.2%
시 330
14.1%
술 327
14.0%
소 327
14.0%
스 37
 
1.6%
이 35
 
1.5%
로 14
 
0.6%
에 13
 
0.6%
타 11
 
0.5%
Other values (224) 575
24.6%
Latin
ValueCountFrequency (%)
V 5
17.9%
S 3
10.7%
P 3
10.7%
C 3
10.7%
F 3
10.7%
E 2
 
7.1%
J 2
 
7.1%
I 2
 
7.1%
N 2
 
7.1%
A 1
 
3.6%
Other values (2) 2
 
7.1%
Common
ValueCountFrequency (%)
6
31.6%
) 5
26.3%
( 5
26.3%
+ 1
 
5.3%
% 1
 
5.3%
1 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2333
98.0%
ASCII 47
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
안 333
14.3%
마 331
14.2%
시 330
14.1%
술 327
14.0%
소 327
14.0%
스 37
 
1.6%
이 35
 
1.5%
로 14
 
0.6%
에 13
 
0.6%
타 11
 
0.5%
Other values (224) 575
24.6%
ASCII
ValueCountFrequency (%)
6
12.8%
) 5
10.6%
( 5
10.6%
V 5
10.6%
S 3
 
6.4%
P 3
 
6.4%
C 3
 
6.4%
F 3
 
6.4%
E 2
 
4.3%
J 2
 
4.3%
Other values (8) 10
21.3%

인허가일자
Real number (ℝ)

Distinct321
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20050347
Minimum19710909
Maximum20200224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T07:37:41.716524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19710909
5-th percentile19970304
Q120021014
median20040910
Q320090114
95-th percentile20151117
Maximum20200224
Range489315
Interquartile range (IQR)69100

Descriptive statistics

Standard deviation53790.751
Coefficient of variation (CV)0.002682784
Kurtosis4.5226321
Mean20050347
Median Absolute Deviation (MAD)29985
Skewness-0.30203447
Sum6.7970677 × 109
Variance2.8934449 × 109
MonotonicityNot monotonic
2023-12-11T07:37:41.863721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20021116 2
 
0.6%
20021111 2
 
0.6%
20041214 2
 
0.6%
20021106 2
 
0.6%
20041022 2
 
0.6%
20040603 2
 
0.6%
20110418 2
 
0.6%
20040422 2
 
0.6%
20040904 2
 
0.6%
20070530 2
 
0.6%
Other values (311) 319
94.1%
ValueCountFrequency (%)
19710909 1
0.3%
19900123 1
0.3%
19950616 1
0.3%
19951107 1
0.3%
19960105 1
0.3%
19960120 1
0.3%
19960401 1
0.3%
19960528 1
0.3%
19960617 1
0.3%
19960621 1
0.3%
ValueCountFrequency (%)
20200224 1
0.3%
20191030 1
0.3%
20190820 1
0.3%
20190816 1
0.3%
20190426 1
0.3%
20190306 1
0.3%
20180419 1
0.3%
20171115 1
0.3%
20171031 1
0.3%
20170224 1
0.3%

영업상태명
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
폐업
206 
영업중
127 
휴업
 
4
기타
 
1
직권폐업
 
1

Length

Max length4
Median length2
Mean length2.380531
Min length2

Unique

Unique2 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 206
60.8%
영업중 127
37.5%
휴업 4
 
1.2%
기타 1
 
0.3%
직권폐업 1
 
0.3%

Length

2023-12-11T07:37:42.009596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:37:42.122377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 206
60.8%
영업중 127
37.5%
휴업 4
 
1.2%
기타 1
 
0.3%
직권폐업 1
 
0.3%

폐업일자
Real number (ℝ)

MISSING 

Distinct193
Distinct (%)95.1%
Missing136
Missing (%)40.1%
Infinite0
Infinite (%)0.0%
Mean20117414
Minimum20050610
Maximum20200603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T07:37:42.224039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20050610
5-th percentile20080629
Q120090622
median20100930
Q320140315
95-th percentile20190722
Maximum20200603
Range149993
Interquartile range (IQR)49693

Descriptive statistics

Standard deviation34768.81
Coefficient of variation (CV)0.0017282942
Kurtosis-0.10635186
Mean20117414
Median Absolute Deviation (MAD)19386
Skewness0.88631095
Sum4.083835 × 109
Variance1.2088701 × 109
MonotonicityNot monotonic
2023-12-11T07:37:42.370404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20090420 3
 
0.9%
20090429 2
 
0.6%
20090108 2
 
0.6%
20090710 2
 
0.6%
20121217 2
 
0.6%
20090626 2
 
0.6%
20090629 2
 
0.6%
20151030 2
 
0.6%
20091102 2
 
0.6%
20130501 1
 
0.3%
Other values (183) 183
54.0%
(Missing) 136
40.1%
ValueCountFrequency (%)
20050610 1
0.3%
20060811 1
0.3%
20061107 1
0.3%
20070625 1
0.3%
20070719 1
0.3%
20071002 1
0.3%
20080404 1
0.3%
20080417 1
0.3%
20080512 1
0.3%
20080602 1
0.3%
ValueCountFrequency (%)
20200603 1
0.3%
20200506 1
0.3%
20200428 1
0.3%
20200420 1
0.3%
20200414 1
0.3%
20200410 1
0.3%
20200117 1
0.3%
20200103 1
0.3%
20191014 1
0.3%
20190813 1
0.3%

병상수(개)
Real number (ℝ)

Distinct34
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.572271
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T07:37:42.510880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q114
median18
Q322
95-th percentile27
Maximum37
Range36
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.4397479
Coefficient of variation (CV)0.36647214
Kurtosis0.21670773
Mean17.572271
Median Absolute Deviation (MAD)4
Skewness-0.39597445
Sum5957
Variance41.470353
MonotonicityNot monotonic
2023-12-11T07:37:42.652159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
20 27
 
8.0%
15 26
 
7.7%
21 26
 
7.7%
22 24
 
7.1%
16 20
 
5.9%
18 20
 
5.9%
24 18
 
5.3%
23 17
 
5.0%
14 15
 
4.4%
17 14
 
4.1%
Other values (24) 132
38.9%
ValueCountFrequency (%)
1 7
2.1%
2 2
 
0.6%
3 2
 
0.6%
4 3
0.9%
5 5
1.5%
6 3
0.9%
7 4
1.2%
8 5
1.5%
9 7
2.1%
10 7
2.1%
ValueCountFrequency (%)
37 1
 
0.3%
36 1
 
0.3%
32 1
 
0.3%
31 1
 
0.3%
30 2
 
0.6%
29 1
 
0.3%
28 4
 
1.2%
27 8
2.4%
26 7
2.1%
25 13
3.8%

의료유사업종별명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
안마시술소
339 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
안마시술소 339
100.0%

Length

2023-12-11T07:37:42.772248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:37:42.861118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안마시술소 339
100.0%

종업원수
Real number (ℝ)

Distinct13
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2566372
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T07:37:42.941523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6.1
Maximum28
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1951214
Coefficient of variation (CV)0.67404542
Kurtosis49.072619
Mean3.2566372
Median Absolute Deviation (MAD)1
Skewness5.2985692
Sum1104
Variance4.8185579
MonotonicityNot monotonic
2023-12-11T07:37:43.043688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 160
47.2%
3 74
21.8%
4 48
 
14.2%
5 25
 
7.4%
6 12
 
3.5%
7 5
 
1.5%
8 3
 
0.9%
11 3
 
0.9%
1 3
 
0.9%
9 2
 
0.6%
Other values (3) 4
 
1.2%
ValueCountFrequency (%)
1 3
 
0.9%
2 160
47.2%
3 74
21.8%
4 48
 
14.2%
5 25
 
7.4%
6 12
 
3.5%
7 5
 
1.5%
8 3
 
0.9%
9 2
 
0.6%
10 2
 
0.6%
ValueCountFrequency (%)
28 1
 
0.3%
12 1
 
0.3%
11 3
 
0.9%
10 2
 
0.6%
9 2
 
0.6%
8 3
 
0.9%
7 5
 
1.5%
6 12
 
3.5%
5 25
7.4%
4 48
14.2%

연면적(㎡)
Real number (ℝ)

Distinct321
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean646.81481
Minimum1
Maximum37642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T07:37:43.151499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile285.438
Q1420.43
median533.3
Q3653.125
95-th percentile797.067
Maximum37642
Range37641
Interquartile range (IQR)232.695

Descriptive statistics

Standard deviation2022.1015
Coefficient of variation (CV)3.1262449
Kurtosis334.38646
Mean646.81481
Median Absolute Deviation (MAD)115.46
Skewness18.224093
Sum219270.22
Variance4088894.3
MonotonicityNot monotonic
2023-12-11T07:37:43.275385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
639.15 4
 
1.2%
502.28 3
 
0.9%
421.39 2
 
0.6%
377.16 2
 
0.6%
437.57 2
 
0.6%
775.46 2
 
0.6%
609.32 2
 
0.6%
324.75 2
 
0.6%
417.0 2
 
0.6%
613.74 2
 
0.6%
Other values (311) 316
93.2%
ValueCountFrequency (%)
1.0 1
0.3%
10.0 1
0.3%
30.61 1
0.3%
51.0 1
0.3%
62.1 1
0.3%
186.51 1
0.3%
201.0 1
0.3%
227.73 1
0.3%
234.1 1
0.3%
244.2 1
0.3%
ValueCountFrequency (%)
37642.0 1
0.3%
830.0 1
0.3%
828.9 1
0.3%
826.4 1
0.3%
824.0 1
0.3%
823.66 1
0.3%
817.47 1
0.3%
815.36 1
0.3%
814.45 1
0.3%
813.47 1
0.3%

소재지도로명주소
Text

MISSING 

Distinct315
Distinct (%)95.5%
Missing9
Missing (%)2.7%
Memory size2.8 KiB
2023-12-11T07:37:43.553679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length47
Mean length32.181818
Min length18

Characters and Unicode

Total characters10620
Distinct characters273
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique301 ?
Unique (%)91.2%

Sample

1st row경기도 고양시 일산동구 정발산로 38 (장항동)
2nd row경기도 고양시 일산동구 정발산로 39 (장항동,대양빌딩 501,502호, 505～510호)
3rd row경기도 고양시 일산동구 무궁화로 34 (장항동,남정씨티프라자Ⅱ505,509,510호)
4th row경기도 고양시 덕양구 호국로777번길 35 (주교동,2층)
5th row경기도 고양시 일산동구 무궁화로 18, 남정 씨티프라자 1 701호 일부, 706~708호호 (장항동)
ValueCountFrequency (%)
경기도 328
 
15.5%
성남시 43
 
2.0%
부천시 39
 
1.8%
수원시 38
 
1.8%
분당구 35
 
1.7%
고양시 28
 
1.3%
안산시 24
 
1.1%
안양시 21
 
1.0%
팔달구 20
 
0.9%
일산동구 19
 
0.9%
Other values (770) 1523
71.9%
2023-12-11T07:37:43.953629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1809
 
17.0%
동 370
 
3.5%
, 352
 
3.3%
시 350
 
3.3%
경 350
 
3.3%
1 348
 
3.3%
기 335
 
3.2%
도 333
 
3.1%
) 325
 
3.1%
( 325
 
3.1%
Other values (263) 5723
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5831
54.9%
Decimal Number 1865
 
17.6%
Space Separator 1809
 
17.0%
Other Punctuation 361
 
3.4%
Close Punctuation 325
 
3.1%
Open Punctuation 325
 
3.1%
Dash Punctuation 72
 
0.7%
Math Symbol 21
 
0.2%
Uppercase Letter 10
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 370
 
6.3%
시 350
 
6.0%
경 350
 
6.0%
기 335
 
5.7%
도 333
 
5.7%
로 313
 
5.4%
구 193
 
3.3%
길 161
 
2.8%
층 143
 
2.5%
번 129
 
2.2%
Other values (237) 3154
54.1%
Decimal Number
ValueCountFrequency (%)
1 348
18.7%
2 231
12.4%
0 220
11.8%
3 220
11.8%
5 161
8.6%
6 159
8.5%
4 153
8.2%
9 130
 
7.0%
7 122
 
6.5%
8 121
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
B 4
40.0%
O 1
 
10.0%
J 1
 
10.0%
S 1
 
10.0%
I 1
 
10.0%
C 1
 
10.0%
H 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 352
97.5%
. 9
 
2.5%
Math Symbol
ValueCountFrequency (%)
~ 17
81.0%
～ 4
 
19.0%
Space Separator
ValueCountFrequency (%)
1809
100.0%
Close Punctuation
ValueCountFrequency (%)
) 325
100.0%
Open Punctuation
ValueCountFrequency (%)
( 325
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5831
54.9%
Common 4778
45.0%
Latin 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 370
 
6.3%
시 350
 
6.0%
경 350
 
6.0%
기 335
 
5.7%
도 333
 
5.7%
로 313
 
5.4%
구 193
 
3.3%
길 161
 
2.8%
층 143
 
2.5%
번 129
 
2.2%
Other values (237) 3154
54.1%
Common
ValueCountFrequency (%)
1809
37.9%
, 352
 
7.4%
1 348
 
7.3%
) 325
 
6.8%
( 325
 
6.8%
2 231
 
4.8%
0 220
 
4.6%
3 220
 
4.6%
5 161
 
3.4%
6 159
 
3.3%
Other values (8) 628
 
13.1%
Latin
ValueCountFrequency (%)
B 4
36.4%
O 1
 
9.1%
J 1
 
9.1%
S 1
 
9.1%
Ⅱ 1
 
9.1%
I 1
 
9.1%
C 1
 
9.1%
H 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5831
54.9%
ASCII 4784
45.0%
None 4
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1809
37.8%
, 352
 
7.4%
1 348
 
7.3%
) 325
 
6.8%
( 325
 
6.8%
2 231
 
4.8%
0 220
 
4.6%
3 220
 
4.6%
5 161
 
3.4%
6 159
 
3.3%
Other values (14) 634
 
13.3%
Hangul
ValueCountFrequency (%)
동 370
 
6.3%
시 350
 
6.0%
경 350
 
6.0%
기 335
 
5.7%
도 333
 
5.7%
로 313
 
5.4%
구 193
 
3.3%
길 161
 
2.8%
층 143
 
2.5%
번 129
 
2.2%
Other values (237) 3154
54.1%
None
ValueCountFrequency (%)
～ 4
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

소재지지번주소
Text

Distinct331
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T07:37:44.224740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length43
Mean length28.454277
Min length15

Characters and Unicode

Total characters9646
Distinct characters242
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique323 ?
Unique (%)95.3%

Sample

1st row경기도 고양시 일산동구 장항동 855번지 장항동이스턴시티 506,506-1,507,507-1,508호
2nd row경기도 고양시일산동구 장항동 856번지 1호 대양빌딩 501,502호, 505～510호
3rd row경기도 고양시일산동구 장항동 778번지 2호 남정씨티프라자Ⅱ505,509,510호
4th row경기도 고양시 덕양구 주교동 608번지 2층
5th row경기도 고양시 일산동구 장항동 760번지 남정 씨티프라자 1
ValueCountFrequency (%)
경기도 337
 
16.4%
부천시 39
 
1.9%
1호 32
 
1.6%
3호 31
 
1.5%
2호 28
 
1.4%
성남시분당구 24
 
1.2%
안양시 22
 
1.1%
화성시 20
 
1.0%
성남시 18
 
0.9%
시흥시 18
 
0.9%
Other values (745) 1492
72.4%
2023-12-11T07:37:44.602303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1763
 
18.3%
1 420
 
4.4%
동 367
 
3.8%
시 360
 
3.7%
도 345
 
3.6%
기 341
 
3.5%
경 340
 
3.5%
지 336
 
3.5%
호 311
 
3.2%
번 306
 
3.2%
Other values (232) 4757
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5478
56.8%
Decimal Number 2150
 
22.3%
Space Separator 1763
 
18.3%
Other Punctuation 107
 
1.1%
Dash Punctuation 87
 
0.9%
Math Symbol 16
 
0.2%
Close Punctuation 14
 
0.1%
Open Punctuation 14
 
0.1%
Uppercase Letter 13
 
0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 367
 
6.7%
시 360
 
6.6%
도 345
 
6.3%
기 341
 
6.2%
경 340
 
6.2%
지 336
 
6.1%
호 311
 
5.7%
번 306
 
5.6%
구 186
 
3.4%
층 146
 
2.7%
Other values (205) 2440
44.5%
Decimal Number
ValueCountFrequency (%)
1 420
19.5%
0 257
12.0%
2 240
11.2%
3 231
10.7%
5 205
9.5%
4 203
9.4%
6 177
8.2%
8 161
 
7.5%
7 159
 
7.4%
9 97
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 7
53.8%
O 1
 
7.7%
J 1
 
7.7%
S 1
 
7.7%
C 1
 
7.7%
I 1
 
7.7%
H 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 96
89.7%
. 11
 
10.3%
Math Symbol
ValueCountFrequency (%)
~ 12
75.0%
～ 4
 
25.0%
Space Separator
ValueCountFrequency (%)
1763
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 87
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Lowercase Letter
ValueCountFrequency (%)
l 3
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5478
56.8%
Common 4151
43.0%
Latin 17
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 367
 
6.7%
시 360
 
6.6%
도 345
 
6.3%
기 341
 
6.2%
경 340
 
6.2%
지 336
 
6.1%
호 311
 
5.7%
번 306
 
5.6%
구 186
 
3.4%
층 146
 
2.7%
Other values (205) 2440
44.5%
Common
ValueCountFrequency (%)
1763
42.5%
1 420
 
10.1%
0 257
 
6.2%
2 240
 
5.8%
3 231
 
5.6%
5 205
 
4.9%
4 203
 
4.9%
6 177
 
4.3%
8 161
 
3.9%
7 159
 
3.8%
Other values (8) 335
 
8.1%
Latin
ValueCountFrequency (%)
B 7
41.2%
l 3
17.6%
O 1
 
5.9%
J 1
 
5.9%
S 1
 
5.9%
Ⅱ 1
 
5.9%
C 1
 
5.9%
I 1
 
5.9%
H 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5478
56.8%
ASCII 4163
43.2%
None 4
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1763
42.3%
1 420
 
10.1%
0 257
 
6.2%
2 240
 
5.8%
3 231
 
5.5%
5 205
 
4.9%
4 203
 
4.9%
6 177
 
4.3%
8 161
 
3.9%
7 159
 
3.8%
Other values (15) 347
 
8.3%
Hangul
ValueCountFrequency (%)
동 367
 
6.7%
시 360
 
6.6%
도 345
 
6.3%
기 341
 
6.2%
경 340
 
6.2%
지 336
 
6.1%
호 311
 
5.7%
번 306
 
5.6%
구 186
 
3.4%
층 146
 
2.7%
Other values (205) 2440
44.5%
None
ValueCountFrequency (%)
～ 4
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct181
Distinct (%)53.7%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean14557.329
Minimum1781
Maximum18593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T07:37:44.724878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1781
5-th percentile10401.8
Q113497
median14580
Q316489
95-th percentile18383.6
Maximum18593
Range16812
Interquartile range (IQR)2992

Descriptive statistics

Standard deviation2418.6491
Coefficient of variation (CV)0.16614649
Kurtosis1.4666202
Mean14557.329
Median Absolute Deviation (MAD)1665
Skewness-0.66788688
Sum4905820
Variance5849863.6
MonotonicityNot monotonic
2023-12-11T07:37:44.842568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13591 7
 
2.1%
16489 7
 
2.1%
13558 7
 
2.1%
15865 7
 
2.1%
13497 6
 
1.8%
10402 6
 
1.8%
10364 5
 
1.5%
16705 5
 
1.5%
14543 5
 
1.5%
15461 5
 
1.5%
Other values (171) 277
81.7%
ValueCountFrequency (%)
1781 1
 
0.3%
6977 1
 
0.3%
10018 2
 
0.6%
10098 1
 
0.3%
10364 5
1.5%
10381 1
 
0.3%
10387 1
 
0.3%
10401 5
1.5%
10402 6
1.8%
10403 1
 
0.3%
ValueCountFrequency (%)
18593 2
0.6%
18577 1
 
0.3%
18567 2
0.6%
18455 2
0.6%
18453 2
0.6%
18412 2
0.6%
18405 4
1.2%
18398 2
0.6%
18380 1
 
0.3%
18258 1
 
0.3%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct295
Distinct (%)88.1%
Missing4
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean37.404783
Minimum36.961137
Maximum37.911359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T07:37:44.959802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.961137
5-th percentile37.132165
Q137.278753
median37.382278
Q337.503901
95-th percentile37.737435
Maximum37.911359
Range0.9502222
Interquartile range (IQR)0.2251476

Descriptive statistics

Standard deviation0.18138979
Coefficient of variation (CV)0.0048493741
Kurtosis0.074904696
Mean37.404783
Median Absolute Deviation (MAD)0.1129808
Skewness0.29742466
Sum12530.602
Variance0.032902254
MonotonicityNot monotonic
2023-12-11T07:37:45.070884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5061752 3
 
0.9%
37.3009782 3
 
0.9%
37.2509933 3
 
0.9%
37.3146714 3
 
0.9%
37.7360679 3
 
0.9%
37.3185929 3
 
0.9%
37.4092535 2
 
0.6%
36.9862174 2
 
0.6%
37.3653375 2
 
0.6%
37.0786265 2
 
0.6%
Other values (285) 309
91.2%
(Missing) 4
 
1.2%
ValueCountFrequency (%)
36.9611367 1
0.3%
36.9862174 2
0.6%
36.9866826423 1
0.3%
36.9887166485 1
0.3%
36.9894903681 1
0.3%
36.991908 1
0.3%
36.9995517 1
0.3%
37.007717 1
0.3%
37.067098 1
0.3%
37.0671546579 1
0.3%
ValueCountFrequency (%)
37.9113589 1
0.3%
37.900189215 1
0.3%
37.8976673 1
0.3%
37.8303410409 1
0.3%
37.828504973 1
0.3%
37.8154499 2
0.6%
37.7618936 1
0.3%
37.7614541 1
0.3%
37.751534 2
0.6%
37.7514263765 1
0.3%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct295
Distinct (%)88.1%
Missing4
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean126.96969
Minimum126.59895
Maximum127.63328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T07:37:45.393628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.59895
5-th percentile126.7492
Q1126.80912
median126.97604
Q3127.09687
95-th percentile127.2136
Maximum127.63328
Range1.0343355
Interquartile range (IQR)0.28775142

Descriptive statistics

Standard deviation0.17605878
Coefficient of variation (CV)0.0013866205
Kurtosis0.33801766
Mean126.96969
Median Absolute Deviation (MAD)0.14314119
Skewness0.55318891
Sum42534.848
Variance0.030996695
MonotonicityNot monotonic
2023-12-11T07:37:45.517618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7553008 3
 
0.9%
126.8646586 3
 
0.9%
127.0759098 3
 
0.9%
126.8287713 3
 
0.9%
127.043904 3
 
0.9%
126.8364676 3
 
0.9%
127.2595639 2
 
0.6%
126.8446414 2
 
0.6%
127.10624 2
 
0.6%
127.0528138 2
 
0.6%
Other values (285) 309
91.2%
(Missing) 4
 
1.2%
ValueCountFrequency (%)
126.5989451 2
0.6%
126.7126325032 1
0.3%
126.7229477 2
0.6%
126.7229957738 1
0.3%
126.7240866123 1
0.3%
126.7291642 1
0.3%
126.7329697765 1
0.3%
126.7347751 1
0.3%
126.7358106 1
0.3%
126.7361694 1
0.3%
ValueCountFrequency (%)
127.6332806 1
0.3%
127.4954375 1
0.3%
127.4910686 1
0.3%
127.4904939908 1
0.3%
127.4494325145 1
0.3%
127.4494325 1
0.3%
127.4476005 2
0.6%
127.4389291 1
0.3%
127.4349175 1
0.3%
127.3150831 1
0.3%

Interactions

2023-12-11T07:37:39.796278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:35.111843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:35.736654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.435136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.089842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.703146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.303332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.942924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:39.881419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:35.187161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:35.807279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.510425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.164581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.785601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.399629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:39.008631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:39.973974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T07:37:35.905882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.594218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.241834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.863897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.496391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:39.081262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:40.062800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:35.349211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:35.998054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.679004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.323275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.941749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.589642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:39.151274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:40.136108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:35.422695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.087651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.764037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.391974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.011811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.672301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:39.215905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:40.224379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:35.502187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.192085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.856903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.481009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.085284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.743831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:39.293123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:40.306917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:35.579976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.291962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.938713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.559161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.158088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.811960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:39.386124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:40.375899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:35.657505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:36.362136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.012426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:37.632209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.223393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:38.877461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:37:39.483446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:37:45.616338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자병상수(개)종업원수연면적(㎡)소재지우편번호WGS84위도WGS84경도
시군명1.0000.5450.5780.0000.4750.2160.0000.9900.9810.979
인허가일자0.5451.0000.2290.3770.0000.0000.0000.2450.3260.158
영업상태명0.5780.2291.0000.0000.0000.0000.0000.0670.3820.000
폐업일자0.0000.3770.0001.0000.0000.0000.0000.0000.0000.000
병상수(개)0.4750.0000.0000.0001.0000.1560.0000.0240.2200.212
종업원수0.2160.0000.0000.0000.1561.0000.0000.1610.1370.000
연면적(㎡)0.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
소재지우편번호0.9900.2450.0670.0000.0240.1610.0001.0000.8390.759
WGS84위도0.9810.3260.3820.0000.2200.1370.0000.8391.0000.646
WGS84경도0.9790.1580.0000.0000.2120.0000.0000.7590.6461.000
2023-12-11T07:37:45.719746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명시군명
영업상태명1.0000.311
시군명0.3111.000
2023-12-11T07:37:45.810571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자병상수(개)종업원수연면적(㎡)소재지우편번호WGS84위도WGS84경도시군명영업상태명
인허가일자1.0000.283-0.006-0.126-0.0460.038-0.012-0.0050.1910.139
폐업일자0.2831.0000.025-0.2560.0560.122-0.034-0.0670.0000.000
병상수(개)-0.0060.0251.0000.1760.4790.083-0.0850.0660.1810.000
종업원수-0.126-0.2560.1761.0000.208-0.1310.122-0.0100.0930.000
연면적(㎡)-0.0460.0560.4790.2081.000-0.0220.032-0.0310.0000.000
소재지우편번호0.0380.1220.083-0.131-0.0221.000-0.8940.0940.9140.000
WGS84위도-0.012-0.034-0.0850.1220.032-0.8941.000-0.2800.8530.167
WGS84경도-0.005-0.0670.066-0.010-0.0310.094-0.2801.0000.8450.000
시군명0.1910.0000.1810.0930.0000.9140.8530.8451.0000.311
영업상태명0.1390.0000.0000.0000.0000.0000.1670.0000.3111.000

Missing values

2023-12-11T07:37:40.483910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:37:40.704302image/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.
2023-12-11T07:37:40.864966image/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

시군명사업장명인허가일자영업상태명폐업일자병상수(개)의료유사업종별명종업원수연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0고양시서원안마시술소20080304영업중<NA>22안마시술소6758.29경기도 고양시 일산동구 정발산로 38 (장항동)경기도 고양시 일산동구 장항동 855번지 장항동이스턴시티 506,506-1,507,507-1,508호1040337.656648126.773115
1고양시리노안마시술소20040705영업중<NA>15안마시술소2684.2경기도 고양시 일산동구 정발산로 39 (장항동,대양빌딩 501,502호, 505～510호)경기도 고양시일산동구 장항동 856번지 1호 대양빌딩 501,502호, 505～510호1040237.657269126.773045
2고양시뉴필안마시술소20040326영업중<NA>23안마시술소3592.99경기도 고양시 일산동구 무궁화로 34 (장항동,남정씨티프라자Ⅱ505,509,510호)경기도 고양시일산동구 장항동 778번지 2호 남정씨티프라자Ⅱ505,509,510호1040137.662522126.768326
3고양시원당여우안마시술소20020603영업중<NA>16안마시술소2513.0경기도 고양시 덕양구 호국로777번길 35 (주교동,2층)경기도 고양시 덕양구 주교동 608번지 2층1046137.656046126.832896
4고양시탑안마시술소20200224영업중<NA>9안마시술소3346.5경기도 고양시 일산동구 무궁화로 18, 남정 씨티프라자 1 701호 일부, 706~708호호 (장항동)경기도 고양시 일산동구 장항동 760번지 남정 씨티프라자 11040137.661411126.766935
5고양시우진안마시술소20090227영업중<NA>11안마시술소3630.09경기도 고양시 일산동구 무궁화로 31-2 (장항동,풍성프라자601~606호)경기도 고양시 일산동구 장항2동 734번지 2호 풍성프라자601~606호1036437.663016126.767826
6고양시슈퍼맨안마시술소20160422영업중<NA>18안마시술소2505.36경기도 고양시 일산동구 중앙로1275번길 60-31, 404~406호 (장항동, 에메랄드빌딩)경기도 고양시 일산동구 장항동 756번지 에메랄드빌딩 404~6호1040137.660017126.768436
7고양시정안마시술소20040910영업중<NA>17안마시술소2527.4경기도 고양시 일산동구 무궁화로 31-13, 5층 (장항동, 로데오휠)경기도 고양시일산동구 장항동 735번지 1호 로데오휠 5층1036437.66296126.767078
8고양시킹안마시술소20070104영업중<NA>9안마시술소2434.09경기도 고양시 덕양구 호국로789번길 7 (주교동,301,302호)경기도 고양시 덕양구 주교동 614번지 301,302호1046137.656717126.835591
9고양시우산안마시술소20051118폐업2009022720안마시술소5630.09경기도 고양시 일산동구 무궁화로 31-2 (장항동,풍성프라자 601～606호)경기도 고양시일산동구 장항동 734번지 2호 풍성프라자 601～606호1036437.663016126.767826
시군명사업장명인허가일자영업상태명폐업일자병상수(개)의료유사업종별명종업원수연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
329화성시MVP안마시술소20030128폐업2009010813안마시술소2330.0<NA>경기도 화성시 남양동 1807번지 1호<NA>37.201822126.819975
330화성시스타안마시술소20080728폐업201111031안마시술소3654.26경기도 화성시 동탄중심상가1길 27 6층 (반송동,프라임빌딩)경기도 화성시 반송동 104번지 6호 프라임빌딩 6층1845537.201078127.072505
331화성시퀸스안마시술소20061023폐업2012082912안마시술소2623.7경기도 화성시 팔탄면 버들로1597번길 지하 28경기도 화성시 팔탄면 월문리 236번지 61호18577<NA><NA>
332화성시오성안마시술소20120625폐업2019011122안마시술소3497.81경기도 화성시 동탄중심상가2길 15 (반송동, 수성프라자402,403호)경기도 화성시 반송동 88번지 8호 수성프라자402,403호1845337.205583127.0735
333화성시진고개안마시술소19981008폐업2015052721안마시술소2552.04경기도 화성시 남양성지로 217 (남양동)경기도 화성시 남양동 1373번지 8호1825837.21321126.825219
334화성시송정안마시술소20020803폐업2011070123안마시술소2535.2경기도 화성시 우정읍 3.1만세로 51-2 (조암리)경기도 화성시 우정읍 조암리 272번지1856737.083172126.81945
335화성시장미안마시술소20040212폐업2020041421안마시술소2511.06경기도 화성시 경기대로 1025-5, 5층 (병점동, 제일빌딩)경기도 화성시 병점동 381번지 17호 제일빌딩 5층1841237.20691127.035356
336화성시요플안마시술소20060829폐업2009021036안마시술소21.0경기도 화성시 효행로 1056 5층 (병점동,탑프라자)경기도 화성시 병점동 844번지 2호 탑프라자 5층1840537.21413127.042809
337<NA>중동안마시술소20090119폐업2009111227안마시술소2814.45서울특별시 노원구 동일로208길 19, 204동 201호 (중계동,중계무지개아파트)서울특별시 노원구 중계동 513번지 26통6반 중계무지개아파트 204동 201호178137.644322127.065212
338<NA>와우안마시술소20090305폐업2009110221안마시술소2755.66서울특별시 동작구 상도로53길 70, 202동 1014호 (상도동,상도에스에이치빌)서울특별시 동작구 상도동 430번지 18통7반 상도에스에이치빌 202동 1014호697737.501839126.95457