Overview

Dataset statistics

Number of variables7
Number of observations1090
Missing cells405
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.9 KiB
Average record size in memory58.1 B

Variable types

Categorical1
Text3
Numeric2
DateTime1

Dataset

Description경기도 군포시 관내 공중위생업 현황으로 업종, 업소명, 소재지, 우편번호, 면적, 전화번호, 신고일자 항목을 제공합니다.
Author경기도 군포시
URLhttps://www.data.go.kr/data/3040814/fileData.do

Alerts

전화번호 has 405 (37.2%) missing valuesMissing
면적 has 26 (2.4%) zerosZeros

Reproduction

Analysis started2023-12-23 07:17:34.876782
Analysis finished2023-12-23 07:17:39.993534
Duration5.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct14
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
일반미용업
475 
피부미용업
108 
세탁업
92 
건물위생관리업
83 
네일미용업
80 
Other values (9)
252 

Length

Max length21
Median length5
Mean length5.7302752
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
일반미용업 475
43.6%
피부미용업 108
 
9.9%
세탁업 92
 
8.4%
건물위생관리업 83
 
7.6%
네일미용업 80
 
7.3%
이용업 69
 
6.3%
숙박업(일반) 50
 
4.6%
네일미용업+화장ㆍ분장 미용업 46
 
4.2%
화장ㆍ분장 미용업 32
 
2.9%
일반미용업+네일미용업 15
 
1.4%
Other values (4) 40
 
3.7%

Length

2023-12-23T07:17:40.405495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 475
40.3%
피부미용업 108
 
9.2%
미용업 93
 
7.9%
세탁업 92
 
7.8%
건물위생관리업 83
 
7.0%
네일미용업 80
 
6.8%
이용업 69
 
5.8%
숙박업(일반 50
 
4.2%
네일미용업+화장ㆍ분장 46
 
3.9%
화장ㆍ분장 32
 
2.7%
Other values (4) 52
 
4.4%

업소명
Text

Distinct1063
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
2023-12-23T07:17:41.714792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length6.1954128
Min length2

Characters and Unicode

Total characters6753
Distinct characters563
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1038 ?
Unique (%)95.2%

Sample

1st row군포여관
2nd row로또파크
3rd row호텔 밴드
4th row타워장여관
5th row007모텔
ValueCountFrequency (%)
헤어 16
 
1.2%
산본점 14
 
1.0%
주식회사 13
 
1.0%
네일 11
 
0.8%
에스테틱 8
 
0.6%
헤어샵 7
 
0.5%
hair 6
 
0.4%
이발관 5
 
0.4%
미용실 5
 
0.4%
토리헤어 4
 
0.3%
Other values (1173) 1255
93.4%
2023-12-23T07:17:43.727786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 335
 
5.0%
헤 330
 
4.9%
256
 
3.8%
이 185
 
2.7%
리 145
 
2.1%
스 141
 
2.1%
미 129
 
1.9%
( 100
 
1.5%
) 100
 
1.5%
일 99
 
1.5%
Other values (553) 4933
73.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5719
84.7%
Space Separator 256
 
3.8%
Lowercase Letter 249
 
3.7%
Uppercase Letter 241
 
3.6%
Open Punctuation 100
 
1.5%
Close Punctuation 100
 
1.5%
Decimal Number 46
 
0.7%
Other Punctuation 32
 
0.5%
Dash Punctuation 4
 
0.1%
Connector Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 335
 
5.9%
헤 330
 
5.8%
이 185
 
3.2%
리 145
 
2.5%
스 141
 
2.5%
미 129
 
2.3%
일 99
 
1.7%
네 94
 
1.6%
세 87
 
1.5%
용 79
 
1.4%
Other values (487) 4095
71.6%
Uppercase Letter
ValueCountFrequency (%)
O 28
 
11.6%
A 21
 
8.7%
H 21
 
8.7%
N 20
 
8.3%
E 15
 
6.2%
I 15
 
6.2%
J 13
 
5.4%
S 13
 
5.4%
T 12
 
5.0%
C 10
 
4.1%
Other values (13) 73
30.3%
Lowercase Letter
ValueCountFrequency (%)
a 34
13.7%
i 26
10.4%
r 23
9.2%
e 22
8.8%
n 21
 
8.4%
o 18
 
7.2%
l 17
 
6.8%
y 12
 
4.8%
u 11
 
4.4%
s 11
 
4.4%
Other values (11) 54
21.7%
Decimal Number
ValueCountFrequency (%)
0 13
28.3%
3 8
17.4%
1 7
15.2%
4 5
 
10.9%
2 3
 
6.5%
5 3
 
6.5%
7 3
 
6.5%
9 2
 
4.3%
6 2
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 11
34.4%
# 9
28.1%
& 5
15.6%
: 3
 
9.4%
' 2
 
6.2%
＆ 1
 
3.1%
* 1
 
3.1%
Space Separator
ValueCountFrequency (%)
256
100.0%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5720
84.7%
Common 541
 
8.0%
Latin 490
 
7.3%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 335
 
5.9%
헤 330
 
5.8%
이 185
 
3.2%
리 145
 
2.5%
스 141
 
2.5%
미 129
 
2.3%
일 99
 
1.7%
네 94
 
1.6%
세 87
 
1.5%
용 79
 
1.4%
Other values (487) 4096
71.6%
Latin
ValueCountFrequency (%)
a 34
 
6.9%
O 28
 
5.7%
i 26
 
5.3%
r 23
 
4.7%
e 22
 
4.5%
A 21
 
4.3%
n 21
 
4.3%
H 21
 
4.3%
N 20
 
4.1%
o 18
 
3.7%
Other values (34) 256
52.2%
Common
ValueCountFrequency (%)
256
47.3%
( 100
 
18.5%
) 100
 
18.5%
0 13
 
2.4%
. 11
 
2.0%
# 9
 
1.7%
3 8
 
1.5%
1 7
 
1.3%
4 5
 
0.9%
& 5
 
0.9%
Other values (11) 27
 
5.0%
Han
ValueCountFrequency (%)
美 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5717
84.7%
ASCII 1030
 
15.3%
None 4
 
0.1%
CJK 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 335
 
5.9%
헤 330
 
5.8%
이 185
 
3.2%
리 145
 
2.5%
스 141
 
2.5%
미 129
 
2.3%
일 99
 
1.7%
네 94
 
1.6%
세 87
 
1.5%
용 79
 
1.4%
Other values (486) 4093
71.6%
ASCII
ValueCountFrequency (%)
256
24.9%
( 100
 
9.7%
) 100
 
9.7%
a 34
 
3.3%
O 28
 
2.7%
i 26
 
2.5%
r 23
 
2.2%
e 22
 
2.1%
A 21
 
2.0%
n 21
 
2.0%
Other values (54) 399
38.7%
None
ValueCountFrequency (%)
㈜ 3
75.0%
＆ 1
 
25.0%
CJK
ValueCountFrequency (%)
美 2
100.0%

소재지
Text

Distinct1076
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
2023-12-23T07:17:45.263439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length44
Mean length31.531193
Min length14

Characters and Unicode

Total characters34369
Distinct characters252
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

Unique1062 ?
Unique (%)97.4%

Sample

1st row군포시 군포역2길 15-1 2 3층 (당동)
2nd row군포시 군포역1길 64 (당동)
3rd row군포시 군포로 647 2 3 4층 (금정동)
4th row군포시 고산로 714-1 (산본동)
5th row군포시 고산로 729 (산본동)
ValueCountFrequency (%)
군포시 1090
 
15.9%
산본동 533
 
7.8%
1층 387
 
5.6%
당동 217
 
3.2%
금정동 185
 
2.7%
산본로323번길 147
 
2.1%
번영로 105
 
1.5%
일부 92
 
1.3%
2층 92
 
1.3%
당정동 63
 
0.9%
Other values (1184) 3959
57.6%
2023-12-23T07:17:46.989076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7125
20.7%
1 1837
 
5.3%
동 1274
 
3.7%
포 1233
 
3.6%
군 1232
 
3.6%
2 1178
 
3.4%
( 1171
 
3.4%
) 1171
 
3.4%
산 1150
 
3.3%
시 1099
 
3.2%
Other values (242) 15899
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17009
49.5%
Decimal Number 7501
21.8%
Space Separator 7125
20.7%
Open Punctuation 1171
 
3.4%
Close Punctuation 1171
 
3.4%
Dash Punctuation 309
 
0.9%
Uppercase Letter 76
 
0.2%
Lowercase Letter 3
 
< 0.1%
Math Symbol 2
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1274
 
7.5%
포 1233
 
7.2%
군 1232
 
7.2%
산 1150
 
6.8%
시 1099
 
6.5%
로 1084
 
6.4%
본 850
 
5.0%
호 850
 
5.0%
번 741
 
4.4%
층 687
 
4.0%
Other values (216) 6809
40.0%
Decimal Number
ValueCountFrequency (%)
1 1837
24.5%
2 1178
15.7%
3 941
12.5%
0 932
12.4%
4 607
 
8.1%
5 589
 
7.9%
6 445
 
5.9%
7 416
 
5.5%
9 316
 
4.2%
8 240
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
B 23
30.3%
A 22
28.9%
D 8
 
10.5%
C 8
 
10.5%
L 7
 
9.2%
G 6
 
7.9%
T 1
 
1.3%
I 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
@ 1
50.0%
Space Separator
ValueCountFrequency (%)
7125
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1171
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 309
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17281
50.3%
Hangul 17009
49.5%
Latin 79
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1274
 
7.5%
포 1233
 
7.2%
군 1232
 
7.2%
산 1150
 
6.8%
시 1099
 
6.5%
로 1084
 
6.4%
본 850
 
5.0%
호 850
 
5.0%
번 741
 
4.4%
층 687
 
4.0%
Other values (216) 6809
40.0%
Common
ValueCountFrequency (%)
7125
41.2%
1 1837
 
10.6%
2 1178
 
6.8%
( 1171
 
6.8%
) 1171
 
6.8%
3 941
 
5.4%
0 932
 
5.4%
4 607
 
3.5%
5 589
 
3.4%
6 445
 
2.6%
Other values (7) 1285
 
7.4%
Latin
ValueCountFrequency (%)
B 23
29.1%
A 22
27.8%
D 8
 
10.1%
C 8
 
10.1%
L 7
 
8.9%
G 6
 
7.6%
e 3
 
3.8%
T 1
 
1.3%
I 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17360
50.5%
Hangul 17009
49.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7125
41.0%
1 1837
 
10.6%
2 1178
 
6.8%
( 1171
 
6.7%
) 1171
 
6.7%
3 941
 
5.4%
0 932
 
5.4%
4 607
 
3.5%
5 589
 
3.4%
6 445
 
2.6%
Other values (16) 1364
 
7.9%
Hangul
ValueCountFrequency (%)
동 1274
 
7.5%
포 1233
 
7.2%
군 1232
 
7.2%
산 1150
 
6.8%
시 1099
 
6.5%
로 1084
 
6.4%
본 850
 
5.0%
호 850
 
5.0%
번 741
 
4.4%
층 687
 
4.0%
Other values (216) 6809
40.0%

우편번호
Real number (ℝ)

Distinct76
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15846.508
Minimum15800
Maximum15888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-12-23T07:17:48.046681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15800
5-th percentile15805
Q115822
median15856
Q315865
95-th percentile15884
Maximum15888
Range88
Interquartile range (IQR)43

Descriptive statistics

Standard deviation25.354754
Coefficient of variation (CV)0.0016000215
Kurtosis-1.2005768
Mean15846.508
Median Absolute Deviation (MAD)15
Skewness-0.41984424
Sum17272694
Variance642.86357
MonotonicityNot monotonic
2023-12-23T07:17:48.762327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15865 240
22.0%
15856 52
 
4.8%
15811 44
 
4.0%
15806 34
 
3.1%
15862 34
 
3.1%
15822 26
 
2.4%
15813 26
 
2.4%
15851 25
 
2.3%
15859 25
 
2.3%
15855 25
 
2.3%
Other values (66) 559
51.3%
ValueCountFrequency (%)
15800 11
 
1.0%
15801 7
 
0.6%
15802 20
1.8%
15803 2
 
0.2%
15804 12
 
1.1%
15805 17
1.6%
15806 34
3.1%
15807 1
 
0.1%
15808 3
 
0.3%
15809 4
 
0.4%
ValueCountFrequency (%)
15888 7
 
0.6%
15887 6
 
0.6%
15886 12
1.1%
15885 23
2.1%
15884 11
1.0%
15881 1
 
0.1%
15880 14
1.3%
15876 3
 
0.3%
15875 17
1.6%
15874 18
1.7%

면적
Real number (ℝ)

ZEROS 

Distinct962
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.127688
Minimum0
Maximum4429.61
Zeros26
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-12-23T07:17:49.531966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.0295
Q121.55
median30.335
Q349.085
95-th percentile394.486
Maximum4429.61
Range4429.61
Interquartile range (IQR)27.535

Descriptive statistics

Standard deviation254.34173
Coefficient of variation (CV)3.0596512
Kurtosis135.21871
Mean83.127688
Median Absolute Deviation (MAD)11.31
Skewness10.033002
Sum90609.18
Variance64689.717
MonotonicityNot monotonic
2023-12-23T07:17:50.294312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
 
2.4%
40.0 4
 
0.4%
18.87 3
 
0.3%
25.96 3
 
0.3%
25.8 3
 
0.3%
31.07 3
 
0.3%
24.0 3
 
0.3%
32.57 3
 
0.3%
18.4 3
 
0.3%
19.8 3
 
0.3%
Other values (952) 1036
95.0%
ValueCountFrequency (%)
0.0 26
2.4%
3.83 1
 
0.1%
4.73 1
 
0.1%
5.04 2
 
0.2%
6.2 1
 
0.1%
6.44 1
 
0.1%
6.72 1
 
0.1%
6.75 1
 
0.1%
7.49 1
 
0.1%
7.65 1
 
0.1%
ValueCountFrequency (%)
4429.61 1
0.1%
3640.53 1
0.1%
3040.0 1
0.1%
1936.1 1
0.1%
1849.0 1
0.1%
1149.28 1
0.1%
1095.82 1
0.1%
1056.39 1
0.1%
998.9 1
0.1%
992.14 1
0.1%

전화번호
Text

MISSING 

Distinct681
Distinct (%)99.4%
Missing405
Missing (%)37.2%
Memory size8.6 KiB
2023-12-23T07:17:52.040799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.024818
Min length12

Characters and Unicode

Total characters8237
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique677 ?
Unique (%)98.8%

Sample

1st row031-452-7501
2nd row031-452-1075
3rd row031-453-8154
4th row031-393-7336
5th row031-397-7791
ValueCountFrequency (%)
031-455-8890 2
 
0.3%
031-397-0808 2
 
0.3%
031-396-3795 2
 
0.3%
031-429-0553 2
 
0.3%
031-395-9994 1
 
0.1%
031-399-3604 1
 
0.1%
031-452-7501 1
 
0.1%
031-398-7466 1
 
0.1%
070-8632-8210 1
 
0.1%
031-461-4630 1
 
0.1%
Other values (672) 672
98.0%
2023-12-23T07:17:54.813373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1466
17.8%
- 1370
16.6%
1 1051
12.8%
0 1040
12.6%
9 723
8.8%
4 512
 
6.2%
5 485
 
5.9%
7 449
 
5.5%
2 424
 
5.1%
8 391
 
4.7%
Other values (2) 326
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6866
83.4%
Dash Punctuation 1370
 
16.6%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1466
21.4%
1 1051
15.3%
0 1040
15.1%
9 723
10.5%
4 512
 
7.5%
5 485
 
7.1%
7 449
 
6.5%
2 424
 
6.2%
8 391
 
5.7%
6 325
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 1370
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1466
17.8%
- 1370
16.6%
1 1051
12.8%
0 1040
12.6%
9 723
8.8%
4 512
 
6.2%
5 485
 
5.9%
7 449
 
5.5%
2 424
 
5.1%
8 391
 
4.7%
Other values (2) 326
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1466
17.8%
- 1370
16.6%
1 1051
12.8%
0 1040
12.6%
9 723
8.8%
4 512
 
6.2%
5 485
 
5.9%
7 449
 
5.5%
2 424
 
5.1%
8 391
 
4.7%
Other values (2) 326
 
4.0%

신고일자
Date

Distinct972
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
Minimum1976-03-12 00:00:00
Maximum2023-11-29 00:00:00
2023-12-23T07:17:55.663742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:17:56.391711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-23T07:17:37.684935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:17:37.001131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:17:37.995722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:17:37.297682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:17:56.921333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종우편번호면적
업종1.0000.3160.758
우편번호0.3161.0000.131
면적0.7580.1311.000
2023-12-23T07:17:57.342034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호면적업종
우편번호1.0000.1230.133
면적0.1231.0000.385
업종0.1330.3851.000

Missing values

2023-12-23T07:17:38.536842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:17:39.490338image/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.

Sample

업종업소명소재지우편번호면적전화번호신고일자
0숙박업(일반)군포여관군포시 군포역2길 15-1 2 3층 (당동)15855510.0031-452-75011981-12-22
1숙박업(일반)로또파크군포시 군포역1길 64 (당동)15855310.0031-452-10751981-12-22
2숙박업(일반)호텔 밴드군포시 군포로 647 2 3 4층 (금정동)15830660.0031-453-81541988-04-29
3숙박업(일반)타워장여관군포시 고산로 714-1 (산본동)15806550.0031-393-73361988-07-25
4숙박업(일반)007모텔군포시 고산로 729 (산본동)15801690.0031-397-77911988-11-24
5숙박업(일반)스카이모텔군포시 군포로 765 (산본동)15805430.0031-399-26641988-12-30
6숙박업(일반)영앤리치 스테이군포시 군포역1길 10-10 (당동)15855421.02031-456-11231989-06-14
7숙박업(일반)하이스테이 금정군포시 고산로 729-1 (산본동)15801483.16031-391-38861989-06-27
8숙박업(일반)수프림텔군포시 군포로 779 (산본동)15805491.94031-395-12121989-12-26
9숙박업(일반)알프스장 모텔군포시 군포로 775 (산본동)15805392.33031-399-80451990-02-24
업종업소명소재지우편번호면적전화번호신고일자
1080피부미용업+네일미용업+화장ㆍ분장 미용업비비드루비군포시 번영로 505 대우디오플러스 3층 306-2호 (산본동)1586534.89<NA>2016-02-05
1081피부미용업+네일미용업+화장ㆍ분장 미용업뷰티바이라군포시 산본로323번길 15 동산상가 5층 503호 (산본동)1586569.44<NA>2018-05-23
1082피부미용업+네일미용업+화장ㆍ분장 미용업네일플로레오(nail_floreo)군포시 산본로323번길 10-18 백운빌딩 405호 (산본동)1586565.48<NA>2019-12-18
1083피부미용업+네일미용업+화장ㆍ분장 미용업오늘도#군포시 송부로 42 송정더드림 2층 204호 (도마교동)1588551.21<NA>2020-03-11
1084피부미용업+네일미용업+화장ㆍ분장 미용업비비엘네일뷰티샵군포시 번영로 497 금화프라자 4층 402호 일부 (가칭402-2호) (산본동)1586536.17<NA>2021-11-10
1085피부미용업+네일미용업+화장ㆍ분장 미용업요조네일군포시 산본로323번길 6 럭키빌딩 3층 302호 (산본동)1586543.7<NA>2021-12-10
1086피부미용업+네일미용업+화장ㆍ분장 미용업뷰티온:유정군포시 산본로323번길 7 금성프라자빌딩 3층 302호일부(302-3호) (산본동)1586561.6<NA>2022-02-07
1087피부미용업+네일미용업+화장ㆍ분장 미용업네일은 피부맑음군포시 산본로432번길 4 목련아파트 상가 1층 104호 (산본동)1580418.2<NA>2022-11-18
1088피부미용업+네일미용업+화장ㆍ분장 미용업러브미 네일군포시 송부로96번길 12 군포 송정 풍산 리치안 A213호 2층 (도마교동)1588541.62<NA>2023-01-26
1089피부미용업+네일미용업+화장ㆍ분장 미용업뷰티욜로와(BEAUTY YOLOWA)군포시 고산로 679 미성프라자 202호일부호 (산본동)158029.0<NA>2023-09-21