Overview

Dataset statistics

Number of variables6
Number of observations1515
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.6 KiB
Average record size in memory49.1 B

Variable types

Categorical2
Text3
Numeric1

Dataset

Description서울특별시 동대문구에 소재한 공중위생업소 현황(* 공중위생업소 : 숙박업, 목욕장업, 이용업, 미용업, 세탁업, 건물위생관리업)
Author서울특별시 동대문구
URLhttps://www.data.go.kr/data/3083937/fileData.do

Alerts

데이터기준일자 has constant value ""Constant

Reproduction

Analysis started2024-04-21 02:55:25.697301
Analysis finished2024-04-21 02:55:27.467922
Duration1.77 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct22
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
일반미용업
527 
세탁업
149 
종합미용업
137 
숙박업(일반)
132 
이용업
118 
Other values (17)
452 

Length

Max length23
Median length5
Mean length5.7881188
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 527
34.8%
세탁업 149
 
9.8%
종합미용업 137
 
9.0%
숙박업(일반) 132
 
8.7%
이용업 118
 
7.8%
피부미용업 113
 
7.5%
건물위생관리업 100
 
6.6%
네일미용업 80
 
5.3%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 31
 
2.0%
목욕장업 25
 
1.7%
Other values (12) 103
 
6.8%

Length

2024-04-21T11:55:27.548106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 579
33.3%
네일미용업 159
 
9.1%
피부미용업 157
 
9.0%
세탁업 149
 
8.6%
종합미용업 137
 
7.9%
숙박업(일반 132
 
7.6%
이용업 118
 
6.8%
건물위생관리업 100
 
5.8%
미용업 99
 
5.7%
화장ㆍ분장 83
 
4.8%
Other values (2) 26
 
1.5%

업소명
Text

Distinct1443
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
2024-04-21T11:55:27.796878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length25
Mean length5.8521452
Min length1

Characters and Unicode

Total characters8866
Distinct characters620
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1381 ?
Unique (%)91.2%

Sample

1st rowM(엠)모텔
2nd row한양
3rd row신흥
4th row칠성
5th row덕성
ValueCountFrequency (%)
헤어 23
 
1.2%
hair 16
 
0.9%
네일 13
 
0.7%
주식회사 10
 
0.5%
태후사랑 9
 
0.5%
nail 8
 
0.4%
청량리점 8
 
0.4%
미용실 6
 
0.3%
살롱 6
 
0.3%
에이바헤어 6
 
0.3%
Other values (1588) 1752
94.3%
2024-04-21T11:55:28.169923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 410
 
4.6%
헤 383
 
4.3%
343
 
3.9%
이 224
 
2.5%
리 178
 
2.0%
) 171
 
1.9%
( 171
 
1.9%
스 166
 
1.9%
미 151
 
1.7%
일 129
 
1.5%
Other values (610) 6540
73.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7228
81.5%
Lowercase Letter 461
 
5.2%
Uppercase Letter 381
 
4.3%
Space Separator 343
 
3.9%
Close Punctuation 171
 
1.9%
Open Punctuation 171
 
1.9%
Decimal Number 54
 
0.6%
Other Punctuation 53
 
0.6%
Dash Punctuation 2
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 410
 
5.7%
헤 383
 
5.3%
이 224
 
3.1%
리 178
 
2.5%
스 166
 
2.3%
미 151
 
2.1%
일 129
 
1.8%
용 121
 
1.7%
네 119
 
1.6%
사 112
 
1.5%
Other values (539) 5235
72.4%
Uppercase Letter
ValueCountFrequency (%)
A 43
 
11.3%
H 36
 
9.4%
I 31
 
8.1%
N 30
 
7.9%
S 26
 
6.8%
M 22
 
5.8%
R 22
 
5.8%
J 21
 
5.5%
O 20
 
5.2%
E 18
 
4.7%
Other values (15) 112
29.4%
Lowercase Letter
ValueCountFrequency (%)
a 57
12.4%
o 55
11.9%
i 52
11.3%
e 43
9.3%
l 40
8.7%
n 38
8.2%
r 31
 
6.7%
h 24
 
5.2%
y 20
 
4.3%
u 18
 
3.9%
Other values (13) 83
18.0%
Decimal Number
ValueCountFrequency (%)
0 10
18.5%
2 10
18.5%
3 9
16.7%
1 8
14.8%
6 5
9.3%
5 4
 
7.4%
4 4
 
7.4%
9 2
 
3.7%
8 1
 
1.9%
7 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
& 18
34.0%
. 13
24.5%
, 10
18.9%
# 6
 
11.3%
· 2
 
3.8%
: 2
 
3.8%
% 1
 
1.9%
' 1
 
1.9%
Space Separator
ValueCountFrequency (%)
343
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7221
81.4%
Latin 842
 
9.5%
Common 796
 
9.0%
Han 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 410
 
5.7%
헤 383
 
5.3%
이 224
 
3.1%
리 178
 
2.5%
스 166
 
2.3%
미 151
 
2.1%
일 129
 
1.8%
용 121
 
1.7%
네 119
 
1.6%
사 112
 
1.6%
Other values (534) 5228
72.4%
Latin
ValueCountFrequency (%)
a 57
 
6.8%
o 55
 
6.5%
i 52
 
6.2%
e 43
 
5.1%
A 43
 
5.1%
l 40
 
4.8%
n 38
 
4.5%
H 36
 
4.3%
r 31
 
3.7%
I 31
 
3.7%
Other values (38) 416
49.4%
Common
ValueCountFrequency (%)
343
43.1%
) 171
21.5%
( 171
21.5%
& 18
 
2.3%
. 13
 
1.6%
0 10
 
1.3%
, 10
 
1.3%
2 10
 
1.3%
3 9
 
1.1%
1 8
 
1.0%
Other values (13) 33
 
4.1%
Han
ValueCountFrequency (%)
美 3
42.9%
明 1
 
14.3%
熹 1
 
14.3%
女 1
 
14.3%
子 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7221
81.4%
ASCII 1636
 
18.5%
CJK 6
 
0.1%
None 2
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 410
 
5.7%
헤 383
 
5.3%
이 224
 
3.1%
리 178
 
2.5%
스 166
 
2.3%
미 151
 
2.1%
일 129
 
1.8%
용 121
 
1.7%
네 119
 
1.6%
사 112
 
1.6%
Other values (534) 5228
72.4%
ASCII
ValueCountFrequency (%)
343
21.0%
) 171
 
10.5%
( 171
 
10.5%
a 57
 
3.5%
o 55
 
3.4%
i 52
 
3.2%
e 43
 
2.6%
A 43
 
2.6%
l 40
 
2.4%
n 38
 
2.3%
Other values (60) 623
38.1%
CJK
ValueCountFrequency (%)
美 3
50.0%
明 1
 
16.7%
熹 1
 
16.7%
子 1
 
16.7%
None
ValueCountFrequency (%)
· 2
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
女 1
100.0%

영업소 주소(도로명)
Text

Distinct1444
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
2024-04-21T11:55:28.404892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length53
Mean length32.056766
Min length22

Characters and Unicode

Total characters48566
Distinct characters267
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

Unique1376 ?
Unique (%)90.8%

Sample

1st row서울특별시 동대문구 하정로 48 (신설동)
2nd row서울특별시 동대문구 왕산로 112-12 (용두동)
3rd row서울특별시 동대문구 답십리로 48-7 (전농동)
4th row서울특별시 동대문구 답십리로 48-9 (전농동)
5th row서울특별시 동대문구 홍릉로8길 2-3 (청량리동)
ValueCountFrequency (%)
서울특별시 1515
 
16.0%
동대문구 1515
 
16.0%
1층 760
 
8.0%
장안동 373
 
3.9%
전농동 221
 
2.3%
답십리동 193
 
2.0%
2층 159
 
1.7%
용두동 133
 
1.4%
이문동 122
 
1.3%
제기동 122
 
1.3%
Other values (1187) 4363
46.0%
2024-04-21T11:55:28.799984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7963
 
16.4%
동 3205
 
6.6%
1 2277
 
4.7%
대 1756
 
3.6%
문 1735
 
3.6%
시 1604
 
3.3%
서 1565
 
3.2%
울 1556
 
3.2%
( 1531
 
3.2%
) 1531
 
3.2%
Other values (257) 23843
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28415
58.5%
Space Separator 7963
 
16.4%
Decimal Number 7349
 
15.1%
Open Punctuation 1531
 
3.2%
Close Punctuation 1531
 
3.2%
Other Punctuation 1441
 
3.0%
Dash Punctuation 241
 
0.5%
Uppercase Letter 69
 
0.1%
Math Symbol 21
 
< 0.1%
Lowercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3205
 
11.3%
대 1756
 
6.2%
문 1735
 
6.1%
시 1604
 
5.6%
서 1565
 
5.5%
울 1556
 
5.5%
특 1515
 
5.3%
구 1515
 
5.3%
별 1515
 
5.3%
로 1510
 
5.3%
Other values (218) 10939
38.5%
Uppercase Letter
ValueCountFrequency (%)
K 14
20.3%
S 14
20.3%
A 8
11.6%
B 8
11.6%
Y 6
8.7%
L 4
 
5.8%
T 2
 
2.9%
E 2
 
2.9%
W 2
 
2.9%
I 1
 
1.4%
Other values (8) 8
11.6%
Decimal Number
ValueCountFrequency (%)
1 2277
31.0%
2 1130
15.4%
3 829
 
11.3%
0 571
 
7.8%
4 552
 
7.5%
6 489
 
6.7%
5 446
 
6.1%
8 404
 
5.5%
7 371
 
5.0%
9 280
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
b 1
20.0%
k 1
20.0%
s 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 1440
99.9%
@ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
7963
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1531
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1531
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 241
100.0%
Math Symbol
ValueCountFrequency (%)
~ 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28415
58.5%
Common 20077
41.3%
Latin 74
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3205
 
11.3%
대 1756
 
6.2%
문 1735
 
6.1%
시 1604
 
5.6%
서 1565
 
5.5%
울 1556
 
5.5%
특 1515
 
5.3%
구 1515
 
5.3%
별 1515
 
5.3%
로 1510
 
5.3%
Other values (218) 10939
38.5%
Latin
ValueCountFrequency (%)
K 14
18.9%
S 14
18.9%
A 8
10.8%
B 8
10.8%
Y 6
8.1%
L 4
 
5.4%
T 2
 
2.7%
E 2
 
2.7%
W 2
 
2.7%
e 2
 
2.7%
Other values (12) 12
16.2%
Common
ValueCountFrequency (%)
7963
39.7%
1 2277
 
11.3%
( 1531
 
7.6%
) 1531
 
7.6%
, 1440
 
7.2%
2 1130
 
5.6%
3 829
 
4.1%
0 571
 
2.8%
4 552
 
2.7%
6 489
 
2.4%
Other values (7) 1764
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28415
58.5%
ASCII 20151
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7963
39.5%
1 2277
 
11.3%
( 1531
 
7.6%
) 1531
 
7.6%
, 1440
 
7.1%
2 1130
 
5.6%
3 829
 
4.1%
0 571
 
2.8%
4 552
 
2.7%
6 489
 
2.4%
Other values (29) 1838
 
9.1%
Hangul
ValueCountFrequency (%)
동 3205
 
11.3%
대 1756
 
6.2%
문 1735
 
6.1%
시 1604
 
5.6%
서 1565
 
5.5%
울 1556
 
5.5%
특 1515
 
5.3%
구 1515
 
5.3%
별 1515
 
5.3%
로 1510
 
5.3%
Other values (218) 10939
38.5%

우편번호(도로명)
Real number (ℝ)

Distinct209
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2541.8264
Minimum2403
Maximum2646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2024-04-21T11:55:28.935585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2403
5-th percentile2427
Q12487
median2550
Q32599
95-th percentile2638
Maximum2646
Range243
Interquartile range (IQR)112

Descriptive statistics

Standard deviation67.194828
Coefficient of variation (CV)0.026435648
Kurtosis-1.0650633
Mean2541.8264
Median Absolute Deviation (MAD)55
Skewness-0.24005697
Sum3850867
Variance4515.1449
MonotonicityNot monotonic
2024-04-21T11:55:29.109676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2624 41
 
2.7%
2566 28
 
1.8%
2453 23
 
1.5%
2637 21
 
1.4%
2639 21
 
1.4%
2490 20
 
1.3%
2507 20
 
1.3%
2488 19
 
1.3%
2582 18
 
1.2%
2524 17
 
1.1%
Other values (199) 1287
85.0%
ValueCountFrequency (%)
2403 1
 
0.1%
2405 2
 
0.1%
2406 6
0.4%
2407 2
 
0.1%
2409 8
0.5%
2410 9
0.6%
2411 1
 
0.1%
2412 6
0.4%
2418 2
 
0.1%
2419 5
0.3%
ValueCountFrequency (%)
2646 2
 
0.1%
2645 2
 
0.1%
2644 16
1.1%
2643 13
0.9%
2642 6
 
0.4%
2641 4
 
0.3%
2640 7
 
0.5%
2639 21
1.4%
2638 12
0.8%
2637 21
1.4%

영업소 주소(지번)
Text

Distinct1315
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
2024-04-21T11:55:29.356299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length42
Mean length23.452805
Min length17

Characters and Unicode

Total characters35531
Distinct characters246
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

Unique1170 ?
Unique (%)77.2%

Sample

1st row서울특별시 동대문구 신설동 89-89
2nd row서울특별시 동대문구 용두동 711-13
3rd row서울특별시 동대문구 전농동 643-88
4th row서울특별시 동대문구 전농동 643-93
5th row서울특별시 동대문구 청량리동 330-0
ValueCountFrequency (%)
서울특별시 1515
22.7%
동대문구 1515
22.7%
장안동 379
 
5.7%
전농동 223
 
3.3%
답십리동 198
 
3.0%
1층 178
 
2.7%
용두동 138
 
2.1%
제기동 123
 
1.8%
이문동 122
 
1.8%
휘경동 114
 
1.7%
Other values (1459) 2165
32.5%
2024-04-21T11:55:29.731867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6640
18.7%
동 3089
 
8.7%
문 1648
 
4.6%
대 1543
 
4.3%
시 1530
 
4.3%
서 1515
 
4.3%
특 1515
 
4.3%
별 1515
 
4.3%
구 1515
 
4.3%
울 1515
 
4.3%
Other values (236) 13506
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20402
57.4%
Decimal Number 7020
 
19.8%
Space Separator 6640
 
18.7%
Dash Punctuation 1356
 
3.8%
Uppercase Letter 56
 
0.2%
Other Punctuation 14
 
< 0.1%
Close Punctuation 13
 
< 0.1%
Open Punctuation 13
 
< 0.1%
Math Symbol 13
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3089
15.1%
문 1648
 
8.1%
대 1543
 
7.6%
시 1530
 
7.5%
서 1515
 
7.4%
특 1515
 
7.4%
별 1515
 
7.4%
구 1515
 
7.4%
울 1515
 
7.4%
안 413
 
2.0%
Other values (198) 4604
22.6%
Uppercase Letter
ValueCountFrequency (%)
K 13
23.2%
S 12
21.4%
Y 6
10.7%
L 4
 
7.1%
A 3
 
5.4%
W 2
 
3.6%
E 2
 
3.6%
T 2
 
3.6%
B 2
 
3.6%
H 2
 
3.6%
Other values (8) 8
14.3%
Decimal Number
ValueCountFrequency (%)
1 1393
19.8%
3 980
14.0%
2 930
13.2%
4 682
9.7%
5 554
 
7.9%
0 533
 
7.6%
6 526
 
7.5%
9 503
 
7.2%
8 480
 
6.8%
7 439
 
6.3%
Lowercase Letter
ValueCountFrequency (%)
e 2
50.0%
k 1
25.0%
s 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 12
85.7%
@ 2
 
14.3%
Space Separator
ValueCountFrequency (%)
6640
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1356
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Math Symbol
ValueCountFrequency (%)
~ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20402
57.4%
Common 15069
42.4%
Latin 60
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3089
15.1%
문 1648
 
8.1%
대 1543
 
7.6%
시 1530
 
7.5%
서 1515
 
7.4%
특 1515
 
7.4%
별 1515
 
7.4%
구 1515
 
7.4%
울 1515
 
7.4%
안 413
 
2.0%
Other values (198) 4604
22.6%
Latin
ValueCountFrequency (%)
K 13
21.7%
S 12
20.0%
Y 6
10.0%
L 4
 
6.7%
A 3
 
5.0%
W 2
 
3.3%
E 2
 
3.3%
T 2
 
3.3%
e 2
 
3.3%
B 2
 
3.3%
Other values (11) 12
20.0%
Common
ValueCountFrequency (%)
6640
44.1%
1 1393
 
9.2%
- 1356
 
9.0%
3 980
 
6.5%
2 930
 
6.2%
4 682
 
4.5%
5 554
 
3.7%
0 533
 
3.5%
6 526
 
3.5%
9 503
 
3.3%
Other values (7) 972
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20402
57.4%
ASCII 15129
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6640
43.9%
1 1393
 
9.2%
- 1356
 
9.0%
3 980
 
6.5%
2 930
 
6.1%
4 682
 
4.5%
5 554
 
3.7%
0 533
 
3.5%
6 526
 
3.5%
9 503
 
3.3%
Other values (28) 1032
 
6.8%
Hangul
ValueCountFrequency (%)
동 3089
15.1%
문 1648
 
8.1%
대 1543
 
7.6%
시 1530
 
7.5%
서 1515
 
7.4%
특 1515
 
7.4%
별 1515
 
7.4%
구 1515
 
7.4%
울 1515
 
7.4%
안 413
 
2.0%
Other values (198) 4604
22.6%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
2024-03-27
1515 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-27
2nd row2024-03-27
3rd row2024-03-27
4th row2024-03-27
5th row2024-03-27

Common Values

ValueCountFrequency (%)
2024-03-27 1515
100.0%

Length

2024-04-21T11:55:29.869684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:55:29.957745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-27 1515
100.0%

Interactions

2024-04-21T11:55:27.160707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:55:30.009484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명우편번호(도로명)
업종명1.0000.213
우편번호(도로명)0.2131.000
2024-04-21T11:55:30.084516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호(도로명)업종명
우편번호(도로명)1.0000.080
업종명0.0801.000

Missing values

2024-04-21T11:55:27.310738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:55:27.415750image/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

업종명업소명영업소 주소(도로명)우편번호(도로명)영업소 주소(지번)데이터기준일자
0숙박업(일반)M(엠)모텔서울특별시 동대문구 하정로 48 (신설동)2582서울특별시 동대문구 신설동 89-892024-03-27
1숙박업(일반)한양서울특별시 동대문구 왕산로 112-12 (용두동)2566서울특별시 동대문구 용두동 711-132024-03-27
2숙박업(일반)신흥서울특별시 동대문구 답십리로 48-7 (전농동)2591서울특별시 동대문구 전농동 643-882024-03-27
3숙박업(일반)칠성서울특별시 동대문구 답십리로 48-9 (전농동)2591서울특별시 동대문구 전농동 643-932024-03-27
4숙박업(일반)덕성서울특별시 동대문구 홍릉로8길 2-3 (청량리동)2484서울특별시 동대문구 청량리동 330-02024-03-27
5숙박업(일반)동대여인숙서울특별시 동대문구 홍릉로3길 13 (제기동)2573서울특별시 동대문구 제기동 486-42024-03-27
6숙박업(일반)동림여인숙서울특별시 동대문구 답십리로19길 22-1 (전농동)2558서울특별시 동대문구 전농동 494-142024-03-27
7숙박업(일반)서울서울특별시 동대문구 홍릉로1가길 1-4 (제기동)2572서울특별시 동대문구 제기동 632-172024-03-27
8숙박업(일반)동아여관서울특별시 동대문구 왕산로28길 3 (용두동)2560서울특별시 동대문구 용두동 23-252024-03-27
9숙박업(일반)우정파크서울특별시 동대문구 천호대로45가길 46 (용두동)2562서울특별시 동대문구 용두동 39-772024-03-27
업종명업소명영업소 주소(도로명)우편번호(도로명)영업소 주소(지번)데이터기준일자
1505일반미용업, 네일미용업, 화장ㆍ분장 미용업반유니서울특별시 동대문구 고산자로34길 70, 청량리역 해링턴플레이스 3층 302호 (용두동)2560서울특별시 동대문구 용두동 797 청량리역 해링턴플레이스2024-03-27
1506일반미용업, 네일미용업, 화장ㆍ분장 미용업에이바헤어 답십리역점서울특별시 동대문구 천호대로 241, 2층 3호 (답십리동, 청계벽산메가트리움)2603서울특별시 동대문구 답십리동 999 청계벽산메가트리움2024-03-27
1507일반미용업, 네일미용업, 화장ㆍ분장 미용업크랭크헤어서울특별시 동대문구 홍릉로 28, 성일빌딩 3층 (청량리동)2490서울특별시 동대문구 청량리동 317 성일빌딩2024-03-27
1508일반미용업, 네일미용업, 화장ㆍ분장 미용업르데이헤어 장안점서울특별시 동대문구 장한로 29, 4층 (장안동)2629서울특별시 동대문구 장안동 430-82024-03-27
1509피부미용업, 네일미용업, 화장ㆍ분장 미용업네일벨리서울특별시 동대문구 답십리로 305, 1층 (장안동)2524서울특별시 동대문구 장안동 331-32024-03-27
1510피부미용업, 네일미용업, 화장ㆍ분장 미용업뷰티끄퀸서울특별시 동대문구 약령시로 6, 2층 (제기동)2574서울특별시 동대문구 제기동 148-202024-03-27
1511피부미용업, 네일미용업, 화장ㆍ분장 미용업수놓다서울특별시 동대문구 장한로 124 (장안동)2637서울특별시 동대문구 장안동 367-72024-03-27
1512피부미용업, 네일미용업, 화장ㆍ분장 미용업다올네일서울특별시 동대문구 전농로 124-4, 1층 (전농동)2531서울특별시 동대문구 전농동 15-462024-03-27
1513피부미용업, 네일미용업, 화장ㆍ분장 미용업니니샵서울특별시 동대문구 전농로10길 20, 상가동 2층 210호 (답십리동, 답십리청솔우성아파트)2536서울특별시 동대문구 답십리동 80 답십리청솔우성아파트2024-03-27
1514피부미용업, 네일미용업, 화장ㆍ분장 미용업큰날 the great day#서울특별시 동대문구 장한로10길 61, 1층 (장안동)2642서울특별시 동대문구 장안동 439-102024-03-27