Overview

Dataset statistics

Number of variables6
Number of observations1165
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.9 KiB
Average record size in memory49.1 B

Variable types

Numeric1
Categorical2
Text3

Dataset

Description서울특별시 중랑구의 공중위생업 중 미용업에관한 현황자료입니다. 데이터구성은 업종명, 업소명, 영업소주소(도로명),영업소주소(지번),업태명으로 구성되어있습니다. 참고해주시기바랍니다. 감사합니다.
Author서울특별시 중랑구
URLhttps://www.data.go.kr/data/15006943/fileData.do

Alerts

연번 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
업태명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:16:37.216286
Analysis finished2023-12-12 20:16:38.341072
Duration1.12 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean583
Minimum1
Maximum1165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-12-13T05:16:38.418383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile59.2
Q1292
median583
Q3874
95-th percentile1106.8
Maximum1165
Range1164
Interquartile range (IQR)582

Descriptive statistics

Standard deviation336.45084
Coefficient of variation (CV)0.57710264
Kurtosis-1.2
Mean583
Median Absolute Deviation (MAD)291
Skewness0
Sum679195
Variance113199.17
MonotonicityStrictly increasing
2023-12-13T05:16:38.607367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
802 1
 
0.1%
782 1
 
0.1%
781 1
 
0.1%
780 1
 
0.1%
779 1
 
0.1%
778 1
 
0.1%
777 1
 
0.1%
776 1
 
0.1%
775 1
 
0.1%
Other values (1155) 1155
99.1%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1165 1
0.1%
1164 1
0.1%
1163 1
0.1%
1162 1
0.1%
1161 1
0.1%
1160 1
0.1%
1159 1
0.1%
1158 1
0.1%
1157 1
0.1%
1156 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
일반미용업
433 
미용업
272 
피부미용업
129 
종합미용업
107 
네일미용업
97 
Other values (10)
127 

Length

Max length23
Median length5
Mean length5.6240343
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 433
37.2%
미용업 272
23.3%
피부미용업 129
 
11.1%
종합미용업 107
 
9.2%
네일미용업 97
 
8.3%
화장ㆍ분장 미용업 29
 
2.5%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 19
 
1.6%
네일미용업, 화장ㆍ분장 미용업 18
 
1.5%
일반미용업, 화장ㆍ분장 미용업 14
 
1.2%
피부미용업, 네일미용업 13
 
1.1%
Other values (5) 34
 
2.9%

Length

2023-12-13T05:16:38.804047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 483
34.8%
미용업 371
26.7%
피부미용업 165
 
11.9%
네일미용업 164
 
11.8%
종합미용업 107
 
7.7%
화장ㆍ분장 99
 
7.1%

업소명
Text

Distinct1121
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
2023-12-13T05:16:39.199452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length5.9527897
Min length1

Characters and Unicode

Total characters6935
Distinct characters562
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1087 ?
Unique (%)93.3%

Sample

1st row수진미용실
2nd row아폴로
3rd row김선희미용실
4th row정
5th row꽃가마
ValueCountFrequency (%)
헤어 36
 
2.4%
hair 20
 
1.3%
네일 18
 
1.2%
미용실 13
 
0.9%
에이바헤어 10
 
0.7%
nail 8
 
0.5%
뷰티 7
 
0.5%
헤어살롱 7
 
0.5%
머리사랑 6
 
0.4%
살롱 6
 
0.4%
Other values (1229) 1351
91.2%
2023-12-13T05:16:39.804022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 506
 
7.3%
헤 480
 
6.9%
317
 
4.6%
미 161
 
2.3%
이 156
 
2.2%
리 148
 
2.1%
스 132
 
1.9%
일 120
 
1.7%
네 116
 
1.7%
( 99
 
1.4%
Other values (552) 4700
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5527
79.7%
Lowercase Letter 454
 
6.5%
Uppercase Letter 341
 
4.9%
Space Separator 317
 
4.6%
Open Punctuation 100
 
1.4%
Close Punctuation 100
 
1.4%
Other Punctuation 64
 
0.9%
Decimal Number 29
 
0.4%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 506
 
9.2%
헤 480
 
8.7%
미 161
 
2.9%
이 156
 
2.8%
리 148
 
2.7%
스 132
 
2.4%
일 120
 
2.2%
네 116
 
2.1%
실 89
 
1.6%
아 86
 
1.6%
Other values (478) 3533
63.9%
Uppercase Letter
ValueCountFrequency (%)
A 35
 
10.3%
S 34
 
10.0%
H 28
 
8.2%
J 24
 
7.0%
N 24
 
7.0%
I 22
 
6.5%
L 19
 
5.6%
B 19
 
5.6%
E 18
 
5.3%
R 18
 
5.3%
Other values (15) 100
29.3%
Lowercase Letter
ValueCountFrequency (%)
a 59
13.0%
i 55
12.1%
e 51
11.2%
n 39
8.6%
o 34
 
7.5%
r 30
 
6.6%
l 26
 
5.7%
h 22
 
4.8%
y 21
 
4.6%
s 20
 
4.4%
Other values (13) 97
21.4%
Other Punctuation
ValueCountFrequency (%)
, 16
25.0%
# 11
17.2%
& 11
17.2%
. 9
14.1%
' 8
12.5%
· 3
 
4.7%
: 2
 
3.1%
; 1
 
1.6%
? 1
 
1.6%
! 1
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 8
27.6%
0 6
20.7%
3 3
 
10.3%
9 3
 
10.3%
2 3
 
10.3%
8 2
 
6.9%
5 2
 
6.9%
7 1
 
3.4%
4 1
 
3.4%
Open Punctuation
ValueCountFrequency (%)
( 99
99.0%
[ 1
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 99
99.0%
] 1
 
1.0%
Space Separator
ValueCountFrequency (%)
317
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5514
79.5%
Latin 795
 
11.5%
Common 613
 
8.8%
Han 13
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 506
 
9.2%
헤 480
 
8.7%
미 161
 
2.9%
이 156
 
2.8%
리 148
 
2.7%
스 132
 
2.4%
일 120
 
2.2%
네 116
 
2.1%
실 89
 
1.6%
아 86
 
1.6%
Other values (472) 3520
63.8%
Latin
ValueCountFrequency (%)
a 59
 
7.4%
i 55
 
6.9%
e 51
 
6.4%
n 39
 
4.9%
A 35
 
4.4%
o 34
 
4.3%
S 34
 
4.3%
r 30
 
3.8%
H 28
 
3.5%
l 26
 
3.3%
Other values (38) 404
50.8%
Common
ValueCountFrequency (%)
317
51.7%
( 99
 
16.2%
) 99
 
16.2%
, 16
 
2.6%
# 11
 
1.8%
& 11
 
1.8%
. 9
 
1.5%
1 8
 
1.3%
' 8
 
1.3%
0 6
 
1.0%
Other values (16) 29
 
4.7%
Han
ValueCountFrequency (%)
美 8
61.5%
手 1
 
7.7%
洙 1
 
7.7%
誠 1
 
7.7%
宇 1
 
7.7%
魂 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5514
79.5%
ASCII 1404
 
20.2%
CJK 13
 
0.2%
None 4
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 506
 
9.2%
헤 480
 
8.7%
미 161
 
2.9%
이 156
 
2.8%
리 148
 
2.7%
스 132
 
2.4%
일 120
 
2.2%
네 116
 
2.1%
실 89
 
1.6%
아 86
 
1.6%
Other values (472) 3520
63.8%
ASCII
ValueCountFrequency (%)
317
22.6%
( 99
 
7.1%
) 99
 
7.1%
a 59
 
4.2%
i 55
 
3.9%
e 51
 
3.6%
n 39
 
2.8%
A 35
 
2.5%
o 34
 
2.4%
S 34
 
2.4%
Other values (62) 582
41.5%
CJK
ValueCountFrequency (%)
美 8
61.5%
手 1
 
7.7%
洙 1
 
7.7%
誠 1
 
7.7%
宇 1
 
7.7%
魂 1
 
7.7%
None
ValueCountFrequency (%)
· 3
75.0%
＆ 1
 
25.0%

영업소 주소(도로명)
Text

Distinct1154
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
2023-12-13T05:16:40.180028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length52
Mean length31.481545
Min length9

Characters and Unicode

Total characters36676
Distinct characters319
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1143 ?
Unique (%)98.1%

Sample

1st row서울특별시 중랑구 사가정로46길 21-22 (면목동)
2nd row서울특별시 중랑구 사가정로50길 67 (면목동)
3rd row서울특별시 중랑구 상봉중앙로1나길 17 (상봉동)
4th row서울특별시 중랑구 상봉로 18 (면목동)
5th row서울특별시 중랑구 면목로73길 13 (면목동)
ValueCountFrequency (%)
서울특별시 1163
 
16.0%
중랑구 1163
 
16.0%
면목동 427
 
5.9%
1층 415
 
5.7%
망우동 150
 
2.1%
상봉동 149
 
2.1%
묵동 149
 
2.1%
중화동 120
 
1.7%
신내동 109
 
1.5%
2층 106
 
1.5%
Other values (1121) 3304
45.5%
2023-12-13T05:16:40.807896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6091
 
16.6%
1 1681
 
4.6%
동 1498
 
4.1%
중 1391
 
3.8%
랑 1229
 
3.4%
시 1190
 
3.2%
) 1173
 
3.2%
( 1173
 
3.2%
구 1169
 
3.2%
서 1168
 
3.2%
Other values (309) 18913
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20899
57.0%
Space Separator 6091
 
16.6%
Decimal Number 6041
 
16.5%
Close Punctuation 1173
 
3.2%
Open Punctuation 1173
 
3.2%
Other Punctuation 1043
 
2.8%
Uppercase Letter 124
 
0.3%
Dash Punctuation 110
 
0.3%
Lowercase Letter 15
 
< 0.1%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1498
 
7.2%
중 1391
 
6.7%
랑 1229
 
5.9%
시 1190
 
5.7%
구 1169
 
5.6%
서 1168
 
5.6%
울 1167
 
5.6%
별 1165
 
5.6%
특 1164
 
5.6%
로 1163
 
5.6%
Other values (260) 8595
41.1%
Uppercase Letter
ValueCountFrequency (%)
S 16
12.9%
A 15
12.1%
B 14
11.3%
E 13
10.5%
C 12
9.7%
O 6
 
4.8%
R 6
 
4.8%
Y 6
 
4.8%
L 5
 
4.0%
H 5
 
4.0%
Other values (8) 26
21.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
20.0%
a 2
13.3%
c 1
 
6.7%
l 1
 
6.7%
y 1
 
6.7%
i 1
 
6.7%
n 1
 
6.7%
s 1
 
6.7%
b 1
 
6.7%
k 1
 
6.7%
Other values (2) 2
13.3%
Decimal Number
ValueCountFrequency (%)
1 1681
27.8%
2 932
15.4%
3 626
 
10.4%
0 574
 
9.5%
4 501
 
8.3%
5 459
 
7.6%
6 356
 
5.9%
9 342
 
5.7%
7 302
 
5.0%
8 268
 
4.4%
Math Symbol
ValueCountFrequency (%)
~ 3
42.9%
< 2
28.6%
> 2
28.6%
Other Punctuation
ValueCountFrequency (%)
, 1040
99.7%
. 3
 
0.3%
Space Separator
ValueCountFrequency (%)
6091
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1173
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1173
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20898
57.0%
Common 15638
42.6%
Latin 139
 
0.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1498
 
7.2%
중 1391
 
6.7%
랑 1229
 
5.9%
시 1190
 
5.7%
구 1169
 
5.6%
서 1168
 
5.6%
울 1167
 
5.6%
별 1165
 
5.6%
특 1164
 
5.6%
로 1163
 
5.6%
Other values (259) 8594
41.1%
Latin
ValueCountFrequency (%)
S 16
 
11.5%
A 15
 
10.8%
B 14
 
10.1%
E 13
 
9.4%
C 12
 
8.6%
O 6
 
4.3%
R 6
 
4.3%
Y 6
 
4.3%
L 5
 
3.6%
H 5
 
3.6%
Other values (20) 41
29.5%
Common
ValueCountFrequency (%)
6091
38.9%
1 1681
 
10.7%
) 1173
 
7.5%
( 1173
 
7.5%
, 1040
 
6.7%
2 932
 
6.0%
3 626
 
4.0%
0 574
 
3.7%
4 501
 
3.2%
5 459
 
2.9%
Other values (9) 1388
 
8.9%
Han
ValueCountFrequency (%)
休 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20898
57.0%
ASCII 15777
43.0%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6091
38.6%
1 1681
 
10.7%
) 1173
 
7.4%
( 1173
 
7.4%
, 1040
 
6.6%
2 932
 
5.9%
3 626
 
4.0%
0 574
 
3.6%
4 501
 
3.2%
5 459
 
2.9%
Other values (39) 1527
 
9.7%
Hangul
ValueCountFrequency (%)
동 1498
 
7.2%
중 1391
 
6.7%
랑 1229
 
5.9%
시 1190
 
5.7%
구 1169
 
5.6%
서 1168
 
5.6%
울 1167
 
5.6%
별 1165
 
5.6%
특 1164
 
5.6%
로 1163
 
5.6%
Other values (259) 8594
41.1%
CJK
ValueCountFrequency (%)
休 1
100.0%

영업소 주소(지번)
Text

Distinct1054
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
2023-12-13T05:16:41.113714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length41
Mean length22.844635
Min length16

Characters and Unicode

Total characters26614
Distinct characters299
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique978 ?
Unique (%)83.9%

Sample

1st row서울특별시 중랑구 면목동 653-2
2nd row서울특별시 중랑구 면목동 353-8
3rd row서울특별시 중랑구 상봉동 190-103
4th row서울특별시 중랑구 면목동 44-17
5th row서울특별시 중랑구 면목동 120-10
ValueCountFrequency (%)
서울특별시 1165
22.4%
중랑구 1165
22.4%
면목동 447
 
8.6%
묵동 157
 
3.0%
망우동 156
 
3.0%
상봉동 152
 
2.9%
중화동 132
 
2.5%
신내동 122
 
2.3%
지상1층 22
 
0.4%
1층 18
 
0.3%
Other values (1310) 1659
31.9%
2023-12-13T05:16:41.566386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5148
19.3%
중 1307
 
4.9%
동 1230
 
4.6%
시 1187
 
4.5%
1 1183
 
4.4%
랑 1171
 
4.4%
서 1170
 
4.4%
구 1169
 
4.4%
울 1169
 
4.4%
별 1167
 
4.4%
Other values (289) 10713
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14821
55.7%
Decimal Number 5509
 
20.7%
Space Separator 5148
 
19.3%
Dash Punctuation 1025
 
3.9%
Uppercase Letter 72
 
0.3%
Lowercase Letter 13
 
< 0.1%
Other Punctuation 11
 
< 0.1%
Close Punctuation 7
 
< 0.1%
Open Punctuation 7
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 1307
 
8.8%
동 1230
 
8.3%
시 1187
 
8.0%
랑 1171
 
7.9%
서 1170
 
7.9%
구 1169
 
7.9%
울 1169
 
7.9%
별 1167
 
7.9%
특 1165
 
7.9%
면 451
 
3.0%
Other values (244) 3635
24.5%
Uppercase Letter
ValueCountFrequency (%)
S 10
13.9%
E 7
9.7%
Y 6
 
8.3%
B 5
 
6.9%
H 5
 
6.9%
O 5
 
6.9%
M 5
 
6.9%
T 4
 
5.6%
R 4
 
5.6%
A 4
 
5.6%
Other values (8) 17
23.6%
Lowercase Letter
ValueCountFrequency (%)
e 2
15.4%
a 2
15.4%
l 1
7.7%
s 1
7.7%
n 1
7.7%
i 1
7.7%
y 1
7.7%
f 1
7.7%
k 1
7.7%
t 1
7.7%
Decimal Number
ValueCountFrequency (%)
1 1183
21.5%
2 688
12.5%
4 590
10.7%
3 573
10.4%
6 496
9.0%
5 477
8.7%
0 459
 
8.3%
7 382
 
6.9%
8 350
 
6.4%
9 311
 
5.6%
Space Separator
ValueCountFrequency (%)
5148
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1025
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14820
55.7%
Common 11708
44.0%
Latin 85
 
0.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 1307
 
8.8%
동 1230
 
8.3%
시 1187
 
8.0%
랑 1171
 
7.9%
서 1170
 
7.9%
구 1169
 
7.9%
울 1169
 
7.9%
별 1167
 
7.9%
특 1165
 
7.9%
면 451
 
3.0%
Other values (243) 3634
24.5%
Latin
ValueCountFrequency (%)
S 10
 
11.8%
E 7
 
8.2%
Y 6
 
7.1%
B 5
 
5.9%
H 5
 
5.9%
O 5
 
5.9%
M 5
 
5.9%
T 4
 
4.7%
R 4
 
4.7%
A 4
 
4.7%
Other values (19) 30
35.3%
Common
ValueCountFrequency (%)
5148
44.0%
1 1183
 
10.1%
- 1025
 
8.8%
2 688
 
5.9%
4 590
 
5.0%
3 573
 
4.9%
6 496
 
4.2%
5 477
 
4.1%
0 459
 
3.9%
7 382
 
3.3%
Other values (6) 687
 
5.9%
Han
ValueCountFrequency (%)
休 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14820
55.7%
ASCII 11793
44.3%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5148
43.7%
1 1183
 
10.0%
- 1025
 
8.7%
2 688
 
5.8%
4 590
 
5.0%
3 573
 
4.9%
6 496
 
4.2%
5 477
 
4.0%
0 459
 
3.9%
7 382
 
3.2%
Other values (35) 772
 
6.5%
Hangul
ValueCountFrequency (%)
중 1307
 
8.8%
동 1230
 
8.3%
시 1187
 
8.0%
랑 1171
 
7.9%
서 1170
 
7.9%
구 1169
 
7.9%
울 1169
 
7.9%
별 1167
 
7.9%
특 1165
 
7.9%
면 451
 
3.0%
Other values (243) 3634
24.5%
CJK
ValueCountFrequency (%)
休 1
100.0%

업태명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
일반미용업
807 
피부미용업
160 
네일아트업
145 
메이크업업
 
44
기타
 
9

Length

Max length5
Median length5
Mean length4.976824
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 807
69.3%
피부미용업 160
 
13.7%
네일아트업 145
 
12.4%
메이크업업 44
 
3.8%
기타 9
 
0.8%

Length

2023-12-13T05:16:41.720172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:16:41.865414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 807
69.3%
피부미용업 160
 
13.7%
네일아트업 145
 
12.4%
메이크업업 44
 
3.8%
기타 9
 
0.8%

Interactions

2023-12-13T05:16:38.033870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:16:41.938240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명업태명
연번1.0000.9310.923
업종명0.9311.0000.974
업태명0.9230.9741.000
2023-12-13T05:16:42.044600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업종명
업태명1.0000.778
업종명0.7781.000
2023-12-13T05:16:42.144040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명업태명
연번1.0000.6860.631
업종명0.6861.0000.778
업태명0.6310.7781.000

Missing values

2023-12-13T05:16:38.174473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:16:38.290208image/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

연번업종명업소명영업소 주소(도로명)영업소 주소(지번)업태명
01미용업수진미용실서울특별시 중랑구 사가정로46길 21-22 (면목동)서울특별시 중랑구 면목동 653-2일반미용업
12미용업아폴로서울특별시 중랑구 사가정로50길 67 (면목동)서울특별시 중랑구 면목동 353-8일반미용업
23미용업김선희미용실서울특별시 중랑구 상봉중앙로1나길 17 (상봉동)서울특별시 중랑구 상봉동 190-103일반미용업
34미용업정서울특별시 중랑구 상봉로 18 (면목동)서울특별시 중랑구 면목동 44-17일반미용업
45미용업꽃가마서울특별시 중랑구 면목로73길 13 (면목동)서울특별시 중랑구 면목동 120-10일반미용업
56미용업박신영미용실서울특별시 중랑구 사가정로46길 37-41 (면목동)서울특별시 중랑구 면목동 655-14일반미용업
67미용업명동서울특별시 중랑구 상봉중앙로1가길 5 (상봉동)서울특별시 중랑구 상봉동 190-76일반미용업
78미용업서울미용실서울특별시 중랑구 동일로96길 20 (면목동,장안제일시장1층8호)서울특별시 중랑구 면목동 160-1 장안제일시장1층8호일반미용업
89미용업연정서울특별시 중랑구 면목로57길 15 (면목동)서울특별시 중랑구 면목동 501-13일반미용업
910미용업아람서울특별시 중랑구 동일로144길 14 (중화동,,13,14)서울특별시 중랑구 중화동 282-3 ,13,14일반미용업
연번업종명업소명영업소 주소(도로명)영업소 주소(지번)업태명
11551156일반미용업, 네일미용업, 화장ㆍ분장 미용업HAIRDRESSER by소영서울특별시 중랑구 망우로76길 33, 스테이빌 1층 (망우동)서울특별시 중랑구 망우동 149-34 스테이빌일반미용업
11561157일반미용업, 네일미용업, 화장ㆍ분장 미용업어반레스트서울특별시 중랑구 면목로55길 35, 1층 (면목동)서울특별시 중랑구 면목동 566-2일반미용업
11571158일반미용업, 네일미용업, 화장ㆍ분장 미용업에이유헤어(AU HAIR)서울특별시 중랑구 중랑역로 244, 엠제이타운 3층 (묵동)서울특별시 중랑구 묵동 234-15 엠제이타운일반미용업
11581159일반미용업, 네일미용업, 화장ㆍ분장 미용업글램아이래쉬서울특별시 중랑구 상봉로 39, MS빌딩 1층 1호 (면목동)서울특별시 중랑구 면목동 72-1 MS빌딩일반미용업
11591160피부미용업, 네일미용업, 화장ㆍ분장 미용업마레드네일(Mairie de nail)서울특별시 중랑구 상봉로 118, 4층 (망우동)서울특별시 중랑구 망우동 506-1피부미용업
11601161피부미용업, 네일미용업, 화장ㆍ분장 미용업멜팅모드(melting mode)서울특별시 중랑구 겸재로 164, 1층 (면목동)서울특별시 중랑구 면목동 118-1네일아트업
11611162피부미용업, 네일미용업, 화장ㆍ분장 미용업블링블링서울특별시 중랑구 봉우재로26길 7, 1층 (면목동)서울특별시 중랑구 면목동 132-27네일아트업
11621163피부미용업, 네일미용업, 화장ㆍ분장 미용업플로라서울특별시 중랑구 용마산로 440, 1층 (면목동)서울특별시 중랑구 면목동 17-1네일아트업
11631164피부미용업, 네일미용업, 화장ㆍ분장 미용업토유네일서울특별시 중랑구 동일로149길 45, 1층 좌측호 (묵동)서울특별시 중랑구 묵동 249-60피부미용업
11641165피부미용업, 네일미용업, 화장ㆍ분장 미용업에이린뷰티 면목서울특별시 중랑구 면목로 427, 2층 (면목동)서울특별시 중랑구 면목동 126-2피부미용업