Overview

Dataset statistics

Number of variables5
Number of observations125
Missing cells106
Missing cells (%)17.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory42.1 B

Variable types

Categorical1
Text3
Numeric1

Dataset

Description부산광역시 북구 관내 미용업소 중 네일미용업을 하는 업소 현황으로 업종명, 업소명, 영업소 소재지, 소재지전화번호 등의 정보를 제공합니다.
Author부산광역시 북구
URLhttps://www.data.go.kr/data/15112937/fileData.do

Alerts

업종명 is highly imbalanced (53.3%)Imbalance
소재지전화 has 106 (84.8%) missing valuesMissing
영업소 주소(도로명) has unique valuesUnique

Reproduction

Analysis started2024-03-23 06:49:39.821126
Analysis finished2024-03-23 06:49:42.244028
Duration2.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

IMBALANCE 

Distinct8
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
네일미용업
94 
네일미용업, 화장ㆍ분장 미용업
12 
피부미용업, 네일미용업
 
7
피부미용업, 네일미용업, 화장ㆍ분장 미용업
 
4
일반미용업
 
3
Other values (3)
 
5

Length

Max length23
Median length5
Mean length7.304
Min length5

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
네일미용업 94
75.2%
네일미용업, 화장ㆍ분장 미용업 12
 
9.6%
피부미용업, 네일미용업 7
 
5.6%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 4
 
3.2%
일반미용업 3
 
2.4%
종합미용업 2
 
1.6%
일반미용업, 피부미용업, 네일미용업 2
 
1.6%
일반미용업, 네일미용업 1
 
0.8%

Length

2024-03-23T06:49:42.657925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T06:49:43.250787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
네일미용업 120
69.4%
화장ㆍ분장 16
 
9.2%
미용업 16
 
9.2%
피부미용업 13
 
7.5%
일반미용업 6
 
3.5%
종합미용업 2
 
1.2%

업소명
Text

Distinct124
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-23T06:49:44.147649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length6.592
Min length2

Characters and Unicode

Total characters824
Distinct characters209
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123 ?
Unique (%)98.4%

Sample

1st row크리에이티브덕천점
2nd row제이오네일(Jo Nail)
3rd row씨네일
4th row한지윤네일
5th row오늘보다 예쁜, 네일
ValueCountFrequency (%)
네일 5
 
3.2%
nail 3
 
1.9%
썬네일 2
 
1.3%
네일은 2
 
1.3%
salon 2
 
1.3%
beauty 2
 
1.3%
화명점 2
 
1.3%
네일앤뷰티 2
 
1.3%
그레이진 1
 
0.6%
엔뷰티스튜디오 1
 
0.6%
Other values (136) 136
86.1%
2024-03-23T06:49:46.081851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
네 97
 
11.8%
일 96
 
11.7%
33
 
4.0%
n 20
 
2.4%
a 18
 
2.2%
) 18
 
2.2%
( 18
 
2.2%
티 17
 
2.1%
뷰 16
 
1.9%
이 15
 
1.8%
Other values (199) 476
57.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 562
68.2%
Lowercase Letter 113
 
13.7%
Uppercase Letter 61
 
7.4%
Space Separator 33
 
4.0%
Close Punctuation 19
 
2.3%
Open Punctuation 19
 
2.3%
Other Punctuation 13
 
1.6%
Connector Punctuation 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
네 97
 
17.3%
일 96
 
17.1%
티 17
 
3.0%
뷰 16
 
2.8%
이 15
 
2.7%
아 8
 
1.4%
라 8
 
1.4%
드 8
 
1.4%
리 7
 
1.2%
다 7
 
1.2%
Other values (152) 283
50.4%
Uppercase Letter
ValueCountFrequency (%)
N 10
16.4%
I 6
 
9.8%
A 4
 
6.6%
L 4
 
6.6%
D 4
 
6.6%
Y 4
 
6.6%
S 3
 
4.9%
H 3
 
4.9%
O 3
 
4.9%
M 3
 
4.9%
Other values (11) 17
27.9%
Lowercase Letter
ValueCountFrequency (%)
n 20
17.7%
a 18
15.9%
i 14
12.4%
l 14
12.4%
e 9
8.0%
o 8
 
7.1%
y 6
 
5.3%
u 5
 
4.4%
d 4
 
3.5%
t 3
 
2.7%
Other values (7) 12
10.6%
Other Punctuation
ValueCountFrequency (%)
, 9
69.2%
' 2
 
15.4%
. 2
 
15.4%
Close Punctuation
ValueCountFrequency (%)
) 18
94.7%
] 1
 
5.3%
Open Punctuation
ValueCountFrequency (%)
( 18
94.7%
[ 1
 
5.3%
Space Separator
ValueCountFrequency (%)
33
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 561
68.1%
Latin 174
 
21.1%
Common 88
 
10.7%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
네 97
 
17.3%
일 96
 
17.1%
티 17
 
3.0%
뷰 16
 
2.9%
이 15
 
2.7%
아 8
 
1.4%
라 8
 
1.4%
드 8
 
1.4%
리 7
 
1.2%
다 7
 
1.2%
Other values (151) 282
50.3%
Latin
ValueCountFrequency (%)
n 20
 
11.5%
a 18
 
10.3%
i 14
 
8.0%
l 14
 
8.0%
N 10
 
5.7%
e 9
 
5.2%
o 8
 
4.6%
y 6
 
3.4%
I 6
 
3.4%
u 5
 
2.9%
Other values (28) 64
36.8%
Common
ValueCountFrequency (%)
33
37.5%
) 18
20.5%
( 18
20.5%
, 9
 
10.2%
_ 4
 
4.5%
' 2
 
2.3%
. 2
 
2.3%
] 1
 
1.1%
[ 1
 
1.1%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 561
68.1%
ASCII 262
31.8%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
네 97
 
17.3%
일 96
 
17.1%
티 17
 
3.0%
뷰 16
 
2.9%
이 15
 
2.7%
아 8
 
1.4%
라 8
 
1.4%
드 8
 
1.4%
리 7
 
1.2%
다 7
 
1.2%
Other values (151) 282
50.3%
ASCII
ValueCountFrequency (%)
33
 
12.6%
n 20
 
7.6%
a 18
 
6.9%
) 18
 
6.9%
( 18
 
6.9%
i 14
 
5.3%
l 14
 
5.3%
N 10
 
3.8%
e 9
 
3.4%
, 9
 
3.4%
Other values (37) 99
37.8%
CJK
ValueCountFrequency (%)
愛 1
100.0%

영업소 주소(도로명)
Text

UNIQUE 

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-23T06:49:47.109453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length44
Mean length36.112
Min length21

Characters and Unicode

Total characters4514
Distinct characters167
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)100.0%

Sample

1st row부산광역시 북구 만덕대로 23 (덕천동,뉴코아 3층)
2nd row부산광역시 북구 금곡대로 238, 지하1층 104호 (화명동, 롯데캐슬멤버스)
3rd row부산광역시 북구 화명대로 47, 4층 (화명동, 롯데마트화명점)
4th row부산광역시 북구 만덕대로40번길 9, 1층 (덕천동)
5th row부산광역시 북구 백양대로1050번길 11 (구포동)
ValueCountFrequency (%)
부산광역시 125
 
14.1%
북구 125
 
14.1%
1층 58
 
6.5%
화명동 41
 
4.6%
덕천동 38
 
4.3%
구포동 22
 
2.5%
만덕동 20
 
2.3%
금곡대로 13
 
1.5%
2층 12
 
1.4%
백양대로 10
 
1.1%
Other values (265) 422
47.6%
2024-03-23T06:49:48.673071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
761
 
16.9%
1 224
 
5.0%
, 162
 
3.6%
동 160
 
3.5%
구 147
 
3.3%
부 143
 
3.2%
시 139
 
3.1%
산 135
 
3.0%
( 127
 
2.8%
) 127
 
2.8%
Other values (157) 2389
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2555
56.6%
Space Separator 761
 
16.9%
Decimal Number 761
 
16.9%
Other Punctuation 162
 
3.6%
Open Punctuation 127
 
2.8%
Close Punctuation 127
 
2.8%
Dash Punctuation 14
 
0.3%
Uppercase Letter 6
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 160
 
6.3%
구 147
 
5.8%
부 143
 
5.6%
시 139
 
5.4%
산 135
 
5.3%
역 127
 
5.0%
북 127
 
5.0%
광 126
 
4.9%
로 124
 
4.9%
덕 102
 
4.0%
Other values (136) 1225
47.9%
Decimal Number
ValueCountFrequency (%)
1 224
29.4%
2 121
15.9%
3 92
12.1%
0 90
11.8%
4 47
 
6.2%
6 43
 
5.7%
5 43
 
5.7%
8 39
 
5.1%
7 34
 
4.5%
9 28
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
D 2
33.3%
A 1
16.7%
C 1
16.7%
L 1
16.7%
H 1
16.7%
Space Separator
ValueCountFrequency (%)
761
100.0%
Other Punctuation
ValueCountFrequency (%)
, 162
100.0%
Open Punctuation
ValueCountFrequency (%)
( 127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 127
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2555
56.6%
Common 1952
43.2%
Latin 7
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 160
 
6.3%
구 147
 
5.8%
부 143
 
5.6%
시 139
 
5.4%
산 135
 
5.3%
역 127
 
5.0%
북 127
 
5.0%
광 126
 
4.9%
로 124
 
4.9%
덕 102
 
4.0%
Other values (136) 1225
47.9%
Common
ValueCountFrequency (%)
761
39.0%
1 224
 
11.5%
, 162
 
8.3%
( 127
 
6.5%
) 127
 
6.5%
2 121
 
6.2%
3 92
 
4.7%
0 90
 
4.6%
4 47
 
2.4%
6 43
 
2.2%
Other values (5) 158
 
8.1%
Latin
ValueCountFrequency (%)
D 2
28.6%
c 1
14.3%
A 1
14.3%
C 1
14.3%
L 1
14.3%
H 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2555
56.6%
ASCII 1959
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
761
38.8%
1 224
 
11.4%
, 162
 
8.3%
( 127
 
6.5%
) 127
 
6.5%
2 121
 
6.2%
3 92
 
4.7%
0 90
 
4.6%
4 47
 
2.4%
6 43
 
2.2%
Other values (11) 165
 
8.4%
Hangul
ValueCountFrequency (%)
동 160
 
6.3%
구 147
 
5.8%
부 143
 
5.6%
시 139
 
5.4%
산 135
 
5.3%
역 127
 
5.0%
북 127
 
5.0%
광 126
 
4.9%
로 124
 
4.9%
덕 102
 
4.0%
Other values (136) 1225
47.9%

우편번호(도로명)
Real number (ℝ)

Distinct51
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46565.64
Minimum46502
Maximum46651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-23T06:49:49.307397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46502
5-th percentile46524.2
Q146532
median46554
Q346599
95-th percentile46641.2
Maximum46651
Range149
Interquartile range (IQR)67

Descriptive statistics

Standard deviation39.866997
Coefficient of variation (CV)0.00085614622
Kurtosis-0.66612184
Mean46565.64
Median Absolute Deviation (MAD)24
Skewness0.70088732
Sum5820705
Variance1589.3774
MonotonicityNot monotonic
2024-03-23T06:49:49.776216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46548 14
 
11.2%
46526 8
 
6.4%
46527 6
 
4.8%
46525 6
 
4.8%
46576 6
 
4.8%
46567 5
 
4.0%
46650 5
 
4.0%
46578 4
 
3.2%
46532 4
 
3.2%
46612 4
 
3.2%
Other values (41) 63
50.4%
ValueCountFrequency (%)
46502 1
 
0.8%
46506 2
 
1.6%
46520 1
 
0.8%
46524 3
 
2.4%
46525 6
4.8%
46526 8
6.4%
46527 6
4.8%
46528 1
 
0.8%
46531 2
 
1.6%
46532 4
3.2%
ValueCountFrequency (%)
46651 1
 
0.8%
46650 5
4.0%
46642 1
 
0.8%
46638 1
 
0.8%
46637 2
 
1.6%
46635 1
 
0.8%
46632 1
 
0.8%
46630 3
2.4%
46623 1
 
0.8%
46622 1
 
0.8%

소재지전화
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing106
Missing (%)84.8%
Memory size1.1 KiB
2024-03-23T06:49:50.363532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters228
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row051-366-2277
2nd row051-363-4143
3rd row051-337-8160
4th row051-341-8661
5th row051-338-1084
ValueCountFrequency (%)
051-366-2277 1
 
5.3%
051-361-4179 1
 
5.3%
051-959-6400 1
 
5.3%
051-911-5975 1
 
5.3%
051-331-7657 1
 
5.3%
051-363-2001 1
 
5.3%
051-337-3111 1
 
5.3%
051-907-9999 1
 
5.3%
051-337-1637 1
 
5.3%
051-341-4107 1
 
5.3%
Other values (9) 9
47.4%
2024-03-23T06:49:51.488326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 40
17.5%
- 38
16.7%
3 31
13.6%
0 27
11.8%
5 26
11.4%
6 16
 
7.0%
7 15
 
6.6%
4 10
 
4.4%
9 10
 
4.4%
2 8
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190
83.3%
Dash Punctuation 38
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 40
21.1%
3 31
16.3%
0 27
14.2%
5 26
13.7%
6 16
 
8.4%
7 15
 
7.9%
4 10
 
5.3%
9 10
 
5.3%
2 8
 
4.2%
8 7
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 40
17.5%
- 38
16.7%
3 31
13.6%
0 27
11.8%
5 26
11.4%
6 16
 
7.0%
7 15
 
6.6%
4 10
 
4.4%
9 10
 
4.4%
2 8
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 40
17.5%
- 38
16.7%
3 31
13.6%
0 27
11.8%
5 26
11.4%
6 16
 
7.0%
7 15
 
6.6%
4 10
 
4.4%
9 10
 
4.4%
2 8
 
3.5%

Interactions

2024-03-23T06:49:41.089282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:49:51.918781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명우편번호(도로명)소재지전화
업종명1.0000.0001.000
우편번호(도로명)0.0001.0001.000
소재지전화1.0001.0001.000
2024-03-23T06:49:52.297909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호(도로명)업종명
우편번호(도로명)1.0000.000
업종명0.0001.000

Missing values

2024-03-23T06:49:41.856191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:49:42.135707image/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)소재지전화
0일반미용업크리에이티브덕천점부산광역시 북구 만덕대로 23 (덕천동,뉴코아 3층)46548051-366-2277
1일반미용업제이오네일(Jo Nail)부산광역시 북구 금곡대로 238, 지하1층 104호 (화명동, 롯데캐슬멤버스)46537051-363-4143
2일반미용업씨네일부산광역시 북구 화명대로 47, 4층 (화명동, 롯데마트화명점)46525051-337-8160
3종합미용업한지윤네일부산광역시 북구 만덕대로40번길 9, 1층 (덕천동)46577<NA>
4종합미용업오늘보다 예쁜, 네일부산광역시 북구 백양대로1050번길 11 (구포동)46635<NA>
5네일미용업엔비(ENVY)부산광역시 북구 만덕대로155번길 15, 삼정그린코아상가2동 109호 (덕천동)46554051-341-8661
6네일미용업영네일부산광역시 북구 와석장터로 6-11 (화명동)46532<NA>
7네일미용업하트부산광역시 북구 덕천2길 11-1, 1층 (덕천동)46576051-338-1084
8네일미용업라라모어 네일앤뷰티부산광역시 북구 금곡대로285번길 19, 208호 (화명동, 리버사이드빌딩)46526051-365-1213
9네일미용업아이엠베스트 화명점부산광역시 북구 화명대로 17, 104호 (화명동, 목양프라자)46524051-362-7477
업종명업소명영업소 주소(도로명)우편번호(도로명)소재지전화
115네일미용업, 화장ㆍ분장 미용업네일그림부산광역시 북구 만덕1로 33, 1층 (만덕동)46561<NA>
116네일미용업, 화장ㆍ분장 미용업단미뷰티부산광역시 북구 금곡대로303번길 80, 코아프라자 1층 103일부호 (화명동)46524<NA>
117네일미용업, 화장ㆍ분장 미용업뷰티인(Beauty_in)부산광역시 북구 화명신도시로 117, 메트로프라자 301일부호 (화명동)46527<NA>
118네일미용업, 화장ㆍ분장 미용업네일로아부산광역시 북구 금곡대로 166, 903동 1층 117호 (화명동, 롯데캐슬카이저)46539<NA>
119일반미용업, 피부미용업, 네일미용업네일하우스부산광역시 북구 만덕대로 28, 3층 (덕천동)46576<NA>
120일반미용업, 피부미용업, 네일미용업포레나뷰티부산광역시 북구 의성로109번길 24, 2층 (덕천동)46567<NA>
121피부미용업, 네일미용업, 화장ㆍ분장 미용업이후진샵부산광역시 북구 백양대로 1200, 5층 (덕천동)46576051-338-6688
122피부미용업, 네일미용업, 화장ㆍ분장 미용업라피네 쉬끄부산광역시 북구 금곡대로8번길 33, 2층 94,95(일부)호 (덕천동, 아남프라자)46548<NA>
123피부미용업, 네일미용업, 화장ㆍ분장 미용업윤이나뷰티(Yunina beauty)부산광역시 북구 화명신도시로 115, 성문타워 202 일부호 (화명동)46527<NA>
124피부미용업, 네일미용업, 화장ㆍ분장 미용업네일인(Inn) 가영부산광역시 북구 은행나무로 25, 1층 (만덕동)46614<NA>