Overview

Dataset statistics

Number of variables5
Number of observations118
Missing cells94
Missing cells (%)15.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory42.1 B

Variable types

Categorical1
Text3
Numeric1

Dataset

Description부산광역시북구_네일미용업현황_20230323
Author부산광역시 북구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15112937

Alerts

업종명 is highly imbalanced (52.7%)Imbalance
소재지전화 has 94 (79.7%) missing valuesMissing
영업소 주소(도로명) has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:51:48.944896
Analysis finished2023-12-10 16:51:49.588999
Duration0.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

IMBALANCE 

Distinct7
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
네일미용업
89 
네일미용업, 화장ㆍ분장 미용업
12 
피부미용업, 네일미용업
 
7
일반미용업
 
5
피부미용업, 네일미용업, 화장ㆍ분장 미용업
 
3
Other values (2)
 
2

Length

Max length23
Median length5
Mean length7.1101695
Min length5

Unique

Unique2 ?
Unique (%)1.7%

Sample

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

Common Values

ValueCountFrequency (%)
네일미용업 89
75.4%
네일미용업, 화장ㆍ분장 미용업 12
 
10.2%
피부미용업, 네일미용업 7
 
5.9%
일반미용업 5
 
4.2%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 3
 
2.5%
종합미용업 1
 
0.8%
일반미용업, 피부미용업, 네일미용업 1
 
0.8%

Length

2023-12-11T01:51:49.705797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:51:49.869920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
네일미용업 112
70.0%
화장ㆍ분장 15
 
9.4%
미용업 15
 
9.4%
피부미용업 11
 
6.9%
일반미용업 6
 
3.8%
종합미용업 1
 
0.6%

업소명
Text

Distinct117
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T01:51:50.233035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length6.7627119
Min length2

Characters and Unicode

Total characters798
Distinct characters203
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

Unique116 ?
Unique (%)98.3%

Sample

1st row네일데이지
2nd row크리에이티브덕천점
3rd row데이지뷰티
4th row제이오네일(Jo Nail)
5th row씨네일
ValueCountFrequency (%)
네일 5
 
3.3%
nail 5
 
3.3%
썬네일 2
 
1.3%
beauty 2
 
1.3%
네일은 2
 
1.3%
화명점 2
 
1.3%
네일앤뷰티 2
 
1.3%
salon 2
 
1.3%
라라뷰티살롱 1
 
0.7%
유니네일 1
 
0.7%
Other values (129) 129
84.3%
2023-12-11T01:51:50.921984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
일 93
 
11.7%
네 93
 
11.7%
35
 
4.4%
a 20
 
2.5%
( 18
 
2.3%
) 18
 
2.3%
l 16
 
2.0%
i 16
 
2.0%
n 16
 
2.0%
이 15
 
1.9%
Other values (193) 458
57.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 526
65.9%
Lowercase Letter 117
 
14.7%
Uppercase Letter 66
 
8.3%
Space Separator 35
 
4.4%
Open Punctuation 19
 
2.4%
Close Punctuation 19
 
2.4%
Other Punctuation 12
 
1.5%
Connector Punctuation 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 93
 
17.7%
네 93
 
17.7%
이 15
 
2.9%
티 14
 
2.7%
뷰 13
 
2.5%
아 10
 
1.9%
다 8
 
1.5%
드 7
 
1.3%
라 7
 
1.3%
나 6
 
1.1%
Other values (146) 260
49.4%
Uppercase Letter
ValueCountFrequency (%)
N 13
19.7%
D 6
 
9.1%
I 5
 
7.6%
L 4
 
6.1%
A 4
 
6.1%
Y 4
 
6.1%
S 3
 
4.5%
H 3
 
4.5%
M 3
 
4.5%
O 3
 
4.5%
Other values (11) 18
27.3%
Lowercase Letter
ValueCountFrequency (%)
a 20
17.1%
l 16
13.7%
i 16
13.7%
n 16
13.7%
e 9
7.7%
o 8
 
6.8%
u 7
 
6.0%
y 6
 
5.1%
b 3
 
2.6%
t 3
 
2.6%
Other values (7) 13
11.1%
Other Punctuation
ValueCountFrequency (%)
, 8
66.7%
. 2
 
16.7%
' 2
 
16.7%
Open Punctuation
ValueCountFrequency (%)
( 18
94.7%
[ 1
 
5.3%
Close Punctuation
ValueCountFrequency (%)
) 18
94.7%
] 1
 
5.3%
Space Separator
ValueCountFrequency (%)
35
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 525
65.8%
Latin 183
 
22.9%
Common 89
 
11.2%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 93
 
17.7%
네 93
 
17.7%
이 15
 
2.9%
티 14
 
2.7%
뷰 13
 
2.5%
아 10
 
1.9%
다 8
 
1.5%
드 7
 
1.3%
라 7
 
1.3%
나 6
 
1.1%
Other values (145) 259
49.3%
Latin
ValueCountFrequency (%)
a 20
 
10.9%
l 16
 
8.7%
i 16
 
8.7%
n 16
 
8.7%
N 13
 
7.1%
e 9
 
4.9%
o 8
 
4.4%
u 7
 
3.8%
D 6
 
3.3%
y 6
 
3.3%
Other values (28) 66
36.1%
Common
ValueCountFrequency (%)
35
39.3%
( 18
20.2%
) 18
20.2%
, 8
 
9.0%
_ 4
 
4.5%
. 2
 
2.2%
' 2
 
2.2%
[ 1
 
1.1%
] 1
 
1.1%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 525
65.8%
ASCII 272
34.1%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
일 93
 
17.7%
네 93
 
17.7%
이 15
 
2.9%
티 14
 
2.7%
뷰 13
 
2.5%
아 10
 
1.9%
다 8
 
1.5%
드 7
 
1.3%
라 7
 
1.3%
나 6
 
1.1%
Other values (145) 259
49.3%
ASCII
ValueCountFrequency (%)
35
 
12.9%
a 20
 
7.4%
( 18
 
6.6%
) 18
 
6.6%
l 16
 
5.9%
i 16
 
5.9%
n 16
 
5.9%
N 13
 
4.8%
e 9
 
3.3%
, 8
 
2.9%
Other values (37) 103
37.9%
CJK
ValueCountFrequency (%)
愛 1
100.0%

영업소 주소(도로명)
Text

UNIQUE 

Distinct118
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T01:51:51.300823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length46
Mean length36.644068
Min length21

Characters and Unicode

Total characters4324
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

Unique118 ?
Unique (%)100.0%

Sample

1st row부산광역시 북구 덕천로234번길 41, 상가동 2-1호 (만덕동, 만덕그린코아)
2nd row부산광역시 북구 만덕대로 23 (덕천동,뉴코아 3층)
3rd row부산광역시 북구 만덕대로 32, 1(일부),2층 (덕천동)
4th row부산광역시 북구 금곡대로 238, 지하1층 104호 (화명동, 롯데캐슬멤버스)
5th row부산광역시 북구 화명대로 47, 4층 (화명동, 롯데마트화명점)
ValueCountFrequency (%)
부산광역시 118
 
14.0%
북구 118
 
14.0%
1층 55
 
6.5%
화명동 38
 
4.5%
덕천동 34
 
4.0%
구포동 23
 
2.7%
만덕동 19
 
2.3%
금곡대로 12
 
1.4%
상가동 11
 
1.3%
2층 10
 
1.2%
Other values (264) 403
47.9%
2023-12-11T01:51:52.138526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
723
 
16.7%
1 217
 
5.0%
동 157
 
3.6%
, 156
 
3.6%
구 142
 
3.3%
부 137
 
3.2%
시 132
 
3.1%
산 128
 
3.0%
( 122
 
2.8%
) 122
 
2.8%
Other values (157) 2288
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2443
56.5%
Decimal Number 732
 
16.9%
Space Separator 723
 
16.7%
Other Punctuation 156
 
3.6%
Open Punctuation 122
 
2.8%
Close Punctuation 122
 
2.8%
Dash Punctuation 18
 
0.4%
Uppercase Letter 7
 
0.2%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 157
 
6.4%
구 142
 
5.8%
부 137
 
5.6%
시 132
 
5.4%
산 128
 
5.2%
역 120
 
4.9%
북 120
 
4.9%
광 119
 
4.9%
로 117
 
4.8%
덕 97
 
4.0%
Other values (135) 1174
48.1%
Decimal Number
ValueCountFrequency (%)
1 217
29.6%
2 118
16.1%
3 88
12.0%
0 83
 
11.3%
4 46
 
6.3%
6 43
 
5.9%
8 42
 
5.7%
5 40
 
5.5%
7 31
 
4.2%
9 24
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
D 2
28.6%
C 1
14.3%
A 1
14.3%
B 1
14.3%
L 1
14.3%
H 1
14.3%
Space Separator
ValueCountFrequency (%)
723
100.0%
Other Punctuation
ValueCountFrequency (%)
, 156
100.0%
Open Punctuation
ValueCountFrequency (%)
( 122
100.0%
Close Punctuation
ValueCountFrequency (%)
) 122
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2443
56.5%
Common 1873
43.3%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 157
 
6.4%
구 142
 
5.8%
부 137
 
5.6%
시 132
 
5.4%
산 128
 
5.2%
역 120
 
4.9%
북 120
 
4.9%
광 119
 
4.9%
로 117
 
4.8%
덕 97
 
4.0%
Other values (135) 1174
48.1%
Common
ValueCountFrequency (%)
723
38.6%
1 217
 
11.6%
, 156
 
8.3%
( 122
 
6.5%
) 122
 
6.5%
2 118
 
6.3%
3 88
 
4.7%
0 83
 
4.4%
4 46
 
2.5%
6 43
 
2.3%
Other values (5) 155
 
8.3%
Latin
ValueCountFrequency (%)
D 2
25.0%
C 1
12.5%
A 1
12.5%
B 1
12.5%
c 1
12.5%
L 1
12.5%
H 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2443
56.5%
ASCII 1881
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
723
38.4%
1 217
 
11.5%
, 156
 
8.3%
( 122
 
6.5%
) 122
 
6.5%
2 118
 
6.3%
3 88
 
4.7%
0 83
 
4.4%
4 46
 
2.4%
6 43
 
2.3%
Other values (12) 163
 
8.7%
Hangul
ValueCountFrequency (%)
동 157
 
6.4%
구 142
 
5.8%
부 137
 
5.6%
시 132
 
5.4%
산 128
 
5.2%
역 120
 
4.9%
북 120
 
4.9%
광 119
 
4.9%
로 117
 
4.8%
덕 97
 
4.0%
Other values (135) 1174
48.1%

우편번호(도로명)
Real number (ℝ)

Distinct53
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46566.102
Minimum46501
Maximum46651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T01:51:52.349862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46501
5-th percentile46524
Q146532
median46554
Q346602
95-th percentile46638.6
Maximum46651
Range150
Interquartile range (IQR)70

Descriptive statistics

Standard deviation40.446523
Coefficient of variation (CV)0.00086858296
Kurtosis-0.83188203
Mean46566.102
Median Absolute Deviation (MAD)27
Skewness0.59150443
Sum5494800
Variance1635.9212
MonotonicityNot monotonic
2023-12-11T01:51:52.509602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46548 13
 
11.0%
46526 8
 
6.8%
46576 6
 
5.1%
46527 6
 
5.1%
46525 5
 
4.2%
46578 4
 
3.4%
46612 4
 
3.4%
46650 3
 
2.5%
46536 3
 
2.5%
46602 3
 
2.5%
Other values (43) 63
53.4%
ValueCountFrequency (%)
46501 1
 
0.8%
46502 1
 
0.8%
46506 2
 
1.7%
46520 1
 
0.8%
46524 3
 
2.5%
46525 5
4.2%
46526 8
6.8%
46527 6
5.1%
46528 1
 
0.8%
46531 1
 
0.8%
ValueCountFrequency (%)
46651 1
 
0.8%
46650 3
2.5%
46643 1
 
0.8%
46642 1
 
0.8%
46638 1
 
0.8%
46637 2
1.7%
46635 1
 
0.8%
46632 1
 
0.8%
46630 3
2.5%
46628 1
 
0.8%

소재지전화
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing94
Missing (%)79.7%
Memory size1.1 KiB
2023-12-11T01:51:52.754664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.208333
Min length12

Characters and Unicode

Total characters293
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

Unique24 ?
Unique (%)100.0%

Sample

1st row051-335-9930
2nd row051-366-2277
3rd row051-337-4111
4th row051-363-4143
5th row051-337-8160
ValueCountFrequency (%)
051-366-2277 1
 
4.2%
051-337-4111 1
 
4.2%
051-911-5975 1
 
4.2%
051-331-7657 1
 
4.2%
051-363-2001 1
 
4.2%
051-337-3111 1
 
4.2%
051-907-9999 1
 
4.2%
051-337-1637 1
 
4.2%
051-361-4179 1
 
4.2%
051-959-6400 1
 
4.2%
Other values (14) 14
58.3%
2023-12-11T01:51:53.137113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 48
16.4%
- 48
16.4%
3 38
13.0%
0 35
11.9%
5 34
11.6%
7 22
7.5%
4 17
 
5.8%
6 16
 
5.5%
9 12
 
4.1%
2 10
 
3.4%
Other values (2) 13
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
82.6%
Dash Punctuation 48
 
16.4%
Space Separator 3
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 48
19.8%
3 38
15.7%
0 35
14.5%
5 34
14.0%
7 22
9.1%
4 17
 
7.0%
6 16
 
6.6%
9 12
 
5.0%
2 10
 
4.1%
8 10
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 48
16.4%
- 48
16.4%
3 38
13.0%
0 35
11.9%
5 34
11.6%
7 22
7.5%
4 17
 
5.8%
6 16
 
5.5%
9 12
 
4.1%
2 10
 
3.4%
Other values (2) 13
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 48
16.4%
- 48
16.4%
3 38
13.0%
0 35
11.9%
5 34
11.6%
7 22
7.5%
4 17
 
5.8%
6 16
 
5.5%
9 12
 
4.1%
2 10
 
3.4%
Other values (2) 13
 
4.4%

Interactions

2023-12-11T01:51:49.278668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:51:53.253632image/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
2023-12-11T01:51:53.399042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호(도로명)업종명
우편번호(도로명)1.0000.000
업종명0.0001.000

Missing values

2023-12-11T01:51:49.424176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:51:49.534103image/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
0일반미용업네일데이지부산광역시 북구 덕천로234번길 41, 상가동 2-1호 (만덕동, 만덕그린코아)46605051-335-9930
1일반미용업크리에이티브덕천점부산광역시 북구 만덕대로 23 (덕천동,뉴코아 3층)46548051-366-2277
2일반미용업데이지뷰티부산광역시 북구 만덕대로 32, 1(일부),2층 (덕천동)46577051-337-4111
3일반미용업제이오네일(Jo Nail)부산광역시 북구 금곡대로 238, 지하1층 104호 (화명동, 롯데캐슬멤버스)46537051-363-4143
4일반미용업씨네일부산광역시 북구 화명대로 47, 4층 (화명동, 롯데마트화명점)46525051-337-8160
5네일미용업엔비(ENVY)부산광역시 북구 만덕대로155번길 15, 삼정그린코아상가2동 109호 (덕천동)46554051-341-8661
6네일미용업영네일부산광역시 북구 와석장터로 6-11 (화명동)46532070-4147-5444
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
108네일미용업, 화장ㆍ분장 미용업도쿄네일살롱부산광역시 북구 덕천1길 13, 5층 (덕천동)46576<NA>
109네일미용업, 화장ㆍ분장 미용업네일,당신과부산광역시 북구 의성로 118, 해광오피스텔 101호 (덕천동)46578<NA>
110네일미용업, 화장ㆍ분장 미용업네일그림부산광역시 북구 만덕1로 33, 1층 (만덕동)46561<NA>
111네일미용업, 화장ㆍ분장 미용업단미뷰티부산광역시 북구 금곡대로303번길 80, 코아프라자 1층 103일부호 (화명동)46524<NA>
112네일미용업, 화장ㆍ분장 미용업뷰티인(Beauty_in)부산광역시 북구 화명신도시로 117, 메트로프라자 301일부호 (화명동)46527<NA>
113네일미용업, 화장ㆍ분장 미용업네일로아부산광역시 북구 금곡대로 166, 903동 1층 117호 (화명동, 롯데캐슬카이저)46539<NA>
114일반미용업, 피부미용업, 네일미용업네일하우스부산광역시 북구 만덕대로 28, 3층 (덕천동)46576<NA>
115피부미용업, 네일미용업, 화장ㆍ분장 미용업이후진샵부산광역시 북구 백양대로 1200, 5층 (덕천동)46576051-338-6688
116피부미용업, 네일미용업, 화장ㆍ분장 미용업라피네 쉬끄부산광역시 북구 금곡대로8번길 33, 2층 94,95(일부)호 (덕천동, 아남프라자)46548<NA>
117피부미용업, 네일미용업, 화장ㆍ분장 미용업윤이나뷰티(Yunina beauty)부산광역시 북구 화명신도시로 115, 성문타워 202 일부호 (화명동)46527<NA>