Overview

Dataset statistics

Number of variables5
Number of observations37
Missing cells2
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory43.6 B

Variable types

Text3
Categorical2

Dataset

Description경상남도 밀양시 착한가격업소 현황에 대한 자료로, 주소, 업소 구분, 업소 명, 전화번호, 업소 자랑거리에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15103723/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
전화번호 has 2 (5.4%) missing valuesMissing
업소명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:59:41.434043
Analysis finished2023-12-12 22:59:41.868462
Duration0.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업소명
Text

UNIQUE 

Distinct37
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-12-13T07:59:42.044084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length8
Mean length4.9189189
Min length2

Characters and Unicode

Total characters182
Distinct characters104
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)100.0%

Sample

1st row경일루
2nd row건강국수
3rd row도도국밥
4th row동아리
5th row리헤어샵
ValueCountFrequency (%)
경일루 1
 
2.5%
건강국수 1
 
2.5%
솔밭만두 1
 
2.5%
아리랑 1
 
2.5%
다방 1
 
2.5%
두레정 1
 
2.5%
식당 1
 
2.5%
삼랑진소머리곰탕 1
 
2.5%
일미랑 1
 
2.5%
대청정 1
 
2.5%
Other values (30) 30
75.0%
2023-12-13T07:59:42.514866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 7
 
3.8%
식 6
 
3.3%
아 6
 
3.3%
국 5
 
2.7%
당 5
 
2.7%
어 4
 
2.2%
동 4
 
2.2%
랑 4
 
2.2%
일 3
 
1.6%
장 3
 
1.6%
Other values (94) 135
74.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 172
94.5%
Space Separator 3
 
1.6%
Uppercase Letter 3
 
1.6%
Close Punctuation 1
 
0.5%
Other Punctuation 1
 
0.5%
Open Punctuation 1
 
0.5%
Letter Number 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 7
 
4.1%
식 6
 
3.5%
아 6
 
3.5%
국 5
 
2.9%
당 5
 
2.9%
어 4
 
2.3%
동 4
 
2.3%
랑 4
 
2.3%
일 3
 
1.7%
장 3
 
1.7%
Other values (86) 125
72.7%
Uppercase Letter
ValueCountFrequency (%)
I 1
33.3%
P 1
33.3%
J 1
33.3%
Space Separator
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 172
94.5%
Common 6
 
3.3%
Latin 4
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 7
 
4.1%
식 6
 
3.5%
아 6
 
3.5%
국 5
 
2.9%
당 5
 
2.9%
어 4
 
2.3%
동 4
 
2.3%
랑 4
 
2.3%
일 3
 
1.7%
장 3
 
1.7%
Other values (86) 125
72.7%
Common
ValueCountFrequency (%)
3
50.0%
) 1
 
16.7%
, 1
 
16.7%
( 1
 
16.7%
Latin
ValueCountFrequency (%)
I 1
25.0%
P 1
25.0%
J 1
25.0%
Ⅱ 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 172
94.5%
ASCII 9
 
4.9%
Number Forms 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 7
 
4.1%
식 6
 
3.5%
아 6
 
3.5%
국 5
 
2.9%
당 5
 
2.9%
어 4
 
2.3%
동 4
 
2.3%
랑 4
 
2.3%
일 3
 
1.7%
장 3
 
1.7%
Other values (86) 125
72.7%
ASCII
ValueCountFrequency (%)
3
33.3%
I 1
 
11.1%
P 1
 
11.1%
) 1
 
11.1%
, 1
 
11.1%
J 1
 
11.1%
( 1
 
11.1%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

업소구분
Categorical

Distinct7
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size428.0 B
한식
25 
이미용
5 
중식
 
2
목욕업
 
2
카페
 
1
Other values (2)
 
2

Length

Max length6
Median length2
Mean length2.3243243
Min length2

Unique

Unique3 ?
Unique (%)8.1%

Sample

1st row중식
2nd row한식
3rd row한식
4th row한식
5th row이미용

Common Values

ValueCountFrequency (%)
한식 25
67.6%
이미용 5
 
13.5%
중식 2
 
5.4%
목욕업 2
 
5.4%
카페 1
 
2.7%
세탁업 1
 
2.7%
기타비요식업 1
 
2.7%

Length

2023-12-13T07:59:42.691272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:59:42.845126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 25
67.6%
이미용 5
 
13.5%
중식 2
 
5.4%
목욕업 2
 
5.4%
카페 1
 
2.7%
세탁업 1
 
2.7%
기타비요식업 1
 
2.7%

주소
Text

UNIQUE 

Distinct37
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-12-13T07:59:43.151162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length19
Mean length16.864865
Min length12

Characters and Unicode

Total characters624
Distinct characters73
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)100.0%

Sample

1st row경남 밀양시 새미4길 46
2nd row경남 밀양시 내이2길 25-1
3rd row경남 밀양시 밀성로 8
4th row경남 밀양시 노상하3길 27
5th row경남 밀양시 내이4길 9
ValueCountFrequency (%)
경남 37
22.6%
밀양시 37
22.6%
산외면 6
 
3.7%
산외로 5
 
3.0%
중앙로 3
 
1.8%
17 2
 
1.2%
범평1길 2
 
1.2%
초동면 2
 
1.2%
삼랑진읍 2
 
1.2%
밀양대로 2
 
1.2%
Other values (60) 66
40.2%
2023-12-13T07:59:43.607796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
127
20.4%
시 41
 
6.6%
밀 40
 
6.4%
남 39
 
6.2%
양 39
 
6.2%
경 37
 
5.9%
1 30
 
4.8%
2 23
 
3.7%
길 21
 
3.4%
로 19
 
3.0%
Other values (63) 208
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 364
58.3%
Space Separator 127
 
20.4%
Decimal Number 119
 
19.1%
Dash Punctuation 11
 
1.8%
Other Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 41
11.3%
밀 40
11.0%
남 39
10.7%
양 39
10.7%
경 37
 
10.2%
길 21
 
5.8%
로 19
 
5.2%
외 12
 
3.3%
산 11
 
3.0%
면 11
 
3.0%
Other values (50) 94
25.8%
Decimal Number
ValueCountFrequency (%)
1 30
25.2%
2 23
19.3%
4 13
10.9%
9 9
 
7.6%
3 9
 
7.6%
8 9
 
7.6%
5 8
 
6.7%
6 7
 
5.9%
7 6
 
5.0%
0 5
 
4.2%
Space Separator
ValueCountFrequency (%)
127
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 364
58.3%
Common 260
41.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 41
11.3%
밀 40
11.0%
남 39
10.7%
양 39
10.7%
경 37
 
10.2%
길 21
 
5.8%
로 19
 
5.2%
외 12
 
3.3%
산 11
 
3.0%
면 11
 
3.0%
Other values (50) 94
25.8%
Common
ValueCountFrequency (%)
127
48.8%
1 30
 
11.5%
2 23
 
8.8%
4 13
 
5.0%
- 11
 
4.2%
9 9
 
3.5%
3 9
 
3.5%
8 9
 
3.5%
5 8
 
3.1%
6 7
 
2.7%
Other values (3) 14
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 364
58.3%
ASCII 260
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
127
48.8%
1 30
 
11.5%
2 23
 
8.8%
4 13
 
5.0%
- 11
 
4.2%
9 9
 
3.5%
3 9
 
3.5%
8 9
 
3.5%
5 8
 
3.1%
6 7
 
2.7%
Other values (3) 14
 
5.4%
Hangul
ValueCountFrequency (%)
시 41
11.3%
밀 40
11.0%
남 39
10.7%
양 39
10.7%
경 37
 
10.2%
길 21
 
5.8%
로 19
 
5.2%
외 12
 
3.3%
산 11
 
3.0%
면 11
 
3.0%
Other values (50) 94
25.8%

전화번호
Text

MISSING 

Distinct35
Distinct (%)100.0%
Missing2
Missing (%)5.4%
Memory size428.0 B
2023-12-13T07:59:43.859938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.142857
Min length12

Characters and Unicode

Total characters425
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

Unique35 ?
Unique (%)100.0%

Sample

1st row055-356-1399
2nd row055-356-7491
3rd row055-352-4303
4th row055-355-6266
5th row055-351-3368
ValueCountFrequency (%)
055-356-1399 1
 
2.9%
055-354-3479 1
 
2.9%
055-356-8333 1
 
2.9%
055-351-3335 1
 
2.9%
055-355-5166 1
 
2.9%
055-353-3399 1
 
2.9%
055-351-1007 1
 
2.9%
055-353-3665 1
 
2.9%
055-354-7510 1
 
2.9%
055-352-6924 1
 
2.9%
Other values (25) 25
71.4%
2023-12-13T07:59:44.280983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 117
27.5%
- 70
16.5%
3 65
15.3%
0 48
11.3%
6 24
 
5.6%
1 21
 
4.9%
4 19
 
4.5%
8 17
 
4.0%
9 15
 
3.5%
2 15
 
3.5%
Other values (2) 14
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 352
82.8%
Dash Punctuation 70
 
16.5%
Space Separator 3
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 117
33.2%
3 65
18.5%
0 48
13.6%
6 24
 
6.8%
1 21
 
6.0%
4 19
 
5.4%
8 17
 
4.8%
9 15
 
4.3%
2 15
 
4.3%
7 11
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 70
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 425
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 117
27.5%
- 70
16.5%
3 65
15.3%
0 48
11.3%
6 24
 
5.6%
1 21
 
4.9%
4 19
 
4.5%
8 17
 
4.0%
9 15
 
3.5%
2 15
 
3.5%
Other values (2) 14
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 117
27.5%
- 70
16.5%
3 65
15.3%
0 48
11.3%
6 24
 
5.6%
1 21
 
4.9%
4 19
 
4.5%
8 17
 
4.0%
9 15
 
3.5%
2 15
 
3.5%
Other values (2) 14
 
3.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-08-21
37 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-21
2nd row2023-08-21
3rd row2023-08-21
4th row2023-08-21
5th row2023-08-21

Common Values

ValueCountFrequency (%)
2023-08-21 37
100.0%

Length

2023-12-13T07:59:44.462514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:59:44.576969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-21 37
100.0%

Correlations

2023-12-13T07:59:44.634999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소명업소구분주소전화번호
업소명1.0001.0001.0001.000
업소구분1.0001.0001.0001.000
주소1.0001.0001.0001.000
전화번호1.0001.0001.0001.000

Missing values

2023-12-13T07:59:41.710250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:59:41.829055image/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

업소명업소구분주소전화번호데이터기준일자
0경일루중식경남 밀양시 새미4길 46055-356-13992023-08-21
1건강국수한식경남 밀양시 내이2길 25-1055-356-74912023-08-21
2도도국밥한식경남 밀양시 밀성로 8055-352-43032023-08-21
3동아리한식경남 밀양시 노상하3길 27055-355-62662023-08-21
4리헤어샵이미용경남 밀양시 내이4길 9055-351-33682023-08-21
5만리향중식경남 밀양시 북성로6길 36055-356-82852023-08-21
6신갈매기한식경남 밀양시 백민로9길 17055-351-88842023-08-21
7쌈지미용실이미용경남 밀양시 하송정3길 28055-355-22032023-08-21
8장터민물장어한식경남 밀양시 북성로 3길 17055-355-11882023-08-21
9헤어Ⅱ이미용경남 밀양시 해천안길 19055-353-07762023-08-21
업소명업소구분주소전화번호데이터기준일자
27대청정한식경남 밀양시 미리벌중앙로2길 25055-356-37702023-08-21
28월성탕목욕업경남 밀양시 밀양대로 1801055-354-34792023-08-21
29참콩나물해장국한식경남 밀양시 소전1길 15-1055-355-88982023-08-21
30일품식당한식경남 밀양시 초동면 범평1길 14055-391-64242023-08-21
31가마솥추어탕한식경남 밀양시 하남읍 초하로 720055-391-59322023-08-21
32진흥사세탁업경남 밀양시 시서상가길 6055-391-12442023-08-21
33수아한식경남 밀양시 초동면 범평1길 14-1055-391-40092023-08-21
34제이아이피(JIP), 임시정부한식경남 밀양시 중앙로 256-1055-354-81632023-08-21
35아줌마우동한식경남 밀양시 중앙로 289-24055-354-75102023-08-21
36솔로몬독서실기타비요식업경남 밀양시 중앙로 234-9, 203호,204호,205호0507-1383-65052023-08-21