Overview

Dataset statistics

Number of variables3
Number of observations345
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory25.4 B

Variable types

Numeric1
Text2

Dataset

Description경상남도 밀양시 담배소매인 지정 현황 정보를 제공합니다. 밀양시 담배소매인 지정 연번, 업소명, 업소 도로명 주소 정보를 확인할 수 있습니다.
Author경상남도 밀양시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15021432

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:12:31.735403
Analysis finished2023-12-11 00:12:32.196954
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct345
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173
Minimum1
Maximum345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-11T09:12:32.571041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.2
Q187
median173
Q3259
95-th percentile327.8
Maximum345
Range344
Interquartile range (IQR)172

Descriptive statistics

Standard deviation99.737155
Coefficient of variation (CV)0.57651534
Kurtosis-1.2
Mean173
Median Absolute Deviation (MAD)86
Skewness0
Sum59685
Variance9947.5
MonotonicityStrictly increasing
2023-12-11T09:12:32.791185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
228 1
 
0.3%
236 1
 
0.3%
235 1
 
0.3%
234 1
 
0.3%
233 1
 
0.3%
232 1
 
0.3%
231 1
 
0.3%
230 1
 
0.3%
229 1
 
0.3%
Other values (335) 335
97.1%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
345 1
0.3%
344 1
0.3%
343 1
0.3%
342 1
0.3%
341 1
0.3%
340 1
0.3%
339 1
0.3%
338 1
0.3%
337 1
0.3%
336 1
0.3%

업소명
Text

Distinct303
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T09:12:33.044400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length16
Mean length6.257971
Min length1

Characters and Unicode

Total characters2159
Distinct characters318
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

Unique296 ?
Unique (%)85.8%

Sample

1st row아이스크림할인점(삼문점)
2nd row현대공인중개사사무소
3rd row지에스(GS)25 밀양예림점
4th row밀양얼큰이
5th row이마트24 밀양세경점
ValueCountFrequency (%)
27
 
6.7%
무 13
 
3.2%
세븐일레븐 8
 
2.0%
씨유 8
 
2.0%
밀양점 5
 
1.2%
필프라이스 4
 
1.0%
gs25 3
 
0.7%
지에스(gs)25 3
 
0.7%
마트 3
 
0.7%
아시아마트 2
 
0.5%
Other values (318) 328
81.2%
2023-12-11T09:12:33.402950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 106
 
4.9%
양 92
 
4.3%
밀 90
 
4.2%
59
 
2.7%
마 54
 
2.5%
트 52
 
2.4%
유 40
 
1.9%
븐 38
 
1.8%
상 38
 
1.8%
이 34
 
1.6%
Other values (308) 1556
72.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1915
88.7%
Space Separator 59
 
2.7%
Uppercase Letter 45
 
2.1%
Decimal Number 43
 
2.0%
Dash Punctuation 28
 
1.3%
Lowercase Letter 22
 
1.0%
Close Punctuation 21
 
1.0%
Open Punctuation 21
 
1.0%
Other Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 106
 
5.5%
양 92
 
4.8%
밀 90
 
4.7%
마 54
 
2.8%
트 52
 
2.7%
유 40
 
2.1%
븐 38
 
2.0%
상 38
 
2.0%
이 34
 
1.8%
일 32
 
1.7%
Other values (274) 1339
69.9%
Lowercase Letter
ValueCountFrequency (%)
o 5
22.7%
a 3
13.6%
p 3
13.6%
y 2
 
9.1%
n 2
 
9.1%
d 2
 
9.1%
h 1
 
4.5%
i 1
 
4.5%
s 1
 
4.5%
e 1
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
S 16
35.6%
G 15
33.3%
D 3
 
6.7%
C 3
 
6.7%
I 2
 
4.4%
A 2
 
4.4%
H 1
 
2.2%
F 1
 
2.2%
Z 1
 
2.2%
W 1
 
2.2%
Decimal Number
ValueCountFrequency (%)
2 20
46.5%
5 17
39.5%
1 3
 
7.0%
4 1
 
2.3%
9 1
 
2.3%
3 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
& 3
60.0%
. 1
 
20.0%
/ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
59
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1915
88.7%
Common 177
 
8.2%
Latin 67
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 106
 
5.5%
양 92
 
4.8%
밀 90
 
4.7%
마 54
 
2.8%
트 52
 
2.7%
유 40
 
2.1%
븐 38
 
2.0%
상 38
 
2.0%
이 34
 
1.8%
일 32
 
1.7%
Other values (274) 1339
69.9%
Latin
ValueCountFrequency (%)
S 16
23.9%
G 15
22.4%
o 5
 
7.5%
D 3
 
4.5%
C 3
 
4.5%
a 3
 
4.5%
p 3
 
4.5%
y 2
 
3.0%
I 2
 
3.0%
n 2
 
3.0%
Other values (11) 13
19.4%
Common
ValueCountFrequency (%)
59
33.3%
- 28
15.8%
) 21
 
11.9%
( 21
 
11.9%
2 20
 
11.3%
5 17
 
9.6%
1 3
 
1.7%
& 3
 
1.7%
4 1
 
0.6%
. 1
 
0.6%
Other values (3) 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1915
88.7%
ASCII 244
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 106
 
5.5%
양 92
 
4.8%
밀 90
 
4.7%
마 54
 
2.8%
트 52
 
2.7%
유 40
 
2.1%
븐 38
 
2.0%
상 38
 
2.0%
이 34
 
1.8%
일 32
 
1.7%
Other values (274) 1339
69.9%
ASCII
ValueCountFrequency (%)
59
24.2%
- 28
11.5%
) 21
 
8.6%
( 21
 
8.6%
2 20
 
8.2%
5 17
 
7.0%
S 16
 
6.6%
G 15
 
6.1%
o 5
 
2.0%
D 3
 
1.2%
Other values (24) 39
16.0%

업소도로명주소
Text

Distinct337
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T09:12:33.666749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length41
Mean length22.898551
Min length1

Characters and Unicode

Total characters7900
Distinct characters194
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

Unique331 ?
Unique (%)95.9%

Sample

1st row경상남도 밀양시 미리벌로 26. 1층 101호 (삼문동. 대림아트빌)
2nd row경상남도 밀양시 삼랑진읍 천태로 116
3rd row경상남도 밀양시 상남면 밀양대로 1549. 2층
4th row경상남도 밀양시 석정로 78 (내일동)
5th row경상남도 밀양시 미리벌로2길 47. 108동 1층 (삼문동. Wellus)
ValueCountFrequency (%)
경상남도 341
19.2%
밀양시 341
19.2%
내이동 61
 
3.4%
삼문동 46
 
2.6%
상남면 28
 
1.6%
중앙로 26
 
1.5%
삼랑진읍 26
 
1.5%
단장면 24
 
1.3%
하남읍 23
 
1.3%
가곡동 22
 
1.2%
Other values (452) 840
47.2%
2023-12-11T09:12:34.114765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1441
18.2%
상 402
 
5.1%
남 401
 
5.1%
밀 378
 
4.8%
양 371
 
4.7%
시 360
 
4.6%
도 352
 
4.5%
경 341
 
4.3%
1 260
 
3.3%
로 233
 
2.9%
Other values (184) 3361
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4910
62.2%
Space Separator 1441
 
18.2%
Decimal Number 1058
 
13.4%
Open Punctuation 175
 
2.2%
Close Punctuation 175
 
2.2%
Dash Punctuation 75
 
0.9%
Other Punctuation 53
 
0.7%
Lowercase Letter 11
 
0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 402
 
8.2%
남 401
 
8.2%
밀 378
 
7.7%
양 371
 
7.6%
시 360
 
7.3%
도 352
 
7.2%
경 341
 
6.9%
로 233
 
4.7%
동 205
 
4.2%
길 135
 
2.7%
Other values (163) 1732
35.3%
Decimal Number
ValueCountFrequency (%)
1 260
24.6%
2 148
14.0%
3 136
12.9%
5 102
 
9.6%
4 91
 
8.6%
7 74
 
7.0%
6 72
 
6.8%
8 64
 
6.0%
0 59
 
5.6%
9 52
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
l 4
36.4%
s 2
18.2%
u 2
18.2%
e 2
18.2%
d 1
 
9.1%
Space Separator
ValueCountFrequency (%)
1441
100.0%
Open Punctuation
ValueCountFrequency (%)
( 175
100.0%
Close Punctuation
ValueCountFrequency (%)
) 175
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%
Other Punctuation
ValueCountFrequency (%)
. 53
100.0%
Uppercase Letter
ValueCountFrequency (%)
W 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4910
62.2%
Common 2977
37.7%
Latin 13
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 402
 
8.2%
남 401
 
8.2%
밀 378
 
7.7%
양 371
 
7.6%
시 360
 
7.3%
도 352
 
7.2%
경 341
 
6.9%
로 233
 
4.7%
동 205
 
4.2%
길 135
 
2.7%
Other values (163) 1732
35.3%
Common
ValueCountFrequency (%)
1441
48.4%
1 260
 
8.7%
( 175
 
5.9%
) 175
 
5.9%
2 148
 
5.0%
3 136
 
4.6%
5 102
 
3.4%
4 91
 
3.1%
- 75
 
2.5%
7 74
 
2.5%
Other values (5) 300
 
10.1%
Latin
ValueCountFrequency (%)
l 4
30.8%
s 2
15.4%
u 2
15.4%
e 2
15.4%
W 2
15.4%
d 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4910
62.2%
ASCII 2990
37.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1441
48.2%
1 260
 
8.7%
( 175
 
5.9%
) 175
 
5.9%
2 148
 
4.9%
3 136
 
4.5%
5 102
 
3.4%
4 91
 
3.0%
- 75
 
2.5%
7 74
 
2.5%
Other values (11) 313
 
10.5%
Hangul
ValueCountFrequency (%)
상 402
 
8.2%
남 401
 
8.2%
밀 378
 
7.7%
양 371
 
7.6%
시 360
 
7.3%
도 352
 
7.2%
경 341
 
6.9%
로 233
 
4.7%
동 205
 
4.2%
길 135
 
2.7%
Other values (163) 1732
35.3%

Interactions

2023-12-11T09:12:31.975637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-11T09:12:32.083672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:12:32.154387image/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

연번업소명업소도로명주소
01아이스크림할인점(삼문점)경상남도 밀양시 미리벌로 26. 1층 101호 (삼문동. 대림아트빌)
12현대공인중개사사무소경상남도 밀양시 삼랑진읍 천태로 116
23지에스(GS)25 밀양예림점경상남도 밀양시 상남면 밀양대로 1549. 2층
34밀양얼큰이경상남도 밀양시 석정로 78 (내일동)
45이마트24 밀양세경점경상남도 밀양시 미리벌로2길 47. 108동 1층 (삼문동. Wellus)
56대현농약사경상남도 밀양시 중앙로 121 (가곡동)
67다담매점경상남도 밀양시 가곡7길 13 (가곡동)
78세븐일레븐 밀양가곡미라점경상남도 밀양시 가곡8길 5. 1층 111호 (가곡동. 삼우아파트)
89밀양호텔커피숍경상남도 밀양시 가곡7길 5-1. 1층 (가곡동)
910씨유밀양예림점경상남도 밀양시 상남면 밀양대로 1531-1
연번업소명업소도로명주소
335336천일상회경상남도 밀양시 단장면 국전로 446
336337심신슈퍼경상남도 밀양시 하남읍 내동2길 2
337338코레일유통(주)경상남도 밀양시 중앙로 62 (가곡동. 밀양역)
338339해동상회경상남도 밀양시 북성로 16 (내이동)
339340솔지방경상남도 밀양시 석정로 11 (내일동)
340341양효식당경상남도 밀양시 무안면 사명로 1110
341342제일이발관경상남도 밀양시 무안면 사명로 503-1
342343무경상남도 밀양시 초동면 대구령길 3-4
343344-경상남도 밀양시 상남면 평촌길 35
344345-경상남도 밀양시 산내면 산내용전3길 2