Overview

Dataset statistics

Number of variables3
Number of observations348
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 00:12:28.762621
Analysis finished2023-12-11 00:12:29.229728
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct348
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.5
Minimum1
Maximum348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-11T09:12:29.315971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.35
Q187.75
median174.5
Q3261.25
95-th percentile330.65
Maximum348
Range347
Interquartile range (IQR)173.5

Descriptive statistics

Standard deviation100.60318
Coefficient of variation (CV)0.57652253
Kurtosis-1.2
Mean174.5
Median Absolute Deviation (MAD)87
Skewness0
Sum60726
Variance10121
MonotonicityStrictly increasing
2023-12-11T09:12:29.480850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
231 1
 
0.3%
239 1
 
0.3%
238 1
 
0.3%
237 1
 
0.3%
236 1
 
0.3%
235 1
 
0.3%
234 1
 
0.3%
233 1
 
0.3%
232 1
 
0.3%
Other values (338) 338
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 (%)
348 1
0.3%
347 1
0.3%
346 1
0.3%
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%

업소명
Text

Distinct301
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T09:12:29.717981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length16
Mean length5.9482759
Min length1

Characters and Unicode

Total characters2070
Distinct characters309
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

Unique292 ?
Unique (%)83.9%

Sample

1st row대신편의점
2nd row마트코리아 신삼문점
3rd row백마강식당
4th row힐링스토리&아이핑거네일
5th row위드미밀양원룸점
ValueCountFrequency (%)
28
 
7.0%
무 15
 
3.7%
세븐일레븐 8
 
2.0%
씨유 6
 
1.5%
필프라이스 5
 
1.2%
밀양점 4
 
1.0%
마트 3
 
0.7%
gs25 3
 
0.7%
할인마트 3
 
0.7%
마트코리아 2
 
0.5%
Other values (315) 325
80.8%
2023-12-11T09:12:30.095049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 95
 
4.6%
양 77
 
3.7%
밀 74
 
3.6%
마 55
 
2.7%
54
 
2.6%
트 53
 
2.6%
상 43
 
2.1%
븐 37
 
1.8%
유 36
 
1.7%
회 35
 
1.7%
Other values (299) 1511
73.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1880
90.8%
Space Separator 54
 
2.6%
Uppercase Letter 33
 
1.6%
Dash Punctuation 29
 
1.4%
Decimal Number 28
 
1.4%
Lowercase Letter 16
 
0.8%
Open Punctuation 12
 
0.6%
Close Punctuation 12
 
0.6%
Other Punctuation 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 95
 
5.1%
양 77
 
4.1%
밀 74
 
3.9%
마 55
 
2.9%
트 53
 
2.8%
상 43
 
2.3%
븐 37
 
2.0%
유 36
 
1.9%
회 35
 
1.9%
일 33
 
1.8%
Other values (269) 1342
71.4%
Lowercase Letter
ValueCountFrequency (%)
p 3
18.8%
o 3
18.8%
a 3
18.8%
y 2
12.5%
h 1
 
6.2%
d 1
 
6.2%
n 1
 
6.2%
i 1
 
6.2%
s 1
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
S 11
33.3%
G 10
30.3%
D 3
 
9.1%
C 3
 
9.1%
I 2
 
6.1%
A 2
 
6.1%
H 1
 
3.0%
F 1
 
3.0%
Decimal Number
ValueCountFrequency (%)
2 12
42.9%
5 10
35.7%
1 3
 
10.7%
9 1
 
3.6%
0 1
 
3.6%
3 1
 
3.6%
Other Punctuation
ValueCountFrequency (%)
& 4
66.7%
/ 1
 
16.7%
. 1
 
16.7%
Space Separator
ValueCountFrequency (%)
54
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1880
90.8%
Common 141
 
6.8%
Latin 49
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 95
 
5.1%
양 77
 
4.1%
밀 74
 
3.9%
마 55
 
2.9%
트 53
 
2.8%
상 43
 
2.3%
븐 37
 
2.0%
유 36
 
1.9%
회 35
 
1.9%
일 33
 
1.8%
Other values (269) 1342
71.4%
Latin
ValueCountFrequency (%)
S 11
22.4%
G 10
20.4%
D 3
 
6.1%
C 3
 
6.1%
p 3
 
6.1%
o 3
 
6.1%
a 3
 
6.1%
I 2
 
4.1%
y 2
 
4.1%
A 2
 
4.1%
Other values (7) 7
14.3%
Common
ValueCountFrequency (%)
54
38.3%
- 29
20.6%
( 12
 
8.5%
2 12
 
8.5%
) 12
 
8.5%
5 10
 
7.1%
& 4
 
2.8%
1 3
 
2.1%
/ 1
 
0.7%
9 1
 
0.7%
Other values (3) 3
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1880
90.8%
ASCII 190
 
9.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 95
 
5.1%
양 77
 
4.1%
밀 74
 
3.9%
마 55
 
2.9%
트 53
 
2.8%
상 43
 
2.3%
븐 37
 
2.0%
유 36
 
1.9%
회 35
 
1.9%
일 33
 
1.8%
Other values (269) 1342
71.4%
ASCII
ValueCountFrequency (%)
54
28.4%
- 29
15.3%
( 12
 
6.3%
2 12
 
6.3%
) 12
 
6.3%
S 11
 
5.8%
5 10
 
5.3%
G 10
 
5.3%
& 4
 
2.1%
D 3
 
1.6%
Other values (20) 33
17.4%

업소도로명주소
Text

Distinct341
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T09:12:30.401269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length40
Mean length22.867816
Min length1

Characters and Unicode

Total characters7958
Distinct characters197
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

Unique334 ?
Unique (%)96.0%

Sample

1st row경상남도 밀양시 하남읍 온천로 1842
2nd row경상남도 밀양시 밀양대로 1783-4 (삼문동. 삼문주공아파트)
3rd row경상남도 밀양시 상남면 상남로 514
4th row경상남도 밀양시 밀양대로 1856 (삼문동)
5th row경상남도 밀양시 밀성로3길 9. 1층 (내이동)
ValueCountFrequency (%)
경상남도 345
19.4%
밀양시 345
19.4%
내이동 62
 
3.5%
삼문동 45
 
2.5%
상남면 32
 
1.8%
하남읍 28
 
1.6%
삼랑진읍 25
 
1.4%
중앙로 24
 
1.3%
단장면 23
 
1.3%
가곡동 21
 
1.2%
Other values (460) 831
46.7%
2023-12-11T09:12:30.859037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1435
18.0%
남 416
 
5.2%
상 412
 
5.2%
밀 382
 
4.8%
양 377
 
4.7%
시 365
 
4.6%
도 357
 
4.5%
경 345
 
4.3%
1 245
 
3.1%
로 228
 
2.9%
Other values (187) 3396
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4991
62.7%
Space Separator 1435
 
18.0%
Decimal Number 1060
 
13.3%
Open Punctuation 168
 
2.1%
Close Punctuation 168
 
2.1%
Dash Punctuation 86
 
1.1%
Other Punctuation 38
 
0.5%
Lowercase Letter 10
 
0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 416
 
8.3%
상 412
 
8.3%
밀 382
 
7.7%
양 377
 
7.6%
시 365
 
7.3%
도 357
 
7.2%
경 345
 
6.9%
로 228
 
4.6%
동 204
 
4.1%
길 144
 
2.9%
Other values (167) 1761
35.3%
Decimal Number
ValueCountFrequency (%)
1 245
23.1%
2 149
14.1%
3 141
13.3%
5 104
9.8%
4 97
 
9.2%
7 75
 
7.1%
6 73
 
6.9%
8 65
 
6.1%
0 60
 
5.7%
9 51
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
l 4
40.0%
s 2
20.0%
u 2
20.0%
e 2
20.0%
Space Separator
ValueCountFrequency (%)
1435
100.0%
Open Punctuation
ValueCountFrequency (%)
( 168
100.0%
Close Punctuation
ValueCountFrequency (%)
) 168
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%
Other Punctuation
ValueCountFrequency (%)
. 38
100.0%
Uppercase Letter
ValueCountFrequency (%)
W 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4991
62.7%
Common 2955
37.1%
Latin 12
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 416
 
8.3%
상 412
 
8.3%
밀 382
 
7.7%
양 377
 
7.6%
시 365
 
7.3%
도 357
 
7.2%
경 345
 
6.9%
로 228
 
4.6%
동 204
 
4.1%
길 144
 
2.9%
Other values (167) 1761
35.3%
Common
ValueCountFrequency (%)
1435
48.6%
1 245
 
8.3%
( 168
 
5.7%
) 168
 
5.7%
2 149
 
5.0%
3 141
 
4.8%
5 104
 
3.5%
4 97
 
3.3%
- 86
 
2.9%
7 75
 
2.5%
Other values (5) 287
 
9.7%
Latin
ValueCountFrequency (%)
l 4
33.3%
s 2
16.7%
u 2
16.7%
W 2
16.7%
e 2
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4991
62.7%
ASCII 2967
37.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1435
48.4%
1 245
 
8.3%
( 168
 
5.7%
) 168
 
5.7%
2 149
 
5.0%
3 141
 
4.8%
5 104
 
3.5%
4 97
 
3.3%
- 86
 
2.9%
7 75
 
2.5%
Other values (10) 299
 
10.1%
Hangul
ValueCountFrequency (%)
남 416
 
8.3%
상 412
 
8.3%
밀 382
 
7.7%
양 377
 
7.6%
시 365
 
7.3%
도 357
 
7.2%
경 345
 
6.9%
로 228
 
4.6%
동 204
 
4.1%
길 144
 
2.9%
Other values (167) 1761
35.3%

Interactions

2023-12-11T09:12:29.023365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-11T09:12:29.132854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:12:29.202466image/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
01대신편의점경상남도 밀양시 하남읍 온천로 1842
12마트코리아 신삼문점경상남도 밀양시 밀양대로 1783-4 (삼문동. 삼문주공아파트)
23백마강식당경상남도 밀양시 상남면 상남로 514
34힐링스토리&아이핑거네일경상남도 밀양시 밀양대로 1856 (삼문동)
45위드미밀양원룸점경상남도 밀양시 밀성로3길 9. 1층 (내이동)
56행복플러스25 삼랑진점경상남도 밀양시 삼랑진읍 천태로 421. 2층
67대영상사경상남도 밀양시 백민로 58-1 (내이동)
78세븐일레븐밀양삼문중앙점경상남도 밀양시 미리벌로2길 18 (삼문동)
89드림마트경상남도 밀양시 수월2길 7-18 (삼문동)
910세븐일레븐밀양터미널중앙점경상남도 밀양시 북성로 10. 1층 (내이동)
연번업소명업소도로명주소
338339천일상회경상남도 밀양시 단장면 국전로 446
339340심신슈퍼경상남도 밀양시 하남읍 내동2길 2
340341코레일유통(주)경상남도 밀양시 중앙로 62 (가곡동. 밀양역)
341342해동상회경상남도 밀양시 북성로 16 (내이동)
342343솔지방경상남도 밀양시 석정로 11 (내일동)
343344양효식당경상남도 밀양시 무안면 사명로 1110
344345제일이발관경상남도 밀양시 무안면 사명로 503-1
345346무경상남도 밀양시 초동면 대구령길 3-4
346347-경상남도 밀양시 상남면 평촌길 35
347348-경상남도 밀양시 산내면 산내용전3길 2