Overview

Dataset statistics

Number of variables6
Number of observations560
Missing cells468
Missing cells (%)13.9%
Duplicate rows2
Duplicate rows (%)0.4%
Total size in memory27.5 KiB
Average record size in memory50.2 B

Variable types

Text4
Numeric2

Dataset

Description전북특별자치도 전주시의 편의점을 제공하며, 상호명, 소재지도로명주소, 소재지지번주소, 위도, 경도, 전화번호를 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=14&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15113231

Alerts

Dataset has 2 (0.4%) duplicate rowsDuplicates
전화번호 has 468 (83.6%) missing valuesMissing

Reproduction

Analysis started2024-03-14 02:33:31.726530
Analysis finished2024-03-14 02:33:32.669009
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct558
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-03-14T11:33:32.854289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length14
Mean length9.7696429
Min length5

Characters and Unicode

Total characters5471
Distinct characters281
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique556 ?
Unique (%)99.3%

Sample

1st rowCUKCC스위첸점
2nd rowCUSK코끼리주유소점
3rd rowCU객리단길점
4th rowCU고시점
5th rowCU공단대로점
ValueCountFrequency (%)
cu전주한옥태조로점 2
 
0.4%
cu전주대스타빌점 2
 
0.4%
gs25전주대뉴타운점 1
 
0.2%
gs25효문여중점 1
 
0.2%
gs25효자원룸점 1
 
0.2%
gs25효자시티점 1
 
0.2%
gs25효자센터점 1
 
0.2%
gs25하가빅스타점 1
 
0.2%
gs25효자서부점 1
 
0.2%
gs25효자꽃길점 1
 
0.2%
Other values (552) 552
97.9%
2024-03-14T11:33:33.185673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
559
 
10.2%
386
 
7.1%
355
 
6.5%
344
 
6.3%
C 210
 
3.8%
U 202
 
3.7%
192
 
3.5%
2 191
 
3.5%
S 188
 
3.4%
G 187
 
3.4%
Other values (271) 2657
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4286
78.3%
Uppercase Letter 795
 
14.5%
Decimal Number 384
 
7.0%
Space Separator 4
 
0.1%
Dash Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
559
 
13.0%
386
 
9.0%
355
 
8.3%
344
 
8.0%
192
 
4.5%
176
 
4.1%
174
 
4.1%
65
 
1.5%
63
 
1.5%
53
 
1.2%
Other values (256) 1919
44.8%
Uppercase Letter
ValueCountFrequency (%)
C 210
26.4%
U 202
25.4%
S 188
23.6%
G 187
23.5%
K 4
 
0.5%
I 3
 
0.4%
V 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 191
49.7%
5 186
48.4%
1 4
 
1.0%
7 2
 
0.5%
3 1
 
0.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4286
78.3%
Latin 795
 
14.5%
Common 390
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
559
 
13.0%
386
 
9.0%
355
 
8.3%
344
 
8.0%
192
 
4.5%
176
 
4.1%
174
 
4.1%
65
 
1.5%
63
 
1.5%
53
 
1.2%
Other values (256) 1919
44.8%
Common
ValueCountFrequency (%)
2 191
49.0%
5 186
47.7%
1 4
 
1.0%
4
 
1.0%
7 2
 
0.5%
- 1
 
0.3%
, 1
 
0.3%
3 1
 
0.3%
Latin
ValueCountFrequency (%)
C 210
26.4%
U 202
25.4%
S 188
23.6%
G 187
23.5%
K 4
 
0.5%
I 3
 
0.4%
V 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4286
78.3%
ASCII 1185
 
21.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
559
 
13.0%
386
 
9.0%
355
 
8.3%
344
 
8.0%
192
 
4.5%
176
 
4.1%
174
 
4.1%
65
 
1.5%
63
 
1.5%
53
 
1.2%
Other values (256) 1919
44.8%
ASCII
ValueCountFrequency (%)
C 210
17.7%
U 202
17.0%
2 191
16.1%
S 188
15.9%
G 187
15.8%
5 186
15.7%
1 4
 
0.3%
K 4
 
0.3%
4
 
0.3%
I 3
 
0.3%
Other values (5) 6
 
0.5%
Distinct555
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-03-14T11:33:33.456855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length80
Median length57
Mean length36.391071
Min length1

Characters and Unicode

Total characters20379
Distinct characters316
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique552 ?
Unique (%)98.6%

Sample

1st row전북특별자치도 전주시 덕진구 오송1길 37-13 (송천동1가)
2nd row전북특별자치도 전주시 완산구 거마평로 47 (삼천동1가)
3rd row전북특별자치도 전주시 완산구 전주객사2길 73 (다가동4가)
4th row전북특별자치도 전주시 덕진구 팔달로 356 진북동 (진북동)
5th row전북특별자치도 전주시 덕진구 기린대로 702 1층 (팔복동1가)
ValueCountFrequency (%)
전북특별자치도 556
 
14.2%
전주시 556
 
14.2%
완산구 282
 
7.2%
덕진구 274
 
7.0%
1층 172
 
4.4%
효자동3가 53
 
1.4%
금암동 39
 
1.0%
101호 37
 
0.9%
서신동 33
 
0.8%
인후동1가 32
 
0.8%
Other values (895) 1891
48.2%
2024-03-14T11:33:33.880319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3557
 
17.5%
1166
 
5.7%
1 946
 
4.6%
737
 
3.6%
675
 
3.3%
595
 
2.9%
590
 
2.9%
580
 
2.8%
567
 
2.8%
567
 
2.8%
Other values (306) 10399
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12963
63.6%
Space Separator 3557
 
17.5%
Decimal Number 2652
 
13.0%
Close Punctuation 555
 
2.7%
Open Punctuation 555
 
2.7%
Dash Punctuation 71
 
0.3%
Uppercase Letter 24
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1166
 
9.0%
737
 
5.7%
675
 
5.2%
595
 
4.6%
590
 
4.6%
580
 
4.5%
567
 
4.4%
567
 
4.4%
558
 
4.3%
558
 
4.3%
Other values (278) 6370
49.1%
Uppercase Letter
ValueCountFrequency (%)
B 4
16.7%
E 3
12.5%
C 3
12.5%
S 2
8.3%
G 2
8.3%
I 2
8.3%
D 2
8.3%
L 1
 
4.2%
A 1
 
4.2%
K 1
 
4.2%
Other values (3) 3
12.5%
Decimal Number
ValueCountFrequency (%)
1 946
35.7%
2 443
16.7%
3 298
 
11.2%
0 228
 
8.6%
4 152
 
5.7%
5 148
 
5.6%
7 135
 
5.1%
6 122
 
4.6%
8 91
 
3.4%
9 89
 
3.4%
Space Separator
ValueCountFrequency (%)
3557
100.0%
Close Punctuation
ValueCountFrequency (%)
) 555
100.0%
Open Punctuation
ValueCountFrequency (%)
( 555
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%
Other Punctuation
ValueCountFrequency (%)
: 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12963
63.6%
Common 7392
36.3%
Latin 24
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1166
 
9.0%
737
 
5.7%
675
 
5.2%
595
 
4.6%
590
 
4.6%
580
 
4.5%
567
 
4.4%
567
 
4.4%
558
 
4.3%
558
 
4.3%
Other values (278) 6370
49.1%
Common
ValueCountFrequency (%)
3557
48.1%
1 946
 
12.8%
) 555
 
7.5%
( 555
 
7.5%
2 443
 
6.0%
3 298
 
4.0%
0 228
 
3.1%
4 152
 
2.1%
5 148
 
2.0%
7 135
 
1.8%
Other values (5) 375
 
5.1%
Latin
ValueCountFrequency (%)
B 4
16.7%
E 3
12.5%
C 3
12.5%
S 2
8.3%
G 2
8.3%
I 2
8.3%
D 2
8.3%
L 1
 
4.2%
A 1
 
4.2%
K 1
 
4.2%
Other values (3) 3
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12963
63.6%
ASCII 7416
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3557
48.0%
1 946
 
12.8%
) 555
 
7.5%
( 555
 
7.5%
2 443
 
6.0%
3 298
 
4.0%
0 228
 
3.1%
4 152
 
2.0%
5 148
 
2.0%
7 135
 
1.8%
Other values (18) 399
 
5.4%
Hangul
ValueCountFrequency (%)
1166
 
9.0%
737
 
5.7%
675
 
5.2%
595
 
4.6%
590
 
4.6%
580
 
4.5%
567
 
4.4%
567
 
4.4%
558
 
4.3%
558
 
4.3%
Other values (278) 6370
49.1%
Distinct558
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-03-14T11:33:34.091914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length57
Mean length34.791071
Min length25

Characters and Unicode

Total characters19483
Distinct characters240
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique556 ?
Unique (%)99.3%

Sample

1st row전북특별자치도 전주시 덕진구 송천동1가 97-6번지
2nd row전북특별자치도 전주시 완산구 삼천동1가 287-15번지
3rd row전북특별자치도 전주시 완산구 다가동4가 57-1번지
4th row전북특별자치도 전주시 덕진구 진북동 368-12번지 진북동
5th row전북특별자치도 전주시 덕진구 팔복동1가 139-15번지 1층
ValueCountFrequency (%)
전북특별자치도 560
16.3%
전주시 560
16.3%
완산구 285
 
8.3%
덕진구 275
 
8.0%
1층 174
 
5.1%
효자동3가 57
 
1.7%
금암동 43
 
1.2%
서신동 41
 
1.2%
인후동1가 37
 
1.1%
상가동 33
 
1.0%
Other values (814) 1380
40.1%
2024-03-14T11:33:34.433024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3268
 
16.8%
1147
 
5.9%
1 1110
 
5.7%
727
 
3.7%
675
 
3.5%
586
 
3.0%
583
 
3.0%
582
 
3.0%
580
 
3.0%
566
 
2.9%