Overview

Dataset statistics

Number of variables5
Number of observations204
Missing cells20
Missing cells (%)2.0%
Duplicate rows1
Duplicate rows (%)0.5%
Total size in memory8.1 KiB
Average record size in memory40.6 B

Variable types

Text4
DateTime1

Dataset

Description전북특별자치도 전주시의 나들가게를 제공하며 매장명, 도로명주소, 지번주소, 위도, 경도, 연락처 등을 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=12&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15020605

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (0.5%) duplicate rowsDuplicates
소재지도로명주소 has 12 (5.9%) missing valuesMissing
소재지연락번호 has 8 (3.9%) missing valuesMissing

Reproduction

Analysis started2024-03-14 02:35:41.105961
Analysis finished2024-03-14 02:35:41.630955
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

매장명
Text

Distinct194
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-14T11:35:41.804933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length5.004902
Min length3

Characters and Unicode

Total characters1021
Distinct characters220
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

Unique185 ?
Unique (%)90.7%

Sample

1st row슈퍼스타편의점
2nd row애플마트(전주대점)
3rd row너구리마트
4th row레드마트25신시가지문학점
5th row여기마트
ValueCountFrequency (%)
코사마트 4
 
1.9%
정마트 3
 
1.4%
푸른마트 2
 
0.9%
프랜드마트 2
 
0.9%
예다음마트 2
 
0.9%
한별마트 2
 
0.9%
애플마트 2
 
0.9%
마트유 2
 
0.9%
그린마트 2
 
0.9%
와이마트 2
 
0.9%
Other values (188) 190
89.2%
2024-03-14T11:35:42.131393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 134
 
13.1%
트 132
 
12.9%
퍼 45
 
4.4%
슈 40
 
3.9%
점 25
 
2.4%
성 15
 
1.5%
우 14
 
1.4%
이 14
 
1.4%
사 13
 
1.3%
편 11
 
1.1%
Other values (210) 578
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 997
97.6%
Space Separator 9
 
0.9%
Decimal Number 9
 
0.9%
Lowercase Letter 4
 
0.4%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 134
 
13.4%
트 132
 
13.2%
퍼 45
 
4.5%
슈 40
 
4.0%
점 25
 
2.5%
성 15
 
1.5%
우 14
 
1.4%
이 14
 
1.4%
사 13
 
1.3%
편 11
 
1.1%
Other values (200) 554
55.6%
Lowercase Letter
ValueCountFrequency (%)
k 1
25.0%
e 1
25.0%
i 1
25.0%
v 1
25.0%
Decimal Number
ValueCountFrequency (%)
2 5
55.6%
5 3
33.3%
4 1
 
11.1%
Space Separator
ValueCountFrequency (%)
9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 997
97.6%
Common 20
 
2.0%
Latin 4
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 134
 
13.4%
트 132
 
13.2%
퍼 45
 
4.5%
슈 40
 
4.0%
점 25
 
2.5%
성 15
 
1.5%
우 14
 
1.4%
이 14
 
1.4%
사 13
 
1.3%
편 11
 
1.1%
Other values (200) 554
55.6%
Common
ValueCountFrequency (%)
9
45.0%
2 5
25.0%
5 3
 
15.0%
) 1
 
5.0%
( 1
 
5.0%
4 1
 
5.0%
Latin
ValueCountFrequency (%)
k 1
25.0%
e 1
25.0%
i 1
25.0%
v 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 997
97.6%
ASCII 24
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 134
 
13.4%
트 132
 
13.2%
퍼 45
 
4.5%
슈 40
 
4.0%
점 25
 
2.5%
성 15
 
1.5%
우 14
 
1.4%
이 14
 
1.4%
사 13
 
1.3%
편 11
 
1.1%
Other values (200) 554
55.6%
ASCII
ValueCountFrequency (%)
9
37.5%
2 5
20.8%
5 3
 
12.5%
k 1
 
4.2%
e 1
 
4.2%
) 1
 
4.2%
( 1
 
4.2%
i 1
 
4.2%
v 1
 
4.2%
4 1
 
4.2%

소재지도로명주소
Text

MISSING 

Distinct190
Distinct (%)99.0%
Missing12
Missing (%)5.9%
Memory size1.7 KiB
2024-03-14T11:35:42.393955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length32
Mean length20.802083
Min length18

Characters and Unicode

Total characters3994
Distinct characters168
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

Unique188 ?
Unique (%)97.9%

Sample

1st row전라북도 전주시 완산구 봉곡2길 35
2nd row전라북도 전주시 완산구 봉곡2길 13
3rd row전라북도 전주시 완산구 척동1길 26
4th row전라북도 전주시 완산구 문학대6길 24
5th row전라북도 전주시 완산구 황강서원2길 5
ValueCountFrequency (%)
전주시 192
19.8%
전라북도 190
19.6%
완산구 100
 
10.3%
덕진구 92
 
9.5%
견훤로 7
 
0.7%
10 5
 
0.5%
매봉로 5
 
0.5%
기린대로 5
 
0.5%
11 5
 
0.5%
26 5
 
0.5%
Other values (267) 362
37.4%
2024-03-14T11:35:42.767450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
776
19.4%
전 394
 
9.9%
주 195
 
4.9%
구 195
 
4.9%
북 194
 
4.9%
라 193
 
4.8%
시 192
 
4.8%
도 191
 
4.8%
1 130
 
3.3%
산 113
 
2.8%
Other values (158) 1421
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2632
65.9%
Space Separator 776
 
19.4%
Decimal Number 545
 
13.6%
Dash Punctuation 35
 
0.9%
Other Punctuation 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 394
15.0%
주 195
 
7.4%
구 195
 
7.4%
북 194
 
7.4%
라 193
 
7.3%
시 192
 
7.3%
도 191
 
7.3%
산 113
 
4.3%
로 107
 
4.1%
완 100
 
3.8%
Other values (145) 758
28.8%
Decimal Number
ValueCountFrequency (%)
1 130
23.9%
2 82
15.0%
3 68
12.5%
4 58
10.6%
5 48
 
8.8%
6 38
 
7.0%
0 36
 
6.6%
7 34
 
6.2%
9 28
 
5.1%
8 23
 
4.2%
Space Separator
ValueCountFrequency (%)
776
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2632
65.9%
Common 1362
34.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 394
15.0%
주 195
 
7.4%
구 195
 
7.4%
북 194
 
7.4%
라 193
 
7.3%
시 192
 
7.3%
도 191
 
7.3%
산 113
 
4.3%
로 107
 
4.1%
완 100
 
3.8%
Other values (145) 758
28.8%
Common
ValueCountFrequency (%)
776
57.0%
1 130
 
9.5%
2 82
 
6.0%
3 68
 
5.0%
4 58
 
4.3%
5 48
 
3.5%
6 38
 
2.8%
0 36
 
2.6%
- 35
 
2.6%
7 34
 
2.5%
Other values (3) 57
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2632
65.9%
ASCII 1362
34.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
776
57.0%
1 130
 
9.5%
2 82
 
6.0%
3 68
 
5.0%
4 58
 
4.3%
5 48
 
3.5%
6 38
 
2.8%
0 36
 
2.6%
- 35
 
2.6%
7 34
 
2.5%
Other values (3) 57
 
4.2%
Hangul
ValueCountFrequency (%)
전 394
15.0%
주 195
 
7.4%
구 195
 
7.4%
북 194
 
7.4%
라 193
 
7.3%
시 192
 
7.3%
도 191
 
7.3%
산 113
 
4.3%
로 107
 
4.1%
완 100
 
3.8%
Other values (145) 758
28.8%

소재지지번주소
Text

Distinct202
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-14T11:35:43.080396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length37
Mean length23.970588
Min length20

Characters and Unicode

Total characters4890
Distinct characters79
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

Unique200 ?
Unique (%)98.0%

Sample

1st row전라북도 전주시 완산구 효자동3가 1731-1
2nd row전라북도 전주시 완산구 효자동3가 1726-2
3rd row전라북도 전주시 완산구 효자동3가 1658-6
4th row전라북도 전주시 완산구 효자동3가 1611-1
5th row전라북도 전주시 완산구 효자동3가 1601-13
ValueCountFrequency (%)
전라북도 204
19.8%
전주시 204
19.8%
완산구 106
 
10.3%
덕진구 98
 
9.5%
삼천동1가 20
 
1.9%
인후동1가 18
 
1.7%
금암동 15
 
1.5%
효자동1가 13
 
1.3%
서신동 10
 
1.0%
인후동2가 9
 
0.9%
Other values (246) 335
32.5%
2024-03-14T11:35:43.657754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
828
16.9%
전 408
 
8.3%
1 261
 
5.3%
북 209
 
4.3%
동 208
 
4.3%
주 206
 
4.2%
구 205
 
4.2%
도 204
 
4.2%
시 204
 
4.2%
라 204
 
4.2%
Other values (69) 1953
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2849
58.3%
Decimal Number 1031
 
21.1%
Space Separator 828
 
16.9%
Dash Punctuation 177
 
3.6%
Other Punctuation 4
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 408
14.3%
북 209
 
7.3%
동 208
 
7.3%
주 206
 
7.2%
구 205
 
7.2%
도 204
 
7.2%
시 204
 
7.2%
라 204
 
7.2%
가 147
 
5.2%
산 116
 
4.1%
Other values (53) 738
25.9%
Decimal Number
ValueCountFrequency (%)
1 261
25.3%
2 163
15.8%
5 107
10.4%
3 100
 
9.7%
4 78
 
7.6%
7 75
 
7.3%
8 73
 
7.1%
6 73
 
7.1%
9 54
 
5.2%
0 47
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 2
50.0%
/ 1
25.0%
. 1
25.0%
Space Separator
ValueCountFrequency (%)
828
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 177
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2849
58.3%
Common 2040
41.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 408
14.3%
북 209
 
7.3%
동 208
 
7.3%
주 206
 
7.2%
구 205
 
7.2%
도 204
 
7.2%
시 204
 
7.2%
라 204
 
7.2%
가 147
 
5.2%
산 116
 
4.1%
Other values (53) 738
25.9%
Common
ValueCountFrequency (%)
828
40.6%
1 261
 
12.8%
- 177
 
8.7%
2 163
 
8.0%
5 107
 
5.2%
3 100
 
4.9%
4 78
 
3.8%
7 75
 
3.7%
8 73
 
3.6%
6 73
 
3.6%
Other values (5) 105
 
5.1%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2849
58.3%
ASCII 2041
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
828
40.6%
1 261
 
12.8%
- 177
 
8.7%
2 163
 
8.0%
5 107
 
5.2%
3 100
 
4.9%
4 78
 
3.8%
7 75
 
3.7%
8 73
 
3.6%
6 73
 
3.6%
Other values (6) 106
 
5.2%
Hangul
ValueCountFrequency (%)
전 408
14.3%
북 209
 
7.3%
동 208
 
7.3%
주 206
 
7.2%
구 205
 
7.2%
도 204
 
7.2%
시 204
 
7.2%
라 204
 
7.2%
가 147
 
5.2%
산 116
 
4.1%
Other values (53) 738
25.9%

소재지연락번호
Text

MISSING 

Distinct194
Distinct (%)99.0%
Missing8
Missing (%)3.9%
Memory size1.7 KiB
2024-03-14T11:35:43.865171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.020408
Min length12

Characters and Unicode

Total characters2356
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192 ?
Unique (%)98.0%

Sample

1st row063-226-0705
2nd row063-227-1173
3rd row063-227-3824
4th row063-237-5999
5th row063-236-2558
ValueCountFrequency (%)
063-275-3222 2
 
1.0%
070-8833-3559 2
 
1.0%
063-276-1171 1
 
0.5%
063-245-5014 1
 
0.5%
063-243-5581 1
 
0.5%
063-245-7383 1
 
0.5%
063-242-2998 1
 
0.5%
063-244-6313 1
 
0.5%
063-246-0887 1
 
0.5%
063-244-4926 1
 
0.5%
Other values (184) 184
93.9%
2024-03-14T11:35:44.170600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 392
16.6%
2 347
14.7%
3 308
13.1%
0 293
12.4%
6 263
11.2%
5 170
7.2%
4 144
 
6.1%
7 135
 
5.7%
8 120
 
5.1%
1 113
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1964
83.4%
Dash Punctuation 392
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 347
17.7%
3 308
15.7%
0 293
14.9%
6 263
13.4%
5 170
8.7%
4 144
7.3%
7 135
 
6.9%
8 120
 
6.1%
1 113
 
5.8%
9 71
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2356
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 392
16.6%
2 347
14.7%
3 308
13.1%
0 293
12.4%
6 263
11.2%
5 170
7.2%
4 144
 
6.1%
7 135
 
5.7%
8 120
 
5.1%
1 113
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 392
16.6%
2 347
14.7%
3 308
13.1%
0 293
12.4%
6 263
11.2%
5 170
7.2%
4 144
 
6.1%
7 135
 
5.7%
8 120
 
5.1%
1 113
 
4.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2021-10-31 00:00:00
Maximum2021-10-31 00:00:00
2024-03-14T11:35:44.281544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:35:44.359830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Missing values

2024-03-14T11:35:41.396485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:35:41.507296image/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.
2024-03-14T11:35:41.585118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

매장명소재지도로명주소소재지지번주소소재지연락번호데이터기준일자
0슈퍼스타편의점전라북도 전주시 완산구 봉곡2길 35전라북도 전주시 완산구 효자동3가 1731-1063-226-07052021-10-31
1애플마트(전주대점)전라북도 전주시 완산구 봉곡2길 13전라북도 전주시 완산구 효자동3가 1726-2063-227-11732021-10-31
2너구리마트전라북도 전주시 완산구 척동1길 26전라북도 전주시 완산구 효자동3가 1658-6063-227-38242021-10-31
3레드마트25신시가지문학점전라북도 전주시 완산구 문학대6길 24전라북도 전주시 완산구 효자동3가 1611-1063-237-59992021-10-31
4여기마트전라북도 전주시 완산구 황강서원2길 5전라북도 전주시 완산구 효자동3가 1601-13063-236-25582021-10-31
5두산마트전라북도 전주시 완산구 서곡로 20-1전라북도 전주시 완산구 효자동3가 1483-2063-251-70462021-10-31
6서곡마트전라북도 전주시 완산구 서곡로 8전라북도 전주시 완산구 효자동3가 1482-1063-271-00192021-10-31
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195대우슈퍼전라북도 전주시 덕진구 매봉로 47전라북도 전주시 덕진구 금암동 1587-17063-275-49722021-10-31
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