Overview

Dataset statistics

Number of variables7
Number of observations41
Missing cells14
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory60.1 B

Variable types

Categorical1
Text3
DateTime2
Numeric1

Dataset

Description전주시 내에 존재하는 대규모, 준대규모 점포들(구분, 업체명, 주소, 전화번호, 개업일시, 면적)에 관한 현황을 제공합니다.자료: 일자리청년정책과
Author전북특별자치도 전주시
URLhttps://www.data.go.kr/data/15042071/fileData.do

Alerts

데이터기준일 has constant value ""Constant
면적(제곱미터) is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 면적(제곱미터)High correlation
전화번호 has 10 (24.4%) missing valuesMissing
면적(제곱미터) has 4 (9.8%) missing valuesMissing
업체명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 18:20:09.686857
Analysis finished2024-03-14 18:20:11.135827
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size456.0 B
준대규모점포
21 
대규모점포
20 

Length

Max length6
Median length6
Mean length5.5121951
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대규모점포
2nd row대규모점포
3rd row대규모점포
4th row대규모점포
5th row대규모점포

Common Values

ValueCountFrequency (%)
준대규모점포 21
51.2%
대규모점포 20
48.8%

Length

2024-03-15T03:20:11.251029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:20:11.597641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
준대규모점포 21
51.2%
대규모점포 20
48.8%

업체명
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size456.0 B
2024-03-15T03:20:12.440836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length8.9756098
Min length4

Characters and Unicode

Total characters368
Distinct characters100
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

Unique41 ?
Unique (%)100.0%

Sample

1st row롯데백화점 전주점
2nd row이마트 전주점
3rd row홈플러스 완산점
4th row홈플러스 전주점
5th row홈플러스 효자점
ValueCountFrequency (%)
롯데슈퍼 8
 
11.9%
홈플러스 4
 
6.0%
전주점 4
 
6.0%
효자점 3
 
4.5%
송천점 3
 
4.5%
노브랜드 2
 
3.0%
이마트 2
 
3.0%
삼천점 2
 
3.0%
전북대점 1
 
1.5%
익스프레스 1
 
1.5%
Other values (37) 37
55.2%
2024-03-15T03:20:13.516807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 30
 
8.2%
26
 
7.1%
) 20
 
5.4%
( 20
 
5.4%
데 18
 
4.9%
롯 16
 
4.3%
시 13
 
3.5%
슈 11
 
3.0%
퍼 10
 
2.7%
전 9
 
2.4%
Other values (90) 195
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 278
75.5%
Space Separator 26
 
7.1%
Close Punctuation 20
 
5.4%
Open Punctuation 20
 
5.4%
Uppercase Letter 16
 
4.3%
Lowercase Letter 6
 
1.6%
Decimal Number 1
 
0.3%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 30
 
10.8%
데 18
 
6.5%
롯 16
 
5.8%
시 13
 
4.7%
슈 11
 
4.0%
퍼 10
 
3.6%
전 9
 
3.2%
마 8
 
2.9%
트 8
 
2.9%
이 7
 
2.5%
Other values (68) 148
53.2%
Uppercase Letter
ValueCountFrequency (%)
L 2
12.5%
G 2
12.5%
S 2
12.5%
A 2
12.5%
M 1
6.2%
E 1
6.2%
V 1
6.2%
W 1
6.2%
C 1
6.2%
N 1
6.2%
Other values (2) 2
12.5%
Lowercase Letter
ValueCountFrequency (%)
l 2
33.3%
a 1
16.7%
f 1
16.7%
o 1
16.7%
m 1
16.7%
Space Separator
ValueCountFrequency (%)
26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 278
75.5%
Common 68
 
18.5%
Latin 22
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 30
 
10.8%
데 18
 
6.5%
롯 16
 
5.8%
시 13
 
4.7%
슈 11
 
4.0%
퍼 10
 
3.6%
전 9
 
3.2%
마 8
 
2.9%
트 8
 
2.9%
이 7
 
2.5%
Other values (68) 148
53.2%
Latin
ValueCountFrequency (%)
L 2
 
9.1%
G 2
 
9.1%
S 2
 
9.1%
A 2
 
9.1%
l 2
 
9.1%
M 1
 
4.5%
E 1
 
4.5%
V 1
 
4.5%
W 1
 
4.5%
C 1
 
4.5%
Other values (7) 7
31.8%
Common
ValueCountFrequency (%)
26
38.2%
) 20
29.4%
( 20
29.4%
2 1
 
1.5%
- 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 278
75.5%
ASCII 90
 
24.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 30
 
10.8%
데 18
 
6.5%
롯 16
 
5.8%
시 13
 
4.7%
슈 11
 
4.0%
퍼 10
 
3.6%
전 9
 
3.2%
마 8
 
2.9%
트 8
 
2.9%
이 7
 
2.5%
Other values (68) 148
53.2%
ASCII
ValueCountFrequency (%)
26
28.9%
) 20
22.2%
( 20
22.2%
L 2
 
2.2%
G 2
 
2.2%
S 2
 
2.2%
A 2
 
2.2%
l 2
 
2.2%
M 1
 
1.1%
E 1
 
1.1%
Other values (12) 12
13.3%

주소
Text

Distinct39
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size456.0 B
2024-03-15T03:20:14.331533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length15.365854
Min length14

Characters and Unicode

Total characters630
Distinct characters76
Distinct categories6 ?
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 (%)90.2%

Sample

1st row전주시 완산구 온고을로 2
2nd row전주시 완산구 당산로 111
3rd row전주시 완산구 기린대로 170
4th row전주시 덕진구 백제대로 771
5th row전주시 완산구 용머리로 31
ValueCountFrequency (%)
전주시 41
25.0%
완산구 27
16.5%
덕진구 14
 
8.5%
백제대로 3
 
1.8%
호암로 2
 
1.2%
20 2
 
1.2%
온고을로 2
 
1.2%
송천중앙로 2
 
1.2%
용머리로 2
 
1.2%
우전로 2
 
1.2%
Other values (61) 67
40.9%
2024-03-15T03:20:15.443770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
19.5%
전 45
 
7.1%
주 43
 
6.8%
시 41
 
6.5%
구 41
 
6.5%
로 30
 
4.8%
산 29
 
4.6%
완 27
 
4.3%
1 24
 
3.8%
2 21
 
3.3%
Other values (66) 206
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 396
62.9%
Space Separator 123
 
19.5%
Decimal Number 106
 
16.8%
Dash Punctuation 3
 
0.5%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 45
11.4%
주 43
10.9%
시 41
10.4%
구 41
10.4%
로 30
 
7.6%
산 29
 
7.3%
완 27
 
6.8%
덕 14
 
3.5%
진 14
 
3.5%
길 11
 
2.8%
Other values (52) 101
25.5%
Decimal Number
ValueCountFrequency (%)
1 24
22.6%
2 21
19.8%
7 9
 
8.5%
0 9
 
8.5%
9 8
 
7.5%
6 8
 
7.5%
5 7
 
6.6%
3 7
 
6.6%
4 7
 
6.6%
8 6
 
5.7%
Space Separator
ValueCountFrequency (%)
123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 396
62.9%
Common 234
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 45
11.4%
주 43
10.9%
시 41
10.4%
구 41
10.4%
로 30
 
7.6%
산 29
 
7.3%
완 27
 
6.8%
덕 14
 
3.5%
진 14
 
3.5%
길 11
 
2.8%
Other values (52) 101
25.5%
Common
ValueCountFrequency (%)
123
52.6%
1 24
 
10.3%
2 21
 
9.0%
7 9
 
3.8%
0 9
 
3.8%
9 8
 
3.4%
6 8
 
3.4%
5 7
 
3.0%
3 7
 
3.0%
4 7
 
3.0%
Other values (4) 11
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 396
62.9%
ASCII 234
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
52.6%
1 24
 
10.3%
2 21
 
9.0%
7 9
 
3.8%
0 9
 
3.8%
9 8
 
3.4%
6 8
 
3.4%
5 7
 
3.0%
3 7
 
3.0%
4 7
 
3.0%
Other values (4) 11
 
4.7%
Hangul
ValueCountFrequency (%)
전 45
11.4%
주 43
10.9%
시 41
10.4%
구 41
10.4%
로 30
 
7.6%
산 29
 
7.3%
완 27
 
6.8%
덕 14
 
3.5%
진 14
 
3.5%
길 11
 
2.8%
Other values (52) 101
25.5%

전화번호
Text

MISSING 

Distinct31
Distinct (%)100.0%
Missing10
Missing (%)24.4%
Memory size456.0 B
2024-03-15T03:20:16.175866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters372
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

Unique31 ?
Unique (%)100.0%

Sample

1st row063-289-3611
2nd row063-259-1001
3rd row063-288-4955
4th row063-249-8124
5th row063-281-8120
ValueCountFrequency (%)
063-289-3611 1
 
3.2%
063-224-4545 1
 
3.2%
063-247-5601 1
 
3.2%
063-225-5620 1
 
3.2%
063-229-4580 1
 
3.2%
063-222-6694 1
 
3.2%
063-715-5081 1
 
3.2%
063-275-1004 1
 
3.2%
063-223-3777 1
 
3.2%
063-237-0035 1
 
3.2%
Other values (21) 21
67.7%
2024-03-15T03:20:17.208978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 62
16.7%
0 55
14.8%
6 48
12.9%
2 46
12.4%
3 44
11.8%
5 29
7.8%
1 27
7.3%
8 18
 
4.8%
4 15
 
4.0%
7 15
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310
83.3%
Dash Punctuation 62
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55
17.7%
6 48
15.5%
2 46
14.8%
3 44
14.2%
5 29
9.4%
1 27
8.7%
8 18
 
5.8%
4 15
 
4.8%
7 15
 
4.8%
9 13
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 62
16.7%
0 55
14.8%
6 48
12.9%
2 46
12.4%
3 44
11.8%
5 29
7.8%
1 27
7.3%
8 18
 
4.8%
4 15
 
4.0%
7 15
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 62
16.7%
0 55
14.8%
6 48
12.9%
2 46
12.4%
3 44
11.8%
5 29
7.8%
1 27
7.3%
8 18
 
4.8%
4 15
 
4.0%
7 15
 
4.0%

개업일시
Date

Distinct39
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size456.0 B
Minimum1982-10-26 00:00:00
Maximum2021-11-29 00:00:00
2024-03-15T03:20:17.510126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:20:18.034636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)100.0%
Missing4
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean6951.4324
Minimum157
Maximum34776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T03:20:18.301327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum157
5-th percentile273
Q1542
median1089
Q310430
95-th percentile25108.6
Maximum34776
Range34619
Interquartile range (IQR)9888

Descriptive statistics

Standard deviation9322.2557
Coefficient of variation (CV)1.3410554
Kurtosis1.3207717
Mean6951.4324
Median Absolute Deviation (MAD)789
Skewness1.4686282
Sum257203
Variance86904451
MonotonicityNot monotonic
2024-03-15T03:20:18.730505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
897 1
 
2.4%
935 1
 
2.4%
551 1
 
2.4%
822 1
 
2.4%
983 1
 
2.4%
637 1
 
2.4%
433 1
 
2.4%
403 1
 
2.4%
390 1
 
2.4%
6571 1
 
2.4%
Other values (27) 27
65.9%
(Missing) 4
 
9.8%
ValueCountFrequency (%)
157 1
2.4%
165 1
2.4%
300 1
2.4%
310 1
2.4%
390 1
2.4%
397 1
2.4%
403 1
2.4%
413 1
2.4%
433 1
2.4%
542 1
2.4%
ValueCountFrequency (%)
34776 1
2.4%
28179 1
2.4%
24341 1
2.4%
22657 1
2.4%
21317 1
2.4%
18192 1
2.4%
15883 1
2.4%
13121 1
2.4%
12433 1
2.4%
10430 1
2.4%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size456.0 B
Minimum2023-11-27 00:00:00
Maximum2023-11-27 00:00:00
2024-03-15T03:20:19.082185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:20:19.389098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T03:20:10.154042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:20:19.610096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분업체명주소전화번호개업일시면적(제곱미터)
구분1.0001.0000.6751.0001.0000.891
업체명1.0001.0001.0001.0001.0001.000
주소0.6751.0001.0001.0000.9980.000
전화번호1.0001.0001.0001.0001.0001.000
개업일시1.0001.0000.9981.0001.0000.810
면적(제곱미터)0.8911.0000.0001.0000.8101.000
2024-03-15T03:20:19.879755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱미터)구분
면적(제곱미터)1.0000.832
구분0.8321.000

Missing values

2024-03-15T03:20:10.515554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:20:10.888716image/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.
2024-03-15T03:20:11.054866image/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

구분업체명주소전화번호개업일시면적(제곱미터)데이터기준일
0대규모점포롯데백화점 전주점전주시 완산구 온고을로 2063-289-36112004-05-21281792023-11-27
1대규모점포이마트 전주점전주시 완산구 당산로 111063-259-10011998-12-0365712023-11-27
2대규모점포홈플러스 완산점전주시 완산구 기린대로 170063-288-49552005-08-05131212023-11-27
3대규모점포홈플러스 전주점전주시 덕진구 백제대로 771063-249-81242006-11-1784712023-11-27
4대규모점포홈플러스 효자점전주시 완산구 용머리로 31063-281-81202011-07-26226572023-11-27
5대규모점포롯데마트 전주점전주시 완산구 우전로 240063-249-25112008-11-19104302023-11-27
6대규모점포롯데마트맥스 송천점전주시 덕진구 송천중앙로 82063-219-25002008-12-17103872023-11-27
7대규모점포NC WAVE 객사점전주시 완산구 전주객사5길 35063-710-50302000-10-1277212023-11-27
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