Overview

Dataset statistics

Number of variables5
Number of observations1019
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.9 KiB
Average record size in memory41.1 B

Variable types

Numeric1
Categorical2
Text2

Dataset

Description번호,브랜드명,자치구,점포명,주소
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15178/S/1/datasetView.do

Alerts

번호 is highly overall correlated with 자치구High correlation
자치구 is highly overall correlated with 번호High correlation
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-11 09:56:43.389875
Analysis finished2023-12-11 09:56:44.052737
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1019
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean510
Minimum1
Maximum1019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2023-12-11T18:56:44.124439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51.9
Q1255.5
median510
Q3764.5
95-th percentile968.1
Maximum1019
Range1018
Interquartile range (IQR)509

Descriptive statistics

Standard deviation294.30426
Coefficient of variation (CV)0.57706718
Kurtosis-1.2
Mean510
Median Absolute Deviation (MAD)255
Skewness0
Sum519690
Variance86615
MonotonicityNot monotonic
2023-12-11T18:56:44.257680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
684 1
 
0.1%
672 1
 
0.1%
673 1
 
0.1%
674 1
 
0.1%
675 1
 
0.1%
676 1
 
0.1%
950 1
 
0.1%
677 1
 
0.1%
678 1
 
0.1%
Other values (1009) 1009
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1019 1
0.1%
1018 1
0.1%
1017 1
0.1%
1016 1
0.1%
1015 1
0.1%
1014 1
0.1%
1013 1
0.1%
1012 1
0.1%
1011 1
0.1%
1010 1
0.1%

브랜드명
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
GS25
319 
CU
271 
세븐일레븐
258 
미니스톱
158 
씨스페이스
 
12

Length

Max length6
Median length5
Mean length3.7350343
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
GS25 319
31.3%
CU 271
26.6%
세븐일레븐 258
25.3%
미니스톱 158
15.5%
씨스페이스 12
 
1.2%
CU(신규) 1
 
0.1%

Length

2023-12-11T18:56:44.398850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:56:44.872769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gs25 319
31.3%
cu 271
26.6%
세븐일레븐 258
25.3%
미니스톱 158
15.5%
씨스페이스 12
 
1.2%
cu(신규 1
 
0.1%

자치구
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
강남구
146 
송파구
 
63
서초구
 
61
동대문구
 
50
강동구
 
47
Other values (21)
652 

Length

Max length4
Median length3
Mean length3.0736016
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강남구 146
 
14.3%
송파구 63
 
6.2%
서초구 61
 
6.0%
동대문구 50
 
4.9%
강동구 47
 
4.6%
마포구 47
 
4.6%
관악구 42
 
4.1%
성북구 42
 
4.1%
영등포구 39
 
3.8%
중구 37
 
3.6%
Other values (16) 445
43.7%

Length

2023-12-11T18:56:45.016775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 146
 
14.3%
송파구 63
 
6.2%
서초구 61
 
6.0%
동대문구 50
 
4.9%
강동구 47
 
4.6%
마포구 47
 
4.6%
관악구 42
 
4.1%
성북구 42
 
4.1%
영등포구 39
 
3.8%
중구 37
 
3.6%
Other values (16) 445
43.7%

점포명
Text

Distinct1004
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
2023-12-11T18:56:45.306235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length5.6084396
Min length2

Characters and Unicode

Total characters5715
Distinct characters383
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique989 ?
Unique (%)97.1%

Sample

1st row광화문광장점
2nd row내자중앙점
3rd row대학로광장점
4th row동대문역점
5th row동숭아트점
ValueCountFrequency (%)
세븐일레븐 48
 
4.4%
b 17
 
1.6%
동부이촌점 2
 
0.2%
수유점 2
 
0.2%
역삼타운점 2
 
0.2%
문정공원점 2
 
0.2%
구일역점 2
 
0.2%
서초염곡점(내곡 2
 
0.2%
포이그린 2
 
0.2%
장안햇살 2
 
0.2%
Other values (995) 1005
92.5%
2023-12-11T18:56:45.874179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 744
 
13.0%
동 167
 
2.9%
대 129
 
2.3%
역 101
 
1.8%
븐 97
 
1.7%
로 91
 
1.6%
일 82
 
1.4%
성 81
 
1.4%
신 80
 
1.4%
삼 78
 
1.4%
Other values (373) 4065
71.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5323
93.1%
Decimal Number 146
 
2.6%
Space Separator 73
 
1.3%
Close Punctuation 62
 
1.1%
Open Punctuation 62
 
1.1%
Uppercase Letter 48
 
0.8%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 744
 
14.0%
동 167
 
3.1%
대 129
 
2.4%
역 101
 
1.9%
븐 97
 
1.8%
로 91
 
1.7%
일 82
 
1.5%
성 81
 
1.5%
신 80
 
1.5%
삼 78
 
1.5%
Other values (342) 3673
69.0%
Uppercase Letter
ValueCountFrequency (%)
B 18
37.5%
C 4
 
8.3%
K 3
 
6.2%
I 3
 
6.2%
E 2
 
4.2%
G 2
 
4.2%
T 2
 
4.2%
M 2
 
4.2%
A 2
 
4.2%
L 2
 
4.2%
Other values (7) 8
16.7%
Decimal Number
ValueCountFrequency (%)
2 48
32.9%
1 33
22.6%
3 31
21.2%
4 13
 
8.9%
5 6
 
4.1%
6 4
 
2.7%
8 4
 
2.7%
7 3
 
2.1%
9 2
 
1.4%
0 2
 
1.4%
Space Separator
ValueCountFrequency (%)
73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%
Open Punctuation
ValueCountFrequency (%)
( 62
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5323
93.1%
Common 344
 
6.0%
Latin 48
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 744
 
14.0%
동 167
 
3.1%
대 129
 
2.4%
역 101
 
1.9%
븐 97
 
1.8%
로 91
 
1.7%
일 82
 
1.5%
성 81
 
1.5%
신 80
 
1.5%
삼 78
 
1.5%
Other values (342) 3673
69.0%
Latin
ValueCountFrequency (%)
B 18
37.5%
C 4
 
8.3%
K 3
 
6.2%
I 3
 
6.2%
E 2
 
4.2%
G 2
 
4.2%
T 2
 
4.2%
M 2
 
4.2%
A 2
 
4.2%
L 2
 
4.2%
Other values (7) 8
16.7%
Common
ValueCountFrequency (%)
73
21.2%
) 62
18.0%
( 62
18.0%
2 48
14.0%
1 33
9.6%
3 31
9.0%
4 13
 
3.8%
5 6
 
1.7%
6 4
 
1.2%
8 4
 
1.2%
Other values (4) 8
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5323
93.1%
ASCII 392
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 744
 
14.0%
동 167
 
3.1%
대 129
 
2.4%
역 101
 
1.9%
븐 97
 
1.8%
로 91
 
1.7%
일 82
 
1.5%
성 81
 
1.5%
신 80
 
1.5%
삼 78
 
1.5%
Other values (342) 3673
69.0%
ASCII
ValueCountFrequency (%)
73
18.6%
) 62
15.8%
( 62
15.8%
2 48
12.2%
1 33
8.4%
3 31
7.9%
B 18
 
4.6%
4 13
 
3.3%
5 6
 
1.5%
C 4
 
1.0%
Other values (21) 42
10.7%

주소
Text

Distinct1018
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
2023-12-11T18:56:46.262681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length42
Mean length22.481845
Min length6

Characters and Unicode

Total characters22909
Distinct characters379
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1017 ?
Unique (%)99.8%

Sample

1st row서울특별시 종로구 신문로1가 1-1번지
2nd row서울특별시 종로구 내자동 61-1번지
3rd row서울특별시 종로구 혜화동 93번지
4th row서울특별시 종로구 창신동 464-16번지
5th row서울특별시 종로구 동숭동 28번지
ValueCountFrequency (%)
서울 353
 
7.9%
서울특별시 320
 
7.1%
강남구 132
 
2.9%
1층 128
 
2.9%
서울시 116
 
2.6%
동대문구 47
 
1.0%
송파구 45
 
1.0%
성북구 40
 
0.9%
강동구 37
 
0.8%
마포구 35
 
0.8%
Other values (2088) 3231
72.1%
2023-12-11T18:56:46.846325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3564
 
15.6%
1 1469
 
6.4%
동 1202
 
5.2%
서 900
 
3.9%
구 883
 
3.9%
울 789
 
3.4%
2 776
 
3.4%
- 753
 
3.3%
3 652
 
2.8%
4 555
 
2.4%
Other values (369) 11366
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11861
51.8%
Decimal Number 5891
25.7%
Space Separator 3564
 
15.6%
Dash Punctuation 753
 
3.3%
Close Punctuation 368
 
1.6%
Open Punctuation 368
 
1.6%
Uppercase Letter 71
 
0.3%
Lowercase Letter 17
 
0.1%
Other Punctuation 15
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1202
 
10.1%
서 900
 
7.6%
구 883
 
7.4%
울 789
 
6.7%
로 477
 
4.0%
시 447
 
3.8%
별 321
 
2.7%
특 320
 
2.7%
길 281
 
2.4%
지 275
 
2.3%
Other values (320) 5966
50.3%
Uppercase Letter
ValueCountFrequency (%)
B 15
21.1%
A 11
15.5%
M 5
 
7.0%
C 5
 
7.0%
S 5
 
7.0%
E 4
 
5.6%
I 3
 
4.2%
D 3
 
4.2%
P 3
 
4.2%
L 3
 
4.2%
Other values (10) 14
19.7%
Lowercase Letter
ValueCountFrequency (%)
a 3
17.6%
o 2
11.8%
e 2
11.8%
k 2
11.8%
l 2
11.8%
t 1
 
5.9%
j 1
 
5.9%
s 1
 
5.9%
g 1
 
5.9%
n 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 1469
24.9%
2 776
13.2%
3 652
11.1%
4 555
 
9.4%
0 470
 
8.0%
6 448
 
7.6%
5 429
 
7.3%
7 394
 
6.7%
8 378
 
6.4%
9 320
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 11
73.3%
@ 3
 
20.0%
/ 1
 
6.7%
Space Separator
ValueCountFrequency (%)
3564
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 753
100.0%
Close Punctuation
ValueCountFrequency (%)
) 368
100.0%
Open Punctuation
ValueCountFrequency (%)
( 368
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11860
51.8%
Common 10960
47.8%
Latin 88
 
0.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1202
 
10.1%
서 900
 
7.6%
구 883
 
7.4%
울 789
 
6.7%
로 477
 
4.0%
시 447
 
3.8%
별 321
 
2.7%
특 320
 
2.7%
길 281
 
2.4%
지 275
 
2.3%
Other values (319) 5965
50.3%
Latin
ValueCountFrequency (%)
B 15
17.0%
A 11
 
12.5%
M 5
 
5.7%
C 5
 
5.7%
S 5
 
5.7%
E 4
 
4.5%
a 3
 
3.4%
I 3
 
3.4%
D 3
 
3.4%
P 3
 
3.4%
Other values (21) 31
35.2%
Common
ValueCountFrequency (%)
3564
32.5%
1 1469
13.4%
2 776
 
7.1%
- 753
 
6.9%
3 652
 
5.9%
4 555
 
5.1%
0 470
 
4.3%
6 448
 
4.1%
5 429
 
3.9%
7 394
 
3.6%
Other values (8) 1450
13.2%
Han
ValueCountFrequency (%)
現 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11860
51.8%
ASCII 11048
48.2%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3564
32.3%
1 1469
13.3%
2 776
 
7.0%
- 753
 
6.8%
3 652
 
5.9%
4 555
 
5.0%
0 470
 
4.3%
6 448
 
4.1%
5 429
 
3.9%
7 394
 
3.6%
Other values (39) 1538
13.9%
Hangul
ValueCountFrequency (%)
동 1202
 
10.1%
서 900
 
7.6%
구 883
 
7.4%
울 789
 
6.7%
로 477
 
4.0%
시 447
 
3.8%
별 321
 
2.7%
특 320
 
2.7%
길 281
 
2.4%
지 275
 
2.3%
Other values (319) 5965
50.3%
CJK
ValueCountFrequency (%)
現 1
100.0%

Interactions

2023-12-11T18:56:43.811141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:56:46.961828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호브랜드명자치구
번호1.0000.3310.984
브랜드명0.3311.0000.396
자치구0.9840.3961.000
2023-12-11T18:56:47.052722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구브랜드명
자치구1.0000.188
브랜드명0.1881.000
2023-12-11T18:56:47.152249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호브랜드명자치구
번호1.0000.1810.889
브랜드명0.1811.0000.188
자치구0.8890.1881.000

Missing values

2023-12-11T18:56:43.927649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:56:44.017879image/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

번호브랜드명자치구점포명주소
01CU종로구광화문광장점서울특별시 종로구 신문로1가 1-1번지
12CU종로구내자중앙점서울특별시 종로구 내자동 61-1번지
23CU종로구대학로광장점서울특별시 종로구 혜화동 93번지
34CU종로구동대문역점서울특별시 종로구 창신동 464-16번지
45CU종로구동숭아트점서울특별시 종로구 동숭동 28번지
56CU종로구마로니에점서울특별시 종로구 연건동 78-2번지
67CU종로구명륜성대점서울특별시 종로구 명륜4가 187번지
78CU종로구종로공원점서울 종로구 율곡로271(종로6가) 1층
89CU종로구종로삼청점서울특별시 종로구 삼청동 22번지
910CU종로구종로신교점서울특별시 종로구 신교동 36번지 수만빌리지
번호브랜드명자치구점포명주소
10091010세븐일레븐강동구명일삼익점서울특별시 강동구 명일동 양재대로 128길 47
10101011세븐일레븐강동구명일점서울 강동구 명일동 306-5
10111012세븐일레븐강동구암사희망점암사동 469-17
10121013세븐일레븐강동구천호쌍용점서울 강동구 천호동 432-10
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