Overview

Dataset statistics

Number of variables3
Number of observations70
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory26.9 B

Variable types

Numeric1
Categorical1
Text1

Dataset

Description서울특별시 용산구 거리가게현황(연번, 구분, 주소)에 대한 데이터를 제공하며 가로판매대, 구두수선대에 대한 주소 데이터를 제공합니다
Author서울특별시 용산구
URLhttps://www.data.go.kr/data/15098948/fileData.do

Alerts

연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:02:33.545611
Analysis finished2023-12-12 05:02:34.013860
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct70
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.5
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 B
2023-12-12T14:02:34.090312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.45
Q118.25
median35.5
Q352.75
95-th percentile66.55
Maximum70
Range69
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation20.351085
Coefficient of variation (CV)0.57327
Kurtosis-1.2
Mean35.5
Median Absolute Deviation (MAD)17.5
Skewness0
Sum2485
Variance414.16667
MonotonicityStrictly increasing
2023-12-12T14:02:34.238451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
46 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
45 1
 
1.4%
54 1
 
1.4%
Other values (60) 60
85.7%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%
63 1
1.4%
62 1
1.4%
61 1
1.4%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size692.0 B
구두수선대
40 
가로판매대
30 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
구두수선대 40
57.1%
가로판매대 30
42.9%

Length

2023-12-12T14:02:34.382660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:02:34.501941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구두수선대 40
57.1%
가로판매대 30
42.9%

주소
Text

UNIQUE 

Distinct70
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size692.0 B
2023-12-12T14:02:34.807000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34.5
Mean length30.3
Min length25

Characters and Unicode

Total characters2121
Distinct characters192
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)100.0%

Sample

1st row서울특별시 용산구 두텁바위로 82 (후암동 용산02번종점 앞)
2nd row서울특별시 용산구 후암로 107 (게이트웨이타워)
3rd row서울특별시 용산구 한강대로 403-1 (서울역 O·K저축은행 앞)
4th row서울특별시 용산구 한강대로 393-1 (서울역 동산빌딩 앞)
5th row서울특별시 용산구 한강대로 291-2 (숙대입구 스타벅스 앞)
ValueCountFrequency (%)
서울특별시 70
 
15.9%
용산구 70
 
15.9%
앞 52
 
11.8%
한강대로 21
 
4.8%
옆 7
 
1.6%
원효로 7
 
1.6%
이촌로 5
 
1.1%
청파로 5
 
1.1%
이태원로 4
 
0.9%
신한은행 4
 
0.9%
Other values (162) 196
44.4%
2023-12-12T14:02:35.705169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
372
 
17.5%
산 88
 
4.1%
용 86
 
4.1%
서 78
 
3.7%
울 74
 
3.5%
구 74
 
3.5%
로 73
 
3.4%
1 72
 
3.4%
시 71
 
3.3%
별 70
 
3.3%
Other values (182) 1063
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1310
61.8%
Space Separator 372
 
17.5%
Decimal Number 248
 
11.7%
Open Punctuation 70
 
3.3%
Close Punctuation 70
 
3.3%
Dash Punctuation 34
 
1.6%
Uppercase Letter 12
 
0.6%
Other Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 88
 
6.7%
용 86
 
6.6%
서 78
 
6.0%
울 74
 
5.6%
구 74
 
5.6%
로 73
 
5.6%
시 71
 
5.4%
별 70
 
5.3%
특 70
 
5.3%
앞 53
 
4.0%
Other values (156) 573
43.7%
Decimal Number
ValueCountFrequency (%)
1 72
29.0%
2 42
16.9%
7 28
 
11.3%
3 24
 
9.7%
0 19
 
7.7%
8 16
 
6.5%
4 15
 
6.0%
9 15
 
6.0%
5 9
 
3.6%
6 8
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
K 3
25.0%
C 2
16.7%
O 2
16.7%
A 1
 
8.3%
B 1
 
8.3%
S 1
 
8.3%
L 1
 
8.3%
F 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
· 2
40.0%
. 1
20.0%
@ 1
20.0%
& 1
20.0%
Space Separator
ValueCountFrequency (%)
372
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1310
61.8%
Common 799
37.7%
Latin 12
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 88
 
6.7%
용 86
 
6.6%
서 78
 
6.0%
울 74
 
5.6%
구 74
 
5.6%
로 73
 
5.6%
시 71
 
5.4%
별 70
 
5.3%
특 70
 
5.3%
앞 53
 
4.0%
Other values (156) 573
43.7%
Common
ValueCountFrequency (%)
372
46.6%
1 72
 
9.0%
( 70
 
8.8%
) 70
 
8.8%
2 42
 
5.3%
- 34
 
4.3%
7 28
 
3.5%
3 24
 
3.0%
0 19
 
2.4%
8 16
 
2.0%
Other values (8) 52
 
6.5%
Latin
ValueCountFrequency (%)
K 3
25.0%
C 2
16.7%
O 2
16.7%
A 1
 
8.3%
B 1
 
8.3%
S 1
 
8.3%
L 1
 
8.3%
F 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1310
61.8%
ASCII 809
38.1%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
372
46.0%
1 72
 
8.9%
( 70
 
8.7%
) 70
 
8.7%
2 42
 
5.2%
- 34
 
4.2%
7 28
 
3.5%
3 24
 
3.0%
0 19
 
2.3%
8 16
 
2.0%
Other values (15) 62
 
7.7%
Hangul
ValueCountFrequency (%)
산 88
 
6.7%
용 86
 
6.6%
서 78
 
6.0%
울 74
 
5.6%
구 74
 
5.6%
로 73
 
5.6%
시 71
 
5.4%
별 70
 
5.3%
특 70
 
5.3%
앞 53
 
4.0%
Other values (156) 573
43.7%
None
ValueCountFrequency (%)
· 2
100.0%

Interactions

2023-12-12T14:02:33.730280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:02:35.819130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분주소
연번1.0000.9971.000
구분0.9971.0001.000
주소1.0001.0001.000
2023-12-12T14:02:35.923575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분
연번1.0000.893
구분0.8931.000

Missing values

2023-12-12T14:02:33.884459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:02:33.975252image/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

연번구분주소
01가로판매대서울특별시 용산구 두텁바위로 82 (후암동 용산02번종점 앞)
12가로판매대서울특별시 용산구 후암로 107 (게이트웨이타워)
23가로판매대서울특별시 용산구 한강대로 403-1 (서울역 O·K저축은행 앞)
34가로판매대서울특별시 용산구 한강대로 393-1 (서울역 동산빌딩 앞)
45가로판매대서울특별시 용산구 한강대로 291-2 (숙대입구 스타벅스 앞)
56가로판매대서울특별시 용산구 한강대로 291-1 (숙대입구 스타벅스 앞)
67가로판매대서울특별시 용산구 한강대로 281-1 (롯데리아 앞)
78가로판매대서울특별시 용산구 한강대로 277-3 (에덴안경점 앞)
89가로판매대서울특별시 용산구 한강대로77길 19 (남영역 앞)
910가로판매대서울특별시 용산구 한강대로77길 17-1 (남영역 파리바게트 앞)
연번구분주소
6061구두수선대서울특별시 용산구 이촌로 200-1 (우리은행 동부이촌동지점 앞)
6162구두수선대서울특별시 용산구 이촌로 262 (삼익상가 앞)
6263구두수선대서울특별시 용산구 이촌로 291 (신한은행 앞)
6364구두수선대서울특별시 용산구 이태원로 159-2 (ABC마트 앞)
6465구두수선대서울특별시 용산구 이태원로 171-1 (퍼스트에비뉴 앞)
6566구두수선대서울특별시 용산구 우사단로 48 (이태원 119안전센터 옆)
6667구두수선대서울특별시 용산구 이태원로 261-1 (꼼데가르송 앞)
6768구두수선대서울특별시 용산구 한남대로 79 (유엔빌리지 편)
6869구두수선대서울특별시 용산구 장문로 101 (파리바게뜨 보광점 앞)
6970구두수선대서울특별시 용산구 녹사평대로 132-1 (크라운호텔 육교 앞)