Overview

Dataset statistics

Number of variables6
Number of observations353
Missing cells16
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.7 KiB
Average record size in memory48.4 B

Variable types

Categorical2
Text4

Dataset

Description청주시에 설치되어 있는 불법 주정차 단속카메라 위치 데이터로 구별, 관리번호, 행정동, 지번, 비고사항을 제공합니다.
URLhttps://www.data.go.kr/data/15040407/fileData.do

Alerts

비고 has constant value ""Constant
지번 has 16 (4.5%) missing valuesMissing
위치 명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:14:58.396049
Analysis finished2023-12-12 13:14:59.198369
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구별
Categorical

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
흥덕구
130 
상당구
91 
서원구
69 
청원구
63 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
흥덕구 130
36.8%
상당구 91
25.8%
서원구 69
19.5%
청원구 63
17.8%

Length

2023-12-12T22:14:59.280761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:59.387886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
흥덕구 130
36.8%
상당구 91
25.8%
서원구 69
19.5%
청원구 63
17.8%

관리번호
Text

Distinct350
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-12T22:14:59.686008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1765
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique347 ?
Unique (%)98.3%

Sample

1st rowA0001
2nd rowA0002
3rd rowA0004
4th rowA0005
5th rowA0006
ValueCountFrequency (%)
c0034 2
 
0.6%
a0031 2
 
0.6%
b0028 2
 
0.6%
d0057 1
 
0.3%
b0072 1
 
0.3%
b0080 1
 
0.3%
b0079 1
 
0.3%
b0078 1
 
0.3%
b0077 1
 
0.3%
b0075 1
 
0.3%
Other values (340) 340
96.3%
2023-12-12T22:15:00.110388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 753
42.7%
B 130
 
7.4%
1 117
 
6.6%
A 91
 
5.2%
2 86
 
4.9%
3 77
 
4.4%
5 75
 
4.2%
4 74
 
4.2%
6 72
 
4.1%
C 69
 
3.9%
Other values (4) 221
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1412
80.0%
Uppercase Letter 353
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 753
53.3%
1 117
 
8.3%
2 86
 
6.1%
3 77
 
5.5%
5 75
 
5.3%
4 74
 
5.2%
6 72
 
5.1%
8 56
 
4.0%
7 54
 
3.8%
9 48
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
B 130
36.8%
A 91
25.8%
C 69
19.5%
D 63
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1412
80.0%
Latin 353
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 753
53.3%
1 117
 
8.3%
2 86
 
6.1%
3 77
 
5.5%
5 75
 
5.3%
4 74
 
5.2%
6 72
 
5.1%
8 56
 
4.0%
7 54
 
3.8%
9 48
 
3.4%
Latin
ValueCountFrequency (%)
B 130
36.8%
A 91
25.8%
C 69
19.5%
D 63
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 753
42.7%
B 130
 
7.4%
1 117
 
6.6%
A 91
 
5.2%
2 86
 
4.9%
3 77
 
4.4%
5 75
 
4.2%
4 74
 
4.2%
6 72
 
4.1%
C 69
 
3.9%
Other values (4) 221
 
12.5%

위치 명칭
Text

UNIQUE 

Distinct353
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-12T22:15:00.335136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length16
Mean length10.246459
Min length4

Characters and Unicode

Total characters3617
Distinct characters320
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique353 ?
Unique (%)100.0%

Sample

1st row무심동로서문대교삼거리부근
2nd row대성로성안행정복지센터부근
3rd row세영첼시빌111동부근
4th row용암북로운암1교사거리부근
5th row용암북로용암오토바이부근
ValueCountFrequency (%)
오송주정차 15
 
4.1%
테크노폴리스지웰아파트 1
 
0.3%
롯데아울렛(에스오일주유소)부근 1
 
0.3%
강서블루지움 1
 
0.3%
오송역a주차장앞 1
 
0.3%
오송사우나부근 1
 
0.3%
오송역버스환승센터출입구 1
 
0.3%
봉명동중부순복음교회부근 1
 
0.3%
오송ibk기업은행부근 1
 
0.3%
복대지구대부근 1
 
0.3%
Other values (346) 346
93.5%
2023-12-12T22:15:00.684486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 310
 
8.6%
근 301
 
8.3%
로 105
 
2.9%
리 99
 
2.7%
사 85
 
2.4%
거 83
 
2.3%
교 82
 
2.3%
초 71
 
2.0%
학 62
 
1.7%
주 59
 
1.6%
Other values (310) 2360
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3374
93.3%
Decimal Number 147
 
4.1%
Uppercase Letter 25
 
0.7%
Close Punctuation 24
 
0.7%
Open Punctuation 24
 
0.7%
Space Separator 17
 
0.5%
Lowercase Letter 3
 
0.1%
Other Punctuation 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 310
 
9.2%
근 301
 
8.9%
로 105
 
3.1%
리 99
 
2.9%
사 85
 
2.5%
거 83
 
2.5%
교 82
 
2.4%
초 71
 
2.1%
학 62
 
1.8%
주 59
 
1.7%
Other values (280) 2117
62.7%
Uppercase Letter
ValueCountFrequency (%)
K 3
12.0%
L 3
12.0%
G 3
12.0%
C 3
12.0%
A 2
8.0%
B 2
8.0%
S 2
8.0%
I 1
 
4.0%
E 1
 
4.0%
J 1
 
4.0%
Other values (4) 4
16.0%
Decimal Number
ValueCountFrequency (%)
1 58
39.5%
2 26
17.7%
0 17
 
11.6%
3 15
 
10.2%
5 9
 
6.1%
8 6
 
4.1%
6 5
 
3.4%
9 4
 
2.7%
4 4
 
2.7%
7 3
 
2.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3374
93.3%
Common 215
 
5.9%
Latin 28
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 310
 
9.2%
근 301
 
8.9%
로 105
 
3.1%
리 99
 
2.9%
사 85
 
2.5%
거 83
 
2.5%
교 82
 
2.4%
초 71
 
2.1%
학 62
 
1.8%
주 59
 
1.7%
Other values (280) 2117
62.7%
Common
ValueCountFrequency (%)
1 58
27.0%
2 26
12.1%
) 24
11.2%
( 24
11.2%
17
 
7.9%
0 17
 
7.9%
3 15
 
7.0%
5 9
 
4.2%
8 6
 
2.8%
6 5
 
2.3%
Other values (5) 14
 
6.5%
Latin
ValueCountFrequency (%)
K 3
10.7%
L 3
10.7%
e 3
10.7%
G 3
10.7%
C 3
10.7%
A 2
 
7.1%
B 2
 
7.1%
S 2
 
7.1%
I 1
 
3.6%
E 1
 
3.6%
Other values (5) 5
17.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3374
93.3%
ASCII 243
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 310
 
9.2%
근 301
 
8.9%
로 105
 
3.1%
리 99
 
2.9%
사 85
 
2.5%
거 83
 
2.5%
교 82
 
2.4%
초 71
 
2.1%
학 62
 
1.8%
주 59
 
1.7%
Other values (280) 2117
62.7%
ASCII
ValueCountFrequency (%)
1 58
23.9%
2 26
10.7%
) 24
9.9%
( 24
9.9%
17
 
7.0%
0 17
 
7.0%
3 15
 
6.2%
5 9
 
3.7%
8 6
 
2.5%
6 5
 
2.1%
Other values (20) 42
17.3%

행정동
Text

Distinct76
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-12T22:15:00.961702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length3
Mean length4.1501416
Min length2

Characters and Unicode

Total characters1465
Distinct characters91
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)8.8%

Sample

1st row서문동
2nd row문화동
3rd row용담동
4th row용암동
5th row용암동
ValueCountFrequency (%)
용암동 34
 
8.0%
복대동 30
 
7.0%
가경동 25
 
5.9%
오창읍 20
 
4.7%
봉명동 16
 
3.7%
봉산리 15
 
3.5%
금천동 12
 
2.8%
분평동 12
 
2.8%
사창동 12
 
2.8%
율량동 12
 
2.8%
Other values (80) 239
56.0%
2023-12-12T22:15:01.371940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 287
 
19.6%
77
 
5.3%
리 59
 
4.0%
1 46
 
3.1%
용 43
 
2.9%
암 42
 
2.9%
산 38
 
2.6%
오 38
 
2.6%
가 37
 
2.5%
창 36
 
2.5%
Other values (81) 762
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1206
82.3%
Decimal Number 123
 
8.4%
Space Separator 77
 
5.3%
Open Punctuation 15
 
1.0%
Close Punctuation 15
 
1.0%
Other Punctuation 15
 
1.0%
Dash Punctuation 14
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 287
23.8%
리 59
 
4.9%
용 43
 
3.6%
암 42
 
3.5%
산 38
 
3.2%
오 38
 
3.2%
가 37
 
3.1%
창 36
 
3.0%
읍 33
 
2.7%
봉 32
 
2.7%
Other values (66) 561
46.5%
Decimal Number
ValueCountFrequency (%)
1 46
37.4%
5 14
 
11.4%
2 13
 
10.6%
8 12
 
9.8%
4 8
 
6.5%
6 8
 
6.5%
3 7
 
5.7%
9 6
 
4.9%
7 5
 
4.1%
0 4
 
3.3%
Space Separator
ValueCountFrequency (%)
77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1206
82.3%
Common 259
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 287
23.8%
리 59
 
4.9%
용 43
 
3.6%
암 42
 
3.5%
산 38
 
3.2%
오 38
 
3.2%
가 37
 
3.1%
창 36
 
3.0%
읍 33
 
2.7%
봉 32
 
2.7%
Other values (66) 561
46.5%
Common
ValueCountFrequency (%)
77
29.7%
1 46
17.8%
( 15
 
5.8%
) 15
 
5.8%
, 15
 
5.8%
5 14
 
5.4%
- 14
 
5.4%
2 13
 
5.0%
8 12
 
4.6%
4 8
 
3.1%
Other values (5) 30
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1206
82.3%
ASCII 259
 
17.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 287
23.8%
리 59
 
4.9%
용 43
 
3.6%
암 42
 
3.5%
산 38
 
3.2%
오 38
 
3.2%
가 37
 
3.1%
창 36
 
3.0%
읍 33
 
2.7%
봉 32
 
2.7%
Other values (66) 561
46.5%
ASCII
ValueCountFrequency (%)
77
29.7%
1 46
17.8%
( 15
 
5.8%
) 15
 
5.8%
, 15
 
5.8%
5 14
 
5.4%
- 14
 
5.4%
2 13
 
5.0%
8 12
 
4.6%
4 8
 
3.1%
Other values (5) 30
 
11.6%

지번
Text

MISSING 

Distinct315
Distinct (%)93.5%
Missing16
Missing (%)4.5%
Memory size2.9 KiB
2023-12-12T22:15:01.788621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.2759644
Min length1

Characters and Unicode

Total characters1441
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique294 ?
Unique (%)87.2%

Sample

1st row162
2nd row18264
3rd row420
4th row2139
5th row1122
ValueCountFrequency (%)
88 3
 
0.9%
32752 2
 
0.6%
338 2
 
0.6%
14885 2
 
0.6%
2458 2
 
0.6%
566 2
 
0.6%
2103 2
 
0.6%
865 2
 
0.6%
90 2
 
0.6%
1459 2
 
0.6%
Other values (305) 316
93.8%
2023-12-12T22:15:02.356965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 252
17.5%
2 192
13.3%
3 144
10.0%
- 124
8.6%
5 122
8.5%
4 120
8.3%
8 112
7.8%
6 108
7.5%
0 98
 
6.8%
7 97
 
6.7%
Other values (2) 72
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1315
91.3%
Dash Punctuation 124
 
8.6%
Other Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 252
19.2%
2 192
14.6%
3 144
11.0%
5 122
9.3%
4 120
9.1%
8 112
8.5%
6 108
8.2%
0 98
 
7.5%
7 97
 
7.4%
9 70
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%
Other Letter
ValueCountFrequency (%)
산 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1439
99.9%
Hangul 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 252
17.5%
2 192
13.3%
3 144
10.0%
- 124
8.6%
5 122
8.5%
4 120
8.3%
8 112
7.8%
6 108
7.5%
0 98
 
6.8%
7 97
 
6.7%
Hangul
ValueCountFrequency (%)
산 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1439
99.9%
Hangul 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 252
17.5%
2 192
13.3%
3 144
10.0%
- 124
8.6%
5 122
8.5%
4 120
8.3%
8 112
7.8%
6 108
7.5%
0 98
 
6.8%
7 97
 
6.7%
Hangul
ValueCountFrequency (%)
산 2
100.0%

비고
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
주정차단속
353 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주정차단속 353
100.0%

Length

2023-12-12T22:15:02.518933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:15:02.604241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주정차단속 353
100.0%

Correlations

2023-12-12T22:15:02.656818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구별행정동
구별1.0001.000
행정동1.0001.000

Missing values

2023-12-12T22:14:59.021123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:14:59.145021image/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

구별관리번호위치 명칭행정동지번비고
0상당구A0001무심동로서문대교삼거리부근서문동162주정차단속
1상당구A0002대성로성안행정복지센터부근문화동18264주정차단속
2상당구A0004세영첼시빌111동부근용담동420주정차단속
3상당구A0005용암북로운암1교사거리부근용암동2139주정차단속
4상당구A0006용암북로용암오토바이부근용암동1122주정차단속
5상당구A0007무농정로농협물류센타부근용암동1775주정차단속
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