Overview

Dataset statistics

Number of variables3
Number of observations76
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory26.7 B

Variable types

Numeric1
Text2

Dataset

Description대구광역시 불법주정차 단속 무인카메라 현황입니다. 카메라 명칭, 카메라 위치인 세부 주소의 내용을 포함하고 있습니다.
Author대구광역시 서구
URLhttps://www.data.go.kr/data/15040416/fileData.do

Alerts

연번 has unique valuesUnique
명 칭 has unique valuesUnique
세 부 주 소 has unique valuesUnique

Reproduction

Analysis started2024-03-14 23:57:59.618553
Analysis finished2024-03-14 23:58:00.857405
Duration1.24 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.5
Minimum1
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T08:58:01.148481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.75
Q119.75
median38.5
Q357.25
95-th percentile72.25
Maximum76
Range75
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation22.083176
Coefficient of variation (CV)0.57358899
Kurtosis-1.2
Mean38.5
Median Absolute Deviation (MAD)19
Skewness0
Sum2926
Variance487.66667
MonotonicityStrictly increasing
2024-03-15T08:58:01.691972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.3%
50 1
 
1.3%
57 1
 
1.3%
56 1
 
1.3%
55 1
 
1.3%
54 1
 
1.3%
53 1
 
1.3%
52 1
 
1.3%
51 1
 
1.3%
49 1
 
1.3%
Other values (66) 66
86.8%
ValueCountFrequency (%)
1 1
1.3%
2 1
1.3%
3 1
1.3%
4 1
1.3%
5 1
1.3%
6 1
1.3%
7 1
1.3%
8 1
1.3%
9 1
1.3%
10 1
1.3%
ValueCountFrequency (%)
76 1
1.3%
75 1
1.3%
74 1
1.3%
73 1
1.3%
72 1
1.3%
71 1
1.3%
70 1
1.3%
69 1
1.3%
68 1
1.3%
67 1
1.3%

명 칭
Text

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size736.0 B
2024-03-15T08:58:02.870146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length8.1184211
Min length4

Characters and Unicode

Total characters617
Distinct characters165
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

Unique76 ?
Unique (%)100.0%

Sample

1st row서구청 건너
2nd row이현공단주유소 옆
3rd row대성초 정문
4th row북부정류장 내
5th row은하수노래방 앞
ValueCountFrequency (%)
앞 20
 
13.1%
정문 17
 
11.1%
입구 4
 
2.6%
서대구역 3
 
2.0%
주변 3
 
2.0%
북편 3
 
2.0%
건너 2
 
1.3%
인지초 2
 
1.3%
비산초 2
 
1.3%
서편 2
 
1.3%
Other values (89) 95
62.1%
2024-03-15T08:58:04.370016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
 
12.5%
서 25
 
4.1%
정 21
 
3.4%
앞 20
 
3.2%
구 19
 
3.1%
초 19
 
3.1%
문 18
 
2.9%
대 15
 
2.4%
이 13
 
2.1%
편 11
 
1.8%
Other values (155) 379
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 517
83.8%
Space Separator 77
 
12.5%
Decimal Number 10
 
1.6%
Uppercase Letter 5
 
0.8%
Close Punctuation 4
 
0.6%
Open Punctuation 4
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 25
 
4.8%
정 21
 
4.1%
앞 20
 
3.9%
구 19
 
3.7%
초 19
 
3.7%
문 18
 
3.5%
대 15
 
2.9%
이 13
 
2.5%
편 11
 
2.1%
리 10
 
1.9%
Other values (142) 346
66.9%
Decimal Number
ValueCountFrequency (%)
7 4
40.0%
1 2
20.0%
2 2
20.0%
3 1
 
10.0%
0 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
20.0%
H 1
20.0%
N 1
20.0%
M 1
20.0%
W 1
20.0%
Space Separator
ValueCountFrequency (%)
77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 517
83.8%
Common 95
 
15.4%
Latin 5
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 25
 
4.8%
정 21
 
4.1%
앞 20
 
3.9%
구 19
 
3.7%
초 19
 
3.7%
문 18
 
3.5%
대 15
 
2.9%
이 13
 
2.5%
편 11
 
2.1%
리 10
 
1.9%
Other values (142) 346
66.9%
Common
ValueCountFrequency (%)
77
81.1%
7 4
 
4.2%
) 4
 
4.2%
( 4
 
4.2%
1 2
 
2.1%
2 2
 
2.1%
3 1
 
1.1%
0 1
 
1.1%
Latin
ValueCountFrequency (%)
B 1
20.0%
H 1
20.0%
N 1
20.0%
M 1
20.0%
W 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 517
83.8%
ASCII 100
 
16.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77
77.0%
7 4
 
4.0%
) 4
 
4.0%
( 4
 
4.0%
1 2
 
2.0%
2 2
 
2.0%
3 1
 
1.0%
0 1
 
1.0%
B 1
 
1.0%
H 1
 
1.0%
Other values (3) 3
 
3.0%
Hangul
ValueCountFrequency (%)
서 25
 
4.8%
정 21
 
4.1%
앞 20
 
3.9%
구 19
 
3.7%
초 19
 
3.7%
문 18
 
3.5%
대 15
 
2.9%
이 13
 
2.5%
편 11
 
2.1%
리 10
 
1.9%
Other values (142) 346
66.9%

세 부 주 소
Text

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size736.0 B
2024-03-15T08:58:05.467686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length11
Min length6

Characters and Unicode

Total characters836
Distinct characters107
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)100.0%

Sample

1st row국채보상로 244, 눈높이 안경콘택트 앞
2nd row국채보상로 185 센스광고 앞
3rd row국채보상로 426
4th row비산동 1856-3, 주차요금소 앞
5th row당산로 259-1, 은하수노래방 앞
ValueCountFrequency (%)
비산동 12
 
6.7%
평리동 10
 
5.6%
앞 9
 
5.1%
이현동 6
 
3.4%
국채보상로 6
 
3.4%
달구벌대로 3
 
1.7%
팔달로 3
 
1.7%
원대동3가 3
 
1.7%
북비산로 3
 
1.7%
35 3
 
1.7%
Other values (110) 120
67.4%
2024-03-15T08:58:06.979568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
103
 
12.3%
1 56
 
6.7%
로 42
 
5.0%
3 40
 
4.8%
2 36
 
4.3%
- 35
 
4.2%
동 34
 
4.1%
6 32
 
3.8%
5 31
 
3.7%
4 29
 
3.5%
Other values (97) 398
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 383
45.8%
Decimal Number 303
36.2%
Space Separator 103
 
12.3%
Dash Punctuation 35
 
4.2%
Other Punctuation 11
 
1.3%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 42
 
11.0%
동 34
 
8.9%
산 17
 
4.4%
길 16
 
4.2%
리 15
 
3.9%
비 15
 
3.9%
달 14
 
3.7%
대 14
 
3.7%
평 13
 
3.4%
구 13
 
3.4%
Other values (83) 190
49.6%
Decimal Number
ValueCountFrequency (%)
1 56
18.5%
3 40
13.2%
2 36
11.9%
6 32
10.6%
5 31
10.2%
4 29
9.6%
7 24
7.9%
0 23
7.6%
8 17
 
5.6%
9 15
 
5.0%
Space Separator
ValueCountFrequency (%)
103
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 452
54.1%
Hangul 384
45.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 42
 
10.9%
동 34
 
8.9%
산 17
 
4.4%
길 16
 
4.2%
리 15
 
3.9%
비 15
 
3.9%
달 14
 
3.6%
대 14
 
3.6%
평 13
 
3.4%
구 13
 
3.4%
Other values (84) 191
49.7%
Common
ValueCountFrequency (%)
103
22.8%
1 56
12.4%
3 40
 
8.8%
2 36
 
8.0%
- 35
 
7.7%
6 32
 
7.1%
5 31
 
6.9%
4 29
 
6.4%
7 24
 
5.3%
0 23
 
5.1%
Other values (3) 43
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 452
54.1%
Hangul 383
45.8%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
103
22.8%
1 56
12.4%
3 40
 
8.8%
2 36
 
8.0%
- 35
 
7.7%
6 32
 
7.1%
5 31
 
6.9%
4 29
 
6.4%
7 24
 
5.3%
0 23
 
5.1%
Other values (3) 43
9.5%
Hangul
ValueCountFrequency (%)
로 42
 
11.0%
동 34
 
8.9%
산 17
 
4.4%
길 16
 
4.2%
리 15
 
3.9%
비 15
 
3.9%
달 14
 
3.7%
대 14
 
3.7%
평 13
 
3.4%
구 13
 
3.4%
Other values (83) 190
49.6%
None
ValueCountFrequency (%)
㈜ 1
100.0%

Interactions

2024-03-15T08:58:00.131849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:58:07.241558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번명 칭세 부 주 소
연번1.0001.0001.000
명 칭1.0001.0001.000
세 부 주 소1.0001.0001.000

Missing values

2024-03-15T08:58:00.485565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:58:00.750756image/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

연번명 칭세 부 주 소
01서구청 건너국채보상로 244, 눈높이 안경콘택트 앞
12이현공단주유소 옆국채보상로 185 센스광고 앞
23대성초 정문국채보상로 426
34북부정류장 내비산동 1856-3, 주차요금소 앞
45은하수노래방 앞당산로 259-1, 은하수노래방 앞
56킹스턴파크 앞서대구로 40, 청춘횟집 앞
67서문시장(한국그릇) 앞큰장로 123
78NH농협은행두류점달구벌대로 1797
89평리푸르지오 입구국채보상로 50길 20, 평리푸르지오 입구
910북부정류장 입구서대구로 299, 와룡축산물판매장 앞
연번명 칭세 부 주 소
6667서대구 농협 앞평리동 1621-13
6768서대구센트럴자이 정문원대동3가 1611-3
6869서대구센트럴자이 남동편원대동3가 1309-2
6970원대역 앞원대동3가 1317-2
7071반도유보라 네거리평리동 1037-1
7172서부도서관 뒷편평리동 1069-3
7273서대구역화성파크드림 722동 북서편평리동 1511-8
7374서대구역화성파크드림 731동 서편평리동 1515-14
7475(화물차) 복개도로이현동 42-179
7576(화물차) 서구 구민운동장 부근비산동 870-6