Overview

Dataset statistics

Number of variables6
Number of observations82
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory52.6 B

Variable types

Numeric3
Text1
Categorical2

Dataset

Description미추홀구 관내의 불법주정차단속 CCTV에 대한 데이터로 설치위치, 구분, 데이터기준일자,위도,경도 등의 항목을 제공합니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15040488&srcSe=7661IVAWM27C61E190

Alerts

구분 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 has unique valuesUnique
설치위치 has unique valuesUnique

Reproduction

Analysis started2024-05-03 19:48:11.734072
Analysis finished2024-05-03 19:48:22.177359
Duration10.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.5
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-05-03T19:48:22.581266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.05
Q121.25
median41.5
Q361.75
95-th percentile77.95
Maximum82
Range81
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation23.815261
Coefficient of variation (CV)0.57386172
Kurtosis-1.2
Mean41.5
Median Absolute Deviation (MAD)20.5
Skewness0
Sum3403
Variance567.16667
MonotonicityStrictly increasing
2024-05-03T19:48:23.260164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
63 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
82 1
1.2%
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%

설치위치
Text

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
2024-05-03T19:48:24.038608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length25
Mean length19.5
Min length13

Characters and Unicode

Total characters1599
Distinct characters177
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

Unique82 ?
Unique (%)100.0%

Sample

1st row용일사거리 (한나루로 505)
2nd row선창빌딩 앞 (인주대로 311)
3rd row맑은교회 앞 (인주대로 359)
4th rowGS25 주안승기점 (인주대로 456)
5th row석바위사거리 (경인로 447)
ValueCountFrequency (%)
앞 13
 
4.8%
삼거리 8
 
3.0%
사거리 7
 
2.6%
경인로 6
 
2.2%
용현동 5
 
1.8%
인주대로 5
 
1.8%
연남로 4
 
1.5%
인하로 4
 
1.5%
35 4
 
1.5%
용현sk스카이뷰 3
 
1.1%
Other values (196) 212
78.2%
2024-05-03T19:48:25.503023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
189
 
11.8%
( 85
 
5.3%
) 85
 
5.3%
1 53
 
3.3%
학 47
 
2.9%
동 47
 
2.9%
로 45
 
2.8%
리 37
 
2.3%
3 37
 
2.3%
4 36
 
2.3%
Other values (167) 938
58.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 920
57.5%
Decimal Number 280
 
17.5%
Space Separator 189
 
11.8%
Open Punctuation 85
 
5.3%
Close Punctuation 85
 
5.3%
Dash Punctuation 30
 
1.9%
Uppercase Letter 10
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 47
 
5.1%
동 47
 
5.1%
로 45
 
4.9%
리 37
 
4.0%
교 35
 
3.8%
거 33
 
3.6%
주 33
 
3.6%
인 27
 
2.9%
등 25
 
2.7%
초 24
 
2.6%
Other values (150) 567
61.6%
Decimal Number
ValueCountFrequency (%)
1 53
18.9%
3 37
13.2%
4 36
12.9%
2 34
12.1%
5 29
10.4%
6 25
8.9%
9 18
 
6.4%
7 17
 
6.1%
0 17
 
6.1%
8 14
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
S 5
50.0%
K 4
40.0%
G 1
 
10.0%
Space Separator
ValueCountFrequency (%)
189
100.0%
Open Punctuation
ValueCountFrequency (%)
( 85
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 920
57.5%
Common 669
41.8%
Latin 10
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 47
 
5.1%
동 47
 
5.1%
로 45
 
4.9%
리 37
 
4.0%
교 35
 
3.8%
거 33
 
3.6%
주 33
 
3.6%
인 27
 
2.9%
등 25
 
2.7%
초 24
 
2.6%
Other values (150) 567
61.6%
Common
ValueCountFrequency (%)
189
28.3%
( 85
12.7%
) 85
12.7%
1 53
 
7.9%
3 37
 
5.5%
4 36
 
5.4%
2 34
 
5.1%
- 30
 
4.5%
5 29
 
4.3%
6 25
 
3.7%
Other values (4) 66
 
9.9%
Latin
ValueCountFrequency (%)
S 5
50.0%
K 4
40.0%
G 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 920
57.5%
ASCII 679
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
189
27.8%
( 85
12.5%
) 85
12.5%
1 53
 
7.8%
3 37
 
5.4%
4 36
 
5.3%
2 34
 
5.0%
- 30
 
4.4%
5 29
 
4.3%
6 25
 
3.7%
Other values (7) 76
11.2%
Hangul
ValueCountFrequency (%)
학 47
 
5.1%
동 47
 
5.1%
로 45
 
4.9%
리 37
 
4.0%
교 35
 
3.8%
거 33
 
3.6%
주 33
 
3.6%
인 27
 
2.9%
등 25
 
2.7%
초 24
 
2.6%
Other values (150) 567
61.6%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
불법주정차단속CCTV
82 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
불법주정차단속CCTV 82
100.0%

Length

2024-05-03T19:48:26.028053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:48:26.379395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불법주정차단속cctv 82
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-04-03
82 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-04-03
2nd row2023-04-03
3rd row2023-04-03
4th row2023-04-03
5th row2023-04-03

Common Values

ValueCountFrequency (%)
2023-04-03 82
100.0%

Length

2024-05-03T19:48:27.003926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:48:27.342329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-04-03 82
100.0%

위도
Real number (ℝ)

Distinct79
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.455689
Minimum37.43767
Maximum37.53756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-05-03T19:48:27.740014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.43767
5-th percentile37.439721
Q137.446849
median37.452414
Q337.461352
95-th percentile37.473357
Maximum37.53756
Range0.0998904
Interquartile range (IQR)0.014503512

Descriptive statistics

Standard deviation0.016146172
Coefficient of variation (CV)0.00043107395
Kurtosis14.411956
Mean37.455689
Median Absolute Deviation (MAD)0.0077493
Skewness3.1786406
Sum3071.3665
Variance0.00026069886
MonotonicityNot monotonic
2024-05-03T19:48:28.450773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.441632 4
 
4.9%
37.4520006 1
 
1.2%
37.4610661 1
 
1.2%
37.45328625 1
 
1.2%
37.43850473 1
 
1.2%
37.46826557 1
 
1.2%
37.45978114 1
 
1.2%
37.44090592 1
 
1.2%
37.45795289 1
 
1.2%
37.46336583 1
 
1.2%
Other values (69) 69
84.1%
ValueCountFrequency (%)
37.4376696 1
 
1.2%
37.43850473 1
 
1.2%
37.43873 1
 
1.2%
37.43953347 1
 
1.2%
37.43970362 1
 
1.2%
37.4400486 1
 
1.2%
37.44090592 1
 
1.2%
37.441632 4
4.9%
37.4421382 1
 
1.2%
37.4421441 1
 
1.2%
ValueCountFrequency (%)
37.53756 1
1.2%
37.53672097 1
1.2%
37.4738951 1
1.2%
37.4738286 1
1.2%
37.47340597 1
1.2%
37.47242161 1
1.2%
37.4720962 1
1.2%
37.4709007 1
1.2%
37.46826557 1
1.2%
37.46797364 1
1.2%

경도
Real number (ℝ)

Distinct79
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.67619
Minimum126.63267
Maximum126.94648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-05-03T19:48:29.059907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63267
5-th percentile126.64122
Q1126.65761
median126.67273
Q3126.68224
95-th percentile126.7015
Maximum126.94648
Range0.313817
Interquartile range (IQR)0.024625625

Descriptive statistics

Standard deviation0.046120695
Coefficient of variation (CV)0.00036408337
Kurtosis27.59604
Mean126.67619
Median Absolute Deviation (MAD)0.0118077
Skewness4.9250029
Sum10387.448
Variance0.0021271185
MonotonicityNot monotonic
2024-05-03T19:48:29.639609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.701501 4
 
4.9%
126.667159 1
 
1.2%
126.677404 1
 
1.2%
126.6414147 1
 
1.2%
126.6722276 1
 
1.2%
126.6646353 1
 
1.2%
126.6490592 1
 
1.2%
126.6627796 1
 
1.2%
126.6798012 1
 
1.2%
126.6462893 1
 
1.2%
Other values (69) 69
84.1%
ValueCountFrequency (%)
126.632667 1
1.2%
126.635652 1
1.2%
126.637349 1
1.2%
126.638066 1
1.2%
126.641213 1
1.2%
126.6412803 1
1.2%
126.6414147 1
1.2%
126.6420222 1
1.2%
126.643485 1
1.2%
126.643488 1
1.2%
ValueCountFrequency (%)
126.946484 1
 
1.2%
126.9432694 1
 
1.2%
126.701501 4
4.9%
126.6962021 1
 
1.2%
126.694154 1
 
1.2%
126.6935643 1
 
1.2%
126.6929667 1
 
1.2%
126.689836 1
 
1.2%
126.68976 1
 
1.2%
126.687214 1
 
1.2%

Interactions

2024-05-03T19:48:20.126804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:48:17.232985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:48:19.007689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:48:20.512224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:48:17.995905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:48:19.439302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:48:20.935255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:48:18.525266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:48:19.807038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T19:48:29.985054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치위치위도경도
연번1.0001.0000.3620.592
설치위치1.0001.0001.0001.000
위도0.3621.0001.0000.667
경도0.5921.0000.6671.000
2024-05-03T19:48:30.478709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.034-0.118
위도0.0341.000-0.052
경도-0.118-0.0521.000

Missing values

2024-05-03T19:48:21.382289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T19:48:21.932686image/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

연번설치위치구분데이터기준일자위도경도
01용일사거리 (한나루로 505)불법주정차단속CCTV2023-04-0337.452001126.667159
12선창빌딩 앞 (인주대로 311)불법주정차단속CCTV2023-04-0337.45221126.671068
23맑은교회 앞 (인주대로 359)불법주정차단속CCTV2023-04-0337.45184126.676459
34GS25 주안승기점 (인주대로 456)불법주정차단속CCTV2023-04-0337.450668126.687214
45석바위사거리 (경인로 447)불법주정차단속CCTV2023-04-0337.458063126.68976
56마실감자탕 (경인로 420)불법주정차단속CCTV2023-04-0337.457803126.686709
67리성창호 (인주대로 423)불법주정차단속CCTV2023-04-0337.451442126.683684
78제일시장사거리 (한나루로 591)불법주정차단속CCTV2023-04-0337.458843126.671819
89주안사거리 (경인로 331)불법주정차단속CCTV2023-04-0337.458884126.676781
910기아자동차 인천서비스센터 앞 (경인로 303)불법주정차단속CCTV2023-04-0337.460274126.674514
연번설치위치구분데이터기준일자위도경도
7273인주초등학교(학익동 40)불법주정차단속CCTV2023-04-0337.442203126.673795
7374용일초등학교(용현동 74-1)불법주정차단속CCTV2023-04-0337.452855126.663526
7475매소홀로442 건너편(학익동 677-1)불법주정차단속CCTV2023-04-0337.439533126.671048
7576주안 더월드스테이트(주안동 913-1)불법주정차단속CCTV2023-04-0337.461447126.693564
7677대화초등학교(도화동 107-2 앞)불법주정차단속CCTV2023-04-0337.536721126.943269
7778서화초등학교(서화초 후문 삼거리 횡단보도)(도화동 1024)불법주정차단속CCTV2023-04-0337.472422126.66137
7879청인학교(도화동 994)불법주정차단속CCTV2023-04-0337.473895126.658796
7980경원초등학교(주안동 829-7)불법주정차단속CCTV2023-04-0337.447649126.67214
8081연학초등학교(주안동 866-152)불법주정차단속CCTV2023-04-0337.443835126.6724
8182학산초등학교(인천병무지청 삼거리)(학익동 536-9)불법주정차단속CCTV2023-04-0337.439704126.657612