Overview

Dataset statistics

Number of variables9
Number of observations44
Missing cells2
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory79.0 B

Variable types

Categorical5
Text2
Numeric2

Dataset

Description충청남도 논산시 이동형무인단속카메라현황(설치연도, 설치주체, 장비기능, 설치장소, 방면, 도로구분, 제한속도, 위도, 경도)
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=388&beforeMenuCd=DOM_000000201001001000&publicdatapk=15049820

Alerts

장비기능 has constant value ""Constant
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
설치연도 is highly overall correlated with 도로구분High correlation
설치주체 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
도로구분 is highly overall correlated with 설치연도 and 1 other fieldsHigh correlation
제한속도 is highly overall correlated with 도로구분High correlation
위도 has 1 (2.3%) missing valuesMissing
경도 has 1 (2.3%) missing valuesMissing

Reproduction

Analysis started2024-01-09 21:50:31.366745
Analysis finished2024-01-09 21:50:32.232705
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

설치연도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size484.0 B
2018
20 
2019
10 
2017
7 
2016
6 
2021
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2018 20
45.5%
2019 10
22.7%
2017 7
 
15.9%
2016 6
 
13.6%
2021 1
 
2.3%

Length

2024-01-10T06:50:32.280442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:50:32.360515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 20
45.5%
2019 10
22.7%
2017 7
 
15.9%
2016 6
 
13.6%
2021 1
 
2.3%

설치주체
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size484.0 B
논산시
28 
논산국토관리
11 
계룡시
5 

Length

Max length6
Median length3
Mean length3.75
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
논산시 28
63.6%
논산국토관리 11
 
25.0%
계룡시 5
 
11.4%

Length

2024-01-10T06:50:32.454990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:50:32.536449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
논산시 28
63.6%
논산국토관리 11
 
25.0%
계룡시 5
 
11.4%

장비기능
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
과속
44 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
과속 44
100.0%

Length

2024-01-10T06:50:32.618829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:50:32.689767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
과속 44
100.0%

설치장소
Text

Distinct41
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-01-10T06:50:32.860627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length20.909091
Min length17

Characters and Unicode

Total characters920
Distinct characters141
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)86.4%

Sample

1st row충청남도 논산시 취암동 공운삼거리
2nd row충청남도 논산시 논산시종합사회복지관앞 상행선
3rd row충청남도 논산시 채운면 장화3리 입구
4th row충청남도 논산시 부창동 금강청과 앞
5th row충청남도 논산시 부창동 해창교 앞
ValueCountFrequency (%)
충청남도 44
21.1%
논산시 39
18.7%
앞 8
 
3.8%
연산면 6
 
2.9%
계룡시 5
 
2.4%
연무읍 5
 
2.4%
채운면 5
 
2.4%
성동면 3
 
1.4%
엄사면 3
 
1.4%
등화동 3
 
1.4%
Other values (75) 88
42.1%
2024-01-10T06:50:33.178809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
165
17.9%
산 56
 
6.1%
도 46
 
5.0%
시 45
 
4.9%
청 45
 
4.9%
충 44
 
4.8%
남 44
 
4.8%
논 42
 
4.6%
리 33
 
3.6%
면 26
 
2.8%
Other values (131) 374
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 748
81.3%
Space Separator 165
 
17.9%
Decimal Number 6
 
0.7%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 56
 
7.5%
도 46
 
6.1%
시 45
 
6.0%
청 45
 
6.0%
충 44
 
5.9%
남 44
 
5.9%
논 42
 
5.6%
리 33
 
4.4%
면 26
 
3.5%
동 17
 
2.3%
Other values (125) 350
46.8%
Decimal Number
ValueCountFrequency (%)
3 2
33.3%
0 2
33.3%
5 1
16.7%
2 1
16.7%
Space Separator
ValueCountFrequency (%)
165
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 748
81.3%
Common 172
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 56
 
7.5%
도 46
 
6.1%
시 45
 
6.0%
청 45
 
6.0%
충 44
 
5.9%
남 44
 
5.9%
논 42
 
5.6%
리 33
 
4.4%
면 26
 
3.5%
동 17
 
2.3%
Other values (125) 350
46.8%
Common
ValueCountFrequency (%)
165
95.9%
3 2
 
1.2%
0 2
 
1.2%
- 1
 
0.6%
5 1
 
0.6%
2 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 748
81.3%
ASCII 172
 
18.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
165
95.9%
3 2
 
1.2%
0 2
 
1.2%
- 1
 
0.6%
5 1
 
0.6%
2 1
 
0.6%
Hangul
ValueCountFrequency (%)
산 56
 
7.5%
도 46
 
6.1%
시 45
 
6.0%
청 45
 
6.0%
충 44
 
5.9%
남 44
 
5.9%
논 42
 
5.6%
리 33
 
4.4%
면 26
 
3.5%
동 17
 
2.3%
Other values (125) 350
46.8%

방면
Text

Distinct35
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-01-10T06:50:33.349126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length6.3181818
Min length5

Characters and Unicode

Total characters278
Distinct characters60
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)63.6%

Sample

1st row계백교→백제병원
2nd row공설운동장→계백교
3rd row강경→논산
4th row강경→논산대교
5th row논산대교→강경
ValueCountFrequency (%)
대전→논산 3
 
6.8%
논산→대전 3
 
6.8%
논산→연무 2
 
4.5%
논산→육군훈련소 2
 
4.5%
논산→강경 2
 
4.5%
논산대교→강경 2
 
4.5%
계룡→논산 2
 
4.5%
폴리텍대학→연무 1
 
2.3%
논산→계룡 1
 
2.3%
공주→논산 1
 
2.3%
Other values (25) 25
56.8%
2024-01-10T06:50:33.647109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
→ 44
15.8%
산 35
 
12.6%
논 28
 
10.1%
대 17
 
6.1%
계 11
 
4.0%
강 11
 
4.0%
전 9
 
3.2%
경 9
 
3.2%
교 7
 
2.5%
룡 7
 
2.5%
Other values (50) 100
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 233
83.8%
Math Symbol 44
 
15.8%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 35
 
15.0%
논 28
 
12.0%
대 17
 
7.3%
계 11
 
4.7%
강 11
 
4.7%
전 9
 
3.9%
경 9
 
3.9%
교 7
 
3.0%
룡 7
 
3.0%
연 6
 
2.6%
Other values (48) 93
39.9%
Math Symbol
ValueCountFrequency (%)
→ 44
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 233
83.8%
Common 45
 
16.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 35
 
15.0%
논 28
 
12.0%
대 17
 
7.3%
계 11
 
4.7%
강 11
 
4.7%
전 9
 
3.9%
경 9
 
3.9%
교 7
 
3.0%
룡 7
 
3.0%
연 6
 
2.6%
Other values (48) 93
39.9%
Common
ValueCountFrequency (%)
→ 44
97.8%
1 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 233
83.8%
Arrows 44
 
15.8%
ASCII 1
 
0.4%

Most frequent character per block

Arrows
ValueCountFrequency (%)
→ 44
100.0%
Hangul
ValueCountFrequency (%)
산 35
 
15.0%
논 28
 
12.0%
대 17
 
7.3%
계 11
 
4.7%
강 11
 
4.7%
전 9
 
3.9%
경 9
 
3.9%
교 7
 
3.0%
룡 7
 
3.0%
연 6
 
2.6%
Other values (48) 93
39.9%
ASCII
ValueCountFrequency (%)
1 1
100.0%

도로구분
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size484.0 B
국도1호
19 
도시계획도로
10 
국도23호
9 
국지도68호
2 
국도4호
2 
Other values (2)
2 

Length

Max length8
Median length7
Mean length4.9090909
Min length4

Unique

Unique2 ?
Unique (%)4.5%

Sample

1st row도시계획도로
2nd row도시계획도로
3rd row국도23호
4th row국도23호
5th row국도23호

Common Values

ValueCountFrequency (%)
국도1호 19
43.2%
도시계획도로 10
22.7%
국도23호 9
20.5%
국지도68호 2
 
4.5%
국도4호 2
 
4.5%
지방도799호 1
 
2.3%
지방도 68호 1
 
2.3%

Length

2024-01-10T06:50:33.761351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:50:33.855083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국도1호 19
42.2%
도시계획도로 10
22.2%
국도23호 9
20.0%
국지도68호 2
 
4.4%
국도4호 2
 
4.4%
지방도799호 1
 
2.2%
지방도 1
 
2.2%
68호 1
 
2.2%

제한속도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size484.0 B
70
20 
60
17 
80
4 
50
 
2
30
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row60
2nd row60
3rd row70
4th row70
5th row70

Common Values

ValueCountFrequency (%)
70 20
45.5%
60 17
38.6%
80 4
 
9.1%
50 2
 
4.5%
30 1
 
2.3%

Length

2024-01-10T06:50:33.953776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:50:34.032813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
70 20
45.5%
60 17
38.6%
80 4
 
9.1%
50 2
 
4.5%
30 1
 
2.3%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)97.7%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean36.19292
Minimum36.079021
Maximum36.308576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-01-10T06:50:34.125067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.079021
5-th percentile36.10636
Q136.160943
median36.19365
Q336.222702
95-th percentile36.279585
Maximum36.308576
Range0.229555
Interquartile range (IQR)0.0617595

Descriptive statistics

Standard deviation0.052539412
Coefficient of variation (CV)0.0014516489
Kurtosis-0.033609567
Mean36.19292
Median Absolute Deviation (MAD)0.029356
Skewness-0.067325923
Sum1556.2956
Variance0.0027603898
MonotonicityNot monotonic
2024-01-10T06:50:34.224073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
36.13933 2
 
4.5%
36.079292 1
 
2.3%
36.212904 1
 
2.3%
36.213846 1
 
2.3%
36.17952 1
 
2.3%
36.174834 1
 
2.3%
36.18134 1
 
2.3%
36.17782 1
 
2.3%
36.177699 1
 
2.3%
36.143034 1
 
2.3%
Other values (32) 32
72.7%
ValueCountFrequency (%)
36.079021 1
2.3%
36.079292 1
2.3%
36.104632 1
2.3%
36.121907 1
2.3%
36.128405 1
2.3%
36.13933 2
4.5%
36.143034 1
2.3%
36.146859 1
2.3%
36.149362 1
2.3%
36.15737 1
2.3%
ValueCountFrequency (%)
36.308576 1
2.3%
36.288886 1
2.3%
36.281037 1
2.3%
36.266522 1
2.3%
36.264061 1
2.3%
36.254789 1
2.3%
36.245451 1
2.3%
36.241617 1
2.3%
36.232836 1
2.3%
36.229579 1
2.3%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)97.7%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean127.11884
Minimum127.00782
Maximum127.2717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-01-10T06:50:34.324909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.00782
5-th percentile127.03137
Q1127.07508
median127.10073
Q3127.16141
95-th percentile127.24408
Maximum127.2717
Range0.263877
Interquartile range (IQR)0.086331

Descriptive statistics

Standard deviation0.07167373
Coefficient of variation (CV)0.00056383248
Kurtosis-0.54671883
Mean127.11884
Median Absolute Deviation (MAD)0.028165
Skewness0.73278872
Sum5466.1101
Variance0.0051371236
MonotonicityNot monotonic
2024-01-10T06:50:34.427503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
127.100835 2
 
4.5%
127.092496 1
 
2.3%
127.14313 1
 
2.3%
127.077765 1
 
2.3%
127.066758 1
 
2.3%
127.058525 1
 
2.3%
127.076864 1
 
2.3%
127.098576 1
 
2.3%
127.098826 1
 
2.3%
127.047007 1
 
2.3%
Other values (32) 32
72.7%
ValueCountFrequency (%)
127.007821 1
2.3%
127.010143 1
2.3%
127.030491 1
2.3%
127.039313 1
2.3%
127.042133 1
2.3%
127.047007 1
2.3%
127.050613 1
2.3%
127.058525 1
2.3%
127.066758 1
2.3%
127.072566 1
2.3%
ValueCountFrequency (%)
127.271698 1
2.3%
127.251978 1
2.3%
127.24469 1
2.3%
127.238632 1
2.3%
127.229972 1
2.3%
127.228071 1
2.3%
127.227709 1
2.3%
127.226376 1
2.3%
127.225865 1
2.3%
127.190809 1
2.3%

Interactions

2024-01-10T06:50:31.879411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:50:31.760323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:50:31.938018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:50:31.819633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:50:34.495653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치연도설치주체설치장소방면도로구분제한속도위도경도
설치연도1.0000.3260.9800.8830.7040.4570.4180.000
설치주체0.3261.0001.0000.6110.5340.0000.8870.880
설치장소0.9801.0001.0000.9261.0001.0001.0001.000
방면0.8830.6110.9261.0001.0001.0000.6350.000
도로구분0.7040.5341.0001.0001.0000.7670.5570.618
제한속도0.4570.0001.0001.0000.7671.0000.2410.594
위도0.4180.8871.0000.6350.5570.2411.0000.683
경도0.0000.8801.0000.0000.6180.5940.6831.000
2024-01-10T06:50:34.601232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제한속도설치주체도로구분설치연도
제한속도1.0000.0000.6160.179
설치주체0.0001.0000.3970.250
도로구분0.6160.3971.0000.536
설치연도0.1790.2500.5361.000
2024-01-10T06:50:34.697123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도설치연도설치주체도로구분제한속도
위도1.0000.5540.2300.7520.3070.060
경도0.5541.0000.0000.5570.3420.369
설치연도0.2300.0001.0000.2500.5360.179
설치주체0.7520.5570.2501.0000.3970.000
도로구분0.3070.3420.5360.3971.0000.616
제한속도0.0600.3690.1790.0000.6161.000

Missing values

2024-01-10T06:50:32.024162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:50:32.124480image/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.
2024-01-10T06:50:32.197835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

설치연도설치주체장비기능설치장소방면도로구분제한속도위도경도
02016논산시과속충청남도 논산시 취암동 공운삼거리계백교→백제병원도시계획도로6036.13933127.100835
12016논산시과속충청남도 논산시 논산시종합사회복지관앞 상행선공설운동장→계백교도시계획도로6036.195637127.101624
22016논산시과속충청남도 논산시 채운면 장화3리 입구강경→논산국도23호7036.168614127.042133
32016논산시과속충청남도 논산시 부창동 금강청과 앞강경→논산대교국도23호7036.195226127.073289
42016논산시과속충청남도 논산시 부창동 해창교 앞논산대교→강경국도23호7036.19365127.072566
52016논산시과속충청남도 논산시 채운면 화정교차로연무→강경국지도68호7036.13933127.100835
62017논산시과속충청남도 논산시 덕지동 논산폐차장 앞논산역→대전도시계획도로6036.210502127.10834
72017논산시과속충청남도 논산시 덕지동 덕지삼거리대전→논산역도시계획도로6036.209529127.100731
82017논산시과속충청남도 논산시 등화동 대건고등학교 앞강산사거리→강경도시계획도로6036.182289127.078354
92017논산시과속충청남도 논산시 강산동 황현마을 입구대건고→강산사거리도시계획도로6036.186985127.086865
설치연도설치주체장비기능설치장소방면도로구분제한속도위도경도
342019논산국토관리과속충청남도 논산시 연무읍 마산리 마산교차로훈련소→논산국도1호6036.128405127.103401
352019논산국토관리과속충청남도 논산시 연무읍 연무삼거리논산→육군훈련소국도1호6036.121907127.098624
362019논산국토관리과속충청남도 논산시 연산면 천호리 개태사역맞은편논산→대전국도1호7036.241617127.227709
372019논산국토관리과속충청남도 논산시 연산면 천호리 화악삼거리대전→논산국도1호7036.245451127.228071
382019논산시과속충청남도 논산시 노성면 하도리 놀뫼플러그 전논산→공주국도23호8036.254789127.118528
392019논산시과속충청남도 논산시 성동면 성동산업단지 램프논산→부여국도4호8036.229579127.050613
402019논산시과속충청남도 논산시 성동면 원북리 건영철강부여→논산국도4호8036.232836127.039313
412019논산시과속충청남도 논산시 성동면 개척리 성광온누리학교구치소→강경지방도799호3036.191316127.010143
422019논산시과속충청남도 논산시 강경읍 강경중학교익산→논산국도23호5036.149362127.007821
432021논산시과속충청남도 논산시 양촌면 산직리 50-20계룡→벌곡지방도 68호60<NA><NA>