Overview

Dataset statistics

Number of variables16
Number of observations61
Missing cells21
Missing cells (%)2.2%
Duplicate rows1
Duplicate rows (%)1.6%
Total size in memory8.1 KiB
Average record size in memory136.2 B

Variable types

Numeric4
Categorical9
Text2
DateTime1

Dataset

Description무인단속(주정차)카메라 현황(무인교통단속카메라관리번호, 시도명, 시군구명 소재지도로명주소, 소재지지번주소, 위도, 경도, 단속구분, 설치년도, 카메라대수, 화소수, 촬영방면정보, 보관일수, 관리부서, 관리기관전화번호)
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=411&beforeMenuCd=DOM_000000201001001000&publicdatapk=15040546

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (1.6%) duplicate rowsDuplicates
관리부서 is highly overall correlated with 무인교통단속카메라관리번호 and 11 other fieldsHigh correlation
관리기관전화번호 is highly overall correlated with 무인교통단속카메라관리번호 and 11 other fieldsHigh correlation
시군구명 is highly overall correlated with 무인교통단속카메라관리번호 and 11 other fieldsHigh correlation
촬영방면정보 is highly overall correlated with 무인교통단속카메라관리번호 and 11 other fieldsHigh correlation
화소수 is highly overall correlated with 무인교통단속카메라관리번호 and 11 other fieldsHigh correlation
시도명 is highly overall correlated with 무인교통단속카메라관리번호 and 11 other fieldsHigh correlation
보관일수 is highly overall correlated with 무인교통단속카메라관리번호 and 11 other fieldsHigh correlation
단속구분 is highly overall correlated with 무인교통단속카메라관리번호 and 11 other fieldsHigh correlation
카메라대수 is highly overall correlated with 시도명 and 7 other fieldsHigh correlation
무인교통단속카메라관리번호 is highly overall correlated with 설치년도 and 8 other fieldsHigh correlation
위도 is highly overall correlated with 시도명 and 7 other fieldsHigh correlation
경도 is highly overall correlated with 시도명 and 7 other fieldsHigh correlation
설치년도 is highly overall correlated with 무인교통단속카메라관리번호 and 8 other fieldsHigh correlation
시도명 is highly imbalanced (71.7%)Imbalance
시군구명 is highly imbalanced (71.7%)Imbalance
단속구분 is highly imbalanced (71.7%)Imbalance
화소수 is highly imbalanced (71.7%)Imbalance
촬영방면정보 is highly imbalanced (71.7%)Imbalance
보관일수 is highly imbalanced (71.7%)Imbalance
관리부서 is highly imbalanced (71.7%)Imbalance
관리기관전화번호 is highly imbalanced (71.7%)Imbalance
무인교통단속카메라관리번호 has 3 (4.9%) missing valuesMissing
소재지도로명주소 has 3 (4.9%) missing valuesMissing
소재지지번주소 has 3 (4.9%) missing valuesMissing
위도 has 3 (4.9%) missing valuesMissing
경도 has 3 (4.9%) missing valuesMissing
설치년도 has 3 (4.9%) missing valuesMissing
데이터기준일자 has 3 (4.9%) missing valuesMissing

Reproduction

Analysis started2024-01-09 21:12:37.739878
Analysis finished2024-01-09 21:12:40.771750
Duration3.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

무인교통단속카메라관리번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)100.0%
Missing3
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean29.5
Minimum1
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2024-01-10T06:12:40.836707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.85
Q115.25
median29.5
Q343.75
95-th percentile55.15
Maximum58
Range57
Interquartile range (IQR)28.5

Descriptive statistics

Standard deviation16.886879
Coefficient of variation (CV)0.57243656
Kurtosis-1.2
Mean29.5
Median Absolute Deviation (MAD)14.5
Skewness0
Sum1711
Variance285.16667
MonotonicityStrictly increasing
2024-01-10T06:12:40.968545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 1
 
1.6%
33 1
 
1.6%
34 1
 
1.6%
35 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
Other values (48) 48
78.7%
(Missing) 3
 
4.9%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%
54 1
1.6%
53 1
1.6%
52 1
1.6%
51 1
1.6%
50 1
1.6%
49 1
1.6%

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
충청남도
58 
<NA>
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 58
95.1%
<NA> 3
 
4.9%

Length

2024-01-10T06:12:41.105504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:12:41.189610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 58
95.1%
na 3
 
4.9%

시군구명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
당진시
58 
<NA>
 
3

Length

Max length4
Median length3
Mean length3.0491803
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
당진시 58
95.1%
<NA> 3
 
4.9%

Length

2024-01-10T06:12:41.281665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:12:41.385027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당진시 58
95.1%
na 3
 
4.9%

소재지도로명주소
Text

MISSING 

Distinct52
Distinct (%)89.7%
Missing3
Missing (%)4.9%
Memory size620.0 B
2024-01-10T06:12:41.586414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length22.5
Mean length18.965517
Min length7

Characters and Unicode

Total characters1100
Distinct characters199
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)82.8%

Sample

1st row당진공영버스터미널 진?출입로 앞
2nd row당진공영버스터미널 앞 3거리
3rd row농협 해나루지점 앞 4거리
4th row구청사 부근 귀빈미용실 앞(탐앤탐스)
5th row당진 로터리 앞
ValueCountFrequency (%)
앞 26
 
11.4%
송악읍 9
 
3.9%
당진시 8
 
3.5%
부근 7
 
3.1%
당진 6
 
2.6%
맞은편 6
 
2.6%
5
 
2.2%
도로 5
 
2.2%
신복운로 5
 
2.2%
읍내동 4
 
1.8%
Other values (116) 147
64.5%
2024-01-10T06:12:41.929976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
183
 
16.6%
로 45
 
4.1%
당 32
 
2.9%
진 32
 
2.9%
앞 30
 
2.7%
( 23
 
2.1%
) 23
 
2.1%
시 21
 
1.9%
교 20
 
1.8%
부 19
 
1.7%
Other values (189) 672
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 823
74.8%
Space Separator 186
 
16.9%
Open Punctuation 23
 
2.1%
Close Punctuation 23
 
2.1%
Decimal Number 20
 
1.8%
Uppercase Letter 15
 
1.4%
Math Symbol 5
 
0.5%
Other Punctuation 3
 
0.3%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 45
 
5.5%
당 32
 
3.9%
진 32
 
3.9%
앞 30
 
3.6%
시 21
 
2.6%
교 20
 
2.4%
부 19
 
2.3%
지 17
 
2.1%
동 16
 
1.9%
차 15
 
1.8%
Other values (171) 576
70.0%
Uppercase Letter
ValueCountFrequency (%)
C 5
33.3%
G 4
26.7%
V 3
20.0%
U 2
 
13.3%
S 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 9
45.0%
2 9
45.0%
3 1
 
5.0%
4 1
 
5.0%
Space Separator
ValueCountFrequency (%)
183
98.4%
　 3
 
1.6%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
? 1
33.3%
Lowercase Letter
ValueCountFrequency (%)
p 1
50.0%
c 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 823
74.8%
Common 260
 
23.6%
Latin 17
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 45
 
5.5%
당 32
 
3.9%
진 32
 
3.9%
앞 30
 
3.6%
시 21
 
2.6%
교 20
 
2.4%
부 19
 
2.3%
지 17
 
2.1%
동 16
 
1.9%
차 15
 
1.8%
Other values (171) 576
70.0%
Common
ValueCountFrequency (%)
183
70.4%
( 23
 
8.8%
) 23
 
8.8%
1 9
 
3.5%
2 9
 
3.5%
~ 5
 
1.9%
　 3
 
1.2%
, 2
 
0.8%
3 1
 
0.4%
? 1
 
0.4%
Latin
ValueCountFrequency (%)
C 5
29.4%
G 4
23.5%
V 3
17.6%
U 2
 
11.8%
p 1
 
5.9%
S 1
 
5.9%
c 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 823
74.8%
ASCII 274
 
24.9%
None 3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
183
66.8%
( 23
 
8.4%
) 23
 
8.4%
1 9
 
3.3%
2 9
 
3.3%
C 5
 
1.8%
~ 5
 
1.8%
G 4
 
1.5%
V 3
 
1.1%
, 2
 
0.7%
Other values (7) 8
 
2.9%
Hangul
ValueCountFrequency (%)
로 45
 
5.5%
당 32
 
3.9%
진 32
 
3.9%
앞 30
 
3.6%
시 21
 
2.6%
교 20
 
2.4%
부 19
 
2.3%
지 17
 
2.1%
동 16
 
1.9%
차 15
 
1.8%
Other values (171) 576
70.0%
None
ValueCountFrequency (%)
　 3
100.0%

소재지지번주소
Text

MISSING 

Distinct51
Distinct (%)87.9%
Missing3
Missing (%)4.9%
Memory size620.0 B
2024-01-10T06:12:42.133627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length13.155172
Min length11

Characters and Unicode

Total characters763
Distinct characters41
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

Unique47 ?
Unique (%)81.0%

Sample

1st row당진시 수청동 980
2nd row당진시 수청동 457
3rd row당진시 읍내동 489-6
4th row당진시 읍내동 546-34
5th row당진시 읍내동 528-54
ValueCountFrequency (%)
당진시 45
25.4%
읍내동 28
15.8%
송악읍 11
 
6.2%
수청동 8
 
4.5%
복운리 7
 
4.0%
1675 5
 
2.8%
합덕읍 3
 
1.7%
운산리 3
 
1.7%
기지시리 3
 
1.7%
대덕동 2
 
1.1%
Other values (57) 62
35.0%
2024-01-10T06:12:42.466014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
130
17.0%
1 61
 
8.0%
시 49
 
6.4%
당 46
 
6.0%
진 45
 
5.9%
동 43
 
5.6%
읍 42
 
5.5%
5 36
 
4.7%
- 31
 
4.1%
내 28
 
3.7%
Other values (31) 252
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 359
47.1%
Decimal Number 243
31.8%
Space Separator 130
 
17.0%
Dash Punctuation 31
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 49
13.6%
당 46
12.8%
진 45
12.5%
동 43
12.0%
읍 42
11.7%
내 28
7.8%
리 14
 
3.9%
운 12
 
3.3%
악 11
 
3.1%
송 11
 
3.1%
Other values (19) 58
16.2%
Decimal Number
ValueCountFrequency (%)
1 61
25.1%
5 36
14.8%
6 27
11.1%
4 22
 
9.1%
2 21
 
8.6%
0 21
 
8.6%
7 20
 
8.2%
9 14
 
5.8%
3 13
 
5.3%
8 8
 
3.3%
Space Separator
ValueCountFrequency (%)
130
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 404
52.9%
Hangul 359
47.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 49
13.6%
당 46
12.8%
진 45
12.5%
동 43
12.0%
읍 42
11.7%
내 28
7.8%
리 14
 
3.9%
운 12
 
3.3%
악 11
 
3.1%
송 11
 
3.1%
Other values (19) 58
16.2%
Common
ValueCountFrequency (%)
130
32.2%
1 61
15.1%
5 36
 
8.9%
- 31
 
7.7%
6 27
 
6.7%
4 22
 
5.4%
2 21
 
5.2%
0 21
 
5.2%
7 20
 
5.0%
9 14
 
3.5%
Other values (2) 21
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 404
52.9%
Hangul 359
47.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130
32.2%
1 61
15.1%
5 36
 
8.9%
- 31
 
7.7%
6 27
 
6.7%
4 22
 
5.4%
2 21
 
5.2%
0 21
 
5.2%
7 20
 
5.0%
9 14
 
3.5%
Other values (2) 21
 
5.2%
Hangul
ValueCountFrequency (%)
시 49
13.6%
당 46
12.8%
진 45
12.5%
동 43
12.0%
읍 42
11.7%
내 28
7.8%
리 14
 
3.9%
운 12
 
3.3%
악 11
 
3.1%
송 11
 
3.1%
Other values (19) 58
16.2%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)100.0%
Missing3
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean36.896461
Minimum36.808141
Maximum36.944645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2024-01-10T06:12:42.593561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.808141
5-th percentile36.869554
Q136.890975
median36.896207
Q336.901211
95-th percentile36.941319
Maximum36.944645
Range0.136504
Interquartile range (IQR)0.01023575

Descriptive statistics

Standard deviation0.026006452
Coefficient of variation (CV)0.0007048495
Kurtosis5.5807467
Mean36.896461
Median Absolute Deviation (MAD)0.00521
Skewness-1.4326544
Sum2139.9948
Variance0.00067633556
MonotonicityNot monotonic
2024-01-10T06:12:42.715194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.900633 1
 
1.6%
36.895025 1
 
1.6%
36.900397 1
 
1.6%
36.899073 1
 
1.6%
36.895059 1
 
1.6%
36.901033 1
 
1.6%
36.902441 1
 
1.6%
36.944645 1
 
1.6%
36.943441 1
 
1.6%
36.941962 1
 
1.6%
Other values (48) 48
78.7%
(Missing) 3
 
4.9%
ValueCountFrequency (%)
36.808141 1
1.6%
36.808762 1
1.6%
36.80979 1
1.6%
36.880101 1
1.6%
36.886426 1
1.6%
36.886713 1
1.6%
36.888168 1
1.6%
36.888907 1
1.6%
36.8894 1
1.6%
36.889623 1
1.6%
ValueCountFrequency (%)
36.944645 1
1.6%
36.943441 1
1.6%
36.941962 1
1.6%
36.941205 1
1.6%
36.940936 1
1.6%
36.938884 1
1.6%
36.937975 1
1.6%
36.903733 1
1.6%
36.903234 1
1.6%
36.902441 1
1.6%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)100.0%
Missing3
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean126.66541
Minimum126.62503
Maximum126.7822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2024-01-10T06:12:42.852164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.62503
5-th percentile126.62696
Q1126.63042
median126.63609
Q3126.68116
95-th percentile126.78158
Maximum126.7822
Range0.157169
Interquartile range (IQR)0.05073925

Descriptive statistics

Standard deviation0.056873038
Coefficient of variation (CV)0.00044900212
Kurtosis0.19696346
Mean126.66541
Median Absolute Deviation (MAD)0.00805
Skewness1.3953772
Sum7346.5938
Variance0.0032345424
MonotonicityNot monotonic
2024-01-10T06:12:43.031014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.693428 1
 
1.6%
126.637078 1
 
1.6%
126.626727 1
 
1.6%
126.627628 1
 
1.6%
126.630545 1
 
1.6%
126.632152 1
 
1.6%
126.631156 1
 
1.6%
126.782201 1
 
1.6%
126.780711 1
 
1.6%
126.779634 1
 
1.6%
Other values (48) 48
78.7%
(Missing) 3
 
4.9%
ValueCountFrequency (%)
126.625032 1
1.6%
126.6251 1
1.6%
126.626727 1
1.6%
126.627004 1
1.6%
126.627063 1
1.6%
126.627628 1
1.6%
126.628161 1
1.6%
126.628747 1
1.6%
126.629021 1
1.6%
126.629363 1
1.6%
ValueCountFrequency (%)
126.782201 1
1.6%
126.782196 1
1.6%
126.781889 1
1.6%
126.781525 1
1.6%
126.781026 1
1.6%
126.780711 1
1.6%
126.779634 1
1.6%
126.775032 1
1.6%
126.775022 1
1.6%
126.773558 1
1.6%

단속구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
불법주정차
58 
<NA>
 
3

Length

Max length5
Median length5
Mean length4.9508197
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
불법주정차 58
95.1%
<NA> 3
 
4.9%

Length

2024-01-10T06:12:43.163901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:12:43.253759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불법주정차 58
95.1%
na 3
 
4.9%

설치년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)13.8%
Missing3
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean2015.7414
Minimum2012
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2024-01-10T06:12:43.340801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014.25
median2015
Q32018
95-th percentile2019
Maximum2019
Range7
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation2.3212243
Coefficient of variation (CV)0.0011515487
Kurtosis-1.1465497
Mean2015.7414
Median Absolute Deviation (MAD)2
Skewness-0.17101721
Sum116913
Variance5.3880823
MonotonicityNot monotonic
2024-01-10T06:12:43.446815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2015 16
26.2%
2018 10
16.4%
2012 8
13.1%
2019 8
13.1%
2017 7
11.5%
2013 4
 
6.6%
2014 3
 
4.9%
2016 2
 
3.3%
(Missing) 3
 
4.9%
ValueCountFrequency (%)
2012 8
13.1%
2013 4
 
6.6%
2014 3
 
4.9%
2015 16
26.2%
2016 2
 
3.3%
2017 7
11.5%
2018 10
16.4%
2019 8
13.1%
ValueCountFrequency (%)
2019 8
13.1%
2018 10
16.4%
2017 7
11.5%
2016 2
 
3.3%
2015 16
26.2%
2014 3
 
4.9%
2013 4
 
6.6%
2012 8
13.1%

화소수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
200만화소
58 
<NA>
 
3

Length

Max length6
Median length6
Mean length5.9016393
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row200만화소
2nd row200만화소
3rd row200만화소
4th row200만화소
5th row200만화소

Common Values

ValueCountFrequency (%)
200만화소 58
95.1%
<NA> 3
 
4.9%

Length

2024-01-10T06:12:43.567410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:12:43.676000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
200만화소 58
95.1%
na 3
 
4.9%

카메라대수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
5
32 
3
22 
4
4 
<NA>
 
3

Length

Max length4
Median length1
Mean length1.147541
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 32
52.5%
3 22
36.1%
4 4
 
6.6%
<NA> 3
 
4.9%

Length

2024-01-10T06:12:43.795726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:12:43.932104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 32
52.5%
3 22
36.1%
4 4
 
6.6%
na 3
 
4.9%

촬영방면정보
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
차량
58 
<NA>
 
3

Length

Max length4
Median length2
Mean length2.0983607
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
차량 58
95.1%
<NA> 3
 
4.9%

Length

2024-01-10T06:12:44.038103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:12:44.156600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
차량 58
95.1%
na 3
 
4.9%

보관일수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
30
58 
<NA>
 
3

Length

Max length4
Median length2
Mean length2.0983607
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30 58
95.1%
<NA> 3
 
4.9%

Length

2024-01-10T06:12:44.265981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:12:44.362624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 58
95.1%
na 3
 
4.9%

관리부서
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
교통과
58 
<NA>
 
3

Length

Max length4
Median length3
Mean length3.0491803
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
교통과 58
95.1%
<NA> 3
 
4.9%

Length

2024-01-10T06:12:44.465822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:12:44.575222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교통과 58
95.1%
na 3
 
4.9%

관리기관전화번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
041-350-4545
58 
<NA>
 
3

Length

Max length12
Median length12
Mean length11.606557
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row041-350-4545
2nd row041-350-4545
3rd row041-350-4545
4th row041-350-4545
5th row041-350-4545

Common Values

ValueCountFrequency (%)
041-350-4545 58
95.1%
<NA> 3
 
4.9%

Length

2024-01-10T06:12:44.680143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:12:44.773502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
041-350-4545 58
95.1%
na 3
 
4.9%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)1.7%
Missing3
Missing (%)4.9%
Memory size620.0 B
Minimum2019-10-22 00:00:00
Maximum2019-10-22 00:00:00
2024-01-10T06:12:44.840864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:44.920841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T06:12:39.474764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:38.507610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:38.815743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:39.131613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:39.565588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:38.575107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:38.888968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:39.215065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:39.660763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:38.650193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:38.964191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:39.298852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:39.777843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:38.733916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:39.050762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:12:39.386264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:12:44.987540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
무인교통단속카메라관리번호소재지도로명주소소재지지번주소위도경도설치년도카메라대수
무인교통단속카메라관리번호1.0000.9600.9720.4760.5920.8880.611
소재지도로명주소0.9601.0000.9961.0001.0001.0001.000
소재지지번주소0.9720.9961.0001.0001.0001.0001.000
위도0.4761.0001.0001.0000.8830.6160.220
경도0.5921.0001.0000.8831.0000.5220.000
설치년도0.8881.0001.0000.6160.5221.0000.506
카메라대수0.6111.0001.0000.2200.0000.5061.000
2024-01-10T06:12:45.088703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리부서관리기관전화번호시군구명촬영방면정보화소수시도명보관일수단속구분카메라대수
관리부서1.0001.0001.0001.0001.0001.0001.0001.0001.000
관리기관전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구명1.0001.0001.0001.0001.0001.0001.0001.0001.000
촬영방면정보1.0001.0001.0001.0001.0001.0001.0001.0001.000
화소수1.0001.0001.0001.0001.0001.0001.0001.0001.000
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.000
보관일수1.0001.0001.0001.0001.0001.0001.0001.0001.000
단속구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
카메라대수1.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-01-10T06:12:45.204238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
무인교통단속카메라관리번호위도경도설치년도시도명시군구명단속구분화소수카메라대수촬영방면정보보관일수관리부서관리기관전화번호
무인교통단속카메라관리번호1.0000.0830.1270.8761.0001.0001.0001.0000.4231.0001.0001.0001.000
위도0.0831.0000.4370.0391.0001.0001.0001.0000.1631.0001.0001.0001.000
경도0.1270.4371.0000.0911.0001.0001.0001.0000.0001.0001.0001.0001.000
설치년도0.8760.0390.0911.0001.0001.0001.0001.0000.4191.0001.0001.0001.000
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
단속구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
화소수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
카메라대수0.4230.1630.0000.4191.0001.0001.0001.0001.0001.0001.0001.0001.000
촬영방면정보1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
보관일수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관리부서1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관리기관전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-01-10T06:12:39.939106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:12:40.154584image/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.
2024-01-10T06:12:40.604941image/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

무인교통단속카메라관리번호시도명시군구명소재지도로명주소소재지지번주소위도경도단속구분설치년도화소수카메라대수촬영방면정보보관일수관리부서관리기관전화번호데이터기준일자
01충청남도당진시당진공영버스터미널 진?출입로 앞당진시 수청동 98036.901811126.645002불법주정차2012200만화소5차량30교통과041-350-45452019-10-22
12충청남도당진시당진공영버스터미널 앞 3거리당진시 수청동 45736.901662126.647439불법주정차2012200만화소4차량30교통과041-350-45452019-10-22
23충청남도당진시농협 해나루지점 앞 4거리당진시 읍내동 489-636.894654126.629751불법주정차2012200만화소5차량30교통과041-350-45452019-10-22
34충청남도당진시구청사 부근 귀빈미용실 앞(탐앤탐스)당진시 읍내동 546-3436.892577126.628747불법주정차2012200만화소5차량30교통과041-350-45452019-10-22
45충청남도당진시당진 로터리 앞당진시 읍내동 528-5436.894075126.630374불법주정차2012200만화소5차량30교통과041-350-45452019-10-22
56충청남도당진시시장오거리 시민약국 앞당진시 읍내동 542-736.891849126.629363불법주정차2012200만화소4차량30교통과041-350-45452019-10-22
67충청남도당진시합덕읍 약손약국 앞(합덕파출소)합덕읍 운산리 262-1136.808141126.775022불법주정차2012200만화소5차량30교통과041-350-45452019-10-22
78충청남도당진시당진 제1교앞(당진정보고)당진시 채운동 252-136.892648126.6251불법주정차2012200만화소5차량30교통과041-350-45452019-10-22
89충청남도당진시푸른정형외과 사거리(서해빌딩)당진시 읍내동 265-636.897686126.634538불법주정차2013200만화소5차량30교통과041-350-45452019-10-22
910충청남도당진시당진터미널맞은편도로(서광위너스외부도로)당진시 수청동 99436.900367126.645301불법주정차2013200만화소5차량30교통과041-350-45452019-10-22
무인교통단속카메라관리번호시도명시군구명소재지도로명주소소재지지번주소위도경도단속구분설치년도화소수카메라대수촬영방면정보보관일수관리부서관리기관전화번호데이터기준일자
5152충청남도당진시당진시 신터미널 맞은편 상가 이면도로당진시 수청동 1001-936.900683126.64579불법주정차2018200만화소3차량30교통과041-350-45452019-10-22
5253충청남도당진시당진시 신터미널 맞은편 상가 이면도로당진시 수청동 1001-1136.901128126.646668불법주정차2018200만화소3차량30교통과041-350-45452019-10-22
5354충청남도당진시당진시 읍내동 구 군청 흥아약국 맞은편당진시 읍내동 517-536.89315126.628161불법주정차2019200만화소3차량30교통과041-350-45452019-10-22
5455충청남도당진시당진시 읍내동 백제한약방 건물 옆 도로당진시 읍내동 166-3036.891908126.630652불법주정차2019200만화소3차량30교통과041-350-45452019-10-22
5556충청남도당진시당진시 읍내동 조약국 옆 도로당진시 읍내동 660-536.889932126.629021불법주정차2019200만화소5차량30교통과041-350-45452019-10-22
5657충청남도당진시당진시 대덕동 남부공영주차장 맞은편 도로당진시 대덕동 172036.886426126.631812불법주정차2019200만화소5차량30교통과041-350-45452019-10-22
5758충청남도당진시당진시 신평면 거산리 서정초등학교 입구 농협 앞당진시 신평면 거산리 41-2936.880101126.754308불법주정차2019200만화소3차량30교통과041-350-45452019-10-22
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무인교통단속카메라관리번호시도명시군구명소재지도로명주소소재지지번주소위도경도단속구분설치년도화소수카메라대수촬영방면정보보관일수관리부서관리기관전화번호데이터기준일자# duplicates
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