Overview

Dataset statistics

Number of variables5
Number of observations6684
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory274.3 KiB
Average record size in memory42.0 B

Variable types

Text2
Numeric2
Boolean1

Dataset

Description위험물질운송차량의 실시간 관제를 통해 우리나라의 주요도로에 설치되어 연계되어 CCTV 정보 목록을 제공합니다.
Author국토교통부
URLhttps://www.data.go.kr/data/15122712/fileData.do

Alerts

사용여부 has constant value ""Constant
경도 is highly skewed (γ1 = -41.4118572)Skewed
위도 is highly skewed (γ1 = 81.75572396)Skewed
아이디 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:45:21.098118
Analysis finished2023-12-12 02:45:22.739173
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아이디
Text

UNIQUE 

Distinct6684
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size52.3 KiB
2023-12-12T11:45:23.112735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9528725
Min length6

Characters and Unicode

Total characters46473
Distinct characters12
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

Unique6684 ?
Unique (%)100.0%

Sample

1st rowE910237
2nd rowE910238
3rd rowE910235
4th rowE910249
5th rowE910184
ValueCountFrequency (%)
e910237 1
 
< 0.1%
e900209 1
 
< 0.1%
e900267 1
 
< 0.1%
e900266 1
 
< 0.1%
e900265 1
 
< 0.1%
e900264 1
 
< 0.1%
e900263 1
 
< 0.1%
e900262 1
 
< 0.1%
e900261 1
 
< 0.1%
e900260 1
 
< 0.1%
Other values (6674) 6674
99.9%
2023-12-12T11:45:23.707261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9403
20.2%
1 7839
16.9%
9 6103
13.1%
E 5000
10.8%
2 3799
8.2%
3 3071
 
6.6%
4 2062
 
4.4%
8 2006
 
4.3%
7 1891
 
4.1%
5 1879
 
4.0%
Other values (2) 3420
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39789
85.6%
Uppercase Letter 6684
 
14.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9403
23.6%
1 7839
19.7%
9 6103
15.3%
2 3799
9.5%
3 3071
 
7.7%
4 2062
 
5.2%
8 2006
 
5.0%
7 1891
 
4.8%
5 1879
 
4.7%
6 1736
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
E 5000
74.8%
L 1684
 
25.2%

Most occurring scripts

ValueCountFrequency (%)
Common 39789
85.6%
Latin 6684
 
14.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9403
23.6%
1 7839
19.7%
9 6103
15.3%
2 3799
9.5%
3 3071
 
7.7%
4 2062
 
5.2%
8 2006
 
5.0%
7 1891
 
4.8%
5 1879
 
4.7%
6 1736
 
4.4%
Latin
ValueCountFrequency (%)
E 5000
74.8%
L 1684
 
25.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9403
20.2%
1 7839
16.9%
9 6103
13.1%
E 5000
10.8%
2 3799
8.2%
3 3071
 
6.6%
4 2062
 
4.4%
8 2006
 
4.3%
7 1891
 
4.1%
5 1879
 
4.0%
Other values (2) 3420
 
7.4%

지점명
Text

Distinct6647
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size52.3 KiB
2023-12-12T11:45:24.086982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length24
Mean length10.601287
Min length2

Characters and Unicode

Total characters70859
Distinct characters568
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6612 ?
Unique (%)98.9%

Sample

1st row(경부선)경주IC
2nd row(경부선)경주터널
3rd row(경부선)경주터널2
4th row(경부선)경주휴게소
5th row(경부선)계룡2교
ValueCountFrequency (%)
외부 51
 
0.7%
앞 16
 
0.2%
4r 15
 
0.2%
남단 14
 
0.2%
1 14
 
0.2%
2 12
 
0.2%
삼거리 11
 
0.2%
ic 8
 
0.1%
광안대교(상부 7
 
0.1%
연초면 6
 
0.1%
Other values (6736) 6977
97.8%
2023-12-12T11:45:24.611351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 5874
 
8.3%
( 5872
 
8.3%
선 4344
 
6.1%
교 2192
 
3.1%
리 1458
 
2.1%
부 1440
 
2.0%
도 1406
 
2.0%
산 1218
 
1.7%
호 1178
 
1.7%
천 1177
 
1.7%
Other values (558) 44700
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53350
75.3%
Close Punctuation 5874
 
8.3%
Open Punctuation 5872
 
8.3%
Decimal Number 4032
 
5.7%
Uppercase Letter 1125
 
1.6%
Space Separator 449
 
0.6%
Lowercase Letter 133
 
0.2%
Other Punctuation 24
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 4344
 
8.1%
교 2192
 
4.1%
리 1458
 
2.7%
부 1440
 
2.7%
도 1406
 
2.6%
산 1218
 
2.3%
호 1178
 
2.2%
천 1177
 
2.2%
거 1116
 
2.1%
주 1043
 
2.0%
Other values (504) 36778
68.9%
Uppercase Letter
ValueCountFrequency (%)
C 402
35.7%
I 305
27.1%
T 85
 
7.6%
J 71
 
6.3%
R 54
 
4.8%
V 28
 
2.5%
A 26
 
2.3%
G 23
 
2.0%
M 21
 
1.9%
P 20
 
1.8%
Other values (12) 90
 
8.0%
Lowercase Letter
ValueCountFrequency (%)
m 37
27.8%
n 17
12.8%
i 14
 
10.5%
j 11
 
8.3%
o 11
 
8.3%
a 9
 
6.8%
u 8
 
6.0%
g 7
 
5.3%
k 4
 
3.0%
e 3
 
2.3%
Other values (7) 12
 
9.0%
Decimal Number
ValueCountFrequency (%)
1 1101
27.3%
2 919
22.8%
3 590
14.6%
4 523
13.0%
7 238
 
5.9%
5 205
 
5.1%
6 156
 
3.9%
0 112
 
2.8%
8 96
 
2.4%
9 92
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 23
95.8%
& 1
 
4.2%
Close Punctuation
ValueCountFrequency (%)
) 5874
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5872
100.0%
Space Separator
ValueCountFrequency (%)
449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53350
75.3%
Common 16251
 
22.9%
Latin 1258
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 4344
 
8.1%
교 2192
 
4.1%
리 1458
 
2.7%
부 1440
 
2.7%
도 1406
 
2.6%
산 1218
 
2.3%
호 1178
 
2.2%
천 1177
 
2.2%
거 1116
 
2.1%
주 1043
 
2.0%
Other values (504) 36778
68.9%
Latin
ValueCountFrequency (%)
C 402
32.0%
I 305
24.2%
T 85
 
6.8%
J 71
 
5.6%
R 54
 
4.3%
m 37
 
2.9%
V 28
 
2.2%
A 26
 
2.1%
G 23
 
1.8%
M 21
 
1.7%
Other values (29) 206
16.4%
Common
ValueCountFrequency (%)
) 5874
36.1%
( 5872
36.1%
1 1101
 
6.8%
2 919
 
5.7%
3 590
 
3.6%
4 523
 
3.2%
449
 
2.8%
7 238
 
1.5%
5 205
 
1.3%
6 156
 
1.0%
Other values (5) 324
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53350
75.3%
ASCII 17509
 
24.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 5874
33.5%
( 5872
33.5%
1 1101
 
6.3%
2 919
 
5.2%
3 590
 
3.4%
4 523
 
3.0%
449
 
2.6%
C 402
 
2.3%
I 305
 
1.7%
7 238
 
1.4%
Other values (44) 1236
 
7.1%
Hangul
ValueCountFrequency (%)
선 4344
 
8.1%
교 2192
 
4.1%
리 1458
 
2.7%
부 1440
 
2.7%
도 1406
 
2.6%
산 1218
 
2.3%
호 1178
 
2.2%
천 1177
 
2.2%
거 1116
 
2.1%
주 1043
 
2.0%
Other values (504) 36778
68.9%

경도
Real number (ℝ)

SKEWED 

Distinct6481
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.66767
Minimum37.648723
Maximum129.57008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.9 KiB
2023-12-12T11:45:24.799088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.648723
5-th percentile126.67651
Q1127.01165
median127.41298
Q3128.49236
95-th percentile129.13168
Maximum129.57008
Range91.921354
Interquartile range (IQR)1.4807138

Descriptive statistics

Standard deviation1.380073
Coefficient of variation (CV)0.010809886
Kurtosis2708.3592
Mean127.66767
Median Absolute Deviation (MAD)0.55668635
Skewness-41.411857
Sum853330.73
Variance1.9046014
MonotonicityNot monotonic
2023-12-12T11:45:25.006598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.074211 10
 
0.1%
129.008259 7
 
0.1%
127.2138035 6
 
0.1%
128.879509 6
 
0.1%
128.894464 5
 
0.1%
128.109142 4
 
0.1%
128.843802 4
 
0.1%
127.25556 3
 
< 0.1%
126.761 3
 
< 0.1%
128.663 3
 
< 0.1%
Other values (6471) 6633
99.2%
ValueCountFrequency (%)
37.64872257 1
< 0.1%
126.1537383 1
< 0.1%
126.2264201 1
< 0.1%
126.326993 1
< 0.1%
126.348759 1
< 0.1%
126.358919 1
< 0.1%
126.366719 1
< 0.1%
126.3761801 1
< 0.1%
126.379666 1
< 0.1%
126.3857917 1
< 0.1%
ValueCountFrequency (%)
129.5700765 1
< 0.1%
129.549216 1
< 0.1%
129.5355717 1
< 0.1%
129.4637865 1
< 0.1%
129.4335794 1
< 0.1%
129.4266691 1
< 0.1%
129.4204679 1
< 0.1%
129.4188336 1
< 0.1%
129.40955 2
< 0.1%
129.409503 1
< 0.1%

위도
Real number (ℝ)

SKEWED 

Distinct6457
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.18099
Minimum34.330779
Maximum425377.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.9 KiB
2023-12-12T11:45:25.159108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.330779
5-th percentile35.085585
Q135.657699
median36.636861
Q337.448061
95-th percentile37.752067
Maximum425377.46
Range425343.13
Interquartile range (IQR)1.7903617

Descriptive statistics

Standard deviation5202.5823
Coefficient of variation (CV)51.931831
Kurtosis6683.9989
Mean100.18099
Median Absolute Deviation (MAD)0.85658779
Skewness81.755724
Sum669609.74
Variance27066862
MonotonicityNot monotonic
2023-12-12T11:45:25.325713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.525613 10
 
0.1%
37.506 9
 
0.1%
35.527382 7
 
0.1%
35.492641 6
 
0.1%
36.54426575 6
 
0.1%
35.492359 5
 
0.1%
37.52 4
 
0.1%
36.384807 4
 
0.1%
37.484 4
 
0.1%
35.505192 4
 
0.1%
Other values (6447) 6625
99.1%
ValueCountFrequency (%)
34.330779 1
< 0.1%
34.68213336 1
< 0.1%
34.684128 1
< 0.1%
34.68534 1
< 0.1%
34.686034 1
< 0.1%
34.686081 1
< 0.1%
34.686387 1
< 0.1%
34.69241717 1
< 0.1%
34.69316667 1
< 0.1%
34.69554351 1
< 0.1%
ValueCountFrequency (%)
425377.46 1
< 0.1%
127.4602376 1
< 0.1%
38.468228 1
< 0.1%
38.441632 1
< 0.1%
38.426695 1
< 0.1%
38.406555 1
< 0.1%
38.384937 1
< 0.1%
38.37765 1
< 0.1%
38.37248603 1
< 0.1%
38.355399 1
< 0.1%

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
True
6684 
ValueCountFrequency (%)
True 6684
100.0%
2023-12-12T11:45:25.437674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-12T11:45:22.305326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:22.078544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:22.404572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:22.190468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:45:25.489903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도위도
경도1.0000.000
위도0.0001.000
2023-12-12T11:45:25.583611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도위도
경도1.000-0.356
위도-0.3561.000

Missing values

2023-12-12T11:45:22.539294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:45:22.683711image/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

아이디지점명경도위도사용여부
0E910237(경부선)경주IC129.06306535.87073Y
1E910238(경부선)경주터널129.06398935.870012Y
2E910235(경부선)경주터널2129.06115735.871995Y
3E910249(경부선)경주휴게소129.19406935.722938Y
4E910184(경부선)계룡2교127.9702836.21306Y
5E910084(경부선)계족127.43107636.373553Y
6E910039(경부선)공도127.15116137.002476Y
7E910020(경부선)공세육교127.10388337.245642Y
8E910265(경부선)광명129.166135.81635Y
9E910242(경부선)광명교129.1552835.81833Y
아이디지점명경도위도사용여부
6674L370055포항시청앞삼거리129.33961736.018038Y
6675L370078해도등외과사거리129.36857236.022609Y
6676L370056현대제철삼거리129.38136536.003617Y
6677L370057형산강변로129.37438736.018829Y
6678L370058형산오거리129.37124636.012476Y
6679L370059형산오거리 (고정)129.37159236.012471Y
6680L370060효자사거리129.33986736.00941Y
6681L370079흥해덕장사거리129.34926536.14077Y
6682L370080흥해옥성삼거리129.34187636.102707Y
6683L370061흥해용전129.34593736.120225Y