Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows76
Duplicate rows (%)0.8%
Total size in memory556.6 KiB
Average record size in memory57.0 B

Variable types

Categorical4
DateTime1
Text1

Dataset

Description구리시 관내 주정차위반 단속위치현황 정보(집계년도, 시군명, 단속일시정보(년월일 시분 단위), 단속장소, 단속방법 등)를 제공합니다.
URLhttps://www.data.go.kr/data/15089201/fileData.do

Alerts

시군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
Dataset has 76 (0.8%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 14:47:31.591176
Analysis finished2023-12-12 14:47:32.282821
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2010
2663 
2013
2191 
2011
1962 
2012
1743 
2014
1441 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2012
3rd row2011
4th row2014
5th row2012

Common Values

ValueCountFrequency (%)
2010 2663
26.6%
2013 2191
21.9%
2011 1962
19.6%
2012 1743
17.4%
2014 1441
14.4%

Length

2023-12-12T23:47:32.352788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:47:32.481918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2010 2663
26.6%
2013 2191
21.9%
2011 1962
19.6%
2012 1743
17.4%
2014 1441
14.4%

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
구리시
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
구리시 10000
100.0%

Length

2023-12-12T23:47:32.597991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:47:32.710015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구리시 10000
100.0%

단속일시정보
Date

Distinct9877
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2010-01-01 11:49:00
Maximum2014-10-30 20:16:00
2023-12-12T23:47:32.823364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:32.972044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

단속장소
Text

Distinct675
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T23:47:33.225413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length7.2722
Min length2

Characters and Unicode

Total characters72722
Distinct characters423
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

Unique332 ?
Unique (%)3.3%

Sample

1st row조은빌딩앞
2nd row수택사거리 주변도로
3rd row장자대로86번길
4th row장자대로(알버트프라자)
5th row건원대로
ValueCountFrequency (%)
주변도로 2947
20.7%
구리우체국 1072
 
7.5%
앞 600
 
4.2%
돌다리사거리 421
 
3.0%
주변 421
 
3.0%
수택사거리 405
 
2.8%
쌍용빌딩 359
 
2.5%
장자대로(새롬상가 307
 
2.2%
신명아파트 298
 
2.1%
세무서사거리 272
 
1.9%
Other values (630) 7162
50.2%
2023-12-12T23:47:33.627222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4270
 
5.9%
로 3977
 
5.5%
주 3781
 
5.2%
리 3696
 
5.1%
변 3574
 
4.9%
도 3101
 
4.3%
앞 1982
 
2.7%
사 1898
 
2.6%
거 1770
 
2.4%
구 1517
 
2.1%
Other values (413) 43156
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65772
90.4%
Space Separator 4270
 
5.9%
Decimal Number 1013
 
1.4%
Close Punctuation 555
 
0.8%
Open Punctuation 555
 
0.8%
Uppercase Letter 464
 
0.6%
Lowercase Letter 71
 
0.1%
Dash Punctuation 20
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 3977
 
6.0%
주 3781
 
5.7%
리 3696
 
5.6%
변 3574
 
5.4%
도 3101
 
4.7%
앞 1982
 
3.0%
사 1898
 
2.9%
거 1770
 
2.7%
구 1517
 
2.3%
국 1459
 
2.2%
Other values (369) 39017
59.3%
Lowercase Letter
ValueCountFrequency (%)
i 40
56.3%
s 4
 
5.6%
d 3
 
4.2%
o 3
 
4.2%
m 3
 
4.2%
k 3
 
4.2%
n 2
 
2.8%
l 2
 
2.8%
a 2
 
2.8%
h 2
 
2.8%
Other values (6) 7
 
9.9%
Uppercase Letter
ValueCountFrequency (%)
S 151
32.5%
K 123
26.5%
G 79
17.0%
L 46
 
9.9%
I 28
 
6.0%
T 18
 
3.9%
P 8
 
1.7%
C 5
 
1.1%
M 2
 
0.4%
E 1
 
0.2%
Other values (3) 3
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 295
29.1%
4 288
28.4%
7 273
26.9%
6 36
 
3.6%
5 28
 
2.8%
2 27
 
2.7%
8 26
 
2.6%
3 24
 
2.4%
9 9
 
0.9%
0 7
 
0.7%
Space Separator
ValueCountFrequency (%)
4270
100.0%
Close Punctuation
ValueCountFrequency (%)
) 555
100.0%
Open Punctuation
ValueCountFrequency (%)
( 555
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65772
90.4%
Common 6415
 
8.8%
Latin 535
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 3977
 
6.0%
주 3781
 
5.7%
리 3696
 
5.6%
변 3574
 
5.4%
도 3101
 
4.7%
앞 1982
 
3.0%
사 1898
 
2.9%
거 1770
 
2.7%
구 1517
 
2.3%
국 1459
 
2.2%
Other values (369) 39017
59.3%
Latin
ValueCountFrequency (%)
S 151
28.2%
K 123
23.0%
G 79
14.8%
L 46
 
8.6%
i 40
 
7.5%
I 28
 
5.2%
T 18
 
3.4%
P 8
 
1.5%
C 5
 
0.9%
s 4
 
0.7%
Other values (19) 33
 
6.2%
Common
ValueCountFrequency (%)
4270
66.6%
) 555
 
8.7%
( 555
 
8.7%
1 295
 
4.6%
4 288
 
4.5%
7 273
 
4.3%
6 36
 
0.6%
5 28
 
0.4%
2 27
 
0.4%
8 26
 
0.4%
Other values (5) 62
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65771
90.4%
ASCII 6950
 
9.6%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4270
61.4%
) 555
 
8.0%
( 555
 
8.0%
1 295
 
4.2%
4 288
 
4.1%
7 273
 
3.9%
S 151
 
2.2%
K 123
 
1.8%
G 79
 
1.1%
L 46
 
0.7%
Other values (34) 315
 
4.5%
Hangul
ValueCountFrequency (%)
로 3977
 
6.0%
주 3781
 
5.7%
리 3696
 
5.6%
변 3574
 
5.4%
도 3101
 
4.7%
앞 1982
 
3.0%
사 1898
 
2.9%
거 1770
 
2.7%
구 1517
 
2.3%
국 1459
 
2.2%
Other values (368) 39016
59.3%
Compat Jamo
ValueCountFrequency (%)
ㅂ 1
100.0%

단속방법
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
고정형CCTV
7616 
일반도보
2383 
<NA>
 
1

Length

Max length7
Median length7
Mean length6.2848
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
고정형CCTV 7616
76.2%
일반도보 2383
 
23.8%
<NA> 1
 
< 0.1%

Length

2023-12-12T23:47:33.805373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:47:34.210292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정형cctv 7616
76.2%
일반도보 2383
 
23.8%
na 1
 
< 0.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-06-15
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-15
2nd row2023-06-15
3rd row2023-06-15
4th row2023-06-15
5th row2023-06-15

Common Values

ValueCountFrequency (%)
2023-06-15 10000
100.0%

Length

2023-12-12T23:47:34.311917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:47:34.403744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-15 10000
100.0%

Correlations

2023-12-12T23:47:34.461220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도단속방법
집계년도1.0000.148
단속방법0.1481.000
2023-12-12T23:47:34.541701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도단속방법
집계년도1.0000.181
단속방법0.1811.000
2023-12-12T23:47:34.626172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도단속방법
집계년도1.0000.181
단속방법0.1811.000

Missing values

2023-12-12T23:47:32.085506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:47:32.216139image/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

집계년도시군명단속일시정보단속장소단속방법데이터기준일자
619222013구리시2013-01-20 11:00조은빌딩앞고정형CCTV2023-06-15
583922012구리시2012-11-02 15:08수택사거리 주변도로고정형CCTV2023-06-15
439522011구리시2011-12-27 16:10장자대로86번길고정형CCTV2023-06-15
842342014구리시2014-03-13 11:41장자대로(알버트프라자)고정형CCTV2023-06-15
480592012구리시2012-03-29 14:36건원대로고정형CCTV2023-06-15
390462011구리시2011-09-20 08:00교문초교앞고정형CCTV2023-06-15
855652014구리시2014-04-12 16:14조은빌딩앞고정형CCTV2023-06-15
730682013구리시2013-07-07 10:51T월드일반도보2023-06-15
533292012구리시2012-07-27 14:49벌말로147번길고정형CCTV2023-06-15
614002013구리시2013-01-11 14:27이문안길두레빌딩앞고정형CCTV2023-06-15
집계년도시군명단속일시정보단속장소단속방법데이터기준일자
538512012구리시2012-08-09 09:08쌍용빌딩고정형CCTV2023-06-15
196782010구리시2010-10-16 19:29돌다리사거리고정형CCTV2023-06-15
231142010구리시2010-12-02 18:41신명아파트 앞고정형CCTV2023-06-15
937842014구리시2014-10-04 08:05수택천로(유수지방향)고정형CCTV2023-06-15
469782012구리시2012-03-07 11:25교문초교사거리고정형CCTV2023-06-15
576092012구리시2012-10-15 20:06한국전력앞고정형CCTV2023-06-15
123402010구리시2010-06-30 14:13정각사입구일반도보2023-06-15
538002012구리시2012-08-08 08:11교문초교사거리고정형CCTV2023-06-15
596832012구리시2012-11-30 19:24배탈고개 주변도로고정형CCTV2023-06-15
548212012구리시2012-08-25 15:13수택사거리 주변도로고정형CCTV2023-06-15

Duplicate rows

Most frequently occurring

집계년도시군명단속일시정보단속장소단속방법데이터기준일자# duplicates
302011구리시2011-07-12 17:56행정복합타운일반도보2023-06-153
522013구리시2013-01-22 15:07건원대로고정형CCTV2023-06-153
752014구리시2014-09-02 20:59용호빌딩일반도보2023-06-153
02010구리시2010-01-14 14:56삼육고일반도보2023-06-152
12010구리시2010-02-07 17:22구리우체국 주변도로고정형CCTV2023-06-152
22010구리시2010-02-24 11:23쌍용타워일반도보2023-06-152
32010구리시2010-02-25 15:04농협인창점일반도보2023-06-152
42010구리시2010-03-03 14:08농협인창점일반도보2023-06-152
52010구리시2010-03-23 10:09125-13 부근고정형CCTV2023-06-152
62010구리시2010-03-23 10:28견인사업소일반도보2023-06-152