Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1538
Duplicate rows (%)15.4%
Total size in memory390.6 KiB
Average record size in memory40.0 B

Variable types

Categorical2
DateTime1
Text1

Dataset

Description경기도 포천시 불법주정차단속시스템에서 제공하는 불법주정차 단속현황(기관명, 단속일시, 단속장소)데이터 입니다.
Author경기도 포천시
URLhttps://www.data.go.kr/data/15061876/fileData.do

Alerts

기관명 has constant value ""Constant
데이터 기준일 has constant value ""Constant
Dataset has 1538 (15.4%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 02:39:30.062018
Analysis finished2023-12-12 02:39:30.599318
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
포천시 10000
100.0%

Length

2023-12-12T11:39:30.672076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:39:30.765846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포천시 10000
100.0%

단속일시
Date

Distinct324
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-01-01 00:00:00
Maximum2023-11-22 00:00:00
2023-12-12T11:39:30.866618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:31.034221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

단속장소
Text

Distinct873
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T11:39:31.291702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length8.7498
Min length3

Characters and Unicode

Total characters87498
Distinct characters572
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique382 ?
Unique (%)3.8%

Sample

1st row농협중앙회 부근
2nd row바다목장 부근
3rd row바다목장 부근
4th row현대정형외과의원 부근
5th row365우리약국 부근
ValueCountFrequency (%)
부근 9860
49.5%
우리병원 341
 
1.7%
필마트 294
 
1.5%
명성아파트 263
 
1.3%
포천세무서 219
 
1.1%
농협중앙회 208
 
1.0%
송우초교 193
 
1.0%
송우로제1공영주차장 187
 
0.9%
바다목장 174
 
0.9%
제일소아과 174
 
0.9%
Other values (858) 8003
40.2%
2023-12-12T11:39:31.721853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 10041
 
11.5%
9921
 
11.3%
근 9866
 
11.3%
우 1541
 
1.8%
트 1420
 
1.6%
아 1231
 
1.4%
장 1226
 
1.4%
송 1145
 
1.3%
주 1031
 
1.2%
천 1030
 
1.2%
Other values (562) 49046
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74978
85.7%
Space Separator 9921
 
11.3%
Decimal Number 1195
 
1.4%
Uppercase Letter 1069
 
1.2%
Lowercase Letter 284
 
0.3%
Other Punctuation 32
 
< 0.1%
Open Punctuation 9
 
< 0.1%
Close Punctuation 9
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 10041
 
13.4%
근 9866
 
13.2%
우 1541
 
2.1%
트 1420
 
1.9%
아 1231
 
1.6%
장 1226
 
1.6%
송 1145
 
1.5%
주 1031
 
1.4%
천 1030
 
1.4%
리 1001
 
1.3%
Other values (517) 45446
60.6%
Uppercase Letter
ValueCountFrequency (%)
K 160
15.0%
C 137
12.8%
F 105
9.8%
E 105
9.8%
O 97
9.1%
P 78
7.3%
Z 74
6.9%
M 64
 
6.0%
G 52
 
4.9%
I 49
 
4.6%
Other values (10) 148
13.8%
Decimal Number
ValueCountFrequency (%)
1 506
42.3%
2 346
29.0%
3 131
 
11.0%
5 87
 
7.3%
6 52
 
4.4%
9 43
 
3.6%
0 20
 
1.7%
4 5
 
0.4%
8 3
 
0.3%
7 2
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
e 77
27.1%
f 77
27.1%
o 55
19.4%
i 22
 
7.7%
a 20
 
7.0%
c 11
 
3.9%
l 9
 
3.2%
k 9
 
3.2%
b 2
 
0.7%
h 2
 
0.7%
Space Separator
ValueCountFrequency (%)
9921
100.0%
Other Punctuation
ValueCountFrequency (%)
. 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74978
85.7%
Common 11167
 
12.8%
Latin 1353
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 10041
 
13.4%
근 9866
 
13.2%
우 1541
 
2.1%
트 1420
 
1.9%
아 1231
 
1.6%
장 1226
 
1.6%
송 1145
 
1.5%
주 1031
 
1.4%
천 1030
 
1.4%
리 1001
 
1.3%
Other values (517) 45446
60.6%
Latin
ValueCountFrequency (%)
K 160
11.8%
C 137
 
10.1%
F 105
 
7.8%
E 105
 
7.8%
O 97
 
7.2%
P 78
 
5.8%
e 77
 
5.7%
f 77
 
5.7%
Z 74
 
5.5%
M 64
 
4.7%
Other values (20) 379
28.0%
Common
ValueCountFrequency (%)
9921
88.8%
1 506
 
4.5%
2 346
 
3.1%
3 131
 
1.2%
5 87
 
0.8%
6 52
 
0.5%
9 43
 
0.4%
. 32
 
0.3%
0 20
 
0.2%
( 9
 
0.1%
Other values (5) 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74978
85.7%
ASCII 12520
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 10041
 
13.4%
근 9866
 
13.2%
우 1541
 
2.1%
트 1420
 
1.9%
아 1231
 
1.6%
장 1226
 
1.6%
송 1145
 
1.5%
주 1031
 
1.4%
천 1030
 
1.4%
리 1001
 
1.3%
Other values (517) 45446
60.6%
ASCII
ValueCountFrequency (%)
9921
79.2%
1 506
 
4.0%
2 346
 
2.8%
K 160
 
1.3%
C 137
 
1.1%
3 131
 
1.0%
F 105
 
0.8%
E 105
 
0.8%
O 97
 
0.8%
5 87
 
0.7%
Other values (35) 925
 
7.4%

데이터 기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-11-22
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-11-22
2nd row2023-11-22
3rd row2023-11-22
4th row2023-11-22
5th row2023-11-22

Common Values

ValueCountFrequency (%)
2023-11-22 10000
100.0%

Length

2023-12-12T11:39:31.865712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:39:31.951858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-11-22 10000
100.0%

Missing values

2023-12-12T11:39:30.465749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:39:30.552060image/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

기관명단속일시단속장소데이터 기준일
11155포천시2023-10-20농협중앙회 부근2023-11-22
1085포천시2023-01-25바다목장 부근2023-11-22
6850포천시2023-06-19바다목장 부근2023-11-22
1151포천시2023-01-27현대정형외과의원 부근2023-11-22
8802포천시2023-08-11365우리약국 부근2023-11-22
410포천시2023-01-09송우초교 부근2023-11-22
5702포천시2023-05-17동아약국 부근2023-11-22
7918포천시2023-07-18피자스쿨 부근2023-11-22
3266포천시2023-03-15피자스쿨 부근2023-11-22
5488포천시2023-05-11법무사임창학사무소 부근2023-11-22
기관명단속일시단속장소데이터 기준일
6854포천시2023-06-19연세그랜드치과의원 부근2023-11-22
6134포천시2023-05-30원일산호3차아파트 부근2023-11-22
525포천시2023-01-11강계식당 부근2023-11-22
9773포천시2023-09-11벨라헤어동안피부샵 부근2023-11-22
7605포천시2023-07-10송우곱창 부근2023-11-22
725포천시2023-01-13더벤티 부근2023-11-22
9135포천시2023-08-22송우로제2공영주차장 부근2023-11-22
5539포천시2023-05-13신읍버스터미널2023-11-22
11666포천시2023-11-01K마트 부근2023-11-22
8538포천시2023-08-04치르치르피쉬앤그릴 부근2023-11-22

Duplicate rows

Most frequently occurring

기관명단속일시단속장소데이터 기준일# duplicates
145포천시2023-01-25송우공영2주차장 부근2023-11-2211
1335포천시2023-10-11포천상운아파트 부근2023-11-2210
1449포천시2023-11-01포천상운아파트 부근2023-11-2210
1368포천시2023-10-18명성아파트 부근2023-11-229
72포천시2023-01-11송우공영2주차장 부근2023-11-228
131포천시2023-01-17송우공영2주차장 부근2023-11-228
350포천시2023-03-03송우공영2주차장 부근2023-11-228
1482포천시2023-11-08포천상운아파트 부근2023-11-228
10포천시2023-01-02필마트 부근2023-11-227
120포천시2023-01-16송우공영2주차장 부근2023-11-227