Overview

Dataset statistics

Number of variables5
Number of observations4121
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory165.1 KiB
Average record size in memory41.0 B

Variable types

Numeric1
Text2
Categorical1
DateTime1

Dataset

Description강서구시설관리공단에서 운영 및 관리하는 거주자우선주차장 부정주차 단속현황입니다. 2023년 3분기 단속현황 업로드입니다. 향후 2023년 4분기 단속현황이 업로드될 예정입니다.
Author강서구시설관리공단
URLhttps://www.data.go.kr/data/15124433/fileData.do

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:01:39.685336
Analysis finished2023-12-12 08:01:40.299271
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct4121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2061
Minimum1
Maximum4121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.3 KiB
2023-12-12T17:01:40.404314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile207
Q11031
median2061
Q33091
95-th percentile3915
Maximum4121
Range4120
Interquartile range (IQR)2060

Descriptive statistics

Standard deviation1189.7746
Coefficient of variation (CV)0.57728023
Kurtosis-1.2
Mean2061
Median Absolute Deviation (MAD)1030
Skewness0
Sum8493381
Variance1415563.5
MonotonicityStrictly increasing
2023-12-12T17:01:40.603536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2706 1
 
< 0.1%
2740 1
 
< 0.1%
2741 1
 
< 0.1%
2742 1
 
< 0.1%
2743 1
 
< 0.1%
2744 1
 
< 0.1%
2745 1
 
< 0.1%
2746 1
 
< 0.1%
2747 1
 
< 0.1%
Other values (4111) 4111
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4121 1
< 0.1%
4120 1
< 0.1%
4119 1
< 0.1%
4118 1
< 0.1%
4117 1
< 0.1%
4116 1
< 0.1%
4115 1
< 0.1%
4114 1
< 0.1%
4113 1
< 0.1%
4112 1
< 0.1%

차량번호
Text

Distinct3168
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
2023-12-12T17:01:40.957927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length7.4343606
Min length4

Characters and Unicode

Total characters30637
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2632 ?
Unique (%)63.9%

Sample

1st row1***241
2nd row1***273
3rd row2***389
4th row3***1577
5th row2***586
ValueCountFrequency (%)
1***9975 27
 
0.7%
2***087 17
 
0.4%
6***154 14
 
0.3%
2***836 13
 
0.3%
8***448 13
 
0.3%
3***2573 12
 
0.3%
7***404 12
 
0.3%
1***779 11
 
0.3%
9***452 11
 
0.3%
1***6306 9
 
0.2%
Other values (3157) 3982
96.6%
2023-12-12T17:01:41.502695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 12363
40.4%
1 2474
 
8.1%
2 2084
 
6.8%
3 1968
 
6.4%
5 1824
 
6.0%
8 1814
 
5.9%
6 1643
 
5.4%
4 1642
 
5.4%
7 1568
 
5.1%
9 1496
 
4.9%
Other values (24) 1761
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17928
58.5%
Other Punctuation 12364
40.4%
Other Letter 345
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 130
37.7%
바 40
 
11.6%
경 37
 
10.7%
자 34
 
9.9%
아 34
 
9.9%
사 29
 
8.4%
배 17
 
4.9%
카 4
 
1.2%
충 3
 
0.9%
나 2
 
0.6%
Other values (12) 15
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 2474
13.8%
2 2084
11.6%
3 1968
11.0%
5 1824
10.2%
8 1814
10.1%
6 1643
9.2%
4 1642
9.2%
7 1568
8.7%
9 1496
8.3%
0 1415
7.9%
Other Punctuation
ValueCountFrequency (%)
* 12363
> 99.9%
, 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30292
98.9%
Hangul 345
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 130
37.7%
바 40
 
11.6%
경 37
 
10.7%
자 34
 
9.9%
아 34
 
9.9%
사 29
 
8.4%
배 17
 
4.9%
카 4
 
1.2%
충 3
 
0.9%
나 2
 
0.6%
Other values (12) 15
 
4.3%
Common
ValueCountFrequency (%)
* 12363
40.8%
1 2474
 
8.2%
2 2084
 
6.9%
3 1968
 
6.5%
5 1824
 
6.0%
8 1814
 
6.0%
6 1643
 
5.4%
4 1642
 
5.4%
7 1568
 
5.2%
9 1496
 
4.9%
Other values (2) 1416
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30292
98.9%
Hangul 345
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 12363
40.8%
1 2474
 
8.2%
2 2084
 
6.9%
3 1968
 
6.5%
5 1824
 
6.0%
8 1814
 
6.0%
6 1643
 
5.4%
4 1642
 
5.4%
7 1568
 
5.2%
9 1496
 
4.9%
Other values (2) 1416
 
4.7%
Hangul
ValueCountFrequency (%)
서 130
37.7%
바 40
 
11.6%
경 37
 
10.7%
자 34
 
9.9%
아 34
 
9.9%
사 29
 
8.4%
배 17
 
4.9%
카 4
 
1.2%
충 3
 
0.9%
나 2
 
0.6%
Other values (12) 15
 
4.3%

위반동명
Categorical

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
우장산동
435 
화곡1동
389 
방화3동
369 
등촌1동
320 
화곡3동
280 
Other values (15)
2328 

Length

Max length4
Median length4
Mean length3.8886193
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화곡2동
2nd row등촌1동
3rd row화곡2동
4th row등촌1동
5th row화곡2동

Common Values

ValueCountFrequency (%)
우장산동 435
10.6%
화곡1동 389
 
9.4%
방화3동 369
 
9.0%
등촌1동 320
 
7.8%
화곡3동 280
 
6.8%
화곡6동 279
 
6.8%
방화2동 273
 
6.6%
공항동 258
 
6.3%
발산1동 245
 
5.9%
방화1동 222
 
5.4%
Other values (10) 1051
25.5%

Length

2023-12-12T17:01:42.086380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
우장산동 435
10.6%
화곡1동 389
 
9.4%
방화3동 369
 
9.0%
등촌1동 320
 
7.8%
화곡3동 280
 
6.8%
화곡6동 279
 
6.8%
방화2동 273
 
6.6%
공항동 258
 
6.3%
발산1동 245
 
5.9%
방화1동 222
 
5.4%
Other values (10) 1051
25.5%

위반구간
Text

Distinct569
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
2023-12-12T17:01:42.423183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.0288765
Min length3

Characters and Unicode

Total characters20724
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)4.9%

Sample

1st row7_03
2nd row2_06
3rd row7_03
4th row2_08
5th row7_03
ValueCountFrequency (%)
22_18 152
 
3.7%
6_63 143
 
3.5%
18_48 108
 
2.6%
18_40 93
 
2.3%
17_37 91
 
2.2%
2_06 87
 
2.1%
21_40 60
 
1.5%
12_14 55
 
1.3%
2_07 53
 
1.3%
22_26 53
 
1.3%
Other values (559) 3226
78.3%
2023-12-12T17:01:42.994343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 4571
22.1%
1 4135
20.0%
2 2695
13.0%
0 1917
9.3%
4 1506
 
7.3%
8 1385
 
6.7%
3 1193
 
5.8%
6 1051
 
5.1%
7 882
 
4.3%
9 831
 
4.0%
Other values (4) 558
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16144
77.9%
Connector Punctuation 4571
 
22.1%
Other Letter 9
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4135
25.6%
2 2695
16.7%
0 1917
11.9%
4 1506
 
9.3%
8 1385
 
8.6%
3 1193
 
7.4%
6 1051
 
6.5%
7 882
 
5.5%
9 831
 
5.1%
5 549
 
3.4%
Other Letter
ValueCountFrequency (%)
한 3
33.3%
광 3
33.3%
고 3
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 4571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20715
> 99.9%
Hangul 9
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 4571
22.1%
1 4135
20.0%
2 2695
13.0%
0 1917
9.3%
4 1506
 
7.3%
8 1385
 
6.7%
3 1193
 
5.8%
6 1051
 
5.1%
7 882
 
4.3%
9 831
 
4.0%
Hangul
ValueCountFrequency (%)
한 3
33.3%
광 3
33.3%
고 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20715
> 99.9%
Hangul 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 4571
22.1%
1 4135
20.0%
2 2695
13.0%
0 1917
9.3%
4 1506
 
7.3%
8 1385
 
6.7%
3 1193
 
5.8%
6 1051
 
5.1%
7 882
 
4.3%
9 831
 
4.0%
Hangul
ValueCountFrequency (%)
한 3
33.3%
광 3
33.3%
고 3
33.3%

단속시간
Date

Distinct3666
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
Minimum2023-07-01 03:07:00
Maximum2023-07-31 21:10:00
2023-12-12T17:01:43.172349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:01:43.365990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T17:01:39.966591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:01:43.467181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위반동명
연번1.0000.194
위반동명0.1941.000
2023-12-12T17:01:43.566983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위반동명
연번1.0000.062
위반동명0.0621.000

Missing values

2023-12-12T17:01:40.109858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:01:40.235105image/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

연번차량번호위반동명위반구간단속시간
011***241화곡2동7_032023-07-31 21:10
121***273등촌1동2_062023-07-31 21:06
232***389화곡2동7_032023-07-31 21:03
343***1577등촌1동2_082023-07-31 21:02
452***586화곡2동7_032023-07-31 21:01
563***8650우장산동18_482023-07-31 20:59
673***765우장산동18_482023-07-31 20:56
781***1509우장산동18_482023-07-31 20:57
891***6110우장산동18_482023-07-31 20:58
9103***6688방화3동22_20_1012023-07-31 20:51
연번차량번호위반동명위반구간단속시간
411141121***5556화곡6동11_732023-07-02 10:27
41124113서***라2172화곡6동11_732023-07-02 10:24
411341141***568화곡1동6_632023-07-01 21:58
411441153***983화곡1동6_632023-07-01 21:55
411541163***8912화곡3동8_022023-07-01 19:30
411641172***1313화곡4동9_782023-07-01 15:05
411741181***8956화곡6동11_772023-07-01 14:36
411841193***140방화2동21_412023-07-01 14:13
411941206***946화곡1동6_632023-07-01 03:11
412041214***554화곡1동6_632023-07-01 03:07