Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1208
Duplicate rows (%)12.1%
Total size in memory556.6 KiB
Average record size in memory57.0 B

Variable types

Categorical3
Text1
Numeric2

Dataset

Description수원시 불법주정차 단속 정보에 대한 데이터로관내 지역별 단속구분에 따른 단속 건수 및 과태료 정보 등의 항목을 제공합니다.
Author경기도 수원시
URLhttps://www.data.go.kr/data/15063802/fileData.do

Alerts

Dataset has 1208 (12.1%) duplicate rowsDuplicates
단속건수 is highly overall correlated with 단속원금High correlation
단속원금 is highly overall correlated with 단속건수High correlation

Reproduction

Analysis started2024-03-15 01:32:43.732642
Analysis finished2024-03-15 01:32:49.915850
Duration6.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
팔달구청
3598 
권선구청
2930 
영통구청
1816 
장안구청
1656 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row팔달구청
2nd row권선구청
3rd row영통구청
4th row팔달구청
5th row권선구청

Common Values

ValueCountFrequency (%)
팔달구청 3598
36.0%
권선구청 2930
29.3%
영통구청 1816
18.2%
장안구청 1656
16.6%

Length

2024-03-15T10:32:50.115842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:32:50.432258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
팔달구청 3598
36.0%
권선구청 2930
29.3%
영통구청 1816
18.2%
장안구청 1656
16.6%

단속구분
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
국민신문고
6203 
주행형
1597 
보행
1070 
고정형
775 
주민신고제
 
355

Length

Max length5
Median length5
Mean length4.2046
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국민신문고
2nd row주행형
3rd row고정형
4th row고정형
5th row국민신문고

Common Values

ValueCountFrequency (%)
국민신문고 6203
62.0%
주행형 1597
 
16.0%
보행 1070
 
10.7%
고정형 775
 
7.8%
주민신고제 355
 
3.5%

Length

2024-03-15T10:32:50.846664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:32:51.261350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국민신문고 6203
62.0%
주행형 1597
 
16.0%
보행 1070
 
10.7%
고정형 775
 
7.8%
주민신고제 355
 
3.5%

단속년월
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-10
1124 
2023-09
1089 
2023-08
930 
2023-11
926 
2023-12
859 
Other values (7)
5072 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08
2nd row2023-12
3rd row2023-05
4th row2023-11
5th row2023-02

Common Values

ValueCountFrequency (%)
2023-10 1124
11.2%
2023-09 1089
10.9%
2023-08 930
9.3%
2023-11 926
9.3%
2023-12 859
8.6%
2023-07 856
8.6%
2023-04 790
7.9%
2023-05 759
7.6%
2023-06 712
7.1%
2023-03 690
6.9%
Other values (2) 1265
12.7%

Length

2024-03-15T10:32:51.767966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-10 1124
11.2%
2023-09 1089
10.9%
2023-08 930
9.3%
2023-11 926
9.3%
2023-12 859
8.6%
2023-07 856
8.6%
2023-04 790
7.9%
2023-05 759
7.6%
2023-06 712
7.1%
2023-03 690
6.9%
Other values (2) 1265
12.7%

단속동
Text

Distinct66
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T10:32:53.004735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0451
Min length2

Characters and Unicode

Total characters30451
Distinct characters74
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row매탄동
2nd row평동
3rd row이의동
4th row매탄동
5th row이의동
ValueCountFrequency (%)
인계동 1579
15.8%
화서동 912
 
9.1%
매탄동 842
 
8.4%
영통동 750
 
7.5%
영통구 745
 
7.4%
우만동 444
 
4.4%
원천동 381
 
3.8%
이의동 338
 
3.4%
망포동 328
 
3.3%
지동 304
 
3.0%
Other values (56) 3377
33.8%
2024-03-15T10:32:54.637865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8696
28.6%
인 1579
 
5.2%
계 1579
 
5.2%
영 1574
 
5.2%
매 1541
 
5.1%
통 1497
 
4.9%
서 989
 
3.2%
화 987
 
3.2%
구 872
 
2.9%
탄 843
 
2.8%
Other values (64) 10294
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29872
98.1%
Decimal Number 579
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8696
29.1%
인 1579
 
5.3%
계 1579
 
5.3%
영 1574
 
5.3%
매 1541
 
5.2%
통 1497
 
5.0%
서 989
 
3.3%
화 987
 
3.3%
구 872
 
2.9%
탄 843
 
2.8%
Other values (61) 9715
32.5%
Decimal Number
ValueCountFrequency (%)
2 291
50.3%
3 150
25.9%
1 138
23.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29872
98.1%
Common 579
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8696
29.1%
인 1579
 
5.3%
계 1579
 
5.3%
영 1574
 
5.3%
매 1541
 
5.2%
통 1497
 
5.0%
서 989
 
3.3%
화 987
 
3.3%
구 872
 
2.9%
탄 843
 
2.8%
Other values (61) 9715
32.5%
Common
ValueCountFrequency (%)
2 291
50.3%
3 150
25.9%
1 138
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29872
98.1%
ASCII 579
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8696
29.1%
인 1579
 
5.3%
계 1579
 
5.3%
영 1574
 
5.3%
매 1541
 
5.2%
통 1497
 
5.0%
서 989
 
3.3%
화 987
 
3.3%
구 872
 
2.9%
탄 843
 
2.8%
Other values (61) 9715
32.5%
ASCII
ValueCountFrequency (%)
2 291
50.3%
3 150
25.9%
1 138
23.8%

단속건수
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0854
Minimum1
Maximum809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:32:55.104008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile26
Maximum809
Range808
Interquartile range (IQR)3

Descriptive statistics

Standard deviation28.995665
Coefficient of variation (CV)4.0923116
Kurtosis278.90836
Mean7.0854
Median Absolute Deviation (MAD)0
Skewness14.396363
Sum70854
Variance840.74858
MonotonicityNot monotonic
2024-03-15T10:32:55.577134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5145
51.4%
2 1574
 
15.7%
3 740
 
7.4%
4 413
 
4.1%
5 268
 
2.7%
6 230
 
2.3%
7 170
 
1.7%
8 120
 
1.2%
9 115
 
1.1%
10 89
 
0.9%
Other values (160) 1136
 
11.4%
ValueCountFrequency (%)
1 5145
51.4%
2 1574
 
15.7%
3 740
 
7.4%
4 413
 
4.1%
5 268
 
2.7%
6 230
 
2.3%
7 170
 
1.7%
8 120
 
1.2%
9 115
 
1.1%
10 89
 
0.9%
ValueCountFrequency (%)
809 1
< 0.1%
733 1
< 0.1%
705 1
< 0.1%
665 1
< 0.1%
654 1
< 0.1%
616 1
< 0.1%
556 1
< 0.1%
545 1
< 0.1%
521 1
< 0.1%
445 1
< 0.1%

단속원금
Real number (ℝ)

HIGH CORRELATION 

Distinct368
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299047
Minimum0
Maximum29210000
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:32:56.009795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40000
Q140000
median80000
Q3160000
95-th percentile1180000
Maximum29210000
Range29210000
Interquartile range (IQR)120000

Descriptive statistics

Standard deviation1137677.7
Coefficient of variation (CV)3.804344
Kurtosis228.97678
Mean299047
Median Absolute Deviation (MAD)40000
Skewness13.020153
Sum2.99047 × 109
Variance1.2943105 × 1012
MonotonicityNot monotonic
2024-03-15T10:32:56.466203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 4667
46.7%
80000 1419
 
14.2%
120000 716
 
7.2%
160000 356
 
3.6%
50000 319
 
3.2%
240000 211
 
2.1%
200000 202
 
2.0%
90000 132
 
1.3%
280000 129
 
1.3%
320000 98
 
1.0%
Other values (358) 1751
 
17.5%
ValueCountFrequency (%)
0 10
 
0.1%
40000 4667
46.7%
50000 319
 
3.2%
80000 1419
 
14.2%
90000 132
 
1.3%
100000 16
 
0.2%
120000 716
 
7.2%
130000 72
 
0.7%
140000 9
 
0.1%
150000 8
 
0.1%
ValueCountFrequency (%)
29210000 1
< 0.1%
27310000 1
< 0.1%
24190000 1
< 0.1%
24090000 1
< 0.1%
23200000 1
< 0.1%
22930000 1
< 0.1%
22180000 1
< 0.1%
20730000 1
< 0.1%
20350000 1
< 0.1%
19130000 1
< 0.1%

Interactions

2024-03-15T10:32:47.553851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:32:44.443455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:32:48.967652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:32:46.591185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:32:56.760626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명단속구분단속년월단속동단속건수단속원금
시군구명1.0000.2310.0550.7970.0540.041
단속구분0.2311.0000.1850.5080.2790.307
단속년월0.0550.1851.0000.5250.0000.009
단속동0.7970.5080.5251.0000.0550.193
단속건수0.0540.2790.0000.0551.0000.988
단속원금0.0410.3070.0090.1930.9881.000
2024-03-15T10:32:57.057529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명단속구분단속년월
시군구명1.0000.1910.026
단속구분0.1911.0000.103
단속년월0.0260.1031.000
2024-03-15T10:32:57.302238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속건수단속원금시군구명단속구분단속년월
단속건수1.0000.8170.0560.2820.035
단속원금0.8171.0000.0240.1320.004
시군구명0.0560.0241.0000.1910.026
단속구분0.2820.1320.1911.0000.103
단속년월0.0350.0040.0260.1031.000

Missing values

2024-03-15T10:32:49.301537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:32:49.755144image/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

시군구명단속구분단속년월단속동단속건수단속원금
40259팔달구청국민신문고2023-08매탄동140000
27513권선구청주행형2023-12평동24960000
53215영통구청고정형2023-05이의동13530000
46035팔달구청고정형2023-11매탄동7320000
11831권선구청국민신문고2023-02이의동3120000
4840장안구청주행형2023-08신풍동140000
8549장안구청국민신문고2023-11매교동240000
30002팔달구청국민신문고2023-02인계동140000
51027영통구청국민신문고2023-03원천동140000
52037영통구청국민신문고2023-04망포동562110000
시군구명단속구분단속년월단속동단속건수단속원금
21746권선구청국민신문고2023-09오목천동140000
57915영통구청주민신고제2023-10영통구280000
16649권선구청국민신문고2023-06이의동140000
38949팔달구청보행2023-08인계동280000
55291영통구청국민신문고2023-08원천동180000
21338권선구청국민신문고2023-09원천동4160000
11923권선구청국민신문고2023-02매탄동140000
24771권선구청국민신문고2023-11인계동18720000
48900팔달구청주행형2023-12지동140000
27101권선구청국민신문고2023-12세류동180000

Duplicate rows

Most frequently occurring

시군구명단속구분단속년월단속동단속건수단속원금# duplicates
409영통구청국민신문고2023-09영통구14000058
465영통구청주민신고제2023-10영통구14000039
158권선구청국민신문고2023-09세류동14000037
201권선구청국민신문고2023-10인계동14000037
136권선구청국민신문고2023-08인계동14000032
478영통구청주민신고제2023-12영통구14000032
752팔달구청국민신문고2023-02인계동14000030
970팔달구청국민신문고2023-10화서동14000030
418영통구청국민신문고2023-10영통구14000028
730팔달구청국민신문고2023-01인계동14000028