Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows7
Duplicate rows (%)0.1%
Total size in memory1.0 MiB
Average record size in memory109.0 B

Variable types

Categorical6
Text1
Numeric4
DateTime1

Dataset

Description광주광역시 서구 교통위반과태료압류정보의 시군구명, 과태료명, 처리상태, 해제일자, 단속구분, 단속일자 등에 대한 현황입니다.
Author광주광역시 서구
URLhttps://www.data.go.kr/data/15090265/fileData.do

Alerts

시군구명 has constant value ""Constant
과태료명 has constant value ""Constant
Dataset has 7 (0.1%) duplicate rowsDuplicates
가산금 is highly overall correlated with 미납금액High correlation
미납금액 is highly overall correlated with 가산금 and 1 other fieldsHigh correlation
납부금액 is highly overall correlated with 미납금액High correlation
처리상태 is highly overall correlated with 감액금액High correlation
감액금액 is highly overall correlated with 처리상태High correlation
처리상태 is highly imbalanced (75.9%)Imbalance
감액금액 is highly imbalanced (99.6%)Imbalance
단속구분 is highly imbalanced (50.0%)Imbalance
차량구분 is highly imbalanced (99.9%)Imbalance
미납금액 has 1253 (12.5%) zerosZeros
납부금액 has 8752 (87.5%) zerosZeros

Reproduction

Analysis started2024-03-15 01:07:21.884047
Analysis finished2024-03-15 01:07:28.282338
Duration6.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 10000
100.0%

Length

2024-03-15T10:07:28.393372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:07:28.553651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 10000
100.0%

과태료명
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주정차위반과태료 10000
100.0%

Length

2024-03-15T10:07:28.906490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:07:29.204145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주정차위반과태료 10000
100.0%

처리상태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
압류
8655 
압류납부
1248 
대납
 
92
부과취소
 
3
법원이송
 
1

Length

Max length6
Median length2
Mean length2.2508
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
압류 8655
86.6%
압류납부 1248
 
12.5%
대납 92
 
0.9%
부과취소 3
 
< 0.1%
법원이송 1
 
< 0.1%
이의신청수용 1
 
< 0.1%

Length

2024-03-15T10:07:29.571162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:07:29.929763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
압류 8655
86.6%
압류납부 1248
 
12.5%
대납 92
 
0.9%
부과취소 3
 
< 0.1%
법원이송 1
 
< 0.1%
이의신청수용 1
 
< 0.1%

해제일자
Text

Distinct148
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T10:07:30.652245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length1
Mean length2.1592
Min length1

Characters and Unicode

Total characters21592
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 8712
87.1%
2023-09-18 50
 
0.5%
2023-10-04 43
 
0.4%
2023-09-19 29
 
0.3%
2023-09-25 23
 
0.2%
2023-12-29 21
 
0.2%
2023-06-30 21
 
0.2%
2024-01-02 20
 
0.2%
2023-09-21 19
 
0.2%
2023-12-26 19
 
0.2%
Other values (138) 1043
 
10.4%
2024-03-15T10:07:31.758580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11404
52.8%
2 3333
 
15.4%
- 2576
 
11.9%
1 1415
 
6.6%
3 1322
 
6.1%
9 385
 
1.8%
4 296
 
1.4%
8 278
 
1.3%
6 241
 
1.1%
7 230
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19016
88.1%
Dash Punctuation 2576
 
11.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11404
60.0%
2 3333
 
17.5%
1 1415
 
7.4%
3 1322
 
7.0%
9 385
 
2.0%
4 296
 
1.6%
8 278
 
1.5%
6 241
 
1.3%
7 230
 
1.2%
5 112
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 2576
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11404
52.8%
2 3333
 
15.4%
- 2576
 
11.9%
1 1415
 
6.6%
3 1322
 
6.1%
9 385
 
1.8%
4 296
 
1.4%
8 278
 
1.3%
6 241
 
1.1%
7 230
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11404
52.8%
2 3333
 
15.4%
- 2576
 
11.9%
1 1415
 
6.6%
3 1322
 
6.1%
9 385
 
1.8%
4 296
 
1.4%
8 278
 
1.3%
6 241
 
1.1%
7 230
 
1.1%

본세
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43647
Minimum10000
Maximum130000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:07:31.960385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile40000
Q140000
median40000
Q340000
95-th percentile80000
Maximum130000
Range120000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14940.271
Coefficient of variation (CV)0.34229779
Kurtosis17.690439
Mean43647
Median Absolute Deviation (MAD)0
Skewness4.1959015
Sum4.3647 × 108
Variance2.2321171 × 108
MonotonicityNot monotonic
2024-03-15T10:07:32.144081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
40000 9154
91.5%
80000 322
 
3.2%
120000 260
 
2.6%
50000 210
 
2.1%
10000 30
 
0.3%
130000 15
 
0.1%
90000 6
 
0.1%
20000 3
 
< 0.1%
ValueCountFrequency (%)
10000 30
 
0.3%
20000 3
 
< 0.1%
40000 9154
91.5%
50000 210
 
2.1%
80000 322
 
3.2%
90000 6
 
0.1%
120000 260
 
2.6%
130000 15
 
0.1%
ValueCountFrequency (%)
130000 15
 
0.1%
120000 260
 
2.6%
90000 6
 
0.1%
80000 322
 
3.2%
50000 210
 
2.1%
40000 9154
91.5%
20000 3
 
< 0.1%
10000 30
 
0.3%

가산금
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3846.09
Minimum420
Maximum19500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:07:32.505232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum420
5-th percentile1680
Q12160
median3600
Q35040
95-th percentile6000
Maximum19500
Range19080
Interquartile range (IQR)2880

Descriptive statistics

Standard deviation2192.1909
Coefficient of variation (CV)0.56997909
Kurtosis12.189699
Mean3846.09
Median Absolute Deviation (MAD)1440
Skewness2.6975358
Sum38460900
Variance4805700.8
MonotonicityNot monotonic
2024-03-15T10:07:32.979645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1680 1449
14.5%
2160 1191
11.9%
2640 1115
11.2%
4080 1052
10.5%
3600 978
9.8%
5040 914
9.1%
3120 906
9.1%
5520 811
8.1%
6000 706
7.1%
4560 66
 
0.7%
Other values (52) 812
8.1%
ValueCountFrequency (%)
420 5
0.1%
540 5
0.1%
660 3
< 0.1%
780 1
 
< 0.1%
900 4
< 0.1%
1020 5
0.1%
1260 1
 
< 0.1%
1380 3
< 0.1%
1500 3
< 0.1%
1560 1
 
< 0.1%
ValueCountFrequency (%)
19500 2
 
< 0.1%
18000 38
0.4%
16560 25
0.2%
16380 2
 
< 0.1%
15120 31
0.3%
13680 3
 
< 0.1%
13500 1
 
< 0.1%
13260 2
 
< 0.1%
12240 33
0.3%
12000 30
0.3%

미납금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41658.6
Minimum0
Maximum149500
Zeros1253
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:07:33.393222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142160
median43120
Q345040
95-th percentile83360
Maximum149500
Range149500
Interquartile range (IQR)2880

Descriptive statistics

Standard deviation22113.958
Coefficient of variation (CV)0.53083777
Kurtosis6.1466433
Mean41658.6
Median Absolute Deviation (MAD)1440
Skewness1.1125714
Sum4.16586 × 108
Variance4.8902716 × 108
MonotonicityNot monotonic
2024-03-15T10:07:33.923038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1253
12.5%
41680 1190
11.9%
42160 1025
10.2%
42640 976
9.8%
44080 922
9.2%
45040 844
8.4%
43600 819
8.2%
45520 789
7.9%
43120 733
7.3%
46000 696
7.0%
Other values (51) 753
7.5%
ValueCountFrequency (%)
0 1253
12.5%
10420 5
 
0.1%
10540 4
 
< 0.1%
10660 3
 
< 0.1%
10780 1
 
< 0.1%
10900 1
 
< 0.1%
11020 4
 
< 0.1%
11260 1
 
< 0.1%
11380 3
 
< 0.1%
11500 3
 
< 0.1%
ValueCountFrequency (%)
149500 2
 
< 0.1%
146380 2
 
< 0.1%
141700 1
 
< 0.1%
140140 1
 
< 0.1%
138000 38
0.4%
137020 3
 
< 0.1%
136560 25
0.2%
135460 4
 
< 0.1%
135120 30
0.3%
133680 1
 
< 0.1%

납부금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5805.074
Minimum0
Maximum143260
Zeros8752
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:07:34.191015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile43600
Maximum143260
Range143260
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16320.087
Coefficient of variation (CV)2.8113487
Kurtosis13.062291
Mean5805.074
Median Absolute Deviation (MAD)0
Skewness3.2105558
Sum58050740
Variance2.6634524 × 108
MonotonicityNot monotonic
2024-03-15T10:07:34.579597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 8752
87.5%
41680 256
 
2.6%
43120 173
 
1.7%
42160 166
 
1.7%
43600 159
 
1.6%
42640 139
 
1.4%
44080 129
 
1.3%
44560 60
 
0.6%
45040 36
 
0.4%
45520 22
 
0.2%
Other values (32) 108
 
1.1%
ValueCountFrequency (%)
0 8752
87.5%
10540 1
 
< 0.1%
10900 3
 
< 0.1%
11020 1
 
< 0.1%
41680 256
 
2.6%
42160 166
 
1.7%
42640 139
 
1.4%
43120 173
 
1.7%
43600 159
 
1.6%
44080 129
 
1.3%
ValueCountFrequency (%)
143260 2
 
< 0.1%
135120 1
 
< 0.1%
133680 2
 
< 0.1%
132240 4
< 0.1%
130800 5
0.1%
129360 5
0.1%
127920 2
 
< 0.1%
126480 4
< 0.1%
125040 6
0.1%
100260 1
 
< 0.1%

감액금액
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9994 
41680
 
4
2400
 
1
125040
 
1

Length

Max length6
Median length1
Mean length1.0024
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9994
99.9%
41680 4
 
< 0.1%
2400 1
 
< 0.1%
125040 1
 
< 0.1%

Length

2024-03-15T10:07:35.009429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:07:35.337942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9994
99.9%
41680 4
 
< 0.1%
2400 1
 
< 0.1%
125040 1
 
< 0.1%

단속구분
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
고정형CCTV
6734 
안전신문고
1689 
주행형CCTV
1497 
버스장착형CCTV
 
57
일반도보
 
18

Length

Max length9
Median length7
Mean length6.6667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고정형CCTV 6734
67.3%
안전신문고 1689
 
16.9%
주행형CCTV 1497
 
15.0%
버스장착형CCTV 57
 
0.6%
일반도보 18
 
0.2%
<NA> 5
 
0.1%

Length

2024-03-15T10:07:35.702639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:07:36.041067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정형cctv 6734
67.3%
안전신문고 1689
 
16.9%
주행형cctv 1497
 
15.0%
버스장착형cctv 57
 
0.6%
일반도보 18
 
0.2%
na 5
 
< 0.1%

단속일시
Date

Distinct9987
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-09-06 13:19:15
Maximum2023-09-05 15:35:04
2024-03-15T10:07:36.414338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:36.919368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

차량구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9999 
미군
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9999
> 99.9%
미군 1
 
< 0.1%

Length

2024-03-15T10:07:37.403706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:07:37.727941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9999
> 99.9%
미군 1
 
< 0.1%

Interactions

2024-03-15T10:07:26.447408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:23.332245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:24.458937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:25.317291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:26.736120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:23.649975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:24.636312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:25.590835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:26.979880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:23.930443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:24.827212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:25.877773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:27.211407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:24.211867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:25.028322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:07:26.164116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:07:37.926877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리상태본세가산금미납금액납부금액감액금액단속구분차량구분
처리상태1.0000.0560.1080.6590.6360.9250.0330.000
본세0.0561.0000.6650.8880.3570.0410.2030.000
가산금0.1080.6651.0000.8420.4080.0000.1410.000
미납금액0.6590.8880.8421.0000.6290.0570.2280.000
납부금액0.6360.3570.4080.6291.0000.0000.0720.000
감액금액0.9250.0410.0000.0570.0001.0000.0000.000
단속구분0.0330.2030.1410.2280.0720.0001.0000.000
차량구분0.0000.0000.0000.0000.0000.0000.0001.000
2024-03-15T10:07:38.240453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속구분차량구분처리상태감액금액
단속구분1.0000.0000.0220.000
차량구분0.0001.0000.0000.000
처리상태0.0220.0001.0000.816
감액금액0.0000.0000.8161.000
2024-03-15T10:07:38.503166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본세가산금미납금액납부금액처리상태감액금액단속구분차량구분
본세1.0000.3450.3790.0020.0210.0260.1430.000
가산금0.3451.0000.812-0.0960.0570.0000.0590.000
미납금액0.3790.8121.000-0.5730.4420.0260.1410.000
납부금액0.002-0.096-0.5731.0000.4460.0000.0460.000
처리상태0.0210.0570.4420.4461.0000.8160.0220.000
감액금액0.0260.0000.0260.0000.8161.0000.0000.000
단속구분0.1430.0590.1410.0460.0220.0001.0000.000
차량구분0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-15T10:07:27.456083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:07:28.138860image/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

시군구명과태료명처리상태해제일자본세가산금미납금액납부금액감액금액단속구분단속일시차량구분
22218서구주정차위반과태료압류04000016804168000고정형CCTV2023-08-24 15:47:58일반
14379서구주정차위반과태료압류04000026404264000고정형CCTV2023-06-15 09:33:20일반
17022서구주정차위반과태료압류04000026404264000고정형CCTV2023-06-09 18:29:04일반
21860서구주정차위반과태료압류04000016804168000안전신문고2023-08-21 10:47:51일반
9348서구주정차위반과태료압류04000040804408000고정형CCTV2023-03-20 17:07:08일반
7231서구주정차위반과태료압류04000050404504000안전신문고2023-01-26 09:08:14일반
10388서구주정차위반과태료압류납부2023-11-308000052800852800안전신문고2023-04-03 19:48:51일반
17049서구주정차위반과태료압류04000026404264000고정형CCTV2023-06-03 16:17:56일반
9897서구주정차위반과태료압류04000040804408000주행형CCTV2023-03-29 10:07:08일반
20851서구주정차위반과태료압류04000016804168000주행형CCTV2023-08-08 18:48:11일반
시군구명과태료명처리상태해제일자본세가산금미납금액납부금액감액금액단속구분단속일시차량구분
2282서구주정차위반과태료압류04000060004600000안전신문고2022-11-29 10:38:28일반
7628서구주정차위반과태료압류납부2023-11-248000081600881600안전신문고2023-01-29 19:23:56일반
5202서구주정차위반과태료압류04000050404504000안전신문고2023-01-03 12:01:13일반
9597서구주정차위반과태료압류04000040804408000안전신문고2023-03-24 15:21:52일반
17340서구주정차위반과태료압류05000027005270000고정형CCTV2023-07-01 09:42:31일반
14174서구주정차위반과태료압류04000031204312000고정형CCTV2023-05-25 08:07:21일반
21799서구주정차위반과태료압류04000016804168000고정형CCTV2023-08-19 17:57:30일반
20834서구주정차위반과태료압류04000016804168000고정형CCTV2023-08-08 17:08:02일반
20119서구주정차위반과태료압류04000016804168000고정형CCTV2023-07-31 15:31:20일반
11164서구주정차위반과태료압류04000036004360000고정형CCTV2023-04-13 09:42:54일반

Duplicate rows

Most frequently occurring

시군구명과태료명처리상태해제일자본세가산금미납금액납부금액감액금액단속구분단속일시차량구분# duplicates
0서구주정차위반과태료압류04000021604216000주행형CCTV2023-07-24 14:41:43일반2
1서구주정차위반과태료압류04000026404264000주행형CCTV2023-06-07 19:15:09일반2
2서구주정차위반과태료압류04000031204312000고정형CCTV2023-05-23 18:41:21일반2
3서구주정차위반과태료압류04000036004360000고정형CCTV2023-04-07 09:07:22일반2
4서구주정차위반과태료압류04000050404504000고정형CCTV2023-01-07 14:21:31일반2
5서구주정차위반과태료압류04000050404504000고정형CCTV2023-01-12 15:52:01일반2
6서구주정차위반과태료압류04000050404504000고정형CCTV2023-01-17 08:05:57일반2