Overview

Dataset statistics

Number of variables8
Number of observations3287
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory215.2 KiB
Average record size in memory67.0 B

Variable types

Categorical3
Text1
DateTime1
Numeric3

Dataset

Description자동차 검사 지연 과태료(회계과목명,부서명,부과번호,부과일자,체납금액,체납본세,체납가산금,압류구분)체납 현황 입니다.
Author전북특별자치도 군산시
URLhttps://www.data.go.kr/data/15086044/fileData.do

Alerts

부서명 has constant value ""Constant
체납금액 is highly overall correlated with 본세 and 1 other fieldsHigh correlation
본세 is highly overall correlated with 체납금액 and 1 other fieldsHigh correlation
가산금 is highly overall correlated with 체납금액 and 1 other fieldsHigh correlation
과목 is highly imbalanced (58.5%)Imbalance

Reproduction

Analysis started2024-03-14 11:49:42.805504
Analysis finished2024-03-14 11:49:45.288572
Duration2.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

과목
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.8 KiB
61-288080 자동차손해배상보장법위반과태료
2100 
61-288131 자동차검사지연과태료
1122 
61-288079 자동차등록위반과태료
 
52
61-288004 건설기계관리법위반과태료
 
10
61-288546 자동차이전등록위반범칙금
 
2

Length

Max length25
Median length25
Mean length23.201095
Min length18

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row61-288080 자동차손해배상보장법위반과태료
2nd row61-288080 자동차손해배상보장법위반과태료
3rd row61-288080 자동차손해배상보장법위반과태료
4th row61-288080 자동차손해배상보장법위반과태료
5th row61-288080 자동차손해배상보장법위반과태료

Common Values

ValueCountFrequency (%)
61-288080 자동차손해배상보장법위반과태료 2100
63.9%
61-288131 자동차검사지연과태료 1122
34.1%
61-288079 자동차등록위반과태료 52
 
1.6%
61-288004 건설기계관리법위반과태료 10
 
0.3%
61-288546 자동차이전등록위반범칙금 2
 
0.1%
41-216007 인증기용증지수입 1
 
< 0.1%

Length

2024-03-14T20:49:45.430214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:49:45.633063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
61-288080 2100
31.9%
자동차손해배상보장법위반과태료 2100
31.9%
61-288131 1122
17.1%
자동차검사지연과태료 1122
17.1%
61-288079 52
 
0.8%
자동차등록위반과태료 52
 
0.8%
61-288004 10
 
0.2%
건설기계관리법위반과태료 10
 
0.2%
61-288546 2
 
< 0.1%
자동차이전등록위반범칙금 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

부과번호
Text

Distinct2765
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Memory size25.8 KiB
2024-03-14T20:49:46.440521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters46018
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

Unique2259 ?
Unique (%)68.7%

Sample

1st row2022-000001-00
2nd row2022-000002-00
3rd row2022-000003-00
4th row2022-000004-00
5th row2022-000005-00
ValueCountFrequency (%)
2022-000020-00 5
 
0.2%
2022-000515-00 3
 
0.1%
2022-000019-00 3
 
0.1%
2022-000069-00 3
 
0.1%
2022-000063-00 3
 
0.1%
2022-000002-00 3
 
0.1%
2022-000516-00 3
 
0.1%
2023-000103-00 3
 
0.1%
2022-000044-00 3
 
0.1%
2022-000479-00 3
 
0.1%
Other values (2755) 3255
99.0%
2024-03-14T20:49:48.151805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19160
41.6%
2 10515
22.8%
- 6574
 
14.3%
3 2322
 
5.0%
1 1829
 
4.0%
4 1037
 
2.3%
5 1004
 
2.2%
6 911
 
2.0%
7 907
 
2.0%
8 901
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39444
85.7%
Dash Punctuation 6574
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19160
48.6%
2 10515
26.7%
3 2322
 
5.9%
1 1829
 
4.6%
4 1037
 
2.6%
5 1004
 
2.5%
6 911
 
2.3%
7 907
 
2.3%
8 901
 
2.3%
9 858
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 6574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46018
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19160
41.6%
2 10515
22.8%
- 6574
 
14.3%
3 2322
 
5.0%
1 1829
 
4.0%
4 1037
 
2.3%
5 1004
 
2.2%
6 911
 
2.0%
7 907
 
2.0%
8 901
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19160
41.6%
2 10515
22.8%
- 6574
 
14.3%
3 2322
 
5.0%
1 1829
 
4.0%
4 1037
 
2.3%
5 1004
 
2.2%
6 911
 
2.0%
7 907
 
2.0%
8 901
 
2.0%

부과일자
Date

Distinct69
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size25.8 KiB
Minimum2022-01-01 00:00:00
Maximum2023-05-04 00:00:00
2024-03-14T20:49:48.914458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:49:49.342397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

체납금액
Real number (ℝ)

HIGH CORRELATION 

Distinct823
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274910.81
Minimum9270
Maximum2755400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.0 KiB
2024-03-14T20:49:49.738695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9270
5-th percentile15630
Q118150
median88280
Q3359400
95-th percentile1024200
Maximum2755400
Range2746130
Interquartile range (IQR)341250

Descriptive statistics

Standard deviation349846.3
Coefficient of variation (CV)1.2725811
Kurtosis4.9902646
Mean274910.81
Median Absolute Deviation (MAD)72650
Skewness1.8257717
Sum9.0363184 × 108
Variance1.2239243 × 1011
MonotonicityNot monotonic
2024-03-14T20:49:50.083563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15450 94
 
2.9%
15630 91
 
2.8%
15810 73
 
2.2%
15990 65
 
2.0%
20600 45
 
1.4%
17610 45
 
1.4%
341400 44
 
1.3%
17430 44
 
1.3%
359400 43
 
1.3%
334200 43
 
1.3%
Other values (813) 2700
82.1%
ValueCountFrequency (%)
9270 7
0.2%
9370 7
0.2%
9470 4
0.1%
9570 1
 
< 0.1%
9670 2
 
0.1%
9770 2
 
0.1%
9870 5
0.2%
10070 1
 
< 0.1%
10170 1
 
< 0.1%
10270 3
0.1%
ValueCountFrequency (%)
2755400 1
< 0.1%
2700200 1
< 0.1%
2672600 1
< 0.1%
2645000 2
0.1%
2617400 1
< 0.1%
2451800 1
< 0.1%
2424200 1
< 0.1%
2187980 1
< 0.1%
2048000 1
< 0.1%
1415510 1
< 0.1%

본세
Real number (ℝ)

HIGH CORRELATION 

Distinct226
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246289.38
Minimum9000
Maximum2300000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.0 KiB
2024-03-14T20:49:50.352670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9000
5-th percentile15000
Q115000
median81000
Q3300000
95-th percentile900000
Maximum2300000
Range2291000
Interquartile range (IQR)285000

Descriptive statistics

Standard deviation312000.26
Coefficient of variation (CV)1.2668035
Kurtosis4.5921059
Mean246289.38
Median Absolute Deviation (MAD)66000
Skewness1.7739202
Sum8.095532 × 108
Variance9.7344162 × 1010
MonotonicityNot monotonic
2024-03-14T20:49:50.795084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15000 818
24.9%
300000 499
15.2%
900000 346
 
10.5%
40000 215
 
6.5%
20000 201
 
6.1%
600000 158
 
4.8%
9000 51
 
1.6%
30000 41
 
1.2%
27000 34
 
1.0%
21000 28
 
0.9%
Other values (216) 896
27.3%
ValueCountFrequency (%)
9000 51
 
1.6%
10800 4
 
0.1%
12600 2
 
0.1%
14400 3
 
0.1%
15000 818
24.9%
19800 2
 
0.1%
20000 201
 
6.1%
21000 28
 
0.9%
21600 1
 
< 0.1%
23400 2
 
0.1%
ValueCountFrequency (%)
2300000 8
 
0.2%
2000000 1
 
< 0.1%
1883000 1
 
< 0.1%
1343000 1
 
< 0.1%
1000000 4
 
0.1%
900000 346
10.5%
897000 1
 
< 0.1%
873000 2
 
0.1%
867000 1
 
< 0.1%
861000 2
 
0.1%

가산금
Real number (ℝ)

HIGH CORRELATION 

Distinct719
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28621.43
Minimum0
Maximum455400
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size29.0 KiB
2024-03-14T20:49:51.213688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile630
Q12245
median7120
Q339600
95-th percentile135000
Maximum455400
Range455400
Interquartile range (IQR)37355

Descriptive statistics

Standard deviation44311.021
Coefficient of variation (CV)1.5481764
Kurtosis11.02906
Mean28621.43
Median Absolute Deviation (MAD)6490
Skewness2.8027787
Sum94078640
Variance1.9634666 × 109
MonotonicityNot monotonic
2024-03-14T20:49:51.837732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
630 95
 
2.9%
450 94
 
2.9%
810 75
 
2.3%
59400 71
 
2.2%
990 65
 
2.0%
37800 60
 
1.8%
48600 59
 
1.8%
27000 51
 
1.6%
2610 46
 
1.4%
600 46
 
1.4%
Other values (709) 2625
79.9%
ValueCountFrequency (%)
0 4
 
0.1%
270 7
 
0.2%
370 7
 
0.2%
450 94
2.9%
470 4
 
0.1%
520 1
 
< 0.1%
570 1
 
< 0.1%
600 46
1.4%
630 95
2.9%
670 2
 
0.1%
ValueCountFrequency (%)
455400 1
 
< 0.1%
400200 1
 
< 0.1%
372600 1
 
< 0.1%
345000 2
 
0.1%
317400 1
 
< 0.1%
304980 1
 
< 0.1%
199800 28
0.9%
189000 20
0.6%
188350 1
 
< 0.1%
188310 1
 
< 0.1%

부서명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.8 KiB
차량등록사업소
3287 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
차량등록사업소 3287
100.0%

Length

2024-03-14T20:49:52.243783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:49:52.551355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
차량등록사업소 3287
100.0%

압류구분
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.8 KiB
압류
2315 
미압류
972 

Length

Max length3
Median length2
Mean length2.2957104
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
압류 2315
70.4%
미압류 972
29.6%

Length

2024-03-14T20:49:52.795602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:49:52.968040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
압류 2315
70.4%
미압류 972
29.6%

Interactions

2024-03-14T20:49:44.398053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:49:43.223699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:49:43.850465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:49:44.556541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:49:43.485711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:49:44.066859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:49:44.711724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:49:43.695319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:49:44.246126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:49:53.086007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과목부과일자체납금액본세가산금압류구분
과목1.0000.9990.5100.4840.2170.227
부과일자0.9991.0000.8780.8860.8590.382
체납금액0.5100.8781.0000.9900.8950.116
본세0.4840.8860.9901.0000.7560.121
가산금0.2170.8590.8950.7561.0000.132
압류구분0.2270.3820.1160.1210.1321.000
2024-03-14T20:49:53.260068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과목압류구분
과목1.0000.163
압류구분0.1631.000
2024-03-14T20:49:53.484028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납금액본세가산금과목압류구분
체납금액1.0000.9890.9400.2820.117
본세0.9891.0000.9050.2950.099
가산금0.9400.9051.0000.1090.132
과목0.2820.2950.1091.0000.163
압류구분0.1170.0990.1320.1631.000

Missing values

2024-03-14T20:49:44.912130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:49:45.133935image/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

과목부과번호부과일자체납금액본세가산금부서명압류구분
061-288080 자동차손해배상보장법위반과태료2022-000001-002022-01-011099800900000199800차량등록사업소압류
161-288080 자동차손해배상보장법위반과태료2022-000002-002022-01-011099800900000199800차량등록사업소압류
261-288080 자동차손해배상보장법위반과태료2022-000003-002022-01-011099800900000199800차량등록사업소압류
361-288080 자동차손해배상보장법위반과태료2022-000004-002022-01-011099800900000199800차량등록사업소압류
461-288080 자동차손해배상보장법위반과태료2022-000005-002022-01-011099800900000199800차량등록사업소압류
561-288080 자동차손해배상보장법위반과태료2022-000006-002022-01-011099800900000199800차량등록사업소압류
661-288080 자동차손해배상보장법위반과태료2022-000007-002022-01-0197860090000078600차량등록사업소미압류
761-288080 자동차손해배상보장법위반과태료2022-000008-002022-01-011075800900000175800차량등록사업소미압류
861-288080 자동차손해배상보장법위반과태료2022-000009-002022-01-0136660030000066600차량등록사업소압류
961-288080 자동차손해배상보장법위반과태료2022-000010-002022-01-011099800900000199800차량등록사업소미압류
과목부과번호부과일자체납금액본세가산금부서명압류구분
327761-288080 자동차손해배상보장법위반과태료2023-001472-002023-05-0492700090000027000차량등록사업소압류
327861-288080 자동차손해배상보장법위반과태료2023-001473-002023-05-0492700090000027000차량등록사업소압류
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