Overview

Dataset statistics

Number of variables19
Number of observations3635
Missing cells0
Missing cells (%)0.0%
Duplicate rows429
Duplicate rows (%)11.8%
Total size in memory564.5 KiB
Average record size in memory159.0 B

Variable types

Categorical11
Numeric6
DateTime2

Dataset

Description오산시 지방세 ARS카드납부시스템의 세외수입에 대한 데이터로 세목, 부과금액, 부과 부서 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15081648/fileData.do

Alerts

자치단체코드 has constant value ""Constant
세구분 has constant value ""Constant
자치단체명 has constant value ""Constant
Dataset has 429 (11.8%) duplicate rowsDuplicates
분납구분 is highly overall correlated with 납기후일자 and 2 other fieldsHigh correlation
부과상태구분 is highly overall correlated with 납기후일자 and 1 other fieldsHigh correlation
납기후일자 is highly overall correlated with 회계연도 and 6 other fieldsHigh correlation
부서명 is highly overall correlated with 과목명 and 2 other fieldsHigh correlation
과목명 is highly overall correlated with 부서명 and 3 other fieldsHigh correlation
감경구분 is highly overall correlated with 납기후일자 and 1 other fieldsHigh correlation
압류구분 is highly overall correlated with 납기후일자 and 1 other fieldsHigh correlation
회계연도 is highly overall correlated with 가산금 and 1 other fieldsHigh correlation
부과금액 is highly overall correlated with 납부금액 and 1 other fieldsHigh correlation
납부금액 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
가산금 is highly overall correlated with 회계연도 and 2 other fieldsHigh correlation
본세 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
통합구분 is highly overall correlated with 납기후일자 and 2 other fieldsHigh correlation
납기후일자 is highly imbalanced (74.2%)Imbalance
부서명 is highly imbalanced (66.2%)Imbalance
과목명 is highly imbalanced (70.3%)Imbalance
분납구분 is highly imbalanced (51.5%)Imbalance
통합구분 is highly imbalanced (70.5%)Imbalance
감경구분 is highly imbalanced (80.7%)Imbalance
가산금 has 403 (11.1%) zerosZeros
납기후금액 has 3200 (88.0%) zerosZeros

Reproduction

Analysis started2023-12-12 09:11:10.607124
Analysis finished2023-12-12 09:11:17.556761
Duration6.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
41370
3635 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41370
2nd row41370
3rd row41370
4th row41370
5th row41370

Common Values

ValueCountFrequency (%)
41370 3635
100.0%

Length

2023-12-12T18:11:17.635010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:17.763707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41370 3635
100.0%

회계연도
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.2768
Minimum1998
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T18:11:17.886631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile2013
Q12020
median2021
Q32022
95-th percentile2022
Maximum2022
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.2681307
Coefficient of variation (CV)0.0016176649
Kurtosis11.640286
Mean2020.2768
Median Absolute Deviation (MAD)1
Skewness-3.2514491
Sum7343706
Variance10.680679
MonotonicityNot monotonic
2023-12-12T18:11:18.037886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2022 1657
45.6%
2021 1030
28.3%
2020 262
 
7.2%
2019 182
 
5.0%
2018 136
 
3.7%
2017 96
 
2.6%
2016 53
 
1.5%
2012 35
 
1.0%
2009 30
 
0.8%
2014 22
 
0.6%
Other values (14) 132
 
3.6%
ValueCountFrequency (%)
1998 1
 
< 0.1%
1999 2
 
0.1%
2001 2
 
0.1%
2002 5
 
0.1%
2003 11
0.3%
2004 7
 
0.2%
2005 11
0.3%
2006 13
0.4%
2007 15
0.4%
2008 20
0.6%
ValueCountFrequency (%)
2022 1657
45.6%
2021 1030
28.3%
2020 262
 
7.2%
2019 182
 
5.0%
2018 136
 
3.7%
2017 96
 
2.6%
2016 53
 
1.5%
2015 11
 
0.3%
2014 22
 
0.6%
2013 6
 
0.2%

부과일자
Date

Distinct455
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
Minimum1998-04-17 00:00:00
Maximum2022-12-06 00:00:00
2023-12-12T18:11:18.209115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:18.402815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최초납기일자
Date

Distinct103
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
Minimum2022-01-03 00:00:00
Maximum2022-12-30 00:00:00
2023-12-12T18:11:18.575319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:18.755820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

납기후일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
<NA>
3200 
2022-08-31
 
88
2022-02-28
 
56
2022-08-01
 
43
2022-10-31
 
41
Other values (7)
 
207

Length

Max length10
Median length4
Mean length4.7180193
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 3200
88.0%
2022-08-31 88
 
2.4%
2022-02-28 56
 
1.5%
2022-08-01 43
 
1.2%
2022-10-31 41
 
1.1%
2022-05-31 37
 
1.0%
2022-11-30 34
 
0.9%
2023-01-02 34
 
0.9%
2022-09-30 32
 
0.9%
2022-06-30 31
 
0.9%
Other values (2) 39
 
1.1%

Length

2023-12-12T18:11:18.957193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3200
88.0%
2022-08-31 88
 
2.4%
2022-02-28 56
 
1.5%
2022-08-01 43
 
1.2%
2022-10-31 41
 
1.1%
2022-05-31 37
 
1.0%
2022-11-30 34
 
0.9%
2023-01-02 34
 
0.9%
2022-09-30 32
 
0.9%
2022-06-30 31
 
0.9%
Other values (2) 39
 
1.1%

세구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
세외수입
3635 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세외수입 3635
100.0%

Length

2023-12-12T18:11:19.117609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:19.284812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세외수입 3635
100.0%

부서명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
스마트교통안전과
2771 
차량등록사업소
464 
도로과
 
127
노인장애인과
 
93
주택과
 
32
Other values (11)
 
148

Length

Max length8
Median length8
Mean length7.4627235
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
스마트교통안전과 2771
76.2%
차량등록사업소 464
 
12.8%
도로과 127
 
3.5%
노인장애인과 93
 
2.6%
주택과 32
 
0.9%
토지정보과 30
 
0.8%
대중교통과 28
 
0.8%
청소자원과 25
 
0.7%
환경과 21
 
0.6%
식품위생과 11
 
0.3%
Other values (6) 33
 
0.9%

Length

2023-12-12T18:11:19.440119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
스마트교통안전과 2771
76.2%
차량등록사업소 464
 
12.8%
도로과 127
 
3.5%
노인장애인과 93
 
2.6%
주택과 32
 
0.9%
토지정보과 30
 
0.8%
대중교통과 28
 
0.8%
청소자원과 25
 
0.7%
환경과 21
 
0.6%
식품위생과 11
 
0.3%
Other values (6) 33
 
0.9%

과목명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct46
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
주정차위반과태료
2757 
자동차손해배상보장법위반과태료
290 
자동차검사지연과태료
 
157
장애인주차구역위반과태료
 
93
부가가치세
 
61
Other values (41)
277 

Length

Max length18
Median length8
Mean length8.7584594
Min length3

Unique

Unique13 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
주정차위반과태료 2757
75.8%
자동차손해배상보장법위반과태료 290
 
8.0%
자동차검사지연과태료 157
 
4.3%
장애인주차구역위반과태료 93
 
2.6%
부가가치세 61
 
1.7%
계속도로점용료 45
 
1.2%
이행강제금 33
 
0.9%
교통유발부담금 25
 
0.7%
미세먼지법위반과태료 21
 
0.6%
폐기물관리법위반과태료 20
 
0.6%
Other values (36) 133
 
3.7%

Length

2023-12-12T18:11:19.596800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주정차위반과태료 2757
75.8%
자동차손해배상보장법위반과태료 290
 
8.0%
자동차검사지연과태료 157
 
4.3%
장애인주차구역위반과태료 93
 
2.6%
부가가치세 61
 
1.7%
계속도로점용료 45
 
1.2%
이행강제금 33
 
0.9%
교통유발부담금 25
 
0.7%
미세먼지법위반과태료 21
 
0.6%
폐기물관리법위반과태료 20
 
0.6%
Other values (36) 133
 
3.7%

부과금액
Real number (ℝ)

HIGH CORRELATION 

Distinct293
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93285.167
Minimum0
Maximum8000000
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T18:11:19.775419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20000
Q140000
median40000
Q340000
95-th percentile300000
Maximum8000000
Range8000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation328254.86
Coefficient of variation (CV)3.5188323
Kurtosis271.42454
Mean93285.167
Median Absolute Deviation (MAD)0
Skewness14.768775
Sum3.3909158 × 108
Variance1.0775125 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:19.996823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 2559
70.4%
50000 159
 
4.4%
100000 109
 
3.0%
300000 85
 
2.3%
15000 83
 
2.3%
120000 59
 
1.6%
80000 56
 
1.5%
20000 49
 
1.3%
900000 17
 
0.5%
5000 15
 
0.4%
Other values (283) 444
 
12.2%
ValueCountFrequency (%)
0 5
 
0.1%
920 1
 
< 0.1%
1190 1
 
< 0.1%
1350 1
 
< 0.1%
2290 1
 
< 0.1%
2690 1
 
< 0.1%
3000 1
 
< 0.1%
3370 1
 
< 0.1%
4040 1
 
< 0.1%
5000 15
0.4%
ValueCountFrequency (%)
8000000 1
< 0.1%
7057900 1
< 0.1%
6037870 1
< 0.1%
6000000 1
< 0.1%
5740600 1
< 0.1%
5407430 1
< 0.1%
4700000 1
< 0.1%
3855180 1
< 0.1%
3641710 1
< 0.1%
2437000 1
< 0.1%

납부금액
Real number (ℝ)

HIGH CORRELATION 

Distinct327
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91498.469
Minimum0
Maximum8000000
Zeros28
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T18:11:20.218967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15000
Q140000
median40000
Q340000
95-th percentile300000
Maximum8000000
Range8000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation327099.55
Coefficient of variation (CV)3.5749183
Kurtosis275.6074
Mean91498.469
Median Absolute Deviation (MAD)0
Skewness14.924857
Sum3.3259693 × 108
Variance1.0699412 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:20.428526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 2521
69.4%
50000 157
 
4.3%
100000 104
 
2.9%
15000 83
 
2.3%
300000 81
 
2.2%
120000 58
 
1.6%
80000 57
 
1.6%
20000 49
 
1.3%
0 28
 
0.8%
5000 15
 
0.4%
Other values (317) 482
 
13.3%
ValueCountFrequency (%)
0 28
0.8%
920 1
 
< 0.1%
1190 1
 
< 0.1%
1350 1
 
< 0.1%
2290 1
 
< 0.1%
2450 1
 
< 0.1%
2690 1
 
< 0.1%
3000 1
 
< 0.1%
3240 1
 
< 0.1%
3370 1
 
< 0.1%
ValueCountFrequency (%)
8000000 1
< 0.1%
7057900 1
< 0.1%
6037870 1
< 0.1%
6000000 1
< 0.1%
5740600 1
< 0.1%
5407430 1
< 0.1%
4700000 1
< 0.1%
3855180 1
< 0.1%
3641710 1
< 0.1%
2437000 1
< 0.1%

가산금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct405
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12451.111
Minimum0
Maximum579550
Zeros403
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T18:11:20.615451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11200
median3600
Q39360
95-th percentile30800
Maximum579550
Range579550
Interquartile range (IQR)8160

Descriptive statistics

Standard deviation38301.835
Coefficient of variation (CV)3.076178
Kurtosis75.940232
Mean12451.111
Median Absolute Deviation (MAD)2400
Skewness7.9281244
Sum45259790
Variance1.4670306 × 109
MonotonicityNot monotonic
2023-12-12T18:11:20.816695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 588
 
16.2%
0 403
 
11.1%
1680 195
 
5.4%
2160 150
 
4.1%
2640 118
 
3.2%
3120 112
 
3.1%
30800 103
 
2.8%
4560 95
 
2.6%
4080 94
 
2.6%
3600 92
 
2.5%
Other values (395) 1685
46.4%
ValueCountFrequency (%)
0 403
11.1%
270 3
 
0.1%
350 1
 
< 0.1%
400 1
 
< 0.1%
450 13
 
0.4%
480 2
 
0.1%
600 10
 
0.3%
630 6
 
0.2%
650 1
 
< 0.1%
680 1
 
< 0.1%
ValueCountFrequency (%)
579550 1
 
< 0.1%
510380 1
 
< 0.1%
462000 5
0.1%
400000 1
 
< 0.1%
383400 2
 
0.1%
378000 1
 
< 0.1%
377300 1
 
< 0.1%
370800 1
 
< 0.1%
361800 1
 
< 0.1%
351000 1
 
< 0.1%

본세
Real number (ℝ)

HIGH CORRELATION 

Distinct640
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103738.53
Minimum480
Maximum8000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T18:11:21.018480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum480
5-th percentile20130
Q141200
median45040
Q361100
95-th percentile332401
Maximum8000000
Range7999520
Interquartile range (IQR)19900

Descriptive statistics

Standard deviation335241.87
Coefficient of variation (CV)3.2316043
Kurtosis248.16024
Mean103738.53
Median Absolute Deviation (MAD)5040
Skewness13.935098
Sum3.7708954 × 108
Variance1.1238711 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:21.218283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 404
 
11.1%
41200 252
 
6.9%
41680 193
 
5.3%
42160 150
 
4.1%
42640 117
 
3.2%
43120 112
 
3.1%
70800 102
 
2.8%
44560 95
 
2.6%
44080 94
 
2.6%
45520 74
 
2.0%
Other values (630) 2042
56.2%
ValueCountFrequency (%)
480 1
< 0.1%
650 1
< 0.1%
920 1
< 0.1%
1190 1
< 0.1%
1350 1
< 0.1%
2290 1
< 0.1%
2690 1
< 0.1%
3370 1
< 0.1%
4040 1
< 0.1%
4950 1
< 0.1%
ValueCountFrequency (%)
8000000 1
< 0.1%
7057900 1
< 0.1%
6037870 1
< 0.1%
6000000 1
< 0.1%
5740600 1
< 0.1%
5407430 1
< 0.1%
4700000 1
< 0.1%
3855180 1
< 0.1%
3641710 1
< 0.1%
2437000 1
< 0.1%

납기후금액
Real number (ℝ)

ZEROS 

Distinct68
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7403.9835
Minimum0
Maximum927000
Zeros3200
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T18:11:21.425131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile41200
Maximum927000
Range927000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation37086.23
Coefficient of variation (CV)5.0089563
Kurtosis236.00078
Mean7403.9835
Median Absolute Deviation (MAD)0
Skewness13.03175
Sum26913480
Variance1.3753885 × 109
MonotonicityNot monotonic
2023-12-12T18:11:21.624841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3200
88.0%
41200 336
 
9.2%
123600 12
 
0.3%
51500 7
 
0.2%
20600 7
 
0.2%
309000 6
 
0.2%
15450 5
 
0.1%
9270 2
 
0.1%
44740 1
 
< 0.1%
478740 1
 
< 0.1%
Other values (58) 58
 
1.6%
ValueCountFrequency (%)
0 3200
88.0%
1190 1
 
< 0.1%
2290 1
 
< 0.1%
2690 1
 
< 0.1%
5020 1
 
< 0.1%
5030 1
 
< 0.1%
6140 1
 
< 0.1%
9270 2
 
0.1%
11380 1
 
< 0.1%
11700 1
 
< 0.1%
ValueCountFrequency (%)
927000 1
< 0.1%
864060 1
< 0.1%
525910 1
< 0.1%
478740 1
< 0.1%
460820 1
< 0.1%
441870 1
< 0.1%
437640 1
< 0.1%
404990 1
< 0.1%
355240 1
< 0.1%
348650 1
< 0.1%

부과상태구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
체납
2987 
부과
648 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
체납 2987
82.2%
부과 648
 
17.8%

Length

2023-12-12T18:11:21.768250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:21.897067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체납 2987
82.2%
부과 648
 
17.8%

분납구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
미분납
3253 
분납
382 

Length

Max length3
Median length3
Mean length2.8949106
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미분납 3253
89.5%
분납 382
 
10.5%

Length

2023-12-12T18:11:22.058636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:22.577029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분납 3253
89.5%
분납 382
 
10.5%

통합구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
미통합
3446 
통합자자료
 
189

Length

Max length5
Median length3
Mean length3.103989
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미통합 3446
94.8%
통합자자료 189
 
5.2%

Length

2023-12-12T18:11:22.725683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:22.870808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미통합 3446
94.8%
통합자자료 189
 
5.2%

압류구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
압류
2342 
미압류
1293 

Length

Max length3
Median length2
Mean length2.3557084
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
압류 2342
64.4%
미압류 1293
35.6%

Length

2023-12-12T18:11:23.034088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:23.176676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
압류 2342
64.4%
미압류 1293
35.6%

자치단체명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
오산시청
3635 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
오산시청 3635
100.0%

Length

2023-12-12T18:11:23.318097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:23.444294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오산시청 3635
100.0%

감경구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
미감경
3527 
감경
 
108

Length

Max length3
Median length3
Mean length2.9702889
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미감경 3527
97.0%
감경 108
 
3.0%

Length

2023-12-12T18:11:23.562283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:23.682621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미감경 3527
97.0%
감경 108
 
3.0%

Interactions

2023-12-12T18:11:16.247854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:12.092137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:12.930725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.738556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.453320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.187358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.379197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:12.206475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.086273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.843585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.571323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.291675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.556591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:12.351757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.205307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.961010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.693120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.415426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.681337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:12.523241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.339461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.071851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.836067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.534557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.806480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:12.651123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.479796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.220587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.943498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.672891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.944024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:12.800210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.625701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.357014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.075897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.782022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:11:23.785680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도납기후일자부서명과목명부과금액납부금액가산금본세납기후금액부과상태구분분납구분통합구분압류구분감경구분
회계연도1.000NaN0.1830.2420.1420.1370.4880.1670.0000.2860.0960.2480.3010.089
납기후일자NaN1.0000.6400.5340.0000.000NaN0.0000.056NaN0.6610.680NaNNaN
부서명0.1830.6401.0000.9980.5240.5230.3300.5240.2480.4720.9350.9280.4490.606
과목명0.2420.5340.9981.0000.6960.6970.4020.6940.5140.4970.9970.9350.4500.687
부과금액0.1420.0000.5240.6961.0001.0000.4481.0000.1680.0360.0460.1080.1210.008
납부금액0.1370.0000.5230.6971.0001.0000.4111.0000.1780.0370.0480.1100.1240.009
가산금0.488NaN0.3300.4020.4480.4111.0000.6080.0000.0850.1640.2100.0630.000
본세0.1670.0000.5240.6941.0001.0000.6081.0000.1480.0380.0580.1520.1230.000
납기후금액0.0000.0560.2480.5140.1680.1780.0000.1481.0000.2110.1840.2310.1290.000
부과상태구분0.286NaN0.4720.4970.0360.0370.0850.0380.2111.0000.3360.2790.8320.553
분납구분0.0960.6610.9350.9970.0460.0480.1640.0580.1840.3361.0000.5060.2600.302
통합구분0.2480.6800.9280.9350.1080.1100.2100.1520.2310.2790.5061.0000.2290.053
압류구분0.301NaN0.4490.4500.1210.1240.0630.1230.1290.8320.2600.2291.0000.358
감경구분0.089NaN0.6060.6870.0080.0090.0000.0000.0000.5530.3020.0530.3581.000
2023-12-12T18:11:23.968655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통합구분분납구분부과상태구분납기후일자부서명과목명감경구분압류구분
통합구분1.0000.3380.1800.6550.7910.8150.0330.147
분납구분0.3381.0000.2180.6350.8000.9530.1960.167
부과상태구분0.1800.2181.0001.0000.3710.3960.3730.626
납기후일자0.6550.6351.0001.0000.4710.3071.0001.000
부서명0.7910.8000.3710.4711.0000.9700.4800.353
과목명0.8150.9530.3960.3070.9701.0000.5550.357
감경구분0.0330.1960.3731.0000.4800.5551.0000.233
압류구분0.1470.1670.6261.0000.3530.3570.2331.000
2023-12-12T18:11:24.115729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도부과금액납부금액가산금본세납기후금액납기후일자부서명과목명부과상태구분분납구분통합구분압류구분감경구분
회계연도1.0000.0010.027-0.632-0.3830.3681.0000.0730.0850.2190.0740.1900.2310.068
부과금액0.0011.0000.9700.1370.761-0.0560.0000.2350.3180.0280.0350.0820.0930.006
납부금액0.0270.9701.0000.1120.771-0.0450.0000.2350.3180.0280.0370.0850.0950.007
가산금-0.6320.1370.1121.0000.567-0.3231.0000.1350.1480.0650.1250.1610.0480.000
본세-0.3830.7610.7710.5671.000-0.3390.0000.2350.3160.0290.0450.1170.0950.000
납기후금액0.368-0.056-0.045-0.323-0.3391.0000.0260.1160.2340.2260.1970.2470.1380.000
납기후일자1.0000.0000.0001.0000.0000.0261.0000.4710.3071.0000.6350.6551.0001.000
부서명0.0730.2350.2350.1350.2350.1160.4711.0000.9700.3710.8000.7910.3530.480
과목명0.0850.3180.3180.1480.3160.2340.3070.9701.0000.3960.9530.8150.3570.555
부과상태구분0.2190.0280.0280.0650.0290.2261.0000.3710.3961.0000.2180.1800.6260.373
분납구분0.0740.0350.0370.1250.0450.1970.6350.8000.9530.2181.0000.3380.1670.196
통합구분0.1900.0820.0850.1610.1170.2470.6550.7910.8150.1800.3381.0000.1470.033
압류구분0.2310.0930.0950.0480.0950.1381.0000.3530.3570.6260.1670.1471.0000.233
감경구분0.0680.0060.0070.0000.0000.0001.0000.4800.5550.3730.1960.0330.2331.000

Missing values

2023-12-12T18:11:17.111609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:11:17.421419image/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

자치단체코드회계연도부과일자최초납기일자납기후일자세구분부서명과목명부과금액납부금액가산금본세납기후금액부과상태구분분납구분통합구분압류구분자치단체명감경구분
04137020182018-09-012022-01-03<NA>세외수입스마트교통안전과주정차위반과태료400004000019440594400체납미분납미통합압류오산시청미감경
14137020192019-03-042022-01-03<NA>세외수입스마트교통안전과주정차위반과태료400004000016560565600체납미분납미통합압류오산시청미감경
24137020212021-10-052022-01-03<NA>세외수입스마트교통안전과주정차위반과태료40000400001680416800체납미분납미통합미압류오산시청미감경
34137020172017-04-042022-01-03<NA>세외수입스마트교통안전과주정차위반과태료400004000028400684000체납미분납미통합압류오산시청미감경
44137020212021-10-052022-01-03<NA>세외수입스마트교통안전과주정차위반과태료40000400001680416800체납미분납미통합미압류오산시청미감경
54137020192019-08-022022-01-03<NA>세외수입스마트교통안전과주정차위반과태료400004000014160541600체납미분납미통합압류오산시청미감경
64137020192019-03-042022-01-03<NA>세외수입스마트교통안전과주정차위반과태료400004000016560565600체납미분납미통합압류오산시청미감경
74137020192019-08-022022-01-03<NA>세외수입스마트교통안전과주정차위반과태료400004000014160541600체납미분납미통합압류오산시청미감경
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94137020192019-08-022022-01-03<NA>세외수입스마트교통안전과주정차위반과태료400004000014160541600체납미분납미통합압류오산시청미감경
자치단체코드회계연도부과일자최초납기일자납기후일자세구분부서명과목명부과금액납부금액가산금본세납기후금액부과상태구분분납구분통합구분압류구분자치단체명감경구분
36254137020222022-12-022022-12-30<NA>세외수입노인장애인과장애인주차구역위반과태료80000800000800000부과분납미통합미압류오산시청감경
36264137020222022-12-022022-12-30<NA>세외수입노인장애인과장애인주차구역위반과태료80000800000800000부과분납미통합미압류오산시청감경
36274137020222022-12-032022-12-30<NA>세외수입도로과시군구재산대부료26950026950002695000부과분납통합자자료미압류오산시청미감경
36284137020222022-12-032022-12-30<NA>세외수입도로과부가가치세26950269500269500부과분납통합자자료미압류오산시청미감경
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36314137020222022-12-022022-12-30<NA>세외수입노인장애인과장애인주차구역위반과태료80000800000800000부과분납미통합미압류오산시청감경
36324137020222022-12-032022-12-30<NA>세외수입도로과시군구재산대부료54074305407430054074300부과분납통합자자료미압류오산시청미감경
36334137020222022-12-032022-12-30<NA>세외수입도로과부가가치세54074054074005407400부과분납통합자자료미압류오산시청미감경
36344137020222022-12-062022-12-30<NA>세외수입도로과국유재산대부료51605160051600부과분납미통합미압류오산시청미감경

Duplicate rows

Most frequently occurring

자치단체코드회계연도부과일자최초납기일자납기후일자세구분부서명과목명부과금액납부금액가산금본세납기후금액부과상태구분분납구분통합구분압류구분자치단체명감경구분# duplicates
2914137020222022-01-052022-02-032022-02-28세외수입스마트교통안전과주정차위반과태료400004000012004000041200부과미분납미통합미압류오산시청미감경47
3924137020222022-07-042022-09-01<NA>세외수입스마트교통안전과주정차위반과태료40000400001200412000체납미분납미통합미압류오산시청미감경38
4104137020222022-09-022022-09-302022-10-31세외수입스마트교통안전과주정차위반과태료400004000012004000041200부과미분납미통합미압류오산시청미감경36
3144137020222022-02-042022-03-31<NA>세외수입스마트교통안전과주정차위반과태료40000400001200412000체납미분납미통합미압류오산시청미감경34
3794137020222022-06-032022-06-302022-08-01세외수입스마트교통안전과주정차위반과태료400004000012004000041200부과미분납미통합미압류오산시청미감경32
3514137020222022-04-052022-05-022022-05-31세외수입스마트교통안전과주정차위반과태료400004000012004000041200부과미분납미통합미압류오산시청미감경31
4234137020222022-11-022022-11-302023-01-02세외수입스마트교통안전과주정차위반과태료400004000012004000041200부과미분납미통합미압류오산시청미감경29
3804137020222022-06-032022-08-01<NA>세외수입스마트교통안전과주정차위반과태료40000400001200412000체납미분납미통합미압류오산시청미감경28
2784137020212021-12-032022-03-31<NA>세외수입스마트교통안전과주정차위반과태료40000400002160421600체납미분납미통합압류오산시청미감경27
2384137020212021-09-042022-02-03<NA>세외수입스마트교통안전과주정차위반과태료40000400002640426400체납미분납미통합압류오산시청미감경25