Overview

Dataset statistics

Number of variables11
Number of observations462
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.1 KiB
Average record size in memory93.3 B

Variable types

Categorical5
Numeric4
Boolean1
DateTime1

Dataset

Description지방세 납부 방법별 현황으로 ars 납부, 위택스 납부, 가상계좌 납부, 은행창구 납부, 인터넷지로납부, 자동이체납부, 자동화기기, 자치단체 방문 납부로 분류됨
URLhttps://www.data.go.kr/data/15078949/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일 has constant value ""Constant
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부건수 is highly overall correlated with 납부금액 and 1 other fieldsHigh correlation
납부금액 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부금액 has unique valuesUnique
납부매체비율 has 19 (4.1%) zerosZeros

Reproduction

Analysis started2023-12-12 10:25:56.000380
Analysis finished2023-12-12 10:25:59.062476
Duration3.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
전라남도
462 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 462
100.0%

Length

2023-12-12T19:25:59.136695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:25:59.244991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 462
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
화순군
462 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
화순군 462
100.0%

Length

2023-12-12T19:25:59.354569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:25:59.451336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화순군 462
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
46790
462 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46790
2nd row46790
3rd row46790
4th row46790
5th row46790

Common Values

ValueCountFrequency (%)
46790 462
100.0%

Length

2023-12-12T19:25:59.566238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:25:59.682716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46790 462
100.0%

납부년도
Real number (ℝ)

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5433
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T19:25:59.776339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.687413
Coefficient of variation (CV)0.00083554189
Kurtosis-1.2295659
Mean2019.5433
Median Absolute Deviation (MAD)1
Skewness-0.049202437
Sum933029
Variance2.8473627
MonotonicityIncreasing
2023-12-12T19:25:59.888324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 87
18.8%
2021 78
16.9%
2022 76
16.5%
2018 74
16.0%
2019 74
16.0%
2017 73
15.8%
ValueCountFrequency (%)
2017 73
15.8%
2018 74
16.0%
2019 74
16.0%
2020 87
18.8%
2021 78
16.9%
2022 76
16.5%
ValueCountFrequency (%)
2022 76
16.5%
2021 78
16.9%
2020 87
18.8%
2019 74
16.0%
2018 74
16.0%
2017 73
15.8%

세목명
Categorical

Distinct14
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
자동차세
61 
재산세
61 
주민세
60 
등록면허세
57 
취득세
53 
Other values (9)
170 

Length

Max length7
Median length5
Mean length4.0411255
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row등록면허세
2nd row자동차세
3rd row재산세
4th row주민세
5th row주민세

Common Values

ValueCountFrequency (%)
자동차세 61
13.2%
재산세 61
13.2%
주민세 60
13.0%
등록면허세 57
12.3%
취득세 53
11.5%
지방소득세 50
10.8%
지역자원시설세 33
7.1%
등록세 30
6.5%
종합토지세 17
 
3.7%
면허세 15
 
3.2%
Other values (4) 25
5.4%

Length

2023-12-12T19:26:00.066451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 61
13.2%
재산세 61
13.2%
주민세 60
13.0%
등록면허세 57
12.3%
취득세 53
11.5%
지방소득세 50
10.8%
지역자원시설세 33
7.1%
등록세 30
6.5%
종합토지세 17
 
3.7%
면허세 15
 
3.2%
Other values (4) 25
5.4%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
가상계좌
66 
은행창구
56 
위택스
54 
기타
53 
자동화기기
51 
Other values (5)
182 

Length

Max length5
Median length4
Mean length3.9069264
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 66
14.3%
은행창구 56
12.1%
위택스 54
11.7%
기타 53
11.5%
자동화기기 51
11.0%
ARS 46
10.0%
인터넷지로 46
10.0%
지자체방문 46
10.0%
자동이체 24
 
5.2%
페이사납부 20
 
4.3%

Length

2023-12-12T19:26:00.251925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:26:00.437313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가상계좌 66
14.3%
은행창구 56
12.1%
위택스 54
11.7%
기타 53
11.5%
자동화기기 51
11.0%
ars 46
10.0%
인터넷지로 46
10.0%
지자체방문 46
10.0%
자동이체 24
 
5.2%
페이사납부 20
 
4.3%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size594.0 B
False
242 
True
220 
ValueCountFrequency (%)
False 242
52.4%
True 220
47.6%
2023-12-12T19:26:00.596486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct333
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2775.342
Minimum1
Maximum39122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T19:26:00.748399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q116
median355
Q33261
95-th percentile12777.7
Maximum39122
Range39121
Interquartile range (IQR)3245

Descriptive statistics

Standard deviation5572.8649
Coefficient of variation (CV)2.0079921
Kurtosis13.307045
Mean2775.342
Median Absolute Deviation (MAD)352
Skewness3.3666171
Sum1282208
Variance31056823
MonotonicityNot monotonic
2023-12-12T19:26:00.929860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 26
 
5.6%
2 14
 
3.0%
3 14
 
3.0%
4 10
 
2.2%
9 9
 
1.9%
16 7
 
1.5%
5 6
 
1.3%
13 6
 
1.3%
6 6
 
1.3%
12 6
 
1.3%
Other values (323) 358
77.5%
ValueCountFrequency (%)
1 26
5.6%
2 14
3.0%
3 14
3.0%
4 10
 
2.2%
5 6
 
1.3%
6 6
 
1.3%
7 5
 
1.1%
8 5
 
1.1%
9 9
 
1.9%
10 2
 
0.4%
ValueCountFrequency (%)
39122 1
0.2%
36165 1
0.2%
33524 1
0.2%
29686 1
0.2%
28375 1
0.2%
27510 1
0.2%
27130 1
0.2%
25380 1
0.2%
23894 1
0.2%
23412 1
0.2%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct462
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2405334 × 109
Minimum1340
Maximum3.5840244 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T19:26:01.159597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1340
5-th percentile27320.5
Q13466642.5
median68090575
Q37.6834461 × 108
95-th percentile5.8707971 × 109
Maximum3.5840244 × 1010
Range3.5840242 × 1010
Interquartile range (IQR)7.6487797 × 108

Descriptive statistics

Standard deviation3.5520437 × 109
Coefficient of variation (CV)2.8633197
Kurtosis46.951715
Mean1.2405334 × 109
Median Absolute Deviation (MAD)68052310
Skewness6.1641554
Sum5.7312643 × 1011
Variance1.2617015 × 1019
MonotonicityNot monotonic
2023-12-12T19:26:01.332378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76710 1
 
0.2%
17034320 1
 
0.2%
5316105110 1
 
0.2%
2256330 1
 
0.2%
7926295000 1
 
0.2%
2273134900 1
 
0.2%
72257730 1
 
0.2%
30750 1
 
0.2%
68640180 1
 
0.2%
28717090880 1
 
0.2%
Other values (452) 452
97.8%
ValueCountFrequency (%)
1340 1
0.2%
2010 1
0.2%
2660 1
0.2%
3090 1
0.2%
3710 1
0.2%
4540 1
0.2%
4790 1
0.2%
6910 1
0.2%
10390 1
0.2%
10680 1
0.2%
ValueCountFrequency (%)
35840243540 1
0.2%
32965429520 1
0.2%
28717090880 1
0.2%
24238599150 1
0.2%
22326213980 1
0.2%
17379868230 1
0.2%
13028361150 1
0.2%
11019245410 1
0.2%
9586547150 1
0.2%
9100828150 1
0.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct354
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.554026
Minimum0
Maximum83.85
Zeros19
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T19:26:01.519556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.31
median5.845
Q317.8575
95-th percentile48.052
Maximum83.85
Range83.85
Interquartile range (IQR)17.5475

Descriptive statistics

Standard deviation15.774881
Coefficient of variation (CV)1.2565595
Kurtosis2.9769279
Mean12.554026
Median Absolute Deviation (MAD)5.805
Skewness1.7053083
Sum5799.96
Variance248.84686
MonotonicityNot monotonic
2023-12-12T19:26:01.707433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
4.1%
0.01 13
 
2.8%
0.02 10
 
2.2%
0.07 8
 
1.7%
0.03 6
 
1.3%
0.31 6
 
1.3%
0.1 5
 
1.1%
0.04 5
 
1.1%
0.13 4
 
0.9%
0.21 4
 
0.9%
Other values (344) 382
82.7%
ValueCountFrequency (%)
0.0 19
4.1%
0.01 13
2.8%
0.02 10
2.2%
0.03 6
 
1.3%
0.04 5
 
1.1%
0.05 4
 
0.9%
0.06 3
 
0.6%
0.07 8
1.7%
0.08 3
 
0.6%
0.09 2
 
0.4%
ValueCountFrequency (%)
83.85 1
0.2%
80.71 1
0.2%
76.7 1
0.2%
72.01 1
0.2%
66.93 1
0.2%
63.29 1
0.2%
60.49 1
0.2%
60.26 1
0.2%
59.16 1
0.2%
58.19 1
0.2%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Minimum2023-08-24 00:00:00
Maximum2023-08-24 00:00:00
2023-12-12T19:26:01.843733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:26:01.974698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T19:25:57.956524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:56.358644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:56.855223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:57.448099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:58.077793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:56.456205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:57.056032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:57.586447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:58.214106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:56.612774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:57.183743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:57.726252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:58.341099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:56.737118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:57.327055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:25:57.856062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:26:02.084971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.2170.0000.1610.5170.619
납부매체0.0000.2171.0000.9980.4740.1990.587
납부매체전자고지여부0.0000.0000.9981.0000.0990.0000.246
납부건수0.0000.1610.4740.0991.0000.2430.755
납부금액0.0000.5170.1990.0000.2431.0000.025
납부매체비율0.0000.6190.5870.2460.7550.0251.000
2023-12-12T19:26:02.230019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체전자고지여부납부매체
세목명1.0000.0000.088
납부매체전자고지여부0.0001.0000.956
납부매체0.0880.9561.000
2023-12-12T19:26:02.325042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부년도1.0000.0270.0370.0390.0000.0000.000
납부건수0.0271.0000.8300.8030.0650.1620.075
납부금액0.0370.8301.0000.6270.2560.0950.000
납부매체비율0.0390.8030.6271.0000.3040.2150.187
세목명0.0000.0650.2560.3041.0000.0880.000
납부매체0.0000.1620.0950.2150.0881.0000.956
납부매체전자고지여부0.0000.0750.0000.1870.0000.9561.000

Missing values

2023-12-12T19:25:58.809269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:25:58.999692image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율데이터기준일
0전라남도화순군467902017등록면허세ARSN7767102.162023-08-24
1전라남도화순군467902017자동차세ARSN1963694049060.492023-08-24
2전라남도화순군467902017재산세ARSN761717172023.462023-08-24
3전라남도화순군467902017주민세ARSN3779575011.422023-08-24
4전라남도화순군467902017주민세ARSY2226600.622023-08-24
5전라남도화순군467902017지방소득세ARSN415762401.232023-08-24
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8전라남도화순군467902017등록면허세가상계좌Y4611838446607.52023-08-24
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452전라남도화순군467902022주민세가상계좌Y1714577263468016.482023-08-24
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456전라남도화순군467902022등록면허세기타N14720285703.512023-08-24
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