Overview

Dataset statistics

Number of variables10
Number of observations395
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.9 KiB
Average record size in memory85.3 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description2017년부터 2021년까지 지방세 납부현황에 대한 세목명, 납부매체, 납부매체전자고지여부, 납부건수, 납부금액, 납부매체비율에 대한 정보
Author경상남도 통영시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15078253

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부매체전자고지여부 is highly overall correlated with 납부매체High 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
납부매체비율 has 24 (6.1%) zerosZeros

Reproduction

Analysis started2023-12-11 01:01:05.489897
Analysis finished2023-12-11 01:01:06.812406
Duration1.32 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
경상남도
395 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 395
100.0%

Length

2023-12-11T10:01:06.865120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T10:01:06.949916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 395
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
통영시
395 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통영시 395
100.0%

Length

2023-12-11T10:01:07.033910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T10:01:07.105775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 395
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
48220
395 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48220
2nd row48220
3rd row48220
4th row48220
5th row48220

Common Values

ValueCountFrequency (%)
48220 395
100.0%

Length

2023-12-11T10:01:07.177368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T10:01:07.248982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48220 395
100.0%

납부년도
Categorical

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2021
84 
2020
83 
2019
80 
2017
74 
2018
74 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2021 84
21.3%
2020 83
21.0%
2019 80
20.3%
2017 74
18.7%
2018 74
18.7%

Length

2023-12-11T10:01:07.321078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T10:01:07.407921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 84
21.3%
2020 83
21.0%
2019 80
20.3%
2017 74
18.7%
2018 74
18.7%

세목명
Categorical

Distinct13
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
등록면허세
53 
자동차세
53 
재산세
53 
주민세
53 
지방소득세
45 
Other values (8)
138 

Length

Max length7
Median length5
Mean length4.0278481
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
등록면허세 53
13.4%
자동차세 53
13.4%
재산세 53
13.4%
주민세 53
13.4%
지방소득세 45
11.4%
취득세 42
10.6%
등록세 26
6.6%
지역자원시설세 24
6.1%
면허세 15
 
3.8%
담배소비세 15
 
3.8%
Other values (3) 16
 
4.1%

Length

2023-12-11T10:01:07.509072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등록면허세 53
13.4%
자동차세 53
13.4%
재산세 53
13.4%
주민세 53
13.4%
지방소득세 45
11.4%
취득세 42
10.6%
등록세 26
6.6%
지역자원시설세 24
6.1%
면허세 15
 
3.8%
담배소비세 15
 
3.8%
Other values (3) 16
 
4.1%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
은행창구
54 
ARS
52 
가상계좌
51 
기타
44 
지자체방문
43 
Other values (5)
151 

Length

Max length5
Median length4
Mean length3.8734177
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
은행창구 54
13.7%
ARS 52
13.2%
가상계좌 51
12.9%
기타 44
11.1%
지자체방문 43
10.9%
위택스 42
10.6%
자동화기기 40
10.1%
인터넷지로 32
8.1%
자동이체 20
 
5.1%
페이사납부 17
 
4.3%

Length

2023-12-11T10:01:07.820798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T10:01:07.925398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
은행창구 54
13.7%
ars 52
13.2%
가상계좌 51
12.9%
기타 44
11.1%
지자체방문 43
10.9%
위택스 42
10.6%
자동화기기 40
10.1%
인터넷지로 32
8.1%
자동이체 20
 
5.1%
페이사납부 17
 
4.3%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size527.0 B
False
211 
True
184 
ValueCountFrequency (%)
False 211
53.4%
True 184
46.6%
2023-12-11T10:01:08.056120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct311
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4608.9797
Minimum1
Maximum50200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-11T10:01:08.149674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q128
median813
Q34408.5
95-th percentile24539.4
Maximum50200
Range50199
Interquartile range (IQR)4380.5

Descriptive statistics

Standard deviation9067.9836
Coefficient of variation (CV)1.9674601
Kurtosis9.1223066
Mean4608.9797
Median Absolute Deviation (MAD)810
Skewness3.0006347
Sum1820547
Variance82228326
MonotonicityNot monotonic
2023-12-11T10:01:08.264000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22
 
5.6%
3 8
 
2.0%
5 6
 
1.5%
6 6
 
1.5%
11 6
 
1.5%
4 6
 
1.5%
13 5
 
1.3%
2 5
 
1.3%
7 4
 
1.0%
8 4
 
1.0%
Other values (301) 323
81.8%
ValueCountFrequency (%)
1 22
5.6%
2 5
 
1.3%
3 8
 
2.0%
4 6
 
1.5%
5 6
 
1.5%
6 6
 
1.5%
7 4
 
1.0%
8 4
 
1.0%
9 2
 
0.5%
10 3
 
0.8%
ValueCountFrequency (%)
50200 1
0.3%
46435 1
0.3%
45849 1
0.3%
45792 1
0.3%
45588 1
0.3%
42316 1
0.3%
41617 1
0.3%
41080 1
0.3%
39784 1
0.3%
39695 1
0.3%

납부금액
Real number (ℝ)

HIGH CORRELATION 

Distinct394
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5235316 × 109
Minimum590
Maximum1.9031349 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-11T10:01:08.405844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum590
5-th percentile47550
Q14487740
median1.808237 × 108
Q31.3976618 × 109
95-th percentile7.1741446 × 109
Maximum1.9031349 × 1010
Range1.9031348 × 1010
Interquartile range (IQR)1.393174 × 109

Descriptive statistics

Standard deviation2.8796883 × 109
Coefficient of variation (CV)1.8901402
Kurtosis9.0526613
Mean1.5235316 × 109
Median Absolute Deviation (MAD)1.8067362 × 108
Skewness2.7817498
Sum6.0179498 × 1011
Variance8.2926046 × 1018
MonotonicityNot monotonic
2023-12-11T10:01:08.536186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15450 2
 
0.5%
5616287850 1
 
0.3%
54400 1
 
0.3%
6727320 1
 
0.3%
673973920 1
 
0.3%
122100400 1
 
0.3%
6652565210 1
 
0.3%
2454126790 1
 
0.3%
315770000 1
 
0.3%
4117937880 1
 
0.3%
Other values (384) 384
97.2%
ValueCountFrequency (%)
590 1
0.3%
3700 1
0.3%
6300 1
0.3%
7590 1
0.3%
10300 1
0.3%
10540 1
0.3%
12500 1
0.3%
12580 1
0.3%
13200 1
0.3%
15450 2
0.5%
ValueCountFrequency (%)
19031348620 1
0.3%
17314984910 1
0.3%
14913355490 1
0.3%
13898647220 1
0.3%
12956592240 1
0.3%
12461844040 1
0.3%
11956361310 1
0.3%
11920642320 1
0.3%
10735355950 1
0.3%
10659738030 1
0.3%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct293
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.151873
Minimum0
Maximum75.04
Zeros24
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-11T10:01:08.659702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.26
median6.49
Q319.695
95-th percentile44.019
Maximum75.04
Range75.04
Interquartile range (IQR)19.435

Descriptive statistics

Standard deviation14.671261
Coefficient of variation (CV)1.2073251
Kurtosis2.0326391
Mean12.151873
Median Absolute Deviation (MAD)6.46
Skewness1.4721732
Sum4799.99
Variance215.24591
MonotonicityNot monotonic
2023-12-11T10:01:08.784797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 24
 
6.1%
0.01 14
 
3.5%
0.02 11
 
2.8%
0.05 8
 
2.0%
0.08 6
 
1.5%
0.04 5
 
1.3%
0.11 5
 
1.3%
0.14 4
 
1.0%
0.03 4
 
1.0%
20.46 3
 
0.8%
Other values (283) 311
78.7%
ValueCountFrequency (%)
0.0 24
6.1%
0.01 14
3.5%
0.02 11
2.8%
0.03 4
 
1.0%
0.04 5
 
1.3%
0.05 8
 
2.0%
0.06 2
 
0.5%
0.07 3
 
0.8%
0.08 6
 
1.5%
0.09 2
 
0.5%
ValueCountFrequency (%)
75.04 1
0.3%
72.82 1
0.3%
65.8 1
0.3%
62.36 1
0.3%
60.0 1
0.3%
52.57 1
0.3%
52.19 1
0.3%
52.05 1
0.3%
52.02 1
0.3%
51.69 1
0.3%

Interactions

2023-12-11T10:01:06.332913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:01:05.827346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:01:06.084574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:01:06.421587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:01:05.912173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:01:06.161770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:01:06.506785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:01:05.997397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:01:06.243762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T10:01:08.869457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.1790.0000.3580.5170.606
납부매체0.0000.1791.0000.9940.5990.2530.630
납부매체전자고지여부0.0000.0000.9941.0000.1680.1180.142
납부건수0.0000.3580.5990.1681.0000.5150.747
납부금액0.0000.5170.2530.1180.5151.0000.398
납부매체비율0.0000.6060.6300.1420.7470.3981.000
2023-12-11T10:01:08.970868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체납부매체전자고지여부납부년도세목명
납부매체1.0000.9220.0000.074
납부매체전자고지여부0.9221.0000.0000.000
납부년도0.0000.0001.0000.000
세목명0.0740.0000.0001.000
2023-12-11T10:01:09.054650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부건수1.0000.7860.8650.0000.1560.2210.127
납부금액0.7861.0000.6720.0000.2510.1160.117
납부매체비율0.8650.6721.0000.0000.3020.2380.107
납부년도0.0000.0000.0001.0000.0000.0000.000
세목명0.1560.2510.3020.0001.0000.0740.000
납부매체0.2210.1160.2380.0000.0741.0000.922
납부매체전자고지여부0.1270.1170.1070.0000.0000.9221.000

Missing values

2023-12-11T10:01:06.611108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T10:01:06.760260image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
0경상남도통영시482202017등록면허세ARSN13520187702.44
1경상남도통영시482202017등록면허세ARSY3334500.05
2경상남도통영시482202017자동차세ARSN287756354828052.05
3경상남도통영시482202017자동차세ARSY2229715100.4
4경상남도통영시482202017재산세ARSN183028297720033.11
5경상남도통영시482202017재산세ARSY116388500.2
6경상남도통영시482202017주민세ARSN563854248010.19
7경상남도통영시482202017주민세ARSY212683300.38
8경상남도통영시482202017지방소득세ARSN47150425300.85
9경상남도통영시482202017취득세ARSN18267604900.33
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
385경상남도통영시482202021주민세지자체방문N2794414995108.83
386경상남도통영시482202021지방소득세지자체방문N6614321548302.09
387경상남도통영시482202021지역자원시설세지자체방문N136380200.04
388경상남도통영시482202021취득세지자체방문N57611192064232018.2
389경상남도통영시482202021등록면허세페이사납부Y19428891904.6
390경상남도통영시482202021자동차세페이사납부Y145923539597034.59
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