Overview

Dataset statistics

Number of variables11
Number of observations387
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.3 KiB
Average record size in memory93.3 B

Variable types

Categorical6
Numeric4
Boolean1

Dataset

Description산청군_지방세 납부현황(시군구명, 납부년도, 세목명, 납부매체, 납부매체전자고지여부, 납부건수, 납부금액, 납부매체비율 등) 자료입니다. 본 자료는 연도별로 생성되는 자료로 2022년 데이터가 최신 데이터임을 알려드립니다.
URLhttps://www.data.go.kr/data/15078808/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 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 6 (1.6%) zerosZeros

Reproduction

Analysis started2023-12-12 10:48:08.094163
Analysis finished2023-12-12 10:48:11.269650
Duration3.18 seconds
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
387 

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 387
100.0%

Length

2023-12-12T19:48:11.360684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:48:11.497555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 387
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
산청군
387 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
산청군 387
100.0%

Length

2023-12-12T19:48:11.646733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:48:11.793696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산청군 387
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
48860
387 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48860
2nd row48860
3rd row48860
4th row48860
5th row48860

Common Values

ValueCountFrequency (%)
48860 387
100.0%

Length

2023-12-12T19:48:11.946982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:48:12.063123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48860 387
100.0%

납부년도
Real number (ℝ)

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5685
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T19:48:12.178541image/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 deviation1.7071439
Coefficient of variation (CV)0.00084530134
Kurtosis-1.2597604
Mean2019.5685
Median Absolute Deviation (MAD)1
Skewness-0.056315376
Sum781573
Variance2.9143404
MonotonicityNot monotonic
2023-12-12T19:48:12.363000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 68
17.6%
2022 68
17.6%
2021 66
17.1%
2018 62
16.0%
2019 62
16.0%
2017 61
15.8%
ValueCountFrequency (%)
2017 61
15.8%
2018 62
16.0%
2019 62
16.0%
2020 68
17.6%
2021 66
17.1%
2022 68
17.6%
ValueCountFrequency (%)
2022 68
17.6%
2021 66
17.1%
2020 68
17.6%
2019 62
16.0%
2018 62
16.0%
2017 61
15.8%

세목명
Categorical

Distinct13
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
자동차세
52 
재산세
52 
주민세
52 
등록면허세
51 
취득세
45 
Other values (8)
135 

Length

Max length7
Median length5
Mean length4.0801034
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 52
13.4%
재산세 52
13.4%
주민세 52
13.4%
등록면허세 51
13.2%
취득세 45
11.6%
지방소득세 44
11.4%
지역자원시설세 33
8.5%
등록세 30
7.8%
담배소비세 15
 
3.9%
면허세 5
 
1.3%
Other values (3) 8
 
2.1%

Length

2023-12-12T19:48:12.578715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 52
13.4%
재산세 52
13.4%
주민세 52
13.4%
등록면허세 51
13.2%
취득세 45
11.6%
지방소득세 44
11.4%
지역자원시설세 33
8.5%
등록세 30
7.8%
담배소비세 15
 
3.9%
면허세 5
 
1.3%
Other values (3) 8
 
2.1%

납부매체
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
은행창구
57 
가상계좌
52 
위택스
52 
지자체방문
50 
자동화기기
47 
Other values (4)
129 

Length

Max length5
Median length4
Mean length4.0465116
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
은행창구 57
14.7%
가상계좌 52
13.4%
위택스 52
13.4%
지자체방문 50
12.9%
자동화기기 47
12.1%
기타 44
11.4%
인터넷지로 41
10.6%
자동이체 24
6.2%
페이사납부 20
 
5.2%

Length

2023-12-12T19:48:12.781021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:48:12.940935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
은행창구 57
14.7%
가상계좌 52
13.4%
위택스 52
13.4%
지자체방문 50
12.9%
자동화기기 47
12.1%
기타 44
11.4%
인터넷지로 41
10.6%
자동이체 24
6.2%
페이사납부 20
 
5.2%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size519.0 B
False
198 
True
189 
ValueCountFrequency (%)
False 198
51.2%
True 189
48.8%
2023-12-12T19:48:13.092503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct311
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2253.5685
Minimum1
Maximum25113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T19:48:13.274175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q157
median514
Q32113
95-th percentile11058.2
Maximum25113
Range25112
Interquartile range (IQR)2056

Descriptive statistics

Standard deviation3911.0002
Coefficient of variation (CV)1.7354699
Kurtosis8.4304917
Mean2253.5685
Median Absolute Deviation (MAD)508
Skewness2.7042989
Sum872131
Variance15295923
MonotonicityNot monotonic
2023-12-12T19:48:13.474912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13
 
3.4%
4 7
 
1.8%
3 6
 
1.6%
2 6
 
1.6%
5 6
 
1.6%
9 5
 
1.3%
7 5
 
1.3%
12 4
 
1.0%
48 3
 
0.8%
97 3
 
0.8%
Other values (301) 329
85.0%
ValueCountFrequency (%)
1 13
3.4%
2 6
1.6%
3 6
1.6%
4 7
1.8%
5 6
1.6%
6 2
 
0.5%
7 5
 
1.3%
8 1
 
0.3%
9 5
 
1.3%
10 2
 
0.5%
ValueCountFrequency (%)
25113 1
0.3%
23221 1
0.3%
20943 1
0.3%
19841 1
0.3%
16837 1
0.3%
14882 1
0.3%
14481 1
0.3%
14457 1
0.3%
14332 1
0.3%
14236 1
0.3%

납부금액
Real number (ℝ)

HIGH CORRELATION 

Distinct385
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2795076 × 108
Minimum6180
Maximum1.3124695 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T19:48:13.671645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6180
5-th percentile177589
Q110649585
median86744570
Q36.5438031 × 108
95-th percentile2.5615715 × 109
Maximum1.3124695 × 1010
Range1.3124689 × 1010
Interquartile range (IQR)6.4373072 × 108

Descriptive statistics

Standard deviation1.3967514 × 109
Coefficient of variation (CV)2.2243008
Kurtosis28.062799
Mean6.2795076 × 108
Median Absolute Deviation (MAD)86379240
Skewness4.6614037
Sum2.4301694 × 1011
Variance1.9509144 × 1018
MonotonicityNot monotonic
2023-12-12T19:48:13.912513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6180 3
 
0.8%
359594630 1
 
0.3%
4787057720 1
 
0.3%
317840 1
 
0.3%
5197370 1
 
0.3%
264131280 1
 
0.3%
1016470 1
 
0.3%
1001892280 1
 
0.3%
207941450 1
 
0.3%
1899226360 1
 
0.3%
Other values (375) 375
96.9%
ValueCountFrequency (%)
6180 3
0.8%
12420 1
 
0.3%
14490 1
 
0.3%
17960 1
 
0.3%
22660 1
 
0.3%
25860 1
 
0.3%
31380 1
 
0.3%
37910 1
 
0.3%
69610 1
 
0.3%
84600 1
 
0.3%
ValueCountFrequency (%)
13124695360 1
0.3%
9488300000 1
0.3%
9468123000 1
0.3%
7484208860 1
0.3%
6524049710 1
0.3%
6438666610 1
0.3%
5916854040 1
0.3%
5866112610 1
0.3%
5607313370 1
0.3%
5474031630 1
0.3%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct325
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.436537
Minimum0
Maximum91.91
Zeros6
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T19:48:14.135919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q11.195
median7.56
Q319.83
95-th percentile49.908
Maximum91.91
Range91.91
Interquartile range (IQR)18.635

Descriptive statistics

Standard deviation16.607071
Coefficient of variation (CV)1.2359636
Kurtosis4.3335942
Mean13.436537
Median Absolute Deviation (MAD)7.3
Skewness1.9694386
Sum5199.94
Variance275.7948
MonotonicityNot monotonic
2023-12-12T19:48:14.339694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 11
 
2.8%
0.06 8
 
2.1%
0.0 6
 
1.6%
0.14 5
 
1.3%
0.12 4
 
1.0%
0.05 4
 
1.0%
0.47 3
 
0.8%
0.17 3
 
0.8%
0.6 3
 
0.8%
0.1 3
 
0.8%
Other values (315) 337
87.1%
ValueCountFrequency (%)
0.0 6
1.6%
0.01 11
2.8%
0.02 1
 
0.3%
0.03 1
 
0.3%
0.04 2
 
0.5%
0.05 4
 
1.0%
0.06 8
2.1%
0.08 1
 
0.3%
0.09 2
 
0.5%
0.1 3
 
0.8%
ValueCountFrequency (%)
91.91 1
0.3%
85.33 1
0.3%
81.77 1
0.3%
81.32 1
0.3%
80.24 1
0.3%
71.33 1
0.3%
68.4 1
0.3%
61.07 1
0.3%
60.05 1
0.3%
60.0 1
0.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2023-08-04
387 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-04
2nd row2023-08-04
3rd row2023-08-04
4th row2023-08-04
5th row2023-08-04

Common Values

ValueCountFrequency (%)
2023-08-04 387
100.0%

Length

2023-12-12T19:48:14.498910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:48:14.625191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-04 387
100.0%

Interactions

2023-12-12T19:48:10.413333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:08.522509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:09.005424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:09.904688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:10.550614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:08.646661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:09.495178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:10.049982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:10.665632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:08.761497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:09.626320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:10.174783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:10.806126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:08.885014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:09.780964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:10.306954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:48:14.714222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.1180.0000.3720.7130.609
납부매체0.0000.1181.0001.0000.4930.3330.481
납부매체전자고지여부0.0000.0001.0001.0000.1540.1810.298
납부건수0.0000.3720.4930.1541.0000.3460.817
납부금액0.0000.7130.3330.1810.3461.0000.000
납부매체비율0.0000.6090.4810.2980.8170.0001.000
2023-12-12T19:48:14.870754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체세목명납부매체전자고지여부
납부매체1.0000.0500.991
세목명0.0501.0000.000
납부매체전자고지여부0.9910.0001.000
2023-12-12T19:48:15.014328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부년도1.0000.0090.0180.0300.0000.0000.000
납부건수0.0091.0000.7170.8300.1620.2500.116
납부금액0.0180.7171.0000.5450.4210.1660.134
납부매체비율0.0300.8300.5451.0000.3040.2420.226
세목명0.0000.1620.4210.3041.0000.0500.000
납부매체0.0000.2500.1660.2420.0501.0000.991
납부매체전자고지여부0.0000.1160.1340.2260.0000.9911.000

Missing values

2023-12-12T19:48:10.955232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:48:11.185883image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율데이터기준일자
0경상남도산청군488602018자동차세자동화기기N255835959463018.182023-08-04
1경상남도산청군488602018재산세자동화기기N804046407402057.142023-08-04
2경상남도산청군488602018주민세자동화기기N1098172708307.82023-08-04
3경상남도산청군488602018지방소득세자동화기기N1971157777001.42023-08-04
4경상남도산청군488602018지역자원시설세자동화기기N95254800.062023-08-04
5경상남도산청군488602018취득세자동화기기N1422177329080010.112023-08-04
6경상남도산청군488602018등록면허세지자체방문N7764641917015.62023-08-04
7경상남도산청군488602018등록세지자체방문N3033543100.62023-08-04
8경상남도산청군488602018자동차세지자체방문N174831070931035.142023-08-04
9경상남도산청군488602018재산세지자체방문N108014216915021.712023-08-04
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율데이터기준일자
377경상남도산청군488602022주민세지자체방문N48475921407.52023-08-04
378경상남도산청군488602022지방소득세지자체방문N143464099502.222023-08-04
379경상남도산청군488602022지역자원시설세지자체방문N92261100.142023-08-04
380경상남도산청군488602022취득세지자체방문N1161131326267018.02023-08-04
381경상남도산청군488602022등록면허세페이사납부Y515870103.852023-08-04
382경상남도산청군488602022자동차세페이사납부Y3284704112024.772023-08-04
383경상남도산청군488602022재산세페이사납부Y7954376011060.052023-08-04
384경상남도산청군488602022주민세페이사납부Y138154787010.422023-08-04
385경상남도산청군488602022지방소득세페이사납부Y45967100.32023-08-04
386경상남도산청군488602022취득세페이사납부Y859060600.62023-08-04