Overview

Dataset statistics

Number of variables10
Number of observations474
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.5 KiB
Average record size in memory85.3 B

Variable types

Categorical5
Numeric4
Boolean1

Dataset

Description경상남도 거창군 지방세 납부현황에 대한 데이터로 납부년도, 세목명, 납부매체, 납부매체전자고지여부, 납부건수, 납부금액, 납부매체비율 항목을 제공합니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079200

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 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 76 (16.0%) zerosZeros

Reproduction

Analysis started2023-12-10 23:15:37.357113
Analysis finished2023-12-10 23:15:39.139016
Duration1.78 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
경상남도
474 

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 474
100.0%

Length

2023-12-11T08:15:39.193139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:39.281033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 474
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
거창군
474 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거창군 474
100.0%

Length

2023-12-11T08:15:39.360820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:39.435939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 474
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
48880
474 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 474
100.0%

Length

2023-12-11T08:15:39.519487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:39.588222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 474
100.0%

납부년도
Real number (ℝ)

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.538
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-11T08:15:39.653332image/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 deviation1.7104433
Coefficient of variation (CV)0.00084694781
Kurtosis-1.2649316
Mean2019.538
Median Absolute Deviation (MAD)1
Skewness-0.038259547
Sum957261
Variance2.9256162
MonotonicityIncreasing
2023-12-11T08:15:39.734890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 81
17.1%
2021 81
17.1%
2022 81
17.1%
2017 78
16.5%
2019 78
16.5%
2018 75
15.8%
ValueCountFrequency (%)
2017 78
16.5%
2018 75
15.8%
2019 78
16.5%
2020 81
17.1%
2021 81
17.1%
2022 81
17.1%
ValueCountFrequency (%)
2022 81
17.1%
2021 81
17.1%
2020 81
17.1%
2019 78
16.5%
2018 75
15.8%
2017 78
16.5%

세목명
Categorical

Distinct14
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
자동차세
64 
주민세
64 
재산세
63 
등록면허세
62 
지방소득세
54 
Other values (9)
167 

Length

Max length7
Median length5
Mean length4.0632911
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 64
13.5%
주민세 64
13.5%
재산세 63
13.3%
등록면허세 62
13.1%
지방소득세 54
11.4%
취득세 52
11.0%
지역자원시설세 39
8.2%
등록세 33
7.0%
면허세 15
 
3.2%
담배소비세 12
 
2.5%
Other values (4) 16
 
3.4%

Length

2023-12-11T08:15:39.832066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 64
13.5%
주민세 64
13.5%
재산세 63
13.3%
등록면허세 62
13.1%
지방소득세 54
11.4%
취득세 52
11.0%
지역자원시설세 39
8.2%
등록세 33
7.0%
면허세 15
 
3.2%
담배소비세 12
 
2.5%
Other values (4) 16
 
3.4%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
ARS
63 
가상계좌
62 
은행창구
53 
지자체방문
53 
위택스
52 
Other values (5)
191 

Length

Max length5
Median length4
Mean length3.9113924
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ARS 63
13.3%
가상계좌 62
13.1%
은행창구 53
11.2%
지자체방문 53
11.2%
위택스 52
11.0%
기타 49
10.3%
자동화기기 49
10.3%
인터넷지로 46
9.7%
자동이체 24
 
5.1%
페이사납부 23
 
4.9%

Length

2023-12-11T08:15:39.936071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:40.304355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ars 63
13.3%
가상계좌 62
13.1%
은행창구 53
11.2%
지자체방문 53
11.2%
위택스 52
11.0%
기타 49
10.3%
자동화기기 49
10.3%
인터넷지로 46
9.7%
자동이체 24
 
5.1%
페이사납부 23
 
4.9%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size606.0 B
False
243 
True
231 
ValueCountFrequency (%)
False 243
51.3%
True 231
48.7%
2023-12-11T08:15:40.439429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct340
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2519.4494
Minimum1
Maximum32223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-11T08:15:40.530291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q116
median359
Q32290.5
95-th percentile12892.5
Maximum32223
Range32222
Interquartile range (IQR)2274.5

Descriptive statistics

Standard deviation5107.3621
Coefficient of variation (CV)2.0271739
Kurtosis10.221647
Mean2519.4494
Median Absolute Deviation (MAD)356
Skewness3.0781327
Sum1194219
Variance26085147
MonotonicityNot monotonic
2023-12-11T08:15:40.658616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25
 
5.3%
2 18
 
3.8%
3 15
 
3.2%
4 15
 
3.2%
5 14
 
3.0%
6 6
 
1.3%
9 5
 
1.1%
7 5
 
1.1%
16 4
 
0.8%
87 4
 
0.8%
Other values (330) 363
76.6%
ValueCountFrequency (%)
1 25
5.3%
2 18
3.8%
3 15
3.2%
4 15
3.2%
5 14
3.0%
6 6
 
1.3%
7 5
 
1.1%
8 4
 
0.8%
9 5
 
1.1%
10 3
 
0.6%
ValueCountFrequency (%)
32223 1
0.2%
29438 1
0.2%
27467 1
0.2%
27106 1
0.2%
25200 1
0.2%
24894 1
0.2%
24615 1
0.2%
23448 1
0.2%
23136 1
0.2%
21911 1
0.2%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct474
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2851543 × 108
Minimum2010
Maximum1.27225 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-11T08:15:40.824793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile18863
Q11749975
median63456335
Q34.8277644 × 108
95-th percentile4.278859 × 109
Maximum1.27225 × 1010
Range1.2722498 × 1010
Interquartile range (IQR)4.8102647 × 108

Descriptive statistics

Standard deviation1.5964855 × 109
Coefficient of variation (CV)2.1914231
Kurtosis14.443986
Mean7.2851543 × 108
Median Absolute Deviation (MAD)63421740
Skewness3.4461191
Sum3.4531632 × 1011
Variance2.5487661 × 1018
MonotonicityNot monotonic
2023-12-11T08:15:40.976698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175210 1
 
0.2%
383068750 1
 
0.2%
5767458860 1
 
0.2%
15513250 1
 
0.2%
5382346000 1
 
0.2%
1529258080 1
 
0.2%
41025050 1
 
0.2%
22629150 1
 
0.2%
5144645640 1
 
0.2%
1103020 1
 
0.2%
Other values (464) 464
97.9%
ValueCountFrequency (%)
2010 1
0.2%
2710 1
0.2%
3150 1
0.2%
3600 1
0.2%
3740 1
0.2%
3780 1
0.2%
6180 1
0.2%
8110 1
0.2%
9270 1
0.2%
9360 1
0.2%
ValueCountFrequency (%)
12722500390 1
0.2%
10106969710 1
0.2%
9929233800 1
0.2%
7581914470 1
0.2%
7398047000 1
0.2%
6830269690 1
0.2%
6544285020 1
0.2%
6353153100 1
0.2%
6190995740 1
0.2%
5997381371 1
0.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct249
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.236414
Minimum0
Maximum85.11
Zeros76
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-11T08:15:41.114753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.255
median5
Q318.3575
95-th percentile52.4215
Maximum85.11
Range85.11
Interquartile range (IQR)18.1025

Descriptive statistics

Standard deviation16.419267
Coefficient of variation (CV)1.3418365
Kurtosis3.6923752
Mean12.236414
Median Absolute Deviation (MAD)5
Skewness1.8874103
Sum5800.06
Variance269.59231
MonotonicityNot monotonic
2023-12-11T08:15:41.240226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 76
 
16.0%
1.0 22
 
4.6%
4.0 12
 
2.5%
2.0 11
 
2.3%
20.0 8
 
1.7%
3.0 8
 
1.7%
10.0 7
 
1.5%
17.0 6
 
1.3%
8.0 6
 
1.3%
0.03 5
 
1.1%
Other values (239) 313
66.0%
ValueCountFrequency (%)
0.0 76
16.0%
0.01 4
 
0.8%
0.02 2
 
0.4%
0.03 5
 
1.1%
0.04 3
 
0.6%
0.05 1
 
0.2%
0.06 1
 
0.2%
0.07 2
 
0.4%
0.08 1
 
0.2%
0.1 1
 
0.2%
ValueCountFrequency (%)
85.11 1
0.2%
84.0 1
0.2%
81.0 1
0.2%
80.0 1
0.2%
75.81 1
0.2%
70.43 1
0.2%
66.0 1
0.2%
64.0 1
0.2%
63.0 1
0.2%
62.66 1
0.2%

Interactions

2023-12-11T08:15:38.641732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:37.644144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:37.968871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:38.286611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:38.713409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:37.716589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:38.042736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:38.376549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:38.784822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:37.790091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:38.119345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:38.464543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:38.871853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:37.894393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:38.213706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:38.555408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:15:41.324738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.2300.0000.2630.5440.615
납부매체0.0000.2301.0000.9940.5850.3750.629
납부매체전자고지여부0.0000.0000.9941.0000.0000.2740.198
납부건수0.0000.2630.5850.0001.0000.5670.742
납부금액0.0000.5440.3750.2740.5671.0000.362
납부매체비율0.0000.6150.6290.1980.7420.3621.000
2023-12-11T08:15:41.426744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체전자고지여부납부매체세목명
납부매체전자고지여부1.0000.9260.000
납부매체0.9261.0000.094
세목명0.0000.0941.000
2023-12-11T08:15:41.550790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부년도1.0000.0530.0640.0900.0000.0000.000
납부건수0.0531.0000.8260.8520.1080.2140.000
납부금액0.0640.8261.0000.7000.2730.1890.205
납부매체비율0.0900.8520.7001.0000.3010.2380.150
세목명0.0000.1080.2730.3011.0000.0940.000
납부매체0.0000.2140.1890.2380.0941.0000.926
납부매체전자고지여부0.0000.0000.2050.1500.0000.9261.000

Missing values

2023-12-11T08:15:38.966867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:15:39.090919image/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
0경상남도거창군488802017등록면허세ARSN161752102.0
1경상남도거창군488802017등록면허세ARSY1120000.0
2경상남도거창군488802017자동차세ARSN5069237097063.0
3경상남도거창군488802017자동차세ARSY57470301.0
4경상남도거창군488802017재산세ARSN1701389874021.0
5경상남도거창군488802017주민세ARSN91131841011.0
6경상남도거창군488802017주민세ARSY5553301.0
7경상남도거창군488802017지방소득세ARSN534334701.0
8경상남도거창군488802017지방소득세ARSY120100.0
9경상남도거창군488802017취득세ARSN327731700.0
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464경상남도거창군488802022주민세지자체방문N386110572603.02
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467경상남도거창군488802022취득세지자체방문N3542540339105027.68
468경상남도거창군488802022등록면허세페이사납부Y100307542504.68
469경상남도거창군488802022자동차세페이사납부Y67111837150031.37
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471경상남도거창군488802022주민세페이사납부Y363433772016.97
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473경상남도거창군488802022취득세페이사납부Y24399872101.12