Overview

Dataset statistics

Number of variables9
Number of observations279
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.8 KiB
Average record size in memory76.5 B

Variable types

Categorical6
Numeric3

Dataset

Description산청군 세원유형별 과세현황(시군구명, 과세년도, 세목명 , 세원 유형명 , 부과건수 , 부과금액 등) 데이터 자료입니다.
Author경상남도 산청군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15078803

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
세원 유형명 is highly overall correlated with 부과건수 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
부과건수 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
부과건수 has 76 (27.2%) zerosZeros
부과금액 has 76 (27.2%) zerosZeros

Reproduction

Analysis started2023-12-11 00:55:21.049887
Analysis finished2023-12-11 00:55:22.179422
Duration1.13 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
경상남도
279 

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 279
100.0%

Length

2023-12-11T09:55:22.252786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:55:22.361191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 279
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
산청군
279 

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 279
100.0%

Length

2023-12-11T09:55:22.487226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:55:22.580355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산청군 279
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
48860
279 

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 279
100.0%

Length

2023-12-11T09:55:22.694118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:55:22.781346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48860 279
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4946
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-11T09:55:22.866685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7045664
Coefficient of variation (CV)0.00084405591
Kurtosis-1.2604133
Mean2019.4946
Median Absolute Deviation (MAD)1
Skewness0.0070616543
Sum563439
Variance2.9055465
MonotonicityNot monotonic
2023-12-11T09:55:23.013763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2019 47
16.8%
2018 47
16.8%
2020 47
16.8%
2017 46
16.5%
2021 46
16.5%
2022 46
16.5%
ValueCountFrequency (%)
2017 46
16.5%
2018 47
16.8%
2019 47
16.8%
2020 47
16.8%
2021 46
16.5%
2022 46
16.5%
ValueCountFrequency (%)
2022 46
16.5%
2021 46
16.5%
2020 47
16.8%
2019 47
16.8%
2018 47
16.8%
2017 46
16.5%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
취득세
54 
주민세
50 
자동차세
42 
재산세
30 
레저세
24 
Other values (8)
79 

Length

Max length7
Median length3
Mean length3.7096774
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자동차세
2nd row담배소비세
3rd row취득세
4th row취득세
5th row취득세

Common Values

ValueCountFrequency (%)
취득세 54
19.4%
주민세 50
17.9%
자동차세 42
15.1%
재산세 30
10.8%
레저세 24
8.6%
지방소득세 24
8.6%
지역자원시설세 14
 
5.0%
등록면허세 12
 
4.3%
담배소비세 6
 
2.2%
지방소비세 6
 
2.2%
Other values (3) 17
 
6.1%

Length

2023-12-11T09:55:23.168750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 54
19.4%
주민세 50
17.9%
자동차세 42
15.1%
재산세 30
10.8%
레저세 24
8.6%
지방소득세 24
8.6%
지역자원시설세 14
 
5.0%
등록면허세 12
 
4.3%
담배소비세 6
 
2.2%
지방소비세 6
 
2.2%
Other values (3) 17
 
6.1%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
기타승용
 
6
토지
 
6
건축물
 
6
지방소득세(법인소득)
 
6
주택(단독)
 
6
Other values (45)
249 

Length

Max length11
Median length8
Mean length6.0394265
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타승용
2nd row담배소비세
3rd row건축물
4th row주택(개별)
5th row주택(단독)

Common Values

ValueCountFrequency (%)
기타승용 6
 
2.2%
토지 6
 
2.2%
건축물 6
 
2.2%
지방소득세(법인소득) 6
 
2.2%
주택(단독) 6
 
2.2%
기타 6
 
2.2%
항공기 6
 
2.2%
기계장비 6
 
2.2%
차량 6
 
2.2%
선박 6
 
2.2%
Other values (40) 219
78.5%

Length

2023-12-11T09:55:23.317614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타승용 6
 
2.2%
주민세(양도소득 6
 
2.2%
승합 6
 
2.2%
토지 6
 
2.2%
주민세(종업원분 6
 
2.2%
주민세(특별징수 6
 
2.2%
주민세(법인세분 6
 
2.2%
화물 6
 
2.2%
주민세(종합소득 6
 
2.2%
지방소득세(특별징수 6
 
2.2%
Other values (40) 219
78.5%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct192
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6666.2581
Minimum0
Maximum116509
Zeros76
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-11T09:55:23.453045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median273
Q34548
95-th percentile25014.7
Maximum116509
Range116509
Interquartile range (IQR)4548

Descriptive statistics

Standard deviation18251.452
Coefficient of variation (CV)2.7378857
Kurtosis22.442231
Mean6666.2581
Median Absolute Deviation (MAD)273
Skewness4.5598916
Sum1859886
Variance3.3311551 × 108
MonotonicityNot monotonic
2023-12-11T09:55:23.639648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76
27.2%
12 5
 
1.8%
7 3
 
1.1%
1 2
 
0.7%
133 2
 
0.7%
155 2
 
0.7%
136 2
 
0.7%
2 2
 
0.7%
107 2
 
0.7%
23905 1
 
0.4%
Other values (182) 182
65.2%
ValueCountFrequency (%)
0 76
27.2%
1 2
 
0.7%
2 2
 
0.7%
4 1
 
0.4%
6 1
 
0.4%
7 3
 
1.1%
9 1
 
0.4%
10 1
 
0.4%
11 1
 
0.4%
12 5
 
1.8%
ValueCountFrequency (%)
116509 1
0.4%
115126 1
0.4%
112728 1
0.4%
109718 1
0.4%
109264 1
0.4%
107328 1
0.4%
56363 1
0.4%
55599 1
0.4%
54812 1
0.4%
54167 1
0.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct204
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8716474 × 108
Minimum0
Maximum1.3124695 × 1010
Zeros76
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-11T09:55:23.790643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.74375 × 108
Q31.143184 × 109
95-th percentile3.4178837 × 109
Maximum1.3124695 × 1010
Range1.3124695 × 1010
Interquartile range (IQR)1.143184 × 109

Descriptive statistics

Standard deviation1.6009411 × 109
Coefficient of variation (CV)1.804559
Kurtosis17.719467
Mean8.8716474 × 108
Median Absolute Deviation (MAD)1.74375 × 108
Skewness3.5088546
Sum2.4751896 × 1011
Variance2.5630124 × 1018
MonotonicityNot monotonic
2023-12-11T09:55:23.939586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76
 
27.2%
7009000 1
 
0.4%
143100000 1
 
0.4%
848367000 1
 
0.4%
502132000 1
 
0.4%
202665000 1
 
0.4%
9488300000 1
 
0.4%
3406144000 1
 
0.4%
1263101000 1
 
0.4%
2472062000 1
 
0.4%
Other values (194) 194
69.5%
ValueCountFrequency (%)
0 76
27.2%
154000 1
 
0.4%
162000 1
 
0.4%
212000 1
 
0.4%
707000 1
 
0.4%
714000 1
 
0.4%
759000 1
 
0.4%
964000 1
 
0.4%
1257000 1
 
0.4%
1609000 1
 
0.4%
ValueCountFrequency (%)
13124695000 1
0.4%
9488300000 1
0.4%
9468123000 1
0.4%
5961207000 1
0.4%
5947184000 1
0.4%
5351510000 1
0.4%
5110137000 1
0.4%
5025916000 1
0.4%
4730376000 1
0.4%
4705033000 1
0.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-08-04
279 

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 (%)
2023-08-04 279
100.0%

Length

2023-12-11T09:55:24.097303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:55:24.201531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-04 279
100.0%

Interactions

2023-12-11T09:55:21.676707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:21.238579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:21.448850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:21.760412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:21.306210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:21.518480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:21.843562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:21.378149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:21.594128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:55:24.265678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0001.0000.8550.707
세원 유형명0.0001.0001.0000.9980.892
부과건수0.0000.8550.9981.0000.464
부과금액0.0000.7070.8920.4641.000
2023-12-11T09:55:24.380035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세원 유형명세목명
세원 유형명1.0000.928
세목명0.9281.000
2023-12-11T09:55:24.477116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과건수부과금액세목명세원 유형명
과세년도1.0000.0090.0380.0000.000
부과건수0.0091.0000.7650.6750.857
부과금액0.0380.7651.0000.4210.566
세목명0.0000.6750.4211.0000.928
세원 유형명0.0000.8570.5660.9281.000

Missing values

2023-12-11T09:55:21.998093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:55:22.126820image/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

시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액데이터기준일자
0경상남도산청군488602019자동차세기타승용8570090002023-08-04
1경상남도산청군488602017담배소비세담배소비세10924580100002023-08-04
2경상남도산청군488602017취득세건축물88813663050002023-08-04
3경상남도산청군488602017취득세주택(개별)116411903660002023-08-04
4경상남도산청군488602017취득세주택(단독)1111294890002023-08-04
5경상남도산청군488602017취득세기타23775180002023-08-04
6경상남도산청군488602017취득세항공기002023-08-04
7경상남도산청군488602017취득세기계장비1632355020002023-08-04
8경상남도산청군488602017취득세차량294422608120002023-08-04
9경상남도산청군488602017취득세선박21540002023-08-04
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액데이터기준일자
269경상남도산청군488602022지방소득세지방소득세(양도소득)7786481740002023-08-04
270경상남도산청군488602022지방소득세지방소득세(종합소득)40785907470002023-08-04
271경상남도산청군488602022등록면허세등록면허세(면허)86601334480002023-08-04
272경상남도산청군488602022등록면허세등록면허세(등록)102208939020002023-08-04
273경상남도산청군488602022지역자원시설세지역자원시설세(소방)108855257940002023-08-04
274경상남도산청군488602022지역자원시설세지역자원시설세(시설)002023-08-04
275경상남도산청군488602022지역자원시설세지역자원시설세(특자)1342393520002023-08-04
276경상남도산청군488602022지방소비세지방소비세9131246950002023-08-04
277경상남도산청군488602022교육세교육세11650935235410002023-08-04
278경상남도산청군488602022체납체납2158211904840002023-08-04