Overview

Dataset statistics

Number of variables9
Number of observations233
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.4 KiB
Average record size in memory76.6 B

Variable types

Categorical6
Numeric2
DateTime1

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
데이터기준일 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 64 (27.5%) zerosZeros
부과금액 has 64 (27.5%) zerosZeros

Reproduction

Analysis started2023-12-11 00:55:26.087149
Analysis finished2023-12-11 00:55:27.095181
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경상남도
233 

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 233
100.0%

Length

2023-12-11T09:55:27.171507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:55:27.275526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 233
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
산청군
233 

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 233
100.0%

Length

2023-12-11T09:55:27.369394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:55:27.474395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산청군 233
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
48860
233 

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 233
100.0%

Length

2023-12-11T09:55:27.598767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:55:27.702482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48860 233
100.0%

과세년도
Categorical

Distinct5
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2019
47 
2018
47 
2020
47 
2017
46 
2021
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2019 47
20.2%
2018 47
20.2%
2020 47
20.2%
2017 46
19.7%
2021 46
19.7%

Length

2023-12-11T09:55:27.836197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:55:27.953647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 47
20.2%
2018 47
20.2%
2020 47
20.2%
2017 46
19.7%
2021 46
19.7%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
취득세
45 
주민세
43 
자동차세
35 
재산세
25 
레저세
20 
Other values (8)
65 

Length

Max length7
Median length3
Mean length3.695279
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 45
19.3%
주민세 43
18.5%
자동차세 35
15.0%
재산세 25
10.7%
레저세 20
8.6%
지방소득세 20
8.6%
지역자원시설세 11
 
4.7%
등록면허세 10
 
4.3%
담배소비세 5
 
2.1%
지방소비세 5
 
2.1%
Other values (3) 14
 
6.0%

Length

2023-12-11T09:55:28.085814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 45
19.3%
주민세 43
18.5%
자동차세 35
15.0%
재산세 25
10.7%
레저세 20
8.6%
지방소득세 20
8.6%
지역자원시설세 11
 
4.7%
등록면허세 10
 
4.3%
담배소비세 5
 
2.1%
지방소비세 5
 
2.1%
Other values (3) 14
 
6.0%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
기타승용
 
5
토지
 
5
건축물
 
5
지방소득세(법인소득)
 
5
주택(단독)
 
5
Other values (45)
208 

Length

Max length11
Median length8
Mean length6.0429185
Min length2

Unique

Unique3 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
기타승용 5
 
2.1%
토지 5
 
2.1%
건축물 5
 
2.1%
지방소득세(법인소득) 5
 
2.1%
주택(단독) 5
 
2.1%
기타 5
 
2.1%
항공기 5
 
2.1%
기계장비 5
 
2.1%
차량 5
 
2.1%
선박 5
 
2.1%
Other values (40) 183
78.5%

Length

2023-12-11T09:55:28.211630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타승용 5
 
2.1%
주민세(양도소득 5
 
2.1%
승합 5
 
2.1%
토지 5
 
2.1%
주민세(종업원분 5
 
2.1%
주민세(특별징수 5
 
2.1%
주민세(법인세분 5
 
2.1%
화물 5
 
2.1%
주민세(종합소득 5
 
2.1%
지방소득세(특별징수 5
 
2.1%
Other values (40) 183
78.5%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct162
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6614.515
Minimum0
Maximum115126
Zeros64
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T09:55:28.627850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median241
Q34482
95-th percentile25235.8
Maximum115126
Range115126
Interquartile range (IQR)4482

Descriptive statistics

Standard deviation18111.338
Coefficient of variation (CV)2.7381203
Kurtosis22.3879
Mean6614.515
Median Absolute Deviation (MAD)241
Skewness4.548516
Sum1541182
Variance3.2802056 × 108
MonotonicityNot monotonic
2023-12-11T09:55:28.787880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
27.5%
12 4
 
1.7%
155 2
 
0.9%
7 2
 
0.9%
136 2
 
0.9%
2 2
 
0.9%
1 2
 
0.9%
229 1
 
0.4%
101 1
 
0.4%
3921 1
 
0.4%
Other values (152) 152
65.2%
ValueCountFrequency (%)
0 64
27.5%
1 2
 
0.9%
2 2
 
0.9%
4 1
 
0.4%
6 1
 
0.4%
7 2
 
0.9%
10 1
 
0.4%
12 4
 
1.7%
14 1
 
0.4%
23 1
 
0.4%
ValueCountFrequency (%)
115126 1
0.4%
112728 1
0.4%
109718 1
0.4%
109264 1
0.4%
107328 1
0.4%
55599 1
0.4%
54812 1
0.4%
54167 1
0.4%
52947 1
0.4%
51408 1
0.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct170
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5026392 × 108
Minimum0
Maximum9.4883 × 109
Zeros64
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T09:55:28.986738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.73896 × 108
Q31.139414 × 109
95-th percentile3.341794 × 109
Maximum9.4883 × 109
Range9.4883 × 109
Interquartile range (IQR)1.139414 × 109

Descriptive statistics

Standard deviation1.4673474 × 109
Coefficient of variation (CV)1.7257552
Kurtosis10.927473
Mean8.5026392 × 108
Median Absolute Deviation (MAD)1.73896 × 108
Skewness2.8899446
Sum1.9811149 × 1011
Variance2.1531083 × 1018
MonotonicityNot monotonic
2023-12-11T09:55:29.176197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
 
27.5%
7009000 1
 
0.4%
215498000 1
 
0.4%
2679667000 1
 
0.4%
5025916000 1
 
0.4%
695107000 1
 
0.4%
2577438000 1
 
0.4%
796348000 1
 
0.4%
2900142000 1
 
0.4%
1257000 1
 
0.4%
Other values (160) 160
68.7%
ValueCountFrequency (%)
0 64
27.5%
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%
1646000 1
 
0.4%
ValueCountFrequency (%)
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%
4694127000 1
0.4%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Minimum2022-07-07 00:00:00
Maximum2022-07-07 00:00:00
2023-12-11T09:55:29.352451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:29.483213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T09:55:26.616506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:26.430014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:26.760123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:55:26.522089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:55:29.555798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0001.0000.8860.749
세원 유형명0.0001.0001.0001.0000.917
부과건수0.0000.8861.0001.0000.660
부과금액0.0000.7490.9170.6601.000
2023-12-11T09:55:29.670846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세원 유형명세목명
과세년도1.0000.0000.000
세원 유형명0.0001.0000.912
세목명0.0000.9121.000
2023-12-11T09:55:29.789872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액과세년도세목명세원 유형명
부과건수1.0000.7670.0000.7260.896
부과금액0.7671.0000.0000.4580.592
과세년도0.0000.0001.0000.0000.000
세목명0.7260.4580.0001.0000.912
세원 유형명0.8960.5920.0000.9121.000

Missing values

2023-12-11T09:55:26.882053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:55:27.031410image/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 유형명부과건수부과금액데이터기준일
0경상남도산청군488602019자동차세기타승용8570090002022-07-07
1경상남도산청군488602017담배소비세담배소비세10924580100002022-07-07
2경상남도산청군488602017취득세건축물88813663050002022-07-07
3경상남도산청군488602017취득세주택(개별)116411903660002022-07-07
4경상남도산청군488602017취득세주택(단독)1111294890002022-07-07
5경상남도산청군488602017취득세기타23775180002022-07-07
6경상남도산청군488602017취득세항공기002022-07-07
7경상남도산청군488602017취득세기계장비1632355020002022-07-07
8경상남도산청군488602017취득세차량294422608120002022-07-07
9경상남도산청군488602017취득세선박21540002022-07-07
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액데이터기준일
223경상남도산청군488602021지방소득세지방소득세(양도소득)8709176730002022-07-07
224경상남도산청군488602021지방소득세지방소득세(종합소득)33544545660002022-07-07
225경상남도산청군488602021등록면허세등록면허세(면허)92891426120002022-07-07
226경상남도산청군488602021등록면허세등록면허세(등록)110609074460002022-07-07
227경상남도산청군488602021지역자원시설세지역자원시설세(소방)111055127150002022-07-07
228경상남도산청군488602021지역자원시설세지역자원시설세(시설)002022-07-07
229경상남도산청군488602021지역자원시설세지역자원시설세(특자)1072433450002022-07-07
230경상남도산청군488602021지방소비세지방소비세794681230002022-07-07
231경상남도산청군488602021교육세교육세11512636102610002022-07-07
232경상남도산청군488602021체납체납2323511714880002022-07-07