Overview

Dataset statistics

Number of variables8
Number of observations274
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.3 KiB
Average record size in memory68.5 B

Variable types

Categorical5
Numeric3

Dataset

Description지방세 과세를 위해 세원이 되는 과세 대상 유형별 부과된 현황을 제공물건 유형에 따른 세부담 수준의 형평성 검토 및 부동산 등 관련분야 규제정책 대상 확인 시 기초자료 활용
Author경상북도 경산시
URLhttps://www.data.go.kr/data/15079715/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
세목명 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
세원 유형명 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 부과건수High correlation
부과건수 has 70 (25.5%) zerosZeros
부과금액 has 70 (25.5%) zerosZeros

Reproduction

Analysis started2024-03-16 06:45:31.248791
Analysis finished2024-03-16 06:45:37.007185
Duration5.76 seconds
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
경상북도
274 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 274
100.0%

Length

2024-03-16T06:45:37.303659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:45:37.805873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 274
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
경산시
274 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경산시 274
100.0%

Length

2024-03-16T06:45:38.170590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:45:38.524059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경산시 274
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
47290
274 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47290
2nd row47290
3rd row47290
4th row47290
5th row47290

Common Values

ValueCountFrequency (%)
47290 274
100.0%

Length

2024-03-16T06:45:38.949345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:45:39.248659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47290 274
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4964
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-03-16T06:45:39.536146image/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 deviation1.72516
Coefficient of variation (CV)0.00085425261
Kurtosis-1.2962786
Mean2019.4964
Median Absolute Deviation (MAD)2
Skewness-0.0067224895
Sum553342
Variance2.9761771
MonotonicityDecreasing
2024-03-16T06:45:40.070123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 47
17.2%
2018 47
17.2%
2017 47
17.2%
2022 46
16.8%
2021 46
16.8%
2019 41
15.0%
ValueCountFrequency (%)
2017 47
17.2%
2018 47
17.2%
2019 41
15.0%
2020 47
17.2%
2021 46
16.8%
2022 46
16.8%
ValueCountFrequency (%)
2022 46
16.8%
2021 46
16.8%
2020 47
17.2%
2019 41
15.0%
2018 47
17.2%
2017 47
17.2%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
취득세
54 
주민세
50 
자동차세
42 
재산세
30 
지방소득세
24 
Other values (8)
74 

Length

Max length7
Median length3
Mean length3.7153285
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교육세
2nd row도시계획세
3rd row취득세
4th row취득세
5th row취득세

Common Values

ValueCountFrequency (%)
취득세 54
19.7%
주민세 50
18.2%
자동차세 42
15.3%
재산세 30
10.9%
지방소득세 24
8.8%
레저세 20
 
7.3%
지역자원시설세 14
 
5.1%
등록면허세 12
 
4.4%
교육세 6
 
2.2%
담배소비세 6
 
2.2%
Other values (3) 16
 
5.8%

Length

2024-03-16T06:45:40.566290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 54
19.7%
주민세 50
18.2%
자동차세 42
15.3%
재산세 30
10.9%
지방소득세 24
8.8%
레저세 20
 
7.3%
지역자원시설세 14
 
5.1%
등록면허세 12
 
4.4%
교육세 6
 
2.2%
담배소비세 6
 
2.2%
Other values (3) 16
 
5.8%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
교육세
 
6
차량
 
6
토지
 
6
건축물
 
6
주택(개별)
 
6
Other values (45)
244 

Length

Max length11
Median length8
Mean length6.0985401
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교육세
2nd row도시계획세
3rd row건축물
4th row주택(개별)
5th row주택(단독)

Common Values

ValueCountFrequency (%)
교육세 6
 
2.2%
차량 6
 
2.2%
토지 6
 
2.2%
건축물 6
 
2.2%
주택(개별) 6
 
2.2%
주택(단독) 6
 
2.2%
등록면허세(등록) 6
 
2.2%
항공기 6
 
2.2%
3륜이하 6
 
2.2%
선박 6
 
2.2%
Other values (40) 214
78.1%

Length

2024-03-16T06:45:41.152594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
교육세 6
 
2.2%
등록면허세(면허 6
 
2.2%
지역자원시설세(소방 6
 
2.2%
기타승용 6
 
2.2%
체납 6
 
2.2%
주민세(특별징수 6
 
2.2%
차량 6
 
2.2%
주민세(양도소득 6
 
2.2%
주민세(종합소득 6
 
2.2%
승용 6
 
2.2%
Other values (40) 214
78.1%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct197
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36496.701
Minimum0
Maximum590806
Zeros70
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-03-16T06:45:41.728885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1893
Q323874.25
95-th percentile185534.55
Maximum590806
Range590806
Interquartile range (IQR)23874.25

Descriptive statistics

Standard deviation93257.591
Coefficient of variation (CV)2.5552335
Kurtosis19.389213
Mean36496.701
Median Absolute Deviation (MAD)1893
Skewness4.1649505
Sum10000096
Variance8.6969782 × 109
MonotonicityNot monotonic
2024-03-16T06:45:42.280478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
25.5%
12 6
 
2.2%
6 2
 
0.7%
17 2
 
0.7%
4202 2
 
0.7%
570450 1
 
0.4%
270 1
 
0.4%
3601 1
 
0.4%
3068 1
 
0.4%
25442 1
 
0.4%
Other values (187) 187
68.2%
ValueCountFrequency (%)
0 70
25.5%
1 1
 
0.4%
6 2
 
0.7%
7 1
 
0.4%
9 1
 
0.4%
11 1
 
0.4%
12 6
 
2.2%
15 1
 
0.4%
16 1
 
0.4%
17 2
 
0.7%
ValueCountFrequency (%)
590806 1
0.4%
572310 1
0.4%
570450 1
0.4%
558829 1
0.4%
545975 1
0.4%
468176 1
0.4%
282298 1
0.4%
280701 1
0.4%
278201 1
0.4%
232160 1
0.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct205
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1742494 × 109
Minimum0
Maximum6.5177022 × 1010
Zeros70
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-03-16T06:45:43.006509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9.009035 × 108
Q31.6120432 × 1010
95-th percentile3.1232731 × 1010
Maximum6.5177022 × 1010
Range6.5177022 × 1010
Interquartile range (IQR)1.6120432 × 1010

Descriptive statistics

Standard deviation1.1624742 × 1010
Coefficient of variation (CV)1.4221173
Kurtosis2.891159
Mean8.1742494 × 109
Median Absolute Deviation (MAD)9.009035 × 108
Skewness1.6621636
Sum2.2397443 × 1012
Variance1.3513462 × 1020
MonotonicityNot monotonic
2024-03-16T06:45:43.580172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
25.5%
4403000 1
 
0.4%
19041659000 1
 
0.4%
18736238000 1
 
0.4%
5598378000 1
 
0.4%
4789585000 1
 
0.4%
19505092000 1
 
0.4%
19696091000 1
 
0.4%
31382221000 1
 
0.4%
16215844000 1
 
0.4%
Other values (195) 195
71.2%
ValueCountFrequency (%)
0 70
25.5%
10000 1
 
0.4%
671000 1
 
0.4%
1008000 1
 
0.4%
1801000 1
 
0.4%
2367000 1
 
0.4%
2372000 1
 
0.4%
2476000 1
 
0.4%
3006000 1
 
0.4%
3152000 1
 
0.4%
ValueCountFrequency (%)
65177022000 1
0.4%
48736618000 1
0.4%
48142383000 1
0.4%
47078621000 1
0.4%
46678232000 1
0.4%
41470954000 1
0.4%
38529717000 1
0.4%
37225081000 1
0.4%
35521170000 1
0.4%
34610307000 1
0.4%

Interactions

2024-03-16T06:45:34.285970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:45:31.755704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:45:33.038414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:45:34.663008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:45:32.088501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:45:33.352774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:45:35.297210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:45:32.477293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:45:33.924310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T06:45:43.976562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0001.0000.8430.609
세원 유형명0.0001.0001.0000.9480.865
부과건수0.0000.8430.9481.0000.560
부과금액0.0000.6090.8650.5601.000
2024-03-16T06:45:44.307291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명
세목명1.0000.926
세원 유형명0.9261.000
2024-03-16T06:45:44.884011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과건수부과금액세목명세원 유형명
과세년도1.000-0.0110.0240.0000.000
부과건수-0.0111.0000.7720.5940.687
부과금액0.0240.7721.0000.3130.489
세목명0.0000.5940.3131.0000.926
세원 유형명0.0000.6870.4890.9261.000

Missing values

2024-03-16T06:45:35.962123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T06:45:36.803314image/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 유형명부과건수부과금액
0경상북도경산시472902022교육세교육세57045031152237000
1경상북도경산시472902022도시계획세도시계획세00
2경상북도경산시472902022취득세건축물216215834195000
3경상북도경산시472902022취득세주택(개별)13755515001000
4경상북도경산시472902022취득세주택(단독)29546315725000
5경상북도경산시472902022취득세기타232976693000
6경상북도경산시472902022취득세항공기00
7경상북도경산시472902022취득세기계장비545550699000
8경상북도경산시472902022취득세차량2125921691072000
9경상북도경산시472902022취득세선박172367000
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
264경상북도경산시472902017지방소득세지방소득세(법인소득)328729879404000
265경상북도경산시472902017지방소득세지방소득세(양도소득)39436917385000
266경상북도경산시472902017지방소득세지방소득세(종합소득)207004611601000
267경상북도경산시472902017담배소비세담배소비세10720914665000
268경상북도경산시472902017지방소비세지방소비세00
269경상북도경산시472902017레저세소싸움00
270경상북도경산시472902017레저세경정00
271경상북도경산시472902017레저세경륜00
272경상북도경산시472902017레저세경마00
273경상북도경산시472902017체납체납28070117457700000