Overview

Dataset statistics

Number of variables8
Number of observations234
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.2 KiB
Average record size in memory66.6 B

Variable types

Categorical6
Text2

Dataset

Description지방세 과세를 위해 세원이 되는 과세 대상 유형별 부과된 현황을 제공하여 물건 유형에 따른 세부담 수준의 형평성 검토 및 부동산 등 관련분야 규제정책 대상 확인시 기초자료로 활용
Author충청북도 영동군
URLhttps://www.data.go.kr/data/15078764/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
세원 유형명 is highly overall correlated with 세목명High correlation
세목명 is highly overall correlated with 세원 유형명High correlation

Reproduction

Analysis started2023-12-12 19:09:55.560496
Analysis finished2023-12-12 19:09:56.423602
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
충청북도
234 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 234
100.0%

Length

2023-12-13T04:09:56.522417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:09:56.648493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 234
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
영동군
234 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영동군 234
100.0%

Length

2023-12-13T04:09:56.775630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:09:56.915616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영동군 234
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
43740
234 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43740
2nd row43740
3rd row43740
4th row43740
5th row43740

Common Values

ValueCountFrequency (%)
43740 234
100.0%

Length

2023-12-13T04:09:57.052392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:09:57.191020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43740 234
100.0%

과세년도
Categorical

Distinct5
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2017
47 
2018
47 
2019
47 
2020
47 
2021
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2017 47
20.1%
2018 47
20.1%
2019 47
20.1%
2020 47
20.1%
2021 46
19.7%

Length

2023-12-13T04:09:57.340576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:09:57.477987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 47
20.1%
2018 47
20.1%
2019 47
20.1%
2020 47
20.1%
2021 46
19.7%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
취득세
45 
주민세
43 
자동차세
35 
재산세
25 
레저세
20 
Other values (8)
66 

Length

Max length7
Median length3
Mean length3.7008547
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row재산세
2nd row재산세
3rd row담배소비세
4th row교육세
5th row도시계획세

Common Values

ValueCountFrequency (%)
취득세 45
19.2%
주민세 43
18.4%
자동차세 35
15.0%
재산세 25
10.7%
레저세 20
8.5%
지방소득세 20
8.5%
지역자원시설세 11
 
4.7%
등록면허세 10
 
4.3%
담배소비세 5
 
2.1%
교육세 5
 
2.1%
Other values (3) 15
 
6.4%

Length

2023-12-13T04:09:57.660716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 45
19.2%
주민세 43
18.4%
자동차세 35
15.0%
재산세 25
10.7%
레저세 20
8.5%
지방소득세 20
8.5%
지역자원시설세 11
 
4.7%
등록면허세 10
 
4.3%
담배소비세 5
 
2.1%
교육세 5
 
2.1%
Other values (3) 15
 
6.4%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
재산세(항공기)
 
5
재산세(주택)
 
5
담배소비세
 
5
재산세(건축물)
 
5
도시계획세
 
5
Other values (45)
209 

Length

Max length11
Median length8
Mean length6.0384615
Min length2

Unique

Unique3 ?
Unique (%)1.3%

Sample

1st row재산세(항공기)
2nd row재산세(선박)
3rd row담배소비세
4th row교육세
5th row도시계획세

Common Values

ValueCountFrequency (%)
재산세(항공기) 5
 
2.1%
재산세(주택) 5
 
2.1%
담배소비세 5
 
2.1%
재산세(건축물) 5
 
2.1%
도시계획세 5
 
2.1%
건축물 5
 
2.1%
주택(개별) 5
 
2.1%
주택(단독) 5
 
2.1%
기타 5
 
2.1%
항공기 5
 
2.1%
Other values (40) 184
78.6%

Length

2023-12-13T04:09:57.803991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
재산세(항공기 5
 
2.1%
교육세 5
 
2.1%
재산세(토지 5
 
2.1%
재산세(주택 5
 
2.1%
재산세(선박 5
 
2.1%
주민세(종업원분 5
 
2.1%
주민세(특별징수 5
 
2.1%
주민세(법인세분 5
 
2.1%
주민세(양도소득 5
 
2.1%
기타승용 5
 
2.1%
Other values (40) 184
78.6%

부과건수
Text

Distinct160
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T04:09:58.159848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length2.9871795
Min length1

Characters and Unicode

Total characters699
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)64.5%

Sample

1st row
2nd row1
3rd row107
4th row114250
5th row
ValueCountFrequency (%)
0 23
 
11.7%
2 8
 
4.1%
12 4
 
2.0%
1 3
 
1.5%
110 2
 
1.0%
105 2
 
1.0%
85 2
 
1.0%
126 2
 
1.0%
16263 1
 
0.5%
1306 1
 
0.5%
Other values (149) 149
75.6%
2023-12-13T04:09:58.732890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 103
14.7%
0 77
11.0%
74
10.6%
2 73
10.4%
6 61
8.7%
4 60
8.6%
7 56
8.0%
3 56
8.0%
5 51
7.3%
8 45
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 625
89.4%
Space Separator 74
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 103
16.5%
0 77
12.3%
2 73
11.7%
6 61
9.8%
4 60
9.6%
7 56
9.0%
3 56
9.0%
5 51
8.2%
8 45
7.2%
9 43
6.9%
Space Separator
ValueCountFrequency (%)
74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 103
14.7%
0 77
11.0%
74
10.6%
2 73
10.4%
6 61
8.7%
4 60
8.6%
7 56
8.0%
3 56
8.0%
5 51
7.3%
8 45
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 103
14.7%
0 77
11.0%
74
10.6%
2 73
10.4%
6 61
8.7%
4 60
8.6%
7 56
8.0%
3 56
8.0%
5 51
7.3%
8 45
6.4%

부과금액
Text

Distinct175
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T04:09:59.039308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.9316239
Min length1

Characters and Unicode

Total characters1622
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)73.5%

Sample

1st row
2nd row4000
3rd row3279856000
4th row3411219000
5th row
ValueCountFrequency (%)
0 23
 
11.7%
6000 2
 
1.0%
1330406000 1
 
0.5%
55427000 1
 
0.5%
48885000 1
 
0.5%
3382178000 1
 
0.5%
61000 1
 
0.5%
3617289000 1
 
0.5%
1281278000 1
 
0.5%
1596000 1
 
0.5%
Other values (164) 164
83.2%
2023-12-13T04:09:59.529008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 641
39.5%
1 133
 
8.2%
2 120
 
7.4%
3 117
 
7.2%
5 105
 
6.5%
4 100
 
6.2%
8 92
 
5.7%
7 88
 
5.4%
6 78
 
4.8%
74
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1548
95.4%
Space Separator 74
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 641
41.4%
1 133
 
8.6%
2 120
 
7.8%
3 117
 
7.6%
5 105
 
6.8%
4 100
 
6.5%
8 92
 
5.9%
7 88
 
5.7%
6 78
 
5.0%
9 74
 
4.8%
Space Separator
ValueCountFrequency (%)
74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1622
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 641
39.5%
1 133
 
8.2%
2 120
 
7.4%
3 117
 
7.2%
5 105
 
6.5%
4 100
 
6.2%
8 92
 
5.7%
7 88
 
5.4%
6 78
 
4.8%
74
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 641
39.5%
1 133
 
8.2%
2 120
 
7.4%
3 117
 
7.2%
5 105
 
6.5%
4 100
 
6.2%
8 92
 
5.7%
7 88
 
5.4%
6 78
 
4.8%
74
 
4.6%

Correlations

2023-12-13T04:09:59.688002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명
과세년도1.0000.0000.000
세목명0.0001.0001.000
세원 유형명0.0001.0001.000
2023-12-13T04:09:59.831730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세원 유형명과세년도세목명
세원 유형명1.0000.0000.912
과세년도0.0001.0000.000
세목명0.9120.0001.000
2023-12-13T04:09:59.947601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명
과세년도1.0000.0000.000
세목명0.0001.0000.912
세원 유형명0.0000.9121.000

Missing values

2023-12-13T04:09:56.172644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:09:56.355499image/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
0충청북도영동군437402017재산세재산세(항공기)
1충청북도영동군437402017재산세재산세(선박)14000
2충청북도영동군437402017담배소비세담배소비세1073279856000
3충청북도영동군437402017교육세교육세1142503411219000
4충청북도영동군437402017도시계획세도시계획세
5충청북도영동군437402017취득세건축물439859507000
6충청북도영동군437402017취득세주택(개별)1141804730000
7충청북도영동군437402017취득세주택(단독)396542784000
8충청북도영동군437402017취득세기타11150000
9충청북도영동군437402017취득세항공기
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
224충청북도영동군437402021지방소득세지방소득세(특별징수)65612371715000
225충청북도영동군437402021지방소득세지방소득세(법인소득)7841560828000
226충청북도영동군437402021지방소득세지방소득세(양도소득)834983632000
227충청북도영동군437402021지방소득세지방소득세(종합소득)4558683266000
228충청북도영동군437402021재산세재산세(주택)16263874575000
229충청북도영동군437402021재산세재산세(토지)499041953361000
230충청북도영동군437402021재산세재산세(항공기)00
231충청북도영동군437402021재산세재산세(선박)210000
232충청북도영동군437402021재산세재산세(건축물)5175849225000
233충청북도영동군437402021체납체납298671407204000