Overview

Dataset statistics

Number of variables9
Number of observations38
Missing cells3
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory77.5 B

Variable types

Categorical5
Text4

Dataset

Description지방세 개방형 데이터 구축자료중 2017년 ~ 2021년도에 대한 경상남도 진주시 지방세 비과 감면율 현황에 대한 자료제공입니다.
Author경상남도 진주시
URLhttps://www.data.go.kr/data/15080408/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
비과세금액 has 3 (7.9%) missing valuesMissing
감면금액 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:49:59.647172
Analysis finished2024-04-06 08:50:00.928100
Duration1.28 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
경상남도
38 

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 38
100.0%

Length

2024-04-06T17:50:01.132861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:50:01.408474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 38
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
진주시
38 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진주시 38
100.0%

Length

2024-04-06T17:50:01.645032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:50:01.901869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진주시 38
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
48170
38 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48170
2nd row48170
3rd row48170
4th row48170
5th row48170

Common Values

ValueCountFrequency (%)
48170 38
100.0%

Length

2024-04-06T17:50:02.188813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:50:02.457262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48170 38
100.0%

세목명
Categorical

Distinct8
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size436.0 B
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
등록면허세
5 
Other values (3)
13 

Length

Max length7
Median length3
Mean length3.9210526
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교육세
2nd row등록세
3rd row재산세
4th row주민세
5th row취득세

Common Values

ValueCountFrequency (%)
재산세 5
13.2%
주민세 5
13.2%
취득세 5
13.2%
자동차세 5
13.2%
등록면허세 5
13.2%
지역자원시설세 5
13.2%
교육세 4
10.5%
등록세 4
10.5%

Length

2024-04-06T17:50:02.741273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:50:03.205431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 5
13.2%
주민세 5
13.2%
취득세 5
13.2%
자동차세 5
13.2%
등록면허세 5
13.2%
지역자원시설세 5
13.2%
교육세 4
10.5%
등록세 4
10.5%

과세년도
Categorical

Distinct5
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size436.0 B
2017
8 
2018
8 
2021
8 
2019
7 
2020
7 

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 8
21.1%
2018 8
21.1%
2021 8
21.1%
2019 7
18.4%
2020 7
18.4%

Length

2024-04-06T17:50:03.635714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:50:04.017268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 8
21.1%
2018 8
21.1%
2021 8
21.1%
2019 7
18.4%
2020 7
18.4%

비과세금액
Text

MISSING 

Distinct32
Distinct (%)91.4%
Missing3
Missing (%)7.9%
Memory size436.0 B
2024-04-06T17:50:04.550016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.2
Min length2

Characters and Unicode

Total characters322
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

Unique31 ?
Unique (%)88.6%

Sample

1st row
2nd row24474517000
3rd row437212000
4th row5001351000
5th row417298000
ValueCountFrequency (%)
24474517000 1
 
3.3%
437212000 1
 
3.3%
54722000 1
 
3.3%
148192000 1
 
3.3%
7241880000 1
 
3.3%
99536000 1
 
3.3%
30257394000 1
 
3.3%
132746000 1
 
3.3%
50545890 1
 
3.3%
136976190 1
 
3.3%
Other values (20) 20
66.7%
2024-04-06T17:50:05.862533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 99
30.7%
48
14.9%
7 25
 
7.8%
4 24
 
7.5%
5 24
 
7.5%
2 21
 
6.5%
9 18
 
5.6%
1 17
 
5.3%
3 16
 
5.0%
8 16
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 274
85.1%
Space Separator 48
 
14.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 99
36.1%
7 25
 
9.1%
4 24
 
8.8%
5 24
 
8.8%
2 21
 
7.7%
9 18
 
6.6%
1 17
 
6.2%
3 16
 
5.8%
8 16
 
5.8%
6 14
 
5.1%
Space Separator
ValueCountFrequency (%)
48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 99
30.7%
48
14.9%
7 25
 
7.8%
4 24
 
7.5%
5 24
 
7.5%
2 21
 
6.5%
9 18
 
5.6%
1 17
 
5.3%
3 16
 
5.0%
8 16
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 99
30.7%
48
14.9%
7 25
 
7.8%
4 24
 
7.5%
5 24
 
7.5%
2 21
 
6.5%
9 18
 
5.6%
1 17
 
5.3%
3 16
 
5.0%
8 16
 
5.0%

감면금액
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2024-04-06T17:50:06.445788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.6315789
Min length4

Characters and Unicode

Total characters366
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

Unique38 ?
Unique (%)100.0%

Sample

1st row4000
2nd row9856000
3rd row5185470000
4th row222503000
5th row12316021000
ValueCountFrequency (%)
4000 1
 
2.7%
12779602000 1
 
2.7%
273137000 1
 
2.7%
268271000 1
 
2.7%
177852000 1
 
2.7%
2150170 1
 
2.7%
4726665060 1
 
2.7%
66692780 1
 
2.7%
15566678320 1
 
2.7%
1335450480 1
 
2.7%
Other values (27) 27
73.0%
2024-04-06T17:50:07.603195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 117
32.0%
41
 
11.2%
6 33
 
9.0%
2 31
 
8.5%
1 28
 
7.7%
5 23
 
6.3%
7 22
 
6.0%
8 21
 
5.7%
3 19
 
5.2%
4 18
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 325
88.8%
Space Separator 41
 
11.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117
36.0%
6 33
 
10.2%
2 31
 
9.5%
1 28
 
8.6%
5 23
 
7.1%
7 22
 
6.8%
8 21
 
6.5%
3 19
 
5.8%
4 18
 
5.5%
9 13
 
4.0%
Space Separator
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 366
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 117
32.0%
41
 
11.2%
6 33
 
9.0%
2 31
 
8.5%
1 28
 
7.7%
5 23
 
6.3%
7 22
 
6.0%
8 21
 
5.7%
3 19
 
5.2%
4 18
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 117
32.0%
41
 
11.2%
6 33
 
9.0%
2 31
 
8.5%
1 28
 
7.7%
5 23
 
6.3%
7 22
 
6.0%
8 21
 
5.7%
3 19
 
5.2%
4 18
 
4.9%

부과금액
Text

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size436.0 B
2024-04-06T17:50:08.205979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.052632
Min length2

Characters and Unicode

Total characters420
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

Unique36 ?
Unique (%)94.7%

Sample

1st row33328066000
2nd row
3rd row47156625000
4th row7879543000
5th row120000000000
ValueCountFrequency (%)
126000000000 1
 
2.9%
74519115900 1
 
2.9%
17963378000 1
 
2.9%
8241187000 1
 
2.9%
85617230 1
 
2.9%
73186974650 1
 
2.9%
9475308820 1
 
2.9%
137572000000 1
 
2.9%
33328066000 1
 
2.9%
24820885950 1
 
2.9%
Other values (25) 25
71.4%
2024-04-06T17:50:09.308508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 133
31.7%
45
 
10.7%
5 30
 
7.1%
6 29
 
6.9%
3 29
 
6.9%
2 28
 
6.7%
8 28
 
6.7%
1 26
 
6.2%
4 24
 
5.7%
7 24
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 375
89.3%
Space Separator 45
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 133
35.5%
5 30
 
8.0%
6 29
 
7.7%
3 29
 
7.7%
2 28
 
7.5%
8 28
 
7.5%
1 26
 
6.9%
4 24
 
6.4%
7 24
 
6.4%
9 24
 
6.4%
Space Separator
ValueCountFrequency (%)
45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 420
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 133
31.7%
45
 
10.7%
5 30
 
7.1%
6 29
 
6.9%
3 29
 
6.9%
2 28
 
6.7%
8 28
 
6.7%
1 26
 
6.2%
4 24
 
5.7%
7 24
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 133
31.7%
45
 
10.7%
5 30
 
7.1%
6 29
 
6.9%
3 29
 
6.9%
2 28
 
6.7%
8 28
 
6.7%
1 26
 
6.2%
4 24
 
5.7%
7 24
 
5.7%

비과세감면율
Text

Distinct20
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
2024-04-06T17:50:09.696096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.5263158
Min length1

Characters and Unicode

Total characters58
Distinct characters10
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

Unique12 ?
Unique (%)31.6%

Sample

1st row
2nd row
3rd row63
4th row8
5th row14
ValueCountFrequency (%)
2 6
18.2%
1 4
12.1%
0 3
 
9.1%
8 2
 
6.1%
14 2
 
6.1%
17 2
 
6.1%
3 2
 
6.1%
13 1
 
3.0%
12 1
 
3.0%
21 1
 
3.0%
Other values (9) 9
27.3%
2024-04-06T17:50:10.343066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
24.1%
10
17.2%
2 9
15.5%
5 5
 
8.6%
0 4
 
6.9%
3 4
 
6.9%
6 4
 
6.9%
4 3
 
5.2%
7 3
 
5.2%
8 2
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48
82.8%
Space Separator 10
 
17.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
29.2%
2 9
18.8%
5 5
 
10.4%
0 4
 
8.3%
3 4
 
8.3%
6 4
 
8.3%
4 3
 
6.2%
7 3
 
6.2%
8 2
 
4.2%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
24.1%
10
17.2%
2 9
15.5%
5 5
 
8.6%
0 4
 
6.9%
3 4
 
6.9%
6 4
 
6.9%
4 3
 
5.2%
7 3
 
5.2%
8 2
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
24.1%
10
17.2%
2 9
15.5%
5 5
 
8.6%
0 4
 
6.9%
3 4
 
6.9%
6 4
 
6.9%
4 3
 
5.2%
7 3
 
5.2%
8 2
 
3.4%

Correlations

2024-04-06T17:50:10.607237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.0001.0001.0000.882
과세년도0.0001.0000.4791.0000.8730.360
비과세금액0.0000.4791.0001.0001.0000.988
감면금액1.0001.0001.0001.0001.0001.000
부과금액1.0000.8731.0001.0001.0001.000
비과세감면율0.8820.3600.9881.0001.0001.000
2024-04-06T17:50:10.900553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2024-04-06T17:50:11.120236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000

Missing values

2024-04-06T17:50:00.408947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:50:00.773562image/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

시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
0경상남도진주시48170교육세2017400033328066000
1경상남도진주시48170등록세2017<NA>9856000
2경상남도진주시48170재산세20172447451700051854700004715662500063
3경상남도진주시48170주민세201743721200022250300078795430008
4경상남도진주시48170취득세201750013510001231602100012000000000014
5경상남도진주시48170자동차세20174172980001134266000662112060002
6경상남도진주시48170등록면허세2017472600004336838000784059600056
7경상남도진주시48170지역자원시설세2017761944000255709000703296400014
8경상남도진주시48170교육세2018600033223162000
9경상남도진주시48170등록세2018<NA>1049000
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
28경상남도진주시48170등록면허세202050545890302688820248208859501
29경상남도진주시48170지역자원시설세20201073404669993200
30경상남도진주시48170교육세20217000389794520000
31경상남도진주시48170등록세2021<NA>108650000
32경상남도진주시48170재산세20213025739400048676570005834885300060
33경상남도진주시48170주민세20219953600033958100094746830005
34경상남도진주시48170취득세202172418800001922420100012443700000021
35경상남도진주시48170자동차세20211481920001465836000696933320002
36경상남도진주시48170등록면허세202154722000273137000251037160001
37경상남도진주시48170지역자원시설세2021846977000205178000941190200011