Overview

Dataset statistics

Number of variables10
Number of observations61
Missing cells8
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory84.2 B

Variable types

Categorical6
Text4

Dataset

Description이 데이터는 남원시의 2017~2021년도 지방세 과세현황에 대하여 세목명, 과세건수, 과세금액, 비과세건수, 비과세금액 등에 대한 데이터 입니다.
URLhttps://www.data.go.kr/data/15079836/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터 기준일자 has constant value ""Constant
비과세건수 has 4 (6.6%) missing valuesMissing
비과세금액 has 4 (6.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 09:25:44.169030
Analysis finished2023-12-12 09:25:44.986184
Duration0.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
전라북도
61 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 61
100.0%

Length

2023-12-12T18:25:45.067014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:25:45.192147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 61
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
남원시
61 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남원시 61
100.0%

Length

2023-12-12T18:25:45.306325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:25:45.463486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남원시 61
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
45190
61 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45190
2nd row45190
3rd row45190
4th row45190
5th row45190

Common Values

ValueCountFrequency (%)
45190 61
100.0%

Length

2023-12-12T18:25:45.576428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:25:45.696480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45190 61
100.0%

과세년도
Categorical

Distinct5
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size620.0 B
2017
13 
2018
13 
2019
13 
2020
11 
2021
11 

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 13
21.3%
2018 13
21.3%
2019 13
21.3%
2020 11
18.0%
2021 11
18.0%

Length

2023-12-12T18:25:45.840111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:25:45.986148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 13
21.3%
2018 13
21.3%
2019 13
21.3%
2020 11
18.0%
2021 11
18.0%

세목명
Categorical

Distinct13
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
취득세
5 
등록세
5 
주민세
5 
재산세
5 
자동차세
5 
Other values (8)
36 

Length

Max length7
Median length5
Mean length4.1639344
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 5
8.2%
등록세 5
8.2%
주민세 5
8.2%
재산세 5
8.2%
자동차세 5
8.2%
담배소비세 5
8.2%
지방소비세 5
8.2%
등록면허세 5
8.2%
지역자원시설세 5
8.2%
지방소득세 5
8.2%
Other values (3) 11
18.0%

Length

2023-12-12T18:25:46.163254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 5
8.2%
등록세 5
8.2%
주민세 5
8.2%
재산세 5
8.2%
자동차세 5
8.2%
담배소비세 5
8.2%
지방소비세 5
8.2%
등록면허세 5
8.2%
지역자원시설세 5
8.2%
지방소득세 5
8.2%
Other values (3) 11
18.0%

과세건수
Text

Distinct49
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
2023-12-12T18:25:46.431130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.9672131
Min length1

Characters and Unicode

Total characters242
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

Unique48 ?
Unique (%)78.7%

Sample

1st row15266
2nd row
3rd row38929
4th row105487
5th row60925
ValueCountFrequency (%)
39203 1
 
2.1%
39410 1
 
2.1%
18839 1
 
2.1%
192753 1
 
2.1%
17471 1
 
2.1%
39828 1
 
2.1%
112134 1
 
2.1%
65501 1
 
2.1%
275 1
 
2.1%
6 1
 
2.1%
Other values (38) 38
79.2%
2023-12-12T18:25:46.950497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 35
14.5%
2 32
13.2%
8 27
11.2%
9 25
10.3%
3 23
9.5%
6 20
8.3%
0 19
7.9%
5 19
7.9%
7 16
6.6%
13
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 229
94.6%
Space Separator 13
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35
15.3%
2 32
14.0%
8 27
11.8%
9 25
10.9%
3 23
10.0%
6 20
8.7%
0 19
8.3%
5 19
8.3%
7 16
7.0%
4 13
 
5.7%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 242
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35
14.5%
2 32
13.2%
8 27
11.2%
9 25
10.3%
3 23
9.5%
6 20
8.3%
0 19
7.9%
5 19
7.9%
7 16
6.6%
13
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35
14.5%
2 32
13.2%
8 27
11.2%
9 25
10.3%
3 23
9.5%
6 20
8.3%
0 19
7.9%
5 19
7.9%
7 16
6.6%
13
 
5.4%

과세금액
Text

Distinct49
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
2023-12-12T18:25:47.304855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.2786885
Min length1

Characters and Unicode

Total characters505
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

Unique48 ?
Unique (%)78.7%

Sample

1st row16397136000
2nd row
3rd row1179715000
4th row6763945000
5th row11254996000
ValueCountFrequency (%)
1984050000 1
 
2.1%
1235958000 1
 
2.1%
10345683000 1
 
2.1%
6535039000 1
 
2.1%
20545799000 1
 
2.1%
1447924000 1
 
2.1%
8031442000 1
 
2.1%
12016374000 1
 
2.1%
5516237000 1
 
2.1%
11533700000 1
 
2.1%
Other values (38) 38
79.2%
2023-12-12T18:25:47.743788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 156
30.9%
1 62
 
12.3%
7 43
 
8.5%
2 38
 
7.5%
4 36
 
7.1%
5 34
 
6.7%
3 33
 
6.5%
9 31
 
6.1%
8 30
 
5.9%
6 29
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 492
97.4%
Space Separator 13
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 156
31.7%
1 62
 
12.6%
7 43
 
8.7%
2 38
 
7.7%
4 36
 
7.3%
5 34
 
6.9%
3 33
 
6.7%
9 31
 
6.3%
8 30
 
6.1%
6 29
 
5.9%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 505
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 156
30.9%
1 62
 
12.3%
7 43
 
8.5%
2 38
 
7.5%
4 36
 
7.1%
5 34
 
6.7%
3 33
 
6.5%
9 31
 
6.1%
8 30
 
5.9%
6 29
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 156
30.9%
1 62
 
12.3%
7 43
 
8.5%
2 38
 
7.5%
4 36
 
7.1%
5 34
 
6.7%
3 33
 
6.5%
9 31
 
6.1%
8 30
 
5.9%
6 29
 
5.7%

비과세건수
Text

MISSING 

Distinct40
Distinct (%)70.2%
Missing4
Missing (%)6.6%
Memory size620.0 B
2023-12-12T18:25:48.053667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.9298246
Min length1

Characters and Unicode

Total characters167
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

Unique39 ?
Unique (%)68.4%

Sample

1st row4280
2nd row50
3rd row6638
4th row26073
5th row9151
ValueCountFrequency (%)
90 1
 
2.6%
10831 1
 
2.6%
4280 1
 
2.6%
5144 1
 
2.6%
2701 1
 
2.6%
83 1
 
2.6%
4371 1
 
2.6%
131 1
 
2.6%
5266 1
 
2.6%
10993 1
 
2.6%
Other values (29) 29
74.4%
2023-12-12T18:25:48.476076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 23
13.8%
18
10.8%
3 18
10.8%
8 17
10.2%
4 16
9.6%
0 15
9.0%
5 15
9.0%
2 12
7.2%
7 12
7.2%
9 11
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 149
89.2%
Space Separator 18
 
10.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23
15.4%
3 18
12.1%
8 17
11.4%
4 16
10.7%
0 15
10.1%
5 15
10.1%
2 12
8.1%
7 12
8.1%
9 11
7.4%
6 10
6.7%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 167
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23
13.8%
18
10.8%
3 18
10.8%
8 17
10.2%
4 16
9.6%
0 15
9.0%
5 15
9.0%
2 12
7.2%
7 12
7.2%
9 11
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23
13.8%
18
10.8%
3 18
10.8%
8 17
10.2%
4 16
9.6%
0 15
9.0%
5 15
9.0%
2 12
7.2%
7 12
7.2%
9 11
6.6%

비과세금액
Text

MISSING 

Distinct40
Distinct (%)70.2%
Missing4
Missing (%)6.6%
Memory size620.0 B
2023-12-12T18:25:48.730896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.0877193
Min length1

Characters and Unicode

Total characters347
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

Unique39 ?
Unique (%)68.4%

Sample

1st row3697974000
2nd row6916000
3rd row55030000
4th row5309512000
5th row483099000
ValueCountFrequency (%)
7883000 1
 
2.6%
454315000 1
 
2.6%
3697974000 1
 
2.6%
199313000 1
 
2.6%
220941000 1
 
2.6%
7000 1
 
2.6%
5253701000 1
 
2.6%
14186000 1
 
2.6%
28240000 1
 
2.6%
429043000 1
 
2.6%
Other values (29) 29
74.4%
2023-12-12T18:25:49.150713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 134
38.6%
4 26
 
7.5%
2 26
 
7.5%
3 24
 
6.9%
5 24
 
6.9%
9 23
 
6.6%
7 22
 
6.3%
1 19
 
5.5%
18
 
5.2%
6 16
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 329
94.8%
Space Separator 18
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 134
40.7%
4 26
 
7.9%
2 26
 
7.9%
3 24
 
7.3%
5 24
 
7.3%
9 23
 
7.0%
7 22
 
6.7%
1 19
 
5.8%
6 16
 
4.9%
8 15
 
4.6%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 134
38.6%
4 26
 
7.5%
2 26
 
7.5%
3 24
 
6.9%
5 24
 
6.9%
9 23
 
6.6%
7 22
 
6.3%
1 19
 
5.5%
18
 
5.2%
6 16
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134
38.6%
4 26
 
7.5%
2 26
 
7.5%
3 24
 
6.9%
5 24
 
6.9%
9 23
 
6.6%
7 22
 
6.3%
1 19
 
5.5%
18
 
5.2%
6 16
 
4.6%

데이터 기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
2021-12-31
61 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-12-31
2nd row2021-12-31
3rd row2021-12-31
4th row2021-12-31
5th row2021-12-31

Common Values

ValueCountFrequency (%)
2021-12-31 61
100.0%

Length

2023-12-12T18:25:49.336730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:25:49.480032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-31 61
100.0%

Correlations

2023-12-12T18:25:49.567897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.6810.6810.7160.716
세목명0.0001.0000.1220.1220.0000.000
과세건수0.6810.1221.0001.0000.9910.991
과세금액0.6810.1221.0001.0000.9910.991
비과세건수0.7160.0000.9910.9911.0001.000
비과세금액0.7160.0000.9910.9911.0001.000
2023-12-12T18:25:49.707203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-12T18:25:49.807555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000

Missing values

2023-12-12T18:25:44.602509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:25:44.774347image/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.
2023-12-12T18:25:44.925993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액데이터 기준일자
0전라북도남원시451902017취득세1526616397136000428036979740002021-12-31
1전라북도남원시451902017등록세5069160002021-12-31
2전라북도남원시451902017주민세3892911797150006638550300002021-12-31
3전라북도남원시451902017재산세10548767639450002607353095120002021-12-31
4전라북도남원시451902017자동차세609251125499600091514830990002021-12-31
5전라북도남원시451902017레저세2021-12-31
6전라북도남원시451902017담배소비세10854543680002021-12-31
7전라북도남원시451902017지방소비세2021-12-31
8전라북도남원시451902017등록면허세32249147006700040182077240002021-12-31
9전라북도남원시451902017도시계획세2021-12-31
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액데이터 기준일자
51전라북도남원시451902021등록세1985145281320297146895402021-12-31
52전라북도남원시451902021주민세3955513748758305293278500002021-12-31
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