Overview

Dataset statistics

Number of variables9
Number of observations65
Missing cells5
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory76.0 B

Variable types

Categorical5
Text4

Dataset

Description충청북도 영동군의 지방세 과세 및 비과세 현황을 연도별, 세목별로 제공하여 국민 조세 혜택 규모를 파악하는데 기초자료로 활용
Author충청북도 영동군
URLhttps://www.data.go.kr/data/15078755/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
비과세금액 has 5 (7.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 01:27:15.964873
Analysis finished2023-12-12 01:27:16.692422
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size652.0 B
충청북도
65 

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 65
100.0%

Length

2023-12-12T10:27:16.795877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:27:16.920026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 65
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size652.0 B
영동군
65 

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 65
100.0%

Length

2023-12-12T10:27:17.028656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:27:17.137061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영동군 65
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size652.0 B
43740
65 

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 65
100.0%

Length

2023-12-12T10:27:17.268260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:27:17.406048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43740 65
100.0%

과세년도
Categorical

Distinct5
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size652.0 B
2017
13 
2018
13 
2019
13 
2020
13 
2021
13 

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
20.0%
2018 13
20.0%
2019 13
20.0%
2020 13
20.0%
2021 13
20.0%

Length

2023-12-12T10:27:17.542996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:27:17.697664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 13
20.0%
2018 13
20.0%
2019 13
20.0%
2020 13
20.0%
2021 13
20.0%

세목명
Categorical

Distinct13
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size652.0 B
취득세
5 
등록세
5 
주민세
5 
재산세
5 
자동차세
5 
Other values (8)
40 

Length

Max length7
Median length5
Mean length4.1538462
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 5
 
7.7%
등록세 5
 
7.7%
주민세 5
 
7.7%
재산세 5
 
7.7%
자동차세 5
 
7.7%
레저세 5
 
7.7%
담배소비세 5
 
7.7%
지방소비세 5
 
7.7%
등록면허세 5
 
7.7%
도시계획세 5
 
7.7%
Other values (3) 15
23.1%

Length

2023-12-12T10:27:17.870517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 5
 
7.7%
등록세 5
 
7.7%
주민세 5
 
7.7%
재산세 5
 
7.7%
자동차세 5
 
7.7%
레저세 5
 
7.7%
담배소비세 5
 
7.7%
지방소비세 5
 
7.7%
등록면허세 5
 
7.7%
도시계획세 5
 
7.7%
Other values (3) 15
23.1%

과세건수
Text

Distinct49
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Memory size652.0 B
2023-12-12T10:27:18.151811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.2923077
Min length1

Characters and Unicode

Total characters279
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)72.3%

Sample

1st row9,307
2nd row
3rd row22,757
4th row66,740
5th row34,319
ValueCountFrequency (%)
0 3
 
6.0%
69,997 1
 
2.0%
17,723 1
 
2.0%
11,446 1
 
2.0%
10,904 1
 
2.0%
115,193 1
 
2.0%
9,419 1
 
2.0%
24,301 1
 
2.0%
71,100 1
 
2.0%
35,359 1
 
2.0%
Other values (38) 38
76.0%
2023-12-12T10:27:18.533123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 44
15.8%
, 32
11.5%
30
10.8%
3 26
9.3%
9 25
9.0%
7 23
8.2%
4 23
8.2%
0 21
7.5%
2 20
7.2%
5 14
 
5.0%
Other values (2) 21
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 217
77.8%
Other Punctuation 32
 
11.5%
Space Separator 30
 
10.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44
20.3%
3 26
12.0%
9 25
11.5%
7 23
10.6%
4 23
10.6%
0 21
9.7%
2 20
9.2%
5 14
 
6.5%
6 13
 
6.0%
8 8
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 32
100.0%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 279
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44
15.8%
, 32
11.5%
30
10.8%
3 26
9.3%
9 25
9.0%
7 23
8.2%
4 23
8.2%
0 21
7.5%
2 20
7.2%
5 14
 
5.0%
Other values (2) 21
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44
15.8%
, 32
11.5%
30
10.8%
3 26
9.3%
9 25
9.0%
7 23
8.2%
4 23
8.2%
0 21
7.5%
2 20
7.2%
5 14
 
5.0%
Other values (2) 21
7.5%

과세금액
Text

Distinct50
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Memory size652.0 B
2023-12-12T10:27:18.783127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length9.2153846
Min length1

Characters and Unicode

Total characters599
Distinct characters12
Distinct categories3 ?
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 (%)73.8%

Sample

1st row8,281,986,000
2nd row
3rd row871,274,000
4th row3,024,348,000
5th row6,086,931,000
ValueCountFrequency (%)
0 3
 
5.9%
4,260,690,000 1
 
2.0%
8,281,986,000 1
 
2.0%
522,109,000 1
 
2.0%
3,449,957,000 1
 
2.0%
10,333,044,000 1
 
2.0%
21,129,000 1
 
2.0%
956,967,000 1
 
2.0%
3,533,747,000 1
 
2.0%
4,954,301,000 1
 
2.0%
Other values (39) 39
76.5%
2023-12-12T10:27:19.129472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 173
28.9%
, 101
16.9%
3 43
 
7.2%
8 39
 
6.5%
4 37
 
6.2%
5 34
 
5.7%
1 32
 
5.3%
2 31
 
5.2%
7 29
 
4.8%
9 29
 
4.8%
Other values (2) 51
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 470
78.5%
Other Punctuation 101
 
16.9%
Space Separator 28
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
36.8%
3 43
 
9.1%
8 39
 
8.3%
4 37
 
7.9%
5 34
 
7.2%
1 32
 
6.8%
2 31
 
6.6%
7 29
 
6.2%
9 29
 
6.2%
6 23
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 101
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
28.9%
, 101
16.9%
3 43
 
7.2%
8 39
 
6.5%
4 37
 
6.2%
5 34
 
5.7%
1 32
 
5.3%
2 31
 
5.2%
7 29
 
4.8%
9 29
 
4.8%
Other values (2) 51
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
28.9%
, 101
16.9%
3 43
 
7.2%
8 39
 
6.5%
4 37
 
6.2%
5 34
 
5.7%
1 32
 
5.3%
2 31
 
5.2%
7 29
 
4.8%
9 29
 
4.8%
Other values (2) 51
 
8.5%

비과세건수
Text

Distinct42
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Memory size652.0 B
2023-12-12T10:27:19.323278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.1076923
Min length1

Characters and Unicode

Total characters202
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)61.5%

Sample

1st row1,959
2nd row2
3rd row4,759
4th row18,945
5th row5,670
ValueCountFrequency (%)
0 5
 
11.1%
810 1
 
2.2%
1,959 1
 
2.2%
872 1
 
2.2%
57 1
 
2.2%
2,166 1
 
2.2%
11 1
 
2.2%
4,398 1
 
2.2%
21,568 1
 
2.2%
6,336 1
 
2.2%
Other values (31) 31
68.9%
2023-12-12T10:27:19.615608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
19.8%
, 20
9.9%
5 20
9.9%
6 17
8.4%
1 17
8.4%
8 17
8.4%
0 16
 
7.9%
7 15
 
7.4%
2 13
 
6.4%
3 10
 
5.0%
Other values (2) 17
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 142
70.3%
Space Separator 40
 
19.8%
Other Punctuation 20
 
9.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 20
14.1%
6 17
12.0%
1 17
12.0%
8 17
12.0%
0 16
11.3%
7 15
10.6%
2 13
9.2%
3 10
7.0%
9 9
6.3%
4 8
 
5.6%
Space Separator
ValueCountFrequency (%)
40
100.0%
Other Punctuation
ValueCountFrequency (%)
, 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 202
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
40
19.8%
, 20
9.9%
5 20
9.9%
6 17
8.4%
1 17
8.4%
8 17
8.4%
0 16
 
7.9%
7 15
 
7.4%
2 13
 
6.4%
3 10
 
5.0%
Other values (2) 17
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
19.8%
, 20
9.9%
5 20
9.9%
6 17
8.4%
1 17
8.4%
8 17
8.4%
0 16
 
7.9%
7 15
 
7.4%
2 13
 
6.4%
3 10
 
5.0%
Other values (2) 17
8.4%

비과세금액
Text

MISSING 

Distinct40
Distinct (%)66.7%
Missing5
Missing (%)7.7%
Memory size652.0 B
2023-12-12T10:27:19.825391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length7.0166667
Min length1

Characters and Unicode

Total characters421
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)61.7%

Sample

1st row2,358,264,000
2nd row43,000
3rd row36,777,000
4th row3,475,684,000
5th row298,390,000
ValueCountFrequency (%)
0 5
 
11.1%
5,000 3
 
6.7%
61871000 1
 
2.2%
263343000 1
 
2.2%
3,735,199,000 1
 
2.2%
218270000 1
 
2.2%
2,358,264,000 1
 
2.2%
219,122,000 1
 
2.2%
2,554,819,000 1
 
2.2%
2,853,000 1
 
2.2%
Other values (29) 29
64.4%
2023-12-12T10:27:20.138828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 137
32.5%
, 65
15.4%
30
 
7.1%
2 29
 
6.9%
7 23
 
5.5%
3 22
 
5.2%
9 21
 
5.0%
8 20
 
4.8%
1 20
 
4.8%
6 19
 
4.5%
Other values (2) 35
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 326
77.4%
Other Punctuation 65
 
15.4%
Space Separator 30
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 137
42.0%
2 29
 
8.9%
7 23
 
7.1%
3 22
 
6.7%
9 21
 
6.4%
8 20
 
6.1%
1 20
 
6.1%
6 19
 
5.8%
5 18
 
5.5%
4 17
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 65
100.0%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 421
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 137
32.5%
, 65
15.4%
30
 
7.1%
2 29
 
6.9%
7 23
 
5.5%
3 22
 
5.2%
9 21
 
5.0%
8 20
 
4.8%
1 20
 
4.8%
6 19
 
4.5%
Other values (2) 35
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 137
32.5%
, 65
15.4%
30
 
7.1%
2 29
 
6.9%
7 23
 
5.5%
3 22
 
5.2%
9 21
 
5.0%
8 20
 
4.8%
1 20
 
4.8%
6 19
 
4.5%
Other values (2) 35
 
8.3%

Correlations

2023-12-12T10:27:20.238472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.7140.7590.7530.856
세목명0.0001.0000.8150.8150.7250.587
과세건수0.7140.8151.0001.0000.9920.989
과세금액0.7590.8151.0001.0000.9940.992
비과세건수0.7530.7250.9920.9941.0001.000
비과세금액0.8560.5870.9890.9921.0001.000
2023-12-12T10:27:20.344488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-12T10:27:20.416160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000

Missing values

2023-12-12T10:27:16.453123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:27:16.624875image/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

시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
0충청북도영동군437402017취득세9,3078,281,986,0001,9592,358,264,000
1충청북도영동군437402017등록세243,000
2충청북도영동군437402017주민세22,757871,274,0004,75936,777,000
3충청북도영동군437402017재산세66,7403,024,348,00018,9453,475,684,000
4충청북도영동군437402017자동차세34,3196,086,931,0005,670298,390,000
5충청북도영동군437402017레저세
6충청북도영동군437402017담배소비세1073,279,856,000
7충청북도영동군437402017지방소비세
8충청북도영동군437402017등록면허세15,955758,433,0003,607102,876,000
9충청북도영동군437402017도시계획세
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
55충청북도영동군437402021재산세713443677171000216133965888000
56충청북도영동군437402021자동차세3520658653310006456263343000
57충청북도영동군437402021레저세0000
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