Overview

Dataset statistics

Number of variables10
Number of observations103
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory83.3 B

Variable types

Categorical6
Text4

Dataset

Description부산광역시해운대구_지방세체납현황_20191231
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15078945

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:29:05.592478
Analysis finished2023-12-10 16:29:06.324518
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
부산광역시
103 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 103
100.0%

Length

2023-12-11T01:29:06.388225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:06.485376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 103
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
해운대구
103 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 103
100.0%

Length

2023-12-11T01:29:06.605430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:06.741853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 103
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
26350
103 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26350
2nd row26350
3rd row26350
4th row26350
5th row26350

Common Values

ValueCountFrequency (%)
26350 103
100.0%

Length

2023-12-11T01:29:06.891007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:07.024366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26350 103
100.0%

과세년도
Categorical

Distinct3
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
2019
39 
2018
33 
2017
31 

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 (%)
2019 39
37.9%
2018 33
32.0%
2017 31
30.1%

Length

2023-12-11T01:29:07.139333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:07.266562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 39
37.9%
2018 33
32.0%
2017 31
30.1%

세목명
Categorical

Distinct7
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size956.0 B
재산세
23 
지방소득세
22 
취득세
19 
주민세
16 
자동차세
12 
Other values (2)
11 

Length

Max length7
Median length3
Mean length3.8543689
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록면허세
2nd row자동차세
3rd row자동차세
4th row자동차세
5th row자동차세

Common Values

ValueCountFrequency (%)
재산세 23
22.3%
지방소득세 22
21.4%
취득세 19
18.4%
주민세 16
15.5%
자동차세 12
11.7%
등록면허세 6
 
5.8%
지역자원시설세 5
 
4.9%

Length

2023-12-11T01:29:07.439156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:07.644138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 23
22.3%
지방소득세 22
21.4%
취득세 19
18.4%
주민세 16
15.5%
자동차세 12
11.7%
등록면허세 6
 
5.8%
지역자원시설세 5
 
4.9%

체납액구간
Categorical

Distinct10
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size956.0 B
10만원 미만
21 
30만원~50만원미만
17 
50만원~1백만원미만
16 
10만원~30만원미만
15 
1백만원~3백만원미만
12 
Other values (5)
22 

Length

Max length11
Median length11
Mean length10.165049
Min length7

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row10만원 미만
2nd row10만원 미만
3rd row10만원~30만원미만
4th row30만원~50만원미만
5th row50만원~1백만원미만

Common Values

ValueCountFrequency (%)
10만원 미만 21
20.4%
30만원~50만원미만 17
16.5%
50만원~1백만원미만 16
15.5%
10만원~30만원미만 15
14.6%
1백만원~3백만원미만 12
11.7%
3백만원~5백만원미만 9
8.7%
5백만원~1천만원미만 7
 
6.8%
1천만원~3천만원미만 3
 
2.9%
3천만원~5천만원미만 2
 
1.9%
1억원~3억원미만 1
 
1.0%

Length

2023-12-11T01:29:07.845572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:08.005255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 21
16.9%
미만 21
16.9%
30만원~50만원미만 17
13.7%
50만원~1백만원미만 16
12.9%
10만원~30만원미만 15
12.1%
1백만원~3백만원미만 12
9.7%
3백만원~5백만원미만 9
7.3%
5백만원~1천만원미만 7
 
5.6%
1천만원~3천만원미만 3
 
2.4%
3천만원~5천만원미만 2
 
1.6%

체납건수
Text

Distinct59
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-11T01:29:08.250153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.1359223
Min length3

Characters and Unicode

Total characters426
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

Unique51 ?
Unique (%)49.5%

Sample

1st row 627
2nd row 1,595
3rd row 2,084
4th row 94
5th row 11
ValueCountFrequency (%)
1 23
22.3%
4 8
 
7.8%
3 7
 
6.8%
2 5
 
4.9%
7 3
 
2.9%
5 2
 
1.9%
133 2
 
1.9%
27 2
 
1.9%
4,839 1
 
1.0%
87 1
 
1.0%
Other values (49) 49
47.6%
2023-12-11T01:29:08.636392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206
48.4%
1 51
 
12.0%
2 33
 
7.7%
3 26
 
6.1%
4 21
 
4.9%
7 18
 
4.2%
9 15
 
3.5%
, 14
 
3.3%
5 13
 
3.1%
8 13
 
3.1%
Other values (2) 16
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator 206
48.4%
Decimal Number 206
48.4%
Other Punctuation 14
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 51
24.8%
2 33
16.0%
3 26
12.6%
4 21
10.2%
7 18
 
8.7%
9 15
 
7.3%
5 13
 
6.3%
8 13
 
6.3%
0 9
 
4.4%
6 7
 
3.4%
Space Separator
ValueCountFrequency (%)
206
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 426
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
206
48.4%
1 51
 
12.0%
2 33
 
7.7%
3 26
 
6.1%
4 21
 
4.9%
7 18
 
4.2%
9 15
 
3.5%
, 14
 
3.3%
5 13
 
3.1%
8 13
 
3.1%
Other values (2) 16
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
206
48.4%
1 51
 
12.0%
2 33
 
7.7%
3 26
 
6.1%
4 21
 
4.9%
7 18
 
4.2%
9 15
 
3.5%
, 14
 
3.3%
5 13
 
3.1%
8 13
 
3.1%
Other values (2) 16
 
3.8%

체납금액
Text

UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-11T01:29:09.033693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.398058
Min length8

Characters and Unicode

Total characters1174
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

Unique103 ?
Unique (%)100.0%

Sample

1st row 22,192,960
2nd row 70,179,930
3rd row 346,254,880
4th row 32,051,980
5th row 6,147,330
ValueCountFrequency (%)
22,192,960 1
 
1.0%
90,022,870 1
 
1.0%
84,225,680 1
 
1.0%
65,724,410 1
 
1.0%
114,798,790 1
 
1.0%
210,573,500 1
 
1.0%
164,669,090 1
 
1.0%
8,335,170 1
 
1.0%
109,869,140 1
 
1.0%
838,447,240 1
 
1.0%
Other values (93) 93
90.3%
2023-12-11T01:29:09.635851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206
17.5%
, 189
16.1%
0 164
14.0%
1 90
7.7%
2 75
 
6.4%
4 75
 
6.4%
3 72
 
6.1%
5 64
 
5.5%
8 63
 
5.4%
7 61
 
5.2%
Other values (2) 115
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 779
66.4%
Space Separator 206
 
17.5%
Other Punctuation 189
 
16.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
21.1%
1 90
11.6%
2 75
9.6%
4 75
9.6%
3 72
9.2%
5 64
 
8.2%
8 63
 
8.1%
7 61
 
7.8%
9 58
 
7.4%
6 57
 
7.3%
Space Separator
ValueCountFrequency (%)
206
100.0%
Other Punctuation
ValueCountFrequency (%)
, 189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
206
17.5%
, 189
16.1%
0 164
14.0%
1 90
7.7%
2 75
 
6.4%
4 75
 
6.4%
3 72
 
6.1%
5 64
 
5.5%
8 63
 
5.4%
7 61
 
5.2%
Other values (2) 115
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
206
17.5%
, 189
16.1%
0 164
14.0%
1 90
7.7%
2 75
 
6.4%
4 75
 
6.4%
3 72
 
6.1%
5 64
 
5.5%
8 63
 
5.4%
7 61
 
5.2%
Other values (2) 115
9.8%

누적체납건수
Text

Distinct82
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-11T01:29:09.964705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.7475728
Min length3

Characters and Unicode

Total characters489
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

Unique72 ?
Unique (%)69.9%

Sample

1st row 1,507
2nd row 8,768
3rd row 7,865
4th row 294
5th row 50
ValueCountFrequency (%)
1 8
 
7.8%
2 4
 
3.9%
4 3
 
2.9%
3 3
 
2.9%
19 3
 
2.9%
18 2
 
1.9%
21 2
 
1.9%
5 2
 
1.9%
20 2
 
1.9%
391 2
 
1.9%
Other values (72) 72
69.9%
2023-12-11T01:29:10.446256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206
42.1%
1 62
 
12.7%
2 30
 
6.1%
3 27
 
5.5%
7 24
 
4.9%
5 22
 
4.5%
8 21
 
4.3%
, 21
 
4.3%
6 21
 
4.3%
9 19
 
3.9%
Other values (2) 36
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 262
53.6%
Space Separator 206
42.1%
Other Punctuation 21
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 62
23.7%
2 30
11.5%
3 27
10.3%
7 24
 
9.2%
5 22
 
8.4%
8 21
 
8.0%
6 21
 
8.0%
9 19
 
7.3%
0 19
 
7.3%
4 17
 
6.5%
Space Separator
ValueCountFrequency (%)
206
100.0%
Other Punctuation
ValueCountFrequency (%)
, 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 489
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
206
42.1%
1 62
 
12.7%
2 30
 
6.1%
3 27
 
5.5%
7 24
 
4.9%
5 22
 
4.5%
8 21
 
4.3%
, 21
 
4.3%
6 21
 
4.3%
9 19
 
3.9%
Other values (2) 36
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 489
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
206
42.1%
1 62
 
12.7%
2 30
 
6.1%
3 27
 
5.5%
7 24
 
4.9%
5 22
 
4.5%
8 21
 
4.3%
, 21
 
4.3%
6 21
 
4.3%
9 19
 
3.9%
Other values (2) 36
 
7.4%

누적체납금액
Text

UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-11T01:29:10.766757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length12.106796
Min length9

Characters and Unicode

Total characters1247
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

Unique103 ?
Unique (%)100.0%

Sample

1st row 51,259,540
2nd row 389,726,940
3rd row 1,276,269,400
4th row 102,340,520
5th row 31,566,290
ValueCountFrequency (%)
51,259,540 1
 
1.0%
186,444,110 1
 
1.0%
148,186,520 1
 
1.0%
85,478,760 1
 
1.0%
247,744,680 1
 
1.0%
422,904,540 1
 
1.0%
478,218,310 1
 
1.0%
46,416,200 1
 
1.0%
269,152,200 1
 
1.0%
2,600,482,680 1
 
1.0%
Other values (93) 93
90.3%
2023-12-11T01:29:11.232106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206
16.5%
, 202
16.2%
0 161
12.9%
2 94
7.5%
1 93
7.5%
4 81
 
6.5%
6 73
 
5.9%
7 73
 
5.9%
3 72
 
5.8%
5 71
 
5.7%
Other values (2) 121
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 839
67.3%
Space Separator 206
 
16.5%
Other Punctuation 202
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 161
19.2%
2 94
11.2%
1 93
11.1%
4 81
9.7%
6 73
8.7%
7 73
8.7%
3 72
8.6%
5 71
8.5%
8 63
 
7.5%
9 58
 
6.9%
Space Separator
ValueCountFrequency (%)
206
100.0%
Other Punctuation
ValueCountFrequency (%)
, 202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
206
16.5%
, 202
16.2%
0 161
12.9%
2 94
7.5%
1 93
7.5%
4 81
 
6.5%
6 73
 
5.9%
7 73
 
5.9%
3 72
 
5.8%
5 71
 
5.7%
Other values (2) 121
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
206
16.5%
, 202
16.2%
0 161
12.9%
2 94
7.5%
1 93
7.5%
4 81
 
6.5%
6 73
 
5.9%
7 73
 
5.9%
3 72
 
5.8%
5 71
 
5.7%
Other values (2) 121
9.7%

Correlations

2023-12-11T01:29:11.332608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수누적체납건수
과세년도1.0000.0000.0000.0000.777
세목명0.0001.0000.0000.0000.000
체납액구간0.0000.0001.0000.0000.000
체납건수0.0000.0000.0001.0000.999
누적체납건수0.7770.0000.0000.9991.000
2023-12-11T01:29:11.427860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.0000.000
체납액구간0.0001.0000.000
세목명0.0000.0001.000
2023-12-11T01:29:11.508005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간
과세년도1.0000.0000.000
세목명0.0001.0000.000
체납액구간0.0000.0001.000

Missing values

2023-12-11T01:29:06.094111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:29:06.266198image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
0부산광역시해운대구263502017등록면허세10만원 미만62722,192,9601,50751,259,540
1부산광역시해운대구263502017자동차세10만원 미만1,59570,179,9308,768389,726,940
2부산광역시해운대구263502017자동차세10만원~30만원미만2,084346,254,8807,8651,276,269,400
3부산광역시해운대구263502017자동차세30만원~50만원미만9432,051,980294102,340,520
4부산광역시해운대구263502017자동차세50만원~1백만원미만116,147,3305031,566,290
5부산광역시해운대구263502017재산세10만원 미만1,94262,750,5207,298222,404,790
6부산광역시해운대구263502017재산세10만원~30만원미만26843,951,330873137,278,270
7부산광역시해운대구263502017재산세1백만원~3백만원미만2026,824,8306385,352,290
8부산광역시해운대구263502017재산세30만원~50만원미만3010,904,8109634,833,710
9부산광역시해운대구263502017재산세50만원~1백만원미만2718,070,8807047,442,820
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
93부산광역시해운대구263502019지방소득세5백만원~1천만원미만527,404,1101056,116,360
94부산광역시해운대구263502019지역자원시설세10만원 미만774,40028244,740
95부산광역시해운대구263502019취득세10만원 미만3194,1101866,216,750
96부산광역시해운대구263502019취득세10만원~30만원미만5893,96011020,767,190
97부산광역시해운대구263502019취득세1백만원~3백만원미만45,922,8802235,976,940
98부산광역시해운대구263502019취득세1천만원~3천만원미만111,682,960111,682,960
99부산광역시해운대구263502019취득세30만원~50만원미만1400,440155,619,490
100부산광역시해운대구263502019취득세3백만원~5백만원미만13,609,930312,688,160
101부산광역시해운대구263502019취득세50만원~1백만원미만31,872,7104834,080,750
102부산광역시해운대구263502019취득세5백만원~1천만원미만210,635,000210,635,000