Overview

Dataset statistics

Number of variables9
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory79.9 B

Variable types

Numeric3
Categorical5
Text1

Dataset

Description부산광역시금정구_세원유형별과세현황_20230503
Author부산광역시 금정구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15079638

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
연번 is highly overall correlated with 세목명High correlation
부과건수 is highly overall correlated with 부과금액High correlation
부과금액 is highly overall correlated with 부과건수High correlation
세목명 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
세원 유형명 has unique valuesUnique
부과건수 has 12 (26.1%) zerosZeros
부과금액 has 12 (26.1%) zerosZeros

Reproduction

Analysis started2023-12-10 16:27:35.120411
Analysis finished2023-12-10 16:27:36.433703
Duration1.31 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.5
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:27:36.812098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.25
Q112.25
median23.5
Q334.75
95-th percentile43.75
Maximum46
Range45
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation13.422618
Coefficient of variation (CV)0.57117522
Kurtosis-1.2
Mean23.5
Median Absolute Deviation (MAD)11.5
Skewness0
Sum1081
Variance180.16667
MonotonicityStrictly increasing
2023-12-11T01:27:37.004837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 1
 
2.2%
36 1
 
2.2%
27 1
 
2.2%
28 1
 
2.2%
29 1
 
2.2%
30 1
 
2.2%
31 1
 
2.2%
32 1
 
2.2%
33 1
 
2.2%
34 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
1 1
2.2%
2 1
2.2%
3 1
2.2%
4 1
2.2%
5 1
2.2%
6 1
2.2%
7 1
2.2%
8 1
2.2%
9 1
2.2%
10 1
2.2%
ValueCountFrequency (%)
46 1
2.2%
45 1
2.2%
44 1
2.2%
43 1
2.2%
42 1
2.2%
41 1
2.2%
40 1
2.2%
39 1
2.2%
38 1
2.2%
37 1
2.2%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
부산광역시
46 

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 46
100.0%

Length

2023-12-11T01:27:37.115715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:27:37.197308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 46
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
금정구
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
금정구 46
100.0%

Length

2023-12-11T01:27:37.278668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:27:37.355372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금정구 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
26410
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26410
2nd row26410
3rd row26410
4th row26410
5th row26410

Common Values

ValueCountFrequency (%)
26410 46
100.0%

Length

2023-12-11T01:27:37.438149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:27:37.518634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26410 46
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
2021
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 46
100.0%

Length

2023-12-11T01:27:37.598354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:27:37.687046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 46
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
취득세
9 
자동차세
7 
주민세
7 
재산세
5 
지방소득세
4 
Other values (8)
14 

Length

Max length7
Median length3
Mean length3.7826087
Min length2

Unique

Unique5 ?
Unique (%)10.9%

Sample

1st row지방소득세
2nd row지방소득세
3rd row지방소득세
4th row지방소득세
5th row지방소비세

Common Values

ValueCountFrequency (%)
취득세 9
19.6%
자동차세 7
15.2%
주민세 7
15.2%
재산세 5
10.9%
지방소득세 4
8.7%
레저세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
지방소비세 1
 
2.2%
담배소비세 1
 
2.2%
Other values (3) 3
 
6.5%

Length

2023-12-11T01:27:37.778709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 9
19.6%
자동차세 7
15.2%
주민세 7
15.2%
재산세 5
10.9%
지방소득세 4
8.7%
레저세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
지방소비세 1
 
2.2%
담배소비세 1
 
2.2%
Other values (3) 3
 
6.5%

세원 유형명
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-11T01:27:38.027954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length6.0217391
Min length2

Characters and Unicode

Total characters277
Distinct characters73
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)100.0%

Sample

1st row지방소득세(특별징수)
2nd row지방소득세(법인소득)
3rd row지방소득세(양도소득)
4th row지방소득세(종합소득)
5th row지방소비세
ValueCountFrequency (%)
지방소득세(특별징수 1
 
2.2%
등록면허세(등록 1
 
2.2%
도시계획세 1
 
2.2%
자동차세(주행 1
 
2.2%
3륜이하 1
 
2.2%
특수 1
 
2.2%
화물 1
 
2.2%
승합 1
 
2.2%
기타승용 1
 
2.2%
승용 1
 
2.2%
Other values (36) 36
78.3%
2023-12-11T01:27:38.392824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 27
 
9.7%
( 24
 
8.7%
) 24
 
8.7%
소 14
 
5.1%
주 11
 
4.0%
지 10
 
3.6%
득 9
 
3.2%
민 7
 
2.5%
방 6
 
2.2%
재 5
 
1.8%
Other values (63) 140
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
82.3%
Open Punctuation 24
 
8.7%
Close Punctuation 24
 
8.7%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
재 5
 
2.2%
시 5
 
2.2%
산 5
 
2.2%
Other values (60) 129
56.6%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
82.3%
Common 49
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
재 5
 
2.2%
시 5
 
2.2%
산 5
 
2.2%
Other values (60) 129
56.6%
Common
ValueCountFrequency (%)
( 24
49.0%
) 24
49.0%
3 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
82.3%
ASCII 49
 
17.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
재 5
 
2.2%
시 5
 
2.2%
산 5
 
2.2%
Other values (60) 129
56.6%
ASCII
ValueCountFrequency (%)
( 24
49.0%
) 24
49.0%
3 1
 
2.0%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28594.391
Minimum0
Maximum473467
Zeros12
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:27:38.578747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median1275.5
Q318300.25
95-th percentile140778
Maximum473467
Range473467
Interquartile range (IQR)18298.5

Descriptive statistics

Standard deviation77410.429
Coefficient of variation (CV)2.7071893
Kurtosis24.898404
Mean28594.391
Median Absolute Deviation (MAD)1275.5
Skewness4.6233155
Sum1315342
Variance5.9923746 × 109
MonotonicityNot monotonic
2023-12-11T01:27:38.731220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 12
26.1%
9 2
 
4.3%
38824 1
 
2.2%
25030 1
 
2.2%
1552 1
 
2.2%
14371 1
 
2.2%
3071 1
 
2.2%
494 1
 
2.2%
114255 1
 
2.2%
38421 1
 
2.2%
Other values (24) 24
52.2%
ValueCountFrequency (%)
0 12
26.1%
7 1
 
2.2%
9 2
 
4.3%
13 1
 
2.2%
38 1
 
2.2%
93 1
 
2.2%
174 1
 
2.2%
494 1
 
2.2%
838 1
 
2.2%
948 1
 
2.2%
ValueCountFrequency (%)
473467 1
2.2%
155397 1
2.2%
149619 1
2.2%
114255 1
2.2%
97510 1
2.2%
86331 1
2.2%
39393 1
2.2%
38824 1
2.2%
38421 1
2.2%
25030 1
2.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2556218 × 109
Minimum0
Maximum2.8430391 × 1010
Zeros12
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:27:38.902964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11353500
median5.304415 × 108
Q37.854483 × 109
95-th percentile2.1086772 × 1010
Maximum2.8430391 × 1010
Range2.8430391 × 1010
Interquartile range (IQR)7.8531295 × 109

Descriptive statistics

Standard deviation7.6563609 × 109
Coefficient of variation (CV)1.4567945
Kurtosis1.0528437
Mean5.2556218 × 109
Median Absolute Deviation (MAD)5.304415 × 108
Skewness1.4383122
Sum2.417586 × 1011
Variance5.8619863 × 1019
MonotonicityNot monotonic
2023-12-11T01:27:39.102970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 12
26.1%
15147677000 1
 
2.2%
1035548000 1
 
2.2%
9828000 1
 
2.2%
48470000 1
 
2.2%
397320000 1
 
2.2%
152138000 1
 
2.2%
31672000 1
 
2.2%
16033834000 1
 
2.2%
5821069000 1
 
2.2%
Other values (25) 25
54.3%
ValueCountFrequency (%)
0 12
26.1%
5414000 1
 
2.2%
9828000 1
 
2.2%
15361000 1
 
2.2%
24505000 1
 
2.2%
31672000 1
 
2.2%
36769000 1
 
2.2%
48470000 1
 
2.2%
152138000 1
 
2.2%
253293000 1
 
2.2%
ValueCountFrequency (%)
28430391000 1
2.2%
21417902000 1
2.2%
21319461000 1
2.2%
20388706000 1
2.2%
19926493000 1
2.2%
16033834000 1
2.2%
15147677000 1
2.2%
13820385000 1
2.2%
13259516000 1
2.2%
13201190000 1
2.2%

Interactions

2023-12-11T01:27:35.866039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:27:35.347161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:27:35.606917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:27:35.978015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:27:35.429009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:27:35.689804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:27:36.064700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:27:35.515742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:27:35.764373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:27:39.225419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세목명세원 유형명부과건수부과금액
연번1.0000.9011.0000.0000.303
세목명0.9011.0001.0000.7550.269
세원 유형명1.0001.0001.0001.0001.000
부과건수0.0000.7551.0001.0000.425
부과금액0.3030.2691.0000.4251.000
2023-12-11T01:27:39.336514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부과건수부과금액세목명
연번1.000-0.077-0.3990.636
부과건수-0.0771.0000.8120.479
부과금액-0.3990.8121.0000.086
세목명0.6360.4790.0861.000

Missing values

2023-12-11T01:27:36.176967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:27:36.354923image/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

연번시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
01부산광역시금정구264102021지방소득세지방소득세(특별징수)3882415147677000
12부산광역시금정구264102021지방소득세지방소득세(법인소득)25337930449000
23부산광역시금정구264102021지방소득세지방소득세(양도소득)494413259516000
34부산광역시금정구264102021지방소득세지방소득세(종합소득)3939313820385000
45부산광역시금정구264102021지방소비세지방소비세75835886000
56부산광역시금정구264102021담배소비세담배소비세00
67부산광역시금정구264102021교육세교육세47346720388706000
78부산광역시금정구264102021취득세건축물10457895037000
89부산광역시금정구264102021취득세주택(개별)154713201190000
910부산광역시금정구264102021취득세주택(단독)495428430391000
연번시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
3637부산광역시금정구264102021지역자원시설세지역자원시설세(특자)94836769000
3738부산광역시금정구264102021주민세주민세(사업소분)120611369224000
3839부산광역시금정구264102021주민세주민세(개인분)86331865661000
3940부산광역시금정구264102021주민세주민세(종업원분)15543057684000
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