Overview

Dataset statistics

Number of variables10
Number of observations44
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory88.0 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description부산광역시_지방세납부현황_20201231
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15079364

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부년도 has constant value ""Constant
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부건수 is highly overall correlated with 납부금액 and 1 other fieldsHigh correlation
납부금액 is highly overall correlated with 납부건수 and 2 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 납부금액High correlation
납부금액 has unique valuesUnique
납부매체비율 has 4 (9.1%) zerosZeros

Reproduction

Analysis started2024-03-13 13:18:22.478625
Analysis finished2024-03-13 13:18:23.840156
Duration1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
부산광역시
44 

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 44
100.0%

Length

2024-03-13T22:18:23.939113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:24.075695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 44
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
부산광역시
44 

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 44
100.0%

Length

2024-03-13T22:18:24.185081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:24.287092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 44
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
26000
44 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26000
2nd row26000
3rd row26000
4th row26000
5th row26000

Common Values

ValueCountFrequency (%)
26000 44
100.0%

Length

2024-03-13T22:18:24.392210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:24.497820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26000 44
100.0%

납부년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
2020
44 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 44
100.0%

Length

2024-03-13T22:18:24.601770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:24.714131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 44
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
자동차세
9 
취득세
9 
주민세
8 
지방소득세
8 
담배소비세
4 
Other values (4)
6 

Length

Max length5
Median length4
Mean length3.9318182
Min length3

Unique

Unique2 ?
Unique (%)4.5%

Sample

1st row담배소비세
2nd row자동차세
3rd row주민세
4th row지방소득세
5th row취득세

Common Values

ValueCountFrequency (%)
자동차세 9
20.5%
취득세 9
20.5%
주민세 8
18.2%
지방소득세 8
18.2%
담배소비세 4
9.1%
등록세 2
 
4.5%
지역개발세 2
 
4.5%
지방소비세 1
 
2.3%
등록면허세 1
 
2.3%

Length

2024-03-13T22:18:24.829672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:25.007246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 9
20.5%
취득세 9
20.5%
주민세 8
18.2%
지방소득세 8
18.2%
담배소비세 4
9.1%
등록세 2
 
4.5%
지역개발세 2
 
4.5%
지방소비세 1
 
2.3%
등록면허세 1
 
2.3%

납부매체
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
기타
8 
은행창구
7 
가상계좌
5 
위택스
5 
자동화기기
5 
Other values (4)
14 

Length

Max length5
Median length4
Mean length3.7727273
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 8
18.2%
은행창구 7
15.9%
가상계좌 5
11.4%
위택스 5
11.4%
자동화기기 5
11.4%
이택스 4
9.1%
인터넷지로 4
9.1%
지자체방문 4
9.1%
페이사납부 2
 
4.5%

Length

2024-03-13T22:18:25.219862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:25.398869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 8
18.2%
은행창구 7
15.9%
가상계좌 5
11.4%
위택스 5
11.4%
자동화기기 5
11.4%
이택스 4
9.1%
인터넷지로 4
9.1%
지자체방문 4
9.1%
페이사납부 2
 
4.5%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size176.0 B
False
24 
True
20 
ValueCountFrequency (%)
False 24
54.5%
True 20
45.5%
2024-03-13T22:18:25.589538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26041.818
Minimum1
Maximum235996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-03-13T22:18:25.700292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median106
Q3544.5
95-th percentile212728.8
Maximum235996
Range235995
Interquartile range (IQR)532.5

Descriptive statistics

Standard deviation65093.884
Coefficient of variation (CV)2.4995906
Kurtosis5.2818365
Mean26041.818
Median Absolute Deviation (MAD)104
Skewness2.5530797
Sum1145840
Variance4.2372138 × 109
MonotonicityNot monotonic
2024-03-13T22:18:25.857435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2 5
 
11.4%
14 2
 
4.5%
3 2
 
4.5%
138 2
 
4.5%
212 2
 
4.5%
4 1
 
2.3%
5 1
 
2.3%
235996 1
 
2.3%
73 1
 
2.3%
122 1
 
2.3%
Other values (26) 26
59.1%
ValueCountFrequency (%)
1 1
 
2.3%
2 5
11.4%
3 2
 
4.5%
4 1
 
2.3%
5 1
 
2.3%
9 1
 
2.3%
13 1
 
2.3%
14 2
 
4.5%
18 1
 
2.3%
24 1
 
2.3%
ValueCountFrequency (%)
235996 1
2.3%
229897 1
2.3%
219006 1
2.3%
177158 1
2.3%
129127 1
2.3%
64146 1
2.3%
45507 1
2.3%
35589 1
2.3%
3945 1
2.3%
1257 1
2.3%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5403735 × 1010
Minimum6300
Maximum9.62814 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-03-13T22:18:26.023452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6300
5-th percentile42296
Q18450595
median1.3547605 × 108
Q32.0905629 × 1010
95-th percentile1.6217885 × 1011
Maximum9.62814 × 1011
Range9.6281399 × 1011
Interquartile range (IQR)2.0897178 × 1010

Descriptive statistics

Standard deviation1.5234016 × 1011
Coefficient of variation (CV)3.3552342
Kurtosis32.15048
Mean4.5403735 × 1010
Median Absolute Deviation (MAD)1.3545326 × 108
Skewness5.4223531
Sum1.9977644 × 1012
Variance2.3207526 × 1022
MonotonicityNot monotonic
2024-03-13T22:18:26.203496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
683550 1
 
2.3%
123290 1
 
2.3%
26808569420 1
 
2.3%
50984170 1
 
2.3%
528026520 1
 
2.3%
80329365630 1
 
2.3%
78602670 1
 
2.3%
96560 1
 
2.3%
7730970 1
 
2.3%
18937981870 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
6300 1
2.3%
12870 1
2.3%
32720 1
2.3%
96560 1
2.3%
100000 1
2.3%
123290 1
2.3%
559970 1
2.3%
683550 1
2.3%
3390970 1
2.3%
5080000 1
2.3%
ValueCountFrequency (%)
962814000000 1
2.3%
291122000000 1
2.3%
169076000000 1
2.3%
123095000000 1
2.3%
120178000000 1
2.3%
80329365630 1
2.3%
78655598600 1
2.3%
54869717670 1
2.3%
34750063100 1
2.3%
27022625320 1
2.3%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.454773
Minimum0
Maximum99.84
Zeros4
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-03-13T22:18:26.374948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median0.255
Q316.49
95-th percentile99.3695
Maximum99.84
Range99.84
Interquartile range (IQR)16.47

Descriptive statistics

Standard deviation36.093885
Coefficient of variation (CV)1.7645703
Kurtosis0.83461944
Mean20.454773
Median Absolute Deviation (MAD)0.255
Skewness1.5843352
Sum900.01
Variance1302.7685
MonotonicityNot monotonic
2024-03-13T22:18:26.517972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.01 5
 
11.4%
0.0 4
 
9.1%
0.03 3
 
6.8%
0.02 3
 
6.8%
0.12 2
 
4.5%
0.05 2
 
4.5%
14.54 2
 
4.5%
99.31 1
 
2.3%
64.6 1
 
2.3%
35.35 1
 
2.3%
Other values (20) 20
45.5%
ValueCountFrequency (%)
0.0 4
9.1%
0.01 5
11.4%
0.02 3
6.8%
0.03 3
6.8%
0.05 2
 
4.5%
0.11 1
 
2.3%
0.12 2
 
4.5%
0.15 1
 
2.3%
0.19 1
 
2.3%
0.32 1
 
2.3%
ValueCountFrequency (%)
99.84 1
2.3%
99.39 1
2.3%
99.38 1
2.3%
99.31 1
2.3%
98.9 1
2.3%
98.16 1
2.3%
85.84 1
2.3%
64.6 1
2.3%
37.72 1
2.3%
35.35 1
2.3%

Interactions

2024-03-13T22:18:23.267171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:22.758431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:22.993191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:23.352177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:22.827055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:23.087665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:23.466113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:22.915839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:23.176275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:18:26.635543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
세목명1.0000.0000.0000.0000.7470.328
납부매체0.0001.0001.0000.1150.0000.000
납부매체전자고지여부0.0001.0001.0000.2990.0000.000
납부건수0.0000.1150.2991.0000.0000.683
납부금액0.7470.0000.0000.0001.0000.000
납부매체비율0.3280.0000.0000.6830.0001.000
2024-03-13T22:18:26.761669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체전자고지여부납부매체세목명
납부매체전자고지여부1.0000.9130.000
납부매체0.9131.0000.000
세목명0.0000.0001.000
2024-03-13T22:18:26.858738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부건수1.0000.7500.8010.0000.0000.198
납부금액0.7501.0000.7070.5530.0000.000
납부매체비율0.8010.7071.0000.1600.0000.000
세목명0.0000.5530.1601.0000.0000.000
납부매체0.0000.0000.0000.0001.0000.913
납부매체전자고지여부0.1980.0000.0000.0000.9131.000

Missing values

2024-03-13T22:18:23.596012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:18:23.772730image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
0부산광역시부산광역시260002020담배소비세가상계좌Y46835500.01
1부산광역시부산광역시260002020자동차세가상계좌Y368964906500.57
2부산광역시부산광역시260002020주민세가상계좌Y263000.0
3부산광역시부산광역시260002020지방소득세가상계좌Y18622744700.03
4부산광역시부산광역시260002020취득세가상계좌Y641463475006310099.39
5부산광역시부산광역시260002020담배소비세기타N35812017800000037.72
6부산광역시부산광역시260002020등록세기타N3139489100.32
7부산광역시부산광역시260002020자동차세기타N902911220000009.48
8부산광역시부산광역시260002020주민세기타N13825647007014.54
9부산광역시부산광역시260002020지방소득세기타N21261485285022.34
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
34부산광역시부산광역시260002020자동차세자동화기기N212452737000.12
35부산광역시부산광역시260002020주민세자동화기기N57198675500.03
36부산광역시부산광역시260002020지방소득세자동화기기N133738466600.01
37부산광역시부산광역시260002020취득세자동화기기N17715812309500000099.84
38부산광역시부산광역시260002020자동차세지자체방문N7086904701.74
39부산광역시부산광역시260002020주민세지자체방문N2128700.05
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