Overview

Dataset statistics

Number of variables10
Number of observations117
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 KiB
Average record size in memory86.1 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description부산광역시_지방세납부현황_20191231
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
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
납부매체비율 has 9 (7.7%) zerosZeros

Reproduction

Analysis started2024-03-13 13:18:27.978169
Analysis finished2024-03-13 13:18:29.473427
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
부산광역시
117 

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 117
100.0%

Length

2024-03-13T22:18:29.577659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:29.666862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 117
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
부산광역시
117 

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 117
100.0%

Length

2024-03-13T22:18:29.779258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:29.889046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 117
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
26000
117 

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 117
100.0%

Length

2024-03-13T22:18:29.997395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:30.091866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26000 117
100.0%

납부년도
Categorical

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2019
44 
2018
37 
2017
36 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2019 44
37.6%
2018 37
31.6%
2017 36
30.8%

Length

2024-03-13T22:18:30.193925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:30.312659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 44
37.6%
2018 37
31.6%
2017 36
30.8%

세목명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
취득세
25 
자동차세
24 
지방소득세
24 
주민세
20 
등록세
13 
Other values (5)
11 

Length

Max length7
Median length5
Mean length3.8205128
Min length3

Unique

Unique2 ?
Unique (%)1.7%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 25
21.4%
자동차세 24
20.5%
지방소득세 24
20.5%
주민세 20
17.1%
등록세 13
11.1%
담배소비세 4
 
3.4%
지방소비세 3
 
2.6%
등록면허세 2
 
1.7%
지역개발세 1
 
0.9%
지역자원시설세 1
 
0.9%

Length

2024-03-13T22:18:30.450630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:30.641801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
취득세 25
21.4%
자동차세 24
20.5%
지방소득세 24
20.5%
주민세 20
17.1%
등록세 13
11.1%
담배소비세 4
 
3.4%
지방소비세 3
 
2.6%
등록면허세 2
 
1.7%
지역개발세 1
 
0.9%
지역자원시설세 1
 
0.9%

납부매체
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
기타
22 
은행창구
18 
자동화기기
16 
위택스
14 
이택스
13 
Other values (4)
34 

Length

Max length5
Median length4
Mean length3.7179487
Min length2

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
기타 22
18.8%
은행창구 18
15.4%
자동화기기 16
13.7%
위택스 14
12.0%
이택스 13
11.1%
가상계좌 12
10.3%
인터넷지로 12
10.3%
지자체방문 9
7.7%
페이사납부 1
 
0.9%

Length

2024-03-13T22:18:30.823507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:31.008178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 22
18.8%
은행창구 18
15.4%
자동화기기 16
13.7%
위택스 14
12.0%
이택스 13
11.1%
가상계좌 12
10.3%
인터넷지로 12
10.3%
지자체방문 9
7.7%
페이사납부 1
 
0.9%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size249.0 B
False
65 
True
52 
ValueCountFrequency (%)
False 65
55.6%
True 52
44.4%
2024-03-13T22:18:31.118439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26904.556
Minimum1
Maximum316181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T22:18:31.218911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median94
Q32494
95-th percentile192694.8
Maximum316181
Range316180
Interquartile range (IQR)2483

Descriptive statistics

Standard deviation66348.624
Coefficient of variation (CV)2.466074
Kurtosis6.2501177
Mean26904.556
Median Absolute Deviation (MAD)90
Skewness2.6449704
Sum3147833
Variance4.4021399 × 109
MonotonicityNot monotonic
2024-03-13T22:18:31.347354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 7
 
6.0%
6 6
 
5.1%
4 5
 
4.3%
3 5
 
4.3%
8 3
 
2.6%
16 3
 
2.6%
24 2
 
1.7%
1 2
 
1.7%
54 2
 
1.7%
94 2
 
1.7%
Other values (77) 80
68.4%
ValueCountFrequency (%)
1 2
 
1.7%
2 7
6.0%
3 5
4.3%
4 5
4.3%
6 6
5.1%
8 3
2.6%
10 1
 
0.9%
11 1
 
0.9%
12 1
 
0.9%
13 1
 
0.9%
ValueCountFrequency (%)
316181 1
0.9%
282492 1
0.9%
245477 1
0.9%
231002 1
0.9%
208762 1
0.9%
197186 1
0.9%
191572 1
0.9%
171912 1
0.9%
169851 1
0.9%
166329 1
0.9%

납부금액
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2607565 × 1010
Minimum1630
Maximum8.2981194 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T22:18:31.477401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1630
5-th percentile50844
Q17009310
median78056530
Q31.4844791 × 1010
95-th percentile2.9798257 × 1011
Maximum8.2981194 × 1011
Range8.2981194 × 1011
Interquartile range (IQR)1.4837782 × 1010

Descriptive statistics

Standard deviation1.2281145 × 1011
Coefficient of variation (CV)2.8823861
Kurtosis19.296933
Mean4.2607565 × 1010
Median Absolute Deviation (MAD)78048520
Skewness4.1342476
Sum4.9850852 × 1012
Variance1.5082653 × 1022
MonotonicityNot monotonic
2024-03-13T22:18:31.610625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109430 2
 
1.7%
1532370 1
 
0.9%
97820 1
 
0.9%
34366130 1
 
0.9%
101301141150 1
 
0.9%
13794240 1
 
0.9%
1996567660 1
 
0.9%
1500000 1
 
0.9%
3631830 1
 
0.9%
114743004590 1
 
0.9%
Other values (106) 106
90.6%
ValueCountFrequency (%)
1630 1
0.9%
8010 1
0.9%
21230 1
0.9%
23130 1
0.9%
30600 1
0.9%
50740 1
0.9%
50870 1
0.9%
76160 1
0.9%
97820 1
0.9%
109430 2
1.7%
ValueCountFrequency (%)
829811938970 1
0.9%
567041937620 1
0.9%
561679195890 1
0.9%
328314351820 1
0.9%
324145655640 1
0.9%
302274616120 1
0.9%
296909564540 1
0.9%
284903380570 1
0.9%
276157722400 1
0.9%
114743004590 1
0.9%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.367863
Minimum0
Maximum100
Zeros9
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T22:18:31.745461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median0.16
Q313.4
95-th percentile99.674
Maximum100
Range100
Interquartile range (IQR)13.38

Descriptive statistics

Standard deviation37.73498
Coefficient of variation (CV)1.7659688
Kurtosis0.14399565
Mean21.367863
Median Absolute Deviation (MAD)0.15
Skewness1.4059146
Sum2500.04
Variance1423.9288
MonotonicityNot monotonic
2024-03-13T22:18:31.888810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 16
 
13.7%
0.0 9
 
7.7%
0.02 6
 
5.1%
0.03 6
 
5.1%
0.15 3
 
2.6%
0.08 3
 
2.6%
0.04 3
 
2.6%
0.1 2
 
1.7%
0.09 2
 
1.7%
0.29 2
 
1.7%
Other values (59) 65
55.6%
ValueCountFrequency (%)
0.0 9
7.7%
0.01 16
13.7%
0.02 6
 
5.1%
0.03 6
 
5.1%
0.04 3
 
2.6%
0.05 2
 
1.7%
0.06 2
 
1.7%
0.07 2
 
1.7%
0.08 3
 
2.6%
0.09 2
 
1.7%
ValueCountFrequency (%)
100.0 1
0.9%
99.87 2
1.7%
99.86 1
0.9%
99.77 1
0.9%
99.73 1
0.9%
99.66 1
0.9%
99.55 1
0.9%
99.54 1
0.9%
99.53 1
0.9%
99.41 1
0.9%

Interactions

2024-03-13T22:18:28.868660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:28.272604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:28.583654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:29.003522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:28.377421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:28.665191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:29.093343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:28.487709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:28.750746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:18:31.971415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0000.2120.0000.9010.724
납부매체0.0000.0001.0001.0000.4510.3580.422
납부매체전자고지여부0.0000.2121.0001.0000.2850.1910.276
납부건수0.0000.0000.4510.2851.0000.6020.795
납부금액0.0000.9010.3580.1910.6021.0000.576
납부매체비율0.0000.7240.4220.2760.7950.5761.000
2024-03-13T22:18:32.064337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체전자고지여부납부매체세목명납부년도
납부매체전자고지여부1.0000.9690.1550.000
납부매체0.9691.0000.0000.000
세목명0.1550.0001.0000.000
납부년도0.0000.0000.0001.000
2024-03-13T22:18:32.146368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부건수1.0000.7590.7980.0000.0000.1560.275
납부금액0.7591.0000.7760.0000.5780.2120.233
납부매체비율0.7980.7761.0000.0000.2990.2040.202
납부년도0.0000.0000.0001.0000.0000.0000.000
세목명0.0000.5780.2990.0001.0000.0000.155
납부매체0.1560.2120.2040.0000.0001.0000.969
납부매체전자고지여부0.2750.2330.2020.0000.1550.9691.000

Missing values

2024-03-13T22:18:29.232945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:18:29.401064image/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
0부산광역시부산광역시260002017자동차세지자체방문N815323700.31
1부산광역시부산광역시260002017지방소득세지자체방문N4123600000.15
2부산광역시부산광역시260002017취득세지자체방문N2583200301094099.54
3부산광역시부산광역시260002018자동차세가상계좌Y150440440000.26
4부산광역시부산광역시260002018지방소득세가상계좌Y4164960600.01
5부산광역시부산광역시260002018취득세가상계좌Y578853110925919099.73
6부산광역시부산광역시260002018담배소비세기타N262828490338057084.75
7부산광역시부산광역시260002018등록세기타N318866900.1
8부산광역시부산광역시260002018자동차세기타N1102969095645403.55
9부산광역시부산광역시260002018주민세기타N861593507802.77
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
107부산광역시부산광역시260002017지방소득세이택스Y513932323500.02
108부산광역시부산광역시260002017취득세이택스Y1399758510517887041.5
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