Overview

Dataset statistics

Number of variables10
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory89.1 B

Variable types

Numeric4
Categorical5
Boolean1

Dataset

Description부산광역시_지방세납부현황_20221231
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15079364

Alerts

시도명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부년도 has constant value ""Constant
납부매체전자고지여부 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
납부매체 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 납부매체 and 1 other fieldsHigh 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
연번 has unique valuesUnique
납부금액 has unique valuesUnique
납부매체비율 has 4 (9.5%) zerosZeros

Reproduction

Analysis started2024-03-13 13:18:11.363304
Analysis finished2024-03-13 13:18:13.742350
Duration2.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.5
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-03-13T22:18:13.821626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.05
Q111.25
median21.5
Q331.75
95-th percentile39.95
Maximum42
Range41
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation12.267844
Coefficient of variation (CV)0.5705974
Kurtosis-1.2
Mean21.5
Median Absolute Deviation (MAD)10.5
Skewness0
Sum903
Variance150.5
MonotonicityStrictly increasing
2024-03-13T22:18:13.940422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 1
 
2.4%
33 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
32 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
42 1
2.4%
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
부산광역시
42 

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 42
100.0%

Length

2024-03-13T22:18:14.084749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:14.168982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 42
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
26000
42 

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 42
100.0%

Length

2024-03-13T22:18:14.255893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:14.352588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26000 42
100.0%

납부년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
2022
42 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 42
100.0%

Length

2024-03-13T22:18:14.452300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:14.536154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 42
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size468.0 B
자동차세
9 
취득세
9 
지방소득세
8 
담배소비세
7 
주민세
6 
Other values (2)
3 

Length

Max length5
Median length4
Mean length3.9761905
Min length3

Unique

Unique1 ?
Unique (%)2.4%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 9
21.4%
취득세 9
21.4%
지방소득세 8
19.0%
담배소비세 7
16.7%
주민세 6
14.3%
등록세 2
 
4.8%
지방소비세 1
 
2.4%

Length

2024-03-13T22:18:14.656287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:14.808932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 9
21.4%
취득세 9
21.4%
지방소득세 8
19.0%
담배소비세 7
16.7%
주민세 6
14.3%
등록세 2
 
4.8%
지방소비세 1
 
2.4%

납부매체
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
기타
6 
은행창구
6 
이택스
6 
가상계좌
5 
위택스
5 
Other values (4)
14 

Length

Max length5
Median length4
Mean length3.7857143
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 6
14.3%
은행창구 6
14.3%
이택스 6
14.3%
가상계좌 5
11.9%
위택스 5
11.9%
자동화기기 5
11.9%
인터넷지로 4
9.5%
지자체방문 3
7.1%
페이사납부 2
 
4.8%

Length

2024-03-13T22:18:14.997897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:15.170632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 6
14.3%
은행창구 6
14.3%
이택스 6
14.3%
가상계좌 5
11.9%
위택스 5
11.9%
자동화기기 5
11.9%
인터넷지로 4
9.5%
지자체방문 3
7.1%
페이사납부 2
 
4.8%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size174.0 B
True
22 
False
20 
ValueCountFrequency (%)
True 22
52.4%
False 20
47.6%
2024-03-13T22:18:15.309491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29955.31
Minimum3
Maximum310720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-03-13T22:18:15.420516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.05
Q116.5
median66.5
Q31196.25
95-th percentile146241
Maximum310720
Range310717
Interquartile range (IQR)1179.75

Descriptive statistics

Standard deviation71268.018
Coefficient of variation (CV)2.3791448
Kurtosis7.0243094
Mean29955.31
Median Absolute Deviation (MAD)62
Skewness2.6728213
Sum1258123
Variance5.0791304 × 109
MonotonicityNot monotonic
2024-03-13T22:18:15.551517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
10 4
 
9.5%
3 2
 
4.8%
82314 1
 
2.4%
255670 1
 
2.4%
135 1
 
2.4%
202 1
 
2.4%
134195 1
 
2.4%
4 1
 
2.4%
352 1
 
2.4%
5 1
 
2.4%
Other values (28) 28
66.7%
ValueCountFrequency (%)
3 2
4.8%
4 1
 
2.4%
5 1
 
2.4%
8 1
 
2.4%
10 4
9.5%
12 1
 
2.4%
16 1
 
2.4%
18 1
 
2.4%
21 1
 
2.4%
26 1
 
2.4%
ValueCountFrequency (%)
310720 1
2.4%
255670 1
2.4%
146535 1
2.4%
140655 1
2.4%
134195 1
2.4%
122850 1
2.4%
82314 1
2.4%
53448 1
2.4%
4017 1
2.4%
3078 1
2.4%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8923846 × 1010
Minimum58250
Maximum1.4343614 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-03-13T22:18:15.702741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58250
5-th percentile104451.5
Q13678312.5
median70729015
Q31.9302392 × 1010
95-th percentile2.202464 × 1011
Maximum1.4343614 × 1012
Range1.4343614 × 1012
Interquartile range (IQR)1.9298714 × 1010

Descriptive statistics

Standard deviation2.2560327 × 1011
Coefficient of variation (CV)3.8287262
Kurtosis35.82142
Mean5.8923846 × 1010
Median Absolute Deviation (MAD)70597820
Skewness5.8273578
Sum2.4748015 × 1012
Variance5.0896837 × 1022
MonotonicityNot monotonic
2024-03-13T22:18:15.836881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2919630 1
 
2.4%
536410 1
 
2.4%
24781768890 1
 
2.4%
55030040 1
 
2.4%
994747800 1
 
2.4%
102873491220 1
 
2.4%
101480 1
 
2.4%
97089700 1
 
2.4%
31272640 1
 
2.4%
40837943980 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
58250 1
2.4%
96260 1
2.4%
101480 1
2.4%
160910 1
2.4%
179200 1
2.4%
186670 1
2.4%
225730 1
2.4%
536410 1
2.4%
959920 1
2.4%
2919630 1
2.4%
ValueCountFrequency (%)
1434361448380 1
2.4%
288197816070 1
2.4%
225806821470 1
2.4%
114598443780 1
2.4%
102873491220 1
2.4%
89237017820 1
2.4%
57546403970 1
2.4%
51173921060 1
2.4%
40837943980 1
2.4%
35257605530 1
2.4%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.427143
Minimum0
Maximum99.9
Zeros4
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-03-13T22:18:15.990093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median0.33
Q324.0325
95-th percentile99.523
Maximum99.9
Range99.9
Interquartile range (IQR)24.0125

Descriptive statistics

Standard deviation36.588845
Coefficient of variation (CV)1.7075933
Kurtosis0.63033355
Mean21.427143
Median Absolute Deviation (MAD)0.33
Skewness1.5222166
Sum899.94
Variance1338.7436
MonotonicityNot monotonic
2024-03-13T22:18:16.130926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.01 6
 
14.3%
0.0 4
 
9.5%
0.07 3
 
7.1%
0.02 2
 
4.8%
5.52 2
 
4.8%
0.43 1
 
2.4%
0.03 1
 
2.4%
0.05 1
 
2.4%
34.39 1
 
2.4%
99.9 1
 
2.4%
Other values (20) 20
47.6%
ValueCountFrequency (%)
0.0 4
9.5%
0.01 6
14.3%
0.02 2
 
4.8%
0.03 1
 
2.4%
0.04 1
 
2.4%
0.05 1
 
2.4%
0.07 3
7.1%
0.19 1
 
2.4%
0.22 1
 
2.4%
0.31 1
 
2.4%
ValueCountFrequency (%)
99.9 1
2.4%
99.62 1
2.4%
99.56 1
2.4%
98.82 1
2.4%
98.78 1
2.4%
98.55 1
2.4%
85.0 1
2.4%
65.52 1
2.4%
34.39 1
2.4%
30.39 1
2.4%

Interactions

2024-03-13T22:18:12.848145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:11.716495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:12.063196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:12.406757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:12.939009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:11.791137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:12.147565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:12.498984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:13.044863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:11.886330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:12.229155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:12.597814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:13.421388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:11.979998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:12.314727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:12.705084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:18:16.258969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
연번1.0000.0000.9300.8450.0300.2300.000
세목명0.0001.0000.0000.0000.0000.6830.615
납부매체0.9300.0001.0001.0000.0000.0000.000
납부매체전자고지여부0.8450.0001.0001.0000.0000.0700.000
납부건수0.0300.0000.0000.0001.0000.0000.945
납부금액0.2300.6830.0000.0700.0001.0000.619
납부매체비율0.0000.6150.0000.0000.9450.6191.000
2024-03-13T22:18:16.364122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체전자고지여부납부매체세목명
납부매체전자고지여부1.0000.9080.000
납부매체0.9081.0000.000
세목명0.0000.0001.000
2024-03-13T22:18:16.467991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
연번1.0000.047-0.0760.0070.0000.7580.604
납부건수0.0471.0000.7620.7460.0000.0000.000
납부금액-0.0760.7621.0000.7360.5260.0000.041
납부매체비율0.0070.7460.7361.0000.2470.0000.000
세목명0.0000.0000.5260.2471.0000.0000.000
납부매체0.7580.0000.0000.0000.0001.0000.908
납부매체전자고지여부0.6040.0000.0410.0000.0000.9081.000

Missing values

2024-03-13T22:18:13.531435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:18:13.674840image/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

연번시도명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
01부산광역시260002022담배소비세가상계좌Y2629196300.02
12부산광역시260002022자동차세가상계좌Y3861044040200.31
23부산광역시260002022주민세가상계좌Y101866700.01
34부산광역시260002022지방소득세가상계좌Y47864279900.04
45부산광역시260002022취득세가상계좌Y1228505754640397099.62
56부산광역시260002022담배소비세기타N101792005.52
67부산광역시260002022자동차세기타N4822580682147026.52
78부산광역시260002022주민세기타N28498230015.47
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