Overview

Dataset statistics

Number of variables10
Number of observations245
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.5 KiB
Average record size in memory85.5 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description부산광역시금정구_지방세납부현황_20191231
Author부산광역시 금정구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15079652

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 1 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부금액 has unique valuesUnique
납부매체비율 has 48 (19.6%) zerosZeros

Reproduction

Analysis started2023-12-10 17:35:39.452899
Analysis finished2023-12-10 17:35:42.496693
Duration3.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
부산광역시
245 

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 245
100.0%

Length

2023-12-11T02:35:42.633245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:35:42.791439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 245
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
금정구
245 

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 245
100.0%

Length

2023-12-11T02:35:42.981989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:35:43.176624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금정구 245
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
26410
245 

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 245
100.0%

Length

2023-12-11T02:35:43.373940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:35:43.556939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26410 245
100.0%

납부년도
Categorical

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2017
84 
2019
81 
2018
80 

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 (%)
2017 84
34.3%
2019 81
33.1%
2018 80
32.7%

Length

2023-12-11T02:35:43.779113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:35:44.009390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 84
34.3%
2019 81
33.1%
2018 80
32.7%

세목명
Categorical

Distinct14
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
등록면허세
28 
자동차세
28 
재산세
28 
주민세
28 
지방소득세
25 
Other values (9)
108 

Length

Max length7
Median length3
Mean length4.0489796
Min length3

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st row등록면허세
2nd row자동차세
3rd row재산세
4th row주민세
5th row지방소득세

Common Values

ValueCountFrequency (%)
등록면허세 28
11.4%
자동차세 28
11.4%
재산세 28
11.4%
주민세 28
11.4%
지방소득세 25
10.2%
취득세 25
10.2%
지역자원시설세 23
9.4%
등록세 17
6.9%
면허세 17
6.9%
종합토지세 12
4.9%
Other values (4) 14
5.7%

Length

2023-12-11T02:35:44.289886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등록면허세 28
11.4%
자동차세 28
11.4%
재산세 28
11.4%
주민세 28
11.4%
지방소득세 25
10.2%
취득세 25
10.2%
지역자원시설세 23
9.4%
등록세 17
6.9%
면허세 17
6.9%
종합토지세 12
4.9%
Other values (4) 14
5.7%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
이택스
34 
은행창구
32 
자동화기기
30 
위택스
28 
기타
27 
Other values (5)
94 

Length

Max length5
Median length4
Mean length3.8897959
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
이택스 34
13.9%
은행창구 32
13.1%
자동화기기 30
12.2%
위택스 28
11.4%
기타 27
11.0%
지자체방문 27
11.0%
인터넷지로 25
10.2%
가상계좌 23
9.4%
자동이체 12
 
4.9%
페이사납부 7
 
2.9%

Length

2023-12-11T02:35:44.607332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:35:44.889936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이택스 34
13.9%
은행창구 32
13.1%
자동화기기 30
12.2%
위택스 28
11.4%
기타 27
11.0%
지자체방문 27
11.0%
인터넷지로 25
10.2%
가상계좌 23
9.4%
자동이체 12
 
4.9%
페이사납부 7
 
2.9%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size377.0 B
True
129 
False
116 
ValueCountFrequency (%)
True 129
52.7%
False 116
47.3%
2023-12-11T02:35:45.166937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct198
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7379.7184
Minimum1
Maximum93800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-11T02:35:45.435074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q133
median817
Q35855
95-th percentile46562
Maximum93800
Range93799
Interquartile range (IQR)5822

Descriptive statistics

Standard deviation16035.371
Coefficient of variation (CV)2.1728974
Kurtosis11.665863
Mean7379.7184
Median Absolute Deviation (MAD)814
Skewness3.3205418
Sum1808031
Variance2.5713311 × 108
MonotonicityNot monotonic
2023-12-11T02:35:45.826164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8
 
3.3%
2 8
 
3.3%
3 7
 
2.9%
9 3
 
1.2%
11 3
 
1.2%
13 3
 
1.2%
4 3
 
1.2%
12 3
 
1.2%
7 3
 
1.2%
83 2
 
0.8%
Other values (188) 202
82.4%
ValueCountFrequency (%)
1 8
3.3%
2 8
3.3%
3 7
2.9%
4 3
 
1.2%
5 2
 
0.8%
6 1
 
0.4%
7 3
 
1.2%
8 1
 
0.4%
9 3
 
1.2%
11 3
 
1.2%
ValueCountFrequency (%)
93800 1
0.4%
91388 1
0.4%
79711 1
0.4%
76652 1
0.4%
73837 1
0.4%
71507 1
0.4%
63915 1
0.4%
59905 1
0.4%
56276 1
0.4%
50005 1
0.4%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct245
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4870795 × 109
Minimum9340
Maximum3.3818224 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-11T02:35:46.173202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9340
5-th percentile79514
Q15131140
median2.223281 × 108
Q31.7578181 × 109
95-th percentile1.2122722 × 1010
Maximum3.3818224 × 1010
Range3.3818214 × 1010
Interquartile range (IQR)1.7526869 × 109

Descriptive statistics

Standard deviation5.1316987 × 109
Coefficient of variation (CV)2.0633432
Kurtosis10.720658
Mean2.4870795 × 109
Median Absolute Deviation (MAD)2.2228001 × 108
Skewness3.0566162
Sum6.0933449 × 1011
Variance2.6334332 × 1019
MonotonicityNot monotonic
2023-12-11T02:35:46.542037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161064710 1
 
0.4%
5893884580 1
 
0.4%
242160 1
 
0.4%
575350 1
 
0.4%
123117230 1
 
0.4%
96899760 1
 
0.4%
13706070 1
 
0.4%
38955280 1
 
0.4%
47210 1
 
0.4%
875269030 1
 
0.4%
Other values (235) 235
95.9%
ValueCountFrequency (%)
9340 1
0.4%
10540 1
0.4%
17170 1
0.4%
18900 1
0.4%
24870 1
0.4%
35800 1
0.4%
47210 1
0.4%
48090 1
0.4%
50000 1
0.4%
56340 1
0.4%
ValueCountFrequency (%)
33818223770 1
0.4%
26534700070 1
0.4%
24997915480 1
0.4%
24706144790 1
0.4%
19242561890 1
0.4%
18914395980 1
0.4%
18540228480 1
0.4%
17689180860 1
0.4%
17142098000 1
0.4%
16342434960 1
0.4%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct132
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.433143
Minimum0
Maximum88.1
Zeros48
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-11T02:35:46.837841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median3.9
Q318.1
95-th percentile42.1
Maximum88.1
Range88.1
Interquartile range (IQR)18

Descriptive statistics

Standard deviation16.058671
Coefficient of variation (CV)1.4045719
Kurtosis5.4097639
Mean11.433143
Median Absolute Deviation (MAD)3.9
Skewness2.088103
Sum2801.12
Variance257.88092
MonotonicityNot monotonic
2023-12-11T02:35:47.061924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 48
 
19.6%
0.1 19
 
7.8%
0.2 10
 
4.1%
0.3 9
 
3.7%
0.9 5
 
2.0%
2.0 4
 
1.6%
15.2 3
 
1.2%
4.6 3
 
1.2%
4.8 2
 
0.8%
1.1 2
 
0.8%
Other values (122) 140
57.1%
ValueCountFrequency (%)
0.0 48
19.6%
0.02 1
 
0.4%
0.1 19
 
7.8%
0.2 10
 
4.1%
0.3 9
 
3.7%
0.4 2
 
0.8%
0.5 1
 
0.4%
0.6 2
 
0.8%
0.9 5
 
2.0%
1.0 2
 
0.8%
ValueCountFrequency (%)
88.1 1
0.4%
87.0 1
0.4%
82.3 1
0.4%
64.1 1
0.4%
63.1 1
0.4%
61.3 1
0.4%
52.5 1
0.4%
50.8 1
0.4%
49.5 1
0.4%
47.0 1
0.4%

Interactions

2023-12-11T02:35:41.458988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:35:40.234371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:35:40.853601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:35:41.619441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:35:40.444293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:35:41.053249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:35:41.814587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:35:40.668365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:35:41.265987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:35:47.209179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0000.0000.2440.3760.634
납부매체0.0000.0001.0001.0000.4280.3180.482
납부매체전자고지여부0.0000.0001.0001.0000.2390.0000.258
납부건수0.0000.2440.4280.2391.0000.6610.687
납부금액0.0000.3760.3180.0000.6611.0000.510
납부매체비율0.0000.6340.4820.2580.6870.5101.000
2023-12-11T02:35:47.428603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체전자고지여부납부매체세목명납부년도
납부매체전자고지여부1.0000.9830.0000.000
납부매체0.9831.0000.0000.000
세목명0.0000.0001.0000.000
납부년도0.0000.0000.0001.000
2023-12-11T02:35:47.638726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부건수1.0000.8170.8540.0000.0990.1430.180
납부금액0.8171.0000.6510.0000.1740.1570.000
납부매체비율0.8540.6511.0000.0000.3260.2420.254
납부년도0.0000.0000.0001.0000.0000.0000.000
세목명0.0990.1740.3260.0001.0000.0000.000
납부매체0.1430.1570.2420.0000.0001.0000.983
납부매체전자고지여부0.1800.0000.2540.0000.0000.9831.000

Missing values

2023-12-11T02:35:42.059819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:35:42.378768image/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
0부산광역시금정구264102017등록면허세가상계좌Y677016106471023.1
1부산광역시금정구264102017자동차세가상계좌Y568191703456019.3
2부산광역시금정구264102017재산세가상계좌Y9367205340967031.9
3부산광역시금정구264102017주민세가상계좌Y426641832027014.5
4부산광역시금정구264102017지방소득세가상계좌Y3158258154024010.8
5부산광역시금정구264102017지역자원시설세가상계좌Y3610817300.1
6부산광역시금정구264102017취득세가상계좌Y989604910600.3
7부산광역시금정구264102017등록면허세기타N26160862000.9
8부산광역시금정구264102017등록세기타N19969700.0
9부산광역시금정구264102017면허세기타N92287700.0
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
235부산광역시금정구264102019주민세지자체방문N229577417016.1
236부산광역시금정구264102019지방소득세지자체방문N48264469003.4
237부산광역시금정구264102019취득세지자체방문N839734869005.8
238부산광역시금정구264102019등록면허세페이사납부Y28860600.0
239부산광역시금정구264102019자동차세페이사납부Y86013866687018.2
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