Overview

Dataset statistics

Number of variables10
Number of observations449
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.4 KiB
Average record size in memory85.3 B

Variable types

Categorical5
Numeric4
Boolean1

Dataset

Description지방세납부현황(납부연도, 세목명, 납부매체, 납부매체전자고지여부, 납부건수, 납부금액, 납부매체비율 등) 정보 공개
Author경기도 동두천시
URLhttps://www.data.go.kr/data/15079308/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부매체전자고지여부 is highly overall correlated with 납부매체High 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
납부매체비율 has 83 (18.5%) zerosZeros

Reproduction

Analysis started2024-03-16 04:12:33.006415
Analysis finished2024-03-16 04:12:36.809338
Duration3.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
경기도
449 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 449
100.0%

Length

2024-03-16T13:12:36.931657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:12:37.109719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 449
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
동두천시
449 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동두천시 449
100.0%

Length

2024-03-16T13:12:37.344607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:12:37.504575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동두천시 449
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
41250
449 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41250
2nd row41250
3rd row41250
4th row41250
5th row41250

Common Values

ValueCountFrequency (%)
41250 449
100.0%

Length

2024-03-16T13:12:37.645812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:12:37.793856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41250 449
100.0%

납부연도
Real number (ℝ)

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5056
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-16T13:12:37.905783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6983685
Coefficient of variation (CV)0.00084098233
Kurtosis-1.2382949
Mean2019.5056
Median Absolute Deviation (MAD)1
Skewness0.0068834132
Sum906758
Variance2.8844555
MonotonicityIncreasing
2024-03-16T13:12:38.033552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2019 79
17.6%
2020 78
17.4%
2022 76
16.9%
2017 73
16.3%
2018 73
16.3%
2021 70
15.6%
ValueCountFrequency (%)
2017 73
16.3%
2018 73
16.3%
2019 79
17.6%
2020 78
17.4%
2021 70
15.6%
2022 76
16.9%
ValueCountFrequency (%)
2022 76
16.9%
2021 70
15.6%
2020 78
17.4%
2019 79
17.6%
2018 73
16.3%
2017 73
16.3%

세목명
Categorical

Distinct15
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
재산세
55 
주민세
55 
자동차세
54 
등록면허세
52 
지방소득세
46 
Other values (10)
187 

Length

Max length7
Median length5
Mean length4.1135857
Min length3

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 55
12.2%
주민세 55
12.2%
자동차세 54
12.0%
등록면허세 52
11.6%
지방소득세 46
10.2%
취득세 45
10.0%
지역자원시설세 40
8.9%
등록세 34
7.6%
면허세 21
 
4.7%
담배소비세 20
 
4.5%
Other values (5) 27
6.0%

Length

2024-03-16T13:12:38.193076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
재산세 55
12.2%
주민세 55
12.2%
자동차세 54
12.0%
등록면허세 52
11.6%
지방소득세 46
10.2%
취득세 45
10.0%
지역자원시설세 40
8.9%
등록세 34
7.6%
면허세 21
 
4.7%
담배소비세 20
 
4.5%
Other values (5) 27
6.0%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
가상계좌
65 
기타
65 
위택스
55 
지자체방문
55 
은행창구
54 
Other values (5)
155 

Length

Max length5
Median length4
Mean length3.9532294
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 65
14.5%
기타 65
14.5%
위택스 55
12.2%
지자체방문 55
12.2%
은행창구 54
12.0%
자동화기기 52
11.6%
인터넷지로 45
10.0%
자동이체 24
 
5.3%
페이사납부 23
 
5.1%
ARS 11
 
2.4%

Length

2024-03-16T13:12:38.392900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:12:38.627157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가상계좌 65
14.5%
기타 65
14.5%
위택스 55
12.2%
지자체방문 55
12.2%
은행창구 54
12.0%
자동화기기 52
11.6%
인터넷지로 45
10.0%
자동이체 24
 
5.3%
페이사납부 23
 
5.1%
ars 11
 
2.4%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size581.0 B
False
237 
True
212 
ValueCountFrequency (%)
False 237
52.8%
True 212
47.2%
2024-03-16T13:12:38.882049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct318
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3175.1247
Minimum1
Maximum40442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-16T13:12:39.101077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q114
median695
Q32833
95-th percentile18966.6
Maximum40442
Range40441
Interquartile range (IQR)2819

Descriptive statistics

Standard deviation6731.5596
Coefficient of variation (CV)2.120093
Kurtosis11.736453
Mean3175.1247
Median Absolute Deviation (MAD)691
Skewness3.3613369
Sum1425631
Variance45313894
MonotonicityNot monotonic
2024-03-16T13:12:39.531101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 27
 
6.0%
2 15
 
3.3%
3 12
 
2.7%
5 8
 
1.8%
4 7
 
1.6%
6 7
 
1.6%
7 7
 
1.6%
9 7
 
1.6%
12 6
 
1.3%
8 6
 
1.3%
Other values (308) 347
77.3%
ValueCountFrequency (%)
1 27
6.0%
2 15
3.3%
3 12
2.7%
4 7
 
1.6%
5 8
 
1.8%
6 7
 
1.6%
7 7
 
1.6%
8 6
 
1.3%
9 7
 
1.6%
10 5
 
1.1%
ValueCountFrequency (%)
40442 1
0.2%
37365 1
0.2%
36821 1
0.2%
35970 1
0.2%
35415 1
0.2%
33916 1
0.2%
33666 1
0.2%
31966 1
0.2%
31575 1
0.2%
30289 1
0.2%

납부금액
Real number (ℝ)

HIGH CORRELATION 

Distinct448
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2031893 × 109
Minimum60
Maximum1.8479626 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-16T13:12:39.799031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile37708
Q13266370
median1.8940555 × 108
Q31.1949444 × 109
95-th percentile6.8790187 × 109
Maximum1.8479626 × 1010
Range1.8479626 × 1010
Interquartile range (IQR)1.1916781 × 109

Descriptive statistics

Standard deviation2.3734193 × 109
Coefficient of variation (CV)1.9726067
Kurtosis10.991762
Mean1.2031893 × 109
Median Absolute Deviation (MAD)1.8924055 × 108
Skewness2.999634
Sum5.40232 × 1011
Variance5.6331193 × 1018
MonotonicityNot monotonic
2024-03-16T13:12:40.009566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10500 2
 
0.4%
1354890 1
 
0.2%
6226637930 1
 
0.2%
2120070 1
 
0.2%
1408669640 1
 
0.2%
164766670 1
 
0.2%
1216830980 1
 
0.2%
1815092520 1
 
0.2%
312346140 1
 
0.2%
61390 1
 
0.2%
Other values (438) 438
97.6%
ValueCountFrequency (%)
60 1
0.2%
640 1
0.2%
2140 1
0.2%
4080 1
0.2%
4740 1
0.2%
4960 1
0.2%
5150 1
0.2%
5830 1
0.2%
10300 1
0.2%
10500 2
0.4%
ValueCountFrequency (%)
18479626170 1
0.2%
13902763340 1
0.2%
10880733370 1
0.2%
10691160710 1
0.2%
10242827430 1
0.2%
10196669870 1
0.2%
10091247650 1
0.2%
9510014180 1
0.2%
9477463590 1
0.2%
9090343980 1
0.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct212
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.153318
Minimum0
Maximum72
Zeros83
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-16T13:12:40.206991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03
median7
Q319.39
95-th percentile43.048
Maximum72
Range72
Interquartile range (IQR)19.36

Descriptive statistics

Standard deviation14.760271
Coefficient of variation (CV)1.2145054
Kurtosis1.7747214
Mean12.153318
Median Absolute Deviation (MAD)7
Skewness1.4403149
Sum5456.84
Variance217.86559
MonotonicityNot monotonic
2024-03-16T13:12:40.434193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 83
 
18.5%
0.01 15
 
3.3%
7.0 10
 
2.2%
1.0 9
 
2.0%
2.0 9
 
2.0%
0.02 8
 
1.8%
8.0 7
 
1.6%
0.03 7
 
1.6%
15.0 7
 
1.6%
4.0 7
 
1.6%
Other values (202) 287
63.9%
ValueCountFrequency (%)
0.0 83
18.5%
0.01 15
 
3.3%
0.02 8
 
1.8%
0.03 7
 
1.6%
0.04 4
 
0.9%
0.05 4
 
0.9%
0.06 5
 
1.1%
0.07 2
 
0.4%
0.08 2
 
0.4%
0.09 3
 
0.7%
ValueCountFrequency (%)
72.0 1
0.2%
70.0 1
0.2%
67.0 1
0.2%
64.0 1
0.2%
60.22 1
0.2%
58.25 1
0.2%
56.0 2
0.4%
55.87 1
0.2%
54.89 1
0.2%
54.0 1
0.2%

Interactions

2024-03-16T13:12:35.734431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:33.553627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:34.241238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:35.112454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:35.869419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:33.709319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:34.494728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:35.250905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:36.006477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:33.854872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:34.755099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:35.425051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:36.144276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:34.033859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:34.923536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:12:35.569452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:12:40.915455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부연도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부연도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0410.0000.2730.5160.666
납부매체0.0000.0411.0001.0000.5310.2680.617
납부매체전자고지여부0.0000.0001.0001.0000.2060.0000.259
납부건수0.0000.2730.5310.2061.0000.5770.683
납부금액0.0000.5160.2680.0000.5771.0000.329
납부매체비율0.0000.6660.6170.2590.6830.3291.000
2024-03-16T13:12:41.116550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체납부매체전자고지여부세목명
납부매체1.0000.9910.013
납부매체전자고지여부0.9911.0000.000
세목명0.0130.0001.000
2024-03-16T13:12:41.281054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부연도납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부연도1.0000.0010.0170.0820.0000.0000.000
납부건수0.0011.0000.7770.8170.1040.1870.156
납부금액0.0170.7771.0000.5820.2490.1300.000
납부매체비율0.0820.8170.5821.0000.3160.2310.197
세목명0.0000.1040.2490.3161.0000.0130.000
납부매체0.0000.1870.1300.2310.0131.0000.991
납부매체전자고지여부0.0000.1560.0000.1970.0000.9911.000

Missing values

2024-03-16T13:12:36.382308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:12:36.713190image/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

시도명시군구명자치단체코드납부연도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
0경기도동두천시412502017자동차세ARSN10135489023.0
1경기도동두천시412502017재산세ARSN18161425042.0
2경기도동두천시412502017주민세ARSN1516500035.0
3경기도동두천시412502017담배소비세가상계좌Y4538900.0
4경기도동두천시412502017등록면허세가상계좌Y81901929492409.0
5경기도동두천시412502017등록세가상계좌Y111584800.0
6경기도동두천시412502017면허세가상계좌Y111153000.0
7경기도동두천시412502017자동차세가상계좌Y30289422357650033.0
8경기도동두천시412502017재산세가상계좌Y28857577021693031.0
9경기도동두천시412502017종합토지세가상계좌Y1315218000.0
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