Overview

Dataset statistics

Number of variables10
Number of observations487
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.6 KiB
Average record size in memory85.3 B

Variable types

Categorical5
Numeric4
Boolean1

Dataset

Description신용카드, 가상계좌, 지로 등 지방세 납부매체별 납부 현황을 제공합니다. 전자송달 시장 규모 및 편익 분석 기초자료로 활용됩니다.
Author충청남도 아산시
URLhttps://www.data.go.kr/data/15079060/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 143 (29.4%) zerosZeros

Reproduction

Analysis started2024-05-04 07:53:08.193569
Analysis finished2024-05-04 07:53:17.454398
Duration9.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
충청남도
487 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 487
100.0%

Length

2024-05-04T07:53:17.738249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:53:18.154555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 487
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
아산시
487 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아산시 487
100.0%

Length

2024-05-04T07:53:18.676303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:53:19.000339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아산시 487
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
44200
487 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44200
2nd row44200
3rd row44200
4th row44200
5th row44200

Common Values

ValueCountFrequency (%)
44200 487
100.0%

Length

2024-05-04T07:53:19.285889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:53:19.570336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44200 487
100.0%

납부년도
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5647
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-04T07:53:19.957809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7019189
Coefficient of variation (CV)0.0008427157
Kurtosis-1.253482
Mean2019.5647
Median Absolute Deviation (MAD)1
Skewness-0.048372965
Sum983528
Variance2.8965278
MonotonicityIncreasing
2024-05-04T07:53:20.480071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 85
17.5%
2020 84
17.2%
2021 83
17.0%
2019 81
16.6%
2018 78
16.0%
2017 76
15.6%
ValueCountFrequency (%)
2017 76
15.6%
2018 78
16.0%
2019 81
16.6%
2020 84
17.2%
2021 83
17.0%
2022 85
17.5%
ValueCountFrequency (%)
2022 85
17.5%
2021 83
17.0%
2020 84
17.2%
2019 81
16.6%
2018 78
16.0%
2017 76
15.6%

세목명
Categorical

Distinct14
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
자동차세
64 
재산세
64 
주민세
59 
등록면허세
56 
취득세
48 
Other values (9)
196 

Length

Max length7
Median length5
Mean length4.0102669
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 64
13.1%
재산세 64
13.1%
주민세 59
12.1%
등록면허세 56
11.5%
취득세 48
9.9%
지방소득세 47
9.7%
등록세 40
8.2%
지역자원시설세 35
7.2%
면허세 30
6.2%
종합토지세 21
 
4.3%
Other values (4) 23
 
4.7%

Length

2024-05-04T07:53:20.977282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 64
13.1%
재산세 64
13.1%
주민세 59
12.1%
등록면허세 56
11.5%
취득세 48
9.9%
지방소득세 47
9.7%
등록세 40
8.2%
지역자원시설세 35
7.2%
면허세 30
6.2%
종합토지세 21
 
4.3%
Other values (4) 23
 
4.7%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
가상계좌
67 
은행창구
65 
기타
59 
인터넷지로
56 
지자체방문
55 
Other values (5)
185 

Length

Max length5
Median length4
Mean length3.963039
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 67
13.8%
은행창구 65
13.3%
기타 59
12.1%
인터넷지로 56
11.5%
지자체방문 55
11.3%
위택스 54
11.1%
자동화기기 52
10.7%
ARS 32
6.6%
자동이체 24
 
4.9%
페이사납부 23
 
4.7%

Length

2024-05-04T07:53:21.553747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:53:22.045902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가상계좌 67
13.8%
은행창구 65
13.3%
기타 59
12.1%
인터넷지로 56
11.5%
지자체방문 55
11.3%
위택스 54
11.1%
자동화기기 52
10.7%
ars 32
6.6%
자동이체 24
 
4.9%
페이사납부 23
 
4.7%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size619.0 B
False
261 
True
226 
ValueCountFrequency (%)
False 261
53.6%
True 226
46.4%
2024-05-04T07:53:22.596458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct373
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12017.275
Minimum1
Maximum177986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-04T07:53:23.064808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q131
median2652
Q311866
95-th percentile54360.2
Maximum177986
Range177985
Interquartile range (IQR)11835

Descriptive statistics

Standard deviation26096.716
Coefficient of variation (CV)2.1716001
Kurtosis16.687234
Mean12017.275
Median Absolute Deviation (MAD)2646
Skewness3.865355
Sum5852413
Variance6.8103861 × 108
MonotonicityNot monotonic
2024-05-04T07:53:23.655134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 19
 
3.9%
2 13
 
2.7%
3 11
 
2.3%
4 8
 
1.6%
11 5
 
1.0%
18 5
 
1.0%
9 5
 
1.0%
5 4
 
0.8%
19 4
 
0.8%
26 4
 
0.8%
Other values (363) 409
84.0%
ValueCountFrequency (%)
1 19
3.9%
2 13
2.7%
3 11
2.3%
4 8
1.6%
5 4
 
0.8%
6 4
 
0.8%
7 3
 
0.6%
8 3
 
0.6%
9 5
 
1.0%
10 3
 
0.6%
ValueCountFrequency (%)
177986 1
0.2%
170749 1
0.2%
160428 1
0.2%
156532 1
0.2%
150322 1
0.2%
138725 1
0.2%
138272 1
0.2%
133152 1
0.2%
131975 1
0.2%
130957 1
0.2%

납부금액
Real number (ℝ)

HIGH CORRELATION 

Distinct485
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4990406 × 109
Minimum3100
Maximum1.71196 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-04T07:53:24.369859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3100
5-th percentile70300
Q17764770
median4.6181628 × 108
Q38.9257232 × 109
95-th percentile3.6467798 × 1010
Maximum1.71196 × 1011
Range1.71196 × 1011
Interquartile range (IQR)8.9179585 × 109

Descriptive statistics

Standard deviation2.0357592 × 1010
Coefficient of variation (CV)2.3952812
Kurtosis25.88683
Mean8.4990406 × 109
Median Absolute Deviation (MAD)4.6171608 × 108
Skewness4.5776493
Sum4.1390328 × 1012
Variance4.1443157 × 1020
MonotonicityNot monotonic
2024-05-04T07:53:24.965194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 2
 
0.4%
12600 2
 
0.4%
15000 1
 
0.2%
7888480 1
 
0.2%
67506090 1
 
0.2%
41373216300 1
 
0.2%
7911169990 1
 
0.2%
914300 1
 
0.2%
39503418220 1
 
0.2%
26601036770 1
 
0.2%
Other values (475) 475
97.5%
ValueCountFrequency (%)
3100 1
0.2%
3140 1
0.2%
6180 1
0.2%
6300 1
0.2%
9000 1
0.2%
9480 1
0.2%
12600 2
0.4%
13780 1
0.2%
15000 1
0.2%
22660 1
0.2%
ValueCountFrequency (%)
171196000000 1
0.2%
164000000000 1
0.2%
145804000000 1
0.2%
116251000000 1
0.2%
113000000000 1
0.2%
111961000000 1
0.2%
111471000000 1
0.2%
110213000000 1
0.2%
88812607500 1
0.2%
85994957810 1
0.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct118
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.10271
Minimum0
Maximum100
Zeros143
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-04T07:53:25.429699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q321
95-th percentile38
Maximum100
Range100
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.043326
Coefficient of variation (CV)1.1603455
Kurtosis3.7644191
Mean12.10271
Median Absolute Deviation (MAD)9
Skewness1.5336812
Sum5894.02
Variance197.215
MonotonicityNot monotonic
2024-05-04T07:53:25.934040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 143
29.4%
14.0 23
 
4.7%
9.0 16
 
3.3%
2.0 14
 
2.9%
27.0 13
 
2.7%
11.0 12
 
2.5%
3.0 12
 
2.5%
1.0 11
 
2.3%
12.0 10
 
2.1%
8.0 10
 
2.1%
Other values (108) 223
45.8%
ValueCountFrequency (%)
0.0 143
29.4%
0.01 4
 
0.8%
0.02 1
 
0.2%
0.04 4
 
0.8%
0.05 1
 
0.2%
0.06 2
 
0.4%
0.09 1
 
0.2%
0.11 1
 
0.2%
0.14 1
 
0.2%
0.15 1
 
0.2%
ValueCountFrequency (%)
100.0 1
0.2%
74.0 1
0.2%
67.0 1
0.2%
58.0 1
0.2%
52.0 2
0.4%
51.0 1
0.2%
50.91 1
0.2%
50.0 1
0.2%
49.0 2
0.4%
44.0 1
0.2%

Interactions

2024-05-04T07:53:15.202524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:12.006508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:12.966399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:13.968832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:15.476574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:12.297660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:13.198533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:14.280370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:15.764173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:12.561677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:13.402969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:14.623758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:16.045754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:12.779433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:13.702800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:53:14.939287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T07:53:26.229918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.2650.0330.2570.3900.577
납부매체0.0000.2651.0000.9950.5350.3350.346
납부매체전자고지여부0.0000.0330.9951.0000.2380.1710.000
납부건수0.0000.2570.5350.2381.0000.3980.443
납부금액0.0000.3900.3350.1710.3981.0000.158
납부매체비율0.0000.5770.3460.0000.4430.1581.000
2024-05-04T07:53:26.751803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체납부매체전자고지여부
세목명1.0000.1090.024
납부매체0.1091.0000.933
납부매체전자고지여부0.0240.9331.000
2024-05-04T07:53:27.022851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부년도1.0000.0220.0220.0640.0000.0000.000
납부건수0.0221.0000.8310.7210.1050.1900.181
납부금액0.0220.8311.0000.5250.1750.1590.170
납부매체비율0.0640.7210.5251.0000.2830.1550.000
세목명0.0000.1050.1750.2831.0000.1090.024
납부매체0.0000.1900.1590.1550.1091.0000.933
납부매체전자고지여부0.0000.1810.1700.0000.0240.9331.000

Missing values

2024-05-04T07:53:16.592238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T07:53:17.098962image/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
0충청남도아산시442002017등록면허세ARSN1150000.0
1충청남도아산시442002017자동차세ARSN771248784034.0
2충청남도아산시442002017자동차세ARSY45003102.0
3충청남도아산시442002017재산세ARSN971280786042.0
4충청남도아산시442002017재산세ARSY34362501.0
5충청남도아산시442002017주민세ARSN4751700021.0
6충청남도아산시442002017등록면허세가상계좌Y264115893751307.0
7충청남도아산시442002017등록세가상계좌Y964585900.0
8충청남도아산시442002017면허세가상계좌Y545973400.0
9충청남도아산시442002017사업소세가상계좌Y25389300.0
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