Overview

Dataset statistics

Number of variables10
Number of observations186
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 KiB
Average record size in memory85.7 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description상기 데이터는 연도별 신용카드, 가상계좌 등 지방세 납부매체별 납부 현황을 제공하여 전자송달 시장 규모 및 편익 분석, 수수료 산정시 기초자료로 활용
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=348&beforeMenuCd=DOM_000000201001001000&publicdatapk=15079997

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 3 (1.6%) zerosZeros

Reproduction

Analysis started2024-01-09 21:38:22.680294
Analysis finished2024-01-09 21:38:23.870152
Duration1.19 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
충청남도
186 

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 186
100.0%

Length

2024-01-10T06:38:23.928053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:38:24.002056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 186
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
부여군
186 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부여군 186
100.0%

Length

2024-01-10T06:38:24.074828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:38:24.149546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부여군 186
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
44760
186 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44760
2nd row44760
3rd row44760
4th row44760
5th row44760

Common Values

ValueCountFrequency (%)
44760 186
100.0%

Length

2024-01-10T06:38:24.227432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:38:24.324361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44760 186
100.0%

납부년도
Categorical

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2019
64 
2017
61 
2018
61 

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 (%)
2019 64
34.4%
2017 61
32.8%
2018 61
32.8%

Length

2024-01-10T06:38:24.409652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:38:24.486506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 64
34.4%
2017 61
32.8%
2018 61
32.8%

세목명
Categorical

Distinct12
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
자동차세
25 
재산세
25 
주민세
25 
등록면허세
24 
취득세
22 
Other values (7)
65 

Length

Max length7
Median length3
Mean length3.9677419
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row등록면허세
2nd row등록세
3rd row면허세
4th row사업소세
5th row자동차세

Common Values

ValueCountFrequency (%)
자동차세 25
13.4%
재산세 25
13.4%
주민세 25
13.4%
등록면허세 24
12.9%
취득세 22
11.8%
지방소득세 21
11.3%
등록세 18
9.7%
지역자원시설세 11
5.9%
종합토지세 7
 
3.8%
면허세 4
 
2.2%
Other values (2) 4
 
2.2%

Length

2024-01-10T06:38:24.582951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 25
13.4%
재산세 25
13.4%
주민세 25
13.4%
등록면허세 24
12.9%
취득세 22
11.8%
지방소득세 21
11.3%
등록세 18
9.7%
지역자원시설세 11
5.9%
종합토지세 7
 
3.8%
면허세 4
 
2.2%
Other values (2) 4
 
2.2%

납부매체
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
가상계좌
31 
은행창구
27 
위택스
24 
인터넷지로
23 
기타
22 
Other values (4)
59 

Length

Max length5
Median length4
Mean length4.0107527
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 31
16.7%
은행창구 27
14.5%
위택스 24
12.9%
인터넷지로 23
12.4%
기타 22
11.8%
지자체방문 22
11.8%
자동화기기 21
11.3%
자동이체 12
 
6.5%
페이사납부 4
 
2.2%

Length

2024-01-10T06:38:24.682993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:38:24.780350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가상계좌 31
16.7%
은행창구 27
14.5%
위택스 24
12.9%
인터넷지로 23
12.4%
기타 22
11.8%
지자체방문 22
11.8%
자동화기기 21
11.3%
자동이체 12
 
6.5%
페이사납부 4
 
2.2%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size318.0 B
True
94 
False
92 
ValueCountFrequency (%)
True 94
50.5%
False 92
49.5%
2024-01-10T06:38:24.882344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3561.0323
Minimum1
Maximum31485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T06:38:24.970298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q1110
median649
Q33363.75
95-th percentile17290.25
Maximum31485
Range31484
Interquartile range (IQR)3253.75

Descriptive statistics

Standard deviation6374.0697
Coefficient of variation (CV)1.78995
Kurtosis6.874179
Mean3561.0323
Median Absolute Deviation (MAD)645
Skewness2.6111309
Sum662352
Variance40628764
MonotonicityNot monotonic
2024-01-10T06:38:25.075440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 8
 
4.3%
2 4
 
2.2%
5 4
 
2.2%
7 3
 
1.6%
307 2
 
1.1%
103 2
 
1.1%
399 2
 
1.1%
71 2
 
1.1%
11 2
 
1.1%
8 2
 
1.1%
Other values (150) 155
83.3%
ValueCountFrequency (%)
1 2
 
1.1%
2 4
2.2%
3 1
 
0.5%
4 8
4.3%
5 4
2.2%
7 3
 
1.6%
8 2
 
1.1%
9 1
 
0.5%
10 1
 
0.5%
11 2
 
1.1%
ValueCountFrequency (%)
31485 1
0.5%
30598 1
0.5%
30484 1
0.5%
27882 1
0.5%
27065 1
0.5%
24642 1
0.5%
21083 1
0.5%
19296 1
0.5%
18214 1
0.5%
17405 1
0.5%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3390709 × 108
Minimum4110
Maximum1.1685465 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T06:38:25.191252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4110
5-th percentile200067.5
Q110241562
median1.2898152 × 108
Q37.9620558 × 108
95-th percentile5.1398716 × 109
Maximum1.1685465 × 1010
Range1.1685461 × 1010
Interquartile range (IQR)7.8596402 × 108

Descriptive statistics

Standard deviation1.8570258 × 109
Coefficient of variation (CV)1.9884481
Kurtosis10.432423
Mean9.3390709 × 108
Median Absolute Deviation (MAD)1.2819811 × 108
Skewness3.0097577
Sum1.7370672 × 1011
Variance3.4485448 × 1018
MonotonicityNot monotonic
2024-01-10T06:38:25.321833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105929160 1
 
0.5%
349028080 1
 
0.5%
17091330 1
 
0.5%
13661940 1
 
0.5%
223736700 1
 
0.5%
151606720 1
 
0.5%
4283270 1
 
0.5%
77460 1
 
0.5%
2940622870 1
 
0.5%
2775413150 1
 
0.5%
Other values (176) 176
94.6%
ValueCountFrequency (%)
4110 1
0.5%
6300 1
0.5%
30610 1
0.5%
35380 1
0.5%
38960 1
0.5%
41520 1
0.5%
77460 1
0.5%
93900 1
0.5%
189590 1
0.5%
198160 1
0.5%
ValueCountFrequency (%)
11685465270 1
0.5%
10188351040 1
0.5%
7282052360 1
0.5%
6999192200 1
0.5%
6410458890 1
0.5%
6392391690 1
0.5%
6174836530 1
0.5%
6142876590 1
0.5%
5493830750 1
0.5%
5308209430 1
0.5%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct160
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.440968
Minimum0
Maximum87.66
Zeros3
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T06:38:25.446828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.8475
median7.165
Q319.2625
95-th percentile50.2175
Maximum87.66
Range87.66
Interquartile range (IQR)18.415

Descriptive statistics

Standard deviation17.319034
Coefficient of variation (CV)1.2885258
Kurtosis4.549794
Mean13.440968
Median Absolute Deviation (MAD)7.055
Skewness2.0090685
Sum2500.02
Variance299.94893
MonotonicityNot monotonic
2024-01-10T06:38:25.770230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 11
 
5.9%
0.11 5
 
2.7%
0.03 3
 
1.6%
0.0 3
 
1.6%
0.01 3
 
1.6%
0.13 2
 
1.1%
0.04 2
 
1.1%
0.24 2
 
1.1%
0.27 2
 
1.1%
20.12 2
 
1.1%
Other values (150) 151
81.2%
ValueCountFrequency (%)
0.0 3
 
1.6%
0.01 3
 
1.6%
0.02 11
5.9%
0.03 3
 
1.6%
0.04 2
 
1.1%
0.07 1
 
0.5%
0.08 1
 
0.5%
0.09 1
 
0.5%
0.1 2
 
1.1%
0.11 5
2.7%
ValueCountFrequency (%)
87.66 1
0.5%
86.66 1
0.5%
86.02 1
0.5%
65.21 1
0.5%
57.32 1
0.5%
56.87 1
0.5%
56.8 1
0.5%
52.76 1
0.5%
51.33 1
0.5%
50.33 1
0.5%

Interactions

2024-01-10T06:38:23.395467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:38:22.993114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:38:23.196589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:38:23.476488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:38:23.063010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:38:23.264543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:38:23.562261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:38:23.135000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:38:23.330385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:38:25.838263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0000.0000.3620.6430.598
납부매체0.0000.0001.0001.0000.3710.3510.679
납부매체전자고지여부0.0000.0001.0001.0000.2440.0000.205
납부건수0.0000.3620.3710.2441.0000.6660.768
납부금액0.0000.6430.3510.0000.6661.0000.670
납부매체비율0.0000.5980.6790.2050.7680.6701.000
2024-01-10T06:38:25.923402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체전자고지여부납부매체세목명납부년도
납부매체전자고지여부1.0000.9810.0000.000
납부매체0.9811.0000.0000.000
세목명0.0000.0001.0000.000
납부년도0.0000.0000.0001.000
2024-01-10T06:38:25.997206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부건수1.0000.7900.8420.0000.1580.1770.182
납부금액0.7901.0000.6340.0000.3360.1180.000
납부매체비율0.8420.6341.0000.0000.3010.2760.201
납부년도0.0000.0000.0001.0000.0000.0000.000
세목명0.1580.3360.3010.0001.0000.0000.000
납부매체0.1770.1180.2760.0000.0001.0000.981
납부매체전자고지여부0.1820.0000.2010.0000.0000.9811.000

Missing values

2024-01-10T06:38:23.669677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:38:23.814490image/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
0충청남도부여군447602017등록면허세가상계좌Y51321059291608.66
1충청남도부여군447602017등록세가상계좌Y1415299400.02
2충청남도부여군447602017면허세가상계좌Y14939000.02
3충청남도부여군447602017사업소세가상계좌Y210786800.0
4충청남도부여군447602017자동차세가상계좌Y18214250499339030.72
5충청남도부여군447602017재산세가상계좌Y24642207835208041.56
6충청남도부여군447602017종합토지세가상계좌Y4306100.01
7충청남도부여군447602017주민세가상계좌Y912527079710015.39
8충청남도부여군447602017지방소득세가상계좌Y169711500645802.86
9충청남도부여군447602017지역자원시설세가상계좌Y29319400.0
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
176충청남도부여군447602019등록세지자체방문N74989700.13
177충청남도부여군447602019자동차세지자체방문N241942326234043.24
178충청남도부여군447602019재산세지자체방문N117815682435021.05
179충청남도부여군447602019주민세지자체방문N10781755302019.27
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