Overview

Dataset statistics

Number of variables11
Number of observations84
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory94.6 B

Variable types

Categorical7
Boolean1
Numeric3

Dataset

Description신용카드, 가상계좌 등 기방세 납부매체별 납부현황에 관한 데이터로 전자송달 시장 규모 및 편의 분석, 수수료 산정 시 기초 자료로 활용됩니다.
Author경상남도 거제시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079867

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부년도 has constant value ""Constant
기준일자 has constant value ""Constant
납부건수 is highly overall correlated with 납부금액(원) and 1 other fieldsHigh correlation
납부금액(원) is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부금액(원) has unique valuesUnique
납부매체비율 has 5 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-11 00:42:53.055797
Analysis finished2023-12-11 00:42:54.495355
Duration1.44 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
경상남도
84 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 84
100.0%

Length

2023-12-11T09:42:54.571924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:54.676460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 84
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
거제시
84 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거제시 84
100.0%

Length

2023-12-11T09:42:54.765698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:54.865039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거제시 84
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
48310
84 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48310
2nd row48310
3rd row48310
4th row48310
5th row48310

Common Values

ValueCountFrequency (%)
48310 84
100.0%

Length

2023-12-11T09:42:54.986591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:55.111877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48310 84
100.0%

납부년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
2022
84 

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 84
100.0%

Length

2023-12-11T09:42:55.238590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:55.365558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 84
100.0%

세목명
Categorical

Distinct13
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Memory size804.0 B
등록면허세
11 
자동차세
11 
재산세
11 
주민세
11 
지방소득세
9 
Other values (8)
31 

Length

Max length7
Median length5
Mean length4.1071429
Min length3

Unique

Unique3 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
등록면허세 11
13.1%
자동차세 11
13.1%
재산세 11
13.1%
주민세 11
13.1%
지방소득세 9
10.7%
취득세 9
10.7%
지역자원시설세 8
9.5%
등록세 6
7.1%
담배소비세 3
 
3.6%
면허세 2
 
2.4%
Other values (3) 3
 
3.6%

Length

2023-12-11T09:42:55.486693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등록면허세 11
13.1%
자동차세 11
13.1%
재산세 11
13.1%
주민세 11
13.1%
지방소득세 9
10.7%
취득세 9
10.7%
지역자원시설세 8
9.5%
등록세 6
7.1%
담배소비세 3
 
3.6%
면허세 2
 
2.4%
Other values (3) 3
 
3.6%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size804.0 B
ARS
11 
가상계좌
11 
기타
10 
위택스
10 
은행창구
8 
Other values (5)
34 

Length

Max length5
Median length4
Mean length3.8690476
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ARS 11
13.1%
가상계좌 11
13.1%
기타 10
11.9%
위택스 10
11.9%
은행창구 8
9.5%
인터넷지로 8
9.5%
자동화기기 8
9.5%
지자체방문 8
9.5%
페이사납부 6
7.1%
자동이체 4
 
4.8%

Length

2023-12-11T09:42:55.652412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:55.805428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ars 11
13.1%
가상계좌 11
13.1%
기타 10
11.9%
위택스 10
11.9%
은행창구 8
9.5%
인터넷지로 8
9.5%
자동화기기 8
9.5%
지자체방문 8
9.5%
페이사납부 6
7.1%
자동이체 4
 
4.8%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size216.0 B
True
43 
False
41 
ValueCountFrequency (%)
True 43
51.2%
False 41
48.8%
2023-12-11T09:42:55.956345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8009.4524
Minimum1
Maximum113102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-11T09:42:56.074504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.15
Q149.5
median1872.5
Q37335.25
95-th percentile27644.35
Maximum113102
Range113101
Interquartile range (IQR)7285.75

Descriptive statistics

Standard deviation17575.929
Coefficient of variation (CV)2.1943983
Kurtosis20.730255
Mean8009.4524
Median Absolute Deviation (MAD)1867.5
Skewness4.2770276
Sum672794
Variance3.0891328 × 108
MonotonicityNot monotonic
2023-12-11T09:42:56.221292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 3
 
3.6%
11 2
 
2.4%
33 2
 
2.4%
2 2
 
2.4%
1 2
 
2.4%
45 2
 
2.4%
295 1
 
1.2%
26 1
 
1.2%
1916 1
 
1.2%
6980 1
 
1.2%
Other values (67) 67
79.8%
ValueCountFrequency (%)
1 2
2.4%
2 2
2.4%
3 1
 
1.2%
4 1
 
1.2%
5 3
3.6%
8 1
 
1.2%
9 1
 
1.2%
11 2
2.4%
17 1
 
1.2%
18 1
 
1.2%
ValueCountFrequency (%)
113102 1
1.2%
89578 1
1.2%
61586 1
1.2%
36981 1
1.2%
28483 1
1.2%
22892 1
1.2%
21405 1
1.2%
19962 1
1.2%
19272 1
1.2%
18407 1
1.2%

납부금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4324916 × 109
Minimum6180
Maximum3.4829659 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-11T09:42:56.360353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6180
5-th percentile66758
Q110098732
median2.819039 × 108
Q33.3049362 × 109
95-th percentile2.034314 × 1010
Maximum3.4829659 × 1010
Range3.4829653 × 1010
Interquartile range (IQR)3.2948375 × 109

Descriptive statistics

Standard deviation7.0380202 × 109
Coefficient of variation (CV)2.0504115
Kurtosis8.4998456
Mean3.4324916 × 109
Median Absolute Deviation (MAD)2.8183528 × 108
Skewness2.9160032
Sum2.8832929 × 1011
Variance4.9533728 × 1019
MonotonicityNot monotonic
2023-12-11T09:42:56.489997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4581840 1
 
1.2%
3233540340 1
 
1.2%
94936760 1
 
1.2%
74831250 1
 
1.2%
3116589000 1
 
1.2%
975329620 1
 
1.2%
149118250 1
 
1.2%
2410911220 1
 
1.2%
1991970 1
 
1.2%
4588706550 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
6180 1
1.2%
25490 1
1.2%
36250 1
1.2%
38710 1
1.2%
65960 1
1.2%
71280 1
1.2%
102730 1
1.2%
242880 1
1.2%
245050 1
1.2%
677470 1
1.2%
ValueCountFrequency (%)
34829659140 1
1.2%
31453910100 1
1.2%
27382333890 1
1.2%
20486497010 1
1.2%
20407724060 1
1.2%
19977164570 1
1.2%
15744869210 1
1.2%
12883150990 1
1.2%
10225989540 1
1.2%
7633795030 1
1.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.904762
Minimum0
Maximum52.89
Zeros5
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-11T09:42:56.624512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0015
Q10.145
median5.905
Q320.7025
95-th percentile39.3605
Maximum52.89
Range52.89
Interquartile range (IQR)20.5575

Descriptive statistics

Standard deviation13.988477
Coefficient of variation (CV)1.1750321
Kurtosis0.38508623
Mean11.904762
Median Absolute Deviation (MAD)5.865
Skewness1.1289421
Sum1000
Variance195.67749
MonotonicityNot monotonic
2023-12-11T09:42:56.764308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
6.0%
0.04 3
 
3.6%
0.07 2
 
2.4%
0.06 2
 
2.4%
0.1 2
 
2.4%
0.01 2
 
2.4%
4.73 1
 
1.2%
19.5 1
 
1.2%
4.23 1
 
1.2%
20.89 1
 
1.2%
Other values (64) 64
76.2%
ValueCountFrequency (%)
0.0 5
6.0%
0.01 2
 
2.4%
0.03 1
 
1.2%
0.04 3
3.6%
0.05 1
 
1.2%
0.06 2
 
2.4%
0.07 2
 
2.4%
0.09 1
 
1.2%
0.1 2
 
2.4%
0.12 1
 
1.2%
ValueCountFrequency (%)
52.89 1
1.2%
50.57 1
1.2%
45.22 1
1.2%
40.64 1
1.2%
39.5 1
1.2%
38.57 1
1.2%
37.25 1
1.2%
34.94 1
1.2%
34.83 1
1.2%
33.65 1
1.2%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
2023-08-24
84 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-24
2nd row2023-08-24
3rd row2023-08-24
4th row2023-08-24
5th row2023-08-24

Common Values

ValueCountFrequency (%)
2023-08-24 84
100.0%

Length

2023-12-11T09:42:56.946285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:57.076393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-24 84
100.0%

Interactions

2023-12-11T09:42:53.928274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:53.364174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:53.649837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:54.014419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:53.449239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:53.744658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:54.104800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:53.563672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:53.834958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:42:57.144024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체납부매체전자고지여부납부건수납부금액(원)납부매체비율
세목명1.0000.0000.0000.0000.5130.244
납부매체0.0001.0000.9940.0000.2410.000
납부매체전자고지여부0.0000.9941.0000.0000.0000.011
납부건수0.0000.0000.0001.0000.6440.659
납부금액(원)0.5130.2410.0000.6441.0000.313
납부매체비율0.2440.0000.0110.6590.3131.000
2023-12-11T09:42:57.245660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체납부매체전자고지여부세목명
납부매체1.0000.8830.000
납부매체전자고지여부0.8831.0000.000
세목명0.0000.0001.000
2023-12-11T09:42:57.377768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액(원)납부매체비율세목명납부매체납부매체전자고지여부
납부건수1.0000.7860.9020.0000.0000.000
납부금액(원)0.7861.0000.6810.2540.1170.000
납부매체비율0.9020.6811.0000.0910.0000.000
세목명0.0000.2540.0911.0000.0000.000
납부매체0.0000.1170.0000.0001.0000.883
납부매체전자고지여부0.0000.0000.0000.0000.8831.000

Missing values

2023-12-11T09:42:54.257250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:42:54.415739image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액(원)납부매체비율기준일자
0경상남도거제시483102022등록면허세ARSN29545818401.712023-08-24
1경상남도거제시483102022등록면허세ARSY5712800.032023-08-24
2경상남도거제시483102022자동차세ARSN6999142955967040.642023-08-24
3경상남도거제시483102022자동차세ARSY89129900.052023-08-24
4경상남도거제시483102022재산세ARSN7789113296227045.222023-08-24
5경상남도거제시483102022재산세ARSY116774700.062023-08-24
6경상남도거제시483102022주민세ARSN18292881139010.622023-08-24
7경상남도거제시483102022주민세ARSY182450500.12023-08-24
8경상남도거제시483102022지방소득세ARSN219602615101.272023-08-24
9경상남도거제시483102022지역자원시설세ARSN2254900.012023-08-24
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액(원)납부매체비율기준일자
74경상남도거제시483102022주민세지자체방문N43411194270507.12023-08-24
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