Overview

Dataset statistics

Number of variables12
Number of observations84
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory103.6 B

Variable types

Numeric4
Categorical7
Boolean1

Dataset

Description신용카드, 가상계좌 등 지방세 납부매체별 납부현황입니다.활용업무: 전자송달 시장 규모 및 편익 분석, 수수료 산정시 기초자료 활용
Author부산광역시 금정구
URLhttps://www.data.go.kr/data/15079652/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부년도 has constant value ""Constant
데이터기준일 has constant value ""Constant
납부매체 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
납부매체전자고지여부 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 납부매체 and 1 other fieldsHigh 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
연번 has unique valuesUnique
납부금액 has unique valuesUnique
납부매체비율 has 6 (7.1%) zerosZeros

Reproduction

Analysis started2023-12-23 07:30:31.289207
Analysis finished2023-12-23 07:30:42.695701
Duration11.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.5
Minimum1
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-23T07:30:43.179922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.15
Q121.75
median42.5
Q363.25
95-th percentile79.85
Maximum84
Range83
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation24.392622
Coefficient of variation (CV)0.57394404
Kurtosis-1.2
Mean42.5
Median Absolute Deviation (MAD)21
Skewness0
Sum3570
Variance595
MonotonicityStrictly increasing
2023-12-23T07:30:43.862202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
55 1
 
1.2%
63 1
 
1.2%
62 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
84 1
1.2%
83 1
1.2%
82 1
1.2%
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%

시도명
Categorical

CONSTANT 

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

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 84
100.0%

Length

2023-12-23T07:30:44.536836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:30:45.004794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 84
100.0%

시군구명
Categorical

CONSTANT 

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

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 84
100.0%

Length

2023-12-23T07:30:45.500739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:30:46.303958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금정구 84
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
26410
84 

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 84
100.0%

Length

2023-12-23T07:30:46.997117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:30:47.874038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26410 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

2023-12-23T07:30:48.400908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:30:49.238845image/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
자동차세
10 
재산세
10 
주민세
10 
등록면허세
10 
지방소득세
9 
Other values (8)
35 

Length

Max length7
Median length3
Mean length4.0357143
Min length3

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st row등록세
2nd row레저세
3rd row면허세
4th row자동차세
5th row재산세

Common Values

ValueCountFrequency (%)
자동차세 10
11.9%
재산세 10
11.9%
주민세 10
11.9%
등록면허세 10
11.9%
지방소득세 9
10.7%
취득세 9
10.7%
지역자원시설세 8
9.5%
등록세 7
8.3%
면허세 5
6.0%
레저세 2
 
2.4%
Other values (3) 4
 
4.8%

Length

2023-12-23T07:30:50.339505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 10
11.9%
재산세 10
11.9%
주민세 10
11.9%
등록면허세 10
11.9%
지방소득세 9
10.7%
취득세 9
10.7%
지역자원시설세 8
9.5%
등록세 7
8.3%
면허세 5
6.0%
레저세 2
 
2.4%
Other values (3) 4
 
4.8%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size804.0 B
기타
11 
이택스
11 
자동화기기
10 
위택스
10 
은행창구
9 
Other values (5)
33 

Length

Max length5
Median length4
Mean length3.8690476
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 11
13.1%
이택스 11
13.1%
자동화기기 10
11.9%
위택스 10
11.9%
은행창구 9
10.7%
인터넷지로 8
9.5%
지자체방문 7
8.3%
페이사납부 7
8.3%
가상계좌 7
8.3%
자동이체 4
 
4.8%

Length

2023-12-23T07:30:51.026703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:30:51.965500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 11
13.1%
이택스 11
13.1%
자동화기기 10
11.9%
위택스 10
11.9%
은행창구 9
10.7%
인터넷지로 8
9.5%
지자체방문 7
8.3%
페이사납부 7
8.3%
가상계좌 7
8.3%
자동이체 4
 
4.8%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size216.0 B
True
47 
False
37 
ValueCountFrequency (%)
True 47
56.0%
False 37
44.0%
2023-12-23T07:30:52.565046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7236.0833
Minimum1
Maximum111663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-23T07:30:53.024208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.15
Q132.75
median997.5
Q36597
95-th percentile28850.5
Maximum111663
Range111662
Interquartile range (IQR)6564.25

Descriptive statistics

Standard deviation16640.566
Coefficient of variation (CV)2.2996648
Kurtosis21.895435
Mean7236.0833
Median Absolute Deviation (MAD)996.5
Skewness4.3183362
Sum607831
Variance2.7690844 × 108
MonotonicityNot monotonic
2023-12-23T07:30:53.946165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
6.0%
2 4
 
4.8%
9 2
 
2.4%
6 2
 
2.4%
32 2
 
2.4%
6174 1
 
1.2%
98 1
 
1.2%
12 1
 
1.2%
15284 1
 
1.2%
900 1
 
1.2%
Other values (64) 64
76.2%
ValueCountFrequency (%)
1 5
6.0%
2 4
4.8%
3 1
 
1.2%
6 2
 
2.4%
9 2
 
2.4%
12 1
 
1.2%
19 1
 
1.2%
22 1
 
1.2%
28 1
 
1.2%
30 1
 
1.2%
ValueCountFrequency (%)
111663 1
1.2%
76574 1
1.2%
52005 1
1.2%
41519 1
1.2%
28978 1
1.2%
28128 1
1.2%
22213 1
1.2%
19696 1
1.2%
15284 1
1.2%
14959 1
1.2%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8715009 × 109
Minimum28350
Maximum2.9863141 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-23T07:30:54.756125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28350
5-th percentile170986
Q17502695
median2.3903241 × 108
Q31.7935075 × 109
95-th percentile1.27791 × 1010
Maximum2.9863141 × 1010
Range2.9863112 × 1010
Interquartile range (IQR)1.7860048 × 109

Descriptive statistics

Standard deviation5.7488965 × 109
Coefficient of variation (CV)2.0020529
Kurtosis8.2408022
Mean2.8715009 × 109
Median Absolute Deviation (MAD)2.389314 × 108
Skewness2.782752
Sum2.4120607 × 1011
Variance3.3049812 × 1019
MonotonicityNot monotonic
2023-12-23T07:30:55.364036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7071640 1
 
1.2%
1033080830 1
 
1.2%
2636550 1
 
1.2%
9194100 1
 
1.2%
880278970 1
 
1.2%
9669100730 1
 
1.2%
7595790 1
 
1.2%
17397044710 1
 
1.2%
1210180670 1
 
1.2%
7221716380 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
28350 1
1.2%
36490 1
1.2%
46350 1
1.2%
46960 1
1.2%
155050 1
1.2%
261290 1
1.2%
514560 1
1.2%
530200 1
1.2%
571590 1
1.2%
588700 1
1.2%
ValueCountFrequency (%)
29863140730 1
1.2%
23982477010 1
1.2%
22290763410 1
1.2%
17397044710 1
1.2%
12861919010 1
1.2%
12309792800 1
1.2%
12053617500 1
1.2%
11145268280 1
1.2%
10608092220 1
1.2%
9669100730 1
1.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.905119
Minimum0
Maximum55.74
Zeros6
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-23T07:30:56.070025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.125
median7.64
Q319.0425
95-th percentile40.8615
Maximum55.74
Range55.74
Interquartile range (IQR)18.9175

Descriptive statistics

Standard deviation14.035027
Coefficient of variation (CV)1.1789069
Kurtosis1.0499651
Mean11.905119
Median Absolute Deviation (MAD)7.605
Skewness1.2613298
Sum1000.03
Variance196.98197
MonotonicityNot monotonic
2023-12-23T07:30:56.775126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
7.1%
0.01 4
 
4.8%
0.02 3
 
3.6%
0.28 2
 
2.4%
0.06 2
 
2.4%
0.03 2
 
2.4%
2.78 1
 
1.2%
18.16 1
 
1.2%
28.55 1
 
1.2%
8.27 1
 
1.2%
Other values (61) 61
72.6%
ValueCountFrequency (%)
0.0 6
7.1%
0.01 4
4.8%
0.02 3
3.6%
0.03 2
 
2.4%
0.04 1
 
1.2%
0.06 2
 
2.4%
0.09 1
 
1.2%
0.1 1
 
1.2%
0.11 1
 
1.2%
0.13 1
 
1.2%
ValueCountFrequency (%)
55.74 1
1.2%
52.38 1
1.2%
49.35 1
1.2%
44.23 1
1.2%
41.19 1
1.2%
39.0 1
1.2%
34.5 1
1.2%
33.31 1
1.2%
32.46 1
1.2%
30.24 1
1.2%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
2023-12-20
84 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-20
2nd row2023-12-20
3rd row2023-12-20
4th row2023-12-20
5th row2023-12-20

Common Values

ValueCountFrequency (%)
2023-12-20 84
100.0%

Length

2023-12-23T07:30:57.535437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:30:57.996165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-20 84
100.0%

Interactions

2023-12-23T07:30:39.200583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:32.649281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:35.048794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:37.120789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:39.629057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:33.263360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:35.634262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:37.663201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:40.037709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:33.883717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:36.132121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:37.995616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:40.324159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:34.485152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:36.629170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:30:38.664271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:30:58.602277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
연번1.0000.0000.9850.9450.1560.0860.000
세목명0.0001.0000.0000.0000.0000.3840.555
납부매체0.9850.0001.0001.0000.0000.0000.000
납부매체전자고지여부0.9450.0001.0001.0000.0000.0580.000
납부건수0.1560.0000.0000.0001.0000.7590.739
납부금액0.0860.3840.0000.0580.7591.0000.575
납부매체비율0.0000.5550.0000.0000.7390.5751.000
2023-12-23T07:30:59.173716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체세목명납부매체전자고지여부
납부매체1.0000.0000.950
세목명0.0001.0000.000
납부매체전자고지여부0.9500.0001.000
2023-12-23T07:30:59.686501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
연번1.0000.1710.1270.0390.0000.7850.761
납부건수0.1711.0000.7850.8390.0000.0000.000
납부금액0.1270.7851.0000.6470.1720.0000.051
납부매체비율0.0390.8390.6471.0000.2580.0000.000
세목명0.0000.0000.1720.2581.0000.0000.000
납부매체0.7850.0000.0000.0000.0001.0000.950
납부매체전자고지여부0.7610.0000.0510.0000.0000.9501.000

Missing values

2023-12-23T07:30:40.962428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:30:42.006174image/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

연번시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율데이터기준일
01부산광역시금정구264102022등록세자동화기기N970716400.012023-12-20
12부산광역시금정구264102022레저세자동화기기N1415296800.02023-12-20
23부산광역시금정구264102022면허세자동화기기N2364900.02023-12-20
34부산광역시금정구264102022자동차세자동화기기N12925199298562017.352023-12-20
45부산광역시금정구264102022재산세자동화기기N41519902970086055.742023-12-20
56부산광역시금정구264102022주민세자동화기기N1417532242915019.032023-12-20
67부산광역시금정구264102022지방소득세자동화기기N206912693750602.782023-12-20
78부산광역시금정구264102022지역자원시설세자동화기기N20772234100.282023-12-20
89부산광역시금정구264102022취득세자동화기기N62344185606000.842023-12-20
910부산광역시금정구264102022등록면허세지자체방문N1033605344010.042023-12-20
연번시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율데이터기준일
7475부산광역시금정구264102022재산세인터넷지로Y5665177386200033.312023-12-20
7576부산광역시금정구264102022주민세인터넷지로Y241134060734014.182023-12-20
7677부산광역시금정구264102022지방소득세인터넷지로Y2484165724620014.612023-12-20
7778부산광역시금정구264102022지역자원시설세인터넷지로Y305302000.182023-12-20
7879부산광역시금정구264102022취득세인터넷지로Y1759021236101.032023-12-20
7980부산광역시금정구264102022등록면허세자동이체Y199618570895021.612023-12-20
8081부산광역시금정구264102022자동차세자동이체Y109516623097011.852023-12-20
8182부산광역시금정구264102022재산세자동이체Y483878945840052.382023-12-20
8283부산광역시금정구264102022주민세자동이체Y13081547350014.162023-12-20
8384부산광역시금정구264102022등록면허세자동화기기N29542473392603.972023-12-20