Overview

Dataset statistics

Number of variables10
Number of observations345
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.8 KiB
Average record size in memory85.4 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description전라남도 영광군의 지방세 납부현황 관련 데이터로 신용카드,가상계좌 등 지방세 납부매체별 납부 현황으로 전자송달 시장 규모 및 편익 분석, 수수료 산정시 기초자료 활용됨.
Author전라남도 영광군
URLhttps://www.data.go.kr/data/15079899/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 납부건수High correlation
납부매체비율 is highly overall correlated with 납부건수High correlation
납부금액 has unique valuesUnique
납부매체비율 has 8 (2.3%) zerosZeros

Reproduction

Analysis started2024-04-17 18:24:13.810952
Analysis finished2024-04-17 18:24:14.853094
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
전라남도
345 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 345
100.0%

Length

2024-04-18T03:24:14.915643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:24:14.983884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 345
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
영광군
345 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영광군 345
100.0%

Length

2024-04-18T03:24:15.056086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:24:15.130287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영광군 345
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
46870
345 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46870
2nd row46870
3rd row46870
4th row46870
5th row46870

Common Values

ValueCountFrequency (%)
46870 345
100.0%

Length

2024-04-18T03:24:15.214926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:24:15.304526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46870 345
100.0%

납부년도
Categorical

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2020
75 
2019
71 
2021
71 
2017
65 
2018
63 

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 (%)
2020 75
21.7%
2019 71
20.6%
2021 71
20.6%
2017 65
18.8%
2018 63
18.3%

Length

2024-04-18T03:24:15.377093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:24:15.454162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 75
21.7%
2019 71
20.6%
2021 71
20.6%
2017 65
18.8%
2018 63
18.3%

세목명
Categorical

Distinct12
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
자동차세
48 
재산세
48 
주민세
48 
등록면허세
47 
지방소득세
41 
Other values (7)
113 

Length

Max length7
Median length5
Mean length3.9797101
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 48
13.9%
재산세 48
13.9%
주민세 48
13.9%
등록면허세 47
13.6%
지방소득세 41
11.9%
취득세 40
11.6%
등록세 28
8.1%
지역자원시설세 18
 
5.2%
담배소비세 12
 
3.5%
종합토지세 7
 
2.0%
Other values (2) 8
 
2.3%

Length

2024-04-18T03:24:15.556329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 48
13.9%
재산세 48
13.9%
주민세 48
13.9%
등록면허세 47
13.6%
지방소득세 41
11.9%
취득세 40
11.6%
등록세 28
8.1%
지역자원시설세 18
 
5.2%
담배소비세 12
 
3.5%
종합토지세 7
 
2.0%
Other values (2) 8
 
2.3%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
가상계좌
49 
은행창구
48 
위택스
41 
지자체방문
40 
자동화기기
38 
Other values (5)
129 

Length

Max length5
Median length4
Mean length3.9652174
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 49
14.2%
은행창구 48
13.9%
위택스 41
11.9%
지자체방문 40
11.6%
자동화기기 38
11.0%
기타 34
9.9%
인터넷지로 33
9.6%
ARS 28
8.1%
자동이체 20
5.8%
페이사납부 14
 
4.1%

Length

2024-04-18T03:24:15.657165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:24:15.751878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가상계좌 49
14.2%
은행창구 48
13.9%
위택스 41
11.9%
지자체방문 40
11.6%
자동화기기 38
11.0%
기타 34
9.9%
인터넷지로 33
9.6%
ars 28
8.1%
자동이체 20
5.8%
페이사납부 14
 
4.1%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size477.0 B
False
188 
True
157 
ValueCountFrequency (%)
False 188
54.5%
True 157
45.5%
2024-04-18T03:24:15.843767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct274
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2574.5971
Minimum1
Maximum26564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-18T03:24:15.927818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q153
median436
Q33319
95-th percentile11959
Maximum26564
Range26563
Interquartile range (IQR)3266

Descriptive statistics

Standard deviation4418.6209
Coefficient of variation (CV)1.7162378
Kurtosis7.7408072
Mean2574.5971
Median Absolute Deviation (MAD)433
Skewness2.6043104
Sum888236
Variance19524211
MonotonicityNot monotonic
2024-04-18T03:24:16.269056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 17
 
4.9%
4 12
 
3.5%
1 9
 
2.6%
3 6
 
1.7%
5 6
 
1.7%
12 5
 
1.4%
7 3
 
0.9%
119 3
 
0.9%
76 3
 
0.9%
65 2
 
0.6%
Other values (264) 279
80.9%
ValueCountFrequency (%)
1 9
2.6%
2 17
4.9%
3 6
 
1.7%
4 12
3.5%
5 6
 
1.7%
6 1
 
0.3%
7 3
 
0.9%
8 1
 
0.3%
9 1
 
0.3%
12 5
 
1.4%
ValueCountFrequency (%)
26564 1
0.3%
25392 1
0.3%
22650 1
0.3%
20759 1
0.3%
20010 1
0.3%
19777 1
0.3%
17868 1
0.3%
17218 1
0.3%
16655 1
0.3%
15547 1
0.3%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct345
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3961539 × 109
Minimum3990
Maximum3.735441 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-18T03:24:16.365930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3990
5-th percentile88474
Q15326360
median1.2754375 × 108
Q37.5479379 × 108
95-th percentile6.4292932 × 109
Maximum3.735441 × 1010
Range3.7354406 × 1010
Interquartile range (IQR)7.4946743 × 108

Descriptive statistics

Standard deviation4.1243207 × 109
Coefficient of variation (CV)2.9540587
Kurtosis39.596516
Mean1.3961539 × 109
Median Absolute Deviation (MAD)1.274229 × 108
Skewness5.8417283
Sum4.816731 × 1011
Variance1.7010021 × 1019
MonotonicityNot monotonic
2024-04-18T03:24:16.473311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141150 1
 
0.3%
76360 1
 
0.3%
757090 1
 
0.3%
1687651220 1
 
0.3%
29092241960 1
 
0.3%
8793483900 1
 
0.3%
1373863750 1
 
0.3%
1586390 1
 
0.3%
9184791300 1
 
0.3%
2965183270 1
 
0.3%
Other values (335) 335
97.1%
ValueCountFrequency (%)
3990 1
0.3%
5320 1
0.3%
9420 1
0.3%
12600 1
0.3%
13750 1
0.3%
15450 1
0.3%
18900 1
0.3%
24150 1
0.3%
25200 1
0.3%
29280 1
0.3%
ValueCountFrequency (%)
37354409910 1
0.3%
31355872660 1
0.3%
30877982950 1
0.3%
29092241960 1
0.3%
23634150270 1
0.3%
16972494150 1
0.3%
12770792280 1
0.3%
9316909290 1
0.3%
9184791300 1
0.3%
9000392280 1
0.3%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct285
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.912957
Minimum0
Maximum89.11
Zeros8
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-18T03:24:16.597083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.76
median9.83
Q321.71
95-th percentile43.624
Maximum89.11
Range89.11
Interquartile range (IQR)20.95

Descriptive statistics

Standard deviation16.087679
Coefficient of variation (CV)1.1563092
Kurtosis3.8852436
Mean13.912957
Median Absolute Deviation (MAD)9.37
Skewness1.7558996
Sum4799.97
Variance258.81342
MonotonicityNot monotonic
2024-04-18T03:24:16.694121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 14
 
4.1%
0.0 8
 
2.3%
0.08 6
 
1.7%
0.02 6
 
1.7%
0.03 5
 
1.4%
0.09 4
 
1.2%
0.49 3
 
0.9%
43.65 2
 
0.6%
0.14 2
 
0.6%
14.07 2
 
0.6%
Other values (275) 293
84.9%
ValueCountFrequency (%)
0.0 8
2.3%
0.01 14
4.1%
0.02 6
1.7%
0.03 5
 
1.4%
0.04 2
 
0.6%
0.06 2
 
0.6%
0.08 6
1.7%
0.09 4
 
1.2%
0.1 2
 
0.6%
0.11 1
 
0.3%
ValueCountFrequency (%)
89.11 1
0.3%
84.97 1
0.3%
83.87 1
0.3%
77.88 1
0.3%
72.62 1
0.3%
61.67 1
0.3%
61.59 1
0.3%
59.9 1
0.3%
56.2 1
0.3%
55.76 1
0.3%

Interactions

2024-04-18T03:24:14.481699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:14.088545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:14.281971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:14.541071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:14.152438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:14.346578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:14.617785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:14.220649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:14.417628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:24:16.777110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0000.0000.3300.4880.659
납부매체0.0000.0001.0001.0000.5960.2820.516
납부매체전자고지여부0.0000.0001.0001.0000.2000.2100.238
납부건수0.0000.3300.5960.2001.0000.4840.597
납부금액0.0000.4880.2820.2100.4841.0000.312
납부매체비율0.0000.6590.5160.2380.5970.3121.000
2024-04-18T03:24:16.881615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부년도납부매체납부매체전자고지여부
세목명1.0000.0000.0000.000
납부년도0.0001.0000.0000.000
납부매체0.0000.0001.0000.988
납부매체전자고지여부0.0000.0000.9881.000
2024-04-18T03:24:16.964809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부건수1.0000.7130.7760.0000.1440.2190.151
납부금액0.7131.0000.4610.0000.2300.1320.207
납부매체비율0.7760.4611.0000.0000.3500.2640.235
납부년도0.0000.0000.0001.0000.0000.0000.000
세목명0.1440.2300.3500.0001.0000.0000.000
납부매체0.2190.1320.2640.0000.0001.0000.988
납부매체전자고지여부0.1510.2070.2350.0000.0000.9881.000

Missing values

2024-04-18T03:24:14.703563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:24:14.809861image/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
0전라남도영광군468702017등록면허세ARSN121411504.35
1전라남도영광군468702017자동차세ARSN1703383299061.59
2전라남도영광군468702017재산세ARSN62789144022.46
3전라남도영광군468702017주민세ARSN2935161010.51
4전라남도영광군468702017지방소득세ARSN153200.36
5전라남도영광군468702017취득세ARSN29046200.72
6전라남도영광군468702017등록면허세가상계좌Y4627800237209.65
7전라남도영광군468702017등록세가상계좌Y11080000.0
8전라남도영광군468702017면허세가상계좌Y2370800.0
9전라남도영광군468702017자동차세가상계좌Y15547230063421032.42
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
335전라남도영광군468702021재산세지자체방문N82013661182020.95
336전라남도영광군468702021주민세지자체방문N5761459006014.72
337전라남도영광군468702021지방소득세지자체방문N99399098502.53
338전라남도영광군468702021취득세지자체방문N65658904801.66
339전라남도영광군468702021등록면허세페이사납부Y526324104.28
340전라남도영광군468702021자동차세페이사납부Y4368838602035.88
341전라남도영광군468702021재산세페이사납부Y4902107707040.33
342전라남도영광군468702021주민세페이사납부Y229275921018.85
343전라남도영광군468702021지방소득세페이사납부Y139900.08
344전라남도영광군468702021취득세페이사납부Y788522100.58