Overview

Dataset statistics

Number of variables10
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.6 KiB
Average record size in memory85.7 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 6 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-12 06:35:15.687053
Analysis finished2023-12-12 06:35:17.387403
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
전라남도
199 

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 199
100.0%

Length

2023-12-12T15:35:17.470534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:17.602541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 199
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
영광군
199 

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 199
100.0%

Length

2023-12-12T15:35:17.738143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:17.855544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영광군 199
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
46870
199 

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 199
100.0%

Length

2023-12-12T15:35:17.964996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:18.097161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46870 199
100.0%

납부년도
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2019
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 (%)
2019 71
35.7%
2017 65
32.7%
2018 63
31.7%

Length

2023-12-12T15:35:18.211111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:18.348151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 71
35.7%
2017 65
32.7%
2018 63
31.7%

세목명
Categorical

Distinct11
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
자동차세
28 
재산세
28 
주민세
28 
등록면허세
27 
지방소득세
24 
Other values (6)
64 

Length

Max length7
Median length5
Mean length3.9949749
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 28
14.1%
재산세 28
14.1%
주민세 28
14.1%
등록면허세 27
13.6%
지방소득세 24
12.1%
취득세 23
11.6%
등록세 16
8.0%
지역자원시설세 12
6.0%
담배소비세 6
 
3.0%
종합토지세 4
 
2.0%

Length

2023-12-12T15:35:18.525587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 28
14.1%
재산세 28
14.1%
주민세 28
14.1%
등록면허세 27
13.6%
지방소득세 24
12.1%
취득세 23
11.6%
등록세 16
8.0%
지역자원시설세 12
6.0%
담배소비세 6
 
3.0%
종합토지세 4
 
2.0%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
가상계좌
30 
은행창구
30 
위택스
23 
자동화기기
23 
지자체방문
22 
Other values (5)
71 

Length

Max length5
Median length4
Mean length3.9346734
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 30
15.1%
은행창구 30
15.1%
위택스 23
11.6%
자동화기기 23
11.6%
지자체방문 22
11.1%
기타 20
10.1%
인터넷지로 19
9.5%
ARS 17
8.5%
자동이체 12
 
6.0%
페이사납부 3
 
1.5%

Length

2023-12-12T15:35:18.709527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:18.892287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가상계좌 30
15.1%
은행창구 30
15.1%
위택스 23
11.6%
자동화기기 23
11.6%
지자체방문 22
11.1%
기타 20
10.1%
인터넷지로 19
9.5%
ars 17
8.5%
자동이체 12
 
6.0%
페이사납부 3
 
1.5%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size331.0 B
False
112 
True
87 
ValueCountFrequency (%)
False 112
56.3%
True 87
43.7%
2023-12-12T15:35:19.055414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2617.7789
Minimum1
Maximum22650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T15:35:19.213416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q160.5
median410
Q33415
95-th percentile12019.7
Maximum22650
Range22649
Interquartile range (IQR)3354.5

Descriptive statistics

Standard deviation4301.4776
Coefficient of variation (CV)1.6431783
Kurtosis5.0408593
Mean2617.7789
Median Absolute Deviation (MAD)407
Skewness2.2388422
Sum520938
Variance18502709
MonotonicityNot monotonic
2023-12-12T15:35:19.395462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9
 
4.5%
4 7
 
3.5%
1 5
 
2.5%
12 4
 
2.0%
3 4
 
2.0%
76 3
 
1.5%
5 3
 
1.5%
119 2
 
1.0%
44 2
 
1.0%
222 2
 
1.0%
Other values (155) 158
79.4%
ValueCountFrequency (%)
1 5
2.5%
2 9
4.5%
3 4
2.0%
4 7
3.5%
5 3
 
1.5%
9 1
 
0.5%
12 4
2.0%
14 1
 
0.5%
15 1
 
0.5%
20 1
 
0.5%
ValueCountFrequency (%)
22650 1
0.5%
20010 1
0.5%
17868 1
0.5%
17218 1
0.5%
16655 1
0.5%
15547 1
0.5%
15114 1
0.5%
14105 1
0.5%
12163 1
0.5%
12035 1
0.5%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3700007 × 109
Minimum5320
Maximum3.735441 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T15:35:19.559622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5320
5-th percentile93683
Q16163720
median1.1660882 × 108
Q37.2445211 × 108
95-th percentile5.3853155 × 109
Maximum3.735441 × 1010
Range3.7354405 × 1010
Interquartile range (IQR)7.1828839 × 108

Descriptive statistics

Standard deviation4.2315242 × 109
Coefficient of variation (CV)3.0887022
Kurtosis42.471693
Mean1.3700007 × 109
Median Absolute Deviation (MAD)1.1646905 × 108
Skewness6.071787
Sum2.7263014 × 1011
Variance1.7905797 × 1019
MonotonicityNot monotonic
2023-12-12T15:35:19.749120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141150 1
 
0.5%
2553831560 1
 
0.5%
209596310 1
 
0.5%
139770 1
 
0.5%
49770440 1
 
0.5%
15255150 1
 
0.5%
706320 1
 
0.5%
93880 1
 
0.5%
452377300 1
 
0.5%
115865460 1
 
0.5%
Other values (189) 189
95.0%
ValueCountFrequency (%)
5320 1
0.5%
9420 1
0.5%
13750 1
0.5%
15450 1
0.5%
18900 1
0.5%
37080 1
0.5%
44730 1
0.5%
85740 1
0.5%
91210 1
0.5%
91910 1
0.5%
ValueCountFrequency (%)
37354409910 1
0.5%
31355872660 1
0.5%
23634150270 1
0.5%
16972494150 1
0.5%
9000392280 1
0.5%
8769722160 1
0.5%
8053630080 1
0.5%
7888368030 1
0.5%
6607369150 1
0.5%
5716989620 1
0.5%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct172
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.070151
Minimum0
Maximum89.11
Zeros6
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T15:35:19.926499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.735
median9.87
Q321.735
95-th percentile44.097
Maximum89.11
Range89.11
Interquartile range (IQR)21

Descriptive statistics

Standard deviation16.682262
Coefficient of variation (CV)1.1856492
Kurtosis4.4916898
Mean14.070151
Median Absolute Deviation (MAD)9.47
Skewness1.8806434
Sum2799.96
Variance278.29788
MonotonicityNot monotonic
2023-12-12T15:35:20.099896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 7
 
3.5%
0.0 6
 
3.0%
0.03 3
 
1.5%
0.02 3
 
1.5%
0.09 3
 
1.5%
43.65 2
 
1.0%
0.04 2
 
1.0%
19.34 2
 
1.0%
1.87 2
 
1.0%
0.25 2
 
1.0%
Other values (162) 167
83.9%
ValueCountFrequency (%)
0.0 6
3.0%
0.01 7
3.5%
0.02 3
1.5%
0.03 3
1.5%
0.04 2
 
1.0%
0.06 2
 
1.0%
0.08 1
 
0.5%
0.09 3
1.5%
0.1 1
 
0.5%
0.12 1
 
0.5%
ValueCountFrequency (%)
89.11 1
0.5%
84.97 1
0.5%
83.87 1
0.5%
61.67 1
0.5%
61.59 1
0.5%
59.9 1
0.5%
55.76 1
0.5%
52.92 1
0.5%
50.49 1
0.5%
48.12 1
0.5%

Interactions

2023-12-12T15:35:16.706653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.050473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.334016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.826541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.141982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.448276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.951826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.243425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.588795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:35:20.218365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0000.0000.3130.4100.623
납부매체0.0000.0001.0001.0000.6510.2830.551
납부매체전자고지여부0.0000.0001.0001.0000.2280.2170.303
납부건수0.0000.3130.6510.2281.0000.4880.705
납부금액0.0000.4100.2830.2170.4881.0000.271
납부매체비율0.0000.6230.5510.3030.7050.2711.000
2023-12-12T15:35:20.359049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체세목명납부매체전자고지여부납부년도
납부매체1.0000.0000.9790.000
세목명0.0001.0000.0000.000
납부매체전자고지여부0.9790.0001.0000.000
납부년도0.0000.0000.0001.000
2023-12-12T15:35:20.485178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부건수1.0000.7240.7830.0000.1370.2510.170
납부금액0.7241.0000.4780.0000.2130.1460.229
납부매체비율0.7830.4781.0000.0000.3520.3000.224
납부년도0.0000.0000.0001.0000.0000.0000.000
세목명0.1370.2130.3520.0001.0000.0000.000
납부매체0.2510.1460.3000.0000.0001.0000.979
납부매체전자고지여부0.1700.2290.2240.0000.0000.9791.000

Missing values

2023-12-12T15:35:17.106231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:35:17.310387image/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
189전라남도영광군468702019등록세지자체방문N310074400.08
190전라남도영광군468702019자동차세지자체방문N189534135026048.12
191전라남도영광군468702019재산세지자체방문N91413518483023.21
192전라남도영광군468702019주민세지자체방문N5481598198013.92
193전라남도영광군468702019지방소득세지자체방문N119370844703.02
194전라남도영광군468702019지역자원시설세지자체방문N42126400.1
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