Overview

Dataset statistics

Number of variables10
Number of observations479
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.9 KiB
Average record size in memory85.3 B

Variable types

Categorical5
Numeric4
Boolean1

Dataset

Description2017년부터 2022년까지 지방세 납부현황에 대한 세목명, 납부매체, 납부매체전자고지여부, 납부건수, 납부금액, 납부매체비율에 대한 정보
Author경상남도 통영시
URLhttps://www.data.go.kr/data/15078253/fileData.do

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(건) is highly overall correlated with 납부금액(원) and 1 other fieldsHigh correlation
납부금액(원) is highly overall correlated with 납부건수(건) and 1 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부건수(건) and 1 other fieldsHigh correlation
납부매체비율 has 147 (30.7%) zerosZeros

Reproduction

Analysis started2024-04-21 01:44:55.418763
Analysis finished2024-04-21 01:44:58.753920
Duration3.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
경상남도
479 

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 479
100.0%

Length

2024-04-21T10:44:58.819347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:44:58.899414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 479
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
통영시
479 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통영시 479
100.0%

Length

2024-04-21T10:44:58.979728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:44:59.059936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 479
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
48220
479 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48220
2nd row48220
3rd row48220
4th row48220
5th row48220

Common Values

ValueCountFrequency (%)
48220 479
100.0%

Length

2024-04-21T10:44:59.148161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:44:59.224635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48220 479
100.0%

납부년도
Real number (ℝ)

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5866
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-21T10:44:59.301423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6992211
Coefficient of variation (CV)0.00084137076
Kurtosis-1.2456811
Mean2019.5866
Median Absolute Deviation (MAD)1
Skewness-0.072219636
Sum967382
Variance2.8873525
MonotonicityIncreasing
2024-04-21T10:44:59.396755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 84
17.5%
2022 84
17.5%
2020 83
17.3%
2019 80
16.7%
2017 74
15.4%
2018 74
15.4%
ValueCountFrequency (%)
2017 74
15.4%
2018 74
15.4%
2019 80
16.7%
2020 83
17.3%
2021 84
17.5%
2022 84
17.5%
ValueCountFrequency (%)
2022 84
17.5%
2021 84
17.5%
2020 83
17.3%
2019 80
16.7%
2018 74
15.4%
2017 74
15.4%

세목명
Categorical

Distinct14
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
등록면허세
64 
자동차세
64 
재산세
64 
주민세
64 
지방소득세
54 
Other values (9)
169 

Length

Max length7
Median length5
Mean length4.0334029
Min length3

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
등록면허세 64
13.4%
자동차세 64
13.4%
재산세 64
13.4%
주민세 64
13.4%
지방소득세 54
11.3%
취득세 51
10.6%
등록세 32
6.7%
지역자원시설세 31
6.5%
면허세 18
 
3.8%
담배소비세 17
 
3.5%
Other values (4) 20
 
4.2%

Length

2024-04-21T10:44:59.513428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등록면허세 64
13.4%
자동차세 64
13.4%
재산세 64
13.4%
주민세 64
13.4%
지방소득세 54
11.3%
취득세 51
10.6%
등록세 32
6.7%
지역자원시설세 31
6.5%
면허세 18
 
3.8%
담배소비세 17
 
3.5%
Other values (4) 20
 
4.2%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
은행창구
63 
ARS
62 
가상계좌
62 
기타
54 
위택스
52 
Other values (5)
186 

Length

Max length5
Median length4
Mean length3.874739
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
은행창구 63
13.2%
ARS 62
12.9%
가상계좌 62
12.9%
기타 54
11.3%
위택스 52
10.9%
지자체방문 51
10.6%
자동화기기 48
10.0%
인터넷지로 40
8.4%
자동이체 24
 
5.0%
페이사납부 23
 
4.8%

Length

2024-04-21T10:44:59.635271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:44:59.762335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
은행창구 63
13.2%
ars 62
12.9%
가상계좌 62
12.9%
기타 54
11.3%
위택스 52
10.9%
지자체방문 51
10.6%
자동화기기 48
10.0%
인터넷지로 40
8.4%
자동이체 24
 
5.0%
페이사납부 23
 
4.8%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size611.0 B
False
252 
True
227 
ValueCountFrequency (%)
False 252
52.6%
True 227
47.4%
2024-04-21T10:44:59.872411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct366
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4553.3925
Minimum1
Maximum51654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-21T10:44:59.970068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q128
median837
Q34332.5
95-th percentile24523.8
Maximum51654
Range51653
Interquartile range (IQR)4304.5

Descriptive statistics

Standard deviation9056.6766
Coefficient of variation (CV)1.9889954
Kurtosis9.7362794
Mean4553.3925
Median Absolute Deviation (MAD)834
Skewness3.0804107
Sum2181075
Variance82023391
MonotonicityNot monotonic
2024-04-21T10:45:00.103724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25
 
5.2%
3 12
 
2.5%
2 8
 
1.7%
13 7
 
1.5%
11 7
 
1.5%
6 6
 
1.3%
5 6
 
1.3%
4 6
 
1.3%
7 5
 
1.0%
8 5
 
1.0%
Other values (356) 392
81.8%
ValueCountFrequency (%)
1 25
5.2%
2 8
 
1.7%
3 12
2.5%
4 6
 
1.3%
5 6
 
1.3%
6 6
 
1.3%
7 5
 
1.0%
8 5
 
1.0%
9 3
 
0.6%
10 3
 
0.6%
ValueCountFrequency (%)
51654 1
0.2%
50200 1
0.2%
46435 1
0.2%
45849 1
0.2%
45792 1
0.2%
45588 1
0.2%
43839 1
0.2%
42316 1
0.2%
41617 1
0.2%
41080 1
0.2%

납부금액(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct477
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.514873 × 109
Minimum590
Maximum1.9031349 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-21T10:45:00.260645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum590
5-th percentile37063
Q14337480
median1.8055241 × 108
Q31.3901682 × 109
95-th percentile7.4475866 × 109
Maximum1.9031349 × 1010
Range1.9031348 × 1010
Interquartile range (IQR)1.3858307 × 109

Descriptive statistics

Standard deviation2.8828579 × 109
Coefficient of variation (CV)1.903036
Kurtosis8.6765958
Mean1.514873 × 109
Median Absolute Deviation (MAD)1.8045813 × 108
Skewness2.7543406
Sum7.2562419 × 1011
Variance8.3108697 × 1018
MonotonicityNot monotonic
2024-04-21T10:45:00.406027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15450 3
 
0.6%
1254800 1
 
0.2%
1392000 1
 
0.2%
7628819840 1
 
0.2%
5860418200 1
 
0.2%
67250 1
 
0.2%
1128830 1
 
0.2%
1008194740 1
 
0.2%
74120 1
 
0.2%
48051300 1
 
0.2%
Other values (467) 467
97.5%
ValueCountFrequency (%)
590 1
0.2%
2980 1
0.2%
3700 1
0.2%
6300 1
0.2%
6480 1
0.2%
7590 1
0.2%
10300 1
0.2%
10540 1
0.2%
11340 1
0.2%
12500 1
0.2%
ValueCountFrequency (%)
19031348620 1
0.2%
17314984910 1
0.2%
15301994500 1
0.2%
14913355490 1
0.2%
13898647220 1
0.2%
12956592240 1
0.2%
12461844040 1
0.2%
11956361310 1
0.2%
11920642320 1
0.2%
10868423520 1
0.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.091858
Minimum0
Maximum75
Zeros147
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-21T10:45:00.686871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q320
95-th percentile41.3
Maximum75
Range75
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.476496
Coefficient of variation (CV)1.1972103
Kurtosis1.8401566
Mean12.091858
Median Absolute Deviation (MAD)6
Skewness1.4222352
Sum5792
Variance209.56895
MonotonicityNot monotonic
2024-04-21T10:45:00.816836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 147
30.7%
2 25
 
5.2%
1 22
 
4.6%
6 19
 
4.0%
20 18
 
3.8%
21 15
 
3.1%
10 13
 
2.7%
18 13
 
2.7%
9 13
 
2.7%
12 13
 
2.7%
Other values (46) 181
37.8%
ValueCountFrequency (%)
0 147
30.7%
1 22
 
4.6%
2 25
 
5.2%
3 11
 
2.3%
4 7
 
1.5%
5 9
 
1.9%
6 19
 
4.0%
7 10
 
2.1%
8 5
 
1.0%
9 13
 
2.7%
ValueCountFrequency (%)
75 1
 
0.2%
73 1
 
0.2%
66 1
 
0.2%
62 1
 
0.2%
60 1
 
0.2%
53 1
 
0.2%
52 5
1.0%
51 1
 
0.2%
49 1
 
0.2%
48 1
 
0.2%

Interactions

2024-04-21T10:44:58.150036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:56.920734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:57.465747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:57.787284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:58.229148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:57.069318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:57.542878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:57.868482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:58.313284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:57.291898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:57.622172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:57.947524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:58.421345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:57.381258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:57.704913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:44:58.054196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:45:00.934131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수(건)납부금액(원)납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.2450.0000.3510.5220.623
납부매체0.0000.2451.0000.9940.6050.3120.618
납부매체전자고지여부0.0000.0000.9941.0000.1480.1030.146
납부건수(건)0.0000.3510.6050.1481.0000.7210.727
납부금액(원)0.0000.5220.3120.1030.7211.0000.465
납부매체비율0.0000.6230.6180.1460.7270.4651.000
2024-04-21T10:45:01.039556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체전자고지여부세목명납부매체
납부매체전자고지여부1.0000.0000.925
세목명0.0001.0000.100
납부매체0.9250.1001.000
2024-04-21T10:45:01.127328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도납부건수(건)납부금액(원)납부매체비율세목명납부매체납부매체전자고지여부
납부년도1.000-0.030-0.013-0.0010.0000.0000.000
납부건수(건)-0.0301.0000.7910.8550.1490.2250.112
납부금액(원)-0.0130.7911.0000.6620.2400.0990.078
납부매체비율-0.0010.8550.6621.0000.3130.2290.100
세목명0.0000.1490.2400.3131.0000.1000.000
납부매체0.0000.2250.0990.2290.1001.0000.925
납부매체전자고지여부0.0000.1120.0780.1000.0000.9251.000

Missing values

2024-04-21T10:44:58.554905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:44:58.700780image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수(건)납부금액(원)납부매체비율
0경상남도통영시482202017등록면허세ARSN13520187702
1경상남도통영시482202017등록면허세ARSY3334500
2경상남도통영시482202017자동차세ARSN287756354828052
3경상남도통영시482202017자동차세ARSY2229715100
4경상남도통영시482202017재산세ARSN183028297720033
5경상남도통영시482202017재산세ARSY116388500
6경상남도통영시482202017주민세ARSN563854248010
7경상남도통영시482202017주민세ARSY212683300
8경상남도통영시482202017지방소득세ARSN47150425301
9경상남도통영시482202017취득세ARSN18267604900
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