Overview

Dataset statistics

Number of variables10
Number of observations312
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.0 KiB
Average record size in memory85.4 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description경상남도 거창군 지방세 납부현황에 대한 데이터로 납부년도, 세목명, 납부매체, 납부매체전자고지여부, 납부건수, 납부금액, 납부매체비율 항목을 제공합니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079200

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 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
납부금액 has unique valuesUnique
납부매체비율 has 70 (22.4%) zerosZeros

Reproduction

Analysis started2023-12-10 23:15:31.259979
Analysis finished2023-12-10 23:15:32.596161
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
경상남도
312 

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 312
100.0%

Length

2023-12-11T08:15:32.657290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:32.755325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 312
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
거창군
312 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거창군 312
100.0%

Length

2023-12-11T08:15:33.098964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:33.197786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 312
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
48880
312 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 312
100.0%

Length

2023-12-11T08:15:33.304481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:33.413511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 312
100.0%

납부년도
Categorical

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2020
81 
2017
78 
2019
78 
2018
75 

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 81
26.0%
2017 78
25.0%
2019 78
25.0%
2018 75
24.0%

Length

2023-12-11T08:15:33.550403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:33.687990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 81
26.0%
2017 78
25.0%
2019 78
25.0%
2018 75
24.0%

세목명
Categorical

Distinct13
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
자동차세
42 
주민세
42 
재산세
41 
등록면허세
40 
지방소득세
35 
Other values (8)
112 

Length

Max length7
Median length5
Mean length4.0576923
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 42
13.5%
주민세 42
13.5%
재산세 41
13.1%
등록면허세 40
12.8%
지방소득세 35
11.2%
취득세 34
10.9%
지역자원시설세 26
8.3%
등록세 22
7.1%
면허세 12
 
3.8%
종합토지세 8
 
2.6%
Other values (3) 10
 
3.2%

Length

2023-12-11T08:15:33.812742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 42
13.5%
주민세 42
13.5%
재산세 41
13.1%
등록면허세 40
12.8%
지방소득세 35
11.2%
취득세 34
10.9%
지역자원시설세 26
8.3%
등록세 22
7.1%
면허세 12
 
3.8%
종합토지세 8
 
2.6%
Other values (3) 10
 
3.2%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
ARS
42 
가상계좌
41 
은행창구
37 
지자체방문
35 
기타
33 
Other values (5)
124 

Length

Max length5
Median length4
Mean length3.900641
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ARS 42
13.5%
가상계좌 41
13.1%
은행창구 37
11.9%
지자체방문 35
11.2%
기타 33
10.6%
위택스 33
10.6%
자동화기기 33
10.6%
인터넷지로 31
9.9%
자동이체 16
 
5.1%
페이사납부 11
 
3.5%

Length

2023-12-11T08:15:33.974620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:34.101792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ars 42
13.5%
가상계좌 41
13.1%
은행창구 37
11.9%
지자체방문 35
11.2%
기타 33
10.6%
위택스 33
10.6%
자동화기기 33
10.6%
인터넷지로 31
9.9%
자동이체 16
 
5.1%
페이사납부 11
 
3.5%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
False
165 
True
147 
ValueCountFrequency (%)
False 165
52.9%
True 147
47.1%
2023-12-11T08:15:34.232785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct231
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2504.6667
Minimum1
Maximum27467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T08:15:34.338162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111.5
median301
Q32256
95-th percentile13071.05
Maximum27467
Range27466
Interquartile range (IQR)2244.5

Descriptive statistics

Standard deviation5044.3512
Coefficient of variation (CV)2.013981
Kurtosis8.4429419
Mean2504.6667
Median Absolute Deviation (MAD)299
Skewness2.8700478
Sum781456
Variance25445479
MonotonicityNot monotonic
2023-12-11T08:15:34.478225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
6.4%
3 13
 
4.2%
2 12
 
3.8%
4 11
 
3.5%
5 8
 
2.6%
6 4
 
1.3%
8 3
 
1.0%
10 3
 
1.0%
87 3
 
1.0%
28 2
 
0.6%
Other values (221) 233
74.7%
ValueCountFrequency (%)
1 20
6.4%
2 12
3.8%
3 13
4.2%
4 11
3.5%
5 8
 
2.6%
6 4
 
1.3%
7 2
 
0.6%
8 3
 
1.0%
9 2
 
0.6%
10 3
 
1.0%
ValueCountFrequency (%)
27467 1
0.3%
27106 1
0.3%
24894 1
0.3%
24615 1
0.3%
23448 1
0.3%
21911 1
0.3%
21878 1
0.3%
20895 1
0.3%
19951 1
0.3%
19051 1
0.3%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct312
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8607954 × 108
Minimum2010
Maximum1.27225 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T08:15:34.629830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile13626
Q11288607.5
median55585310
Q34.7921286 × 108
95-th percentile3.5852888 × 109
Maximum1.27225 × 1010
Range1.2722498 × 1010
Interquartile range (IQR)4.7792425 × 108

Descriptive statistics

Standard deviation1.5481635 × 109
Coefficient of variation (CV)2.2565365
Kurtosis18.729575
Mean6.8607954 × 108
Median Absolute Deviation (MAD)55561585
Skewness3.8317568
Sum2.1405682 × 1011
Variance2.3968103 × 1018
MonotonicityNot monotonic
2023-12-11T08:15:34.790818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175210 1
 
0.3%
395156890 1
 
0.3%
23704600 1
 
0.3%
672618290 1
 
0.3%
490745700 1
 
0.3%
1149890 1
 
0.3%
57605520 1
 
0.3%
67037850 1
 
0.3%
928839340 1
 
0.3%
7945500 1
 
0.3%
Other values (302) 302
96.8%
ValueCountFrequency (%)
2010 1
0.3%
2710 1
0.3%
3150 1
0.3%
3780 1
0.3%
6180 1
0.3%
8110 1
0.3%
9270 1
0.3%
9450 1
0.3%
10700 1
0.3%
11000 1
0.3%
ValueCountFrequency (%)
12722500390 1
0.3%
9929233800 1
0.3%
7581914470 1
0.3%
6830269690 1
0.3%
6353153100 1
0.3%
5988294950 1
0.3%
5754320920 1
0.3%
5703508900 1
0.3%
5604891180 1
0.3%
5436505130 1
0.3%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.179583
Minimum0
Maximum85.11
Zeros70
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T08:15:34.934960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1075
median4
Q319
95-th percentile54
Maximum85.11
Range85.11
Interquartile range (IQR)18.8925

Descriptive statistics

Standard deviation16.82063
Coefficient of variation (CV)1.3810514
Kurtosis4.0413941
Mean12.179583
Median Absolute Deviation (MAD)4
Skewness1.9558691
Sum3800.03
Variance282.93361
MonotonicityNot monotonic
2023-12-11T08:15:35.057570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 70
22.4%
1.0 22
 
7.1%
4.0 12
 
3.8%
2.0 11
 
3.5%
20.0 8
 
2.6%
3.0 8
 
2.6%
10.0 7
 
2.2%
17.0 6
 
1.9%
8.0 6
 
1.9%
16.0 5
 
1.6%
Other values (113) 157
50.3%
ValueCountFrequency (%)
0.0 70
22.4%
0.01 3
 
1.0%
0.03 1
 
0.3%
0.04 1
 
0.3%
0.05 1
 
0.3%
0.08 1
 
0.3%
0.1 1
 
0.3%
0.11 1
 
0.3%
0.14 1
 
0.3%
0.15 1
 
0.3%
ValueCountFrequency (%)
85.11 1
0.3%
84.0 1
0.3%
81.0 1
0.3%
80.0 1
0.3%
66.0 1
0.3%
64.0 1
0.3%
63.0 1
0.3%
62.66 1
0.3%
58.0 1
0.3%
57.0 1
0.3%

Interactions

2023-12-11T08:15:32.122469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:31.593558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:31.846628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:32.202588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:31.668885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:31.927152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:32.282870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:31.753361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:32.039828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:15:35.135241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0000.0000.3370.4640.609
납부매체0.0000.0001.0000.9940.5780.3760.490
납부매체전자고지여부0.0000.0000.9941.0000.0000.2550.138
납부건수0.0000.3370.5780.0001.0000.6500.661
납부금액0.0000.4640.3760.2550.6501.0000.372
납부매체비율0.0000.6090.4900.1380.6610.3721.000
2023-12-11T08:15:35.227258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체전자고지여부납부년도납부매체세목명
납부매체전자고지여부1.0000.0000.9220.000
납부년도0.0001.0000.0000.000
납부매체0.9220.0001.0000.000
세목명0.0000.0000.0001.000
2023-12-11T08:15:35.307679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부건수1.0000.8330.8530.0000.1450.2100.000
납부금액0.8331.0000.7120.0000.2270.1890.190
납부매체비율0.8530.7121.0000.0000.3130.2470.136
납부년도0.0000.0000.0001.0000.0000.0000.000
세목명0.1450.2270.3130.0001.0000.0000.000
납부매체0.2100.1890.2470.0000.0001.0000.922
납부매체전자고지여부0.0000.1900.1360.0000.0000.9221.000

Missing values

2023-12-11T08:15:32.397540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:15:32.545478image/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
0경상남도거창군488802017등록면허세ARSN161752102.0
1경상남도거창군488802017등록면허세ARSY1120000.0
2경상남도거창군488802017자동차세ARSN5069237097063.0
3경상남도거창군488802017자동차세ARSY57470301.0
4경상남도거창군488802017재산세ARSN1701389874021.0
5경상남도거창군488802017주민세ARSN91131841011.0
6경상남도거창군488802017주민세ARSY5553301.0
7경상남도거창군488802017지방소득세ARSN534334701.0
8경상남도거창군488802017지방소득세ARSY120100.0
9경상남도거창군488802017취득세ARSN327731700.0
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
302경상남도거창군488802020주민세지자체방문N491140398403.77
303경상남도거창군488802020지방소득세지자체방문N1971378402001.51
304경상남도거창군488802020지역자원시설세지자체방문N51677200.04
305경상남도거창군488802020취득세지자체방문N3710517168909028.48
306경상남도거창군488802020등록면허세페이사납부Y559077005.83
307경상남도거창군488802020자동차세페이사납부Y3085364758032.63
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