Overview

Dataset statistics

Number of variables9
Number of observations21
Missing cells1
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory84.3 B

Variable types

Categorical5
Numeric4

Dataset

Description경상남도 사천시 지방세 비과/감면율 현황(2018 ~ 2020년)에 대한 데이터로 과세액 중 비과세액과 감면액이 차지하는 비율 현황을 제공합니다.
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079585

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
비과세금액 is highly overall correlated with 감면금액 and 2 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
비과세금액 has 1 (4.8%) missing valuesMissing
감면금액 has unique valuesUnique
부과금액 has unique valuesUnique
비과세금액 has 3 (14.3%) zerosZeros
부과금액 has 1 (4.8%) zerosZeros
비과세감면율 has 3 (14.3%) zerosZeros

Reproduction

Analysis started2023-12-10 23:08:57.555048
Analysis finished2023-12-10 23:08:59.659615
Duration2.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
경상남도
21 

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 21
100.0%

Length

2023-12-11T08:08:59.744275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:08:59.850865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 21
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
사천시
21 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사천시 21
100.0%

Length

2023-12-11T08:08:59.963564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:09:00.081093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사천시 21
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
48240
21 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48240
2nd row48240
3rd row48240
4th row48240
5th row48240

Common Values

ValueCountFrequency (%)
48240 21
100.0%

Length

2023-12-11T08:09:00.208287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:09:00.316902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48240 21
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Memory size300.0 B
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
등록면허세
3 
Other values (3)
6 

Length

Max length7
Median length3
Mean length3.8095238
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교육세
2nd row등록세
3rd row재산세
4th row주민세
5th row취득세

Common Values

ValueCountFrequency (%)
재산세 3
14.3%
주민세 3
14.3%
취득세 3
14.3%
자동차세 3
14.3%
등록면허세 3
14.3%
교육세 2
9.5%
등록세 2
9.5%
지역자원시설세 2
9.5%

Length

2023-12-11T08:09:00.447186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:09:00.605662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 3
14.3%
주민세 3
14.3%
취득세 3
14.3%
자동차세 3
14.3%
등록면허세 3
14.3%
교육세 2
9.5%
등록세 2
9.5%
지역자원시설세 2
9.5%

과세년도
Categorical

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
2018
8 
2019
7 
2020
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 8
38.1%
2019 7
33.3%
2020 6
28.6%

Length

2023-12-11T08:09:00.752905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:09:00.880091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 8
38.1%
2019 7
33.3%
2020 6
28.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)90.0%
Missing1
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean2.2796495 × 109
Minimum0
Maximum1.1046709 × 1010
Zeros3
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T08:09:01.019465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113497895
median77155405
Q34.1177149 × 109
95-th percentile1.0341033 × 1010
Maximum1.1046709 × 1010
Range1.1046709 × 1010
Interquartile range (IQR)4.104217 × 109

Descriptive statistics

Standard deviation3.845427 × 109
Coefficient of variation (CV)1.6868501
Kurtosis0.89515808
Mean2.2796495 × 109
Median Absolute Deviation (MAD)77155405
Skewness1.518639
Sum4.5592989 × 1010
Variance1.4787309 × 1019
MonotonicityNot monotonic
2023-12-11T08:09:01.142834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 3
 
14.3%
73673000 1
 
4.8%
14252860 1
 
4.8%
80637810 1
 
4.8%
3985539860 1
 
4.8%
32082750 1
 
4.8%
11046708860 1
 
4.8%
159631000 1
 
4.8%
11233000 1
 
4.8%
4514240000 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
0 3
14.3%
9592000 1
 
4.8%
11233000 1
 
4.8%
14252860 1
 
4.8%
32082750 1
 
4.8%
63053000 1
 
4.8%
66481000 1
 
4.8%
73673000 1
 
4.8%
80637810 1
 
4.8%
155435000 1
 
4.8%
ValueCountFrequency (%)
11046708860 1
4.8%
10303892000 1
4.8%
9628532000 1
4.8%
5254972000 1
4.8%
4514240000 1
4.8%
3985539860 1
4.8%
193033000 1
4.8%
159631000 1
4.8%
155435000 1
4.8%
80637810 1
4.8%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3626474 × 109
Minimum2000
Maximum8.482798 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T08:09:01.270750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile3000
Q197609000
median2.3522016 × 108
Q31.662038 × 109
95-th percentile7.2304599 × 109
Maximum8.482798 × 109
Range8.482796 × 109
Interquartile range (IQR)1.564429 × 109

Descriptive statistics

Standard deviation2.4437714 × 109
Coefficient of variation (CV)1.7933996
Kurtosis3.9608439
Mean1.3626474 × 109
Median Absolute Deviation (MAD)2.3504816 × 108
Skewness2.2007653
Sum2.8615596 × 1010
Variance5.9720187 × 1018
MonotonicityNot monotonic
2023-12-11T08:09:01.400172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2000 1
 
4.8%
172000 1
 
4.8%
235220160 1
 
4.8%
496686010 1
 
4.8%
7230459920 1
 
4.8%
273938150 1
 
4.8%
1870536150 1
 
4.8%
9250650 1
 
4.8%
69550000 1
 
4.8%
232993000 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
2000 1
4.8%
3000 1
4.8%
172000 1
4.8%
9250650 1
4.8%
69550000 1
4.8%
97609000 1
4.8%
100645000 1
4.8%
101975000 1
4.8%
205461000 1
4.8%
232993000 1
4.8%
ValueCountFrequency (%)
8482798000 1
4.8%
7230459920 1
4.8%
4896266000 1
4.8%
1870536150 1
4.8%
1742729000 1
4.8%
1662038000 1
4.8%
498461000 1
4.8%
496686010 1
4.8%
408803000 1
4.8%
273938150 1
4.8%

부과금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4529706 × 1010
Minimum0
Maximum5.0762147 × 1010
Zeros1
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T08:09:01.512364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile92203970
Q14.2025266 × 109
median1.2307546 × 1010
Q31.8311761 × 1010
95-th percentile4.5379562 × 1010
Maximum5.0762147 × 1010
Range5.0762147 × 1010
Interquartile range (IQR)1.4109234 × 1010

Descriptive statistics

Standard deviation1.4249652 × 1010
Coefficient of variation (CV)0.98072541
Kurtosis1.3718695
Mean1.4529706 × 1010
Median Absolute Deviation (MAD)8.090194 × 109
Skewness1.3726907
Sum3.0512383 × 1011
Variance2.0305259 × 1020
MonotonicityNot monotonic
2023-12-11T08:09:01.618854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
13188211000 1
 
4.8%
0 1
 
4.8%
4202526590 1
 
4.8%
19329643170 1
 
4.8%
50762146860 1
 
4.8%
6251739090 1
 
4.8%
24709556630 1
 
4.8%
92203970 1
 
4.8%
3055611000 1
 
4.8%
3717398000 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
0 1
4.8%
92203970 1
4.8%
2932833000 1
4.8%
3055611000 1
4.8%
3717398000 1
4.8%
4202526590 1
4.8%
4217352000 1
4.8%
5751814000 1
4.8%
6239520000 1
4.8%
6251739090 1
4.8%
ValueCountFrequency (%)
50762146860 1
4.8%
45379562000 1
4.8%
34633536000 1
4.8%
24709556630 1
4.8%
19329643170 1
4.8%
18311761000 1
4.8%
17314661000 1
4.8%
16746932000 1
4.8%
15979278000 1
4.8%
13188211000 1
4.8%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.581429
Minimum0
Maximum65.79
Zeros3
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T08:09:01.779946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.99
median5.94
Q322.1
95-th percentile65.21
Maximum65.79
Range65.79
Interquartile range (IQR)19.11

Descriptive statistics

Standard deviation20.938385
Coefficient of variation (CV)1.3438039
Kurtosis1.638155
Mean15.581429
Median Absolute Deviation (MAD)3.24
Skewness1.6688074
Sum327.21
Variance438.41597
MonotonicityNot monotonic
2023-12-11T08:09:01.914286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 3
 
14.3%
65.21 1
 
4.8%
5.94 1
 
4.8%
2.99 1
 
4.8%
22.1 1
 
4.8%
4.89 1
 
4.8%
52.28 1
 
4.8%
10.03 1
 
4.8%
7.5 1
 
4.8%
6.57 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
0.0 3
14.3%
2.7 1
 
4.8%
2.85 1
 
4.8%
2.99 1
 
4.8%
3.42 1
 
4.8%
3.77 1
 
4.8%
4.89 1
 
4.8%
5.1 1
 
4.8%
5.94 1
 
4.8%
6.57 1
 
4.8%
ValueCountFrequency (%)
65.79 1
4.8%
65.21 1
4.8%
52.28 1
4.8%
30.27 1
4.8%
27.17 1
4.8%
22.1 1
4.8%
10.03 1
4.8%
8.63 1
4.8%
7.5 1
4.8%
6.57 1
4.8%

Interactions

2023-12-11T08:08:59.005941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:57.857738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:58.253689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:58.626751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:59.119349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:57.960104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:58.336867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:58.710369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:59.206381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:58.060800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:58.428182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:58.806317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:59.308998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:58.151958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:58.523367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:58.895104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:09:02.007321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.6570.4610.9450.779
과세년도0.0001.0000.0000.0000.0000.000
비과세금액0.6570.0001.0001.0000.8340.913
감면금액0.4610.0001.0001.0000.9010.967
부과금액0.9450.0000.8340.9011.0000.891
비과세감면율0.7790.0000.9130.9670.8911.000
2023-12-11T08:09:02.127929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-11T08:09:02.521507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율세목명과세년도
비과세금액1.0000.7860.6460.7110.4080.000
감면금액0.7861.0000.8090.6750.2580.000
부과금액0.6460.8091.0000.3820.6200.000
비과세감면율0.7110.6750.3821.0000.5270.000
세목명0.4080.2580.6200.5271.0000.000
과세년도0.0000.0000.0000.0000.0001.000

Missing values

2023-12-11T08:08:59.430430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:08:59.603409image/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

시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
0경상남도사천시48240교육세201802000131882110000.0
1경상남도사천시48240등록세2018<NA>17200000.0
2경상남도사천시48240재산세2018962853200016620380001731466100065.21
3경상남도사천시48240주민세20186305300010064500057518140002.85
4경상남도사천시48240취득세2018525497200084827980004537956200030.27
5경상남도사천시48240자동차세2018193033000408803000159792780003.77
6경상남도사천시48240등록면허세2018959200020546100042173520005.1
7경상남도사천시48240지역자원시설세20181554350009760900029328330008.63
8경상남도사천시48240교육세201903000123075460000.0
9경상남도사천시48240재산세20191030389200017427290001831176100065.79
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
11경상남도사천시48240취득세2019451424000048962660003463353600027.17
12경상남도사천시48240자동차세201973673000498461000167469320003.42
13경상남도사천시48240등록면허세20191123300023299300037173980006.57
14경상남도사천시48240지역자원시설세20191596310006955000030556110007.5
15경상남도사천시48240등록세2020092506509220397010.03
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