Overview

Dataset statistics

Number of variables9
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory81.5 B

Variable types

Categorical5
Numeric4

Dataset

Description연도별 지방세 과세 및 비과세 현황을 표준지방세시스템을 이용하여 세목명, 과세건수, 과세금액, 비과세건수, 비과세금액을 열람 및 조회할 수 있음
Author전라남도 고흥군
URLhttps://www.data.go.kr/data/15079062/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세건수 is highly overall correlated with 과세금액 and 3 other fieldsHigh correlation
과세금액 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세금액 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 3 other fieldsHigh correlation
과세건수 has 11 (28.9%) zerosZeros
과세금액 has 11 (28.9%) zerosZeros
비과세건수 has 14 (36.8%) zerosZeros
비과세금액 has 14 (36.8%) zerosZeros

Reproduction

Analysis started2023-12-12 09:11:35.971982
Analysis finished2023-12-12 09:11:38.526372
Duration2.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
전라남도
38 

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 38
100.0%

Length

2023-12-12T18:11:38.608620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:38.711081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 38
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
고흥군
38 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고흥군 38
100.0%

Length

2023-12-12T18:11:38.809785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:38.909184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고흥군 38
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
46770
38 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46770
2nd row46770
3rd row46770
4th row46770
5th row46770

Common Values

ValueCountFrequency (%)
46770 38
100.0%

Length

2023-12-12T18:11:39.011927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:39.488739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46770 38
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size436.0 B
2017
13 
2019
13 
2018
12 

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 (%)
2017 13
34.2%
2019 13
34.2%
2018 12
31.6%

Length

2023-12-12T18:11:39.597121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:39.714930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 13
34.2%
2019 13
34.2%
2018 12
31.6%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size436.0 B
취득세
3 
등록세
3 
주민세
3 
재산세
3 
자동차세
3 
Other values (8)
23 

Length

Max length7
Median length5
Mean length4.1315789
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 3
 
7.9%
등록세 3
 
7.9%
주민세 3
 
7.9%
재산세 3
 
7.9%
자동차세 3
 
7.9%
레저세 3
 
7.9%
담배소비세 3
 
7.9%
지방소비세 3
 
7.9%
등록면허세 3
 
7.9%
지역자원시설세 3
 
7.9%
Other values (3) 8
21.1%

Length

2023-12-12T18:11:39.862385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 3
 
7.9%
등록세 3
 
7.9%
주민세 3
 
7.9%
재산세 3
 
7.9%
자동차세 3
 
7.9%
레저세 3
 
7.9%
담배소비세 3
 
7.9%
지방소비세 3
 
7.9%
등록면허세 3
 
7.9%
지역자원시설세 3
 
7.9%
Other values (3) 8
21.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33608.974
Minimum0
Maximum171530
Zeros11
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T18:11:40.002375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13273.5
Q335787
95-th percentile162327
Maximum171530
Range171530
Interquartile range (IQR)35787

Descriptive statistics

Standard deviation49390.767
Coefficient of variation (CV)1.4695708
Kurtosis2.3976376
Mean33608.974
Median Absolute Deviation (MAD)13273.5
Skewness1.8396935
Sum1277141
Variance2.4394479 × 109
MonotonicityNot monotonic
2023-12-12T18:11:40.129831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 11
28.9%
14340 1
 
2.6%
33008 1
 
2.6%
171530 1
 
2.6%
12076 1
 
2.6%
13140 1
 
2.6%
33644 1
 
2.6%
84 1
 
2.6%
43663 1
 
2.6%
112788 1
 
2.6%
Other values (18) 18
47.4%
ValueCountFrequency (%)
0 11
28.9%
84 1
 
2.6%
88 1
 
2.6%
107 1
 
2.6%
10172 1
 
2.6%
11031 1
 
2.6%
12076 1
 
2.6%
12564 1
 
2.6%
13140 1
 
2.6%
13407 1
 
2.6%
ValueCountFrequency (%)
171530 1
2.6%
165880 1
2.6%
161700 1
2.6%
112788 1
2.6%
109882 1
2.6%
107145 1
2.6%
43663 1
2.6%
42049 1
2.6%
41049 1
2.6%
36062 1
2.6%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1994347 × 109
Minimum0
Maximum1.3110485 × 1010
Zeros11
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T18:11:40.263335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.327802 × 109
Q34.5379465 × 109
95-th percentile1.2403815 × 1010
Maximum1.3110485 × 1010
Range1.3110485 × 1010
Interquartile range (IQR)4.5379465 × 109

Descriptive statistics

Standard deviation3.7889508 × 109
Coefficient of variation (CV)1.1842564
Kurtosis1.245649
Mean3.1994347 × 109
Median Absolute Deviation (MAD)1.327802 × 109
Skewness1.3621759
Sum1.2157852 × 1011
Variance1.4356148 × 1019
MonotonicityNot monotonic
2023-12-12T18:11:40.378857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 11
28.9%
12299808000 1
 
2.6%
1210121000 1
 
2.6%
4682637000 1
 
2.6%
4758430000 1
 
2.6%
560395000 1
 
2.6%
1445483000 1
 
2.6%
4330543000 1
 
2.6%
8879503000 1
 
2.6%
3604604000 1
 
2.6%
Other values (18) 18
47.4%
ValueCountFrequency (%)
0 11
28.9%
445556000 1
 
2.6%
524481000 1
 
2.6%
560395000 1
 
2.6%
835167000 1
 
2.6%
838580000 1
 
2.6%
961349000 1
 
2.6%
996693000 1
 
2.6%
1210121000 1
 
2.6%
1445483000 1
 
2.6%
ValueCountFrequency (%)
13110485000 1
2.6%
12993186000 1
2.6%
12299808000 1
2.6%
8879503000 1
2.6%
8306735000 1
2.6%
7376845000 1
2.6%
4758430000 1
2.6%
4682637000 1
2.6%
4593823000 1
2.6%
4539553000 1
2.6%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6604.9211
Minimum0
Maximum44576
Zeros14
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T18:11:40.493779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median484
Q35796.75
95-th percentile40688
Maximum44576
Range44576
Interquartile range (IQR)5796.75

Descriptive statistics

Standard deviation11700.316
Coefficient of variation (CV)1.7714544
Kurtosis5.1470015
Mean6604.9211
Median Absolute Deviation (MAD)484
Skewness2.3808046
Sum250987
Variance1.368974 × 108
MonotonicityNot monotonic
2023-12-12T18:11:40.608633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 14
36.8%
40 2
 
5.3%
3132 1
 
2.6%
4715 1
 
2.6%
537 1
 
2.6%
5869 1
 
2.6%
5580 1
 
2.6%
12490 1
 
2.6%
44576 1
 
2.6%
16552 1
 
2.6%
Other values (14) 14
36.8%
ValueCountFrequency (%)
0 14
36.8%
14 1
 
2.6%
40 2
 
5.3%
424 1
 
2.6%
431 1
 
2.6%
537 1
 
2.6%
2736 1
 
2.6%
3132 1
 
2.6%
3168 1
 
2.6%
4345 1
 
2.6%
ValueCountFrequency (%)
44576 1
2.6%
41521 1
2.6%
40541 1
2.6%
16552 1
2.6%
16127 1
2.6%
15975 1
2.6%
12490 1
2.6%
11070 1
2.6%
10441 1
2.6%
5869 1
2.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2118029 × 108
Minimum0
Maximum4.856063 × 109
Zeros14
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T18:11:40.723375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15543500
Q31.74362 × 108
95-th percentile4.1710473 × 109
Maximum4.856063 × 109
Range4.856063 × 109
Interquartile range (IQR)1.74362 × 108

Descriptive statistics

Standard deviation1.3390219 × 109
Coefficient of variation (CV)2.1556091
Kurtosis3.72715
Mean6.2118029 × 108
Median Absolute Deviation (MAD)15543500
Skewness2.2306379
Sum2.3604851 × 1010
Variance1.7929796 × 1018
MonotonicityNot monotonic
2023-12-12T18:11:40.842004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 14
36.8%
71000 2
 
5.3%
4160238000 1
 
2.6%
98797000 1
 
2.6%
89000 1
 
2.6%
171032000 1
 
2.6%
133356000 1
 
2.6%
399135000 1
 
2.6%
2752857000 1
 
2.6%
63636000 1
 
2.6%
Other values (14) 14
36.8%
ValueCountFrequency (%)
0 14
36.8%
71000 2
 
5.3%
89000 1
 
2.6%
1999000 1
 
2.6%
7213000 1
 
2.6%
23874000 1
 
2.6%
56887000 1
 
2.6%
59816000 1
 
2.6%
63636000 1
 
2.6%
98797000 1
 
2.6%
ValueCountFrequency (%)
4856063000 1
2.6%
4232300000 1
2.6%
4160238000 1
2.6%
2752857000 1
2.6%
2728874000 1
2.6%
2557542000 1
2.6%
406081000 1
2.6%
403029000 1
2.6%
399135000 1
2.6%
175472000 1
2.6%

Interactions

2023-12-12T18:11:37.897673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:36.351873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:36.913281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.457182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.999180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:36.497498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.050582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.567148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:38.093996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:36.638318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.191046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.703100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:38.176726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:36.774935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.312677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.795556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:11:40.933846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0001.0000.9250.9830.866
과세건수0.0001.0001.0000.7810.9570.595
과세금액0.0000.9250.7811.0000.8210.852
비과세건수0.0000.9830.9570.8211.0000.593
비과세금액0.0000.8660.5950.8520.5931.000
2023-12-12T18:11:41.053049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-12T18:11:41.141995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.6590.7770.6360.0000.870
과세금액0.6591.0000.2990.4470.0000.684
비과세건수0.7770.2991.0000.8580.0000.844
비과세금액0.6360.4470.8581.0000.0000.610
과세년도0.0000.0000.0000.0001.0000.000
세목명0.8700.6840.8440.6100.0001.000

Missing values

2023-12-12T18:11:38.299433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:11:38.457375image/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

시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
0전라남도고흥군467702017취득세143401229980800031324160238000
1전라남도고흥군467702017등록세00141999000
2전라남도고흥군467702017주민세349448351670001612756887000
3전라남도고흥군467702017재산세1071453123390000405412557542000
4전라남도고흥군467702017자동차세41049830673500011070406081000
5전라남도고흥군467702017레저세0000
6전라남도고흥군467702017담배소비세107450029300000
7전라남도고흥군467702017지방소비세0000
8전라남도고흥군467702017등록면허세300859613490004345162917000
9전라남도고흥군467702017도시계획세0000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
28전라남도고흥군467702019재산세1127883604604000445762752857000
29전라남도고흥군467702019자동차세43663887950300012490399135000
30전라남도고흥군467702019레저세0000
31전라남도고흥군467702019담배소비세84433054300000
32전라남도고흥군467702019지방소비세0000
33전라남도고흥군467702019등록면허세3364414454830005580133356000
34전라남도고흥군467702019도시계획세0000
35전라남도고흥군467702019지역자원시설세131405603950005869171032000
36전라남도고흥군467702019지방소득세12076475843000000
37전라남도고흥군467702019교육세171530468263700053789000