Overview

Dataset statistics

Number of variables8
Number of observations110
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory69.2 B

Variable types

Categorical5
Numeric3

Dataset

Description지방세 세원이 되는 과세물건 유형별 부과된 현황에 대해 과세년도, 세목명, 세원 유형명, 부과건수, 부과금액 등을 제공합니다.
Author울산광역시
URLhttps://www.data.go.kr/data/15080363/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
세목명 is highly overall correlated with 세원 유형명High correlation
세원 유형명 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
부과건수 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수High correlation
부과건수 has 74 (67.3%) zerosZeros
부과금액 has 74 (67.3%) zerosZeros

Reproduction

Analysis started2023-12-12 04:27:52.595054
Analysis finished2023-12-12 04:27:54.172464
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1012.0 B
울산광역시
110 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울산광역시 110
100.0%

Length

2023-12-12T13:27:54.233343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:27:54.319525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산광역시 110
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1012.0 B
울산광역시
110 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울산광역시 110
100.0%

Length

2023-12-12T13:27:54.413914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:27:54.524749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산광역시 110
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1012.0 B
31000
110 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31000
2nd row31000
3rd row31000
4th row31000
5th row31000

Common Values

ValueCountFrequency (%)
31000 110
100.0%

Length

2023-12-12T13:27:54.637561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:27:54.748512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31000 110
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4636
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:27:54.851856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7223691
Coefficient of variation (CV)0.00085288445
Kurtosis-1.283807
Mean2019.4636
Median Absolute Deviation (MAD)1.5
Skewness0.029773298
Sum222141
Variance2.9665555
MonotonicityNot monotonic
2023-12-12T13:27:54.971779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2018 19
17.3%
2017 19
17.3%
2019 18
16.4%
2020 18
16.4%
2021 18
16.4%
2022 18
16.4%
ValueCountFrequency (%)
2017 19
17.3%
2018 19
17.3%
2019 18
16.4%
2020 18
16.4%
2021 18
16.4%
2022 18
16.4%
ValueCountFrequency (%)
2022 18
16.4%
2021 18
16.4%
2020 18
16.4%
2019 18
16.4%
2018 19
17.3%
2017 19
17.3%

세목명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size1012.0 B
취득세
42 
자동차세
42 
교육세
6 
체납
6 
담배소비세
6 
Other values (2)
8 

Length

Max length5
Median length4
Mean length3.5818182
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 42
38.2%
자동차세 42
38.2%
교육세 6
 
5.5%
체납 6
 
5.5%
담배소비세 6
 
5.5%
지방소비세 6
 
5.5%
등록면허세 2
 
1.8%

Length

2023-12-12T13:27:55.082986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:27:55.196665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
취득세 42
38.2%
자동차세 42
38.2%
교육세 6
 
5.5%
체납 6
 
5.5%
담배소비세 6
 
5.5%
지방소비세 6
 
5.5%
등록면허세 2
 
1.8%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size1012.0 B
토지
 
6
건축물
 
6
지방소비세
 
6
선박
 
6
차량
 
6
Other values (14)
80 

Length

Max length9
Median length8
Mean length3.2727273
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row토지
2nd row건축물
3rd row선박
4th row차량
5th row기계장비

Common Values

ValueCountFrequency (%)
토지 6
 
5.5%
건축물 6
 
5.5%
지방소비세 6
 
5.5%
선박 6
 
5.5%
차량 6
 
5.5%
기계장비 6
 
5.5%
항공기 6
 
5.5%
기타 6
 
5.5%
교육세 6
 
5.5%
승용 6
 
5.5%
Other values (9) 50
45.5%

Length

2023-12-12T13:27:55.350619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
토지 6
 
5.5%
기타승용 6
 
5.5%
담배소비세 6
 
5.5%
체납 6
 
5.5%
자동차세(주행 6
 
5.5%
3륜이하 6
 
5.5%
특수 6
 
5.5%
화물 6
 
5.5%
승합 6
 
5.5%
승용 6
 
5.5%
Other values (9) 50
45.5%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4685.7273
Minimum0
Maximum86709
Zeros74
Zeros (%)67.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:27:55.505444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile42354.6
Maximum86709
Range86709
Interquartile range (IQR)12

Descriptive statistics

Standard deviation18555.754
Coefficient of variation (CV)3.9600585
Kurtosis14.207794
Mean4685.7273
Median Absolute Deviation (MAD)0
Skewness3.9850564
Sum515430
Variance3.4431601 × 108
MonotonicityNot monotonic
2023-12-12T13:27:55.650792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 74
67.3%
12 6
 
5.5%
8 2
 
1.8%
10 2
 
1.8%
2070 1
 
0.9%
642 1
 
0.9%
2133 1
 
0.9%
1874 1
 
0.9%
74751 1
 
0.9%
491 1
 
0.9%
Other values (20) 20
 
18.2%
ValueCountFrequency (%)
0 74
67.3%
6 1
 
0.9%
8 2
 
1.8%
9 1
 
0.9%
10 2
 
1.8%
12 6
 
5.5%
89 1
 
0.9%
92 1
 
0.9%
112 1
 
0.9%
285 1
 
0.9%
ValueCountFrequency (%)
86709 1
0.9%
85092 1
0.9%
81382 1
0.9%
80940 1
0.9%
79820 1
0.9%
74751 1
0.9%
2759 1
0.9%
2301 1
0.9%
2133 1
0.9%
2125 1
0.9%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9529685 × 1010
Minimum0
Maximum4.8630648 × 1011
Zeros74
Zeros (%)67.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T13:27:55.790618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.8410627 × 1010
95-th percentile1.4863073 × 1011
Maximum4.8630648 × 1011
Range4.8630648 × 1011
Interquartile range (IQR)2.8410627 × 1010

Descriptive statistics

Standard deviation7.2286507 × 1010
Coefficient of variation (CV)2.4479268
Kurtosis18.750834
Mean2.9529685 × 1010
Median Absolute Deviation (MAD)0
Skewness3.9493489
Sum3.2482654 × 1012
Variance5.2253391 × 1021
MonotonicityNot monotonic
2023-12-12T13:27:55.972002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 74
67.3%
63511146000 1
 
0.9%
28762586000 1
 
0.9%
94673306000 1
 
0.9%
65008542000 1
 
0.9%
194981505000 1
 
0.9%
105492137000 1
 
0.9%
3178202000 1
 
0.9%
28428962000 1
 
0.9%
64224731000 1
 
0.9%
Other values (27) 27
 
24.5%
ValueCountFrequency (%)
0 74
67.3%
2937726000 1
 
0.9%
3033418000 1
 
0.9%
3048620000 1
 
0.9%
3178202000 1
 
0.9%
3455364000 1
 
0.9%
4085500000 1
 
0.9%
27190142000 1
 
0.9%
28355623000 1
 
0.9%
28428962000 1
 
0.9%
ValueCountFrequency (%)
486306478000 1
0.9%
361634538000 1
0.9%
274369235000 1
0.9%
194981505000 1
0.9%
191393932000 1
0.9%
181996397000 1
0.9%
107850475000 1
0.9%
105492137000 1
0.9%
102191166000 1
0.9%
94693249000 1
0.9%

Interactions

2023-12-12T13:27:53.389423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:27:52.838776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:27:53.141283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:27:53.500180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:27:52.923261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:27:53.227959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:27:53.879355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:27:53.028268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:27:53.309158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:27:56.067864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0001.0000.0000.665
세원 유형명0.0001.0001.0000.8090.761
부과건수0.0000.0000.8091.0000.675
부과금액0.0000.6650.7610.6751.000
2023-12-12T13:27:56.223255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명
세목명1.0000.940
세원 유형명0.9401.000
2023-12-12T13:27:56.335505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과건수부과금액세목명세원 유형명
과세년도1.0000.0140.0180.0000.000
부과건수0.0141.0000.9310.0000.584
부과금액0.0180.9311.0000.4330.422
세목명0.0000.0000.4331.0000.940
세원 유형명0.0000.5840.4220.9401.000

Missing values

2023-12-12T13:27:53.997443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:27:54.123630image/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

시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
0울산광역시울산광역시310002018취득세토지00
1울산광역시울산광역시310002018취득세건축물00
2울산광역시울산광역시310002018취득세선박00
3울산광역시울산광역시310002018취득세차량8094091096237000
4울산광역시울산광역시310002018취득세기계장비21013048620000
5울산광역시울산광역시310002018취득세항공기00
6울산광역시울산광역시310002018취득세기타00
7울산광역시울산광역시310002018등록면허세등록면허세(등록)00
8울산광역시울산광역시310002018교육세교육세165928355623000
9울산광역시울산광역시310002018자동차세승용00
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
100울산광역시울산광역시310002022자동차세승용00
101울산광역시울산광역시310002022자동차세기타승용00
102울산광역시울산광역시310002022자동차세승합00
103울산광역시울산광역시310002022자동차세화물00
104울산광역시울산광역시310002022자동차세특수00
105울산광역시울산광역시310002022자동차세3륜이하00
106울산광역시울산광역시310002022자동차세자동차세(주행)1250227374000
107울산광역시울산광역시310002022체납체납00
108울산광역시울산광역시310002022담배소비세담배소비세64264365435000
109울산광역시울산광역시310002022지방소비세지방소비세10486306478000