Overview

Dataset statistics

Number of variables8
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory70.9 B

Variable types

Categorical5
Text1
Numeric2

Dataset

Description지방세 과세를 위해 세원이 되는 과세 대상 유형별 부과된 현황을 제공합니다. 물건 유형에 따른 세부담 수준의 형평성 검토 및 부동산 등 관련 분야 규제정책 대상 확인 시 기초자료로 활용됩니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=349&beforeMenuCd=DOM_000000201001001000&publicdatapk=15079034

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
부과건수 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수High correlation
세목명 is highly overall correlated with 부과건수High correlation
세원 유형명 has unique valuesUnique
부과건수 has 11 (23.9%) zerosZeros
부과금액 has 11 (23.9%) zerosZeros

Reproduction

Analysis started2024-01-09 22:38:01.070958
Analysis finished2024-01-09 22:38:01.733040
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
충청남도
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 46
100.0%

Length

2024-01-10T07:38:01.786877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:38:01.876436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 46
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
아산시
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아산시 46
100.0%

Length

2024-01-10T07:38:01.984117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:38:02.059826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아산시 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
44200
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44200
2nd row44200
3rd row44200
4th row44200
5th row44200

Common Values

ValueCountFrequency (%)
44200 46
100.0%

Length

2024-01-10T07:38:02.139024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:38:02.212697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44200 46
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
2021
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 46
100.0%

Length

2024-01-10T07:38:02.287074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:38:02.359581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 46
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
취득세
9 
자동차세
7 
주민세
7 
재산세
5 
레저세
4 
Other values (8)
14 

Length

Max length7
Median length3
Mean length3.7826087
Min length2

Unique

Unique5 ?
Unique (%)10.9%

Sample

1st row담배소비세
2nd row교육세
3rd row도시계획세
4th row취득세
5th row취득세

Common Values

ValueCountFrequency (%)
취득세 9
19.6%
자동차세 7
15.2%
주민세 7
15.2%
재산세 5
10.9%
레저세 4
8.7%
지방소득세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
담배소비세 1
 
2.2%
교육세 1
 
2.2%
Other values (3) 3
 
6.5%

Length

2024-01-10T07:38:02.444827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 9
19.6%
자동차세 7
15.2%
주민세 7
15.2%
재산세 5
10.9%
레저세 4
8.7%
지방소득세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
담배소비세 1
 
2.2%
교육세 1
 
2.2%
Other values (3) 3
 
6.5%

세원 유형명
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2024-01-10T07:38:02.633913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length6.0217391
Min length2

Characters and Unicode

Total characters277
Distinct characters73
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)100.0%

Sample

1st row담배소비세
2nd row교육세
3rd row도시계획세
4th row건축물
5th row주택(개별)
ValueCountFrequency (%)
담배소비세 1
 
2.2%
등록면허세(면허 1
 
2.2%
주민세(종합소득 1
 
2.2%
승합 1
 
2.2%
기타승용 1
 
2.2%
승용 1
 
2.2%
지방소득세(특별징수 1
 
2.2%
지방소득세(법인소득 1
 
2.2%
지방소득세(양도소득 1
 
2.2%
지방소득세(종합소득 1
 
2.2%
Other values (36) 36
78.3%
2024-01-10T07:38:02.964374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 27
 
9.7%
( 24
 
8.7%
) 24
 
8.7%
소 14
 
5.1%
주 11
 
4.0%
지 10
 
3.6%
득 9
 
3.2%
민 7
 
2.5%
방 6
 
2.2%
자 5
 
1.8%
Other values (63) 140
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
82.3%
Open Punctuation 24
 
8.7%
Close Punctuation 24
 
8.7%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
자 5
 
2.2%
기 5
 
2.2%
시 5
 
2.2%
Other values (60) 129
56.6%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
82.3%
Common 49
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
자 5
 
2.2%
기 5
 
2.2%
시 5
 
2.2%
Other values (60) 129
56.6%
Common
ValueCountFrequency (%)
( 24
49.0%
) 24
49.0%
3 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
82.3%
ASCII 49
 
17.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
자 5
 
2.2%
기 5
 
2.2%
시 5
 
2.2%
Other values (60) 129
56.6%
ASCII
ValueCountFrequency (%)
( 24
49.0%
) 24
49.0%
3 1
 
2.0%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48227.674
Minimum0
Maximum731058
Zeros11
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-01-10T07:38:03.089751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median2164.5
Q329896.5
95-th percentile215150.5
Maximum731058
Range731058
Interquartile range (IQR)29888.5

Descriptive statistics

Standard deviation121285.17
Coefficient of variation (CV)2.514846
Kurtosis22.983413
Mean48227.674
Median Absolute Deviation (MAD)2164.5
Skewness4.4360036
Sum2218473
Variance1.4710094 × 1010
MonotonicityNot monotonic
2024-01-10T07:38:03.214753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 11
23.9%
479 1
 
2.2%
94154 1
 
2.2%
239457 1
 
2.2%
75629 1
 
2.2%
6162 1
 
2.2%
7338 1
 
2.2%
55186 1
 
2.2%
7 1
 
2.2%
50776 1
 
2.2%
Other values (26) 26
56.5%
ValueCountFrequency (%)
0 11
23.9%
7 1
 
2.2%
11 1
 
2.2%
12 1
 
2.2%
56 1
 
2.2%
174 1
 
2.2%
244 1
 
2.2%
479 1
 
2.2%
1065 1
 
2.2%
1226 1
 
2.2%
ValueCountFrequency (%)
731058 1
2.2%
302397 1
2.2%
239457 1
2.2%
142231 1
2.2%
128803 1
2.2%
115672 1
2.2%
108698 1
2.2%
94154 1
2.2%
75629 1
2.2%
55186 1
2.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6939811 × 1010
Minimum0
Maximum1.2376702 × 1011
Zeros11
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-01-10T07:38:03.332489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13171000
median2.1368505 × 109
Q32.7924094 × 1010
95-th percentile6.4014455 × 1010
Maximum1.2376702 × 1011
Range1.2376702 × 1011
Interquartile range (IQR)2.7920923 × 1010

Descriptive statistics

Standard deviation2.6824283 × 1010
Coefficient of variation (CV)1.5835055
Kurtosis6.6743806
Mean1.6939811 × 1010
Median Absolute Deviation (MAD)2.1368505 × 109
Skewness2.4224739
Sum7.792313 × 1011
Variance7.1954215 × 1020
MonotonicityNot monotonic
2024-01-10T07:38:03.446478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 11
23.9%
29118492000 1
 
2.2%
13231220000 1
 
2.2%
34022593000 1
 
2.2%
67180775000 1
 
2.2%
105051228000 1
 
2.2%
15176469000 1
 
2.2%
7134922000 1
 
2.2%
17222412000 1
 
2.2%
823748000 1
 
2.2%
Other values (26) 26
56.5%
ValueCountFrequency (%)
0 11
23.9%
1348000 1
 
2.2%
8640000 1
 
2.2%
15322000 1
 
2.2%
66561000 1
 
2.2%
72705000 1
 
2.2%
97371000 1
 
2.2%
332069000 1
 
2.2%
556574000 1
 
2.2%
804926000 1
 
2.2%
ValueCountFrequency (%)
123767025000 1
2.2%
105051228000 1
2.2%
67180775000 1
2.2%
54515494000 1
2.2%
40496078000 1
2.2%
37715496000 1
2.2%
34392625000 1
2.2%
34022593000 1
2.2%
32021074000 1
2.2%
30536872000 1
2.2%

Interactions

2024-01-10T07:38:01.402136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:38:01.215297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:38:01.477191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:38:01.308363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:38:03.524579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명부과건수부과금액
세목명1.0001.0000.8440.594
세원 유형명1.0001.0001.0001.000
부과건수0.8441.0001.0000.718
부과금액0.5941.0000.7181.000
2024-01-10T07:38:03.606346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액세목명
부과건수1.0000.7470.593
부과금액0.7471.0000.287
세목명0.5930.2871.000

Missing values

2024-01-10T07:38:01.587005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:38:01.692755image/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
0충청남도아산시442002021담배소비세담배소비세47929118492000
1충청남도아산시442002021교육세교육세73105854515494000
2충청남도아산시442002021도시계획세도시계획세00
3충청남도아산시442002021취득세건축물483737715496000
4충청남도아산시442002021취득세주택(개별)263419997727000
5충청남도아산시442002021취득세주택(단독)1175732021074000
6충청남도아산시442002021취득세기타2442409913000
7충청남도아산시442002021취득세항공기00
8충청남도아산시442002021취득세기계장비15011863788000
9충청남도아산시442002021취득세차량3017534392625000
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
36충청남도아산시442002021지역자원시설세지역자원시설세(시설)1166561000
37충청남도아산시442002021지역자원시설세지역자원시설세(특자)1065556574000
38충청남도아산시442002021주민세주민세(사업소분)192564452449000
39충청남도아산시442002021주민세주민세(개인분)1288031297421000
40충청남도아산시442002021주민세주민세(종업원분)649126919519000
41충청남도아산시442002021주민세주민세(특별징수)00
42충청남도아산시442002021주민세주민세(법인세분)00
43충청남도아산시442002021주민세주민세(양도소득)00
44충청남도아산시442002021주민세주민세(종합소득)00
45충청남도아산시442002021체납체납30239730536872000