Overview

Dataset statistics

Number of variables8
Number of observations44
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory71.0 B

Variable types

Categorical6
Numeric2

Dataset

Description지방세 과세를 위해 세원이 되는 과세 대상 유형별 부과된 현황을 제공(과세년도, 세목명, 세원 유형명, 부과건수, 부과금액등 데이터 제공)
URLhttps://www.data.go.kr/data/15079747/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
세목명 is highly overall correlated with 부과건수 and 2 other fieldsHigh correlation
세원 유형명 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과건수 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수 and 2 other fieldsHigh correlation
부과건수 has 24 (54.5%) zerosZeros
부과금액 has 24 (54.5%) zerosZeros

Reproduction

Analysis started2023-12-12 23:28:02.963121
Analysis finished2023-12-12 23:28:03.771227
Duration0.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
인천광역시
44 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 44
100.0%

Length

2023-12-13T08:28:03.835406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:28:03.931534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 44
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
인천광역시
44 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 44
100.0%

Length

2023-12-13T08:28:04.091411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:28:04.193413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 44
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
28000
44 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28000
2nd row28000
3rd row28000
4th row28000
5th row28000

Common Values

ValueCountFrequency (%)
28000 44
100.0%

Length

2023-12-13T08:28:04.312380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:28:04.477502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28000 44
100.0%

과세년도
Categorical

Distinct4
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size484.0 B
2021
11 
2018
11 
2019
11 
2020
11 

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 11
25.0%
2018 11
25.0%
2019 11
25.0%
2020 11
25.0%

Length

2023-12-13T08:28:04.583291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:28:04.715675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 11
25.0%
2018 11
25.0%
2019 11
25.0%
2020 11
25.0%

세목명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size484.0 B
자동차세
28 
교육세
4 
체납
4 
담배소비세
4 
지방소비세
4 

Length

Max length5
Median length4
Mean length3.9090909
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교육세
2nd row자동차세
3rd row자동차세
4th row자동차세
5th row자동차세

Common Values

ValueCountFrequency (%)
자동차세 28
63.6%
교육세 4
 
9.1%
체납 4
 
9.1%
담배소비세 4
 
9.1%
지방소비세 4
 
9.1%

Length

2023-12-13T08:28:04.861141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:28:05.001781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 28
63.6%
교육세 4
 
9.1%
체납 4
 
9.1%
담배소비세 4
 
9.1%
지방소비세 4
 
9.1%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
교육세
4 
승용
4 
기타승용
4 
승합
4 
화물
4 
Other values (6)
24 

Length

Max length8
Median length5
Mean length3.5454545
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교육세
2nd row승용
3rd row기타승용
4th row승합
5th row화물

Common Values

ValueCountFrequency (%)
교육세 4
9.1%
승용 4
9.1%
기타승용 4
9.1%
승합 4
9.1%
화물 4
9.1%
특수 4
9.1%
3륜이하 4
9.1%
자동차세(주행) 4
9.1%
체납 4
9.1%
담배소비세 4
9.1%

Length

2023-12-13T08:28:05.130104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
교육세 4
9.1%
승용 4
9.1%
기타승용 4
9.1%
승합 4
9.1%
화물 4
9.1%
특수 4
9.1%
3륜이하 4
9.1%
자동차세(주행 4
9.1%
체납 4
9.1%
담배소비세 4
9.1%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81209.114
Minimum0
Maximum1013628
Zeros24
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T08:28:05.244287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3102
95-th percentile831322.7
Maximum1013628
Range1013628
Interquartile range (IQR)102

Descriptive statistics

Standard deviation260555.01
Coefficient of variation (CV)3.2084454
Kurtosis7.4346585
Mean81209.114
Median Absolute Deviation (MAD)0
Skewness2.9962486
Sum3573201
Variance6.7888913 × 1010
MonotonicityNot monotonic
2023-12-13T08:28:05.362266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 24
54.5%
12 4
 
9.1%
102 2
 
4.5%
115 2
 
4.5%
1308 1
 
2.3%
6 1
 
2.3%
9 1
 
2.3%
761 1
 
2.3%
1013628 1
 
2.3%
757 1
 
2.3%
Other values (6) 6
 
13.6%
ValueCountFrequency (%)
0 24
54.5%
6 1
 
2.3%
8 1
 
2.3%
9 1
 
2.3%
10 1
 
2.3%
12 4
 
9.1%
102 2
 
4.5%
115 2
 
4.5%
757 1
 
2.3%
761 1
 
2.3%
ValueCountFrequency (%)
1013628 1
2.3%
912994 1
2.3%
835763 1
2.3%
806161 1
2.3%
1314 1
2.3%
1308 1
2.3%
761 1
2.3%
757 1
2.3%
115 2
4.5%
102 2
4.5%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6920047 × 1010
Minimum0
Maximum5.5215662 × 1011
Zeros24
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T08:28:05.473571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.8428731 × 1011
95-th percentile3.3025712 × 1011
Maximum5.5215662 × 1011
Range5.5215662 × 1011
Interquartile range (IQR)1.8428731 × 1011

Descriptive statistics

Standard deviation1.3336466 × 1011
Coefficient of variation (CV)1.5343372
Kurtosis2.6465835
Mean8.6920047 × 1010
Median Absolute Deviation (MAD)0
Skewness1.7053547
Sum3.8244821 × 1012
Variance1.7786133 × 1022
MonotonicityNot monotonic
2023-12-13T08:28:05.590986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 24
54.5%
92296671000 1
 
2.3%
334571475000 1
 
2.3%
195170897000 1
 
2.3%
29534865000 1
 
2.3%
220950144000 1
 
2.3%
85855668000 1
 
2.3%
421457636000 1
 
2.3%
182311519000 1
 
2.3%
35106313000 1
 
2.3%
Other values (11) 11
25.0%
ValueCountFrequency (%)
0 24
54.5%
29534865000 1
 
2.3%
35106313000 1
 
2.3%
37463913000 1
 
2.3%
45798146000 1
 
2.3%
80198546000 1
 
2.3%
83675133000 1
 
2.3%
85855668000 1
 
2.3%
92296671000 1
 
2.3%
182311519000 1
 
2.3%
ValueCountFrequency (%)
552156625000 1
2.3%
421457636000 1
2.3%
334571475000 1
2.3%
305809137000 1
2.3%
251322419000 1
2.3%
243206716000 1
2.3%
223410897000 1
2.3%
220950144000 1
2.3%
213970695000 1
2.3%
195170897000 1
2.3%

Interactions

2023-12-13T08:28:03.338581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:03.175259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:03.425590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:03.253061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:28:05.677918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0001.0000.5890.907
세원 유형명0.0001.0001.0000.5560.873
부과건수0.0000.5890.5561.0000.000
부과금액0.0000.9070.8730.0001.000
2023-12-13T08:28:05.797132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명
과세년도1.0000.0000.000
세목명0.0001.0000.920
세원 유형명0.0000.9201.000
2023-12-13T08:28:05.914515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액과세년도세목명세원 유형명
부과건수1.0000.7890.0000.5080.329
부과금액0.7891.0000.0000.8080.636
과세년도0.0000.0001.0000.0000.000
세목명0.5080.8080.0001.0000.920
세원 유형명0.3290.6360.0000.9201.000

Missing values

2023-12-13T08:28:03.554456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:28:03.710752image/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
0인천광역시인천광역시280002021교육세교육세130892296671000
1인천광역시인천광역시280002021자동차세승용00
2인천광역시인천광역시280002021자동차세기타승용00
3인천광역시인천광역시280002021자동차세승합00
4인천광역시인천광역시280002021자동차세화물00
5인천광역시인천광역시280002021자동차세특수00
6인천광역시인천광역시280002021자동차세3륜이하00
7인천광역시인천광역시280002021자동차세자동차세(주행)12223410897000
8인천광역시인천광역시280002021체납체납83576345798146000
9인천광역시인천광역시280002021담배소비세담배소비세1314213970695000
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
34인천광역시인천광역시280002020자동차세승용00
35인천광역시인천광역시280002020자동차세기타승용00
36인천광역시인천광역시280002020자동차세승합00
37인천광역시인천광역시280002020자동차세화물00
38인천광역시인천광역시280002020자동차세특수00
39인천광역시인천광역시280002020자동차세3륜이하00
40인천광역시인천광역시280002020자동차세자동차세(주행)12220950144000
41인천광역시인천광역시280002020체납체납101362829534865000
42인천광역시인천광역시280002020담배소비세담배소비세761195170897000
43인천광역시인천광역시280002020지방소비세지방소비세9334571475000