Overview

Dataset statistics

Number of variables8
Number of observations273
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.3 KiB
Average record size in memory68.5 B

Variable types

Categorical5
Numeric3

Dataset

Description지방세 과세를 위해 세원이 되는 과세 대상 유형별 부과현황 데이터로 세원 유형명, 부과건수, 부과금액 등의 항목을 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15078344/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 1 other fieldsHigh correlation
세원 유형명 is highly overall correlated with 부과건수 and 2 other fieldsHigh correlation
부과건수 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
부과건수 has 63 (23.1%) zerosZeros
부과금액 has 63 (23.1%) zerosZeros

Reproduction

Analysis started2023-12-12 13:21:14.439488
Analysis finished2023-12-12 13:21:16.007991
Duration1.57 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
대전광역시
273 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 273
100.0%

Length

2023-12-12T22:21:16.084743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:21:16.191376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 273
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
대덕구
273 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대덕구 273
100.0%

Length

2023-12-12T22:21:16.294601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:21:16.396574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대덕구 273
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
30230
273 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30230
2nd row30230
3rd row30230
4th row30230
5th row30230

Common Values

ValueCountFrequency (%)
30230 273
100.0%

Length

2023-12-12T22:21:16.515573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:21:16.625309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30230 273
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4982
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T22:21:16.718276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7280654
Coefficient of variation (CV)0.00085569048
Kurtosis-1.3015612
Mean2019.4982
Median Absolute Deviation (MAD)2
Skewness-0.0098053058
Sum551323
Variance2.9862099
MonotonicityIncreasing
2023-12-12T22:21:16.834195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 47
17.2%
2018 47
17.2%
2020 47
17.2%
2021 46
16.8%
2022 46
16.8%
2019 40
14.7%
ValueCountFrequency (%)
2017 47
17.2%
2018 47
17.2%
2019 40
14.7%
2020 47
17.2%
2021 46
16.8%
2022 46
16.8%
ValueCountFrequency (%)
2022 46
16.8%
2021 46
16.8%
2020 47
17.2%
2019 40
14.7%
2018 47
17.2%
2017 47
17.2%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
취득세
54 
주민세
50 
자동차세
42 
재산세
30 
지방소득세
24 
Other values (8)
73 

Length

Max length7
Median length3
Mean length3.7106227
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 54
19.8%
주민세 50
18.3%
자동차세 42
15.4%
재산세 30
11.0%
지방소득세 24
8.8%
레저세 20
 
7.3%
지역자원시설세 14
 
5.1%
등록면허세 12
 
4.4%
교육세 6
 
2.2%
체납 6
 
2.2%
Other values (3) 15
 
5.5%

Length

2023-12-12T22:21:16.951284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 54
19.8%
주민세 50
18.3%
자동차세 42
15.4%
재산세 30
11.0%
지방소득세 24
8.8%
레저세 20
 
7.3%
지역자원시설세 14
 
5.1%
등록면허세 12
 
4.4%
교육세 6
 
2.2%
체납 6
 
2.2%
Other values (3) 15
 
5.5%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
기타승용
 
6
체납
 
6
재산세(항공기)
 
6
기타
 
6
건축물
 
6
Other values (45)
243 

Length

Max length11
Median length8
Mean length6.1025641
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타승용
2nd row승용
3rd row지방소득세(특별징수)
4th row지방소득세(법인소득)
5th row지방소득세(양도소득)

Common Values

ValueCountFrequency (%)
기타승용 6
 
2.2%
체납 6
 
2.2%
재산세(항공기) 6
 
2.2%
기타 6
 
2.2%
건축물 6
 
2.2%
교육세 6
 
2.2%
등록면허세(면허) 6
 
2.2%
선박 6
 
2.2%
지방소득세(특별징수) 6
 
2.2%
차량 6
 
2.2%
Other values (40) 213
78.0%

Length

2023-12-12T22:21:17.074817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타승용 6
 
2.2%
승합 6
 
2.2%
승용 6
 
2.2%
화물 6
 
2.2%
재산세(건축물 6
 
2.2%
주민세(종업원분 6
 
2.2%
체납 6
 
2.2%
주민세(법인세분 6
 
2.2%
주민세(양도소득 6
 
2.2%
재산세(선박 6
 
2.2%
Other values (40) 213
78.0%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct205
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22456.015
Minimum0
Maximum351513
Zeros63
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T22:21:17.198867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median1143
Q318457
95-th percentile110663
Maximum351513
Range351513
Interquartile range (IQR)18449

Descriptive statistics

Standard deviation57081.159
Coefficient of variation (CV)2.5419096
Kurtosis20.687079
Mean22456.015
Median Absolute Deviation (MAD)1143
Skewness4.2904384
Sum6130492
Variance3.2582587 × 109
MonotonicityNot monotonic
2023-12-12T22:21:17.338066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
 
23.1%
6 2
 
0.7%
23 2
 
0.7%
11 2
 
0.7%
20 2
 
0.7%
884 2
 
0.7%
5 2
 
0.7%
18912 1
 
0.4%
2424 1
 
0.4%
3209 1
 
0.4%
Other values (195) 195
71.4%
ValueCountFrequency (%)
0 63
23.1%
3 1
 
0.4%
5 2
 
0.7%
6 2
 
0.7%
8 1
 
0.4%
9 1
 
0.4%
11 2
 
0.7%
15 1
 
0.4%
16 1
 
0.4%
17 1
 
0.4%
ValueCountFrequency (%)
351513 1
0.4%
349226 1
0.4%
347696 1
0.4%
345579 1
0.4%
341559 1
0.4%
338359 1
0.4%
158809 1
0.4%
146330 1
0.4%
123335 1
0.4%
113422 1
0.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct211
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0835178 × 109
Minimum0
Maximum3.9523683 × 1010
Zeros63
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T22:21:17.768832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1693000
median4.88022 × 108
Q35.670999 × 109
95-th percentile1.789371 × 1010
Maximum3.9523683 × 1010
Range3.9523683 × 1010
Interquartile range (IQR)5.670306 × 109

Descriptive statistics

Standard deviation6.9339469 × 109
Coefficient of variation (CV)1.6980327
Kurtosis7.3099266
Mean4.0835178 × 109
Median Absolute Deviation (MAD)4.88022 × 108
Skewness2.513173
Sum1.1148004 × 1012
Variance4.8079619 × 1019
MonotonicityNot monotonic
2023-12-12T22:21:17.912508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
 
23.1%
2133000 1
 
0.4%
10057060000 1
 
0.4%
491481000 1
 
0.4%
162120000 1
 
0.4%
27553000 1
 
0.4%
14392532000 1
 
0.4%
5555108000 1
 
0.4%
22022614000 1
 
0.4%
32976942000 1
 
0.4%
Other values (201) 201
73.6%
ValueCountFrequency (%)
0 63
23.1%
441000 1
 
0.4%
448000 1
 
0.4%
482000 1
 
0.4%
489000 1
 
0.4%
498000 1
 
0.4%
693000 1
 
0.4%
1127000 1
 
0.4%
1296000 1
 
0.4%
1471000 1
 
0.4%
ValueCountFrequency (%)
39523683000 1
0.4%
35293349000 1
0.4%
34463398000 1
0.4%
32976942000 1
0.4%
32875235000 1
0.4%
32808380000 1
0.4%
23479682000 1
0.4%
22063116000 1
0.4%
22022614000 1
0.4%
20690800000 1
0.4%

Interactions

2023-12-12T22:21:15.430888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:14.700629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:15.070445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:15.535123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:14.817203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:15.188502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:15.640371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:14.952271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:15.302901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:21:18.010415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0001.0000.8550.598
세원 유형명0.0001.0001.0000.9760.933
부과건수0.0000.8550.9761.0000.547
부과금액0.0000.5980.9330.5471.000
2023-12-12T22:21:18.106127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명
세목명1.0000.926
세원 유형명0.9261.000
2023-12-12T22:21:18.194940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과건수부과금액세목명세원 유형명
과세년도1.0000.0010.0410.0000.000
부과건수0.0011.0000.8330.6360.787
부과금액0.0410.8331.0000.3190.639
세목명0.0000.6360.3191.0000.926
세원 유형명0.0000.7870.6390.9261.000

Missing values

2023-12-12T22:21:15.799359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:21:15.941913image/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
0대전광역시대덕구302302017자동차세기타승용392133000
1대전광역시대덕구302302017자동차세승용11342214475290000
2대전광역시대덕구302302017지방소득세지방소득세(특별징수)2880318899202000
3대전광역시대덕구302302017지방소득세지방소득세(법인소득)320539523683000
4대전광역시대덕구302302017지방소득세지방소득세(양도소득)21002060985000
5대전광역시대덕구302302017지방소득세지방소득세(종합소득)159343480056000
6대전광역시대덕구302302017지방소비세지방소비세00
7대전광역시대덕구302302017담배소비세담배소비세00
8대전광역시대덕구302302017교육세교육세34557911182372000
9대전광역시대덕구302302017도시계획세도시계획세00
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
263대전광역시대덕구302302022지역자원시설세지역자원시설세(시설)1118564000
264대전광역시대덕구302302022지역자원시설세지역자원시설세(특자)39116930000
265대전광역시대덕구302302022자동차세자동차세(주행)00
266대전광역시대덕구302302022자동차세3륜이하7909073000
267대전광역시대덕구302302022자동차세특수151753628000
268대전광역시대덕구302302022자동차세화물18551492461000
269대전광역시대덕구302302022자동차세승합2982151139000
270대전광역시대덕구302302022자동차세기타승용134469977000
271대전광역시대덕구302302022자동차세승용11306015026210000
272대전광역시대덕구302302022체납체납1078366460710000