Overview

Dataset statistics

Number of variables9
Number of observations274
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.7 KiB
Average record size in memory77.5 B

Variable types

Numeric4
Categorical5

Dataset

Description지방세 과세를 위해 세원이 되는 과세 대상 유형별 부과된 현황을 제공하여,물건 유형에 따른 세부담 수준의 형평성 검토 및 부동산 등 관련분야 규제정책 대상 확인 시 기초자료 활용하고자 함에 목적을 두고 있습니다.
URLhttps://www.data.go.kr/data/15080229/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
연번 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 연번High 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
연번 has unique valuesUnique
부과건수 has 54 (19.7%) zerosZeros
부과금액 has 55 (20.1%) zerosZeros

Reproduction

Analysis started2023-12-12 21:59:41.455707
Analysis finished2023-12-12 21:59:44.389119
Duration2.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct274
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.5
Minimum1
Maximum274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-13T06:59:44.447141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.65
Q169.25
median137.5
Q3205.75
95-th percentile260.35
Maximum274
Range273
Interquartile range (IQR)136.5

Descriptive statistics

Standard deviation79.241193
Coefficient of variation (CV)0.57629959
Kurtosis-1.2
Mean137.5
Median Absolute Deviation (MAD)68.5
Skewness0
Sum37675
Variance6279.1667
MonotonicityStrictly increasing
2023-12-13T06:59:44.565892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
182 1
 
0.4%
188 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
184 1
 
0.4%
183 1
 
0.4%
181 1
 
0.4%
207 1
 
0.4%
Other values (264) 264
96.4%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
274 1
0.4%
273 1
0.4%
272 1
0.4%
271 1
0.4%
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%
266 1
0.4%
265 1
0.4%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
울산광역시
274 

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 274
100.0%

Length

2023-12-13T06:59:44.680924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:59:44.801376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산광역시 274
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
울주군
274 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울주군 274
100.0%

Length

2023-12-13T06:59:44.915307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:59:45.005298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울주군 274
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
31710
274 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31710
2nd row31710
3rd row31710
4th row31710
5th row31710

Common Values

ValueCountFrequency (%)
31710 274
100.0%

Length

2023-12-13T06:59:45.107501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:59:45.191712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31710 274
100.0%

과세년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5182
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-13T06:59:45.267235image/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 deviation1.7122792
Coefficient of variation (CV)0.0008478652
Kurtosis-1.2557429
Mean2019.5182
Median Absolute Deviation (MAD)1
Skewness-0.031289271
Sum553348
Variance2.9319002
MonotonicityIncreasing
2023-12-13T06:59:45.360982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 47
17.2%
2019 47
17.2%
2020 47
17.2%
2021 46
16.8%
2022 46
16.8%
2018 41
15.0%
ValueCountFrequency (%)
2017 47
17.2%
2018 41
15.0%
2019 47
17.2%
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 47
17.2%
2018 41
15.0%
2017 47
17.2%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
취득세
54 
주민세
50 
자동차세
42 
재산세
30 
지방소득세
24 
Other values (8)
74 

Length

Max length7
Median length3
Mean length3.7153285
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 54
19.7%
주민세 50
18.2%
자동차세 42
15.3%
재산세 30
10.9%
지방소득세 24
8.8%
레저세 20
 
7.3%
지역자원시설세 14
 
5.1%
등록면허세 12
 
4.4%
담배소비세 6
 
2.2%
교육세 6
 
2.2%
Other values (3) 16
 
5.8%

Length

2023-12-13T06:59:45.463234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 54
19.7%
주민세 50
18.2%
자동차세 42
15.3%
재산세 30
10.9%
지방소득세 24
8.8%
레저세 20
 
7.3%
지역자원시설세 14
 
5.1%
등록면허세 12
 
4.4%
담배소비세 6
 
2.2%
교육세 6
 
2.2%
Other values (3) 16
 
5.8%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
담배소비세
 
6
건축물
 
6
선박
 
6
기계장비
 
6
3륜이하
 
6
Other values (45)
244 

Length

Max length11
Median length8
Mean length6.0985401
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row담배소비세
2nd row교육세
3rd row도시계획세
4th row건축물
5th row주택(단독)

Common Values

ValueCountFrequency (%)
담배소비세 6
 
2.2%
건축물 6
 
2.2%
선박 6
 
2.2%
기계장비 6
 
2.2%
3륜이하 6
 
2.2%
항공기 6
 
2.2%
등록면허세(면허) 6
 
2.2%
주택(개별) 6
 
2.2%
주택(단독) 6
 
2.2%
토지 6
 
2.2%
Other values (40) 214
78.1%

Length

2023-12-13T06:59:45.577449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
담배소비세 6
 
2.2%
주민세(종합소득 6
 
2.2%
등록면허세(등록 6
 
2.2%
기타승용 6
 
2.2%
체납 6
 
2.2%
교육세 6
 
2.2%
건축물 6
 
2.2%
주민세(법인세분 6
 
2.2%
주민세(양도소득 6
 
2.2%
승용 6
 
2.2%
Other values (40) 214
78.1%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct207
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33340.321
Minimum0
Maximum541637
Zeros54
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-13T06:59:45.695744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median2235
Q323614
95-th percentile159040.25
Maximum541637
Range541637
Interquartile range (IQR)23602

Descriptive statistics

Standard deviation85408.767
Coefficient of variation (CV)2.561726
Kurtosis23.219413
Mean33340.321
Median Absolute Deviation (MAD)2235
Skewness4.5426408
Sum9135248
Variance7.2946575 × 109
MonotonicityNot monotonic
2023-12-13T06:59:45.852840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
19.7%
1 7
 
2.6%
12 6
 
2.2%
4169 2
 
0.7%
207 2
 
0.7%
229 2
 
0.7%
1612 1
 
0.4%
277 1
 
0.4%
204 1
 
0.4%
4103 1
 
0.4%
Other values (197) 197
71.9%
ValueCountFrequency (%)
0 54
19.7%
1 7
 
2.6%
5 1
 
0.4%
6 1
 
0.4%
9 1
 
0.4%
11 1
 
0.4%
12 6
 
2.2%
18 1
 
0.4%
24 1
 
0.4%
29 1
 
0.4%
ValueCountFrequency (%)
541637 1
0.4%
539280 1
0.4%
534961 1
0.4%
532020 1
0.4%
521850 1
0.4%
516993 1
0.4%
170431 1
0.4%
165688 1
0.4%
164891 1
0.4%
164272 1
0.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct215
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0882448 × 1010
Minimum0
Maximum1.0479256 × 1011
Zeros55
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-13T06:59:46.057370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112684750
median8.983745 × 108
Q31.5349857 × 1010
95-th percentile4.6170732 × 1010
Maximum1.0479256 × 1011
Range1.0479256 × 1011
Interquartile range (IQR)1.5337172 × 1010

Descriptive statistics

Standard deviation1.7360797 × 1010
Coefficient of variation (CV)1.5953026
Kurtosis7.8540308
Mean1.0882448 × 1010
Median Absolute Deviation (MAD)8.983745 × 108
Skewness2.4978565
Sum2.9817906 × 1012
Variance3.0139727 × 1020
MonotonicityNot monotonic
2023-12-13T06:59:46.232508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
20.1%
5000 6
 
2.2%
18596060000 1
 
0.4%
189162000 1
 
0.4%
31365549000 1
 
0.4%
60579438000 1
 
0.4%
5932258000 1
 
0.4%
4781620000 1
 
0.4%
18220913000 1
 
0.4%
17478777000 1
 
0.4%
Other values (205) 205
74.8%
ValueCountFrequency (%)
0 55
20.1%
5000 6
 
2.2%
4204000 1
 
0.4%
5537000 1
 
0.4%
6267000 1
 
0.4%
7105000 1
 
0.4%
9122000 1
 
0.4%
10320000 1
 
0.4%
10636000 1
 
0.4%
11933000 1
 
0.4%
ValueCountFrequency (%)
104792564000 1
0.4%
102041000000 1
0.4%
92901261000 1
0.4%
71581094000 1
0.4%
68080849000 1
0.4%
64834022000 1
0.4%
60579438000 1
0.4%
58373224000 1
0.4%
54704862000 1
0.4%
54170739000 1
0.4%

Interactions

2023-12-13T06:59:43.713683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:41.773941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:42.519954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:43.103630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:43.870839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:41.878257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:42.673197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:43.252517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:44.015340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:41.962723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:42.807308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:43.407612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:44.116018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:42.055545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:42.949884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:43.537039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:59:46.325997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도세목명세원 유형명부과건수부과금액
연번1.0000.9440.3300.0000.0000.060
과세년도0.9441.0000.0000.0000.0000.000
세목명0.3300.0001.0001.0000.8200.609
세원 유형명0.0000.0001.0001.0000.9800.879
부과건수0.0000.0000.8200.9801.0000.624
부과금액0.0600.0000.6090.8790.6241.000
2023-12-13T06:59:46.428370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명
세목명1.0000.926
세원 유형명0.9261.000
2023-12-13T06:59:46.510286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도부과건수부과금액세목명세원 유형명
연번1.0000.9860.0070.0010.1410.000
과세년도0.9861.000-0.0150.0080.0000.000
부과건수0.007-0.0151.0000.7310.6170.765
부과금액0.0010.0080.7311.0000.3130.513
세목명0.1410.0000.6170.3131.0000.926
세원 유형명0.0000.0000.7650.5130.9261.000

Missing values

2023-12-13T06:59:44.240044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:59:44.346212image/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

연번시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
01울산광역시울주군317102017담배소비세담배소비세10718596060000
12울산광역시울주군317102017교육세교육세52185032995105000
23울산광역시울주군317102017도시계획세도시계획세00
34울산광역시울주군317102017취득세건축물276417056144000
45울산광역시울주군317102017취득세주택(단독)478618411633000
56울산광역시울주군317102017취득세주택(개별)222912079972000
67울산광역시울주군317102017취득세기타2073007708000
78울산광역시울주군317102017취득세항공기00
89울산광역시울주군317102017취득세기계장비8877776000
910울산광역시울주군317102017취득세차량1300233469000
연번시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
264265울산광역시울주군317102022등록면허세등록면허세(면허)43320618113000
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