Overview

Dataset statistics

Number of variables9
Number of observations134
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.1 KiB
Average record size in memory77.0 B

Variable types

Categorical7
Numeric2

Dataset

Description제공범위(대상) : 지방세 세원이 되는 과세물건 유형별 부과된 현황을 제공 관련법령 : 지방세법 소관기관 : 지방자치단체 제공기관 : 시군구 표준데이터셋 제공시스템 : 표준지방세시스템 자료기준일 : 매년 12월31일 2022년 자료는 올해 6월에 나올 예정
URLhttps://www.data.go.kr/data/15079036/fileData.do

Alerts

시도명 has constant value ""Constant
과세년도 has constant value ""Constant
데이터기준일 has constant value ""Constant
시군구명 is highly overall correlated with 자치단체코드High correlation
자치단체코드 is highly overall correlated with 시군구명High correlation
부과건수 is highly overall correlated with 부과금액High correlation
부과금액 is highly overall correlated with 부과건수High correlation
세목명 is highly overall correlated with 세원 유형명High correlation
세원 유형명 is highly overall correlated with 세목명High correlation
부과건수 has 67 (50.0%) zerosZeros
부과금액 has 67 (50.0%) zerosZeros

Reproduction

Analysis started2023-12-12 08:49:31.982734
Analysis finished2023-12-12 08:49:33.260112
Duration1.28 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
경상북도
134 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 134
100.0%

Length

2023-12-12T17:49:33.335127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:49:33.443171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 134
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
포항시북구
46 
포항시남구
46 
포항시
42 

Length

Max length5
Median length5
Mean length4.3731343
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포항시북구
2nd row포항시북구
3rd row포항시북구
4th row포항시남구
5th row포항시남구

Common Values

ValueCountFrequency (%)
포항시북구 46
34.3%
포항시남구 46
34.3%
포항시 42
31.3%

Length

2023-12-12T17:49:33.575088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:49:33.723319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포항시북구 46
34.3%
포항시남구 46
34.3%
포항시 42
31.3%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
47113
46 
47111
46 
47110
42 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47113
2nd row47113
3rd row47113
4th row47111
5th row47111

Common Values

ValueCountFrequency (%)
47113 46
34.3%
47111 46
34.3%
47110 42
31.3%

Length

2023-12-12T17:49:33.906535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:49:34.018261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47113 46
34.3%
47111 46
34.3%
47110 42
31.3%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2021
134 

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 134
100.0%

Length

2023-12-12T17:49:34.164143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:49:34.256511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 134
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
취득세
25 
자동차세
21 
주민세
20 
재산세
15 
레저세
12 
Other values (8)
41 

Length

Max length7
Median length3
Mean length3.7910448
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 25
18.7%
자동차세 21
15.7%
주민세 20
14.9%
재산세 15
11.2%
레저세 12
9.0%
지방소득세 11
8.2%
지역자원시설세 9
 
6.7%
등록면허세 6
 
4.5%
지방소비세 3
 
2.2%
담배소비세 3
 
2.2%
Other values (3) 9
 
6.7%

Length

2023-12-12T17:49:34.367425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 25
18.7%
자동차세 21
15.7%
주민세 20
14.9%
재산세 15
11.2%
레저세 12
9.0%
지방소득세 11
8.2%
지역자원시설세 9
 
6.7%
등록면허세 6
 
4.5%
지방소비세 3
 
2.2%
담배소비세 3
 
2.2%
Other values (3) 9
 
6.7%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
차량
 
3
특수
 
3
경마
 
3
토지
 
3
기타
 
3
Other values (41)
119 

Length

Max length11
Median length8
Mean length5.9626866
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row차량
2nd row선박
3rd row토지
4th row주택(개별)
5th row주택(단독)

Common Values

ValueCountFrequency (%)
차량 3
 
2.2%
특수 3
 
2.2%
경마 3
 
2.2%
토지 3
 
2.2%
기타 3
 
2.2%
항공기 3
 
2.2%
기계장비 3
 
2.2%
건축물 3
 
2.2%
소싸움 3
 
2.2%
경정 3
 
2.2%
Other values (36) 104
77.6%

Length

2023-12-12T17:49:34.498793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
차량 3
 
2.2%
지방소비세 3
 
2.2%
승용 3
 
2.2%
주민세(사업소분 3
 
2.2%
주민세(개인분 3
 
2.2%
주민세(특별징수 3
 
2.2%
주민세(법인세분 3
 
2.2%
주민세(양도소득 3
 
2.2%
주민세(종합소득 3
 
2.2%
교육세 3
 
2.2%
Other values (36) 104
77.6%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22721.97
Minimum0
Maximum581480
Zeros67
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:49:34.641724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q34106.75
95-th percentile116390.85
Maximum581480
Range581480
Interquartile range (IQR)4106.75

Descriptive statistics

Standard deviation72974.49
Coefficient of variation (CV)3.2116269
Kurtosis35.870615
Mean22721.97
Median Absolute Deviation (MAD)2.5
Skewness5.532195
Sum3044744
Variance5.3252762 × 109
MonotonicityNot monotonic
2023-12-12T17:49:34.804702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
50.0%
473 3
 
2.2%
160 2
 
1.5%
21161 1
 
0.7%
31322 1
 
0.7%
91046 1
 
0.7%
259 1
 
0.7%
123353 1
 
0.7%
52348 1
 
0.7%
63483 1
 
0.7%
Other values (55) 55
41.0%
ValueCountFrequency (%)
0 67
50.0%
5 1
 
0.7%
7 1
 
0.7%
12 1
 
0.7%
97 1
 
0.7%
99 1
 
0.7%
120 1
 
0.7%
160 2
 
1.5%
259 1
 
0.7%
396 1
 
0.7%
ValueCountFrequency (%)
581480 1
0.7%
470781 1
0.7%
192273 1
0.7%
178378 1
0.7%
170489 1
0.7%
162918 1
0.7%
123353 1
0.7%
112642 1
0.7%
101965 1
0.7%
97221 1
0.7%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.403397 × 109
Minimum0
Maximum5.8975527 × 1010
Zeros67
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:49:34.973885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6153000
Q36.696458 × 109
95-th percentile2.5251196 × 1010
Maximum5.8975527 × 1010
Range5.8975527 × 1010
Interquartile range (IQR)6.696458 × 109

Descriptive statistics

Standard deviation1.0675077 × 1010
Coefficient of variation (CV)1.9756234
Kurtosis8.1016311
Mean5.403397 × 109
Median Absolute Deviation (MAD)6153000
Skewness2.6677916
Sum7.240552 × 1011
Variance1.1395728 × 1020
MonotonicityNot monotonic
2023-12-12T17:49:35.123341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
50.0%
23631638000 1
 
0.7%
5787522000 1
 
0.7%
10261158000 1
 
0.7%
13721503000 1
 
0.7%
14055518000 1
 
0.7%
160319000 1
 
0.7%
7657267000 1
 
0.7%
45916000 1
 
0.7%
5464286000 1
 
0.7%
Other values (58) 58
43.3%
ValueCountFrequency (%)
0 67
50.0%
12306000 1
 
0.7%
13550000 1
 
0.7%
19243000 1
 
0.7%
33838000 1
 
0.7%
45916000 1
 
0.7%
67981000 1
 
0.7%
82061000 1
 
0.7%
93797000 1
 
0.7%
107988000 1
 
0.7%
ValueCountFrequency (%)
58975527000 1
0.7%
54107701000 1
0.7%
40879280000 1
0.7%
37190023000 1
0.7%
33162325000 1
0.7%
28924780000 1
0.7%
26831542000 1
0.7%
24400241000 1
0.7%
24341910000 1
0.7%
23631638000 1
0.7%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2021-12-31
134 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-12-31
2nd row2021-12-31
3rd row2021-12-31
4th row2021-12-31
5th row2021-12-31

Common Values

ValueCountFrequency (%)
2021-12-31 134
100.0%

Length

2023-12-12T17:49:35.265454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:49:35.358435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-31 134
100.0%

Interactions

2023-12-12T17:49:32.515288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:49:32.317316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:49:32.617038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:49:32.405405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:49:35.420987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명세원 유형명부과건수부과금액
시군구명1.0001.0000.0000.0000.1580.206
자치단체코드1.0001.0000.0000.0000.1580.206
세목명0.0000.0001.0001.0000.6430.513
세원 유형명0.0000.0001.0001.0000.5020.702
부과건수0.1580.1580.6430.5021.0000.535
부과금액0.2060.2060.5130.7020.5351.000
2023-12-12T17:49:35.547857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명세원 유형명자치단체코드세목명
시군구명1.0000.0001.0000.000
세원 유형명0.0001.0000.0000.853
자치단체코드1.0000.0001.0000.000
세목명0.0000.8530.0001.000
2023-12-12T17:49:35.702602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액시군구명자치단체코드세목명세원 유형명
부과건수1.0000.9220.0630.0630.3750.193
부과금액0.9221.0000.1290.1290.2550.291
시군구명0.0630.1291.0001.0000.0000.000
자치단체코드0.0630.1291.0001.0000.0000.000
세목명0.3750.2550.0000.0001.0000.853
세원 유형명0.1930.2910.0000.0000.8531.000

Missing values

2023-12-12T17:49:33.041049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:49:33.195423image/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 유형명부과건수부과금액데이터기준일
0경상북도포항시북구471132021취득세차량21161236316380002021-12-31
1경상북도포항시북구471132021취득세선박973226240002021-12-31
2경상북도포항시북구471132021취득세토지6744541077010002021-12-31
3경상북도포항시남구471112021취득세주택(개별)166172742410002021-12-31
4경상북도포항시남구471112021취득세주택(단독)415095379490002021-12-31
5경상북도포항시남구471112021취득세기타998330660002021-12-31
6경상북도포항시남구471112021취득세항공기002021-12-31
7경상북도포항시남구471112021취득세기계장비77215213550002021-12-31
8경상북도포항시남구471112021취득세차량19796213212400002021-12-31
9경상북도포항시남구471112021취득세선박1202560540002021-12-31
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액데이터기준일
124경상북도포항시북구471132021취득세주택(단독)9167210789920002021-12-31
125경상북도포항시북구471132021취득세기타1608394720002021-12-31
126경상북도포항시북구471132021취득세항공기002021-12-31
127경상북도포항시북구471132021취득세기계장비121816441360002021-12-31
128경상북도포항시북구471132021교육세교육세581480232175760002021-12-31
129경상북도포항시남구471112021교육세교육세470781182324200002021-12-31
130경상북도포항시471102021교육세교육세473163598900002021-12-31
131경상북도포항시북구471132021체납체납170489147860600002021-12-31
132경상북도포항시남구471112021체납체납178378161615430002021-12-31
133경상북도포항시471102021체납체납002021-12-31