Overview

Dataset statistics

Number of variables9
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory78.9 B

Variable types

Categorical5
Text1
Numeric2
DateTime1

Dataset

Description지방세 과세를 위해 세원이 되는 과세 대상 유형별 부과된 현황을 제공 물건 유형에 따른 세부담 수준의 형평성 검토 및 부동산 등 관련분야 규제정책 대상 확인 시 기초자료 활용
URLhttps://www.data.go.kr/data/15078748/fileData.do

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 부과건수 High correlation
세목명 is highly overall correlated with 부과건수 High correlation
세원 유형명 has unique valuesUnique
부과건수 has 10 (21.7%) zerosZeros
부과금액 has 10 (21.7%) zerosZeros

Reproduction

Analysis started2023-12-12 00:45:53.374545
Analysis finished2023-12-12 00:45:54.543748
Duration1.17 second
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
경기도
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 46
100.0%

Length

2023-12-12T09:45:54.611136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:45:54.695070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 46
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광주시 46
100.0%

Length

2023-12-12T09:45:54.779423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:45:54.869861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주시 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
41610
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41610
2nd row41610
3rd row41610
4th row41610
5th row41610

Common Values

ValueCountFrequency (%)
41610 46
100.0%

Length

2023-12-12T09:45:54.972840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:45:55.078688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41610 46
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
2022
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 46
100.0%

Length

2023-12-12T09:45:55.167319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:45:55.269460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 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 row도시계획세
3rd row취득세
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%
도시계획세 1
 
2.2%
Other values (3) 3
 
6.5%

Length

2023-12-12T09:45:55.383828image/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%
도시계획세 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
2023-12-12T09:45:55.616665image/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 row도시계획세
3rd row건축물
4th row주택(개별)
5th row주택(단독)
ValueCountFrequency (%)
담배소비세 1
 
2.2%
주민세(종합소득 1
 
2.2%
지역자원시설세(특자 1
 
2.2%
기타승용 1
 
2.2%
승용 1
 
2.2%
주민세(사업소분 1
 
2.2%
주민세(개인분 1
 
2.2%
주민세(종업원분 1
 
2.2%
주민세(특별징수 1
 
2.2%
주민세(법인세분 1
 
2.2%
Other values (36) 36
78.3%
2023-12-12T09:45:56.015096image/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%
산 5
 
2.2%
재 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%
산 5
 
2.2%
재 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%
산 5
 
2.2%
재 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 

Distinct37
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60214.913
Minimum0
Maximum905773
Zeros10
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T09:45:56.172757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.25
median2170
Q336755.5
95-th percentile288651
Maximum905773
Range905773
Interquartile range (IQR)36744.25

Descriptive statistics

Standard deviation150261.62
Coefficient of variation (CV)2.495422
Kurtosis22.767288
Mean60214.913
Median Absolute Deviation (MAD)2170
Skewness4.3858535
Sum2769886
Variance2.2578554 × 1010
MonotonicityNot monotonic
2023-12-12T09:45:56.316690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 10
 
21.7%
640 1
 
2.2%
5933 1
 
2.2%
1761 1
 
2.2%
256569 1
 
2.2%
30334 1
 
2.2%
157583 1
 
2.2%
3167 1
 
2.2%
115353 1
 
2.2%
8212 1
 
2.2%
Other values (27) 27
58.7%
ValueCountFrequency (%)
0 10
21.7%
9 1
 
2.2%
11 1
 
2.2%
12 1
 
2.2%
34 1
 
2.2%
37 1
 
2.2%
93 1
 
2.2%
566 1
 
2.2%
640 1
 
2.2%
674 1
 
2.2%
ValueCountFrequency (%)
905773 1
2.2%
326898 1
2.2%
299345 1
2.2%
256569 1
2.2%
157583 1
2.2%
146167 1
2.2%
118308 1
2.2%
115353 1
2.2%
102602 1
2.2%
74940 1
2.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6636871 × 1010
Minimum0
Maximum9.8535186 × 1010
Zeros10
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T09:45:56.454484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112969250
median2.3684605 × 109
Q32.89012 × 1010
95-th percentile6.1651538 × 1010
Maximum9.8535186 × 1010
Range9.8535186 × 1010
Interquartile range (IQR)2.888823 × 1010

Descriptive statistics

Standard deviation2.3557438 × 1010
Coefficient of variation (CV)1.4159777
Kurtosis2.204615
Mean1.6636871 × 1010
Median Absolute Deviation (MAD)2.3684605 × 109
Skewness1.5789018
Sum7.6529606 × 1011
Variance5.5495289 × 1020
MonotonicityNot monotonic
2023-12-12T09:45:56.608596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 10
 
21.7%
29447674000 1
 
2.2%
27261776000 1
 
2.2%
110627000 1
 
2.2%
38173913000 1
 
2.2%
3167445000 1
 
2.2%
1569476000 1
 
2.2%
5405355000 1
 
2.2%
30241326000 1
 
2.2%
45529766000 1
 
2.2%
Other values (27) 27
58.7%
ValueCountFrequency (%)
0 10
21.7%
1638000 1
 
2.2%
10156000 1
 
2.2%
21409000 1
 
2.2%
31096000 1
 
2.2%
37818000 1
 
2.2%
58843000 1
 
2.2%
110627000 1
 
2.2%
148374000 1
 
2.2%
405730000 1
 
2.2%
ValueCountFrequency (%)
98535186000 1
2.2%
69446485000 1
2.2%
63495150000 1
2.2%
56120700000 1
2.2%
52112625000 1
2.2%
45529766000 1
2.2%
44569700000 1
2.2%
44461385000 1
2.2%
38173913000 1
2.2%
35852928000 1
2.2%

데이터기준일
Date

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
Minimum1900-01-20 00:00:00
Maximum2023-07-19 00:00:00
2023-12-12T09:45:56.749839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:56.874159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2023-12-12T09:45:54.085141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:53.925756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:54.193040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:53.997154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:45:56.957325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명부과건수부과금액데이터기준일
세목명1.0001.0000.7900.7141.000
세원 유형명1.0001.0001.0001.0001.000
부과건수0.7901.0001.0000.7590.000
부과금액0.7141.0000.7591.0000.418
데이터기준일1.0001.0000.0000.4181.000
2023-12-12T09:45:57.051227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액세목명
부과건수1.0000.7460.520
부과금액0.7461.0000.377
세목명0.5200.3771.000

Missing values

2023-12-12T09:45:54.339067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:45:54.477830image/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
0경기도광주시416102022담배소비세담배소비세640294476740001900-01-20
1경기도광주시416102022도시계획세도시계획세002023-07-19
2경기도광주시416102022취득세건축물3741445697000002023-07-19
3경기도광주시416102022취득세주택(개별)1677175132290002023-07-19
4경기도광주시416102022취득세주택(단독)10177561207000002023-07-19
5경기도광주시416102022취득세기타56647551990002023-07-19
6경기도광주시416102022취득세항공기002023-07-19
7경기도광주시416102022취득세기계장비6749023570002023-07-19
8경기도광주시416102022취득세차량34705444613850002023-07-19
9경기도광주시416102022취득세선박37101560002023-07-19
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액데이터기준일
36경기도광주시416102022지방소득세지방소득세(양도소득)5933272617760002023-07-19
37경기도광주시416102022지방소득세지방소득세(종합소득)118308185690460002023-07-19
38경기도광주시416102022지방소비세지방소비세9133492440002023-07-19
39경기도광주시416102022교육세교육세905773634951500002023-07-19
40경기도광주시416102022등록면허세등록면허세(면허)7494010343570002023-07-19
41경기도광주시416102022등록면허세등록면허세(등록)102602176821880002023-07-19
42경기도광주시416102022지역자원시설세지역자원시설세(소방)29934595904480002023-07-19
43경기도광주시416102022지역자원시설세지역자원시설세(시설)002023-07-19
44경기도광주시416102022지역자원시설세지역자원시설세(특자)981214090002023-07-19
45경기도광주시416102022체납체납326898521126250002023-07-19