Overview

Dataset statistics

Number of variables9
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory79.8 B

Variable types

Numeric3
Categorical5
Text1

Dataset

Description세원 유형별 과세현황, 지방세 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
Author강원특별자치도 양양군
URLhttps://www.data.go.kr/data/15079475/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
연번 is highly overall correlated with 세목명High correlation
부과건수 is highly overall correlated with 부과금액High correlation
부과금액 is highly overall correlated with 부과건수High correlation
세목명 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
세원 유형명 has unique valuesUnique
부과건수 has 8 (17.4%) zerosZeros
부과금액 has 8 (17.4%) zerosZeros

Reproduction

Analysis started2024-03-14 20:09:13.552732
Analysis finished2024-03-14 20:09:16.610437
Duration3.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.5
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size542.0 B
2024-03-15T05:09:16.894692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.25
Q112.25
median23.5
Q334.75
95-th percentile43.75
Maximum46
Range45
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation13.422618
Coefficient of variation (CV)0.57117522
Kurtosis-1.2
Mean23.5
Median Absolute Deviation (MAD)11.5
Skewness0
Sum1081
Variance180.16667
MonotonicityStrictly increasing
2024-03-15T05:09:17.458954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 1
 
2.2%
36 1
 
2.2%
27 1
 
2.2%
28 1
 
2.2%
29 1
 
2.2%
30 1
 
2.2%
31 1
 
2.2%
32 1
 
2.2%
33 1
 
2.2%
34 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
1 1
2.2%
2 1
2.2%
3 1
2.2%
4 1
2.2%
5 1
2.2%
6 1
2.2%
7 1
2.2%
8 1
2.2%
9 1
2.2%
10 1
2.2%
ValueCountFrequency (%)
46 1
2.2%
45 1
2.2%
44 1
2.2%
43 1
2.2%
42 1
2.2%
41 1
2.2%
40 1
2.2%
39 1
2.2%
38 1
2.2%
37 1
2.2%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 B
강원특별자치도
46 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원특별자치도 46
100.0%

Length

2024-03-15T05:09:17.938005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:09:18.244807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원특별자치도 46
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 B
양양군
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양양군 46
100.0%

Length

2024-03-15T05:09:18.723267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:09:19.099060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양양군 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 B
51830
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row51830
2nd row51830
3rd row51830
4th row51830
5th row51830

Common Values

ValueCountFrequency (%)
51830 46
100.0%

Length

2024-03-15T05:09:19.425013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:09:19.729835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
51830 46
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 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

2024-03-15T05:09:20.017979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:09:20.242757image/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 size496.0 B
취득세
9 
자동차세
7 
주민세
7 
재산세
5 
지방소득세
4 
Other values (8)
14 

Length

Max length7
Median length3
Mean length3.7826087
Min length2

Unique

Unique5 ?
Unique (%)10.9%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 9
19.6%
자동차세 7
15.2%
주민세 7
15.2%
재산세 5
10.9%
지방소득세 4
8.7%
레저세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
교육세 1
 
2.2%
도시계획세 1
 
2.2%
Other values (3) 3
 
6.5%

Length

2024-03-15T05:09:20.422806image/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%
지방소득세 4
8.7%
레저세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
교육세 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 size496.0 B
2024-03-15T05:09:21.332306image/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 row지방소득세(특별징수)
2nd row지방소득세(법인소득)
3rd row지방소득세(양도소득)
4th row지방소득세(종합소득)
5th row교육세
ValueCountFrequency (%)
지방소득세(특별징수 1
 
2.2%
경정 1
 
2.2%
담배소비세 1
 
2.2%
기타승용 1
 
2.2%
승용 1
 
2.2%
등록면허세(면허 1
 
2.2%
등록면허세(등록 1
 
2.2%
지역자원시설세(소방 1
 
2.2%
지역자원시설세(시설 1
 
2.2%
지역자원시설세(특자 1
 
2.2%
Other values (36) 36
78.3%
2024-03-15T05:09:22.383901image/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%
시 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%
시 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%
시 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 

Distinct39
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5571.6739
Minimum0
Maximum91908
Zeros8
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size542.0 B
2024-03-15T05:09:22.801509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median323
Q34839.5
95-th percentile18736.75
Maximum91908
Range91908
Interquartile range (IQR)4825.5

Descriptive statistics

Standard deviation14744.338
Coefficient of variation (CV)2.646303
Kurtosis27.121085
Mean5571.6739
Median Absolute Deviation (MAD)323
Skewness4.8639275
Sum256297
Variance2.1739549 × 108
MonotonicityNot monotonic
2024-03-15T05:09:23.128442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 8
 
17.4%
7052 1
 
2.2%
94 1
 
2.2%
4589 1
 
2.2%
456 1
 
2.2%
145 1
 
2.2%
19264 1
 
2.2%
9869 1
 
2.2%
8684 1
 
2.2%
12672 1
 
2.2%
Other values (29) 29
63.0%
ValueCountFrequency (%)
0 8
17.4%
1 1
 
2.2%
9 1
 
2.2%
11 1
 
2.2%
12 1
 
2.2%
20 1
 
2.2%
27 1
 
2.2%
34 1
 
2.2%
88 1
 
2.2%
94 1
 
2.2%
ValueCountFrequency (%)
91908 1
2.2%
35484 1
2.2%
19264 1
2.2%
17155 1
2.2%
13654 1
2.2%
13334 1
2.2%
12672 1
2.2%
9869 1
2.2%
8684 1
2.2%
7052 1
2.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4954551 × 109
Minimum0
Maximum1.8430297 × 1010
Zeros8
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size542.0 B
2024-03-15T05:09:23.363511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15017750
median2.55702 × 108
Q31.6301092 × 109
95-th percentile5.9066862 × 109
Maximum1.8430297 × 1010
Range1.8430297 × 1010
Interquartile range (IQR)1.6250915 × 109

Descriptive statistics

Standard deviation3.062623 × 109
Coefficient of variation (CV)2.0479538
Kurtosis21.026385
Mean1.4954551 × 109
Median Absolute Deviation (MAD)2.55702 × 108
Skewness4.1480121
Sum6.8790934 × 1010
Variance9.3796596 × 1018
MonotonicityNot monotonic
2024-03-15T05:09:23.697803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 8
 
17.4%
1931079000 1
 
2.2%
61004000 1
 
2.2%
136664000 1
 
2.2%
25056000 1
 
2.2%
10223000 1
 
2.2%
2579575000 1
 
2.2%
150204000 1
 
2.2%
1025473000 1
 
2.2%
1002727000 1
 
2.2%
Other values (29) 29
63.0%
ValueCountFrequency (%)
0 8
17.4%
994000 1
 
2.2%
2527000 1
 
2.2%
4213000 1
 
2.2%
4558000 1
 
2.2%
6397000 1
 
2.2%
10020000 1
 
2.2%
10223000 1
 
2.2%
11498000 1
 
2.2%
25056000 1
 
2.2%
ValueCountFrequency (%)
18430297000 1
2.2%
6916167000 1
2.2%
6046685000 1
2.2%
5486690000 1
2.2%
4141162000 1
2.2%
3243150000 1
2.2%
3189724000 1
2.2%
2579575000 1
2.2%
2499791000 1
2.2%
1931079000 1
2.2%

Interactions

2024-03-15T05:09:15.153390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:09:13.848876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:09:14.604271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:09:15.405918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:09:14.287762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:09:14.732471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:09:15.599252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:09:14.449970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:09:14.899137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:09:23.868394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세목명세원 유형명부과건수부과금액
연번1.0000.9131.0000.3560.288
세목명0.9131.0001.0000.6860.655
세원 유형명1.0001.0001.0001.0001.000
부과건수0.3560.6861.0001.0000.843
부과금액0.2880.6551.0000.8431.000
2024-03-15T05:09:24.106076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부과건수부과금액세목명
연번1.000-0.303-0.3750.665
부과건수-0.3031.0000.7380.407
부과금액-0.3750.7381.0000.378
세목명0.6650.4070.3781.000

Missing values

2024-03-15T05:09:15.969099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:09:16.437396image/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
01강원특별자치도양양군518302022지방소득세지방소득세(특별징수)70521931079000
12강원특별자치도양양군518302022지방소득세지방소득세(법인소득)6471535226000
23강원특별자치도양양군518302022지방소득세지방소득세(양도소득)7603189724000
34강원특별자치도양양군518302022지방소득세지방소득세(종합소득)49231055455000
45강원특별자치도양양군518302022교육세교육세919086046685000
56강원특별자치도양양군518302022도시계획세도시계획세00
67강원특별자치도양양군518302022취득세건축물13136916167000
78강원특별자치도양양군518302022취득세주택(개별)6911661737000
89강원특별자치도양양군518302022취득세주택(단독)454999227000
910강원특별자치도양양군518302022취득세기타177849676000
연번시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
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