Overview

Dataset statistics

Number of variables8
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory70.8 B

Variable types

Categorical5
Text1
Numeric2

Dataset

Description지방세 과세를 위해 세원이 되는 과세 대상 유형별 부과된 현황을 제공합니다.(연도별, 세월 유형명, 부과건수, 부과금액 등)
Author전북특별자치도 익산시
URLhttps://www.data.go.kr/data/15079915/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 부과건수High correlation
세목명 is highly overall correlated with 부과건수High correlation
세원 유형명 has unique valuesUnique
부과건수 has 11 (23.9%) zerosZeros
부과금액 has 11 (23.9%) zerosZeros

Reproduction

Analysis started2024-03-15 01:32:33.311094
Analysis finished2024-03-15 01:32:35.036248
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

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

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북특별자치도 46
100.0%

Length

2024-03-15T10:32:35.238904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:32:35.454387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북특별자치도 46
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
익산시 46
100.0%

Length

2024-03-15T10:32:35.618069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:32:35.773705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
익산시 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 B
45140
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45140
2nd row45140
3rd row45140
4th row45140
5th row45140

Common Values

ValueCountFrequency (%)
45140 46
100.0%

Length

2024-03-15T10:32:35.933192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:32:36.103218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45140 46
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 B
2021
46 

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 46
100.0%

Length

2024-03-15T10:32:36.366498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:32:36.639909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 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 
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 row교육세
3rd row도시계획세
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%
교육세 1
 
2.2%
Other values (3) 3
 
6.5%

Length

2024-03-15T10:32:36.964667image/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%
교육세 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
2024-03-15T10:32:37.878112image/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 row교육세
3rd row도시계획세
4th row건축물
5th row주택(개별)
ValueCountFrequency (%)
담배소비세 1
 
2.2%
등록면허세(면허 1
 
2.2%
주민세(종합소득 1
 
2.2%
승합 1
 
2.2%
기타승용 1
 
2.2%
승용 1
 
2.2%
지방소비세 1
 
2.2%
지방소득세(특별징수 1
 
2.2%
지방소득세(법인소득 1
 
2.2%
지방소득세(양도소득 1
 
2.2%
Other values (36) 36
78.3%
2024-03-15T10:32:39.241505image/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%
기 5
 
2.2%
시 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%
기 5
 
2.2%
시 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%
기 5
 
2.2%
시 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 

Distinct36
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37325.391
Minimum0
Maximum570217
Zeros11
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size542.0 B
2024-03-15T10:32:39.479011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median1567.5
Q327392.75
95-th percentile161377.5
Maximum570217
Range570217
Interquartile range (IQR)27384.75

Descriptive statistics

Standard deviation94971.395
Coefficient of variation (CV)2.5444179
Kurtosis22.695719
Mean37325.391
Median Absolute Deviation (MAD)1567.5
Skewness4.4120058
Sum1716968
Variance9.0195658 × 109
MonotonicityNot monotonic
2024-03-15T10:32:39.972009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 11
23.9%
479 1
 
2.2%
64580 1
 
2.2%
178898 1
 
2.2%
7 1
 
2.2%
35885 1
 
2.2%
4079 1
 
2.2%
5231 1
 
2.2%
37301 1
 
2.2%
42169 1
 
2.2%
Other values (26) 26
56.5%
ValueCountFrequency (%)
0 11
23.9%
7 1
 
2.2%
11 1
 
2.2%
12 1
 
2.2%
16 1
 
2.2%
66 1
 
2.2%
104 1
 
2.2%
479 1
 
2.2%
768 1
 
2.2%
790 1
 
2.2%
ValueCountFrequency (%)
570217 1
2.2%
244020 1
2.2%
178898 1
2.2%
108816 1
2.2%
101814 1
2.2%
101218 1
2.2%
99337 1
2.2%
64580 1
2.2%
42169 1
2.2%
37301 1
2.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1204644 × 109
Minimum0
Maximum2.7683041 × 1010
Zeros11
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size542.0 B
2024-03-15T10:32:40.372604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1974500
median9.74924 × 108
Q31.1297637 × 1010
95-th percentile2.469998 × 1010
Maximum2.7683041 × 1010
Range2.7683041 × 1010
Interquartile range (IQR)1.1296662 × 1010

Descriptive statistics

Standard deviation9.1712177 × 109
Coefficient of variation (CV)1.2880084
Kurtosis-0.42499886
Mean7.1204644 × 109
Median Absolute Deviation (MAD)9.74924 × 108
Skewness1.0273594
Sum3.2754136 × 1011
Variance8.4111234 × 1019
MonotonicityNot monotonic
2024-03-15T10:32:40.835031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 11
23.9%
19084890000 1
 
2.2%
6835568000 1
 
2.2%
24802809000 1
 
2.2%
11041239000 1
 
2.2%
18787374000 1
 
2.2%
20924562000 1
 
2.2%
6589561000 1
 
2.2%
5820861000 1
 
2.2%
857977000 1
 
2.2%
Other values (26) 26
56.5%
ValueCountFrequency (%)
0 11
23.9%
659000 1
 
2.2%
1921000 1
 
2.2%
8835000 1
 
2.2%
31352000 1
 
2.2%
44350000 1
 
2.2%
61532000 1
 
2.2%
61898000 1
 
2.2%
278391000 1
 
2.2%
818332000 1
 
2.2%
ValueCountFrequency (%)
27683041000 1
2.2%
26727641000 1
2.2%
24802809000 1
2.2%
24391493000 1
2.2%
22920888000 1
2.2%
20924562000 1
2.2%
20920428000 1
2.2%
20064677000 1
2.2%
19084890000 1
2.2%
18787374000 1
2.2%

Interactions

2024-03-15T10:32:34.099961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:32:33.634046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:32:34.314913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:32:33.885760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:32:41.328520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명부과건수부과금액
세목명1.0001.0000.8500.651
세원 유형명1.0001.0001.0001.000
부과건수0.8501.0001.0000.683
부과금액0.6511.0000.6831.000
2024-03-15T10:32:41.597624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액세목명
부과건수1.0000.7400.603
부과금액0.7401.0000.320
세목명0.6030.3201.000

Missing values

2024-03-15T10:32:34.654620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:32:34.876204image/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
0전북특별자치도익산시451402021담배소비세담배소비세47919084890000
1전북특별자치도익산시451402021교육세교육세57021726727641000
2전북특별자치도익산시451402021도시계획세도시계획세00
3전북특별자치도익산시451402021취득세건축물170711383103000
4전북특별자치도익산시451402021취득세주택(개별)29367902025000
5전북특별자치도익산시451402021취득세주택(단독)691420920428000
6전북특별자치도익산시451402021취득세기타104839456000
7전북특별자치도익산시451402021취득세항공기00
8전북특별자치도익산시451402021취득세기계장비768844384000
9전북특별자치도익산시451402021취득세차량2252322920888000
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
36전북특별자치도익산시451402021지역자원시설세지역자원시설세(시설)1131352000
37전북특별자치도익산시451402021지역자원시설세지역자원시설세(특자)84644350000
38전북특별자치도익산시451402021주민세주민세(사업소분)158122074411000
39전북특별자치도익산시451402021주민세주민세(개인분)1088161091871000
40전북특별자치도익산시451402021주민세주민세(종업원분)20755648651000
41전북특별자치도익산시451402021주민세주민세(특별징수)00
42전북특별자치도익산시451402021주민세주민세(법인세분)00
43전북특별자치도익산시451402021주민세주민세(양도소득)00
44전북특별자치도익산시451402021주민세주민세(종합소득)00
45전북특별자치도익산시451402021체납체납24402020064677000