Overview

Dataset statistics

Number of variables8
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory71.2 B

Variable types

Categorical5
Text1
Numeric2

Dataset

Description대전광역시 동구에서 부과되는 지방세의 세목의 세원 유형별(건축물, 토지, 차량, 선박 등)의 분야를 세분화하여 연도별 세목별로 분류한 자료입니다.
URLhttps://www.data.go.kr/data/15078370/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 9 (22.0%) zerosZeros
부과금액 has 9 (22.0%) zerosZeros

Reproduction

Analysis started2023-12-12 19:25:58.589349
Analysis finished2023-12-12 19:25:59.364892
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
대전광역시
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 41
100.0%

Length

2023-12-13T04:25:59.725026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:25:59.834619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 41
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
동구
41 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동구 41
100.0%

Length

2023-12-13T04:25:59.939622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:26:00.041846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
30110
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30110
2nd row30110
3rd row30110
4th row30110
5th row30110

Common Values

ValueCountFrequency (%)
30110 41
100.0%

Length

2023-12-13T04:26:00.150284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:26:00.272316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30110 41
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
2021
41 

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 41
100.0%

Length

2023-12-13T04:26:00.390838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:26:00.489301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 41
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Memory size460.0 B
취득세
9 
자동차세
7 
주민세
7 
재산세
5 
지방소득세
4 
Other values (6)
9 

Length

Max length7
Median length3
Mean length3.8292683
Min length2

Unique

Unique4 ?
Unique (%)9.8%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 9
22.0%
자동차세 7
17.1%
주민세 7
17.1%
재산세 5
12.2%
지방소득세 4
9.8%
지역자원시설세 3
 
7.3%
등록면허세 2
 
4.9%
교육세 1
 
2.4%
도시계획세 1
 
2.4%
지방소비세 1
 
2.4%

Length

2023-12-13T04:26:00.599905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 9
22.0%
자동차세 7
17.1%
주민세 7
17.1%
재산세 5
12.2%
지방소득세 4
9.8%
지역자원시설세 3
 
7.3%
등록면허세 2
 
4.9%
교육세 1
 
2.4%
도시계획세 1
 
2.4%
지방소비세 1
 
2.4%

세원 유형명
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-13T04:26:00.816871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length6.4146341
Min length2

Characters and Unicode

Total characters263
Distinct characters66
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

Unique41 ?
Unique (%)100.0%

Sample

1st row교육세
2nd row도시계획세
3rd row건축물
4th row주택(개별)
5th row주택(단독)
ValueCountFrequency (%)
교육세 1
 
2.4%
기타승용 1
 
2.4%
지방소비세 1
 
2.4%
등록면허세(면허 1
 
2.4%
등록면허세(등록 1
 
2.4%
지역자원시설세(소방 1
 
2.4%
지역자원시설세(시설 1
 
2.4%
지역자원시설세(특자 1
 
2.4%
지방소득세(특별징수 1
 
2.4%
지방소득세(법인소득 1
 
2.4%
Other values (31) 31
75.6%
2023-12-13T04:26:01.234052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 26
 
9.9%
) 24
 
9.1%
( 24
 
9.1%
소 12
 
4.6%
주 11
 
4.2%
지 10
 
3.8%
득 9
 
3.4%
민 7
 
2.7%
방 6
 
2.3%
재 5
 
1.9%
Other values (56) 129
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 214
81.4%
Close Punctuation 24
 
9.1%
Open Punctuation 24
 
9.1%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 26
 
12.1%
소 12
 
5.6%
주 11
 
5.1%
지 10
 
4.7%
득 9
 
4.2%
민 7
 
3.3%
방 6
 
2.8%
재 5
 
2.3%
산 5
 
2.3%
자 5
 
2.3%
Other values (53) 118
55.1%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 214
81.4%
Common 49
 
18.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 26
 
12.1%
소 12
 
5.6%
주 11
 
5.1%
지 10
 
4.7%
득 9
 
4.2%
민 7
 
3.3%
방 6
 
2.8%
재 5
 
2.3%
산 5
 
2.3%
자 5
 
2.3%
Other values (53) 118
55.1%
Common
ValueCountFrequency (%)
) 24
49.0%
( 24
49.0%
3 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 214
81.4%
ASCII 49
 
18.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 26
 
12.1%
소 12
 
5.6%
주 11
 
5.1%
지 10
 
4.7%
득 9
 
4.2%
민 7
 
3.3%
방 6
 
2.8%
재 5
 
2.3%
산 5
 
2.3%
자 5
 
2.3%
Other values (53) 118
55.1%
ASCII
ValueCountFrequency (%)
) 24
49.0%
( 24
49.0%
3 1
 
2.0%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28903.122
Minimum0
Maximum422993
Zeros9
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T04:26:01.432063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median1662
Q324081
95-th percentile122933
Maximum422993
Range422993
Interquartile range (IQR)24070

Descriptive statistics

Standard deviation72447.261
Coefficient of variation (CV)2.5065549
Kurtosis22.496946
Mean28903.122
Median Absolute Deviation (MAD)1662
Skewness4.3896063
Sum1185028
Variance5.2486057 × 109
MonotonicityNot monotonic
2023-12-13T04:26:01.586156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 9
 
22.0%
422993 1
 
2.4%
29618 1
 
2.4%
122933 1
 
2.4%
7 1
 
2.4%
24081 1
 
2.4%
41027 1
 
2.4%
111786 1
 
2.4%
326 1
 
2.4%
1975 1
 
2.4%
Other values (23) 23
56.1%
ValueCountFrequency (%)
0 9
22.0%
7 1
 
2.4%
11 1
 
2.4%
13 1
 
2.4%
18 1
 
2.4%
65 1
 
2.4%
326 1
 
2.4%
800 1
 
2.4%
831 1
 
2.4%
941 1
 
2.4%
ValueCountFrequency (%)
422993 1
2.4%
135123 1
2.4%
122933 1
2.4%
111786 1
2.4%
84893 1
2.4%
79649 1
2.4%
41027 1
2.4%
32963 1
2.4%
29618 1
2.4%
28475 1
2.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5758862 × 109
Minimum0
Maximum2.3856017 × 1010
Zeros9
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T04:26:01.767770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13406000
median8.37957 × 108
Q37.197231 × 109
95-th percentile1.6351929 × 1010
Maximum2.3856017 × 1010
Range2.3856017 × 1010
Interquartile range (IQR)7.193825 × 109

Descriptive statistics

Standard deviation6.5256662 × 109
Coefficient of variation (CV)1.4260989
Kurtosis1.6633719
Mean4.5758862 × 109
Median Absolute Deviation (MAD)8.37957 × 108
Skewness1.5364231
Sum1.8761133 × 1011
Variance4.258432 × 1019
MonotonicityNot monotonic
2023-12-13T04:26:01.951952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 9
 
22.0%
15070951000 1
 
2.4%
12058928000 1
 
2.4%
16351929000 1
 
2.4%
7197231000 1
 
2.4%
980503000 1
 
2.4%
3733409000 1
 
2.4%
3898174000 1
 
2.4%
10258000 1
 
2.4%
5448329000 1
 
2.4%
Other values (23) 23
56.1%
ValueCountFrequency (%)
0 9
22.0%
670000 1
 
2.4%
3406000 1
 
2.4%
3467000 1
 
2.4%
10258000 1
 
2.4%
10715000 1
 
2.4%
46898000 1
 
2.4%
48256000 1
 
2.4%
77512000 1
 
2.4%
174165000 1
 
2.4%
ValueCountFrequency (%)
23856017000 1
2.4%
22790066000 1
2.4%
16351929000 1
2.4%
15070951000 1
2.4%
13279094000 1
2.4%
12767566000 1
2.4%
12344975000 1
2.4%
12058928000 1
2.4%
8829416000 1
2.4%
8067764000 1
2.4%

Interactions

2023-12-13T04:25:58.949148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:25:58.778144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:25:59.034717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:25:58.860753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:26:02.076871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명부과건수부과금액
세목명1.0001.0000.8420.666
세원 유형명1.0001.0001.0001.000
부과건수0.8421.0001.0000.603
부과금액0.6661.0000.6031.000
2023-12-13T04:26:02.247746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액세목명
부과건수1.0000.7870.604
부과금액0.7871.0000.376
세목명0.6040.3761.000

Missing values

2023-12-13T04:25:59.193609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:25:59.312944image/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
0대전광역시동구301102021교육세교육세42299315070951000
1대전광역시동구301102021도시계획세도시계획세00
2대전광역시동구301102021취득세건축물11198829416000
3대전광역시동구301102021취득세주택(개별)166212767566000
4대전광역시동구301102021취득세주택(단독)590923856017000
5대전광역시동구301102021취득세기타1877512000
6대전광역시동구301102021취득세항공기00
7대전광역시동구301102021취득세기계장비113467000
8대전광역시동구301102021취득세차량2064296455000
9대전광역시동구301102021취득세선박133406000
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
31대전광역시동구301102021지방소득세지방소득세(양도소득)31186165212000
32대전광역시동구301102021지방소득세지방소득세(종합소득)329633541323000
33대전광역시동구301102021주민세주민세(사업소분)113151296769000
34대전광역시동구301102021주민세주민세(개인분)84893837957000
35대전광역시동구301102021주민세주민세(종업원분)11353141659000
36대전광역시동구301102021주민세주민세(특별징수)00
37대전광역시동구301102021주민세주민세(법인세분)00
38대전광역시동구301102021주민세주민세(양도소득)00
39대전광역시동구301102021주민세주민세(종합소득)00
40대전광역시동구301102021체납체납1351238067764000