Overview

Dataset statistics

Number of variables8
Number of observations47
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

Description제공범위 : 지방세 세원이 되는 과세물건 유형별 부과된 현황을 제공. 관련 법령 : 지방세법. 소관기관 : 지방자치단체. 제공기관 : 시군구. 표준데이터 셋 제공시스템 : 표준지방세시스템. 자료기준일 : 매년 12월 31일
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=351&beforeMenuCd=DOM_000000201001001000&publicdatapk=15078560

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 부과금액High correlation
부과금액 is highly overall correlated with 부과건수High correlation
세원 유형명 has unique valuesUnique
부과건수 has 11 (23.4%) zerosZeros
부과금액 has 12 (25.5%) zerosZeros

Reproduction

Analysis started2024-01-09 23:10:13.086622
Analysis finished2024-01-09 23:10:14.157452
Duration1.07 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
충청남도
47 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 47
100.0%

Length

2024-01-10T08:10:14.212499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:10:14.305772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 47
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
홍성군
47 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
홍성군 47
100.0%

Length

2024-01-10T08:10:14.402014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:10:14.491636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
홍성군 47
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
44800
47 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44800
2nd row44800
3rd row44800
4th row44800
5th row44800

Common Values

ValueCountFrequency (%)
44800 47
100.0%

Length

2024-01-10T08:10:14.590023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:10:14.673669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44800 47
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
2020
47 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 47
100.0%

Length

2024-01-10T08:10:14.772537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:10:14.866516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 47
100.0%

세목명
Categorical

Distinct13
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size508.0 B
취득세
9 
주민세
9 
자동차세
7 
재산세
5 
레저세
4 
Other values (8)
13 

Length

Max length7
Median length3
Mean length3.6808511
Min length2

Unique

Unique5 ?
Unique (%)10.6%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 9
19.1%
주민세 9
19.1%
자동차세 7
14.9%
재산세 5
10.6%
레저세 4
8.5%
지방소득세 4
8.5%
등록면허세 2
 
4.3%
지역자원시설세 2
 
4.3%
담배소비세 1
 
2.1%
교육세 1
 
2.1%
Other values (3) 3
 
6.4%

Length

2024-01-10T08:10:14.953654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 9
19.1%
주민세 9
19.1%
자동차세 7
14.9%
재산세 5
10.6%
레저세 4
8.5%
지방소득세 4
8.5%
등록면허세 2
 
4.3%
지역자원시설세 2
 
4.3%
담배소비세 1
 
2.1%
교육세 1
 
2.1%
Other values (3) 3
 
6.4%

세원 유형명
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2024-01-10T08:10:15.150900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length6.0425532
Min length2

Characters and Unicode

Total characters284
Distinct characters74
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

Unique47 ?
Unique (%)100.0%

Sample

1st row담배소비세
2nd row교육세
3rd row도시계획세
4th row건축물
5th row주택(개별)
ValueCountFrequency (%)
담배소비세 1
 
2.1%
주민세(재산분 1
 
2.1%
주민세(특별징수 1
 
2.1%
주민세(법인세분 1
 
2.1%
주민세(양도소득 1
 
2.1%
주민세(종합소득 1
 
2.1%
주민세(법인균등 1
 
2.1%
주민세(개인사업 1
 
2.1%
주민세(개인균등 1
 
2.1%
지방소비세 1
 
2.1%
Other values (37) 37
78.7%
2024-01-10T08:10:15.475992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 28
 
9.9%
( 25
 
8.8%
) 25
 
8.8%
소 13
 
4.6%
주 13
 
4.6%
득 9
 
3.2%
지 9
 
3.2%
민 9
 
3.2%
재 6
 
2.1%
산 6
 
2.1%
Other values (64) 141
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 233
82.0%
Open Punctuation 25
 
8.8%
Close Punctuation 25
 
8.8%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 28
 
12.0%
소 13
 
5.6%
주 13
 
5.6%
득 9
 
3.9%
지 9
 
3.9%
민 9
 
3.9%
재 6
 
2.6%
산 6
 
2.6%
방 6
 
2.6%
기 5
 
2.1%
Other values (61) 129
55.4%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 233
82.0%
Common 51
 
18.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 28
 
12.0%
소 13
 
5.6%
주 13
 
5.6%
득 9
 
3.9%
지 9
 
3.9%
민 9
 
3.9%
재 6
 
2.6%
산 6
 
2.6%
방 6
 
2.6%
기 5
 
2.1%
Other values (61) 129
55.4%
Common
ValueCountFrequency (%)
( 25
49.0%
) 25
49.0%
3 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 233
82.0%
ASCII 51
 
18.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 28
 
12.0%
소 13
 
5.6%
주 13
 
5.6%
득 9
 
3.9%
지 9
 
3.9%
민 9
 
3.9%
재 6
 
2.6%
산 6
 
2.6%
방 6
 
2.6%
기 5
 
2.1%
Other values (61) 129
55.4%
ASCII
ValueCountFrequency (%)
( 25
49.0%
) 25
49.0%
3 1
 
2.0%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14039.298
Minimum0
Maximum240784
Zeros11
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-10T08:10:15.600451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.5
median641
Q39956.5
95-th percentile55337.7
Maximum240784
Range240784
Interquartile range (IQR)9949

Descriptive statistics

Standard deviation37867.109
Coefficient of variation (CV)2.6972224
Kurtosis28.90893
Mean14039.298
Median Absolute Deviation (MAD)641
Skewness5.0103727
Sum659847
Variance1.4339179 × 109
MonotonicityNot monotonic
2024-01-10T08:10:15.727652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 11
23.4%
273 1
 
2.1%
25525 1
 
2.1%
641 1
 
2.1%
574 1
 
2.1%
1663 1
 
2.1%
3735 1
 
2.1%
40392 1
 
2.1%
6 1
 
2.1%
21563 1
 
2.1%
Other values (27) 27
57.4%
ValueCountFrequency (%)
0 11
23.4%
6 1
 
2.1%
9 1
 
2.1%
11 1
 
2.1%
12 1
 
2.1%
17 1
 
2.1%
35 1
 
2.1%
273 1
 
2.1%
279 1
 
2.1%
402 1
 
2.1%
ValueCountFrequency (%)
240784 1
2.1%
76286 1
2.1%
61743 1
2.1%
40392 1
2.1%
37485 1
2.1%
37436 1
2.1%
34697 1
2.1%
25525 1
2.1%
21563 1
2.1%
17834 1
2.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4120523 × 109
Minimum0
Maximum1.2867591 × 1010
Zeros12
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-10T08:10:15.869287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1282000
median3.06905 × 108
Q33.1566 × 109
95-th percentile1.0374992 × 1010
Maximum1.2867591 × 1010
Range1.2867591 × 1010
Interquartile range (IQR)3.156318 × 109

Descriptive statistics

Standard deviation3.6121581 × 109
Coefficient of variation (CV)1.4975455
Kurtosis1.3170502
Mean2.4120523 × 109
Median Absolute Deviation (MAD)3.06905 × 108
Skewness1.5561543
Sum1.1336646 × 1011
Variance1.3047686 × 1019
MonotonicityNot monotonic
2024-01-10T08:10:16.022728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 12
25.5%
7632105000 1
 
2.1%
2479557000 1
 
2.1%
8637773000 1
 
2.1%
306905000 1
 
2.1%
990911000 1
 
2.1%
115007000 1
 
2.1%
187206000 1
 
2.1%
404194000 1
 
2.1%
249081000 1
 
2.1%
Other values (26) 26
55.3%
ValueCountFrequency (%)
0 12
25.5%
564000 1
 
2.1%
2679000 1
 
2.1%
3343000 1
 
2.1%
4948000 1
 
2.1%
16785000 1
 
2.1%
21556000 1
 
2.1%
105881000 1
 
2.1%
115007000 1
 
2.1%
180743000 1
 
2.1%
ValueCountFrequency (%)
12867591000 1
2.1%
11364359000 1
2.1%
10734610000 1
2.1%
9535884000 1
2.1%
9058889000 1
2.1%
8637773000 1
2.1%
7632105000 1
2.1%
6464154000 1
2.1%
4801431000 1
2.1%
4794802000 1
2.1%

Interactions

2024-01-10T08:10:13.444530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:10:13.255210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:10:13.538961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:10:13.343445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T08:10:16.106853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명부과건수부과금액
세목명1.0001.0000.7030.529
세원 유형명1.0001.0001.0001.000
부과건수0.7031.0001.0000.832
부과금액0.5291.0000.8321.000
2024-01-10T08:10:16.188917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액세목명
부과건수1.0000.7890.425
부과금액0.7891.0000.232
세목명0.4250.2321.000

Missing values

2024-01-10T08:10:13.676372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T08:10:14.111726image/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
0충청남도홍성군448002020담배소비세담배소비세2737632105000
1충청남도홍성군448002020교육세교육세24078410734610000
2충청남도홍성군448002020도시계획세도시계획세00
3충청남도홍성군448002020취득세건축물12474801431000
4충청남도홍성군448002020취득세주택(개별)12692363451000
5충청남도홍성군448002020취득세주택(단독)16933151358000
6충청남도홍성군448002020취득세기타17180743000
7충청남도홍성군448002020취득세항공기00
8충청남도홍성군448002020취득세기계장비510566320000
9충청남도홍성군448002020취득세차량86029058889000
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
37충청남도홍성군448002020지역자원시설세지역자원시설세(특자)112679000
38충청남도홍성군448002020레저세소싸움00
39충청남도홍성군448002020레저세경정00
40충청남도홍성군448002020레저세경륜00
41충청남도홍성군448002020레저세경마00
42충청남도홍성군448002020지방소득세지방소득세(특별징수)178346464154000
43충청남도홍성군448002020지방소득세지방소득세(법인소득)16194794802000
44충청남도홍성군448002020지방소득세지방소득세(양도소득)13761426051000
45충청남도홍성군448002020지방소득세지방소득세(종합소득)105122142775000
46충청남도홍성군448002020체납체납374853161842000