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 memory70.9 B

Variable types

Categorical5
Text1
Numeric2

Dataset

Description지방세 과세를 위해 세원이 되는 과세 대상 유형별로 부과된 현황(부과건수, 부과금액 등)에 대한 데이터를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=352&beforeMenuCd=DOM_000000201001001000&publicdatapk=15078636

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 12 (26.1%) zerosZeros
부과금액 has 12 (26.1%) zerosZeros

Reproduction

Analysis started2024-01-09 20:42:16.097175
Analysis finished2024-01-09 20:42:16.988029
Duration0.89 seconds
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
충청남도
46 

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 46
100.0%

Length

2024-01-10T05:42:17.039923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:42:17.114701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 46
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
예산군 46
100.0%

Length

2024-01-10T05:42:17.211976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:42:17.320427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
예산군 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
44810
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44810
2nd row44810
3rd row44810
4th row44810
5th row44810

Common Values

ValueCountFrequency (%)
44810 46
100.0%

Length

2024-01-10T05:42:17.411440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:42:17.492467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44810 46
100.0%

과세년도
Categorical

CONSTANT 

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

2024-01-10T05:42:17.574360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:42:17.652639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 46
100.0%

세목명
Categorical

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 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

2024-01-10T05:42:17.740338image/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 size500.0 B
2024-01-10T05:42:17.943000image/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%
지역자원시설세(특자 1
 
2.2%
주민세(종합소득 1
 
2.2%
승합 1
 
2.2%
기타승용 1
 
2.2%
승용 1
 
2.2%
지방소비세 1
 
2.2%
등록면허세(면허 1
 
2.2%
등록면허세(등록 1
 
2.2%
지역자원시설세(소방 1
 
2.2%
Other values (36) 36
78.3%
2024-01-10T05:42:18.268283image/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%
산 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%
산 5
 
2.2%
기 5
 
2.2%
자 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%
산 5
 
2.2%
기 5
 
2.2%
자 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%
산 5
 
2.2%
기 5
 
2.2%
자 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 

Distinct35
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11252.043
Minimum0
Maximum182343
Zeros12
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-01-10T05:42:18.385929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median523.5
Q39119
95-th percentile46633.25
Maximum182343
Range182343
Interquartile range (IQR)9118.25

Descriptive statistics

Standard deviation29938.898
Coefficient of variation (CV)2.6607521
Kurtosis24.647018
Mean11252.043
Median Absolute Deviation (MAD)523.5
Skewness4.6431874
Sum517594
Variance8.9633761 × 108
MonotonicityNot monotonic
2024-01-10T05:42:18.497578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 12
26.1%
478 1
 
2.2%
205 1
 
2.2%
424 1
 
2.2%
50881 1
 
2.2%
7 1
 
2.2%
16854 1
 
2.2%
22241 1
 
2.2%
25905 1
 
2.2%
14558 1
 
2.2%
Other values (25) 25
54.3%
ValueCountFrequency (%)
0 12
26.1%
3 1
 
2.2%
7 1
 
2.2%
10 1
 
2.2%
12 1
 
2.2%
43 1
 
2.2%
115 1
 
2.2%
205 1
 
2.2%
359 1
 
2.2%
424 1
 
2.2%
ValueCountFrequency (%)
182343 1
2.2%
79144 1
2.2%
50881 1
2.2%
33890 1
2.2%
27021 1
2.2%
25905 1
2.2%
22241 1
2.2%
16854 1
2.2%
14558 1
2.2%
13537 1
2.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6248922 × 109
Minimum0
Maximum1.7904607 × 1010
Zeros12
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-01-10T05:42:18.612433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q198500
median5.500085 × 108
Q34.365761 × 109
95-th percentile9.6297625 × 109
Maximum1.7904607 × 1010
Range1.7904607 × 1010
Interquartile range (IQR)4.3656625 × 109

Descriptive statistics

Standard deviation3.8833097 × 109
Coefficient of variation (CV)1.4794169
Kurtosis4.1627135
Mean2.6248922 × 109
Median Absolute Deviation (MAD)5.500085 × 108
Skewness1.9116483
Sum1.2074504 × 1011
Variance1.5080095 × 1019
MonotonicityNot monotonic
2024-01-10T05:42:18.723488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 12
26.1%
6460384000 1
 
2.2%
63618000 1
 
2.2%
29728000 1
 
2.2%
6928360000 1
 
2.2%
10057140000 1
 
2.2%
237304000 1
 
2.2%
2356822000 1
 
2.2%
2241297000 1
 
2.2%
6355648000 1
 
2.2%
Other values (25) 25
54.3%
ValueCountFrequency (%)
0 12
26.1%
394000 1
 
2.2%
980000 1
 
2.2%
4566000 1
 
2.2%
7629000 1
 
2.2%
20330000 1
 
2.2%
29728000 1
 
2.2%
63618000 1
 
2.2%
102040000 1
 
2.2%
237304000 1
 
2.2%
ValueCountFrequency (%)
17904607000 1
2.2%
10057140000 1
2.2%
9642859000 1
2.2%
9590473000 1
2.2%
8504652000 1
2.2%
7161681000 1
2.2%
6928360000 1
2.2%
6460384000 1
2.2%
6416054000 1
2.2%
6355648000 1
2.2%

Interactions

2024-01-10T05:42:16.665611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:16.281808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:16.742107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:16.593034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:42:18.804541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명부과건수부과금액
세목명1.0001.0000.7180.644
세원 유형명1.0001.0001.0001.000
부과건수0.7181.0001.0000.607
부과금액0.6441.0000.6071.000
2024-01-10T05:42:18.886290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액세목명
부과건수1.0000.7710.439
부과금액0.7711.0000.329
세목명0.4390.3291.000

Missing values

2024-01-10T05:42:16.846811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:42:16.947103image/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
0충청남도예산군448102021담배소비세담배소비세4786460384000
1충청남도예산군448102021교육세교육세1823439642859000
2충청남도예산군448102021도시계획세도시계획세00
3충청남도예산군448102021취득세건축물14545235188000
4충청남도예산군448102021취득세주택(개별)16992023525000
5충청남도예산군448102021취득세주택(단독)9621269169000
6충청남도예산군448102021취득세기타115376688000
7충청남도예산군448102021취득세항공기00
8충청남도예산군448102021취득세기계장비549714879000
9충청남도예산군448102021취득세차량72747161681000
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
36충청남도예산군448102021지방소득세지방소득세(양도소득)16402492336000
37충청남도예산군448102021지방소득세지방소득세(종합소득)102211939808000
38충청남도예산군448102021주민세주민세(사업소분)3971809067000
39충청남도예산군448102021주민세주민세(개인분)00
40충청남도예산군448102021주민세주민세(종업원분)9801616796000
41충청남도예산군448102021주민세주민세(특별징수)00
42충청남도예산군448102021주민세주민세(법인세분)00
43충청남도예산군448102021주민세주민세(양도소득)00
44충청남도예산군448102021주민세주민세(종합소득)00
45충청남도예산군448102021체납체납270214830320000