Overview

Dataset statistics

Number of variables8
Number of observations193
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.8 KiB
Average record size in memory67.7 B

Variable types

Categorical5
Numeric2
Boolean1

Dataset

Description세목별 납세자 유형, 관내외 여부, 납세 인원을 제공함으로써 관외 납세자에 대한 부과징수 정책 수립시 기초자료로 활용하고자 합니다.
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/15078925/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납세자수 is highly overall correlated with 납세자유형High correlation
납세자유형 is highly overall correlated with 납세자수High correlation

Reproduction

Analysis started2024-04-21 01:49:26.605037
Analysis finished2024-04-21 01:49:28.896528
Duration2.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
부산광역시
193 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 193
100.0%

Length

2024-04-21T10:49:28.959290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:49:29.061128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 193
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
해운대구
193 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 193
100.0%

Length

2024-04-21T10:49:29.149173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:49:29.234817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 193
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
26350
193 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26350
2nd row26350
3rd row26350
4th row26350
5th row26350

Common Values

ValueCountFrequency (%)
26350 193
100.0%

Length

2024-04-21T10:49:29.323251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:49:29.413329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26350 193
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5699
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-21T10:49:29.495882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7128584
Coefficient of variation (CV)0.00084813026
Kurtosis-1.2774682
Mean2019.5699
Median Absolute Deviation (MAD)1
Skewness-0.051209044
Sum389777
Variance2.9338839
MonotonicityIncreasing
2024-04-21T10:49:29.600302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 34
17.6%
2022 34
17.6%
2018 32
16.6%
2020 32
16.6%
2019 31
16.1%
2017 30
15.5%
ValueCountFrequency (%)
2017 30
15.5%
2018 32
16.6%
2019 31
16.1%
2020 32
16.6%
2021 34
17.6%
2022 34
17.6%
ValueCountFrequency (%)
2022 34
17.6%
2021 34
17.6%
2020 32
16.6%
2019 31
16.1%
2018 32
16.6%
2017 30
15.5%

세목명
Categorical

Distinct10
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
재산세
24 
주민세
24 
취득세
24 
자동차세
24 
등록면허세
24 
Other values (5)
73 

Length

Max length7
Median length5
Mean length4.1502591
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록세
2nd row등록세
3rd row재산세
4th row재산세
5th row재산세

Common Values

ValueCountFrequency (%)
재산세 24
12.4%
주민세 24
12.4%
취득세 24
12.4%
자동차세 24
12.4%
등록면허세 24
12.4%
지방소득세 24
12.4%
지역자원시설세 24
12.4%
등록세 20
10.4%
지방소비세 3
 
1.6%
레저세 2
 
1.0%

Length

2024-04-21T10:49:29.725908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:49:29.845596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 24
12.4%
주민세 24
12.4%
취득세 24
12.4%
자동차세 24
12.4%
등록면허세 24
12.4%
지방소득세 24
12.4%
지역자원시설세 24
12.4%
등록세 20
10.4%
지방소비세 3
 
1.6%
레저세 2
 
1.0%

납세자유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
개인
98 
법인
95 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row개인
3rd row개인
4th row개인
5th row법인

Common Values

ValueCountFrequency (%)
개인 98
50.8%
법인 95
49.2%

Length

2024-04-21T10:49:29.964152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:49:30.057014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 98
50.8%
법인 95
49.2%

관내_관외
Boolean

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size325.0 B
True
98 
False
95 
ValueCountFrequency (%)
True 98
50.8%
False 95
49.2%
2024-04-21T10:49:30.222852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납세자수
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19671.114
Minimum1
Maximum143957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-21T10:49:30.384936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q1238
median3093
Q317604
95-th percentile115346.2
Maximum143957
Range143956
Interquartile range (IQR)17366

Descriptive statistics

Standard deviation36257.537
Coefficient of variation (CV)1.8431868
Kurtosis3.8279071
Mean19671.114
Median Absolute Deviation (MAD)3047
Skewness2.2175007
Sum3796525
Variance1.314609 × 109
MonotonicityNot monotonic
2024-04-21T10:49:30.533442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9
 
4.7%
60 3
 
1.6%
66 3
 
1.6%
63 2
 
1.0%
2 2
 
1.0%
21 2
 
1.0%
188 2
 
1.0%
32 2
 
1.0%
2006 1
 
0.5%
44 1
 
0.5%
Other values (166) 166
86.0%
ValueCountFrequency (%)
1 9
4.7%
2 2
 
1.0%
3 1
 
0.5%
5 1
 
0.5%
16 1
 
0.5%
19 1
 
0.5%
21 2
 
1.0%
22 1
 
0.5%
24 1
 
0.5%
28 1
 
0.5%
ValueCountFrequency (%)
143957 1
0.5%
143955 1
0.5%
143208 1
0.5%
140506 1
0.5%
136336 1
0.5%
134225 1
0.5%
122083 1
0.5%
121557 1
0.5%
115880 1
0.5%
115528 1
0.5%

Interactions

2024-04-21T10:49:28.479128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:28.116397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:28.563851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:49:28.252870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:49:30.649110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명납세자유형관내_관외납세자수
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0000.0000.0000.653
납세자유형0.0000.0001.0000.0000.749
관내_관외0.0000.0000.0001.0000.540
납세자수0.0000.6530.7490.5401.000
2024-04-21T10:49:30.739825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형관내_관외
세목명1.0000.0000.000
납세자유형0.0001.0000.000
관내_관외0.0000.0001.000
2024-04-21T10:49:30.824193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도납세자수세목명납세자유형관내_관외
과세년도1.000-0.0480.0000.0000.000
납세자수-0.0481.0000.2520.5760.408
세목명0.0000.2521.0000.0000.000
납세자유형0.0000.5760.0001.0000.000
관내_관외0.0000.4080.0000.0001.000

Missing values

2024-04-21T10:49:28.696214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:49:28.839694image/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

시도명시군구명자치단체코드과세년도세목명납세자유형관내_관외납세자수
0부산광역시해운대구263502017등록세개인N55
1부산광역시해운대구263502017등록세개인Y60
2부산광역시해운대구263502017재산세개인N55535
3부산광역시해운대구263502017재산세개인Y106956
4부산광역시해운대구263502017재산세법인N1309
5부산광역시해운대구263502017재산세법인Y1171
6부산광역시해운대구263502017주민세개인N32056
7부산광역시해운대구263502017주민세개인Y134225
8부산광역시해운대구263502017주민세법인N1753
9부산광역시해운대구263502017주민세법인Y4598
시도명시군구명자치단체코드과세년도세목명납세자유형관내_관외납세자수
183부산광역시해운대구263502022등록면허세법인Y5196
184부산광역시해운대구263502022지방소득세개인N20998
185부산광역시해운대구263502022지방소득세개인Y86073
186부산광역시해운대구263502022지방소득세법인N2667
187부산광역시해운대구263502022지방소득세법인Y5740
188부산광역시해운대구263502022지방소비세법인Y1
189부산광역시해운대구263502022지역자원시설세개인N32
190부산광역시해운대구263502022지역자원시설세개인Y144
191부산광역시해운대구263502022지역자원시설세법인N16
192부산광역시해운대구263502022지역자원시설세법인Y51