Overview

Dataset statistics

Number of variables8
Number of observations139
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 KiB
Average record size in memory67.9 B

Variable types

Categorical6
Boolean1
Numeric1

Dataset

Description2017년 세목별 납세자유형 및 납세자 수, 2018년 세목별 납세자유형 및 납세자 수, 2019년 세목별 납세자유형 및 납세자 수, 2020년, 2021년, 2022년 세목별 납세자유형 및 납세자 수에 대한 자료입니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079205

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant

Reproduction

Analysis started2023-12-11 00:06:15.048947
Analysis finished2023-12-11 00:06:15.574131
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
경상남도
139 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 139
100.0%

Length

2023-12-11T09:06:15.651810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:06:15.765806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 139
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
거창군
139 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거창군 139
100.0%

Length

2023-12-11T09:06:15.886489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:06:15.992516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 139
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
48880
139 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 139
100.0%

Length

2023-12-11T09:06:16.084822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:06:16.183702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 139
100.0%

과세년도
Categorical

Distinct4
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2020
36 
2019
35 
2017
34 
2018
34 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2020 36
25.9%
2019 35
25.2%
2017 34
24.5%
2018 34
24.5%

Length

2023-12-11T09:06:16.295603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:06:16.423586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 36
25.9%
2019 35
25.2%
2017 34
24.5%
2018 34
24.5%

세목명
Categorical

Distinct10
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
재산세
16 
주민세
16 
취득세
16 
자동차세
16 
등록면허세
16 
Other values (5)
59 

Length

Max length7
Median length5
Mean length4.2086331
Min length3

Unique

Unique1 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 16
11.5%
주민세 16
11.5%
취득세 16
11.5%
자동차세 16
11.5%
등록면허세 16
11.5%
지방소득세 16
11.5%
담배소비세 15
10.8%
지역자원시설세 14
10.1%
등록세 13
9.4%
지방소비세 1
 
0.7%

Length

2023-12-11T09:06:16.542816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:06:17.002033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 16
11.5%
주민세 16
11.5%
취득세 16
11.5%
자동차세 16
11.5%
등록면허세 16
11.5%
지방소득세 16
11.5%
담배소비세 15
10.8%
지역자원시설세 14
10.1%
등록세 13
9.4%
지방소비세 1
 
0.7%

납세자유형
Categorical

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
개인
71 
법인
68 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 71
51.1%
법인 68
48.9%

Length

2023-12-11T09:06:17.188027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:06:17.296985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 71
51.1%
법인 68
48.9%

관내_관외
Boolean

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size271.0 B
True
73 
False
66 
ValueCountFrequency (%)
True 73
52.5%
False 66
47.5%
2023-12-11T09:06:17.392696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납세자수
Real number (ℝ)

Distinct116
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4222.8489
Minimum1
Maximum32173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T09:06:17.502990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q140
median516
Q32139.5
95-th percentile30102.6
Maximum32173
Range32172
Interquartile range (IQR)2099.5

Descriptive statistics

Standard deviation8735.028
Coefficient of variation (CV)2.0685154
Kurtosis4.355107
Mean4222.8489
Median Absolute Deviation (MAD)510
Skewness2.3877672
Sum586976
Variance76300713
MonotonicityNot monotonic
2023-12-11T09:06:17.641291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
3.6%
2 5
 
3.6%
6 3
 
2.2%
7 3
 
2.2%
4 3
 
2.2%
5 3
 
2.2%
201 2
 
1.4%
11 2
 
1.4%
9 2
 
1.4%
88 2
 
1.4%
Other values (106) 109
78.4%
ValueCountFrequency (%)
1 5
3.6%
2 5
3.6%
3 2
 
1.4%
4 3
2.2%
5 3
2.2%
6 3
2.2%
7 3
2.2%
9 2
 
1.4%
10 2
 
1.4%
11 2
 
1.4%
ValueCountFrequency (%)
32173 1
0.7%
32151 1
0.7%
32102 1
0.7%
31691 1
0.7%
31548 1
0.7%
31193 1
0.7%
30738 1
0.7%
30032 1
0.7%
27725 1
0.7%
27411 1
0.7%

Interactions

2023-12-11T09:06:15.266017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:06:17.744652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명납세자유형관내_관외납세자수
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0000.0000.0000.620
납세자유형0.0000.0001.0000.0000.425
관내_관외0.0000.0000.0001.0000.316
납세자수0.0000.6200.4250.3161.000
2023-12-11T09:06:17.862831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명납세자유형관내_관외
과세년도1.0000.0000.0000.000
세목명0.0001.0000.0000.000
납세자유형0.0000.0001.0000.000
관내_관외0.0000.0000.0001.000
2023-12-11T09:06:17.958293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납세자수과세년도세목명납세자유형관내_관외
납세자수1.0000.0000.3700.4470.332
과세년도0.0001.0000.0000.0000.000
세목명0.3700.0001.0000.0000.000
납세자유형0.4470.0000.0001.0000.000
관내_관외0.3320.0000.0000.0001.000

Missing values

2023-12-11T09:06:15.396634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:06:15.526303image/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
0경상남도거창군488802017등록세개인N231
1경상남도거창군488802017등록세개인Y216
2경상남도거창군488802017등록세법인Y10
3경상남도거창군488802017재산세개인N30032
4경상남도거창군488802017재산세개인Y31548
5경상남도거창군488802017재산세법인N497
6경상남도거창군488802017재산세법인Y2521
7경상남도거창군488802017주민세개인N2234
8경상남도거창군488802017주민세개인Y26512
9경상남도거창군488802017주민세법인N134
시도명시군구명자치단체코드과세년도세목명납세자유형관내_관외납세자수
129경상남도거창군488802020등록면허세법인Y920
130경상남도거창군488802020지방소득세개인N818
131경상남도거창군488802020지방소득세개인Y7064
132경상남도거창군488802020지방소득세법인N247
133경상남도거창군488802020지방소득세법인Y845
134경상남도거창군488802020지방소비세법인Y1
135경상남도거창군488802020지역자원시설세개인N6
136경상남도거창군488802020지역자원시설세개인Y38
137경상남도거창군488802020지역자원시설세법인N2
138경상남도거창군488802020지역자원시설세법인Y11