Overview

Dataset statistics

Number of variables8
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory70.9 B

Variable types

Categorical6
Boolean1
Numeric1

Dataset

Description세목별 납세 인원 현황에 대한 데이터로 과세연도, 세목명, 납세자 유형, 관내 관외, 납세자 수 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15079615/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-12 17:05:31.052375
Analysis finished2023-12-12 17:05:31.468536
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
경상북도
34 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 34
100.0%

Length

2023-12-13T02:05:31.520205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:05:31.595357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 34
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
영양군
34 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영양군 34
100.0%

Length

2023-12-13T02:05:31.671030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:05:31.750632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영양군 34
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
47760
34 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47760
2nd row47760
3rd row47760
4th row47760
5th row47760

Common Values

ValueCountFrequency (%)
47760 34
100.0%

Length

2023-12-13T02:05:31.830305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:05:32.252350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47760 34
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
2022
34 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 34
100.0%

Length

2023-12-13T02:05:32.344351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:05:32.440952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 34
100.0%

세목명
Categorical

Distinct10
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size404.0 B
등록세
4 
재산세
4 
주민세
4 
취득세
4 
자동차세
4 
Other values (5)
14 

Length

Max length5
Median length3
Mean length3.8235294
Min length3

Unique

Unique1 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
등록세 4
11.8%
재산세 4
11.8%
주민세 4
11.8%
취득세 4
11.8%
자동차세 4
11.8%
등록면허세 4
11.8%
지방소득세 4
11.8%
담배소비세 3
8.8%
레저세 2
5.9%
지방소비세 1
 
2.9%

Length

2023-12-13T02:05:32.554399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:05:32.703950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록세 4
11.8%
재산세 4
11.8%
주민세 4
11.8%
취득세 4
11.8%
자동차세 4
11.8%
등록면허세 4
11.8%
지방소득세 4
11.8%
담배소비세 3
8.8%
레저세 2
5.9%
지방소비세 1
 
2.9%

납세자유형
Categorical

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
법인
18 
개인
16 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
법인 18
52.9%
개인 16
47.1%

Length

2023-12-13T02:05:32.855513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:05:32.999241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법인 18
52.9%
개인 16
47.1%

관내_관외
Boolean

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size166.0 B
False
18 
True
16 
ValueCountFrequency (%)
False 18
52.9%
True 16
47.1%
2023-12-13T02:05:33.150354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납세자수
Real number (ℝ)

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1729.1176
Minimum1
Maximum17189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T02:05:33.330235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q142.75
median312.5
Q3852.5
95-th percentile10348.75
Maximum17189
Range17188
Interquartile range (IQR)809.75

Descriptive statistics

Standard deviation3986.4101
Coefficient of variation (CV)2.3054592
Kurtosis9.1108941
Mean1729.1176
Median Absolute Deviation (MAD)280
Skewness3.0678448
Sum58790
Variance15891466
MonotonicityNot monotonic
2023-12-13T02:05:33.543837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 4
 
11.8%
3 2
 
5.9%
382 1
 
2.9%
394 1
 
2.9%
321 1
 
2.9%
110 1
 
2.9%
2196 1
 
2.9%
434 1
 
2.9%
329 1
 
2.9%
350 1
 
2.9%
Other values (20) 20
58.8%
ValueCountFrequency (%)
1 4
11.8%
3 2
5.9%
7 1
 
2.9%
28 1
 
2.9%
37 1
 
2.9%
60 1
 
2.9%
61 1
 
2.9%
92 1
 
2.9%
110 1
 
2.9%
186 1
 
2.9%
ValueCountFrequency (%)
17189 1
2.9%
14538 1
2.9%
8093 1
2.9%
6011 1
2.9%
2424 1
2.9%
2196 1
2.9%
1841 1
2.9%
1056 1
2.9%
981 1
2.9%
467 1
2.9%

Interactions

2023-12-13T02:05:31.234740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:05:33.709912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형관내_관외납세자수
세목명1.0000.0000.0000.000
납세자유형0.0001.0000.0000.568
관내_관외0.0000.0001.0000.496
납세자수0.0000.5680.4961.000
2023-12-13T02:05:33.861823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관내_관외납세자유형세목명
관내_관외1.0000.0000.000
납세자유형0.0001.0000.000
세목명0.0000.0001.000
2023-12-13T02:05:33.998644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납세자수세목명납세자유형관내_관외
납세자수1.0000.0000.3800.329
세목명0.0001.0000.0000.000
납세자유형0.3800.0001.0000.000
관내_관외0.3290.0000.0001.000

Missing values

2023-12-13T02:05:31.338629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:05:31.433013image/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
0경상북도영양군477602022등록세개인N382
1경상북도영양군477602022등록세개인Y284
2경상북도영양군477602022등록세법인N1
3경상북도영양군477602022등록세법인Y7
4경상북도영양군477602022레저세개인N1
5경상북도영양군477602022레저세법인N3
6경상북도영양군477602022재산세개인N17189
7경상북도영양군477602022재산세개인Y14538
8경상북도영양군477602022재산세법인N270
9경상북도영양군477602022재산세법인Y467
시도명시군구명자치단체코드과세년도세목명납세자유형관내_관외납세자수
24경상북도영양군477602022담배소비세법인Y1
25경상북도영양군477602022등록면허세개인N981
26경상북도영양군477602022등록면허세개인Y2424
27경상북도영양군477602022등록면허세법인N350
28경상북도영양군477602022등록면허세법인Y329
29경상북도영양군477602022지방소득세개인N434
30경상북도영양군477602022지방소득세개인Y2196
31경상북도영양군477602022지방소득세법인N110
32경상북도영양군477602022지방소득세법인Y321
33경상북도영양군477602022지방소비세법인Y1