Overview

Dataset statistics

Number of variables8
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory71.3 B

Variable types

Categorical6
Boolean1
Numeric1

Dataset

Description지방세 납세자 현황: 세목별 납세 인원 현황을 제공(2022년) 활용분야: 관외 납세자에 대한 부과징수 정책 수립시 기초자료로 활용
URLhttps://www.data.go.kr/data/15078847/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
납세자수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:12:07.879073
Analysis finished2023-12-12 05:12:08.518217
Duration0.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
대전광역시
31 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 31
100.0%

Length

2023-12-12T14:12:08.600992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:12:08.727125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 31
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
서구
31 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 31
100.0%

Length

2023-12-12T14:12:08.879398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:12:09.013823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 31
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
30170
31 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30170
2nd row30170
3rd row30170
4th row30170
5th row30170

Common Values

ValueCountFrequency (%)
30170 31
100.0%

Length

2023-12-12T14:12:09.140803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:12:09.263378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30170 31
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2022
31 

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 31
100.0%

Length

2023-12-12T14:12:09.392543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:12:09.527510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 31
100.0%

세목명
Categorical

Distinct9
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
재산세
4 
주민세
4 
취득세
4 
자동차세
4 
등록면허세
4 
Other values (4)
11 

Length

Max length7
Median length5
Mean length4.2258065
Min length3

Unique

Unique1 ?
Unique (%)3.2%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 4
12.9%
주민세 4
12.9%
취득세 4
12.9%
자동차세 4
12.9%
등록면허세 4
12.9%
지방소득세 4
12.9%
지역자원시설세 4
12.9%
레저세 2
6.5%
지방소비세 1
 
3.2%

Length

2023-12-12T14:12:09.658468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:12:09.873355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 4
12.9%
주민세 4
12.9%
취득세 4
12.9%
자동차세 4
12.9%
등록면허세 4
12.9%
지방소득세 4
12.9%
지역자원시설세 4
12.9%
레저세 2
6.5%
지방소비세 1
 
3.2%

납세자유형
Categorical

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
법인
16 
개인
15 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
법인 16
51.6%
개인 15
48.4%

Length

2023-12-12T14:12:10.055170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:12:10.184334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법인 16
51.6%
개인 15
48.4%

관내/관외
Boolean

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size163.0 B
False
16 
True
15 
ValueCountFrequency (%)
False 16
51.6%
True 15
48.4%
2023-12-12T14:12:10.300449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납세자수
Real number (ℝ)

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24816.71
Minimum1
Maximum171251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T14:12:10.423478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q1271.5
median3223
Q319065.5
95-th percentile136817.5
Maximum171251
Range171250
Interquartile range (IQR)18794

Descriptive statistics

Standard deviation46653.599
Coefficient of variation (CV)1.8799269
Kurtosis4.0529898
Mean24816.71
Median Absolute Deviation (MAD)3193
Skewness2.2341247
Sum769318
Variance2.1765583 × 109
MonotonicityNot monotonic
2023-12-12T14:12:10.891073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 1
 
3.2%
3 1
 
3.2%
62 1
 
3.2%
30 1
 
3.2%
51 1
 
3.2%
26 1
 
3.2%
1 1
 
3.2%
5278 1
 
3.2%
2707 1
 
3.2%
102313 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1 1
3.2%
2 1
3.2%
3 1
3.2%
26 1
3.2%
30 1
3.2%
51 1
3.2%
62 1
3.2%
271 1
3.2%
272 1
3.2%
528 1
3.2%
ValueCountFrequency (%)
171251 1
3.2%
153317 1
3.2%
120318 1
3.2%
102313 1
3.2%
51421 1
3.2%
43317 1
3.2%
29518 1
3.2%
19762 1
3.2%
18369 1
3.2%
17065 1
3.2%

Interactions

2023-12-12T14:12:08.116722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:12:11.033229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형관내/관외납세자수
세목명1.0000.0000.0000.000
납세자유형0.0001.0000.0000.684
관내/관외0.0000.0001.0000.399
납세자수0.0000.6840.3991.000
2023-12-12T14:12:11.188920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형관내/관외
세목명1.0000.0000.000
납세자유형0.0001.0000.000
관내/관외0.0000.0001.000
2023-12-12T14:12:11.319091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납세자수세목명납세자유형관내/관외
납세자수1.0000.0000.4580.254
세목명0.0001.0000.0000.000
납세자유형0.4580.0001.0000.000
관내/관외0.2540.0000.0001.000

Missing values

2023-12-12T14:12:08.290769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:12:08.449524image/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
0대전광역시서구301702022레저세개인N2
1대전광역시서구301702022레저세법인N3
2대전광역시서구301702022재산세개인N51421
3대전광역시서구301702022재산세개인Y120318
4대전광역시서구301702022재산세법인N1290
5대전광역시서구301702022재산세법인Y1534
6대전광역시서구301702022주민세개인N18369
7대전광역시서구301702022주민세개인Y171251
8대전광역시서구301702022주민세법인N2265
9대전광역시서구301702022주민세법인Y5809
시도명시군구명자치단체코드과세년도세목명납세자유형관내/관외납세자수
21대전광역시서구301702022등록면허세법인Y4507
22대전광역시서구301702022지방소득세개인N29518
23대전광역시서구301702022지방소득세개인Y102313
24대전광역시서구301702022지방소득세법인N2707
25대전광역시서구301702022지방소득세법인Y5278
26대전광역시서구301702022지방소비세법인Y1
27대전광역시서구301702022지역자원시설세개인N26
28대전광역시서구301702022지역자원시설세개인Y51
29대전광역시서구301702022지역자원시설세법인N30
30대전광역시서구301702022지역자원시설세법인Y62