Overview

Dataset statistics

Number of variables8
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory71.1 B

Variable types

Categorical6
Boolean1
Numeric1

Dataset

Description지방세 세목별 관내 및 관외의 납세 인원에 대한 현황을 제공합니다.(과세년도 세목명, 납세자 유형, 관내 관외, 납세자 수)
URLhttps://www.data.go.kr/data/15078850/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 23:52:02.976620
Analysis finished2023-12-12 23:52:03.437044
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
인천광역시
32 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 32
100.0%

Length

2023-12-13T08:52:03.497206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:52:03.597076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 32
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
계양구
32 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
계양구 32
100.0%

Length

2023-12-13T08:52:03.702577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:52:03.798174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계양구 32
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
28245
32 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28245
2nd row28245
3rd row28245
4th row28245
5th row28245

Common Values

ValueCountFrequency (%)
28245 32
100.0%

Length

2023-12-13T08:52:03.888427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:52:03.990922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28245 32
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
2021
32 

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 32
100.0%

Length

2023-12-13T08:52:04.116105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:52:04.210692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 32
100.0%

세목명
Categorical

Distinct9
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
재산세
4 
주민세
4 
취득세
4 
자동차세
4 
등록면허세
4 
Other values (4)
12 

Length

Max length7
Median length5
Mean length4.1875
Min length3

Unique

Unique1 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 4
12.5%
주민세 4
12.5%
취득세 4
12.5%
자동차세 4
12.5%
등록면허세 4
12.5%
지방소득세 4
12.5%
지역자원시설세 4
12.5%
등록세 3
9.4%
지방소비세 1
 
3.1%

Length

2023-12-13T08:52:04.312745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:52:04.445095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 4
12.5%
주민세 4
12.5%
취득세 4
12.5%
자동차세 4
12.5%
등록면허세 4
12.5%
지방소득세 4
12.5%
지역자원시설세 4
12.5%
등록세 3
9.4%
지방소비세 1
 
3.1%

납세자유형
Categorical

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
개인
16 
법인
16 

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 16
50.0%
법인 16
50.0%

Length

2023-12-13T08:52:04.579633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:52:04.690323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 16
50.0%
법인 16
50.0%

관내_관외
Boolean

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size164.0 B
True
17 
False
15 
ValueCountFrequency (%)
True 17
53.1%
False 15
46.9%
2023-12-13T08:52:04.798191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납세자수
Real number (ℝ)

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15536.438
Minimum1
Maximum106972
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:52:04.910804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.1
Q1220.5
median1906
Q312797
95-th percentile84307.35
Maximum106972
Range106971
Interquartile range (IQR)12576.5

Descriptive statistics

Standard deviation28392.269
Coefficient of variation (CV)1.8274633
Kurtosis3.9532816
Mean15536.438
Median Absolute Deviation (MAD)1893
Skewness2.188052
Sum497166
Variance8.0612092 × 108
MonotonicityNot monotonic
2023-12-13T08:52:05.030497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
19 2
 
6.2%
33 1
 
3.1%
17 1
 
3.1%
9 1
 
3.1%
5 1
 
3.1%
1 1
 
3.1%
2749 1
 
3.1%
852 1
 
3.1%
57909 1
 
3.1%
15875 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
1 1
3.1%
3 1
3.1%
5 1
3.1%
9 1
3.1%
17 1
3.1%
19 2
6.2%
33 1
3.1%
283 1
3.1%
583 1
3.1%
617 1
3.1%
ValueCountFrequency (%)
106972 1
3.1%
87796 1
3.1%
81453 1
3.1%
57909 1
3.1%
40035 1
3.1%
25747 1
3.1%
24743 1
3.1%
15875 1
3.1%
11771 1
3.1%
10757 1
3.1%

Interactions

2023-12-13T08:52:03.148927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:52:05.115310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형관내_관외납세자수
세목명1.0000.0000.0000.000
납세자유형0.0001.0000.0000.718
관내_관외0.0000.0001.0000.454
납세자수0.0000.7180.4541.000
2023-12-13T08:52:05.219963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납세자유형세목명관내_관외
납세자유형1.0000.0000.000
세목명0.0001.0000.000
관내_관외0.0000.0001.000
2023-12-13T08:52:05.315115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납세자수세목명납세자유형관내_관외
납세자수1.0000.0000.4860.295
세목명0.0001.0000.0000.000
납세자유형0.4860.0001.0000.000
관내_관외0.2950.0000.0001.000

Missing values

2023-12-13T08:52:03.264556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:52:03.390511image/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
0인천광역시계양구282452021등록세개인N33
1인천광역시계양구282452021등록세개인Y17
2인천광역시계양구282452021등록세법인Y3
3인천광역시계양구282452021재산세개인N40035
4인천광역시계양구282452021재산세개인Y81453
5인천광역시계양구282452021재산세법인N705
6인천광역시계양구282452021재산세법인Y583
7인천광역시계양구282452021주민세개인N10757
8인천광역시계양구282452021주민세개인Y106972
9인천광역시계양구282452021주민세법인N738
시도명시군구명자치단체코드과세년도세목명납세자유형관내_관외납세자수
22인천광역시계양구282452021등록면허세법인Y2434
23인천광역시계양구282452021지방소득세개인N15875
24인천광역시계양구282452021지방소득세개인Y57909
25인천광역시계양구282452021지방소득세법인N852
26인천광역시계양구282452021지방소득세법인Y2749
27인천광역시계양구282452021지방소비세법인Y1
28인천광역시계양구282452021지역자원시설세개인N19
29인천광역시계양구282452021지역자원시설세개인Y19
30인천광역시계양구282452021지역자원시설세법인N5
31인천광역시계양구282452021지역자원시설세법인Y9