Overview

Dataset statistics

Number of variables9
Number of observations90
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory78.5 B

Variable types

Numeric3
Categorical6

Dataset

Description2017년부터 2021년까지 지방세 세원이 되는 과세물건 유형별 부과된 현황을 제공하여 물건 유형에 따른 세부담 수준의 형평성 검토 및 부동산 등 관련분야 규제정책 대상 확인 시 기초자료 활용
URLhttps://www.data.go.kr/data/15079337/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
세목명 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
세원 유형명 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 과세년도High correlation
부과건수 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
과세년도 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
부과건수 has 55 (61.1%) zerosZeros
부과금액 has 55 (61.1%) zerosZeros

Reproduction

Analysis started2023-12-12 02:47:06.160625
Analysis finished2023-12-12 02:47:07.503706
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.5
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T11:47:07.586314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.45
Q123.25
median45.5
Q367.75
95-th percentile85.55
Maximum90
Range89
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation26.124701
Coefficient of variation (CV)0.57416925
Kurtosis-1.2
Mean45.5
Median Absolute Deviation (MAD)22.5
Skewness0
Sum4095
Variance682.5
MonotonicityStrictly increasing
2023-12-12T11:47:07.771090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
69 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
61 1
 
1.1%
60 1
 
1.1%
Other values (80) 80
88.9%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%
82 1
1.1%
81 1
1.1%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
대전광역시
90 

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 90
100.0%

Length

2023-12-12T11:47:07.924816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:08.028535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 90
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
대전광역시
90 

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 90
100.0%

Length

2023-12-12T11:47:08.129220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:08.247409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 90
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
30000
90 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30000
2nd row30000
3rd row30000
4th row30000
5th row30000

Common Values

ValueCountFrequency (%)
30000 90
100.0%

Length

2023-12-12T11:47:08.397358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:08.506830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30000 90
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size852.0 B
2017
18 
2018
18 
2019
18 
2020
18 
2021
18 

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 (%)
2017 18
20.0%
2018 18
20.0%
2019 18
20.0%
2020 18
20.0%
2021 18
20.0%

Length

2023-12-12T11:47:08.607055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:08.746023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 18
20.0%
2018 18
20.0%
2019 18
20.0%
2020 18
20.0%
2021 18
20.0%

세목명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size852.0 B
취득세
35 
자동차세
35 
교육세
5 
체납
5 
담배소비세
5 

Length

Max length5
Median length4
Mean length3.5555556
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 35
38.9%
자동차세 35
38.9%
교육세 5
 
5.6%
체납 5
 
5.6%
담배소비세 5
 
5.6%
지방소비세 5
 
5.6%

Length

2023-12-12T11:47:08.907218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:09.022045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
취득세 35
38.9%
자동차세 35
38.9%
교육세 5
 
5.6%
체납 5
 
5.6%
담배소비세 5
 
5.6%
지방소비세 5
 
5.6%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size852.0 B
토지
 
5
건축물
 
5
선박
 
5
차량
 
5
기계장비
 
5
Other values (13)
65 

Length

Max length8
Median length6.5
Mean length3.1666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row토지
2nd row건축물
3rd row선박
4th row차량
5th row기계장비

Common Values

ValueCountFrequency (%)
토지 5
 
5.6%
건축물 5
 
5.6%
선박 5
 
5.6%
차량 5
 
5.6%
기계장비 5
 
5.6%
항공기 5
 
5.6%
기타 5
 
5.6%
교육세 5
 
5.6%
승용 5
 
5.6%
기타승용 5
 
5.6%
Other values (8) 40
44.4%

Length

2023-12-12T11:47:09.178432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
토지 5
 
5.6%
건축물 5
 
5.6%
담배소비세 5
 
5.6%
체납 5
 
5.6%
자동차세(주행 5
 
5.6%
3륜이하 5
 
5.6%
특수 5
 
5.6%
화물 5
 
5.6%
승합 5
 
5.6%
기타승용 5
 
5.6%
Other values (8) 40
44.4%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6008.7333
Minimum0
Maximum110358
Zeros55
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T11:47:09.307943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3107.75
95-th percentile55795.7
Maximum110358
Range110358
Interquartile range (IQR)107.75

Descriptive statistics

Standard deviation23884.01
Coefficient of variation (CV)3.9748827
Kurtosis13.965809
Mean6008.7333
Median Absolute Deviation (MAD)0
Skewness3.952214
Sum540786
Variance5.7044595 × 108
MonotonicityNot monotonic
2023-12-12T11:47:09.449436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 55
61.1%
12 5
 
5.6%
8 2
 
2.2%
9 1
 
1.1%
110358 1
 
1.1%
1345 1
 
1.1%
1208 1
 
1.1%
794 1
 
1.1%
273 1
 
1.1%
1685 1
 
1.1%
Other values (21) 21
 
23.3%
ValueCountFrequency (%)
0 55
61.1%
6 1
 
1.1%
8 2
 
2.2%
9 1
 
1.1%
10 1
 
1.1%
12 5
 
5.6%
86 1
 
1.1%
92 1
 
1.1%
113 1
 
1.1%
273 1
 
1.1%
ValueCountFrequency (%)
110358 1
1.1%
104759 1
1.1%
102519 1
1.1%
101705 1
1.1%
99911 1
1.1%
1877 1
1.1%
1685 1
1.1%
1648 1
1.1%
1599 1
1.1%
1593 1
1.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7658226 × 1010
Minimum0
Maximum4.7441066 × 1011
Zeros55
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T11:47:09.613671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.1645872 × 1010
95-th percentile1.8685699 × 1011
Maximum4.7441066 × 1011
Range4.7441066 × 1011
Interquartile range (IQR)4.1645872 × 1010

Descriptive statistics

Standard deviation8.0746474 × 1010
Coefficient of variation (CV)2.1441922
Kurtosis11.88793
Mean3.7658226 × 1010
Median Absolute Deviation (MAD)0
Skewness3.1450376
Sum3.3892403 × 1012
Variance6.5199931 × 1021
MonotonicityNot monotonic
2023-12-12T11:47:09.742486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 55
61.1%
3858501000 1
 
1.1%
362665600000 1
 
1.1%
131102514000 1
 
1.1%
1826742000 1
 
1.1%
42637822000 1
 
1.1%
110663079000 1
 
1.1%
4101865000 1
 
1.1%
96726766000 1
 
1.1%
251200878000 1
 
1.1%
Other values (26) 26
28.9%
ValueCountFrequency (%)
0 55
61.1%
1704891000 1
 
1.1%
1826742000 1
 
1.1%
2077189000 1
 
1.1%
2329503000 1
 
1.1%
2654554000 1
 
1.1%
2690717000 1
 
1.1%
3858501000 1
 
1.1%
4070721000 1
 
1.1%
4101865000 1
 
1.1%
ValueCountFrequency (%)
474410661000 1
1.1%
362665600000 1
1.1%
251200878000 1
1.1%
232491214000 1
1.1%
232474288000 1
1.1%
131102514000 1
1.1%
128615820000 1
1.1%
110663079000 1
1.1%
110575916000 1
1.1%
109284432000 1
1.1%

Interactions

2023-12-12T11:47:06.942539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:06.412838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:06.643327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:07.030916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:06.484069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:06.727416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:07.128265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:06.564673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:06.828886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:47:09.839067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도세목명세원 유형명부과건수부과금액
연번1.0001.0000.4590.0000.0000.000
과세년도1.0001.0000.0000.0000.0000.000
세목명0.4590.0001.0001.0000.2750.694
세원 유형명0.0000.0001.0001.0001.0000.778
부과건수0.0000.0000.2751.0001.0000.512
부과금액0.0000.0000.6940.7780.5121.000
2023-12-12T11:47:09.940676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명과세년도
세목명1.0000.9260.000
세원 유형명0.9261.0000.000
과세년도0.0000.0001.000
2023-12-12T11:47:10.047451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부과건수부과금액과세년도세목명세원 유형명
연번1.0000.0560.0970.9700.2540.000
부과건수0.0561.0000.8870.0000.1910.905
부과금액0.0970.8871.0000.0000.5020.456
과세년도0.9700.0000.0001.0000.0000.000
세목명0.2540.1910.5020.0001.0000.926
세원 유형명0.0000.9050.4560.0000.9261.000

Missing values

2023-12-12T11:47:07.263242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:47:07.425910image/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

연번시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
01대전광역시대전광역시300002017취득세토지00
12대전광역시대전광역시300002017취득세건축물00
23대전광역시대전광역시300002017취득세선박00
34대전광역시대전광역시300002017취득세차량104759105141345000
45대전광역시대전광역시300002017취득세기계장비16482329503000
56대전광역시대전광역시300002017취득세항공기00
67대전광역시대전광역시300002017취득세기타00
78대전광역시대전광역시300002017교육세교육세122041951197000
89대전광역시대전광역시300002017자동차세승용00
910대전광역시대전광역시300002017자동차세기타승용00
연번시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
8081대전광역시대전광역시300002021자동차세승용00
8182대전광역시대전광역시300002021자동차세기타승용00
8283대전광역시대전광역시300002021자동차세승합00
8384대전광역시대전광역시300002021자동차세화물00
8485대전광역시대전광역시300002021자동차세특수00
8586대전광역시대전광역시300002021자동차세3륜이하00
8687대전광역시대전광역시300002021자동차세자동차세(주행)12107743677000
8788대전광역시대전광역시300002021체납체납18779191447000
8889대전광역시대전광역시300002021담배소비세담배소비세48795447408000
8990대전광역시대전광역시300002021지방소비세지방소비세10474410661000