Overview

Dataset statistics

Number of variables9
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory81.4 B

Variable types

Categorical5
Numeric4

Dataset

Description연도별 지방세 과세 및 비과세 현황을 세목별로 제공하는 데이터로, 이 데이터는 국민 조세 혜택 규모를 파악하는 데 사용합니다.
URLhttps://www.data.go.kr/data/15080070/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세건수 is highly overall correlated with 과세금액 and 3 other fieldsHigh correlation
과세금액 is highly overall correlated with 과세건수 and 3 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세금액 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
과세건수 has 14 (35.9%) zerosZeros
과세금액 has 14 (35.9%) zerosZeros
비과세건수 has 15 (38.5%) zerosZeros
비과세금액 has 15 (38.5%) zerosZeros

Reproduction

Analysis started2023-12-12 15:15:13.132015
Analysis finished2023-12-12 15:15:15.340410
Duration2.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
제주특별자치도
39 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주특별자치도 39
100.0%

Length

2023-12-13T00:15:15.396037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:15:15.477952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주특별자치도 39
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
서귀포시
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서귀포시 39
100.0%

Length

2023-12-13T00:15:15.558069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:15:15.643124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서귀포시 39
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
50130
39 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50130
2nd row50130
3rd row50130
4th row50130
5th row50130

Common Values

ValueCountFrequency (%)
50130 39
100.0%

Length

2023-12-13T00:15:15.738798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:15:15.826968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50130 39
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
2019
13 
2020
13 
2021
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 13
33.3%
2020 13
33.3%
2021 13
33.3%

Length

2023-12-13T00:15:15.920171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:15:16.008775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 13
33.3%
2020 13
33.3%
2021 13
33.3%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size444.0 B
취득세
3 
등록세
3 
주민세
3 
재산세
3 
자동차세
3 
Other values (8)
24 

Length

Max length7
Median length5
Mean length4.1538462
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row취득세
2nd row등록세
3rd row주민세
4th row재산세
5th row자동차세

Common Values

ValueCountFrequency (%)
취득세 3
 
7.7%
등록세 3
 
7.7%
주민세 3
 
7.7%
재산세 3
 
7.7%
자동차세 3
 
7.7%
레저세 3
 
7.7%
교육세 3
 
7.7%
지방소비세 3
 
7.7%
등록면허세 3
 
7.7%
도시계획세 3
 
7.7%
Other values (3) 9
23.1%

Length

2023-12-13T00:15:16.122991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 3
 
7.7%
등록세 3
 
7.7%
주민세 3
 
7.7%
재산세 3
 
7.7%
자동차세 3
 
7.7%
레저세 3
 
7.7%
교육세 3
 
7.7%
지방소비세 3
 
7.7%
등록면허세 3
 
7.7%
도시계획세 3
 
7.7%
Other values (3) 9
23.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100448.92
Minimum0
Maximum523620
Zeros14
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T00:15:16.236890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median53106
Q394421
95-th percentile506621.3
Maximum523620
Range523620
Interquartile range (IQR)94421

Descriptive statistics

Standard deviation143934.47
Coefficient of variation (CV)1.432912
Kurtosis3.4273409
Mean100448.92
Median Absolute Deviation (MAD)53106
Skewness1.9921371
Sum3917508
Variance2.0717132 × 1010
MonotonicityNot monotonic
2023-12-13T00:15:16.351606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 14
35.9%
38230 1
 
2.6%
514562 1
 
2.6%
61670 1
 
2.6%
93178 1
 
2.6%
93742 1
 
2.6%
523620 1
 
2.6%
153586 1
 
2.6%
284338 1
 
2.6%
85424 1
 
2.6%
Other values (16) 16
41.0%
ValueCountFrequency (%)
0 14
35.9%
846 1
 
2.6%
33174 1
 
2.6%
38230 1
 
2.6%
38493 1
 
2.6%
52055 1
 
2.6%
53106 1
 
2.6%
61670 1
 
2.6%
84741 1
 
2.6%
85090 1
 
2.6%
ValueCountFrequency (%)
523620 1
2.6%
514562 1
2.6%
505739 1
2.6%
284338 1
2.6%
279711 1
2.6%
267440 1
2.6%
153586 1
2.6%
151225 1
2.6%
150783 1
2.6%
95100 1
2.6%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3914075 × 1010
Minimum0
Maximum1.58 × 1011
Zeros14
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T00:15:16.475835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.527782 × 109
Q32.7293748 × 1010
95-th percentile1.21918 × 1011
Maximum1.58 × 1011
Range1.58 × 1011
Interquartile range (IQR)2.7293748 × 1010

Descriptive statistics

Standard deviation3.9146705 × 1010
Coefficient of variation (CV)1.6369734
Kurtosis4.5732832
Mean2.3914075 × 1010
Median Absolute Deviation (MAD)6.527782 × 109
Skewness2.2215857
Sum9.3264893 × 1011
Variance1.5324645 × 1021
MonotonicityNot monotonic
2023-12-13T00:15:16.611443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 14
35.9%
138181000000 1
 
2.6%
25051822000 1
 
2.6%
45290855000 1
 
2.6%
6947462000 1
 
2.6%
11051865000 1
 
2.6%
28631389000 1
 
2.6%
16127820000 1
 
2.6%
71349476000 1
 
2.6%
3895697000 1
 
2.6%
Other values (16) 16
41.0%
ValueCountFrequency (%)
0 14
35.9%
176300000 1
 
2.6%
3871337000 1
 
2.6%
3895697000 1
 
2.6%
4040102000 1
 
2.6%
5985577000 1
 
2.6%
6527782000 1
 
2.6%
6947462000 1
 
2.6%
10871605000 1
 
2.6%
11051865000 1
 
2.6%
ValueCountFrequency (%)
158000000000 1
2.6%
138181000000 1
2.6%
120111000000 1
2.6%
71349476000 1
2.6%
66935866000 1
2.6%
63026372000 1
2.6%
45290855000 1
2.6%
43108658000 1
2.6%
34134336000 1
2.6%
28631389000 1
2.6%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39541.077
Minimum0
Maximum1117023
Zeros15
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T00:15:16.769616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median548
Q313346
95-th percentile112592.5
Maximum1117023
Range1117023
Interquartile range (IQR)13346

Descriptive statistics

Standard deviation178914.65
Coefficient of variation (CV)4.5247794
Kurtosis37.292385
Mean39541.077
Median Absolute Deviation (MAD)548
Skewness6.054676
Sum1542102
Variance3.2010452 × 1010
MonotonicityNot monotonic
2023-12-13T00:15:16.905747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 15
38.5%
7168 1
 
2.6%
548 1
 
2.6%
2977 1
 
2.6%
13419 1
 
2.6%
682 1
 
2.6%
27005 1
 
2.6%
116404 1
 
2.6%
17204 1
 
2.6%
81 1
 
2.6%
Other values (15) 15
38.5%
ValueCountFrequency (%)
0 15
38.5%
40 1
 
2.6%
52 1
 
2.6%
81 1
 
2.6%
364 1
 
2.6%
548 1
 
2.6%
682 1
 
2.6%
2391 1
 
2.6%
2559 1
 
2.6%
2977 1
 
2.6%
ValueCountFrequency (%)
1117023 1
2.6%
116404 1
2.6%
112169 1
2.6%
27005 1
2.6%
26719 1
2.6%
24777 1
2.6%
17204 1
2.6%
16281 1
2.6%
13667 1
2.6%
13419 1
2.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3839156 × 109
Minimum0
Maximum3.074932 × 1010
Zeros15
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T00:15:17.029991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22463000
Q31.025152 × 109
95-th percentile2.8537615 × 1010
Maximum3.074932 × 1010
Range3.074932 × 1010
Interquartile range (IQR)1.025152 × 109

Descriptive statistics

Standard deviation9.8613198 × 109
Coefficient of variation (CV)2.2494319
Kurtosis2.3624806
Mean4.3839156 × 109
Median Absolute Deviation (MAD)22463000
Skewness2.0356309
Sum1.7097271 × 1011
Variance9.7245629 × 1019
MonotonicityNot monotonic
2023-12-13T00:15:17.173320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 15
38.5%
30749320000 1
 
2.6%
133000 1
 
2.6%
597580000 1
 
2.6%
1108955000 1
 
2.6%
174000 1
 
2.6%
947921000 1
 
2.6%
29371385000 1
 
2.6%
118751000 1
 
2.6%
22463000 1
 
2.6%
Other values (15) 15
38.5%
ValueCountFrequency (%)
0 15
38.5%
81000 1
 
2.6%
133000 1
 
2.6%
174000 1
 
2.6%
15707000 1
 
2.6%
22463000 1
 
2.6%
27962000 1
 
2.6%
102027000 1
 
2.6%
117596000 1
 
2.6%
118751000 1
 
2.6%
ValueCountFrequency (%)
30749320000 1
2.6%
29371385000 1
2.6%
28444974000 1
2.6%
25090700000 1
2.6%
24457895000 1
2.6%
24329041000 1
2.6%
1167251000 1
2.6%
1144047000 1
2.6%
1108955000 1
2.6%
1049946000 1
2.6%

Interactions

2023-12-13T00:15:14.510072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:13.382231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:13.789392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:14.194073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:14.592077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:13.485394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:13.897565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:14.271525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:14.690101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:13.591474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:14.004272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:14.360786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:14.782925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:13.688120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:14.094122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:14.432841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:15:17.272048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.176
세목명0.0001.0000.9880.8560.6990.581
과세건수0.0000.9881.0000.7930.6870.411
과세금액0.0000.8560.7931.0000.8080.996
비과세건수0.0000.6990.6870.8081.0000.694
비과세금액0.1760.5810.4110.9960.6941.000
2023-12-13T00:15:17.390141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-13T00:15:17.475261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.7360.7670.6320.0000.859
과세금액0.7361.0000.6040.6750.0000.560
비과세건수0.7670.6041.0000.9070.0000.441
비과세금액0.6320.6750.9071.0000.1580.315
과세년도0.0000.0000.0000.1581.0000.000
세목명0.8590.5600.4410.3150.0001.000

Missing values

2023-12-13T00:15:14.886948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:15:15.290481image/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

시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
0제주특별자치도서귀포시501302019취득세38230138181000000716830749320000
1제주특별자치도서귀포시501302019등록세005227962000
2제주특별자치도서귀포시501302019주민세84741404010200013667102027000
3제주특별자치도서귀포시501302019재산세2674406302637200011216925090700000
4제주특별자치도서귀포시501302019자동차세15078315920318000247771049946000
5제주특별자치도서귀포시501302019레저세0000
6제주특별자치도서귀포시501302019교육세5057392595610700036481000
7제주특별자치도서귀포시501302019지방소비세0000
8제주특별자치도서귀포시501302019등록면허세9322610871605000132731167251000
9제주특별자치도서귀포시501302019도시계획세0000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
29제주특별자치도서귀포시501302021재산세2843387134947600011640429371385000
30제주특별자치도서귀포시501302021자동차세1535861612782000027005947921000
31제주특별자치도서귀포시501302021레저세0000
32제주특별자치도서귀포시501302021교육세52362028631389000682174000
33제주특별자치도서귀포시501302021지방소비세0000
34제주특별자치도서귀포시501302021등록면허세9374211051865000134191108955000
35제주특별자치도서귀포시501302021도시계획세0000
36제주특별자치도서귀포시501302021지역자원시설세9317869474620002977597580000
37제주특별자치도서귀포시501302021지방소득세616704529085500000
38제주특별자치도서귀포시501302021담배소비세0000