Overview

Dataset statistics

Number of variables9
Number of observations74
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory79.7 B

Variable types

Categorical4
Numeric5

Dataset

Description연도별 지방세 과세 및 비과세 현황을 3년간 연도별 세목별로 제공하여, 국민 조세 혜택 규모를 파악하는 데 사용합니다. 자세한 사항은 붙임파일 내 내용을 참고하시기 바랍니다.
Author울산광역시 울주군
URLhttps://www.data.go.kr/data/15080221/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 2 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 2 other fieldsHigh correlation
과세건수 has 16 (21.6%) zerosZeros
과세금액 has 17 (23.0%) zerosZeros
비과세건수 has 27 (36.5%) zerosZeros
비과세금액 has 27 (36.5%) zerosZeros

Reproduction

Analysis started2024-04-13 12:54:07.170188
Analysis finished2024-04-13 12:54:15.864610
Duration8.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size720.0 B
울산광역시
74 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울산광역시 74
100.0%

Length

2024-04-13T21:54:16.076466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:54:16.399320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산광역시 74
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size720.0 B
울주군
74 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울주군 74
100.0%

Length

2024-04-13T21:54:16.732159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:54:17.046543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울주군 74
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size720.0 B
31710
74 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31710
2nd row31710
3rd row31710
4th row31710
5th row31710

Common Values

ValueCountFrequency (%)
31710 74
100.0%

Length

2024-04-13T21:54:17.378114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:54:17.689243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31710 74
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5405
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.0 B
2024-04-13T21:54:17.981310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7295909
Coefficient of variation (CV)0.00085642791
Kurtosis-1.2499212
Mean2019.5405
Median Absolute Deviation (MAD)1
Skewness-0.053917346
Sum149446
Variance2.9914846
MonotonicityIncreasing
2024-04-13T21:54:18.346389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 13
17.6%
2019 13
17.6%
2020 13
17.6%
2022 13
17.6%
2021 12
16.2%
2018 10
13.5%
ValueCountFrequency (%)
2017 13
17.6%
2018 10
13.5%
2019 13
17.6%
2020 13
17.6%
2021 12
16.2%
2022 13
17.6%
ValueCountFrequency (%)
2022 13
17.6%
2021 12
16.2%
2020 13
17.6%
2019 13
17.6%
2018 10
13.5%
2017 13
17.6%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size720.0 B
취득세
6 
주민세
6 
재산세
6 
자동차세
6 
담배소비세
6 
Other values (8)
44 

Length

Max length7
Median length5
Mean length4.1621622
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 6
 
8.1%
주민세 6
 
8.1%
재산세 6
 
8.1%
자동차세 6
 
8.1%
담배소비세 6
 
8.1%
등록면허세 6
 
8.1%
지역자원시설세 6
 
8.1%
지방소득세 6
 
8.1%
교육세 6
 
8.1%
등록세 5
 
6.8%
Other values (3) 15
20.3%

Length

2024-04-13T21:54:18.777324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 6
 
8.1%
주민세 6
 
8.1%
재산세 6
 
8.1%
자동차세 6
 
8.1%
담배소비세 6
 
8.1%
등록면허세 6
 
8.1%
지역자원시설세 6
 
8.1%
지방소득세 6
 
8.1%
교육세 6
 
8.1%
등록세 5
 
6.8%
Other values (3) 15
20.3%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110262.08
Minimum0
Maximum541637
Zeros16
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size794.0 B
2024-04-13T21:54:19.193371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median96450
Q3111623.75
95-th percentile525409.5
Maximum541637
Range541637
Interquartile range (IQR)111605.75

Descriptive statistics

Standard deviation145047.45
Coefficient of variation (CV)1.315479
Kurtosis3.3888828
Mean110262.08
Median Absolute Deviation (MAD)96363.5
Skewness1.931822
Sum8159394
Variance2.1038764 × 1010
MonotonicityNot monotonic
2024-04-13T21:54:19.633438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
21.6%
24736 1
 
1.4%
270 1
 
1.4%
5 1
 
1.4%
106508 1
 
1.4%
109020 1
 
1.4%
86728 1
 
1.4%
539280 1
 
1.4%
21308 1
 
1.4%
106124 1
 
1.4%
Other values (49) 49
66.2%
ValueCountFrequency (%)
0 16
21.6%
5 1
 
1.4%
6 1
 
1.4%
9 1
 
1.4%
45 1
 
1.4%
84 1
 
1.4%
89 1
 
1.4%
107 1
 
1.4%
270 1
 
1.4%
478 1
 
1.4%
ValueCountFrequency (%)
541637 1
1.4%
539280 1
1.4%
534961 1
1.4%
532020 1
1.4%
521850 1
1.4%
516993 1
1.4%
224052 1
1.4%
220413 1
1.4%
216720 1
1.4%
211945 1
1.4%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.872812 × 1010
Minimum0
Maximum1.6505 × 1011
Zeros17
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size794.0 B
2024-04-13T21:54:20.046463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.2660555 × 109
median1.8902042 × 1010
Q35.8271356 × 1010
95-th percentile1.2205 × 1011
Maximum1.6505 × 1011
Range1.6505 × 1011
Interquartile range (IQR)5.30053 × 1010

Descriptive statistics

Standard deviation4.291176 × 1010
Coefficient of variation (CV)1.1080259
Kurtosis0.64763322
Mean3.872812 × 1010
Median Absolute Deviation (MAD)1.8902042 × 1010
Skewness1.237521
Sum2.8658809 × 1012
Variance1.8414192 × 1021
MonotonicityNot monotonic
2024-04-13T21:54:20.492817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
23.0%
75174071000 1
 
1.4%
58282003000 1
 
1.4%
17603102000 1
 
1.4%
10363006000 1
 
1.4%
31285599000 1
 
1.4%
103000000000 1
 
1.4%
33305169000 1
 
1.4%
121000000000 1
 
1.4%
17452740000 1
 
1.4%
Other values (48) 48
64.9%
ValueCountFrequency (%)
0 17
23.0%
119104000 1
 
1.4%
4004785000 1
 
1.4%
9049867000 1
 
1.4%
9755752000 1
 
1.4%
10207985000 1
 
1.4%
10256866000 1
 
1.4%
10363006000 1
 
1.4%
10507892000 1
 
1.4%
11232337000 1
 
1.4%
ValueCountFrequency (%)
165050000000 1
1.4%
152000000000 1
1.4%
145000000000 1
1.4%
124000000000 1
1.4%
121000000000 1
1.4%
120000000000 1
1.4%
116000000000 1
1.4%
109000000000 1
1.4%
108000000000 1
1.4%
103000000000 1
1.4%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7502.5541
Minimum0
Maximum60556
Zeros27
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size794.0 B
2024-04-13T21:54:20.898487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1511
Q37094.25
95-th percentile52228.4
Maximum60556
Range60556
Interquartile range (IQR)7094.25

Descriptive statistics

Standard deviation15003.98
Coefficient of variation (CV)1.9998496
Kurtosis6.3308148
Mean7502.5541
Median Absolute Deviation (MAD)1511
Skewness2.6900081
Sum555189
Variance2.2511942 × 108
MonotonicityNot monotonic
2024-04-13T21:54:21.326771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 27
36.5%
2 2
 
2.7%
18092 1
 
1.4%
6672 1
 
1.4%
60556 1
 
1.4%
17839 1
 
1.4%
4812 1
 
1.4%
2183 1
 
1.4%
29 1
 
1.4%
7207 1
 
1.4%
Other values (37) 37
50.0%
ValueCountFrequency (%)
0 27
36.5%
2 2
 
2.7%
8 1
 
1.4%
9 1
 
1.4%
14 1
 
1.4%
21 1
 
1.4%
29 1
 
1.4%
38 1
 
1.4%
148 1
 
1.4%
1110 1
 
1.4%
ValueCountFrequency (%)
60556 1
1.4%
59331 1
1.4%
57403 1
1.4%
54506 1
1.4%
51002 1
1.4%
46267 1
1.4%
19145 1
1.4%
18092 1
1.4%
17839 1
1.4%
15536 1
1.4%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1597092 × 109
Minimum0
Maximum3.5803692 × 1010
Zeros27
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size794.0 B
2024-04-13T21:54:21.734924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12367500
Q37.4787725 × 108
95-th percentile3.2613192 × 1010
Maximum3.5803692 × 1010
Range3.5803692 × 1010
Interquartile range (IQR)7.4787725 × 108

Descriptive statistics

Standard deviation1.1407271 × 1010
Coefficient of variation (CV)2.2108361
Kurtosis1.9324077
Mean5.1597092 × 109
Median Absolute Deviation (MAD)12367500
Skewness1.938458
Sum3.8181848 × 1011
Variance1.3012584 × 1020
MonotonicityNot monotonic
2024-04-13T21:54:22.183369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 27
36.5%
24227890000 1
 
1.4%
304972000 1
 
1.4%
172913000 1
 
1.4%
32799383000 1
 
1.4%
1009919000 1
 
1.4%
317448000 1
 
1.4%
678839000 1
 
1.4%
3000 1
 
1.4%
35576872000 1
 
1.4%
Other values (38) 38
51.4%
ValueCountFrequency (%)
0 27
36.5%
2000 1
 
1.4%
3000 1
 
1.4%
4000 1
 
1.4%
39000 1
 
1.4%
81000 1
 
1.4%
300000 1
 
1.4%
926000 1
 
1.4%
1439000 1
 
1.4%
5188000 1
 
1.4%
ValueCountFrequency (%)
35803692000 1
1.4%
35576872000 1
1.4%
33993066000 1
1.4%
32799383000 1
1.4%
32512935000 1
1.4%
32144227000 1
1.4%
31037403000 1
1.4%
30394551000 1
1.4%
27609362000 1
1.4%
26679592000 1
1.4%

Interactions

2024-04-13T21:54:13.735948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:08.726985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:09.975489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:11.211196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:12.477584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:13.985723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:08.978595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:10.220973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:11.460570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:12.725793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:14.233855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:09.219154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:10.458568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:11.707145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:12.970349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:14.492007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:09.471351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:10.707835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:11.960197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:13.231087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:14.744490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:09.717514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:10.955243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:12.214714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:54:13.476382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T21:54:22.591687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9240.9010.8150.663
과세건수0.0000.9241.0000.8220.6940.470
과세금액0.0000.9010.8221.0000.7810.830
비과세건수0.0000.8150.6940.7811.0000.757
비과세금액0.0000.6630.4700.8300.7571.000
2024-04-13T21:54:22.869972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도과세건수과세금액비과세건수비과세금액세목명
과세년도1.0000.0640.0140.009-0.0260.000
과세건수0.0641.0000.6440.6810.5690.741
과세금액0.0140.6441.0000.4810.5090.657
비과세건수0.0090.6810.4811.0000.9370.531
비과세금액-0.0260.5690.5090.9371.0000.409
세목명0.0000.7410.6570.5310.4091.000

Missing values

2024-04-13T21:54:15.101742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T21:54:15.683921image/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
0울산광역시울주군317102017취득세24736116000000000692124227890000
1울산광역시울주군317102017등록세0088532000
2울산광역시울주군317102017주민세102248171150120003029329525000
3울산광역시울주군317102017재산세193924583062420004626725333426000
4울산광역시울주군317102017자동차세18648658239415000108641011590000
5울산광역시울주군317102017레저세0000
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