Overview

Dataset statistics

Number of variables9
Number of observations31
Missing cells3
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory82.3 B

Variable types

Categorical5
Numeric4

Dataset

Description4년간 지방세 부과 징수 자료에 따른 지방세 세목별 통계자료를 근거로 연도별 지방세 비과세 및 감면율 현황을 추출한 자료에 해당됩니다
Author충청북도 진천군
URLhttps://www.data.go.kr/data/15079484/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
비과세금액 is highly overall correlated with 감면금액 and 3 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
부과금액 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
비과세금액 has 3 (9.7%) missing valuesMissing
비과세금액 has 7 (22.6%) zerosZeros
부과금액 has 3 (9.7%) zerosZeros
비과세감면율 has 6 (19.4%) zerosZeros

Reproduction

Analysis started2024-03-23 04:06:19.099390
Analysis finished2024-03-23 04:06:26.187627
Duration7.09 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
충청북도
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 31
100.0%

Length

2024-03-23T04:06:26.555310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T04:06:26.956316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 31
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진천군 31
100.0%

Length

2024-03-23T04:06:27.511768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T04:06:27.888484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진천군 31
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
43750
31 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43750
2nd row43750
3rd row43750
4th row43750
5th row43750

Common Values

ValueCountFrequency (%)
43750 31
100.0%

Length

2024-03-23T04:06:28.306681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T04:06:28.768867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43750 31
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size380.0 B
등록세
4 
재산세
4 
주민세
4 
취득세
4 
자동차세
4 
Other values (3)
11 

Length

Max length7
Median length3
Mean length3.9032258
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등록세 4
12.9%
재산세 4
12.9%
주민세 4
12.9%
취득세 4
12.9%
자동차세 4
12.9%
등록면허세 4
12.9%
지역자원시설세 4
12.9%
교육세 3
9.7%

Length

2024-03-23T04:06:29.260751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T04:06:29.751771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록세 4
12.9%
재산세 4
12.9%
주민세 4
12.9%
취득세 4
12.9%
자동차세 4
12.9%
등록면허세 4
12.9%
지역자원시설세 4
12.9%
교육세 3
9.7%

과세년도
Categorical

Distinct4
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size380.0 B
2020
8 
2021
8 
2022
8 
2019
7 

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 (%)
2020 8
25.8%
2021 8
25.8%
2022 8
25.8%
2019 7
22.6%

Length

2024-03-23T04:06:30.177200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T04:06:30.484200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 8
25.8%
2021 8
25.8%
2022 8
25.8%
2019 7
22.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)78.6%
Missing3
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean1.520627 × 109
Minimum0
Maximum9.0948 × 109
Zeros7
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-03-23T04:06:30.926170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1599250
median1.55993 × 108
Q31.128759 × 109
95-th percentile8.1504472 × 109
Maximum9.0948 × 109
Range9.0948 × 109
Interquartile range (IQR)1.1281598 × 109

Descriptive statistics

Standard deviation2.9149872 × 109
Coefficient of variation (CV)1.9169639
Kurtosis1.9905117
Mean1.520627 × 109
Median Absolute Deviation (MAD)1.55993 × 108
Skewness1.8793669
Sum4.2577557 × 1010
Variance8.4971503 × 1018
MonotonicityNot monotonic
2024-03-23T04:06:31.470241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 7
22.6%
8446704000 1
 
3.2%
242729000 1
 
3.2%
6602000 1
 
3.2%
163881000 1
 
3.2%
1247085000 1
 
3.2%
9094800000 1
 
3.2%
232624000 1
 
3.2%
6055000 1
 
3.2%
157091000 1
 
3.2%
Other values (12) 12
38.7%
(Missing) 3
 
9.7%
ValueCountFrequency (%)
0 7
22.6%
799000 1
 
3.2%
1669000 1
 
3.2%
6055000 1
 
3.2%
6602000 1
 
3.2%
16460000 1
 
3.2%
134077000 1
 
3.2%
154895000 1
 
3.2%
157091000 1
 
3.2%
163881000 1
 
3.2%
ValueCountFrequency (%)
9094800000 1
3.2%
8446704000 1
3.2%
7600256000 1
3.2%
7413684000 1
3.2%
4703959000 1
3.2%
1494720000 1
3.2%
1247085000 1
3.2%
1089317000 1
3.2%
242729000 1
3.2%
232624000 1
3.2%

감면금액
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9607492 × 109
Minimum1000
Maximum1.3418682 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-03-23T04:06:32.222440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3000
Q145960500
median97893000
Q31.311457 × 109
95-th percentile1.2601024 × 1010
Maximum1.3418682 × 1010
Range1.3418681 × 1010
Interquartile range (IQR)1.2654965 × 109

Descriptive statistics

Standard deviation4.0921883 × 109
Coefficient of variation (CV)2.0870534
Kurtosis3.5127668
Mean1.9607492 × 109
Median Absolute Deviation (MAD)97078000
Skewness2.2146339
Sum6.0783224 × 1010
Variance1.6746005 × 1019
MonotonicityNot monotonic
2024-03-23T04:06:32.818562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3000 2
 
6.5%
39461000 1
 
3.2%
68095000 1
 
3.2%
97893000 1
 
3.2%
254062000 1
 
3.2%
9874303000 1
 
3.2%
52460000 1
 
3.2%
2622131000 1
 
3.2%
2238000 1
 
3.2%
65248000 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
1000 1
3.2%
3000 2
6.5%
815000 1
3.2%
2238000 1
3.2%
11692000 1
3.2%
19203000 1
3.2%
39461000 1
3.2%
52460000 1
3.2%
54293000 1
3.2%
64925000 1
3.2%
ValueCountFrequency (%)
13418682000 1
3.2%
12686167000 1
3.2%
12515880000 1
3.2%
9874303000 1
3.2%
2695470000 1
3.2%
2622131000 1
3.2%
2517903000 1
3.2%
2358281000 1
3.2%
264633000 1
3.2%
254514000 1
3.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5163934 × 1010
Minimum0
Maximum6.3676996 × 1010
Zeros3
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-03-23T04:06:33.538936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5780105 × 109
median9.458244 × 109
Q31.8429276 × 1010
95-th percentile5.1746376 × 1010
Maximum6.3676996 × 1010
Range6.3676996 × 1010
Interquartile range (IQR)1.3851266 × 1010

Descriptive statistics

Standard deviation1.6288194 × 1010
Coefficient of variation (CV)1.0741404
Kurtosis2.9369533
Mean1.5163934 × 1010
Median Absolute Deviation (MAD)5.98377 × 109
Skewness1.8021356
Sum4.7008196 × 1011
Variance2.6530527 × 1020
MonotonicityNot monotonic
2024-03-23T04:06:34.287724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 3
 
9.7%
19300353000 1
 
3.2%
5132218000 1
 
3.2%
5217281000 1
 
3.2%
15292452000 1
 
3.2%
59183338000 1
 
3.2%
9458244000 1
 
3.2%
22480173000 1
 
3.2%
15549914000 1
 
3.2%
4812251000 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
0 3
9.7%
30744000 1
 
3.2%
3474474000 1
 
3.2%
3894591000 1
 
3.2%
4164039000 1
 
3.2%
4343770000 1
 
3.2%
4812251000 1
 
3.2%
5132218000 1
 
3.2%
5217281000 1
 
3.2%
5643144000 1
 
3.2%
ValueCountFrequency (%)
63676996000 1
3.2%
59183338000 1
3.2%
44309413000 1
3.2%
41517803000 1
3.2%
22480173000 1
3.2%
20515825000 1
3.2%
19402864000 1
3.2%
19300353000 1
3.2%
17558200000 1
3.2%
17081751000 1
3.2%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.049355
Minimum0
Maximum62.46
Zeros6
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-03-23T04:06:35.271088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.45
median2.73
Q321.105
95-th percentile53.215
Maximum62.46
Range62.46
Interquartile range (IQR)20.655

Descriptive statistics

Standard deviation20.456557
Coefficient of variation (CV)1.4560496
Kurtosis0.23636417
Mean14.049355
Median Absolute Deviation (MAD)2.73
Skewness1.3526515
Sum435.53
Variance418.47074
MonotonicityNot monotonic
2024-03-23T04:06:35.961555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 6
 
19.4%
54.31 1
 
3.2%
6.06 1
 
3.2%
2.0 1
 
3.2%
2.73 1
 
3.2%
18.79 1
 
3.2%
0.55 1
 
3.2%
52.12 1
 
3.2%
6.19 1
 
3.2%
3.75 1
 
3.2%
Other values (16) 16
51.6%
ValueCountFrequency (%)
0.0 6
19.4%
0.01 1
 
3.2%
0.35 1
 
3.2%
0.55 1
 
3.2%
0.62 1
 
3.2%
2.0 1
 
3.2%
2.33 1
 
3.2%
2.36 1
 
3.2%
2.4 1
 
3.2%
2.65 1
 
3.2%
ValueCountFrequency (%)
62.46 1
3.2%
54.31 1
3.2%
52.12 1
3.2%
51.46 1
3.2%
51.33 1
3.2%
41.89 1
3.2%
30.7 1
3.2%
23.42 1
3.2%
18.79 1
3.2%
6.48 1
3.2%

Interactions

2024-03-23T04:06:23.818413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:19.623984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:21.295279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:22.601396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:24.061474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:19.906609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:21.565139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:22.936431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:24.422906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:20.563634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:21.977190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:23.247885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:24.772442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:20.884019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:22.316423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:06:23.533963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T04:06:36.612583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.7410.7500.9420.709
과세년도0.0001.0000.0000.0000.0000.000
비과세금액0.7410.0001.0000.9660.8100.889
감면금액0.7500.0000.9661.0000.8140.808
부과금액0.9420.0000.8100.8141.0000.860
비과세감면율0.7090.0000.8890.8080.8601.000
2024-03-23T04:06:37.312212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2024-03-23T04:06:37.829252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율세목명과세년도
비과세금액1.0000.8090.5510.7790.5320.000
감면금액0.8091.0000.6370.7270.5550.000
부과금액0.5510.6371.0000.4210.8250.000
비과세감면율0.7790.7270.4211.0000.4630.000
세목명0.5320.5550.8250.4631.0000.000
과세년도0.0000.0000.0000.0000.0001.000

Missing values

2024-03-23T04:06:25.210901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T04:06:25.954591image/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

시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
0충청북도진천군43750등록세2019<NA>6696100000.0
1충청북도진천군43750재산세2019741368400025179030001930035300051.46
2충청북도진천군43750주민세2019164600001169200080081660000.35
3충청북도진천군43750취득세20191089317000125158800004430941300030.7
4충청북도진천군43750자동차세2019134077000264633000150432730002.65
5충청북도진천군43750등록면허세201979900013222700056431440002.36
6충청북도진천군43750지역자원시설세20191750900009489600041640390006.48
7충청북도진천군43750교육세202001000128902990000.0
8충청북도진천군43750등록세20200192030003074400062.46
9충청북도진천군43750재산세2020760025600023582810001940286400051.33
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
21충청북도진천군43750등록면허세2021605500013991000038945910003.75
22충청북도진천군43750지역자원시설세20212326240006524800048122510006.19
23충청북도진천군43750교육세202203000155499140000.0
24충청북도진천군43750등록세2022<NA>223800000.0
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