Overview

Dataset statistics

Number of variables9
Number of observations35
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory81.8 B

Variable types

Categorical5
Numeric4

Dataset

Description과세액 중 비과세액과 감면액이 차지하는 비율 현황 제공 (2017-2021년) - 활용업무: 국민조세 혜택 규모를 파악하는 데 사용
URLhttps://www.data.go.kr/data/15080274/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 3 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
세목명 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
비과세금액 has 1 (2.9%) missing valuesMissing
감면금액 has unique valuesUnique
비과세금액 has 4 (11.4%) zerosZeros
부과금액 has 4 (11.4%) zerosZeros
비과세감면율 has 4 (11.4%) zerosZeros

Reproduction

Analysis started2023-12-12 06:23:11.083587
Analysis finished2023-12-12 06:23:13.658307
Duration2.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
대전광역시
35 

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 35
100.0%

Length

2023-12-12T15:23:13.749229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:23:13.882189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 35
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
유성구
35 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 35
100.0%

Length

2023-12-12T15:23:14.003583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:23:14.131938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 35
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
30200
35 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30200
2nd row30200
3rd row30200
4th row30200
5th row30200

Common Values

ValueCountFrequency (%)
30200 35
100.0%

Length

2023-12-12T15:23:14.268710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:23:14.376372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30200 35
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
등록세
5 
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
Other values (2)
10 

Length

Max length7
Median length3
Mean length4
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등록세 5
14.3%
재산세 5
14.3%
주민세 5
14.3%
취득세 5
14.3%
자동차세 5
14.3%
등록면허세 5
14.3%
지역자원시설세 5
14.3%

Length

2023-12-12T15:23:14.532405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:23:14.702151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록세 5
14.3%
재산세 5
14.3%
주민세 5
14.3%
취득세 5
14.3%
자동차세 5
14.3%
등록면허세 5
14.3%
지역자원시설세 5
14.3%

과세년도
Categorical

Distinct5
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size412.0 B
2017
7 
2018
7 
2019
7 
2020
7 
2021
7 

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 7
20.0%
2018 7
20.0%
2019 7
20.0%
2020 7
20.0%
2021 7
20.0%

Length

2023-12-12T15:23:14.865976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:23:14.983420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 7
20.0%
2018 7
20.0%
2019 7
20.0%
2020 7
20.0%
2021 7
20.0%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct31
Distinct (%)91.2%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean9.4205049 × 109
Minimum0
Maximum5.5490648 × 1010
Zeros4
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T15:23:15.098973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.0513075 × 108
median1.391621 × 109
Q36.8097965 × 109
95-th percentile4.732202 × 1010
Maximum5.5490648 × 1010
Range5.5490648 × 1010
Interquartile range (IQR)6.6046658 × 109

Descriptive statistics

Standard deviation1.6597092 × 1010
Coefficient of variation (CV)1.761805
Kurtosis2.1595784
Mean9.4205049 × 109
Median Absolute Deviation (MAD)1.391621 × 109
Skewness1.8997554
Sum3.2029717 × 1011
Variance2.7546348 × 1020
MonotonicityNot monotonic
2023-12-12T15:23:15.220123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 4
 
11.4%
41051396000 1
 
2.9%
1381397000 1
 
2.9%
101248000 1
 
2.9%
234207000 1
 
2.9%
19143398000 1
 
2.9%
3347474000 1
 
2.9%
55490648000 1
 
2.9%
1367473000 1
 
2.9%
1051281000 1
 
2.9%
Other values (21) 21
60.0%
ValueCountFrequency (%)
0 4
11.4%
53298000 1
 
2.9%
53429000 1
 
2.9%
54844000 1
 
2.9%
101248000 1
 
2.9%
198721000 1
 
2.9%
224360000 1
 
2.9%
234207000 1
 
2.9%
1051281000 1
 
2.9%
1310548000 1
 
2.9%
ValueCountFrequency (%)
55490648000 1
2.9%
49111513000 1
2.9%
46358447000 1
2.9%
43790277000 1
2.9%
41051396000 1
2.9%
19143398000 1
2.9%
16686164000 1
2.9%
8299018000 1
2.9%
7015218000 1
2.9%
6193532000 1
2.9%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8815773 × 109
Minimum1365000
Maximum6.3036575 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T15:23:15.368664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1365000
5-th percentile18492600
Q12.39032 × 108
median1.431823 × 109
Q31.4252453 × 1010
95-th percentile4.7881912 × 1010
Maximum6.3036575 × 1010
Range6.303521 × 1010
Interquartile range (IQR)1.4013421 × 1010

Descriptive statistics

Standard deviation1.7691776 × 1010
Coefficient of variation (CV)1.7903798
Kurtosis3.264439
Mean9.8815773 × 109
Median Absolute Deviation (MAD)1.256291 × 109
Skewness2.0523006
Sum3.4585521 × 1011
Variance3.1299895 × 1020
MonotonicityNot monotonic
2023-12-12T15:23:15.547078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
25266000 1
 
2.9%
14745079000 1
 
2.9%
494231000 1
 
2.9%
2688000 1
 
2.9%
15550315000 1
 
2.9%
1613120000 1
 
2.9%
61663677000 1
 
2.9%
2805190000 1
 
2.9%
190418000 1
 
2.9%
512232000 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1365000 1
2.9%
2688000 1
2.9%
25266000 1
2.9%
31538000 1
2.9%
34129000 1
2.9%
143748000 1
2.9%
175532000 1
2.9%
181126000 1
2.9%
190418000 1
2.9%
287646000 1
2.9%
ValueCountFrequency (%)
63036575000 1
2.9%
61663677000 1
2.9%
41975441000 1
2.9%
41546341000 1
2.9%
40390337000 1
2.9%
16072992000 1
2.9%
15550315000 1
2.9%
14811832000 1
2.9%
14745079000 1
2.9%
13759827000 1
2.9%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.418332 × 1010
Minimum0
Maximum2.1718038 × 1011
Zeros4
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T15:23:15.709669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1671956 × 1010
median2.2647121 × 1010
Q37.4260564 × 1010
95-th percentile1.3916112 × 1011
Maximum2.1718038 × 1011
Range2.1718038 × 1011
Interquartile range (IQR)6.2588608 × 1010

Descriptive statistics

Standard deviation5.1099388 × 1010
Coefficient of variation (CV)1.1565312
Kurtosis2.848002
Mean4.418332 × 1010
Median Absolute Deviation (MAD)1.3239444 × 1010
Skewness1.7032422
Sum1.5464162 × 1012
Variance2.6111474 × 1021
MonotonicityNot monotonic
2023-12-12T15:23:15.862731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 4
 
11.4%
12295142000 1
 
2.9%
13174111000 1
 
2.9%
14353913000 1
 
2.9%
37976672000 1
 
2.9%
217180383000 1
 
2.9%
24878893000 1
 
2.9%
95425780000 1
 
2.9%
12424535000 1
 
2.9%
13470334000 1
 
2.9%
Other values (22) 22
62.9%
ValueCountFrequency (%)
0 4
11.4%
39880000 1
 
2.9%
10021820000 1
 
2.9%
10250695000 1
 
2.9%
10737463000 1
 
2.9%
11581551000 1
 
2.9%
11762362000 1
 
2.9%
12295142000 1
 
2.9%
12424535000 1
 
2.9%
13174111000 1
 
2.9%
ValueCountFrequency (%)
217180383000 1
2.9%
155871086000 1
2.9%
131999711000 1
2.9%
116235199000 1
2.9%
97896590000 1
2.9%
95425780000 1
2.9%
88293278000 1
2.9%
81554132000 1
2.9%
76526876000 1
2.9%
71994252000 1
2.9%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.192
Minimum0
Maximum77.5
Zeros4
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T15:23:16.040122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.88
median16.16
Q337.315
95-th percentile75.067
Maximum77.5
Range77.5
Interquartile range (IQR)30.435

Descriptive statistics

Standard deviation25.165086
Coefficient of variation (CV)1.0402235
Kurtosis0.028759419
Mean24.192
Median Absolute Deviation (MAD)12.98
Skewness1.1249749
Sum846.72
Variance633.28158
MonotonicityNot monotonic
2023-12-12T15:23:16.219633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.0 4
 
11.4%
1.87 1
 
2.9%
14.41 1
 
2.9%
1.71 1
 
2.9%
7.33 1
 
2.9%
37.84 1
 
2.9%
20.27 1
 
2.9%
74.99 1
 
2.9%
15.13 1
 
2.9%
9.22 1
 
2.9%
Other values (22) 22
62.9%
ValueCountFrequency (%)
0.0 4
11.4%
1.71 1
 
2.9%
1.87 1
 
2.9%
2.29 1
 
2.9%
3.18 1
 
2.9%
6.74 1
 
2.9%
7.02 1
 
2.9%
7.14 1
 
2.9%
7.33 1
 
2.9%
7.35 1
 
2.9%
ValueCountFrequency (%)
77.5 1
2.9%
75.2 1
2.9%
75.01 1
2.9%
74.99 1
2.9%
73.24 1
2.9%
50.27 1
2.9%
49.74 1
2.9%
41.44 1
2.9%
37.84 1
2.9%
36.79 1
2.9%

Interactions

2023-12-12T15:23:12.746288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:11.339456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:11.790737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:12.240760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:12.871916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:11.436328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:11.896374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:12.352382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:13.011816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:11.554363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:11.994078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:12.486573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:13.136326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:11.660916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:12.100290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:23:12.605123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:23:16.328727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.7080.8480.7460.836
과세년도0.0001.0000.0000.0000.0000.000
비과세금액0.7080.0001.0001.0000.8720.743
감면금액0.8480.0001.0001.0000.9970.979
부과금액0.7460.0000.8720.9971.0000.942
비과세감면율0.8360.0000.7430.9790.9421.000
2023-12-12T15:23:16.459029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-12T15:23:16.589622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율세목명과세년도
비과세금액1.0000.8460.8070.9410.5070.000
감면금액0.8461.0000.9450.8530.7330.000
부과금액0.8070.9451.0000.7560.5080.000
비과세감면율0.9410.8530.7561.0000.6310.000
세목명0.5070.7330.5080.6311.0000.000
과세년도0.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T15:23:13.360548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:23:13.598352image/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
0대전광역시유성구30200등록세201702526600000.0
1대전광역시유성구30200재산세201741051396000147450790007199425200077.5
2대전광역시유성구30200주민세2017292055500014318230002060515200021.12
3대전광역시유성구30200취득세201761935320004197544100011623519900041.44
4대전광역시유성구30200자동차세201713242840001081239000342840430007.02
5대전광역시유성구30200등록면허세201753429000287646000107374630003.18
6대전광역시유성구30200지역자원시설세2017131054800012237280001002182000025.29
7대전광역시유성구30200등록세20180136500000.0
8대전광역시유성구30200재산세201843790277000137598270007652687600075.2
9대전광역시유성구30200주민세2018296558300014610380002170026600020.4
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
25대전광역시유성구30200자동차세20202243600002805190000368708010008.22
26대전광역시유성구30200등록면허세20201051281000190418000134703340009.22
27대전광역시유성구30200지역자원시설세202013674730005122320001242453500015.13
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