Overview

Dataset statistics

Number of variables9
Number of observations48
Missing cells5
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory80.8 B

Variable types

Categorical4
Numeric5

Dataset

Description2017년 세목별 지방세 비과, 감면 금액, 2018년 세목별 지방세 비과, 감면 금액, 2019년 세목별 지방세 비과, 감면 금액, 2020년, 2021년, 2022년 세목별 지방세 비과, 감면 금액에 관한 자료입니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079219

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
비과세금액 is highly overall correlated with 감면금액 and 2 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
부과금액 is highly overall correlated with 감면금액 and 1 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
비과세금액 has 5 (10.4%) missing valuesMissing
비과세금액 has 7 (14.6%) zerosZeros
부과금액 has 5 (10.4%) zerosZeros
비과세감면율 has 11 (22.9%) zerosZeros

Reproduction

Analysis started2023-12-10 22:57:54.391015
Analysis finished2023-12-10 22:57:57.886856
Duration3.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
경상남도
48 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 48
100.0%

Length

2023-12-11T07:57:57.952375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:57:58.034581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 48
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
거창군
48 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거창군 48
100.0%

Length

2023-12-11T07:57:58.395505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:57:58.486254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 48
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
48880
48 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 48
100.0%

Length

2023-12-11T07:57:58.605754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:57:58.702240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 48
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size516.0 B
교육세
6 
등록세
6 
재산세
6 
주민세
6 
취득세
6 
Other values (3)
18 

Length

Max length7
Median length3
Mean length3.875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
교육세 6
12.5%
등록세 6
12.5%
재산세 6
12.5%
주민세 6
12.5%
취득세 6
12.5%
자동차세 6
12.5%
등록면허세 6
12.5%
지역자원시설세 6
12.5%

Length

2023-12-11T07:57:58.815046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:57:58.939227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교육세 6
12.5%
등록세 6
12.5%
재산세 6
12.5%
주민세 6
12.5%
취득세 6
12.5%
자동차세 6
12.5%
등록면허세 6
12.5%
지역자원시설세 6
12.5%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T07:57:59.074160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019.5
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7258979
Coefficient of variation (CV)0.00085461642
Kurtosis-1.2751304
Mean2019.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum96936
Variance2.9787234
MonotonicityIncreasing
2023-12-11T07:57:59.190832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 8
16.7%
2018 8
16.7%
2019 8
16.7%
2020 8
16.7%
2021 8
16.7%
2022 8
16.7%
ValueCountFrequency (%)
2017 8
16.7%
2018 8
16.7%
2019 8
16.7%
2020 8
16.7%
2021 8
16.7%
2022 8
16.7%
ValueCountFrequency (%)
2022 8
16.7%
2021 8
16.7%
2020 8
16.7%
2019 8
16.7%
2018 8
16.7%
2017 8
16.7%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct37
Distinct (%)86.0%
Missing5
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean5.7989888 × 108
Minimum0
Maximum3.705327 × 109
Zeros7
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T07:57:59.322783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14722000
median54408000
Q34.779185 × 108
95-th percentile3.1995832 × 109
Maximum3.705327 × 109
Range3.705327 × 109
Interquartile range (IQR)4.731965 × 108

Descriptive statistics

Standard deviation1.0915395 × 109
Coefficient of variation (CV)1.8822928
Kurtosis2.4822185
Mean5.7989888 × 108
Median Absolute Deviation (MAD)54408000
Skewness1.9851993
Sum2.4935652 × 1010
Variance1.1914585 × 1018
MonotonicityNot monotonic
2023-12-11T07:57:59.440729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 7
 
14.6%
185419000 1
 
2.1%
808902000 1
 
2.1%
52293000 1
 
2.1%
4784000 1
 
2.1%
109292000 1
 
2.1%
3382424000 1
 
2.1%
6595000 1
 
2.1%
1060275000 1
 
2.1%
54408000 1
 
2.1%
Other values (27) 27
56.2%
(Missing) 5
 
10.4%
ValueCountFrequency (%)
0 7
14.6%
3379000 1
 
2.1%
3601000 1
 
2.1%
4628000 1
 
2.1%
4660000 1
 
2.1%
4784000 1
 
2.1%
4823000 1
 
2.1%
5534000 1
 
2.1%
5839000 1
 
2.1%
6595000 1
 
2.1%
ValueCountFrequency (%)
3705327000 1
2.1%
3382424000 1
2.1%
3215869000 1
2.1%
3053011000 1
2.1%
2912794000 1
2.1%
2691326000 1
2.1%
1060275000 1
2.1%
951174000 1
2.1%
808902000 1
2.1%
798762000 1
2.1%

감면금액
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2855244 × 108
Minimum7000
Maximum2.385683 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T07:57:59.580966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7000
5-th percentile8350
Q115055250
median98129000
Q33.52751 × 108
95-th percentile2.3251468 × 109
Maximum2.385683 × 109
Range2.385676 × 109
Interquartile range (IQR)3.3769575 × 108

Descriptive statistics

Standard deviation7.2866107 × 108
Coefficient of variation (CV)1.7002845
Kurtosis2.5233495
Mean4.2855244 × 108
Median Absolute Deviation (MAD)97379000
Skewness1.9592617
Sum2.0570517 × 1010
Variance5.3094695 × 1017
MonotonicityNot monotonic
2023-12-11T07:57:59.712187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
8000 2
 
4.2%
9000 2
 
4.2%
220758000 1
 
2.1%
17378000 1
 
2.1%
1975018000 1
 
2.1%
221491000 1
 
2.1%
131697000 1
 
2.1%
47492000 1
 
2.1%
1848000 1
 
2.1%
754653000 1
 
2.1%
Other values (36) 36
75.0%
ValueCountFrequency (%)
7000 1
2.1%
8000 2
4.2%
9000 2
4.2%
39000 1
2.1%
1461000 1
2.1%
1848000 1
2.1%
2208000 1
2.1%
3025000 1
2.1%
7936000 1
2.1%
10634000 1
2.1%
ValueCountFrequency (%)
2385683000 1
2.1%
2375071000 1
2.1%
2334875000 1
2.1%
2307080000 1
2.1%
1975018000 1
2.1%
1963264000 1
2.1%
827217000 1
2.1%
800890000 1
2.1%
754653000 1
2.1%
754447000 1
2.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2555519 × 109
Minimum0
Maximum1.9208463 × 1010
Zeros5
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T07:57:59.837522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.902105 × 108
median3.076067 × 109
Q37.44689 × 109
95-th percentile1.8442341 × 1010
Maximum1.9208463 × 1010
Range1.9208463 × 1010
Interquartile range (IQR)6.4566795 × 109

Descriptive statistics

Standard deviation5.7183474 × 109
Coefficient of variation (CV)1.0880584
Kurtosis0.55562153
Mean5.2555519 × 109
Median Absolute Deviation (MAD)2.4484925 × 109
Skewness1.216449
Sum2.5226649 × 1011
Variance3.2699497 × 1019
MonotonicityNot monotonic
2023-12-11T07:57:59.969759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 5
 
10.4%
18935443000 1
 
2.1%
6175053000 1
 
2.1%
1190665000 1
 
2.1%
17526581000 1
 
2.1%
10771066000 1
 
2.1%
1283450000 1
 
2.1%
943961000 1
 
2.1%
5493902000 1
 
2.1%
6513581000 1
 
2.1%
Other values (34) 34
70.8%
ValueCountFrequency (%)
0 5
10.4%
48236000 1
 
2.1%
765944000 1
 
2.1%
913142000 1
 
2.1%
943961000 1
 
2.1%
945587000 1
 
2.1%
977747000 1
 
2.1%
985619000 1
 
2.1%
991741000 1
 
2.1%
1091524000 1
 
2.1%
ValueCountFrequency (%)
19208463000 1
2.1%
19181784000 1
2.1%
18935443000 1
2.1%
17526581000 1
2.1%
16491397000 1
2.1%
12949698000 1
2.1%
10771066000 1
2.1%
10564068000 1
2.1%
10320834000 1
2.1%
10168298000 1
2.1%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.593125
Minimum0
Maximum68.55
Zeros11
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T07:58:00.114266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5025
median9.115
Q317.3475
95-th percentile64.5785
Maximum68.55
Range68.55
Interquartile range (IQR)14.845

Descriptive statistics

Standard deviation20.240173
Coefficient of variation (CV)1.2980191
Kurtosis2.1734884
Mean15.593125
Median Absolute Deviation (MAD)8.135
Skewness1.7983536
Sum748.47
Variance409.66462
MonotonicityNot monotonic
2023-12-11T07:58:00.230888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 11
22.9%
20.53 2
 
4.2%
17.0 2
 
4.2%
2.84 2
 
4.2%
2.67 1
 
2.1%
3.0 1
 
2.1%
11.0 1
 
2.1%
63.51 1
 
2.1%
9.18 1
 
2.1%
17.78 1
 
2.1%
Other values (25) 25
52.1%
ValueCountFrequency (%)
0.0 11
22.9%
2.0 1
 
2.1%
2.67 1
 
2.1%
2.7 1
 
2.1%
2.71 1
 
2.1%
2.84 2
 
4.2%
3.0 1
 
2.1%
3.53 1
 
2.1%
6.0 1
 
2.1%
7.61 1
 
2.1%
ValueCountFrequency (%)
68.55 1
2.1%
66.07 1
2.1%
64.89 1
2.1%
64.0 1
2.1%
63.79 1
2.1%
63.51 1
2.1%
22.62 1
2.1%
20.53 2
4.2%
19.31 1
2.1%
17.78 1
2.1%

Interactions

2023-12-11T07:57:57.022307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:54.671214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:55.264508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:55.878603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:56.439957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:57.149017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:54.792820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:55.367851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:55.988429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:56.536794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:57.268531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:54.914703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:55.489449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:56.095106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:56.645398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:57.376454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:55.005981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:55.623989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:56.216439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:56.754128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:57.491828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:55.122411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:55.761240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:56.340105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:56.891430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:58:00.323815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.7410.7890.9490.852
과세년도0.0001.0000.0000.0000.0000.000
비과세금액0.7410.0001.0000.8440.8340.733
감면금액0.7890.0000.8441.0000.8160.886
부과금액0.9490.0000.8340.8161.0000.820
비과세감면율0.8520.0000.7330.8860.8201.000
2023-12-11T07:58:00.433597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도비과세금액감면금액부과금액비과세감면율세목명
과세년도1.000-0.033-0.0050.102-0.1070.000
비과세금액-0.0331.0000.7160.3260.8500.515
감면금액-0.0050.7161.0000.6880.6760.619
부과금액0.1020.3260.6881.0000.2100.642
비과세감면율-0.1070.8500.6760.2101.0000.713
세목명0.0000.5150.6190.6420.7131.000

Missing values

2023-12-11T07:57:57.676258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:57:57.833123image/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
0경상남도거창군48880교육세20170800051257020000.0
1경상남도거창군48880등록세2017<NA>1063400000.0
2경상남도거창군48880재산세20172691326000800890000509442000068.55
3경상남도거창군48880주민세20171854190001690800098561900020.53
4경상남도거창군48880취득세201780890200023750710001649139700019.31
5경상남도거창군48880자동차세201752742000233110000105640680002.71
6경상남도거창군48880등록면허세20173601000166786000111656500015.26
7경상남도거창군48880지역자원시설세20171022900005498400076594400020.53
8경상남도거창군48880교육세20180900051143980000.0
9경상남도거창군48880등록세2018<NA>793600000.0
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
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40경상남도거창군48880교육세202203900055552170000.0
41경상남도거창군48880등록세2022<NA>146100000.0
42경상남도거창군48880재산세20223705327000827217000686065300066.07
43경상남도거창군48880주민세2022583900010182300012119140008.88
44경상남도거창군48880취득세202241203600019632640001920846300012.37
45경상남도거창군48880자동차세20225419400020761300092056010002.84
46경상남도거창군48880등록면허세202255340009322700012970730007.61
47경상남도거창군48880지역자원시설세20221179470005238500099174100017.18