Overview

Dataset statistics

Number of variables9
Number of observations78
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory79.7 B

Variable types

Categorical4
Numeric5

Dataset

Description경상남도 거창군 지방세 과세현황에 대한 데이터로 과세년도, 세목명, 과세건수, 과세금액 비과세건수, 비과세금액 항목을 제공합니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079161

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 1 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
비과세금액 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
과세건수 has 20 (25.6%) zerosZeros
과세금액 has 20 (25.6%) zerosZeros
비과세건수 has 30 (38.5%) zerosZeros
비과세금액 has 30 (38.5%) zerosZeros

Reproduction

Analysis started2023-12-10 23:23:00.361478
Analysis finished2023-12-10 23:23:03.676318
Duration3.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size756.0 B
경상남도
78 

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 78
100.0%

Length

2023-12-11T08:23:04.059931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:23:04.171049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 78
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size756.0 B
거창군
78 

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 78
100.0%

Length

2023-12-11T08:23:04.267363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:23:04.370511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 78
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size756.0 B
48880
78 

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 78
100.0%

Length

2023-12-11T08:23:04.460476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:23:04.563549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 78
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:23:04.656816image/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 deviation1.7188791
Coefficient of variation (CV)0.00085114094
Kurtosis-1.2727579
Mean2019.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum157521
Variance2.9545455
MonotonicityIncreasing
2023-12-11T08:23:04.758920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 13
16.7%
2018 13
16.7%
2019 13
16.7%
2020 13
16.7%
2021 13
16.7%
2022 13
16.7%
ValueCountFrequency (%)
2017 13
16.7%
2018 13
16.7%
2019 13
16.7%
2020 13
16.7%
2021 13
16.7%
2022 13
16.7%
ValueCountFrequency (%)
2022 13
16.7%
2021 13
16.7%
2020 13
16.7%
2019 13
16.7%
2018 13
16.7%
2017 13
16.7%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size756.0 B
취득세
6 
등록세
6 
주민세
6 
재산세
6 
자동차세
6 
Other values (8)
48 

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 6
 
7.7%
등록세 6
 
7.7%
주민세 6
 
7.7%
재산세 6
 
7.7%
자동차세 6
 
7.7%
레저세 6
 
7.7%
담배소비세 6
 
7.7%
지방소비세 6
 
7.7%
등록면허세 6
 
7.7%
도시계획세 6
 
7.7%
Other values (3) 18
23.1%

Length

2023-12-11T08:23:04.896113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 6
 
7.7%
등록세 6
 
7.7%
주민세 6
 
7.7%
재산세 6
 
7.7%
자동차세 6
 
7.7%
레저세 6
 
7.7%
담배소비세 6
 
7.7%
지방소비세 6
 
7.7%
등록면허세 6
 
7.7%
도시계획세 6
 
7.7%
Other values (3) 18
23.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29908.795
Minimum0
Maximum157460
Zeros20
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:23:05.032305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5
median14358
Q330518.75
95-th percentile148950.25
Maximum157460
Range157460
Interquartile range (IQR)30517.25

Descriptive statistics

Standard deviation43060.96
Coefficient of variation (CV)1.4397424
Kurtosis2.8214295
Mean29908.795
Median Absolute Deviation (MAD)14358
Skewness1.9078094
Sum2332886
Variance1.8542463 × 109
MonotonicityNot monotonic
2023-12-11T08:23:05.180735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
25.6%
21186 2
 
2.6%
89595 1
 
1.3%
45902 1
 
1.3%
276 1
 
1.3%
6 1
 
1.3%
24979 1
 
1.3%
14275 1
 
1.3%
153635 1
 
1.3%
14441 1
 
1.3%
Other values (48) 48
61.5%
ValueCountFrequency (%)
0 20
25.6%
6 1
 
1.3%
7 1
 
1.3%
9 1
 
1.3%
45 1
 
1.3%
81 1
 
1.3%
87 1
 
1.3%
109 1
 
1.3%
276 1
 
1.3%
471 1
 
1.3%
ValueCountFrequency (%)
157460 1
1.3%
157301 1
1.3%
153635 1
1.3%
149320 1
1.3%
148885 1
1.3%
147832 1
1.3%
90926 1
1.3%
89595 1
1.3%
89594 1
1.3%
88237 1
1.3%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3582457 × 109
Minimum0
Maximum1.9208463 × 1010
Zeros20
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:23:05.314196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18399500
median2.6157905 × 109
Q36.4064692 × 109
95-th percentile1.665391 × 1010
Maximum1.9208463 × 1010
Range1.9208463 × 1010
Interquartile range (IQR)6.3980698 × 109

Descriptive statistics

Standard deviation5.0231148 × 109
Coefficient of variation (CV)1.1525543
Kurtosis1.6829928
Mean4.3582457 × 109
Median Absolute Deviation (MAD)2.6157905 × 109
Skewness1.4231217
Sum3.3994316 × 1011
Variance2.5231682 × 1019
MonotonicityNot monotonic
2023-12-11T08:23:05.454995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
25.6%
16491397000 1
 
1.3%
4138694000 1
 
1.3%
5376600000 1
 
1.3%
1283450000 1
 
1.3%
943961000 1
 
1.3%
7175553000 1
 
1.3%
5377549000 1
 
1.3%
18935443000 1
 
1.3%
1134642000 1
 
1.3%
Other values (49) 49
62.8%
ValueCountFrequency (%)
0 20
25.6%
33598000 1
 
1.3%
765944000 1
 
1.3%
913142000 1
 
1.3%
943961000 1
 
1.3%
945587000 1
 
1.3%
977747000 1
 
1.3%
985619000 1
 
1.3%
991741000 1
 
1.3%
1091524000 1
 
1.3%
ValueCountFrequency (%)
19208463000 1
1.3%
19181784000 1
1.3%
18935443000 1
1.3%
17574816000 1
1.3%
16491397000 1
1.3%
12949698000 1
1.3%
10771066000 1
1.3%
10564068000 1
1.3%
10320834000 1
1.3%
10168298000 1
1.3%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4044.6282
Minimum0
Maximum35195
Zeros30
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:23:05.577474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median108
Q34044.75
95-th percentile24246.35
Maximum35195
Range35195
Interquartile range (IQR)4044.75

Descriptive statistics

Standard deviation7845.6187
Coefficient of variation (CV)1.9397626
Kurtosis7.3816483
Mean4044.6282
Median Absolute Deviation (MAD)108
Skewness2.7666139
Sum315481
Variance61553733
MonotonicityNot monotonic
2023-12-11T08:23:05.688875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 30
38.5%
5 2
 
2.6%
8054 2
 
2.6%
4069 1
 
1.3%
6844 1
 
1.3%
29110 1
 
1.3%
7664 1
 
1.3%
3456 1
 
1.3%
1499 1
 
1.3%
95 1
 
1.3%
Other values (37) 37
47.4%
ValueCountFrequency (%)
0 30
38.5%
5 2
 
2.6%
10 1
 
1.3%
15 1
 
1.3%
25 1
 
1.3%
36 1
 
1.3%
95 1
 
1.3%
97 1
 
1.3%
107 1
 
1.3%
109 1
 
1.3%
ValueCountFrequency (%)
35195 1
1.3%
35140 1
1.3%
29110 1
1.3%
26736 1
1.3%
23807 1
1.3%
23052 1
1.3%
9649 1
1.3%
9595 1
1.3%
8551 1
1.3%
8054 2
2.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8341242 × 108
Minimum0
Maximum4.532544 × 109
Zeros30
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:23:05.805767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2616500
Q31.85555 × 108
95-th percentile3.6461626 × 109
Maximum4.532544 × 109
Range4.532544 × 109
Interquartile range (IQR)1.85555 × 108

Descriptive statistics

Standard deviation1.2551351 × 109
Coefficient of variation (CV)2.1513686
Kurtosis2.6436292
Mean5.8341242 × 108
Median Absolute Deviation (MAD)2616500
Skewness2.0726224
Sum4.5506169 × 1010
Variance1.5753642 × 1018
MonotonicityNot monotonic
2023-12-11T08:23:05.921055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 30
38.5%
8000 2
 
2.6%
9000 2
 
2.6%
3183973000 1
 
1.3%
275166000 1
 
1.3%
273784000 1
 
1.3%
136481000 1
 
1.3%
156784000 1
 
1.3%
7000 1
 
1.3%
3367355000 1
 
1.3%
Other values (37) 37
47.4%
ValueCountFrequency (%)
0 30
38.5%
7000 1
 
1.3%
8000 2
 
2.6%
9000 2
 
2.6%
39000 1
 
1.3%
1461000 1
 
1.3%
1848000 1
 
1.3%
2208000 1
 
1.3%
3025000 1
 
1.3%
7936000 1
 
1.3%
ValueCountFrequency (%)
4532544000 1
1.3%
4137077000 1
1.3%
3970316000 1
1.3%
3769099000 1
1.3%
3624468000 1
1.3%
3492216000 1
1.3%
3367355000 1
1.3%
3183973000 1
1.3%
3133637000 1
1.3%
2929484000 1
1.3%

Interactions

2023-12-11T08:23:02.802120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:00.610508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:01.152795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:01.699042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:02.278894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:02.911262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:00.711499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:01.244409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:01.791304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:02.382570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:03.057415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:00.824924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:01.355954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:01.906317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:02.493353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:03.179170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:00.944789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:01.485082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:02.029201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:02.597907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:03.306630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:01.057537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:01.593892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:02.155031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:02.704752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:23:05.993541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9540.8610.7630.719
과세건수0.0000.9541.0000.7020.7340.734
과세금액0.0000.8610.7021.0000.3830.730
비과세건수0.0000.7630.7340.3831.0000.830
비과세금액0.0000.7190.7340.7300.8301.000
2023-12-11T08:23:06.079496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도과세건수과세금액비과세건수비과세금액세목명
과세년도1.0000.0730.1410.014-0.0280.000
과세건수0.0731.0000.5910.7590.6220.817
과세금액0.1410.5911.0000.3890.4530.602
비과세건수0.0140.7590.3891.0000.9210.466
비과세금액-0.0280.6220.4530.9211.0000.436
세목명0.0000.8170.6020.4660.4361.000

Missing values

2023-12-11T08:23:03.457581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:23:03.621715image/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
0경상남도거창군488802017취득세121921649139700029613183973000
1경상남도거창군488802017등록세003610634000
2경상남도거창군488802017주민세296759856190007518202327000
3경상남도거창군488802017재산세843185094420000230523492216000
4경상남도거창군488802017자동차세42688105640680006285285852000
5경상남도거창군488802017레저세0000
6경상남도거창군488802017담배소비세109443012100000
7경상남도거창군488802017지방소비세0000
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9경상남도거창군488802017도시계획세0000
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70경상남도거창군488802022레저세453359800000
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74경상남도거창군488802022도시계획세0000
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