Overview

Dataset statistics

Number of variables9
Number of observations77
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory79.7 B

Variable types

Categorical4
Numeric5

Dataset

Description2017년부터 2022년까지 지방세 과세현황의 세목명, 과세건수, 과세금액, 비과세건수, 비과세금액에 대한 정보
Author경상남도 통영시
URLhttps://www.data.go.kr/data/15078532/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세건수(건) is highly overall correlated with 과세금액(원) and 3 other fieldsHigh correlation
과세금액(원) is highly overall correlated with 과세건수(건) and 2 other fieldsHigh correlation
비과세건수(건) is highly overall correlated with 과세건수(건) and 2 other fieldsHigh correlation
비과세금액(원) is highly overall correlated with 과세건수(건) and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수(건) and 2 other fieldsHigh correlation
과세건수(건) has 19 (24.7%) zerosZeros
과세금액(원) has 19 (24.7%) zerosZeros
비과세건수(건) has 29 (37.7%) zerosZeros
비과세금액(원) has 29 (37.7%) zerosZeros

Reproduction

Analysis started2024-04-21 01:47:29.075098
Analysis finished2024-04-21 01:47:33.537011
Duration4.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size748.0 B
경상남도
77 

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 77
100.0%

Length

2024-04-21T10:47:33.593550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:47:33.672683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 77
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size748.0 B
통영시
77 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통영시 77
100.0%

Length

2024-04-21T10:47:33.757623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:47:33.860261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 77
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size748.0 B
48220
77 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48220
2nd row48220
3rd row48220
4th row48220
5th row48220

Common Values

ValueCountFrequency (%)
48220 77
100.0%

Length

2024-04-21T10:47:33.954261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:47:34.037778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48220 77
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4935
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-04-21T10:47:34.115799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation1.7291873
Coefficient of variation (CV)0.00085624803
Kurtosis-1.2924608
Mean2019.4935
Median Absolute Deviation (MAD)2
Skewness0.011239594
Sum155501
Variance2.9900889
MonotonicityIncreasing
2024-04-21T10:47:34.254026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 13
16.9%
2018 13
16.9%
2019 13
16.9%
2021 13
16.9%
2022 13
16.9%
2020 12
15.6%
ValueCountFrequency (%)
2017 13
16.9%
2018 13
16.9%
2019 13
16.9%
2020 12
15.6%
2021 13
16.9%
2022 13
16.9%
ValueCountFrequency (%)
2022 13
16.9%
2021 13
16.9%
2020 12
15.6%
2019 13
16.9%
2018 13
16.9%
2017 13
16.9%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size748.0 B
취득세
6 
등록세
6 
주민세
6 
재산세
6 
자동차세
6 
Other values (8)
47 

Length

Max length7
Median length5
Mean length4.1428571
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 6
 
7.8%
등록세 6
 
7.8%
주민세 6
 
7.8%
재산세 6
 
7.8%
자동차세 6
 
7.8%
레저세 6
 
7.8%
담배소비세 6
 
7.8%
지방소비세 6
 
7.8%
등록면허세 6
 
7.8%
지역자원시설세 6
 
7.8%
Other values (3) 17
22.1%

Length

2024-04-21T10:47:34.404599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 6
 
7.8%
등록세 6
 
7.8%
주민세 6
 
7.8%
재산세 6
 
7.8%
자동차세 6
 
7.8%
레저세 6
 
7.8%
담배소비세 6
 
7.8%
지방소비세 6
 
7.8%
등록면허세 6
 
7.8%
지역자원시설세 6
 
7.8%
Other values (3) 17
22.1%

과세건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55088.494
Minimum0
Maximum284162
Zeros19
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-04-21T10:47:34.542045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median32807
Q368788
95-th percentile274864
Maximum284162
Range284162
Interquartile range (IQR)68782

Descriptive statistics

Standard deviation74957.521
Coefficient of variation (CV)1.3606747
Kurtosis3.8491267
Mean55088.494
Median Absolute Deviation (MAD)32807
Skewness2.0354922
Sum4241814
Variance5.61863 × 109
MonotonicityNot monotonic
2024-04-21T10:47:34.678585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
24.7%
16925 1
 
1.3%
274 1
 
1.3%
6 1
 
1.3%
53515 1
 
1.3%
68788 1
 
1.3%
32807 1
 
1.3%
282730 1
 
1.3%
16854 1
 
1.3%
60159 1
 
1.3%
Other values (49) 49
63.6%
ValueCountFrequency (%)
0 19
24.7%
6 1
 
1.3%
7 1
 
1.3%
9 1
 
1.3%
45 1
 
1.3%
83 1
 
1.3%
88 1
 
1.3%
107 1
 
1.3%
274 1
 
1.3%
479 1
 
1.3%
ValueCountFrequency (%)
284162 1
1.3%
282730 1
1.3%
282268 1
1.3%
277976 1
1.3%
274086 1
1.3%
272429 1
1.3%
116000 1
1.3%
114702 1
1.3%
114677 1
1.3%
114424 1
1.3%

과세금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3607372 × 109
Minimum0
Maximum4.2969276 × 1010
Zeros19
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-04-21T10:47:34.950174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q167432000
median5.194805 × 109
Q31.4941487 × 1010
95-th percentile2.7160715 × 1010
Maximum4.2969276 × 1010
Range4.2969276 × 1010
Interquartile range (IQR)1.4874055 × 1010

Descriptive statistics

Standard deviation9.4130598 × 109
Coefficient of variation (CV)1.0055896
Kurtosis1.0250144
Mean9.3607372 × 109
Median Absolute Deviation (MAD)5.194805 × 109
Skewness1.0603515
Sum7.2077676 × 1011
Variance8.8605695 × 1019
MonotonicityNot monotonic
2024-04-21T10:47:35.088796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
24.7%
29599210000 1
 
1.3%
10744373000 1
 
1.3%
5430000000 1
 
1.3%
3244778000 1
 
1.3%
5056720000 1
 
1.3%
13014996000 1
 
1.3%
12146918000 1
 
1.3%
28038509000 1
 
1.3%
2113367000 1
 
1.3%
Other values (49) 49
63.6%
ValueCountFrequency (%)
0 19
24.7%
67432000 1
 
1.3%
2011936000 1
 
1.3%
2113367000 1
 
1.3%
2251581000 1
 
1.3%
2268935000 1
 
1.3%
2280887000 1
 
1.3%
2492014000 1
 
1.3%
2927221000 1
 
1.3%
3046957000 1
 
1.3%
ValueCountFrequency (%)
42969276000 1
1.3%
31771634000 1
1.3%
29599210000 1
1.3%
28038509000 1
1.3%
26941267000 1
1.3%
25958307000 1
1.3%
20915098000 1
1.3%
20802206000 1
1.3%
20632500000 1
1.3%
20530854000 1
1.3%

비과세건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4864.1818
Minimum0
Maximum34470
Zeros29
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-04-21T10:47:35.226922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36
Q34815
95-th percentile30119.2
Maximum34470
Range34470
Interquartile range (IQR)4815

Descriptive statistics

Standard deviation8713.6829
Coefficient of variation (CV)1.7913974
Kurtosis5.3724131
Mean4864.1818
Median Absolute Deviation (MAD)36
Skewness2.4387974
Sum374542
Variance75928270
MonotonicityNot monotonic
2024-04-21T10:47:35.353450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 29
37.7%
3 3
 
3.9%
36 2
 
2.6%
2774 1
 
1.3%
34182 1
 
1.3%
12355 1
 
1.3%
33787 1
 
1.3%
10377 1
 
1.3%
3267 1
 
1.3%
4599 1
 
1.3%
Other values (36) 36
46.8%
ValueCountFrequency (%)
0 29
37.7%
2 1
 
1.3%
3 3
 
3.9%
6 1
 
1.3%
17 1
 
1.3%
19 1
 
1.3%
28 1
 
1.3%
36 2
 
2.6%
40 1
 
1.3%
137 1
 
1.3%
ValueCountFrequency (%)
34470 1
1.3%
34182 1
1.3%
33787 1
1.3%
33116 1
1.3%
29370 1
1.3%
25898 1
1.3%
13382 1
1.3%
12475 1
1.3%
12355 1
1.3%
11046 1
1.3%

비과세금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1353925 × 109
Minimum0
Maximum9.203382 × 109
Zeros29
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-04-21T10:47:35.482703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median640000
Q32.97691 × 108
95-th percentile8.023254 × 109
Maximum9.203382 × 109
Range9.203382 × 109
Interquartile range (IQR)2.97691 × 108

Descriptive statistics

Standard deviation2.5680928 × 109
Coefficient of variation (CV)2.2618545
Kurtosis3.6300888
Mean1.1353925 × 109
Median Absolute Deviation (MAD)640000
Skewness2.270306
Sum8.7425226 × 1010
Variance6.5951006 × 1018
MonotonicityNot monotonic
2024-04-21T10:47:35.602423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 29
37.7%
2000 3
 
3.9%
3000 2
 
2.6%
102867000 1
 
1.3%
8203394000 1
 
1.3%
439675000 1
 
1.3%
108603000 1
 
1.3%
283508000 1
 
1.3%
3836266000 1
 
1.3%
640000 1
 
1.3%
Other values (36) 36
46.8%
ValueCountFrequency (%)
0 29
37.7%
2000 3
 
3.9%
3000 2
 
2.6%
23000 1
 
1.3%
74000 1
 
1.3%
125000 1
 
1.3%
610000 1
 
1.3%
640000 1
 
1.3%
3445000 1
 
1.3%
23260000 1
 
1.3%
ValueCountFrequency (%)
9203382000 1
1.3%
8679023000 1
1.3%
8478555000 1
1.3%
8203394000 1
1.3%
7978219000 1
1.3%
7878774000 1
1.3%
7545724000 1
1.3%
7255250000 1
1.3%
4772996000 1
1.3%
3936505000 1
1.3%

Interactions

2024-04-21T10:47:32.883345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:30.640540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.292748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.704402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:32.351888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:32.962048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:30.800363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.383687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.839175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:32.453037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:33.097073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.033128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.462421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.954877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:32.568573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:33.200307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.114919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.535049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:32.123417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:32.674419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:33.277433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.208925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:31.626941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:32.248351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:32.784774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:47:35.678633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수(건)과세금액(원)비과세건수(건)비과세금액(원)
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9490.9040.8370.614
과세건수(건)0.0000.9491.0000.6540.8420.559
과세금액(원)0.0000.9040.6541.0000.6120.740
비과세건수(건)0.0000.8370.8420.6121.0000.877
비과세금액(원)0.0000.6140.5590.7400.8771.000
2024-04-21T10:47:35.777864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도과세건수(건)과세금액(원)비과세건수(건)비과세금액(원)세목명
과세년도1.0000.0250.0450.0490.0120.000
과세건수(건)0.0251.0000.6200.7440.6050.802
과세금액(원)0.0450.6201.0000.3930.5210.687
비과세건수(건)0.0490.7440.3931.0000.9210.565
비과세금액(원)0.0120.6050.5210.9211.0000.324
세목명0.0000.8020.6870.5650.3241.000

Missing values

2024-04-21T10:47:33.374086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:47:33.489117image/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

시도명시군구명자치단체코드과세년도세목명과세건수(건)과세금액(원)비과세건수(건)비과세금액(원)
0경상남도통영시482202017취득세169252959921000027747878774000
1경상남도통영시482202017등록세001723260000
2경상남도통영시482202017주민세6283524920140006188163984000
3경상남도통영시482202017재산세10650418842572000258987255250000
4경상남도통영시482202017자동차세85382168202910007423476988000
5경상남도통영시482202017레저세0000
6경상남도통영시482202017담배소비세1071210692700000
7경상남도통영시482202017지방소비세0000
8경상남도통영시482202017등록면허세5283929272210002629152470000
9경상남도통영시482202017도시계획세0000
시도명시군구명자치단체코드과세년도세목명과세건수(건)과세금액(원)비과세건수(건)비과세금액(원)
67경상남도통영시482202022재산세11600020915098000344709203382000
68경상남도통영시482202022자동차세841641473857500011046408570000
69경상남도통영시482202022레저세456743200000
70경상남도통영시482202022담배소비세6391062715100000
71경상남도통영시482202022지방소비세91086842300000
72경상남도통영시482202022등록면허세483153361364000433974373000
73경상남도통영시482202022도시계획세0000
74경상남도통영시482202022지역자원시설세6120748315010004513299623000
75경상남도통영시482202022지방소득세444801548133200000
76경상남도통영시482202022교육세2724291215670100013723000