Overview

Dataset statistics

Number of variables9
Number of observations73
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory79.8 B

Variable types

Categorical4
Numeric5

Dataset

Description연도별 지방세 과세 및 비과세 현황을 세목별로 제공국민 조세 혜택 규모를 파악하는 데 사용과세건수 및 금액 : 지방세 부과된 과세건수 및 금액의 합계비과세건수 및 금액 : 지방세를 부과하지 않은 건수 및 금액의 합계
Author경상북도 경산시
URLhttps://www.data.go.kr/data/15079714/fileData.do

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 15 (20.5%) zerosZeros
과세금액 has 15 (20.5%) zerosZeros
비과세건수 has 27 (37.0%) zerosZeros
비과세금액 has 27 (37.0%) zerosZeros

Reproduction

Analysis started2024-03-16 06:32:37.195365
Analysis finished2024-03-16 06:32:51.452130
Duration14.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size716.0 B
경상북도
73 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 73
100.0%

Length

2024-03-16T06:32:51.639917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:32:52.056973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 73
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size716.0 B
경산시
73 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경산시 73
100.0%

Length

2024-03-16T06:32:52.323483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:32:52.660737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경산시 73
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size716.0 B
47290
73 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47290
2nd row47290
3rd row47290
4th row47290
5th row47290

Common Values

ValueCountFrequency (%)
47290 73
100.0%

Length

2024-03-16T06:32:53.281969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:32:53.576553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47290 73
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5068
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-16T06:32:53.916659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7648047
Coefficient of variation (CV)0.00087387906
Kurtosis-1.3492864
Mean2019.5068
Median Absolute Deviation (MAD)2
Skewness-0.019440521
Sum147424
Variance3.1145358
MonotonicityDecreasing
2024-03-16T06:32:54.316226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 13
17.8%
2020 13
17.8%
2018 13
17.8%
2017 13
17.8%
2021 12
16.4%
2019 9
12.3%
ValueCountFrequency (%)
2017 13
17.8%
2018 13
17.8%
2019 9
12.3%
2020 13
17.8%
2021 12
16.4%
2022 13
17.8%
ValueCountFrequency (%)
2022 13
17.8%
2021 12
16.4%
2020 13
17.8%
2019 9
12.3%
2018 13
17.8%
2017 13
17.8%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size716.0 B
취득세
6 
주민세
6 
재산세
6 
자동차세
6 
담배소비세
6 
Other values (8)
43 

Length

Max length7
Median length5
Mean length4.1780822
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 6
8.2%
주민세 6
8.2%
재산세 6
8.2%
자동차세 6
8.2%
담배소비세 6
8.2%
등록면허세 6
8.2%
지역자원시설세 6
8.2%
지방소득세 6
8.2%
교육세 6
8.2%
레저세 5
 
6.8%
Other values (3) 14
19.2%

Length

2024-03-16T06:32:54.799441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 6
8.2%
주민세 6
8.2%
재산세 6
8.2%
자동차세 6
8.2%
담배소비세 6
8.2%
등록면허세 6
8.2%
지역자원시설세 6
8.2%
지방소득세 6
8.2%
교육세 6
8.2%
레저세 5
 
6.8%
Other values (3) 14
19.2%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116323.26
Minimum0
Maximum590806
Zeros15
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-16T06:32:55.295518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145
median83415
Q3157273
95-th percentile551116.6
Maximum590806
Range590806
Interquartile range (IQR)157228

Descriptive statistics

Standard deviation150795.39
Coefficient of variation (CV)1.2963477
Kurtosis3.5832227
Mean116323.26
Median Absolute Deviation (MAD)83370
Skewness1.9547686
Sum8491598
Variance2.2739248 × 1010
MonotonicityNot monotonic
2024-03-16T06:32:55.737317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
20.5%
33739 1
 
1.4%
38019 1
 
1.4%
121367 1
 
1.4%
187712 1
 
1.4%
214256 1
 
1.4%
83 1
 
1.4%
97352 1
 
1.4%
144332 1
 
1.4%
73314 1
 
1.4%
Other values (49) 49
67.1%
ValueCountFrequency (%)
0 15
20.5%
6 1
 
1.4%
7 1
 
1.4%
9 1
 
1.4%
45 1
 
1.4%
83 1
 
1.4%
86 1
 
1.4%
107 1
 
1.4%
276 1
 
1.4%
477 1
 
1.4%
ValueCountFrequency (%)
590806 1
1.4%
572310 1
1.4%
570450 1
1.4%
558829 1
1.4%
545975 1
1.4%
468176 1
1.4%
220248 1
1.4%
219600 1
1.4%
218770 1
1.4%
214256 1
1.4%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9149141 × 1010
Minimum0
Maximum1.4438989 × 1011
Zeros15
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-16T06:32:56.273347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.269323 × 109
median1.9505092 × 1010
Q34.816586 × 1010
95-th percentile1.107772 × 1011
Maximum1.4438989 × 1011
Range1.4438989 × 1011
Interquartile range (IQR)4.1896537 × 1010

Descriptive statistics

Standard deviation3.3555895 × 1010
Coefficient of variation (CV)1.1511795
Kurtosis2.3233147
Mean2.9149141 × 1010
Median Absolute Deviation (MAD)1.9505092 × 1010
Skewness1.5736824
Sum2.1278873 × 1012
Variance1.1259981 × 1021
MonotonicityNot monotonic
2024-03-16T06:32:56.839739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
20.5%
89415469000 1
 
1.4%
110177512000 1
 
1.4%
8980167000 1
 
1.4%
51044775000 1
 
1.4%
62181102000 1
 
1.4%
19505092000 1
 
1.4%
8053287000 1
 
1.4%
6370627000 1
 
1.4%
48165860000 1
 
1.4%
Other values (49) 49
67.1%
ValueCountFrequency (%)
0 15
20.5%
146316000 1
 
1.4%
5052253000 1
 
1.4%
5758626000 1
 
1.4%
6269323000 1
 
1.4%
6370627000 1
 
1.4%
6608676000 1
 
1.4%
6884005000 1
 
1.4%
6977076000 1
 
1.4%
6999105000 1
 
1.4%
ValueCountFrequency (%)
144389891000 1
1.4%
126416306000 1
1.4%
119500691000 1
1.4%
111676744000 1
1.4%
110177512000 1
1.4%
89415469000 1
1.4%
62181102000 1
1.4%
60161804000 1
1.4%
58583464000 1
1.4%
58128711000 1
1.4%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11241.233
Minimum0
Maximum120278
Zeros27
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-16T06:32:57.566532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2106
Q312039
95-th percentile52154
Maximum120278
Range120278
Interquartile range (IQR)12039

Descriptive statistics

Standard deviation20290.78
Coefficient of variation (CV)1.8050316
Kurtosis11.192247
Mean11241.233
Median Absolute Deviation (MAD)2106
Skewness2.9109833
Sum820610
Variance4.1171577 × 108
MonotonicityNot monotonic
2024-03-16T06:32:58.036151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 27
37.0%
2 2
 
2.7%
3816 1
 
1.4%
28946 1
 
1.4%
4005 1
 
1.4%
2106 1
 
1.4%
19 1
 
1.4%
12104 1
 
1.4%
15 1
 
1.4%
12039 1
 
1.4%
Other values (36) 36
49.3%
ValueCountFrequency (%)
0 27
37.0%
2 2
 
2.7%
15 1
 
1.4%
16 1
 
1.4%
19 1
 
1.4%
20 1
 
1.4%
23 1
 
1.4%
27 1
 
1.4%
114 1
 
1.4%
2106 1
 
1.4%
ValueCountFrequency (%)
120278 1
1.4%
59843 1
1.4%
57142 1
1.4%
55175 1
1.4%
50140 1
1.4%
45298 1
1.4%
36809 1
1.4%
35025 1
1.4%
33036 1
1.4%
30040 1
1.4%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5717836 × 109
Minimum0
Maximum2.7926893 × 1010
Zeros27
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-16T06:32:59.028845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23986000
Q31.351941 × 109
95-th percentile2.1672397 × 1010
Maximum2.7926893 × 1010
Range2.7926893 × 1010
Interquartile range (IQR)1.351941 × 109

Descriptive statistics

Standard deviation7.608963 × 109
Coefficient of variation (CV)2.1302979
Kurtosis2.6050101
Mean3.5717836 × 109
Median Absolute Deviation (MAD)23986000
Skewness2.0427066
Sum2.607402 × 1011
Variance5.7896318 × 1019
MonotonicityNot monotonic
2024-03-16T06:32:59.704865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 27
37.0%
1000 2
 
2.7%
2000 2
 
2.7%
27926893000 1
 
1.4%
1507607000 1
 
1.4%
1408956000 1
 
1.4%
243832000 1
 
1.4%
811023000 1
 
1.4%
21586444000 1
 
1.4%
21452000 1
 
1.4%
Other values (35) 35
47.9%
ValueCountFrequency (%)
0 27
37.0%
1000 2
 
2.7%
2000 2
 
2.7%
28000 1
 
1.4%
432000 1
 
1.4%
1635000 1
 
1.4%
21452000 1
 
1.4%
23360000 1
 
1.4%
23986000 1
 
1.4%
124554000 1
 
1.4%
ValueCountFrequency (%)
27926893000 1
1.4%
25175164000 1
1.4%
23291459000 1
1.4%
21801327000 1
1.4%
21586444000 1
1.4%
21210556000 1
1.4%
20527152000 1
1.4%
18992600000 1
1.4%
17723632000 1
1.4%
17591759000 1
1.4%

Interactions

2024-03-16T06:32:48.910018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:41.792071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:44.101956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:45.875531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:47.427238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:49.145096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:42.245110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:44.402312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:46.220863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:47.706200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:49.386432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:42.854603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:44.837879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:46.493305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:47.969352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:49.723888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:43.282503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:45.216724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:46.765661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:48.234321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:50.065349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:43.726879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:45.546327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:47.106912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T06:32:48.637664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T06:33:00.072948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9360.8410.7310.643
과세건수0.0000.9361.0000.7130.7110.000
과세금액0.0000.8410.7131.0000.4190.836
비과세건수0.0000.7310.7110.4191.0000.761
비과세금액0.0000.6430.0000.8360.7611.000
2024-03-16T06:33:00.469208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도과세건수과세금액비과세건수비과세금액세목명
과세년도1.0000.0570.0860.0110.0210.000
과세건수0.0571.0000.5400.6580.5430.767
과세금액0.0860.5401.0000.4300.4600.551
비과세건수0.0110.6580.4301.0000.9280.446
비과세금액0.0210.5430.4600.9281.0000.362
세목명0.0000.7670.5510.4460.3621.000

Missing values

2024-03-16T06:32:50.558403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T06:32:51.286094image/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
0경상북도경산시472902022취득세33739894154690001009727926893000
1경상북도경산시472902022등록세002432000
2경상북도경산시472902022주민세114088868649400028132864433000
3경상북도경산시472902022재산세200599601618040005984321801327000
4경상북도경산시472902022자동차세21877044197541000368091435267000
5경상북도경산시472902022레저세4514631600000
6경상북도경산시472902022담배소비세6712091888800000
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