Overview

Dataset statistics

Number of variables9
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory81.4 B

Variable types

Categorical5
Numeric4

Dataset

Description제공범위 : 연도별 지방세 과세 및 비과세 현황을 세목별로 제공. 관련 법령 : 지방세법. 소관기관 : 지방자치단체. 제공기관 : 시군구. 표준데이터 셋 제공시스템 : 표준지방세시스템. 자료기준일 : 매년 12월 31일.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=351&beforeMenuCd=DOM_000000201001001000&publicdatapk=15078554

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 3 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 3 other fieldsHigh correlation
비과세금액 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
과세건수 has 12 (30.8%) zerosZeros
과세금액 has 12 (30.8%) zerosZeros
비과세건수 has 15 (38.5%) zerosZeros
비과세금액 has 15 (38.5%) zerosZeros

Reproduction

Analysis started2024-01-09 21:36:09.853432
Analysis finished2024-01-09 21:36:11.305248
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
충청남도
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 39
100.0%

Length

2024-01-10T06:36:11.355104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:36:11.430589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 39
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
홍성군
39 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
홍성군 39
100.0%

Length

2024-01-10T06:36:11.506590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:36:11.577092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
홍성군 39
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
44800
39 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44800
2nd row44800
3rd row44800
4th row44800
5th row44800

Common Values

ValueCountFrequency (%)
44800 39
100.0%

Length

2024-01-10T06:36:11.649668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:36:11.724345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44800 39
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
2017
13 
2018
13 
2019
13 

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 13
33.3%
2018 13
33.3%
2019 13
33.3%

Length

2024-01-10T06:36:11.796106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:36:11.875160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 13
33.3%
2018 13
33.3%
2019 13
33.3%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size444.0 B
취득세
3 
등록세
3 
주민세
3 
재산세
3 
자동차세
3 
Other values (8)
24 

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 3
 
7.7%
등록세 3
 
7.7%
주민세 3
 
7.7%
재산세 3
 
7.7%
자동차세 3
 
7.7%
레저세 3
 
7.7%
담배소비세 3
 
7.7%
지방소비세 3
 
7.7%
등록면허세 3
 
7.7%
도시계획세 3
 
7.7%
Other values (3) 9
23.1%

Length

2024-01-10T06:36:11.970259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 3
 
7.7%
등록세 3
 
7.7%
주민세 3
 
7.7%
재산세 3
 
7.7%
자동차세 3
 
7.7%
레저세 3
 
7.7%
담배소비세 3
 
7.7%
지방소비세 3
 
7.7%
등록면허세 3
 
7.7%
도시계획세 3
 
7.7%
Other values (3) 9
23.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45691.718
Minimum0
Maximum234656
Zeros12
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-01-10T06:36:12.066223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26517
Q346315.5
95-th percentile231566.1
Maximum234656
Range234656
Interquartile range (IQR)46315.5

Descriptive statistics

Standard deviation64875.364
Coefficient of variation (CV)1.4198495
Kurtosis3.4997845
Mean45691.718
Median Absolute Deviation (MAD)26517
Skewness1.9889599
Sum1781977
Variance4.2088128 × 109
MonotonicityNot monotonic
2024-01-10T06:36:12.155815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 12
30.8%
16947 1
 
2.6%
40237 1
 
2.6%
234656 1
 
2.6%
29587 1
 
2.6%
34208 1
 
2.6%
41096 1
 
2.6%
84 1
 
2.6%
77704 1
 
2.6%
121152 1
 
2.6%
Other values (18) 18
46.2%
ValueCountFrequency (%)
0 12
30.8%
84 1
 
2.6%
90 1
 
2.6%
109 1
 
2.6%
16661 1
 
2.6%
16947 1
 
2.6%
17290 1
 
2.6%
23950 1
 
2.6%
26517 1
 
2.6%
29587 1
 
2.6%
ValueCountFrequency (%)
234656 1
2.6%
234186 1
2.6%
231275 1
2.6%
121152 1
2.6%
119148 1
2.6%
116484 1
2.6%
77704 1
2.6%
76754 1
2.6%
74945 1
2.6%
46428 1
2.6%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7497183 × 109
Minimum0
Maximum3.5263392 × 1010
Zeros12
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-01-10T06:36:12.247932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.395894 × 109
Q31.4984658 × 1010
95-th percentile2.6384681 × 1010
Maximum3.5263392 × 1010
Range3.5263392 × 1010
Interquartile range (IQR)1.4984658 × 1010

Descriptive statistics

Standard deviation9.0719974 × 109
Coefficient of variation (CV)1.1706229
Kurtosis1.0793647
Mean7.7497183 × 109
Median Absolute Deviation (MAD)2.395894 × 109
Skewness1.2329303
Sum3.0223901 × 1011
Variance8.2301136 × 1019
MonotonicityNot monotonic
2024-01-10T06:36:12.343835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 12
30.8%
35263392000 1
 
2.6%
2395894000 1
 
2.6%
10011336000 1
 
2.6%
15675490000 1
 
2.6%
2108230000 1
 
2.6%
2548132000 1
 
2.6%
7160561000 1
 
2.6%
16342731000 1
 
2.6%
16251813000 1
 
2.6%
Other values (18) 18
46.2%
ValueCountFrequency (%)
0 12
30.8%
1760053000 1
 
2.6%
1789728000 1
 
2.6%
1907700000 1
 
2.6%
1931187000 1
 
2.6%
1987695000 1
 
2.6%
2108230000 1
 
2.6%
2383446000 1
 
2.6%
2395894000 1
 
2.6%
2548132000 1
 
2.6%
ValueCountFrequency (%)
35263392000 1
2.6%
27986906000 1
2.6%
26206656000 1
2.6%
17932083000 1
2.6%
16607849000 1
2.6%
16342731000 1
2.6%
16251813000 1
2.6%
15881572000 1
2.6%
15675490000 1
2.6%
15129658000 1
2.6%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4408.3846
Minimum0
Maximum31274
Zeros15
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-01-10T06:36:12.438290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median83
Q34997
95-th percentile25656.6
Maximum31274
Range31274
Interquartile range (IQR)4997

Descriptive statistics

Standard deviation7979.0851
Coefficient of variation (CV)1.8099794
Kurtosis5.4540686
Mean4408.3846
Median Absolute Deviation (MAD)83
Skewness2.4165785
Sum171927
Variance63665800
MonotonicityNot monotonic
2024-01-10T06:36:12.524141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 15
38.5%
3858 1
 
2.6%
4751 1
 
2.6%
83 1
 
2.6%
1197 1
 
2.6%
5243 1
 
2.6%
12221 1
 
2.6%
31274 1
 
2.6%
8172 1
 
2.6%
20 1
 
2.6%
Other values (15) 15
38.5%
ValueCountFrequency (%)
0 15
38.5%
13 1
 
2.6%
20 1
 
2.6%
44 1
 
2.6%
72 1
 
2.6%
83 1
 
2.6%
84 1
 
2.6%
1197 1
 
2.6%
1308 1
 
2.6%
1463 1
 
2.6%
ValueCountFrequency (%)
31274 1
2.6%
29874 1
2.6%
25188 1
2.6%
12221 1
2.6%
12005 1
2.6%
10808 1
2.6%
8172 1
2.6%
6994 1
2.6%
5374 1
2.6%
5243 1
2.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4607463 × 109
Minimum0
Maximum9.715471 × 109
Zeros15
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-01-10T06:36:12.625092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10855000
Q34.907835 × 108
95-th percentile8.8353938 × 109
Maximum9.715471 × 109
Range9.715471 × 109
Interquartile range (IQR)4.907835 × 108

Descriptive statistics

Standard deviation3.1240322 × 109
Coefficient of variation (CV)2.1386549
Kurtosis2.2233621
Mean1.4607463 × 109
Median Absolute Deviation (MAD)10855000
Skewness2.006753
Sum5.6969104 × 1010
Variance9.7595771 × 1018
MonotonicityNot monotonic
2024-01-10T06:36:12.717046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 15
38.5%
6000 2
 
5.1%
8920019000 1
 
2.6%
204225000 1
 
2.6%
7000 1
 
2.6%
389279000 1
 
2.6%
203246000 1
 
2.6%
535434000 1
 
2.6%
9715471000 1
 
2.6%
525527000 1
 
2.6%
Other values (14) 14
35.9%
ValueCountFrequency (%)
0 15
38.5%
6000 2
 
5.1%
7000 1
 
2.6%
2684000 1
 
2.6%
10855000 1
 
2.6%
41116000 1
 
2.6%
203246000 1
 
2.6%
204225000 1
 
2.6%
248905000 1
 
2.6%
389279000 1
 
2.6%
ValueCountFrequency (%)
9715471000 1
2.6%
8920019000 1
2.6%
8825991000 1
2.6%
8449401000 1
2.6%
8363851000 1
2.6%
7656583000 1
2.6%
559255000 1
2.6%
543136000 1
2.6%
535434000 1
2.6%
525527000 1
2.6%

Interactions

2024-01-10T06:36:10.889074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.087124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.350952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.622926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.947799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.157140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.411120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.680858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:11.017320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.229847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.487284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.754879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:11.079290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.289294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.555836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:36:10.822345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:36:12.782804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9480.8580.9170.581
과세건수0.0000.9481.0000.6150.9760.550
과세금액0.0000.8580.6151.0000.5520.704
비과세건수0.0000.9170.9760.5521.0000.640
비과세금액0.0000.5810.5500.7040.6401.000
2024-01-10T06:36:12.863374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-01-10T06:36:12.933622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.6650.7640.6210.0000.747
과세금액0.6651.0000.5390.5820.0000.559
비과세건수0.7640.5391.0000.9400.0000.677
비과세금액0.6210.5820.9401.0000.0000.315
과세년도0.0000.0000.0000.0001.0000.000
세목명0.7470.5590.6770.3150.0001.000

Missing values

2024-01-10T06:36:11.166122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:36:11.266260image/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
0충청남도홍성군448002017취득세169473526339200038588920019000
1충청남도홍성군448002017등록세00442684000
2충청남도홍성군448002017주민세4612517600530005374454086000
3충청남도홍성군448002017재산세11648415129658000251888825991000
4충청남도홍성군448002017자동차세749451793208300010808559255000
5충청남도홍성군448002017레저세0000
6충청남도홍성군448002017담배소비세109745377300000
7충청남도홍성군448002017지방소비세0000
8충청남도홍성군448002017등록면허세3906123834460004218248905000
9충청남도홍성군448002017도시계획세0000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
29충청남도홍성군448002019재산세12115216251813000312749715471000
30충청남도홍성군448002019자동차세777041634273100012221535434000
31충청남도홍성군448002019레저세0000
32충청남도홍성군448002019담배소비세84716056100000
33충청남도홍성군448002019지방소비세0000
34충청남도홍성군448002019등록면허세4109625481320005243203246000
35충청남도홍성군448002019도시계획세0000
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