Overview

Dataset statistics

Number of variables11
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory94.2 B

Variable types

Categorical6
Text4
Numeric1

Dataset

Description영월군 지방세 부과액에 대한 세목별 징수현화을 제공하여 영월군의 재정자주도, 재정자립도를 산출하는 기초 및 납세 협력도 및 조세 순응도를 확인하는 자료로 활용
Author강원도 영월군
URLhttps://www.data.go.kr/data/15079642/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
징수율 is highly overall correlated with 세목명 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 징수율High correlation
결손금액 is highly overall correlated with 징수율High correlation
징수율 has 11 (26.8%) zerosZeros

Reproduction

Analysis started2023-12-12 02:24:59.193134
Analysis finished2023-12-12 02:25:00.022345
Duration0.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
강원도
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원도 41
100.0%

Length

2023-12-12T11:25:00.131315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:25:00.267250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 41
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
영월군
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영월군 41
100.0%

Length

2023-12-12T11:25:00.411156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:25:00.511168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영월군 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
42750
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42750
2nd row42750
3rd row42750
4th row42750
5th row42750

Common Values

ValueCountFrequency (%)
42750 41
100.0%

Length

2023-12-12T11:25:00.642507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:25:00.766999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
42750 41
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
2017
14 
2018
14 
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 14
34.1%
2018 14
34.1%
2019 13
31.7%

Length

2023-12-12T11:25:00.884276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:25:01.008791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 14
34.1%
2018 14
34.1%
2019 13
31.7%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Memory size460.0 B
레저세
3 
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
Other values (9)
26 

Length

Max length7
Median length5
Mean length4.3902439
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도축세
2nd row레저세
3rd row재산세
4th row주민세
5th row취득세

Common Values

ValueCountFrequency (%)
레저세 3
 
7.3%
재산세 3
 
7.3%
주민세 3
 
7.3%
취득세 3
 
7.3%
자동차세 3
 
7.3%
과년도수입 3
 
7.3%
담배소비세 3
 
7.3%
도시계획세 3
 
7.3%
등록면허세 3
 
7.3%
지방교육세 3
 
7.3%
Other values (4) 11
26.8%

Length

2023-12-12T11:25:01.162852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 3
 
7.3%
재산세 3
 
7.3%
주민세 3
 
7.3%
취득세 3
 
7.3%
자동차세 3
 
7.3%
과년도수입 3
 
7.3%
담배소비세 3
 
7.3%
도시계획세 3
 
7.3%
등록면허세 3
 
7.3%
지방교육세 3
 
7.3%
Other values (4) 11
26.8%

부과금액
Text

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T11:25:01.357787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length10.170732
Min length3

Characters and Unicode

Total characters417
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)73.2%

Sample

1st row -
2nd row -
3rd row3,427,096,000
4th row968,274,000
5th row10,939,116,000
ValueCountFrequency (%)
11
26.8%
3,120,391,000 1
 
2.4%
6,541,099,000 1
 
2.4%
3,519,696,000 1
 
2.4%
826,356,000 1
 
2.4%
2,924,848,000 1
 
2.4%
4,309,009,000 1
 
2.4%
4,770,592,000 1
 
2.4%
9,836,294,000 1
 
2.4%
1,180,220,000 1
 
2.4%
Other values (21) 21
51.2%
2023-12-12T11:25:02.095661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 111
26.6%
, 86
20.6%
1 26
 
6.2%
4 24
 
5.8%
9 24
 
5.8%
22
 
5.3%
3 22
 
5.3%
2 22
 
5.3%
7 20
 
4.8%
6 18
 
4.3%
Other values (3) 42
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 298
71.5%
Other Punctuation 86
 
20.6%
Space Separator 22
 
5.3%
Dash Punctuation 11
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111
37.2%
1 26
 
8.7%
4 24
 
8.1%
9 24
 
8.1%
3 22
 
7.4%
2 22
 
7.4%
7 20
 
6.7%
6 18
 
6.0%
8 16
 
5.4%
5 15
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 86
100.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111
26.6%
, 86
20.6%
1 26
 
6.2%
4 24
 
5.8%
9 24
 
5.8%
22
 
5.3%
3 22
 
5.3%
2 22
 
5.3%
7 20
 
4.8%
6 18
 
4.3%
Other values (3) 42
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111
26.6%
, 86
20.6%
1 26
 
6.2%
4 24
 
5.8%
9 24
 
5.8%
22
 
5.3%
3 22
 
5.3%
2 22
 
5.3%
7 20
 
4.8%
6 18
 
4.3%
Other values (3) 42
 
10.1%

수납급액
Text

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T11:25:02.301478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length10
Min length3

Characters and Unicode

Total characters410
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)73.2%

Sample

1st row -
2nd row -
3rd row3,293,647,000
4th row931,933,000
5th row9,965,681,000
ValueCountFrequency (%)
11
26.8%
3,120,391,000 1
 
2.4%
5,908,148,000 1
 
2.4%
3,404,190,000 1
 
2.4%
823,081,000 1
 
2.4%
2,924,848,000 1
 
2.4%
970,862,000 1
 
2.4%
4,444,707,000 1
 
2.4%
9,823,134,000 1
 
2.4%
1,107,167,000 1
 
2.4%
Other values (21) 21
51.2%
2023-12-12T11:25:02.621239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 111
27.1%
, 83
20.2%
1 34
 
8.3%
3 29
 
7.1%
22
 
5.4%
8 20
 
4.9%
9 19
 
4.6%
5 19
 
4.6%
4 18
 
4.4%
7 17
 
4.1%
Other values (3) 38
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
71.7%
Other Punctuation 83
 
20.2%
Space Separator 22
 
5.4%
Dash Punctuation 11
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111
37.8%
1 34
 
11.6%
3 29
 
9.9%
8 20
 
6.8%
9 19
 
6.5%
5 19
 
6.5%
4 18
 
6.1%
7 17
 
5.8%
6 15
 
5.1%
2 12
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 83
100.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111
27.1%
, 83
20.2%
1 34
 
8.3%
3 29
 
7.1%
22
 
5.4%
8 20
 
4.9%
9 19
 
4.6%
5 19
 
4.6%
4 18
 
4.4%
7 17
 
4.1%
Other values (3) 38
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111
27.1%
, 83
20.2%
1 34
 
8.3%
3 29
 
7.1%
22
 
5.4%
8 20
 
4.9%
9 19
 
4.6%
5 19
 
4.6%
4 18
 
4.4%
7 17
 
4.1%
Other values (3) 38
 
9.3%

환급금액
Text

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T11:25:02.821079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7
Min length3

Characters and Unicode

Total characters287
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)65.9%

Sample

1st row -
2nd row -
3rd row615,000
4th row875,000
5th row21,911,000
ValueCountFrequency (%)
14
34.1%
615,000 1
 
2.4%
83,039,000 1
 
2.4%
14,726,000 1
 
2.4%
3,270,000 1
 
2.4%
182,657,000 1
 
2.4%
41,549,000 1
 
2.4%
52,832,000 1
 
2.4%
343,000 1
 
2.4%
2,042,000 1
 
2.4%
Other values (18) 18
43.9%
2023-12-12T11:25:03.168659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 89
31.0%
, 47
16.4%
28
 
9.8%
3 17
 
5.9%
4 17
 
5.9%
1 16
 
5.6%
- 14
 
4.9%
5 12
 
4.2%
2 12
 
4.2%
9 12
 
4.2%
Other values (3) 23
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 198
69.0%
Other Punctuation 47
 
16.4%
Space Separator 28
 
9.8%
Dash Punctuation 14
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89
44.9%
3 17
 
8.6%
4 17
 
8.6%
1 16
 
8.1%
5 12
 
6.1%
2 12
 
6.1%
9 12
 
6.1%
6 8
 
4.0%
8 8
 
4.0%
7 7
 
3.5%
Other Punctuation
ValueCountFrequency (%)
, 47
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89
31.0%
, 47
16.4%
28
 
9.8%
3 17
 
5.9%
4 17
 
5.9%
1 16
 
5.6%
- 14
 
4.9%
5 12
 
4.2%
2 12
 
4.2%
9 12
 
4.2%
Other values (3) 23
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89
31.0%
, 47
16.4%
28
 
9.8%
3 17
 
5.9%
4 17
 
5.9%
1 16
 
5.6%
- 14
 
4.9%
5 12
 
4.2%
2 12
 
4.2%
9 12
 
4.2%
Other values (3) 23
 
8.0%

결손금액
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Memory size460.0 B
-
29 
64,304,000
 
1
36,727,000
 
1
54,000
 
1
2,769,000
 
1
Other values (8)
8 

Length

Max length11
Median length3
Mean length4.6341463
Min length3

Unique

Unique12 ?
Unique (%)29.3%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row64,304,000

Common Values

ValueCountFrequency (%)
- 29
70.7%
64,304,000 1
 
2.4%
36,727,000 1
 
2.4%
54,000 1
 
2.4%
2,769,000 1
 
2.4%
51,997,000 1
 
2.4%
28,000 1
 
2.4%
334,362,000 1
 
2.4%
5,200,000 1
 
2.4%
52,000 1
 
2.4%
Other values (3) 3
 
7.3%

Length

2023-12-12T11:25:03.352608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29
70.7%
64,304,000 1
 
2.4%
36,727,000 1
 
2.4%
54,000 1
 
2.4%
2,769,000 1
 
2.4%
51,997,000 1
 
2.4%
28,000 1
 
2.4%
334,362,000 1
 
2.4%
5,200,000 1
 
2.4%
52,000 1
 
2.4%
Other values (3) 3
 
7.3%

미수납 금액
Text

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T11:25:03.561230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length8.0731707
Min length3

Characters and Unicode

Total characters331
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)65.9%

Sample

1st row -
2nd row -
3rd row133,449,000
4th row36,341,000
5th row909,131,000
ValueCountFrequency (%)
14
34.1%
133,449,000 1
 
2.4%
588,209,000 1
 
2.4%
115,501,000 1
 
2.4%
3,275,000 1
 
2.4%
2,856,535,000 1
 
2.4%
325,885,000 1
 
2.4%
13,160,000 1
 
2.4%
73,001,000 1
 
2.4%
110,068,000 1
 
2.4%
Other values (18) 18
43.9%
2023-12-12T11:25:03.896368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 92
27.8%
, 57
17.2%
28
 
8.5%
3 25
 
7.6%
1 24
 
7.3%
6 18
 
5.4%
4 15
 
4.5%
- 14
 
4.2%
5 14
 
4.2%
8 14
 
4.2%
Other values (3) 30
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 232
70.1%
Other Punctuation 57
 
17.2%
Space Separator 28
 
8.5%
Dash Punctuation 14
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92
39.7%
3 25
 
10.8%
1 24
 
10.3%
6 18
 
7.8%
4 15
 
6.5%
5 14
 
6.0%
8 14
 
6.0%
2 12
 
5.2%
7 11
 
4.7%
9 7
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 57
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92
27.8%
, 57
17.2%
28
 
8.5%
3 25
 
7.6%
1 24
 
7.3%
6 18
 
5.4%
4 15
 
4.5%
- 14
 
4.2%
5 14
 
4.2%
8 14
 
4.2%
Other values (3) 30
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92
27.8%
, 57
17.2%
28
 
8.5%
3 25
 
7.6%
1 24
 
7.3%
6 18
 
5.4%
4 15
 
4.5%
- 14
 
4.2%
5 14
 
4.2%
8 14
 
4.2%
Other values (3) 30
 
9.1%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.998537
Minimum0
Maximum100
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:25:04.060555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median94.89
Q398.25
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)98.25

Descriptive statistics

Standard deviation44.98305
Coefficient of variation (CV)0.6920625
Kurtosis-1.5166365
Mean64.998537
Median Absolute Deviation (MAD)4.71
Skewness-0.7160628
Sum2664.94
Variance2023.4748
MonotonicityNot monotonic
2023-12-12T11:25:04.187869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 11
26.8%
100.0 3
 
7.3%
99.66 1
 
2.4%
99.25 1
 
2.4%
90.32 1
 
2.4%
96.72 1
 
2.4%
99.6 1
 
2.4%
22.53 1
 
2.4%
93.17 1
 
2.4%
99.87 1
 
2.4%
Other values (19) 19
46.3%
ValueCountFrequency (%)
0.0 11
26.8%
7.7 1
 
2.4%
21.58 1
 
2.4%
22.53 1
 
2.4%
90.32 1
 
2.4%
91.1 1
 
2.4%
91.91 1
 
2.4%
93.17 1
 
2.4%
93.81 1
 
2.4%
93.92 1
 
2.4%
ValueCountFrequency (%)
100.0 3
7.3%
99.87 1
 
2.4%
99.66 1
 
2.4%
99.61 1
 
2.4%
99.6 1
 
2.4%
99.25 1
 
2.4%
99.11 1
 
2.4%
99.08 1
 
2.4%
98.25 1
 
2.4%
97.79 1
 
2.4%

Interactions

2023-12-12T11:24:59.583429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:25:04.287211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.1930.1930.1510.0530.1510.000
세목명0.0001.0000.7900.7900.7730.2650.7730.919
부과금액0.1930.7901.0001.0001.0001.0001.0001.000
수납급액0.1930.7901.0001.0001.0001.0001.0001.000
환급금액0.1510.7731.0001.0001.0001.0001.0000.908
결손금액0.0530.2651.0001.0001.0001.0001.0000.793
미수납 금액0.1510.7731.0001.0001.0001.0001.0000.908
징수율0.0000.9191.0001.0000.9080.7930.9081.000
2023-12-12T11:25:04.401406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도결손금액
세목명1.0000.0000.000
과세년도0.0001.0000.000
결손금액0.0000.0001.000
2023-12-12T11:25:04.504502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
징수율과세년도세목명결손금액
징수율1.0000.0000.7110.552
과세년도0.0001.0000.0000.000
세목명0.7110.0001.0000.000
결손금액0.5520.0000.0001.000

Missing values

2023-12-12T11:24:59.723789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:24:59.923475image/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

시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
0강원도영월군427502017도축세-----0.0
1강원도영월군427502017레저세-----0.0
2강원도영월군427502017재산세3,427,096,0003,293,647,000615,000-133,449,00096.11
3강원도영월군427502017주민세968,274,000931,933,000875,000-36,341,00096.25
4강원도영월군427502017취득세10,939,116,0009,965,681,00021,911,00064,304,000909,131,00091.1
5강원도영월군427502017자동차세4,520,714,0004,155,113,00031,819,000-365,601,00091.91
6강원도영월군427502017과년도수입3,257,808,000703,192,000136,423,00036,727,0002,517,889,00021.58
7강원도영월군427502017담배소비세2,974,035,0002,974,035,000---100.0
8강원도영월군427502017도시계획세-----0.0
9강원도영월군427502017등록면허세777,637,000774,596,0005,035,00054,0002,987,00099.61
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
31강원도영월군427502019취득세9,836,294,0009,823,134,00052,832,000-13,160,00099.87
32강원도영월군427502019자동차세4,770,592,0004,444,707,00041,549,000-325,885,00093.17
33강원도영월군427502019과년도수입4,309,009,000970,862,000182,657,000481,612,0002,856,535,00022.53
34강원도영월군427502019담배소비세2,924,848,0002,924,848,000---100.0
35강원도영월군427502019도시계획세-----0.0
36강원도영월군427502019등록면허세826,356,000823,081,0003,270,000-3,275,00099.6
37강원도영월군427502019지방교육세3,519,696,0003,404,190,00014,726,0005,000115,501,00096.72
38강원도영월군427502019지방소득세6,541,099,0005,908,148,00083,039,00044,742,000588,209,00090.32
39강원도영월군427502019지방소비세-----0.0
40강원도영월군427502019지역자원시설세1,529,954,0001,518,532,000426,000-11,422,00099.25