Overview

Dataset statistics

Number of variables11
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory93.0 B

Variable types

Categorical5
Text5
Numeric1

Dataset

Description2017~2021년도 충청남도 보령시 지방세 부과액에 대한 데이터로 각 재산세, 주민세 등의 세목별로 징수현황 항목을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=353&beforeMenuCd=DOM_000000201001001000&publicdatapk=15078783

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
징수율 is highly overall correlated with 세목명High correlation
세목명 is highly overall correlated with 징수율High correlation
징수율 has 15 (22.4%) zerosZeros

Reproduction

Analysis started2024-01-09 21:47:49.889617
Analysis finished2024-01-09 21:47:50.430103
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
충청남도
67 

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 67
100.0%

Length

2024-01-10T06:47:50.496753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:47:50.590185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 67
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
보령시
67 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
보령시 67
100.0%

Length

2024-01-10T06:47:50.687645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:47:50.783520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보령시 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
44180
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44180
2nd row44180
3rd row44180
4th row44180
5th row44180

Common Values

ValueCountFrequency (%)
44180 67
100.0%

Length

2024-01-10T06:47:50.881228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:47:50.978597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44180 67
100.0%

과세년도
Categorical

Distinct5
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
2017
14 
2018
14 
2019
13 
2020
13 
2021
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
20.9%
2018 14
20.9%
2019 13
19.4%
2020 13
19.4%
2021 13
19.4%

Length

2024-01-10T06:47:51.059794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:47:51.142959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 14
20.9%
2018 14
20.9%
2019 13
19.4%
2020 13
19.4%
2021 13
19.4%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Memory size668.0 B
레저세
5 
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
Other values (9)
42 

Length

Max length7
Median length5
Mean length4.4179104
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 5
 
7.5%
재산세 5
 
7.5%
주민세 5
 
7.5%
취득세 5
 
7.5%
자동차세 5
 
7.5%
과년도수입 5
 
7.5%
담배소비세 5
 
7.5%
도시계획세 5
 
7.5%
등록면허세 5
 
7.5%
지방교육세 5
 
7.5%
Other values (4) 17
25.4%

Length

2024-01-10T06:47:51.244099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 5
 
7.5%
재산세 5
 
7.5%
주민세 5
 
7.5%
취득세 5
 
7.5%
자동차세 5
 
7.5%
과년도수입 5
 
7.5%
담배소비세 5
 
7.5%
도시계획세 5
 
7.5%
등록면허세 5
 
7.5%
지방교육세 5
 
7.5%
Other values (4) 17
25.4%

부과금액
Text

Distinct54
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-01-10T06:47:51.440487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.6268657
Min length1

Characters and Unicode

Total characters578
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)77.6%

Sample

1st row
2nd row
3rd row13979808000
4th row3106859000
5th row44325307000
ValueCountFrequency (%)
0 2
 
3.7%
11088195000 1
 
1.9%
7397300000 1
 
1.9%
15933224000 1
 
1.9%
14800402000 1
 
1.9%
17039818000 1
 
1.9%
3684630000 1
 
1.9%
36752074000 1
 
1.9%
12529013000 1
 
1.9%
4890014000 1
 
1.9%
Other values (43) 43
79.6%
2024-01-10T06:47:51.717780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 198
34.3%
1 57
 
9.9%
3 50
 
8.7%
8 40
 
6.9%
9 39
 
6.7%
7 37
 
6.4%
4 36
 
6.2%
6 35
 
6.1%
5 32
 
5.5%
2 28
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 552
95.5%
Space Separator 26
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 198
35.9%
1 57
 
10.3%
3 50
 
9.1%
8 40
 
7.2%
9 39
 
7.1%
7 37
 
6.7%
4 36
 
6.5%
6 35
 
6.3%
5 32
 
5.8%
2 28
 
5.1%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 198
34.3%
1 57
 
9.9%
3 50
 
8.7%
8 40
 
6.9%
9 39
 
6.7%
7 37
 
6.4%
4 36
 
6.2%
6 35
 
6.1%
5 32
 
5.5%
2 28
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 198
34.3%
1 57
 
9.9%
3 50
 
8.7%
8 40
 
6.9%
9 39
 
6.7%
7 37
 
6.4%
4 36
 
6.2%
6 35
 
6.1%
5 32
 
5.5%
2 28
 
4.8%

수납급액
Text

Distinct54
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-01-10T06:47:51.909414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.6119403
Min length1

Characters and Unicode

Total characters577
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)77.6%

Sample

1st row
2nd row
3rd row13489153000
4th row3026057000
5th row44099099000
ValueCountFrequency (%)
0 2
 
3.7%
10671126000 1
 
1.9%
7397300000 1
 
1.9%
15263675000 1
 
1.9%
14733585000 1
 
1.9%
16496164000 1
 
1.9%
3576658000 1
 
1.9%
36487561000 1
 
1.9%
11654218000 1
 
1.9%
1591080000 1
 
1.9%
Other values (43) 43
79.6%
2024-01-10T06:47:52.191446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 183
31.7%
1 55
 
9.5%
6 45
 
7.8%
3 44
 
7.6%
9 42
 
7.3%
5 39
 
6.8%
2 38
 
6.6%
7 38
 
6.6%
4 34
 
5.9%
8 33
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 551
95.5%
Space Separator 26
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 183
33.2%
1 55
 
10.0%
6 45
 
8.2%
3 44
 
8.0%
9 42
 
7.6%
5 39
 
7.1%
2 38
 
6.9%
7 38
 
6.9%
4 34
 
6.2%
8 33
 
6.0%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 183
31.7%
1 55
 
9.5%
6 45
 
7.8%
3 44
 
7.6%
9 42
 
7.3%
5 39
 
6.8%
2 38
 
6.6%
7 38
 
6.6%
4 34
 
5.9%
8 33
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 183
31.7%
1 55
 
9.5%
6 45
 
7.8%
3 44
 
7.6%
9 42
 
7.3%
5 39
 
6.8%
2 38
 
6.6%
7 38
 
6.6%
4 34
 
5.9%
8 33
 
5.7%

환급금액
Text

Distinct50
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-01-10T06:47:52.363975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.2985075
Min length1

Characters and Unicode

Total characters422
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)71.6%

Sample

1st row
2nd row
3rd row4488000
4th row7483000
5th row327941000
ValueCountFrequency (%)
0 3
 
5.9%
39267000 1
 
2.0%
725077000 1
 
2.0%
418810000 1
 
2.0%
15929000 1
 
2.0%
11418000 1
 
2.0%
853000 1
 
2.0%
260410000 1
 
2.0%
127770000 1
 
2.0%
1319356000 1
 
2.0%
Other values (39) 39
76.5%
2024-01-10T06:47:52.626790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 168
39.8%
1 45
 
10.7%
32
 
7.6%
9 31
 
7.3%
4 27
 
6.4%
3 24
 
5.7%
7 23
 
5.5%
2 22
 
5.2%
6 19
 
4.5%
8 16
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 390
92.4%
Space Separator 32
 
7.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
43.1%
1 45
 
11.5%
9 31
 
7.9%
4 27
 
6.9%
3 24
 
6.2%
7 23
 
5.9%
2 22
 
5.6%
6 19
 
4.9%
8 16
 
4.1%
5 15
 
3.8%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 422
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168
39.8%
1 45
 
10.7%
32
 
7.6%
9 31
 
7.3%
4 27
 
6.4%
3 24
 
5.7%
7 23
 
5.5%
2 22
 
5.2%
6 19
 
4.5%
8 16
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168
39.8%
1 45
 
10.7%
32
 
7.6%
9 31
 
7.3%
4 27
 
6.4%
3 24
 
5.7%
7 23
 
5.5%
2 22
 
5.2%
6 19
 
4.5%
8 16
 
3.8%

결손금액
Text

Distinct41
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-01-10T06:47:52.785791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.641791
Min length1

Characters and Unicode

Total characters311
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)58.2%

Sample

1st row
2nd row
3rd row1891000
4th row150000
5th row
ValueCountFrequency (%)
0 5
 
11.4%
18631000 1
 
2.3%
31000 1
 
2.3%
447000 1
 
2.3%
169911000 1
 
2.3%
2528000 1
 
2.3%
524000 1
 
2.3%
241000 1
 
2.3%
1384000 1
 
2.3%
587984000 1
 
2.3%
Other values (30) 30
68.2%
2024-01-10T06:47:53.030754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 137
44.1%
46
 
14.8%
1 29
 
9.3%
8 18
 
5.8%
9 15
 
4.8%
4 13
 
4.2%
5 13
 
4.2%
2 12
 
3.9%
3 11
 
3.5%
7 10
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 265
85.2%
Space Separator 46
 
14.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 137
51.7%
1 29
 
10.9%
8 18
 
6.8%
9 15
 
5.7%
4 13
 
4.9%
5 13
 
4.9%
2 12
 
4.5%
3 11
 
4.2%
7 10
 
3.8%
6 7
 
2.6%
Space Separator
ValueCountFrequency (%)
46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 311
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 137
44.1%
46
 
14.8%
1 29
 
9.3%
8 18
 
5.8%
9 15
 
4.8%
4 13
 
4.2%
5 13
 
4.2%
2 12
 
3.9%
3 11
 
3.5%
7 10
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 137
44.1%
46
 
14.8%
1 29
 
9.3%
8 18
 
5.8%
9 15
 
4.8%
4 13
 
4.2%
5 13
 
4.2%
2 12
 
3.9%
3 11
 
3.5%
7 10
 
3.2%

미수납 금액
Text

Distinct47
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-01-10T06:47:53.199113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.5671642
Min length1

Characters and Unicode

Total characters440
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)67.2%

Sample

1st row
2nd row
3rd row488764000
4th row80652000
5th row226208000
ValueCountFrequency (%)
0 4
 
8.2%
12223000 1
 
2.0%
482917000 1
 
2.0%
316639000 1
 
2.0%
499638000 1
 
2.0%
64289000 1
 
2.0%
543130000 1
 
2.0%
107731000 1
 
2.0%
264513000 1
 
2.0%
873411000 1
 
2.0%
Other values (36) 36
73.5%
2024-01-10T06:47:53.473456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 166
37.7%
1 37
 
8.4%
36
 
8.2%
4 35
 
8.0%
2 31
 
7.0%
7 26
 
5.9%
8 23
 
5.2%
6 22
 
5.0%
3 22
 
5.0%
9 21
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 404
91.8%
Space Separator 36
 
8.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 166
41.1%
1 37
 
9.2%
4 35
 
8.7%
2 31
 
7.7%
7 26
 
6.4%
8 23
 
5.7%
6 22
 
5.4%
3 22
 
5.4%
9 21
 
5.2%
5 21
 
5.2%
Space Separator
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 166
37.7%
1 37
 
8.4%
36
 
8.2%
4 35
 
8.0%
2 31
 
7.0%
7 26
 
5.9%
8 23
 
5.2%
6 22
 
5.0%
3 22
 
5.0%
9 21
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 166
37.7%
1 37
 
8.4%
36
 
8.2%
4 35
 
8.0%
2 31
 
7.0%
7 26
 
5.9%
8 23
 
5.2%
6 22
 
5.0%
3 22
 
5.0%
9 21
 
4.8%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.983284
Minimum0
Maximum100
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T06:47:53.587759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q134.14
median96.78
Q399.36
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)65.22

Descriptive statistics

Standard deviation42.082111
Coefficient of variation (CV)0.59284536
Kurtosis-0.92367647
Mean70.983284
Median Absolute Deviation (MAD)2.81
Skewness-0.99850867
Sum4755.88
Variance1770.904
MonotonicityNot monotonic
2024-01-10T06:47:53.691700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0 15
22.4%
100.0 7
 
10.4%
99.49 2
 
3.0%
99.55 2
 
3.0%
99.32 1
 
1.5%
96.81 1
 
1.5%
97.07 1
 
1.5%
99.28 1
 
1.5%
93.02 1
 
1.5%
32.54 1
 
1.5%
Other values (35) 35
52.2%
ValueCountFrequency (%)
0.0 15
22.4%
18.48 1
 
1.5%
32.54 1
 
1.5%
35.74 1
 
1.5%
37.53 1
 
1.5%
40.05 1
 
1.5%
91.75 1
 
1.5%
92.09 1
 
1.5%
93.02 1
 
1.5%
93.98 1
 
1.5%
ValueCountFrequency (%)
100.0 7
10.4%
99.67 1
 
1.5%
99.64 1
 
1.5%
99.63 1
 
1.5%
99.59 1
 
1.5%
99.55 2
 
3.0%
99.53 1
 
1.5%
99.49 2
 
3.0%
99.4 1
 
1.5%
99.32 1
 
1.5%

Interactions

2024-01-10T06:47:50.150109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:47:53.762105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.6930.6930.7440.7550.7440.000
세목명0.0001.0000.5580.5580.4320.0000.4680.808
부과금액0.6930.5581.0001.0001.0001.0001.0001.000
수납급액0.6930.5581.0001.0001.0001.0001.0001.000
환급금액0.7440.4321.0001.0001.0001.0001.0000.986
결손금액0.7550.0001.0001.0001.0001.0001.0000.950
미수납 금액0.7440.4681.0001.0001.0001.0001.0000.961
징수율0.0000.8081.0001.0000.9860.9500.9611.000
2024-01-10T06:47:53.844236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-01-10T06:47:53.906473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
징수율과세년도세목명
징수율1.0000.0000.543
과세년도0.0001.0000.000
세목명0.5430.0001.000

Missing values

2024-01-10T06:47:50.235437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:47:50.371429image/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
0충청남도보령시441802017도축세0.0
1충청남도보령시441802017레저세0.0
2충청남도보령시441802017재산세13979808000134891530004488000189100048876400096.49
3충청남도보령시441802017주민세3106859000302605700074830001500008065200097.4
4충청남도보령시441802017취득세443253070004409909900032794100022620800099.49
5충청남도보령시441802017자동차세150507350001385978500086481000942000119000800092.09
6충청남도보령시441802017과년도수입285127900010190300003866494000679281000319102800035.74
7충청남도보령시441802017담배소비세86577790008657779000100.0
8충청남도보령시441802017도시계획세0.0
9충청남도보령시441802017등록면허세2597891000257775400010139000250002011200099.22
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
57충청남도보령시441802021취득세4381653800043634743000245653000018179500099.59
58충청남도보령시441802021자동차세1583993800014969426000122587000180900086870300094.5
59충청남도보령시441802021과년도수입49543230001859398000729243000609019000248590600037.53
60충청남도보령시441802021담배소비세84672940008467294000125000000100.0
61충청남도보령시441802021도시계획세000000.0
62충청남도보령시441802021등록면허세43141660004299764000134410003050001409700099.67
63충청남도보령시441802021지방교육세11930870000115547070005491500088600037527700096.85
64충청남도보령시441802021지방소득세1715691500016669197000521816000480100048291700097.16
65충청남도보령시441802021지방소비세73966960007396696000000100.0
66충청남도보령시441802021지역자원시설세128195380001274221500079540001600007716300099.4