Overview

Dataset statistics

Number of variables12
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory106.9 B

Variable types

Categorical6
Numeric6

Dataset

Description미환급 유형별 미환급금 현황 및 연간 누적률에 대한 데이터로 당해 미환급 건수, 당해 미환급 금액, 누적 미환급 건수, 누적 미환급 금액 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15078436/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
미환급유형 has constant value ""Constant
당해미환급건수 is highly overall correlated with 당해미환급금액 and 2 other fieldsHigh correlation
당해미환급금액 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급건수 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급금액 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급금액증감 has 5 (14.7%) zerosZeros

Reproduction

Analysis started2023-12-12 10:10:13.219569
Analysis finished2023-12-12 10:10:18.249186
Duration5.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
대전광역시
34 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 34
100.0%

Length

2023-12-12T19:10:18.347029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:10:18.481411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 34
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
대덕구
34 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대덕구 34
100.0%

Length

2023-12-12T19:10:18.616054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:10:18.771870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대덕구 34
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
30230
34 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30230
2nd row30230
3rd row30230
4th row30230
5th row30230

Common Values

ValueCountFrequency (%)
30230 34
100.0%

Length

2023-12-12T19:10:18.927824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:10:19.073694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30230 34
100.0%

세목명
Categorical

Distinct5
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
자동차세
12 
지방소득세
12 
주민세
4 
재산세
4 
등록면허세
2 

Length

Max length5
Median length4
Mean length4.1764706
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록면허세
2nd row자동차세
3rd row자동차세
4th row주민세
5th row지방소득세

Common Values

ValueCountFrequency (%)
자동차세 12
35.3%
지방소득세 12
35.3%
주민세 4
 
11.8%
재산세 4
 
11.8%
등록면허세 2
 
5.9%

Length

2023-12-12T19:10:19.221202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:10:19.366747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 12
35.3%
지방소득세 12
35.3%
주민세 4
 
11.8%
재산세 4
 
11.8%
등록면허세 2
 
5.9%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.6471
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T19:10:19.495431image/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 deviation1.7902489
Coefficient of variation (CV)0.0008864167
Kurtosis-1.312244
Mean2019.6471
Median Absolute Deviation (MAD)1.5
Skewness-0.13758721
Sum68668
Variance3.2049911
MonotonicityIncreasing
2023-12-12T19:10:19.635040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 7
20.6%
2017 6
17.6%
2019 6
17.6%
2021 6
17.6%
2020 5
14.7%
2018 4
11.8%
ValueCountFrequency (%)
2017 6
17.6%
2018 4
11.8%
2019 6
17.6%
2020 5
14.7%
2021 6
17.6%
2022 7
20.6%
ValueCountFrequency (%)
2022 7
20.6%
2021 6
17.6%
2020 5
14.7%
2019 6
17.6%
2018 4
11.8%
2017 6
17.6%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
신규
34 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신규 34
100.0%

Length

2023-12-12T19:10:19.808601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:10:19.937148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 34
100.0%

납세자유형
Categorical

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
개인
19 
법인
15 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row법인
2nd row개인
3rd row법인
4th row법인
5th row개인

Common Values

ValueCountFrequency (%)
개인 19
55.9%
법인 15
44.1%

Length

2023-12-12T19:10:20.079034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:10:20.220075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 19
55.9%
법인 15
44.1%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.529412
Minimum1
Maximum457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T19:10:20.353017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14.25
median15
Q388
95-th percentile346.3
Maximum457
Range456
Interquartile range (IQR)83.75

Descriptive statistics

Standard deviation117.79907
Coefficient of variation (CV)1.6241558
Kurtosis3.8397764
Mean72.529412
Median Absolute Deviation (MAD)14
Skewness2.1206896
Sum2466
Variance13876.62
MonotonicityNot monotonic
2023-12-12T19:10:20.480731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 5
 
14.7%
4 3
 
8.8%
5 3
 
8.8%
16 2
 
5.9%
88 2
 
5.9%
12 2
 
5.9%
457 1
 
2.9%
384 1
 
2.9%
114 1
 
2.9%
19 1
 
2.9%
Other values (13) 13
38.2%
ValueCountFrequency (%)
1 5
14.7%
3 1
 
2.9%
4 3
8.8%
5 3
8.8%
6 1
 
2.9%
11 1
 
2.9%
12 2
 
5.9%
14 1
 
2.9%
16 2
 
5.9%
19 1
 
2.9%
ValueCountFrequency (%)
457 1
2.9%
384 1
2.9%
326 1
2.9%
282 1
2.9%
158 1
2.9%
141 1
2.9%
114 1
2.9%
100 1
2.9%
88 2
5.9%
57 1
2.9%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean813364.41
Minimum930
Maximum5122010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T19:10:20.623458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum930
5-th percentile943
Q116527.5
median138365
Q31050467.5
95-th percentile4116476
Maximum5122010
Range5121080
Interquartile range (IQR)1033940

Descriptive statistics

Standard deviation1393706.5
Coefficient of variation (CV)1.713508
Kurtosis3.855469
Mean813364.41
Median Absolute Deviation (MAD)136695
Skewness2.1273224
Sum27654390
Variance1.9424177 × 1012
MonotonicityNot monotonic
2023-12-12T19:10:20.765605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
930 2
 
5.9%
27500 1
 
2.9%
7200 1
 
2.9%
5122010 1
 
2.9%
1462880 1
 
2.9%
138120 1
 
2.9%
138610 1
 
2.9%
4988490 1
 
2.9%
169200 1
 
2.9%
2512710 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
930 2
5.9%
950 1
2.9%
2390 1
2.9%
3300 1
2.9%
3500 1
2.9%
7200 1
2.9%
8910 1
2.9%
12870 1
2.9%
27500 1
2.9%
36480 1
2.9%
ValueCountFrequency (%)
5122010 1
2.9%
4988490 1
2.9%
3646930 1
2.9%
2723360 1
2.9%
2512710 1
2.9%
1462880 1
2.9%
1386180 1
2.9%
1353600 1
2.9%
1200330 1
2.9%
600880 1
2.9%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.11765
Minimum1
Maximum739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T19:10:20.917230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17.5
median30.5
Q3159
95-th percentile647.95
Maximum739
Range738
Interquartile range (IQR)151.5

Descriptive statistics

Standard deviation214.04414
Coefficient of variation (CV)1.5724937
Kurtosis2.7758917
Mean136.11765
Median Absolute Deviation (MAD)29
Skewness1.9366073
Sum4628
Variance45814.895
MonotonicityNot monotonic
2023-12-12T19:10:21.045704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 4
 
11.8%
5 2
 
5.9%
259 1
 
2.9%
33 1
 
2.9%
737 1
 
2.9%
120 1
 
2.9%
19 1
 
2.9%
55 1
 
2.9%
600 1
 
2.9%
28 1
 
2.9%
Other values (20) 20
58.8%
ValueCountFrequency (%)
1 4
11.8%
2 1
 
2.9%
5 2
5.9%
6 1
 
2.9%
7 1
 
2.9%
9 1
 
2.9%
11 1
 
2.9%
12 1
 
2.9%
14 1
 
2.9%
17 1
 
2.9%
ValueCountFrequency (%)
739 1
2.9%
737 1
2.9%
600 1
2.9%
568 1
2.9%
337 1
2.9%
259 1
2.9%
257 1
2.9%
181 1
2.9%
169 1
2.9%
129 1
2.9%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1420518.5
Minimum930
Maximum8775880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T19:10:21.212566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum930
5-th percentile2496.5
Q120862.5
median210325
Q31740465
95-th percentile7327359
Maximum8775880
Range8774950
Interquartile range (IQR)1719602.5

Descriptive statistics

Standard deviation2354589.1
Coefficient of variation (CV)1.657556
Kurtosis3.5081398
Mean1420518.5
Median Absolute Deviation (MAD)209395
Skewness2.0761839
Sum48297630
Variance5.5440896 × 1012
MonotonicityNot monotonic
2023-12-12T19:10:21.350433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
930 2
 
5.9%
27500 1
 
2.9%
7200 1
 
2.9%
7181360 1
 
2.9%
1833190 1
 
2.9%
139050 1
 
2.9%
151480 1
 
2.9%
8775880 1
 
2.9%
184160 1
 
2.9%
4907140 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
930 2
5.9%
3340 1
2.9%
3500 1
2.9%
4290 1
2.9%
7200 1
2.9%
13200 1
2.9%
15260 1
2.9%
18650 1
2.9%
27500 1
2.9%
58080 1
2.9%
ValueCountFrequency (%)
8775880 1
2.9%
7598500 1
2.9%
7181360 1
2.9%
4907140 1
2.9%
4124360 1
2.9%
2520740 1
2.9%
2133720 1
2.9%
1876860 1
2.9%
1833190 1
2.9%
1462290 1
2.9%

누적미환급금액증감
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.487059
Minimum0
Maximum362.42
Zeros5
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T19:10:21.806922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.295
median67.565
Q3142.385
95-th percentile321.6215
Maximum362.42
Range362.42
Interquartile range (IQR)129.09

Descriptive statistics

Standard deviation104.29825
Coefficient of variation (CV)1.04836
Kurtosis0.59624982
Mean99.487059
Median Absolute Deviation (MAD)58.5
Skewness1.2037543
Sum3382.56
Variance10878.126
MonotonicityNot monotonic
2023-12-12T19:10:21.935765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0 5
 
14.7%
51.44 1
 
2.9%
231.9 1
 
2.9%
108.35 1
 
2.9%
5.49 1
 
2.9%
166.77 1
 
2.9%
305.49 1
 
2.9%
95.29 1
 
2.9%
8.84 1
 
2.9%
75.92 1
 
2.9%
Other values (20) 20
58.8%
ValueCountFrequency (%)
0.0 5
14.7%
0.67 1
 
2.9%
5.49 1
 
2.9%
8.84 1
 
2.9%
9.29 1
 
2.9%
25.31 1
 
2.9%
39.75 1
 
2.9%
40.21 1
 
2.9%
44.91 1
 
2.9%
48.15 1
 
2.9%
ValueCountFrequency (%)
362.42 1
2.9%
351.58 1
2.9%
305.49 1
2.9%
243.21 1
2.9%
231.9 1
2.9%
223.46 1
2.9%
212.35 1
2.9%
166.77 1
2.9%
153.73 1
2.9%
108.35 1
2.9%

Interactions

2023-12-12T19:10:17.204907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:13.605484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:14.578132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:15.210181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:15.900665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:16.534541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:17.315879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:13.724251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:14.681882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:15.312912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:16.011828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:16.637411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:17.400214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:14.149982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:14.774066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:15.422534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:16.118486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:16.743778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:17.505502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:14.247983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:14.882030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:15.529717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:16.219325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:16.841173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:17.636477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:14.361749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:14.999786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:15.641366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:16.326933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:16.954937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:17.751018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:14.465271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:15.105988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:15.772471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:16.420421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:10:17.061594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:10:22.040195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
세목명1.0000.0000.2420.0000.0000.0000.0000.231
과세년도0.0001.0000.0000.3700.0400.0000.1780.000
납세자유형0.2420.0001.0000.6240.2120.5710.4580.327
당해미환급건수0.0000.3700.6241.0000.9200.9840.9320.000
당해미환급금액0.0000.0400.2120.9201.0000.8850.9800.403
누적미환급건수0.0000.0000.5710.9840.8851.0000.9430.000
누적미환급금액0.0000.1780.4580.9320.9800.9431.0000.000
누적미환급금액증감0.2310.0000.3270.0000.4030.0000.0001.000
2023-12-12T19:10:22.176246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형
세목명1.0000.275
납세자유형0.2751.000
2023-12-12T19:10:22.280446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명납세자유형
과세년도1.0000.4040.3760.3420.374-0.0260.0000.000
당해미환급건수0.4041.0000.9380.9810.9560.3400.0000.421
당해미환급금액0.3760.9381.0000.9190.9720.1670.0000.184
누적미환급건수0.3420.9810.9191.0000.9590.4410.0000.382
누적미환급금액0.3740.9560.9720.9591.0000.3390.0000.421
누적미환급금액증감-0.0260.3400.1670.4410.3391.0000.1070.207
세목명0.0000.0000.0000.0000.0000.1071.0000.275
납세자유형0.0000.4210.1840.3820.4210.2070.2751.000

Missing values

2023-12-12T19:10:17.963578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:10:18.162088image/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

시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
0대전광역시대덕구30230등록면허세2017신규법인1275001275000.0
1대전광역시대덕구30230자동차세2017신규개인56439230112904200105.86
2대전광역시대덕구30230자동차세2017신규법인43648075808059.21
3대전광역시대덕구30230주민세2017신규법인193019300.0
4대전광역시대덕구30230지방소득세2017신규개인1614789039478370223.46
5대전광역시대덕구30230지방소득세2017신규법인323905334039.75
6대전광역시대덕구30230자동차세2018신규개인573717801691275980243.21
7대전광역시대덕구30230자동차세2018신규법인4377801195860153.73
8대전광역시대덕구30230지방소득세2018신규개인5445502093933390105.13
9대전광역시대덕구30230지방소득세2018신규법인495094290351.58
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
24대전광역시대덕구30230주민세2021신규개인11138610121514809.29
25대전광역시대덕구30230지방소득세2021신규개인3264988490568877588075.92
26대전광역시대덕구30230지방소득세2021신규법인12169200281841608.84
27대전광역시대덕구30230등록면허세2022신규법인17200172000.0
28대전광역시대덕구30230자동차세2022신규개인2822512710600490714095.29
29대전광역시대덕구30230자동차세2022신규법인1912780055518220305.49
30대전광역시대덕구30230재산세2022신규개인167409019197650166.77
31대전광역시대덕구30230주민세2022신규개인114138618012014622905.49
32대전광역시대덕구30230지방소득세2022신규개인38436469307377598500108.35
33대전광역시대덕구30230지방소득세2022신규법인126564033217860231.9