Overview

Dataset statistics

Number of variables12
Number of observations69
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory104.9 B

Variable types

Categorical7
Numeric5

Dataset

Description미환급 유형별 미환급금 현황 및 연간 누적률 제공 (2017-2021년) -활용업무 : 자치단체의 환급금 해소노력
URLhttps://www.data.go.kr/data/15080276/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
당해미환급건수 is highly overall correlated with 당해미환급금액 and 1 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 당해미환급금액 and 2 other fieldsHigh correlation
납세자유형 is highly overall correlated with 누적미환급금액High correlation
당해미환급금액 has unique valuesUnique
누적미환급금액증감 has 5 (7.2%) zerosZeros

Reproduction

Analysis started2023-12-12 15:36:23.969467
Analysis finished2023-12-12 15:36:27.690741
Duration3.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
대전광역시
69 

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 69
100.0%

Length

2023-12-13T00:36:27.785840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:36:27.925022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 69
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
유성구
69 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 69
100.0%

Length

2023-12-13T00:36:28.087044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:36:28.231632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 69
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
30200
69 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30200
2nd row30200
3rd row30200
4th row30200
5th row30200

Common Values

ValueCountFrequency (%)
30200 69
100.0%

Length

2023-12-13T00:36:28.388614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:36:28.527567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30200 69
100.0%

세목명
Categorical

Distinct6
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size684.0 B
자동차세
25 
지방소득세
24 
등록면허세
8 
재산세
5 
주민세
5 

Length

Max length5
Median length4
Mean length4.2898551
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 25
36.2%
지방소득세 24
34.8%
등록면허세 8
 
11.6%
재산세 5
 
7.2%
주민세 5
 
7.2%
취득세 2
 
2.9%

Length

2023-12-13T00:36:28.691196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:36:28.915222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 25
36.2%
지방소득세 24
34.8%
등록면허세 8
 
11.6%
재산세 5
 
7.2%
주민세 5
 
7.2%
취득세 2
 
2.9%

과세년도
Categorical

Distinct5
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size684.0 B
2021
23 
2020
14 
2018
12 
2017
11 
2019
9 

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 (%)
2021 23
33.3%
2020 14
20.3%
2018 12
17.4%
2017 11
15.9%
2019 9
 
13.0%

Length

2023-12-13T00:36:29.117163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:36:29.293120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 23
33.3%
2020 14
20.3%
2018 12
17.4%
2017 11
15.9%
2019 9
 
13.0%

미환급유형
Categorical

Distinct6
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size684.0 B
신규
36 
송달분 미수령
13 
주소불명
10 
폐업 또는 부도
6 
기타
 
3

Length

Max length8
Median length2
Mean length3.7536232
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
신규 36
52.2%
송달분 미수령 13
 
18.8%
주소불명 10
 
14.5%
폐업 또는 부도 6
 
8.7%
기타 3
 
4.3%
사망 1
 
1.4%

Length

2023-12-13T00:36:29.487366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:36:29.668602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 36
38.3%
송달분 13
 
13.8%
미수령 13
 
13.8%
주소불명 10
 
10.6%
폐업 6
 
6.4%
또는 6
 
6.4%
부도 6
 
6.4%
기타 3
 
3.2%
사망 1
 
1.1%

납세자유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size684.0 B
법인
35 
개인
34 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
법인 35
50.7%
개인 34
49.3%

Length

2023-12-13T00:36:29.856774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:36:29.972561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법인 35
50.7%
개인 34
49.3%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.101449
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T00:36:30.095324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q311
95-th percentile148
Maximum243
Range242
Interquartile range (IQR)10

Descriptive statistics

Standard deviation50.957348
Coefficient of variation (CV)2.1142856
Kurtosis7.740609
Mean24.101449
Median Absolute Deviation (MAD)2
Skewness2.823041
Sum1663
Variance2596.6513
MonotonicityNot monotonic
2023-12-13T00:36:30.281150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 19
27.5%
2 12
17.4%
6 5
 
7.2%
3 4
 
5.8%
5 4
 
5.8%
7 3
 
4.3%
4 3
 
4.3%
15 2
 
2.9%
101 1
 
1.4%
18 1
 
1.4%
Other values (15) 15
21.7%
ValueCountFrequency (%)
1 19
27.5%
2 12
17.4%
3 4
 
5.8%
4 3
 
4.3%
5 4
 
5.8%
6 5
 
7.2%
7 3
 
4.3%
10 1
 
1.4%
11 1
 
1.4%
13 1
 
1.4%
ValueCountFrequency (%)
243 1
1.4%
211 1
1.4%
174 1
1.4%
158 1
1.4%
133 1
1.4%
101 1
1.4%
92 1
1.4%
81 1
1.4%
65 1
1.4%
62 1
1.4%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483777.54
Minimum20
Maximum6110130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T00:36:30.481562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile1554
Q115090
median84410
Q3373680
95-th percentile2403410
Maximum6110130
Range6110110
Interquartile range (IQR)358590

Descriptive statistics

Standard deviation1052980.3
Coefficient of variation (CV)2.1765796
Kurtosis15.09547
Mean483777.54
Median Absolute Deviation (MAD)81570
Skewness3.6638726
Sum33380650
Variance1.1087675 × 1012
MonotonicityNot monotonic
2023-12-13T00:36:30.681562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40890 1
 
1.4%
108420 1
 
1.4%
107930 1
 
1.4%
4759520 1
 
1.4%
87060 1
 
1.4%
210020 1
 
1.4%
2130 1
 
1.4%
559900 1
 
1.4%
525940 1
 
1.4%
347130 1
 
1.4%
Other values (59) 59
85.5%
ValueCountFrequency (%)
20 1
1.4%
480 1
1.4%
740 1
1.4%
1490 1
1.4%
1650 1
1.4%
2120 1
1.4%
2130 1
1.4%
2840 1
1.4%
3650 1
1.4%
4050 1
1.4%
ValueCountFrequency (%)
6110130 1
1.4%
4759520 1
1.4%
2596160 1
1.4%
2459590 1
1.4%
2319140 1
1.4%
2269680 1
1.4%
1263090 1
1.4%
1165790 1
1.4%
962770 1
1.4%
946600 1
1.4%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.01449
Minimum1
Maximum552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T00:36:30.838439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median48
Q3144
95-th percentile454
Maximum552
Range551
Interquartile range (IQR)138

Descriptive statistics

Standard deviation159.28401
Coefficient of variation (CV)1.2948394
Kurtosis0.91022284
Mean123.01449
Median Absolute Deviation (MAD)46
Skewness1.4226926
Sum8488
Variance25371.397
MonotonicityNot monotonic
2023-12-13T00:36:31.372139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2 6
 
8.7%
34 6
 
8.7%
3 4
 
5.8%
48 4
 
5.8%
357 4
 
5.8%
127 4
 
5.8%
24 3
 
4.3%
552 3
 
4.3%
13 3
 
4.3%
454 3
 
4.3%
Other values (16) 29
42.0%
ValueCountFrequency (%)
1 2
 
2.9%
2 6
8.7%
3 4
5.8%
4 2
 
2.9%
5 2
 
2.9%
6 2
 
2.9%
7 2
 
2.9%
13 3
4.3%
19 1
 
1.4%
24 3
4.3%
ValueCountFrequency (%)
552 3
4.3%
454 3
4.3%
393 1
 
1.4%
357 4
5.8%
279 2
2.9%
235 2
2.9%
228 2
2.9%
144 2
2.9%
142 2
2.9%
127 4
5.8%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2354185.8
Minimum2230
Maximum9001430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T00:36:31.582728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2230
5-th percentile13486
Q1197470
median1448740
Q32825430
95-th percentile8647640
Maximum9001430
Range8999200
Interquartile range (IQR)2627960

Descriptive statistics

Standard deviation2818816.3
Coefficient of variation (CV)1.1973636
Kurtosis0.23350046
Mean2354185.8
Median Absolute Deviation (MAD)1261470
Skewness1.2452783
Sum1.6243882 × 108
Variance7.9457252 × 1012
MonotonicityNot monotonic
2023-12-13T00:36:31.801065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1762850 4
 
5.8%
7693530 4
 
5.8%
2170980 4
 
5.8%
1685950 3
 
4.3%
5596060 3
 
4.3%
8647640 3
 
4.3%
641310 3
 
4.3%
453420 3
 
4.3%
2230 2
 
2.9%
3615270 2
 
2.9%
Other values (29) 38
55.1%
ValueCountFrequency (%)
2230 2
2.9%
3670 1
1.4%
9050 1
1.4%
20140 1
1.4%
20690 1
1.4%
24340 1
1.4%
25610 1
1.4%
35040 1
1.4%
40890 1
1.4%
58600 1
1.4%
ValueCountFrequency (%)
9001430 2
2.9%
8647640 3
4.3%
7693530 4
5.8%
5884950 1
 
1.4%
5596060 3
4.3%
5543950 2
2.9%
3615270 2
2.9%
2825430 2
2.9%
2434390 2
2.9%
2170980 4
5.8%

누적미환급금액증감
Real number (ℝ)

ZEROS 

Distinct65
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6484.2071
Minimum0
Maximum102304.72
Zeros5
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T00:36:32.013628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q192.73
median525.41
Q35267.46
95-th percentile31025.176
Maximum102304.72
Range102304.72
Interquartile range (IQR)5174.73

Descriptive statistics

Standard deviation16571.661
Coefficient of variation (CV)2.5556958
Kurtosis20.199615
Mean6484.2071
Median Absolute Deviation (MAD)523.06
Skewness4.2514609
Sum447410.29
Variance2.7461994 × 108
MonotonicityNot monotonic
2023-12-13T00:36:32.215106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
7.2%
7912.27 1
 
1.4%
114.17 1
 
1.4%
1616.31 1
 
1.4%
18250.0 1
 
1.4%
201.35 1
 
1.4%
49.66 1
 
1.4%
125.4 1
 
1.4%
2024.44 1
 
1.4%
220.56 1
 
1.4%
Other values (55) 55
79.7%
ValueCountFrequency (%)
0.0 5
7.2%
0.86 1
 
1.4%
1.05 1
 
1.4%
2.35 1
 
1.4%
10.18 1
 
1.4%
21.52 1
 
1.4%
33.26 1
 
1.4%
47.32 1
 
1.4%
49.66 1
 
1.4%
53.85 1
 
1.4%
ValueCountFrequency (%)
102304.72 1
1.4%
75624.76 1
1.4%
41039.58 1
1.4%
35803.26 1
1.4%
23858.05 1
1.4%
22481.34 1
1.4%
18250.0 1
1.4%
13565.45 1
1.4%
13233.25 1
1.4%
11249.7 1
1.4%

Interactions

2023-12-13T00:36:26.675641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:24.506046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:25.132624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:25.672786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:26.177520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:26.781619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:24.629767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:25.224077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:25.770973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:26.278249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:26.906386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:24.768191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:25.326606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:25.892456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:26.394814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:27.011227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:24.893233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:25.438259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:25.990791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:26.482354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:27.121847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:25.008740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:25.548492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:26.078310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:26.572503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:36:32.375701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
세목명1.0000.0000.0000.0000.0000.0000.1600.3620.000
과세년도0.0001.0000.0000.0000.0000.1660.5570.6160.216
미환급유형0.0000.0001.0000.4590.0000.0000.0000.3020.412
납세자유형0.0000.0000.4591.0000.1770.2180.5270.8210.000
당해미환급건수0.0000.0000.0000.1771.0000.9640.8290.6200.000
당해미환급금액0.0000.1660.0000.2180.9641.0000.7390.5530.000
누적미환급건수0.1600.5570.0000.5270.8290.7391.0000.9110.132
누적미환급금액0.3620.6160.3020.8210.6200.5530.9111.0000.123
누적미환급금액증감0.0000.2160.4120.0000.0000.0000.1320.1231.000
2023-12-13T00:36:32.571288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형미환급유형과세년도
세목명1.0000.0000.0000.000
납세자유형0.0001.0000.3200.000
미환급유형0.0000.3201.0000.000
과세년도0.0000.0000.0001.000
2023-12-13T00:36:32.743351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명과세년도미환급유형납세자유형
당해미환급건수1.0000.8140.5330.499-0.3290.0000.0000.0000.120
당해미환급금액0.8141.0000.5480.588-0.4560.0000.0980.0000.222
누적미환급건수0.5330.5481.0000.9680.3660.0660.3500.0000.500
누적미환급금액0.4990.5880.9681.0000.3510.2200.4040.1540.612
누적미환급금액증감-0.329-0.4560.3660.3511.0000.0000.1330.2560.000
세목명0.0000.0000.0660.2200.0001.0000.0000.0000.000
과세년도0.0000.0980.3500.4040.1330.0001.0000.0000.000
미환급유형0.0000.0000.0000.1540.2560.0000.0001.0000.320
납세자유형0.1200.2220.5000.6120.0000.0000.0000.3201.000

Missing values

2023-12-13T00:36:27.279794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:36:27.585731image/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
0대전광역시유성구30200등록면허세2017신규개인1408901408900.0
1대전광역시유성구30200자동차세2017송달분 미수령개인414165014224343901618.6
2대전광역시유성구30200자동차세2017신규개인811263090142243439092.73
3대전광역시유성구30200자동차세2017송달분 미수령법인25800244534207717.59
4대전광역시유성구30200자동차세2017신규법인153731102445342021.52
5대전광역시유성구30200자동차세2017폐업 또는 부도법인113080244534203366.51
6대전광역시유성구30200재산세2017신규개인1365029050147.95
7대전광역시유성구30200지방소득세2017송달분 미수령개인7187820761810020863.7
8대전광역시유성구30200지방소득세2017신규개인38761930761810020137.56
9대전광역시유성구30200지방소득세2017신규법인21650718727011249.7
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
59대전광역시유성구30200지방소득세2021사망개인29466003577693530712.75
60대전광역시유성구30200지방소득세2021송달분 미수령개인523981035776935303108.18
61대전광역시유성구30200지방소득세2021신규개인17423191403577693530231.74
62대전광역시유성구30200지방소득세2021주소불명개인312573035776935306019.09
63대전광역시유성구30200지방소득세2021송달분 미수령법인4237204817628507331.91
64대전광역시유성구30200지방소득세2021신규법인18275660481762850539.5
65대전광역시유성구30200지방소득세2021주소불명법인2506004817628503383.89
66대전광역시유성구30200지방소득세2021폐업 또는 부도법인2491048176285035803.26
67대전광역시유성구30200취득세2021신규개인5350405350400.0
68대전광역시유성구30200취득세2021신규법인1243401243400.0